CN119382350B - Electric power corridor air hidden danger wide area monitoring method and related device - Google Patents
Electric power corridor air hidden danger wide area monitoring method and related device Download PDFInfo
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Abstract
The invention provides a wide-area monitoring method and a related device for air hidden danger of an electric power corridor, and relates to the technical field of electric power systems. The method comprises the steps of obtaining a low-orbit satellite echo signal, carrying out range and Doppler Fourier transform according to the low-orbit satellite echo signal to generate a range-Doppler image, preprocessing the range-Doppler image to construct a range-Doppler image database, training the range-Doppler image database to generate an aerial hidden danger wide area monitoring network, determining hidden danger type and two-dimensional coordinate information according to the low-orbit satellite echo signal, determining hidden danger image information through the two-dimensional coordinate information and combining a preset image acquisition device, determining three-dimensional coordinates according to the hidden danger image information, and tracking the track of the low-orbit satellite echo signal according to the hidden danger type. Through collaborative analysis of the target type, the position and the motion trail, wide-area situation awareness of the air hidden danger in the electric power corridor service area is realized, and safe operation of the electric power system is ensured.
Description
Technical Field
The application relates to the technical field of power systems, in particular to a wide-area monitoring method and a related device for air hidden danger of a power corridor.
Background
With the continuous expansion of power transmission networks in the global scope, the monitoring and prevention of hidden air hazards become an important issue for the safe operation of power grids. Especially in the electric power corridor of remote mountain area and complicated topography, because ground condition is complicated, traditional ground monitoring means has the limitation in coverage and real-time response, has difficulty in comprehensively catching the dynamic change of aerial hidden danger. Therefore, how to efficiently implement wide area monitoring of air hazards becomes an important challenge for grid management.
At present, the air hidden danger monitoring mainly depends on modes such as ground cameras and unmanned aerial vehicle inspection. Although these methods have a certain operability, they generally have problems such as limited monitoring range, insufficient inspection frequency, and difficulty in coping with bad weather. Especially for the sudden hidden trouble possibly caused by fast moving air obstacles or severe weather conditions, the traditional means is difficult to realize timely early warning, and then potential threat is caused to the safety of the electric power corridor.
The existing method for monitoring the hidden danger in the air mainly comprises the modes of monitoring by a ground camera, inspection by an unmanned aerial vehicle, radar monitoring and the like. The ground camera monitoring is suitable for continuous monitoring of fixed positions, but the monitoring range is limited, and remote and complex terrain areas are difficult to cover. Unmanned aerial vehicle inspection has flexibility, can be used to specific regional hidden danger investigation, but because battery duration restriction, inspection frequency is lower, is difficult to realize long-time continuous monitoring. The radar monitoring can detect the air obstacle to a certain extent, is suitable for the environment with better weather conditions, but the performance of the radar monitoring is obviously reduced under severe weather conditions. In addition, these methods do not respond well to fast moving air hazards and are difficult to provide effective pre-warning and emergency treatment. The continuous and rapid monitoring of the air hidden trouble in the electric power corridor service area is realized by utilizing the advantages brought by the full coverage characteristic of the low orbit satellite and the rapid convergence of the precise positioning due to the rapid change of the geometric figure in the signal coverage range. In addition, the motion characteristics of the hidden danger in the air can be further extracted by combining the distance-Doppler image received by the satellite terminal, and effective support is provided for early warning and emergency treatment of the hidden danger.
However, the existing method for monitoring the hidden danger in the air by simply utilizing the low-orbit satellite has the following defects that (1) the method lacks of the assistance of a ground camera, the hidden danger is difficult to accurately position, satellite monitoring can only provide rough two-dimensional position information and cannot meet the fine tracking requirement on hidden danger targets, (2) satellite signals are greatly influenced by environmental factors, particularly under the conditions of complex terrains and severe weather, the signal quality is easily interfered, the monitoring result is unstable, and (3) the low-orbit satellite is covered widely, but because of the high-speed operation, the monitoring has a certain discontinuity, and continuous and real-time fine monitoring cannot be realized. The defects limit the practical application effect of purely depending on satellite monitoring.
Therefore, how to realize wide-area situation awareness of the air hidden danger in the electric power gallery service area and powerfully ensure the safe operation of the electric power system becomes a technical problem to be solved urgently.
Disclosure of Invention
In order to realize wide-area situation awareness of the air hidden danger in the electric power corridor service area and powerfully ensure the safe operation of an electric power system, the application provides a wide-area monitoring method and a related device for the air hidden danger in the electric power corridor.
In a first aspect, the method for monitoring the air hidden danger of the electric power corridor adopts the following technical scheme:
A wide area monitoring method for electric power corridor air hidden danger includes:
acquiring a low-orbit satellite echo signal through a preset communication device;
performing a range and doppler fourier transform from the low orbit satellite echo signals to generate a range-doppler image;
Preprocessing the range-doppler image to construct a range-doppler image database;
acquiring a preset fusion model and training based on the distance-Doppler image database to generate an air hidden danger wide area monitoring network;
Inputting the low-orbit satellite echo signals into the aerial hidden danger wide area monitoring network to determine hidden danger types and two-dimensional coordinate information;
determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device;
And determining three-dimensional coordinates according to the hidden danger image information and tracking the track of the low-orbit satellite echo signals according to the hidden danger type.
