CN116382348A - Unmanned aerial vehicle inspection method and system for power distribution equipment - Google Patents
Unmanned aerial vehicle inspection method and system for power distribution equipment Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle inspection, and provides an unmanned aerial vehicle inspection method and system for power distribution equipment, wherein the method comprises the following steps: generating an unmanned aerial vehicle inspection two-dimensional map according to the unmanned aerial vehicle inspection range; acquiring position information and signal strength sent by an unmanned aerial vehicle, and generating a signal thermodynamic diagram; and judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending out alarm information. According to the invention, the signal thermodynamic diagram is generated according to the position information and the signal strength of the unmanned aerial vehicle during inspection, the operation risk of the unmanned aerial vehicle is judged based on the color of the position of the unmanned aerial vehicle in the current signal thermodynamic diagram, and the alarm information is correspondingly sent, so that the unmanned aerial vehicle can be timely inspected by workers, the problem of inspection data loss caused by sudden signal weakness or signal disconnection during inspection is avoided, and the inspection reliability is improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle inspection method and system for power distribution equipment.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Distribution equipment arranged in a field environment needs to be regularly inspected to ensure the reliability of equipment operation, and automatic inspection by adopting an unmanned aerial vehicle is an effective means for realizing equipment inspection. The unmanned aerial vehicle can adopt the frequency band of 2.4GHz or 5.8GHz, does not need a relay station, and directly transmits signals with the ground, but the transmission distance of the mode is shorter, is easy to interfere, and is not suitable for remote automatic inspection.
To unmanned aerial vehicle remote automatic inspection, can set up 4G communication module on unmanned aerial vehicle, however, because many distribution equipment lay the position remote, network signal probably is weaker, leads to when inspecting, can appear signal weak or signal disconnection's problem to lead to inspecting data unable in time to upload or data loss.
Disclosure of Invention
In order to solve the problems, the invention provides a power distribution equipment unmanned aerial vehicle inspection method and system, which are used for generating a signal thermodynamic diagram according to position information and signal strength during unmanned aerial vehicle inspection, judging the operation risk of the unmanned aerial vehicle based on the color of the position of the unmanned aerial vehicle in the signal thermodynamic diagram, and correspondingly sending alarm information, so that the inspection reliability is improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
one or more embodiments provide a power distribution equipment unmanned aerial vehicle inspection method, including the following steps:
step 1, generating an unmanned aerial vehicle inspection two-dimensional map according to an unmanned aerial vehicle inspection range;
step 2, acquiring position information and signal strength sent by an unmanned aerial vehicle, and generating a signal thermodynamic diagram;
step 3, judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending out alarm information;
in step 2, according to the obtained position information and signal intensity sent by the unmanned aerial vehicle, calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point, wherein the attribute value represents the signal intensity of the unmanned aerial vehicle element point, the attribute correction value represents the difference of the attribute values of the unmanned aerial vehicle element points to be combined, and the attribute value change rate represents the change degree of the attribute values between adjacent unmanned aerial vehicle element points.
Preferably, the obtaining the position information and the signal strength sent by the unmanned aerial vehicle, and generating the signal thermodynamic diagram specifically include:
step 21, generating unmanned aerial vehicle element points according to the position information and the signal intensity sent by the unmanned aerial vehicle;
step 22, calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point;
step 23, calculating the gray value of each unmanned aerial vehicle element point according to the attribute value, the attribute correction value and the attribute value change rate of each unmanned aerial vehicle element point to obtain a signal gray map;
and 24, coloring the signal gray level map to obtain a signal thermodynamic diagram.
Preferably, the generating the unmanned aerial vehicle element point includes: and judging the distance between the unmanned aerial vehicle essential point to be generated and the existing unmanned aerial vehicle essential point, merging the unmanned aerial vehicle essential point to be generated with the previous unmanned aerial vehicle essential point if the distance between the two points is smaller than half pixel on the unmanned aerial vehicle inspection two-dimensional map, and generating a new unmanned aerial vehicle essential point if the distance between the two points is greater than half pixel on the unmanned aerial vehicle inspection two-dimensional map.