Optionally, the step of performing a range and Doppler Fourier transform from the low-orbit satellite echo signals to generate a range-Doppler image comprises defining the low-orbit satellite echo signals as a matrix s (K, L) of size K L:
;
Wherein, Representing the signal amplitude, e is a natural index, and pi represents the circumference ratio; the Doppler frequency of the speed information representing the movement of a target, T represents a period interval, L represents a certain period of a data frame, and L represents the frequency modulation period number contained in the data frame of a signal received by a frame terminal; The distance frequency of the distance information between the terminal and the target is represented, K represents a certain sampling point, K represents the number of sampling points in one frequency modulation period, s (K) represents a distance dimension Fourier transform function, and s (l) represents a Doppler dimension transform function;
Performing Fourier transform of a distance dimension on the low-orbit satellite echo signal, taking each frequency modulation period as a basic unit, considering all sampling points in the period as time domain characterization, and then performing Fourier transform:
;
wherein w (K) represents an index after performing distance dimension Fourier transform using a window function for K points, u represents a complex unit;
In a frame of ground satellite terminal received signal data frame, using the frequency modulation period as a coordinate, windowing data at the same distance, and performing Doppler Fourier transform, thereby obtaining a distance-Doppler diagram:
;
where w (L) denotes an index after performing distance dimension fourier transform using a window function for L points;
After two-dimensional fourier transform processing, a frame of signal data received by the terminal is converted into a data matrix form including a distance dimension and a doppler dimension:
;
A distance unit is arranged The signal intensity in the range-Doppler graph is obtained by converting the signal intensity into the logarithmic domain。
Optionally, the step of preprocessing the range-doppler image to construct a range-doppler image database includes:
acquiring actual environmental characteristics of a current electric power corridor, and determining an air hidden danger set according to the actual environmental characteristics;
Converting the range-doppler plot into a gray scale image using a logarithmic conversion function:
Let the gray level diagram be Obtained by the following formula:
;
Wherein the method comprises the steps of As a set of real numbers,AndControlling the dynamic range and the gray level respectively, wherein Z represents a distance-Doppler matrix, and Z|max represents the maximum modulus of the distance-Doppler matrix Z;
And constructing a distance-Doppler image database according to the gray level image and the air hidden danger set.
Optionally, the preset fusion model comprises a GCN fusion model and a CNN fusion model.
Optionally, the step of acquiring a preset fusion model and training based on the distance-doppler image database to generate an air hidden danger wide area monitoring network includes:
Inputting the range-doppler image database into the CNN to extract local features through a multi-layer convolution operation;
converting the range-Doppler image into an adjacency matrix according to the range-Doppler image and inputting the adjacency matrix into the GCN to extract global features;
Fusing the local features and the global features to obtain a feature H result;
Predicting class results by Softmax function ;
;
Wherein, And d is a weight matrix, d is a bias vector, and the trained preset fusion model is used as an air hidden danger wide area monitoring network.
Optionally, the step of determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device includes:
acquiring image information of a low-orbit satellite echo signal through a preset image acquisition device;
Processing the image information through an image dilation and image erosion strategy to generate first image information;
Wherein, g (x) and b (x) are used to represent two discrete functions in a two-dimensional space, and g (x) describes a gray image, and b (x) represents a structural element;
the expansion process is expressed as: ;
the corrosion process is expressed as: ;
separating the background from the foreground of the first image information to generate second image information;
Is set to have no noise Interference, then the image sequence is trackedFrom background imagesWith moving objectsIs formed by the combination of the components;
;
Image processing apparatus Is a combination of moving object and noise:
applying a threshold segmentation algorithm:
;
wherein m represents a threshold value;
based on the result of the threshold segmentation process, normalizing the color components by using a normalization-based RGB differential algorithm, wherein r=R/(R+G+B), g=G/(R+G+B), and b=B/(R+G+B);
Calculating Gaussian model parameters of each pixel point in background image And u and sigma represent the average value and variance of corresponding points in the background respectively;
performing differential operation to obtain binary image based on the established background model The foreground and background pixels are denoted 1 and 0, respectively;
;
in the formula, Representative pixelThe measured value is used to determine, for each of the measured values,Belonging to the threshold parameter, if the r, g, b component of the pixel is varied, treating it as a foreground pixel to generate second image information;
fusing the second image information through a panoramic stitching algorithm;
Setting the width of an image as W, the height of the image as H, presetting the focal length f shot by an image acquisition device, (x, y) as the coordinates of any point p in a view plane, and (xc, yc, zc) as the projection coordinates of the point p on a cylindrical curved surface;
;
and taking the fused image information as target image information corresponding to the low-orbit satellite echo signal.
Optionally, the step of acquiring the low-orbit satellite echo signal by the preset communication device includes:
acquiring position and attitude information of a satellite communication terminal as a low-orbit satellite echo signal by a preset communication strategy and combining a global navigation satellite system and an inertial measurement unit;
Acquiring orbit data of a low-orbit satellite by combining ephemeris, and determining the azimuth of a visible satellite in the range of an electric power corridor;
estimating the motion trail of the low orbit satellite based on the position and the gesture of the satellite communication terminal, and converting the satellite position from an earth fixed coordinate system to a terminal antenna coordinate system through multiple coordinate system conversion;
Controlling a satellite communication terminal antenna to accurately point to a visible satellite in azimuth, elevation and polarization directions;
A satellite handoff algorithm is applied to switch to the next visible satellite when the currently tracked low-orbit satellite is not visible, stops transmitting or the signal is below a set threshold.
In a second aspect, the present application provides a wide-area monitoring system for electric power corridor air hidden danger, the wide-area monitoring system for electric power corridor air hidden danger comprising:
the signal acquisition module is used for acquiring a low-orbit satellite echo signal through a preset communication device;
an image generation module for performing a range and doppler fourier transform from the low orbit satellite echo signals to generate a range-doppler image;
A database generation module for preprocessing the range-doppler image to construct a range-doppler image database;
The network training module is used for acquiring a preset fusion model and training based on the distance-Doppler image database to generate an air hidden danger wide area monitoring network;
The coordinate information acquisition module is used for inputting the low-orbit satellite echo signals into the aerial hidden danger wide-area monitoring network to determine hidden danger types and two-dimensional coordinate information;
the hidden danger image determining module is used for determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device;
and the track tracking module is used for determining three-dimensional coordinates according to the hidden danger image information and tracking the track of the low-orbit satellite echo signals according to the hidden danger type.
In a third aspect, the application provides a computer device comprising a memory, a processor which, when executing computer instructions stored by the memory, performs a method as described above.
In a fourth aspect, the application provides a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform a method as described above.
In summary, the application comprises the following beneficial technical effects:
The method comprises the steps of obtaining a low-orbit satellite echo signal through a preset communication device, carrying out range and Doppler Fourier transform according to the low-orbit satellite echo signal to generate a range-Doppler image, preprocessing the range-Doppler image to construct a range-Doppler image database, obtaining a preset fusion model and training based on the range-Doppler image database to generate an aerial hidden danger wide-area monitoring network, inputting the low-orbit satellite echo signal into the aerial hidden danger wide-area monitoring network to determine hidden danger types and two-dimensional coordinate information, determining hidden danger image information through two-dimensional coordinate information and combining a preset image acquisition device, determining three-dimensional coordinates according to hidden danger image information, and carrying out track tracking on the low-orbit satellite echo signal according to hidden danger types. Through collaborative analysis of the type, the position and the motion trail of the target, wide-area situation awareness of the air hidden danger in the service area of the electric power gallery is realized, and the safe operation of the electric power system is powerfully ensured.
Drawings
FIG. 1 is a schematic diagram of a computer device in a hardware operating environment according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a first embodiment of the method for monitoring the air hidden danger of the electric power gallery.
FIG. 3 is a flow chart of a data frame processed into a range Doppler graph in the wide area monitoring method of the electric power corridor air hidden trouble of the present application;
FIG. 4 is a flowchart of an air hidden danger wide area monitoring method based on GCN and CNN fusion in the electric power corridor air hidden danger wide area monitoring method of the application;
fig. 5 is a block diagram of a first embodiment of the power corridor air hidden danger wide area monitoring system of the present application.
Detailed Description
The present application will be described in further detail below with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Referring to fig. 1, fig. 1 is a schematic diagram of a computer device structure of a hardware running environment according to an embodiment of the present application.
As shown in FIG. 1, the computer device may include a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is not limiting of a computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include an operating system, a network communication module, a user interface module, and a power corridor air hidden danger wide area monitoring program.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server, the user interface 1003 is mainly used for data interaction with a user, the processor 1001 and the memory 1005 in the application can be arranged in the computer device, and the computer device invokes the electric power gallery air hidden danger wide area monitoring program stored in the memory 1005 through the processor 1001 and executes the electric power gallery air hidden danger wide area monitoring method provided by the embodiment of the application.
The embodiment of the application provides a wide-area monitoring method for electric power corridor air hidden danger, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the wide-area monitoring method for electric power corridor air hidden danger.
In this embodiment, the method for monitoring the air hidden trouble of the electric power gallery includes the following steps:
and S10, acquiring a low-orbit satellite echo signal through a preset communication device.