Preferably, the attribute value of each unmanned aerial vehicle element point is calculated by the signal intensity, the upper limit value of the signal intensity and the lower limit value of the signal intensity of the unmanned aerial vehicle element point.
Preferably, the attribute correction value of the unmanned aerial vehicle essential points after combination is obtained through calculation by using a range and a range coefficient, wherein the range is obtained through calculation by using the maximum value of the attribute values in the unmanned aerial vehicle essential points to be combined and the minimum value of the attribute values in the unmanned aerial vehicle essential points to be combined.
Preferably, the calculation method of the attribute correction value s of the combined unmanned aerial vehicle essential points is as follows:
wherein m is the number of unmanned aerial vehicle essential points to be combined,
for j th unmanned aerial vehicle essential point to be combined +.>
Attribute value of->
The average value of the attribute values of m unmanned aerial vehicle essential points to be combined is obtained.
Preferably, the gray value of each unmanned aerial vehicle principal point i
The calculation method of (1) is as follows:
in the method, in the process of the invention,
attribute value for unmanned aerial vehicle element i,/->
Adjusting coefficients for attribute values, +.>
The attribute correction value of the unmanned aerial vehicle essential point i is represented by y, which is an attribute correction value adjustment coefficient,/->
And z is an attribute value change rate adjusting coefficient for the unmanned aerial vehicle element point i.
One or more embodiments provide a power distribution equipment unmanned aerial vehicle inspection system, comprising:
the unmanned aerial vehicle inspection two-dimensional map generation module is used for generating an unmanned aerial vehicle inspection two-dimensional map according to the unmanned aerial vehicle inspection range;
the signal thermodynamic diagram generating module is used for acquiring the position information and the signal strength sent by the unmanned aerial vehicle and generating a signal thermodynamic diagram; calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point according to the acquired position information and signal strength sent by the unmanned aerial vehicle, wherein the attribute value represents the signal strength of the unmanned aerial vehicle element point, the attribute correction value represents the difference of the attribute values of the unmanned aerial vehicle element points to be combined, and the attribute value change rate represents the change degree of the attribute values between adjacent unmanned aerial vehicle element points;
and the alarm module is used for judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending alarm information.
A medium having stored thereon a program which, when executed by a processor, performs the steps of a method for low-carbon scheduling of an integrated energy system as described above.
An electronic device comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in the low-carbon scheduling method of the integrated energy system when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the signal thermodynamic diagram is generated according to the position information and the signal strength of the unmanned aerial vehicle during inspection, the operation risk of the unmanned aerial vehicle is judged based on the color of the position of the unmanned aerial vehicle in the signal thermodynamic diagram, and the alarm information is correspondingly sent, so that the current network state of the unmanned aerial vehicle can be timely reminded to the personnel to inspect, the problem of inspection data loss caused by sudden signal weakness or signal disconnection during inspection is avoided, and the inspection reliability is improved.
2. When the signal gray level map is generated, three variables of the attribute value, the attribute correction value and the attribute value change rate of the unmanned aerial vehicle essential points are introduced for weighting calculation, three influencing factors of the signal intensity of the unmanned aerial vehicle essential points, the difference of the attribute values of the unmanned aerial vehicle essential points to be combined and the change degree of the attribute values between adjacent unmanned aerial vehicle essential points, which influence the data transmission stability, are fully considered, and the capability of finding potential data transmission risks is improved.
The advantages of the present invention, as well as additional aspects of the invention, will be described in detail in the following detailed examples.
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Fig. 1 is an overall flow chart of a first embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
Example 1
In one or more embodiments, as shown in fig. 1, there is provided an unmanned aerial vehicle inspection method for power distribution equipment, including the following steps:
step 1, generating an unmanned aerial vehicle inspection two-dimensional map according to an unmanned aerial vehicle inspection range;
step 2, acquiring the information of the transmitting position and the signal strength of the unmanned aerial vehicle, and generating a signal thermodynamic diagram;
and 3, judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending out alarm information.