It can be understood that the step of acquiring the low-orbit satellite echo signal through the preset communication device comprises the steps of acquiring the position and posture information of the satellite communication terminal as the low-orbit satellite echo signal through a preset communication strategy by combining a global navigation satellite system and an inertial measurement unit, wherein the preset communication strategy comprises the steps of acquiring the orbit data of the low-orbit satellite by combining ephemeris, determining the azimuth of the visible satellite in the electric corridor range, estimating the motion track of the low-orbit satellite based on the position and the posture of the satellite communication terminal, converting the satellite position from an earth fixed coordinate system to a terminal antenna coordinate system through multiple coordinate system conversion, controlling the satellite communication terminal antenna to precisely point to the visible satellite in azimuth, elevation angle and polarization directions, and switching to the next visible satellite by applying a satellite switching algorithm when the currently tracked low-orbit satellite is invisible, stops transmitting or the signal is lower than a set threshold value.
It should be noted that, in order to realize the high-efficiency wide-area monitoring of the hidden danger in the electric power corridor, the attitude of the satellite terminal on the transmission tower needs to be controlled to keep continuous uninterrupted communication with each visible satellite, and reliable technical support is provided for the wide-area monitoring of the electric power corridor on the basis of ensuring stable transmission of electric power signals.
GNSS provides high accuracy geographic position measurements, and IMU's are used to sense in real time changes in terminal attitude, including tilt, pitch, and yaw angles.
Based on the position and the gesture of the satellite communication terminal, the motion track of the low orbit satellite is estimated, and the satellite position is converted from an earth fixed coordinate system to a terminal antenna coordinate system through multiple coordinate system conversion, so that unified and accurate conversion among different coordinate systems is realized.
The satellite communication terminal antenna is then controlled to be precisely pointed at the visible satellites in azimuth, elevation and polarization directions to ensure continuity and reliability of communication.
Finally, a satellite handoff algorithm is applied. When the currently tracked low-orbit satellite is invisible, stops transmitting or the signal is lower than a set threshold value, the next visible satellite is automatically switched to ensure that the communication is not interrupted. By combining the low orbit satellite switching protocol with the electronic steerable antenna, seamless switching is realized, and uninterrupted communication between the ground and the satellite is continuously ensured.
Step S20, performing a range and doppler fourier transform based on the low-orbit satellite echo signal to generate a range-doppler image.
The step of performing a range-and-doppler fourier transform to generate a range-doppler image from the low-orbit satellite echo signal includes:
Definition the low-orbit satellite echo signal is a matrix s (K, L) of size k×l:
;
Wherein, Representing the signal amplitude, e is a natural index, and pi represents the circumference ratio; the Doppler frequency of the speed information representing the movement of a target, T represents a period interval, L represents a certain period of a data frame, and L represents the frequency modulation period number contained in the data frame of a signal received by a frame terminal; The distance frequency of the distance information between the terminal and the target is represented, K represents a certain sampling point, K represents the number of sampling points in one frequency modulation period, s (K) represents a distance dimension Fourier transform function, and s (l) represents a Doppler dimension transform function;
performing Fourier transform of a distance dimension on the low-orbit satellite echo signal, taking each frequency modulation period as a basic unit, considering all sampling points in the period as time domain characterization, and then performing Fourier transform:
;
wherein w (K) represents an index after performing distance dimension Fourier transform using a window function for K points, u represents a complex unit;
In a frame of ground satellite terminal received signal data frame, using the frequency modulation period as a coordinate, windowing data at the same distance, and performing Doppler Fourier transform, thereby obtaining a distance-Doppler diagram:
;
where w (L) denotes an index after performing distance dimension fourier transform using a window function for L points;
After two-dimensional fourier transform processing, a frame of signal data received by the terminal is converted into a data matrix form including a distance dimension and a doppler dimension:
;
A distance unit is arranged The signal intensity in the range-Doppler graph is obtained by converting the signal intensity into the logarithmic domain。
It will be appreciated that the range-doppler plot, as shown in fig. 3, contains range information and doppler dimension information from a ground satellite terminal during movement of an object at an overhead potential, while implying a time dimension correlation between neighboring plots.
Step S30, preprocessing the range-doppler image to construct a range-doppler image database.
Based on the actual environmental characteristics of the electric power corridor, the hidden air trouble needing to be closely monitored should be clearly defined. These include, but are not limited to, birds or other flying animals nesting, perching or flying on the electrical facilities, possibly causing short circuits or equipment damage, possibly causing risks of equipment loosening, broken wires, damaged insulation layers, etc. under severe weather conditions such as strong winds, lightning, etc., illegal intrusion of flying objects such as aircrafts, unmanned aerial vehicles, etc. in the vicinity, potentially threatening the electrical facilities, and light objects such as plastic bags, kite lines, etc. floating in the air, possibly hanging on overhead lines or equipment, causing faults. In addition, airborne hazards may also include vegetation overgrowth encroaching on power lines, etc., all of which may affect the safe operation of the power corridor. And recording the hidden danger information as the actual environmental characteristics of the current electric power corridor.