In this embodiment, a signal thermodynamic diagram is generated according to a real-time inspection state of the current unmanned aerial vehicle, and according to the color of the position of the unmanned aerial vehicle in the signal thermodynamic diagram, the operation risk of the unmanned aerial vehicle is judged, and alarm information is sent, so that the problem that inspection frequency data cannot be uploaded or the inspection data is lost in time due to weak signals or broken signals in the inspection process is avoided.
In step 1, the inspection range of the unmanned aerial vehicle is defined in advance according to actual needs, and the size of the two-dimensional map inspected by the unmanned aerial vehicle can be predetermined according to inspection requirements and the inspection range (inspection track) of each unmanned aerial vehicle.
In step 2, position information and signal strength sent by the unmanned aerial vehicle are obtained, and a signal thermodynamic diagram is generated, which specifically includes:
step 21, generating unmanned aerial vehicle element points according to the position information and the signal intensity sent by the unmanned aerial vehicle;
step 22, calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point;
step 23, calculating the gray value of each unmanned aerial vehicle element point according to the attribute value, the attribute correction value and the attribute value change rate of each unmanned aerial vehicle element point to obtain a signal gray map;
and 24, coloring the signal gray level map to obtain a signal thermodynamic diagram.
In step 21, the unmanned aerial vehicle is equipped with a GPS positioning device, so that the position information of the unmanned aerial vehicle can be obtained in real time. Because the interference in the vertical direction in the long-distance inspection is usually small, and the inspection height of the unmanned aerial vehicle is not reduced too low, the signal intensity variation degree in the vertical direction is far smaller than the signal intensity variation degree in the horizontal direction, so that the embodiment finally establishes a two-dimensional thermodynamic diagram, namely the signal intensity variation in the horizontal position is considered, and therefore longitude and latitude information acquired by the GPS is used.
The signal strength of the unmanned aerial vehicle adopts an absolute strength value, the unit is decibel milliwatt (dBm), and the absolute value of power is represented. The unmanned aerial vehicle acquires the position information and the signal strength of the unmanned aerial vehicle in real time, packages the position information and the signal strength, and uploads the packaged position information and the signal strength to the receiving terminal in real time.
The receiving terminal continuously receives the position information and the signal strength sent by the unmanned aerial vehicle. Firstly, a factor point marking layer is newly built on an unmanned aerial vehicle inspection two-dimensional map, the size of the factor point marking layer is the same as that of the unmanned aerial vehicle inspection two-dimensional map, the resolution of the factor point marking layer needs to comprehensively consider factors such as the inspection range, the terrain complexity of the inspection position and the like, and if the inspection range is large and the terrain is flat, the network signal of the unmanned aerial vehicle is usually stable at the moment, so that the resolution of the factor point marking layer can be properly reduced; however, if the terrain is complex in mountains, hills, etc., or if there are many factors (such as trees, buildings, etc.) on the ground that may interfere with the signal, the resolution of the element point mark layer needs to be improved. In addition, the resolution of the layer needs to take into account the visibility of the gray scale map and the thermodynamic diagram generated later, and the size of the pixel is preferably such that the color patch of each pixel can be clearly distinguished from the thermodynamic diagram. Typically, one can choose to have the width of one pixel on the map correspond to 0.1 meters of the actual distance.
And marking the corresponding signal intensity on the element point marking layer according to the position information of the unmanned aerial vehicle, wherein each marking point is an unmanned aerial vehicle element point. The unmanned aerial vehicle element points are used for generating a signal thermodynamic diagram, and the attribute value of each unmanned aerial vehicle element point determines the color of the element point in the signal thermodynamic diagram.
In addition, when the unmanned aerial vehicle element points are generated, the distance between the unmanned aerial vehicle element points to be generated and the existing unmanned aerial vehicle element points is judged, if the distance between the two points is smaller than half a pixel on the unmanned aerial vehicle inspection two-dimensional map, the newly generated unmanned aerial vehicle element points are combined with the previous unmanned aerial vehicle element points, and if the distance between the two points is greater than half a pixel, a new unmanned aerial vehicle element point is generated.
In step 22, first, an upper limit value and a lower limit value of the signal intensity are determined, and then an attribute value of each unmanned plane element point is calculated.
The attribute value of each element point characterizes the magnitude of the signal strength of that element point. In general, unmanned aerial vehicles for realizing communication and data transceiving through the Internet of things have signal strength ranging from-30 dBm to-110 dBm. The upper limit of the signal intensity can be set to be-30 dBm, the lower limit of the signal intensity is the minimum signal intensity which can meet the transmission requirement in actual inspection, and the lower limit of the signal intensity can be set according to the actual requirement, for example, when the inspection of real-time video transmission is required, the requirement on the signal intensity is higher, the lower limit of the signal intensity can be set to be-85 dBm at the moment, and when only an inspection photo needs to be taken, the lower limit of the signal intensity can be reduced to be-95 dBm.
Attribute value of each unmanned aerial vehicle element point i
Obtained by the following formula:
in the method, in the process of the invention,
signal intensity for unmanned aerial vehicle element i, < +.>
Is the upper limit value of signal intensity, +.>
Is the lower limit value of the signal intensity. According to the above, attribute value +.>
The larger the signal intensity +.>
The closer to the lower limit value of the signal intensity +.>
I.e. the weaker the signal at this time.
In addition, if a certain unmanned aerial vehicle element point is obtained by combining a plurality of unmanned aerial vehicle element points, that is, when the unmanned aerial vehicle hovers in the horizontal direction for shooting, slowly moves or moves only in the vertical direction, the distance between the newly generated unmanned aerial vehicle element point and the previous unmanned aerial vehicle element point is smaller than half a pixel, the unmanned aerial vehicle element points need to be combined, and the attribute value of the combined unmanned aerial vehicle element point is the average value of the attribute values of a plurality of unmanned aerial vehicle element points to be combined.
However, using the mean alone may lose much effective information, for example, if the difference between attribute values between a plurality of unmanned aerial vehicle pixels to be combined is larger, it may indicate that the signal strength has a certain gradient of change in the horizontal or vertical direction near the actual environment corresponding to the unmanned aerial vehicle pixels, or where the signal stability is poor, and a certain network fluctuation may exist. Thus, using only the mean value, potentially valid information as described above may be lost.
Based on this, considering the unmanned aerial vehicle principal points obtained by combining the plurality of unmanned aerial vehicle principal points to be combined in the scene, similar to the scene of repeatedly measuring the same observed quantity for a plurality of times, the embodiment creatively introduces the statistical idea into the calculation of the attribute values, and introduces the attribute correction value capable of reflecting the difference of the attribute values among the plurality of unmanned aerial vehicle principal points to be combined on the basis of the calculation mean value.
Specifically, aiming at m unmanned aerial vehicle essential points to be combined
First, the mean of the attribute values of the m points is calculated:
in the method, in the process of the invention,
for the mean value of the attribute values of m unmanned aerial vehicle principal points to be combined, namely the combined unmanned aerial vehicle principal points +.>
Attribute value of->
For j th unmanned aerial vehicle essential point to be combined +.>
Is a property value of (a).
And then, calculating the attribute correction value in two different modes according to the number m of the unmanned aerial vehicle essential points to be combined.
When (when)
The calculation method of the attribute correction value s is as follows:
wherein R is very bad,
，/>
for the maximum value of the attribute values in the unmanned aerial vehicle principal points to be combined,
and C is the range coefficient for the minimum value of the attribute values in the unmanned aerial vehicle essential points to be combined.
When (when)
The calculation method of the attribute correction value s is as follows:
wherein m is the number of unmanned aerial vehicle essential points to be combined,
for j th unmanned aerial vehicle essential point to be combined +.>
Attribute value of->
The average value of the attribute values of m unmanned aerial vehicle essential points to be combined is obtained.