It will be appreciated that the step of preprocessing the range-doppler image to construct a range-doppler image database comprises obtaining actual environmental characteristics of the current power corridor, determining a set of air hidden danger from the actual environmental characteristics, converting the range-doppler image to a gray image using a logarithmic conversion function, setting the gray image to be the gray imageObtained by the following formula:
;
Wherein the method comprises the steps of As a set of real numbers,AndThe dynamic range and the gray level are controlled respectively, Z represents a distance-Doppler matrix, z|max represents the maximum modulus value of the distance-Doppler matrix Z, and a distance-Doppler image database is constructed according to the gray image and the air hidden danger set.
And S40, acquiring a preset fusion model and training based on a distance-Doppler image database to generate an air hidden danger wide area monitoring network.
The preset fusion model includes a RD diagram of the low-orbit satellite signal received by the terminal under different hidden danger conditions obtained by the network input of the GCN and CNN fusion model as shown in fig. 4, and the network output is hidden danger category obtained by pattern recognition and two-dimensional coordinate position interval information corresponding to the hidden danger.
In a specific implementation, the step of acquiring a preset fusion model and training based on a distance-doppler image database to generate an air hidden danger wide area monitoring network includes:
the method comprises the steps of inputting a distance-Doppler image database into a CNN, extracting local features through multi-layer convolution operation, converting the distance-Doppler image into an adjacent matrix according to the distance-Doppler image, inputting the adjacent matrix into a GCN to extract global features, and fusing the local features and the global features to obtain features H result;
Predicting class results by Softmax function ;
;
Wherein, And d is a weight matrix, d is a bias vector, and the trained preset fusion model is used as an air hidden danger wide area monitoring network.
In a specific implementation, in this embodiment, the CNN is first used to process the range-doppler plot of the satellite terminal received signal. CNN takes as input a range-doppler plot, and extracts local features of the image by a multi-layer convolution operation. At the same time, the GCN module starts to construct the graph data in order to better extract the global features. Since GCN can only act on graph structure data, it is necessary to convert RD images into topology graph structures. This process includes dividing the range-doppler plot into a plurality of H x H pixel-sized sub-plots, treating each sub-plot as an independent node, and calculating the connection relationships between the nodes to generate an adjacency matrix.
After completion of the graph data construction, these nodes and their corresponding adjacency matrices are input into the GCN module to further extract global features. The GCN flow is summarized as follows:
1) And the node characteristic aggregation is to synchronously integrate the information of the node and the adjacent nodes, thereby realizing the deep integration and intersection of the characteristic information.
2) And (3) global weight weighting, namely multiplying the fused information with a global unified weight matrix, and adjusting the influence of the characteristics of each node through weight distribution, so that the mutual correlation among the nodes is more accurately described.
3) Nonlinear mapping, namely introducing an activation function to implement nonlinear mapping, and endowing the model with the capability of learning complex nonlinear association.
In this way, the CNN and GCN modules each focus on extracting local and global features, which complement each other. Then, the features extracted from the two are fused to obtain a feature Hresult, the feature Hresult is input into a full connection, and then the class result is predicted through a Softmax function, so that the end-to-end identification of the hidden danger in the air is realized, and the following formula is adopted:
;
Wherein the method comprises the steps of And d is the weight matrix and the bias vector, respectively.
The GCN and CNN combined model is trained, so that the network can accurately identify different types of hidden dangers in the air and two-dimensional coordinate intervals where the hidden dangers are located. And finally, aiming at the environmental characteristics and the historical monitoring data of the electric power corridor, fine adjustment is carried out on the model so as to improve the accuracy and adaptability of identification.
And S50, inputting the low-orbit satellite echo signals into an aerial hidden danger wide area monitoring network to determine hidden danger types and two-dimensional coordinate information.
And S60, determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device.
In order to improve the image quality and solve the image quality difference caused by factors such as illumination change, climate environment, equipment vibration and the like, morphological filtering is firstly carried out on an initial image so as to balance the brightness and contrast of the image, thereby improving the image fusion effect of multiple cameras, enhancing the identification precision of the hidden danger targets in the air and ensuring that the hidden danger targets in a complex scene can be accurately identified.
In specific implementation, the step of determining hidden danger image information by combining two-dimensional coordinate information with a preset image acquisition device comprises the steps of acquiring image information of a low-orbit satellite echo signal by the preset image acquisition device, processing the image information by an image expansion and image corrosion strategy to generate first image information, wherein g (x) and b (x) are used for representing two discrete functions in a two-dimensional space, g (x) describes a gray level image, and b (x) represents a structural element;
the expansion process is expressed as: ;
the corrosion process is expressed as: ;
separating the background from the foreground of the first image information to generate second image information;
Is set to have no noise Interference, then the image sequence is trackedFrom background imagesWith moving objectsIs formed by the combination of the components;
;
Image processing apparatus Is a combination of moving object and noise:
;
applying a threshold segmentation algorithm:
;
wherein m represents a threshold value;
based on the result of the threshold segmentation process, normalizing the color components by using a normalization-based RGB differential algorithm, wherein r=R/(R+G+B), g=G/(R+G+B), and b=B/(R+G+B);
Calculating Gaussian model parameters of each pixel point in background image And u and sigma represent the average value and variance of corresponding points in the background respectively;
performing differential operation to obtain binary image based on the established background model The foreground and background pixels are denoted 1 and 0, respectively;
;
in the formula, Representative pixelThe measured value is used to determine, for each of the measured values,Belonging to the threshold parameter, if the r, g, b component of the pixel is varied, treating it as a foreground pixel to generate second image information;
fusing the second image information through a panoramic stitching algorithm;
Setting the width of an image as W, the height of the image as H, presetting the focal length f shot by an image acquisition device, (x, y) as the coordinates of any point p in a view plane, and (xc, yc, zc) as the projection coordinates of the point p on a cylindrical curved surface;
;
And taking the fused image information as target image information corresponding to the low-orbit satellite echo signals.
It can be understood that the moving path of the target is captured in real time through linkage control of the camera, so that the flight track of the hidden danger in the air can be accurately recorded, and continuous monitoring and position updating of the target are ensured.
And step S70, determining three-dimensional coordinates according to the hidden danger image information and tracking the track of the low-orbit satellite echo signals according to the hidden danger type.
In specific implementation, the three-dimensional position information of the hidden danger in the air is further determined by utilizing the two-dimensional rough coordinate information obtained in the previous step and the high-resolution cameras arranged on the transmission towers, and real-time track tracking is carried out on the three-dimensional position information.
Secondly, a comprehensive alarm mechanism is established. When the system detects that the hidden danger in the air enters the electric corridor area, an alarm is immediately triggered, and alarm information including the real-time position, the flying speed and related image data of the target is sent to the control center.
In order to reduce false alarm, a multi-level target verification mechanism is built in the system, multiple confirmation is carried out through Doppler frequency shift analysis and imaging characteristics, and the accuracy and reliability of alarm are ensured.
And finally, ensuring the safe operation of the power transmission line. After triggering an alarm, the system can automatically schedule the unmanned aerial vehicle to track the hidden danger in the air in a short distance and acquire more detailed flight data. Meanwhile, ground inspection personnel can be dispatched to reach the potential risk area, and inspection and risk assessment can be performed on the power transmission equipment.
In addition, the present embodiment may analyze the type and threat level of the air hidden trouble to determine whether to take additional defensive measures, such as driving off or quarantining. The cooperative coping mechanism can rapidly respond to the hidden danger in the air, and ensures that the threat to the power transmission line is timely and effectively controlled and processed.
It should be noted that, the present embodiment relates to generating a distance-doppler image (RD diagram) by using multipath effect of low-orbit satellite communication, and fusing a graphic neural network (GCN) and a Convolutional Neural Network (CNN) to realize preliminary identification and positioning of air hidden danger in a service area of an electric power corridor, and simultaneously controlling a high-definition camera preset on a ground tower in a linkage manner to capture an accurate position and a motion track of a target. First, the communication channel information of the low-orbit satellite Internet and the ground power communication terminal is utilized to carry out Fourier transform of distance and Doppler dimensions on a data frame of a signal received by the terminal, and an RD chart for monitoring hidden danger in the air is generated. And then analyzing the generated RD image through a pattern recognition network fused with the GCN and the CNN, and preliminarily determining the type of the hidden danger target and the two-dimensional position information thereof. Then, the system controls the ground high-definition cameras deployed on the adjacent towers in a linkage way. According to the preliminary identification result, the shooting angle of the camera is adjusted and panoramic image fusion is carried out, so that the three-dimensional position and the motion trail of the target are accurately obtained, and the accurate tracking of the target is realized. The system realizes wide-area situation awareness of the air hidden danger in the electric power corridor service area through collaborative analysis of the type, the position and the movement track of the target, and powerfully ensures the safe operation of the electric power system.
The embodiment comprises the steps of acquiring a low-orbit satellite echo signal through a preset communication device, carrying out range and Doppler Fourier transform according to the low-orbit satellite echo signal to generate a range-Doppler image, preprocessing the range-Doppler image to construct a range-Doppler image database, acquiring a preset fusion model and training based on the range-Doppler image database to generate an aerial hidden danger wide-area monitoring network, inputting the low-orbit satellite echo signal into the aerial hidden danger wide-area monitoring network to determine hidden danger types and two-dimensional coordinate information, determining hidden danger image information through two-dimensional coordinate information and combining a preset image acquisition device, determining three-dimensional coordinates according to hidden danger image information, and carrying out track tracking on the low-orbit satellite echo signal according to the hidden danger types. Through collaborative analysis of the type, the position and the motion trail of the target, wide-area situation awareness of the air hidden danger in the service area of the electric power gallery is realized, and the safe operation of the electric power system is powerfully ensured.
In addition, the embodiment of the application also provides a computer readable storage medium, and the storage medium stores a program for monitoring the wide area of the electric power corridor air hidden danger, and the program for monitoring the wide area of the electric power corridor air hidden danger realizes the steps of the method for monitoring the wide area of the electric power corridor air hidden danger.
Referring to fig. 5, fig. 5 is a block diagram of a first embodiment of the electric power corridor air hidden danger wide area monitoring system of the present application.