The calculated attribute correction value characterizes the difference of attribute values of m unmanned aerial vehicle main points to be combined, namely reflects the change degree of the signal intensity of the unmanned aerial vehicle at the position, and the larger the attribute correction value is, the more severe the signal intensity of the unmanned aerial vehicle at the point is, the more severe the signal intensity of the unmanned aerial vehicle is, the problems that the signal in the horizontal direction has change gradient, electromagnetic interference, poor signal stability and the like are likely to exist, or the larger change rate in the vertical direction is likely to exist.
Because the meanings of the attribute correction value and the attribute value representation are different and cannot be directly combined, an attribute correction value layer needs to be newly built on the unmanned aerial vehicle inspection two-dimensional map, and the size and resolution of the attribute correction value layer are equal to those of the element point marking layer and are used for marking the attribute correction value corresponding to the unmanned aerial vehicle element point.
Because the change amplitude of the attribute value of the adjacent unmanned aerial vehicle element point may be small, the thermodynamic diagram is generated only by the size of the attribute value, and the change condition of the attribute value is difficult to intuitively reflect, the calculation of the attribute value change rate of the adjacent unmanned aerial vehicle element point is additionally added, and the attribute value change rate is taken as a factor influencing the final thermodynamic diagram color, so that the final thermodynamic diagram has richer information.
The unmanned aerial vehicle element points are continuous points generated in real time according to real-time position information and signal intensity sent during unmanned aerial vehicle inspection, and the distance between two adjacent unmanned aerial vehicle element points is 1 time of the pixel width on an element point mark layer (the two unmanned aerial vehicle element points are adjacent transversely or longitudinally) or
Multiple (two unmanned aerial vehicle principal points are obliquely adjacent).
Assume that the newly generated unmanned aerial vehicle principal point is
The previous unmanned aerial vehicle principal point adjacent to the point is +.>
The calculation method of the attribute value change rate of the adjacent unmanned aerial vehicle element points comprises the following steps:
in the method, in the process of the invention,
for newly generated unmanned aerial vehicle principal point +.>
Relative to the previous unmanned aerial vehicle principal point +.>
Attribute value change rate +_>
For newly generated unmanned aerial vehicle principal point +.>
Attribute value of->
For the previous unmanned aerial vehicle principal point +.>
L is the newly generated unmanned aerial vehicle principal point +.>
And the previous unmanned aerial vehicle principal point +.>
Distance between them.
The attribute value change rate characterizes the change degree of the attribute values between the adjacent unmanned aerial vehicle element points, when the attribute values change in a small amplitude, namely the signal intensity of the unmanned aerial vehicle changes in a small amplitude, the thermodynamic diagram generated by the attribute values is only used, the small-amplitude change cannot be displayed very intuitively, and after the calculation of the attribute value change rate is increased, the small-amplitude change can be highlighted.
In order to superimpose the attribute value change rate into the thermodynamic diagram, an attribute value change rate layer needs to be newly built on the unmanned aerial vehicle inspection two-dimensional map, and the size and resolution of the attribute value change rate layer are equal to those of the element point marking layer, so that the calculated attribute value change rate of each unmanned aerial vehicle element point relative to the previous unmanned aerial vehicle element point can be marked on the corresponding position of the attribute value change rate layer.
In step 23, the gray value of the unmanned aerial vehicle principal point i
Obtained by the following formula:
in the method, in the process of the invention,
attributes for unmanned aerial vehicle element iValue of->
Adjusting coefficients for attribute values, +.>
The attribute correction value of the unmanned aerial vehicle essential point i is represented by y, which is an attribute correction value adjustment coefficient,/->
And z is an attribute value change rate adjusting coefficient for the unmanned aerial vehicle element point i. The attribute value adjustment factor x, the attribute correction value adjustment factor y and the attribute value change rate adjustment factor z are all in the range of 0 to 1, and are used for adjusting the weight relationship between the attribute value, the attribute correction value and the attribute value change rate on the one hand and for enabling ∈ ->
Ranging between 0 and 1.