As shown in fig. 5, the electric power corridor air hidden danger wide area monitoring system provided by the embodiment of the present application includes:
A signal acquisition module 10, configured to acquire a low-orbit satellite echo signal through a preset communication device;
An image generation module 20 for performing a range and doppler fourier transform from the low orbit satellite echo signals to generate a range-doppler image;
A database generation module 30 for preprocessing the range-doppler image to construct a range-doppler image database;
The network training module 40 is configured to acquire a preset fusion model and perform training based on the distance-doppler image database to generate an air hidden danger wide area monitoring network;
The coordinate information acquisition module 50 is used for inputting the low-orbit satellite echo signals into the wide-area monitoring network for the hidden danger in the air so as to determine the hidden danger type and the two-dimensional coordinate information;
the hidden danger image determining module 60 is configured to determine hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device;
the track tracking module 70 is used for determining three-dimensional coordinates according to the hidden danger image information and tracking the track of the low-orbit satellite echo signals according to the hidden danger type.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the application as desired, and the application is not limited thereto.
The embodiment comprises the steps of acquiring a low-orbit satellite echo signal through a preset communication device, carrying out range and Doppler Fourier transform according to the low-orbit satellite echo signal to generate a range-Doppler image, preprocessing the range-Doppler image to construct a range-Doppler image database, acquiring a preset fusion model and training based on the range-Doppler image database to generate an aerial hidden danger wide-area monitoring network, inputting the low-orbit satellite echo signal into the aerial hidden danger wide-area monitoring network to determine hidden danger types and two-dimensional coordinate information, determining hidden danger image information through two-dimensional coordinate information and combining a preset image acquisition device, determining three-dimensional coordinates according to hidden danger image information, and carrying out track tracking on the low-orbit satellite echo signal according to the hidden danger types. Through collaborative analysis of the type, the position and the motion trail of the target, wide-area situation awareness of the air hidden danger in the service area of the electric power gallery is realized, and the safe operation of the electric power system is powerfully ensured.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present application, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details which are not described in detail in the present embodiment can refer to the method for monitoring the air hidden danger wide area of the electric power gallery provided in any embodiment of the present application, and are not described herein again.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The utility model provides a power corridor air hidden danger wide area monitoring method which is characterized in that the method comprises the following steps:
acquiring a low-orbit satellite echo signal through a preset communication device;
performing a range and doppler fourier transform from the low orbit satellite echo signals to generate a range-doppler image;
Preprocessing the range-doppler image to construct a range-doppler image database;
acquiring a preset fusion model and training based on the distance-Doppler image database to generate an air hidden danger wide area monitoring network;
Inputting the low-orbit satellite echo signals into the aerial hidden danger wide area monitoring network to determine hidden danger types and two-dimensional coordinate information;
determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device;
And determining three-dimensional coordinates according to the hidden danger image information and tracking the track of the low-orbit satellite echo signals according to the hidden danger type.
2. The method of claim 1, wherein the step of performing a range and doppler fourier transform from the low-orbit satellite echo signals to generate a range-doppler image comprises:
Defining the low-orbit satellite echo signal as a matrix s (K, L) of size k×l:
;
Wherein, Representing the signal amplitude, e is a natural index, and pi represents the circumference ratio; the Doppler frequency of the speed information representing the movement of a target, T represents a period interval, L represents a certain period of a data frame, and L represents the frequency modulation period number contained in the data frame of a signal received by a frame terminal; The distance frequency of the distance information between the terminal and the target is represented, K represents a certain sampling point, K represents the number of sampling points in one frequency modulation period, s (K) represents a distance dimension Fourier transform function, and s (l) represents a Doppler dimension transform function;
Performing Fourier transform of a distance dimension on the low-orbit satellite echo signal, taking each frequency modulation period as a basic unit, considering all sampling points in the period as time domain characterization, and then performing Fourier transform:
;
wherein w (K) represents an index after performing distance dimension Fourier transform using a window function for K points, u represents a complex unit;
In a frame of ground satellite terminal received signal data frame, using the frequency modulation period as a coordinate, windowing data at the same distance, and performing Doppler Fourier transform, thereby obtaining a distance-Doppler diagram:
;
where w (L) denotes an index after performing distance dimension fourier transform using a window function for L points;
After two-dimensional fourier transform processing, a frame of signal data received by the terminal is converted into a data matrix form including a distance dimension and a doppler dimension:
;
A distance unit is arranged The signal intensity in the range-Doppler graph is obtained by converting the signal intensity into the logarithmic domain。
3. The method of wide area monitoring of electrical power corridor air hazards as recited in claim 2, wherein the step of preprocessing the range-doppler image to build a range-doppler image database comprises:
acquiring actual environmental characteristics of a current electric power corridor, and determining an air hidden danger set according to the actual environmental characteristics;
Converting the range-doppler plot into a gray scale image using a logarithmic conversion function:
Let the gray level diagram be Obtained by the following formula:
;
Wherein the method comprises the steps of As a set of real numbers,AndControlling the dynamic range and the gray level respectively, wherein Z represents a distance-Doppler matrix, and Z max represents the maximum modulus of the distance-Doppler matrix Z;
And constructing a distance-Doppler image database according to the gray level image and the air hidden danger set.