The gray value range of the complete gray scale band is 0-255, black is 0, white is 255, and the gray value of unfilled pixels on the gray scale layer is 0. According to
As can be seen from the calculation formula of (a) for the unmanned aerial vehicle element point i, the gray value of the unmanned aerial vehicle element point i is positively correlated with the attribute value, the attribute correction value and the attribute value change rate of the point, and when the attribute value is larger (the signal intensity is weaker), the attribute correction value is larger (the signal intensity change is stronger at the point or in the vertical direction of the point), and the attribute value change rate is larger (the signal intensity change is stronger in the horizontal direction), the gray value of the point is larger, and the color filled in the gray map is brighter.
In step 22, the attribute value, the attribute correction value, and the attribute value change rate are respectively marked in the element point marking layer, the attribute correction value layer, and the attribute value change rate layer, thereby obtaining the gray value
Essentially, the values on the three layers are superimposedAnd (3) a process.
After the gray value of each unmanned aerial vehicle pixel is calculated, a gray level layer is newly built, filling is carried out on the gray level layer according to the calculated gray value of each unmanned aerial vehicle pixel, and meanwhile, progressive filling is carried out on pixels which are not filled in a set radius around each unmanned aerial vehicle pixel, so that a signal gray level image is obtained.
And gradually filling, namely setting pixels which are not filled in the radius around each unmanned aerial vehicle essential point, and filling by adopting a linear interpolation method according to the distance between the pixels and the unmanned aerial vehicle essential points, wherein the closer the distance between the pixels and the unmanned aerial vehicle essential points is, the larger the gray value is, and the lighter the corresponding color is. When a certain pixel is simultaneously positioned in the progressive filling range of a plurality of unmanned aerial vehicle element points, the gray value of the progressive filling of the unmanned aerial vehicle element point closest to the pixel is determined.
In step 24, the signal gray scale map is colored based on the gray scale values in the signal gray scale map, thereby obtaining a signal thermodynamic diagram.
In the coloring, a rainbow color band containing 256 colors may be used for filling in accordance with a gray value of 0 to 255, or a threshold range may be set as required, and the color may be filled with several colors such as red, orange, yellow, green, blue, etc. In this example, five colors of red, orange, yellow, green and blue are used for filling.
In step 3, the operation risk of the unmanned aerial vehicle is judged and alarm information is sent out, and the judgment can be carried out according to the generated signal thermodynamic diagram or can be directly carried out according to the gray value in the signal gray diagram. The purpose of generating the signal thermodynamic diagram is to facilitate the operator to more intuitively see the change in signal strength on the map.
Judging the operation risk of the unmanned aerial vehicle and sending out alarm information according to the signal thermodynamic diagram, and specifically comprising the following steps:
if the corresponding color of the current position of the unmanned aerial vehicle in the signal thermodynamic diagram is blue, the current unmanned aerial vehicle is in a signal stable area, and the inspection task can be normally executed;
if the color corresponding to the current position of the unmanned aerial vehicle in the signal thermodynamic diagram is green, the current unmanned aerial vehicle is in a relatively stable signal area, so that the inspection task can be normally executed, but the signal intensity and the data transmission condition need to be continuously monitored;
if the corresponding color of the current position of the unmanned aerial vehicle in the signal thermodynamic diagram is yellow, the situation that the network signal is poor or unstable conditions such as network fluctuation or the like or signal interference can exist near the current position is indicated; setting the unmanned aerial vehicle state as three-level operation risk, pushing three-level risk early warning to staff, prompting that network fluctuation or disconnection risk possibly exists in the current environment, continuously executing the inspection task, but reducing the flying speed of the unmanned aerial vehicle, avoiding data interruption caused by sudden disconnection, and avoiding the unmanned aerial vehicle from being out of control caused by sudden disconnection or network fluctuation if the unmanned aerial vehicle works in a manual control mode, simultaneously continuously focusing on the signal intensity and data transmission condition of the unmanned aerial vehicle, and setting functions such as automatic return in advance if necessary;
if the color corresponding to the current position of the unmanned aerial vehicle in the signal thermodynamic diagram is orange, the current position of the unmanned aerial vehicle is indicated to be poor in network signal, interference is likely to exist, and the problems of network fluctuation and the like occur in a large probability; at the moment, the unmanned plane state is set to be a secondary operation risk, secondary risk early warning is pushed to workers, the risk of network fluctuation or disconnection is high under the current environment, the continuous execution of an automatic inspection task is not recommended, the unmanned plane can fly slowly under manual control, and inspection work with low network requirements such as photo shooting can be carried out in a short time; if the condition allows, the communication can be tried to be switched to be directly communicated with the ground by adopting a frequency band of 2.4GHz or 5.8 GHz;
if the corresponding color of the current position of the unmanned aerial vehicle in the signal thermodynamic diagram is red, the situation that the network signal is poor at the current position of the unmanned aerial vehicle is indicated, the disconnection risk appears in a very high probability, the unmanned aerial vehicle state is set to be the primary operation risk, the primary risk early warning is pushed to the staff, the disconnection risk is high, the patrol task cannot be continuously executed, and the unmanned aerial vehicle needs to be controlled to return or fly to a place with good signal in time.
According to the unmanned aerial vehicle inspection method for the power distribution equipment, the signal thermodynamic diagram is generated according to the position information and the signal intensity when the unmanned aerial vehicle is inspected, the operation risk of the unmanned aerial vehicle is judged and the alarm information is sent correspondingly based on the color of the position of the unmanned aerial vehicle in the signal thermodynamic diagram, workers can be reminded of inspecting the current network state of the unmanned aerial vehicle in time, the problem that inspection data are lost due to sudden signal weakness or signal disconnection when the unmanned aerial vehicle is inspected is avoided, and the inspection reliability is improved.
Example 2
Based on embodiment 1, in this embodiment, a power distribution equipment unmanned aerial vehicle inspection system includes:
the unmanned aerial vehicle inspection two-dimensional map generation module is used for generating an unmanned aerial vehicle inspection two-dimensional map according to the unmanned aerial vehicle inspection range;
the signal thermodynamic diagram generating module is used for acquiring the position information and the signal strength sent by the unmanned aerial vehicle and generating a signal thermodynamic diagram; calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point according to the acquired position information and signal strength sent by the unmanned aerial vehicle, wherein the attribute value represents the signal strength of the unmanned aerial vehicle element point, the attribute correction value represents the difference of the attribute values of the unmanned aerial vehicle element points to be combined, and the attribute value change rate represents the change degree of the attribute values between adjacent unmanned aerial vehicle element points;
and the alarm module is used for judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending alarm information.
Here, the modules in this embodiment are in one-to-one correspondence with the steps in embodiment 1, and the implementation process is the same, which is not described here.
Example 3
The present embodiment provides a medium on which a program is stored, characterized in that the program, when executed by a processor, implements the steps of the method as in embodiment 1.
Example 4
The present embodiment provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps recited in the method of embodiment 1.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The unmanned aerial vehicle inspection method for the power distribution equipment is characterized by comprising the following steps of:
step 1, generating an unmanned aerial vehicle inspection two-dimensional map according to an unmanned aerial vehicle inspection range;
step 2, acquiring position information and signal strength sent by an unmanned aerial vehicle, and generating a signal thermodynamic diagram;
step 3, judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending out alarm information;
in step 2, according to the obtained position information and signal intensity sent by the unmanned aerial vehicle, calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point, wherein the attribute value represents the signal intensity of the unmanned aerial vehicle element point, the attribute correction value represents the difference of the attribute values of the unmanned aerial vehicle element points to be combined, and the attribute value change rate represents the change degree of the attribute values between adjacent unmanned aerial vehicle element points.