4. The method for monitoring the air hidden danger of the electric power corridor according to claim 1, wherein the preset fusion model comprises a GCN and CNN fusion model.
5. The method of claim 4, wherein the step of obtaining a preset fusion model and training based on the range-doppler image database to generate an air borne risk wide area monitoring network comprises:
Inputting the range-doppler image database into the CNN to extract local features through a multi-layer convolution operation;
converting the range-Doppler image into an adjacency matrix according to the range-Doppler image and inputting the adjacency matrix into the GCN to extract global features;
Fusing the local features and the global features to obtain a feature H result;
Predicting class results by Softmax function ;
;
Wherein, And d is a weight matrix, d is a bias vector, and the trained preset fusion model is used as an air hidden danger wide area monitoring network.
6. The method for monitoring the air hidden danger wide area of the electric power corridor according to claim 1, wherein the step of determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device comprises the following steps:
acquiring image information of a low-orbit satellite echo signal through a preset image acquisition device;
Processing the image information through an image dilation and image erosion strategy to generate first image information;
Wherein, g (x) and b (x) are used to represent two discrete functions in a two-dimensional space, and g (x) describes a gray image, and b (x) represents a structural element;
the expansion process is expressed as: ;
the corrosion process is expressed as: ;
separating the background from the foreground of the first image information to generate second image information;
Is set to have no noise Interference, then the image sequence is trackedFrom background imagesWith moving objectsIs formed by the combination of the components;
;
Image processing apparatus Is a combination of moving object and noise:
;
applying a threshold segmentation algorithm:
;
wherein m represents a threshold value;
based on the result of the threshold segmentation process, normalizing the color components by using a normalization-based RGB differential algorithm, wherein r=R/(R+G+B), g=G/(R+G+B), and b=B/(R+G+B);
Calculating Gaussian model parameters of each pixel point in background image And u and sigma represent the average value and variance of corresponding points in the background respectively;
performing differential operation to obtain binary image based on the established background model The foreground and background pixels are denoted 1 and 0, respectively;
;
in the formula, Representative pixelThe measured value is used to determine, for each of the measured values,Belonging to the threshold parameter, if the r, g, b component of the pixel is varied, treating it as a foreground pixel to generate second image information;
fusing the second image information through a panoramic stitching algorithm;
Setting the width of an image as W, the height of the image as H, presetting the focal length f shot by an image acquisition device, (x, y) as the coordinate of any point p in a view plane, and (x c, yc, zc) as the projection coordinate of the point p on a cylindrical curved surface;
;
and taking the fused image information as target image information corresponding to the low-orbit satellite echo signal.
7. The method of any one of claims 1 to 6, wherein the step of acquiring the low-orbit satellite echo signal by the preset communication device comprises:
acquiring position and attitude information of a satellite communication terminal as a low-orbit satellite echo signal by a preset communication strategy and combining a global navigation satellite system and an inertial measurement unit;
Acquiring orbit data of a low-orbit satellite by combining ephemeris, and determining the azimuth of a visible satellite in the range of an electric power corridor;
estimating the motion trail of the low orbit satellite based on the position and the gesture of the satellite communication terminal, and converting the satellite position from an earth fixed coordinate system to a terminal antenna coordinate system through multiple coordinate system conversion;
Controlling a satellite communication terminal antenna to accurately point to a visible satellite in azimuth, elevation and polarization directions;
A satellite handoff algorithm is applied to switch to the next visible satellite when the currently tracked low-orbit satellite is not visible, stops transmitting or the signal is below a set threshold.
8. An electric power corridor aerial hidden danger wide area monitoring system, characterized in that the electric power corridor aerial hidden danger wide area monitoring system includes:
the signal acquisition module is used for acquiring a low-orbit satellite echo signal through a preset communication device;
an image generation module for performing a range and doppler fourier transform from the low orbit satellite echo signals to generate a range-doppler image;
A database generation module for preprocessing the range-doppler image to construct a range-doppler image database;
The network training module is used for acquiring a preset fusion model and training based on the distance-Doppler image database to generate an air hidden danger wide area monitoring network;
The coordinate information acquisition module is used for inputting the low-orbit satellite echo signals into the aerial hidden danger wide-area monitoring network to determine hidden danger types and two-dimensional coordinate information;
the hidden danger image determining module is used for determining hidden danger image information by combining the two-dimensional coordinate information with a preset image acquisition device;
and the track tracking module is used for determining three-dimensional coordinates according to the hidden danger image information and tracking the track of the low-orbit satellite echo signals according to the hidden danger type.
9. A computer device comprising a memory, a processor, which when executing computer instructions stored in the memory, performs the method of any one of claims 1 to 7.
10. A computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 7.
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