2. The unmanned aerial vehicle inspection method of claim 1, wherein the acquiring the position information and the signal strength sent by the unmanned aerial vehicle to generate the signal thermodynamic diagram specifically comprises:
step 21, generating unmanned aerial vehicle element points according to the position information and the signal intensity sent by the unmanned aerial vehicle;
step 22, calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point;
step 23, calculating the gray value of each unmanned aerial vehicle element point according to the attribute value, the attribute correction value and the attribute value change rate of each unmanned aerial vehicle element point to obtain a signal gray map;
and 24, coloring the signal gray level map to obtain a signal thermodynamic diagram.
3. The method for inspecting a power distribution equipment unmanned aerial vehicle according to claim 2, wherein the generating the unmanned aerial vehicle element point comprises: and judging the distance between the unmanned aerial vehicle essential point to be generated and the existing unmanned aerial vehicle essential point, merging the unmanned aerial vehicle essential point to be generated with the previous unmanned aerial vehicle essential point if the distance between the two points is smaller than half pixel on the unmanned aerial vehicle inspection two-dimensional map, and generating a new unmanned aerial vehicle essential point if the distance between the two points is greater than half pixel on the unmanned aerial vehicle inspection two-dimensional map.
4. The unmanned aerial vehicle inspection method of claim 2, wherein the attribute value of each unmanned aerial vehicle element point is calculated by the signal intensity, the upper signal intensity limit value and the lower signal intensity limit value of the unmanned aerial vehicle element point.
5. A method for unmanned aerial vehicle inspection of a power distribution device according to claim 3, wherein the attribute correction value of the combined unmanned aerial vehicle essential points is calculated by a range and a range coefficient, and the range is calculated by a maximum value of attribute values in the unmanned aerial vehicle essential points to be combined and a minimum value of attribute values in the unmanned aerial vehicle essential points to be combined.
6. A method for unmanned aerial vehicle inspection of a power distribution device as claimed in claim 3, wherein the method for calculating the attribute correction value s of the combined unmanned aerial vehicle essential point is as follows:
wherein m is the number of unmanned aerial vehicle essential points to be combined,
is the firstj unmanned aerial vehicle principal points to be combined +.>
Is used to determine the value of the attribute of (c),
the average value of the attribute values of m unmanned aerial vehicle essential points to be combined is obtained.
7. A method for inspection of unmanned aerial vehicle of power distribution equipment as claimed in claim 2, wherein the gray value of each unmanned aerial vehicle element i
The calculation method of (1) is as follows:
in the method, in the process of the invention,
attribute value for unmanned aerial vehicle element i,/->
Adjusting coefficients for attribute values, +.>
The attribute correction value of the unmanned aerial vehicle essential point i is represented by y, which is an attribute correction value adjustment coefficient,/->
And z is an attribute value change rate adjusting coefficient for the unmanned aerial vehicle element point i.
8. A power distribution equipment unmanned aerial vehicle inspection system, comprising:
the unmanned aerial vehicle inspection two-dimensional map generation module is used for generating an unmanned aerial vehicle inspection two-dimensional map according to the unmanned aerial vehicle inspection range;
the signal thermodynamic diagram generating module is used for acquiring the position information and the signal strength sent by the unmanned aerial vehicle and generating a signal thermodynamic diagram; calculating an attribute value, an attribute correction value and an attribute value change rate of each unmanned aerial vehicle element point according to the acquired position information and signal strength sent by the unmanned aerial vehicle, wherein the attribute value represents the signal strength of the unmanned aerial vehicle element point, the attribute correction value represents the difference of the attribute values of the unmanned aerial vehicle element points to be combined, and the attribute value change rate represents the change degree of the attribute values between adjacent unmanned aerial vehicle element points;
and the alarm module is used for judging the operation risk of the unmanned aerial vehicle according to the signal thermodynamic diagram and sending alarm information.
9. A medium having stored thereon a program which when executed by a processor performs the steps of a method of drone inspection of electrical distribution equipment as claimed in any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor performs the steps of a method for drone inspection of a power distribution device as claimed in any one of claims 1 to 7 when the program is executed by the processor.
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