CN105045274A - Intelligent tower connected graph construction method for unmanned aerial vehicle inspection track planning - Google Patents
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Abstract
The invention provides a method for constructing an intelligent tower connectivity graph for planning an unmanned aerial vehicle inspection track, which comprises the following steps of: A. predicting the distribution of the power transmission line towers by adopting a unitary nonlinear regression prediction method to generate a plurality of tower lines, wherein each tower line covers a plurality of towers; B. connecting the plurality of tower lines to form a line connection diagram by using the critical condition of tower distribution; wherein the critical conditions include: the method comprises the following steps of (1) crossing conditions, parallel distribution conditions of a plurality of lines at close distances, tower steering conditions and tower branch conditions; C. and constructing a storage structure of the line connection graph. The invention can utilize the algorithm to carry out intelligent planning on the power transmission line, thereby improving the planning efficiency; the invention provides a whole-course inspection scheme, which not only inspects the route, but also inspects the route, thereby greatly improving the efficiency of line inspection; according to the invention, through an intelligent traversal algorithm, the optimal patrol route at the planned position in a line network is met, and traversal of all towers in the area under the condition of the shortest patrol distance is met.
Description
Technical Field
The invention relates to a method for constructing an intelligent tower connectivity graph for planning an unmanned aerial vehicle inspection track.
Background
The inspection of the overhead line is the basic work of a power grid company on the routine maintenance of the power transmission line, and the inspection modes can be divided into manual inspection, inspection by a manned helicopter and inspection by an unmanned aerial vehicle. Although manual inspection is the most commonly used inspection method, the defects of slow efficiency, restriction by climatic geographic environment and the like exist all the time, the mechanical inspection method is widely researched and applied, especially the safety and high efficiency characteristic of unmanned aerial vehicle inspection, and an overhead power line inspection Flying Robot (FROPI) has greater and greater application value.
The unmanned aerial vehicle system comprises an unmanned aerial vehicle body platform, a task load and a data wireless transmission part. The existing unmanned aerial vehicle platform applied to power transmission line inspection mainly comprises a micro multi-rotor unmanned aerial vehicle, an unmanned aerial vehicle helicopter and the like; the task load mainly comprises the charge of polling imaging equipment such as a visible light camera, a thermal infrared imager and the like; the wireless transmission mainly comprises the transmission (data transmission) of remote control instructions and the transmission (image transmission) of high-bandwidth image data, which are collectively called as a measurement and control data chain.
The unmanned aerial vehicle system is a main body of power transmission line inspection work, an inspection area is firstly determined in the autonomous line inspection process of the unmanned aerial vehicle, the flight line of the unmanned aerial vehicle is planned according to a shooting angle and a flight safety distance on the basis of obtaining accurate line geographic coordinates, and the unmanned aerial vehicle is sent by a starting/landing point to inspect and fly the line according to the flight line uploaded in advance. The unmanned aerial vehicle acquires ideal patrol data as much as possible in the patrol process, and ensures absolute safety and reliability of flight, the flight area and flight route of the unmanned aerial vehicle directly influence patrol quality, patrol efficiency and even the safety of the unmanned aerial vehicle flight and overhead lines, so that the mission planning of the unmanned aerial vehicle for the power transmission line plays a crucial role. Meanwhile, overhead transmission lines are distributed in mountainous areas in many ways, cross-over areas exist, and transmission lines in a large area are complicated, so that the selection of the optimal routing inspection line in a transmission line network in a large area plays a key role in improving the operation efficiency of the unmanned aerial vehicle, improving the energy efficiency ratio and flying safely.
At present, a manual planning mode is mostly adopted for task planning of power transmission line unmanned aerial vehicle inspection, the mode ensures the flight safety of the unmanned aerial vehicle, but the efficiency is low, the requirement of large-scale unmanned aerial vehicle inspection cannot be met, and meanwhile, the manual planning mode is difficult to realize optimal planning in a large area.
The intelligent unmanned aerial vehicle line patrol task planning firstly needs to construct a data structure for all tower positions in the whole overhead line area so as to carry out intelligent algorithm planning.
Chinese patent application 201410283175.8 discloses a high-precision three-dimensional reconstruction method for power transmission lines and corridors. The method comprises the steps of collecting three-dimensional point cloud data of a power transmission line and a corridor through an airborne laser radar, detecting the three-dimensional point cloud data, eliminating wrong points and points with abnormal elevations, automatically classifying the three-dimensional point cloud data by using a voxel classification based method, and finally automatically reconstructing the power transmission line and the corridor through an algorithm.
Chinese patent application 201310676886.7 discloses an unmanned aerial vehicle flight path planning algorithm based on Dubins path and sparse a search. Combining the Dubins path with the sparse A search algorithm, adopting the Dubins path length as a heuristic function of the sparse A search algorithm, and searching nodes in a space by using the heuristic function to realize the flight path planning of the unmanned aerial vehicle.
The defects of the technical scheme are as follows: 1. the existing autonomous task planning means of the overhead transmission line mainly depends on a manual planning mode, the efficiency is low, and the requirement of patrol of large-area complex lines cannot be met; 2. the flight path planning is generally only one-way task planning, namely, the inspection is only carried out on a one-way route, and the task inspection is not carried out in the return process, so that the energy consumption is wasted; 3. in a power transmission line network with cross spanning, the optimal inspection line is difficult to find out from the overall consideration by a manual planning method, so that the flight distance is shortest on the premise of finishing inspection of all lines.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for constructing a connection diagram of an intelligent tower for planning an inspection flight path of an unmanned aerial vehicle.
In order to achieve the purpose, the invention provides a method for constructing an intelligent tower connectivity graph for unmanned aerial vehicle inspection track planning, which comprises the following steps:
A. predicting the distribution of the power transmission line towers by adopting a unitary nonlinear regression prediction method to generate a plurality of tower lines, wherein each tower line covers a plurality of towers;
in the step, firstly, a three-dimensional space model is simplified into a two-dimensional space model, and the distribution of the power transmission line towers is predicted by adopting a unitary nonlinear regression prediction method; and the prediction mode adopts a confidence interval mode, and new input data (explanatory variables) are predicted and judged according to historical tower data (response variables). Due to the particularity of tower distribution, the special case types are summarized, and the existence condition of each special case is determined so as to be classified in the algorithm processing process.
B. Connecting a plurality of tower lines to form a line connection diagram by using the critical condition of tower distribution; wherein the critical conditions include: the method comprises the following steps of (1) crossing conditions, parallel distribution conditions of a plurality of lines at close distances, tower steering conditions and tower branch conditions;
in the step, if the pole tower distribution reaches a critical condition due to the occurrence of special conditions, critical type judgment is carried out, and processing is carried out; the critical types of tower distribution are divided into the following types:
cross-over situation: predicting that a plurality of points are generated in the interval, the number of the points exceeds the set number of the towers, and intersection points exist;
the parallel distribution condition that the distance of a plurality of lines is short: predicting that a plurality of points appear in the interval, exceed the set number of towers and have no intersection points;
the tower turns to the condition: compared with a prediction equation, a unique inflection point appears in a prediction interval;
the tower branch situation: in contrast to the prediction equation, a plurality of inflection points occur within the prediction interval.
C. And constructing a storage structure of the line connection diagram.
According to the method, the tower connected graph is intelligently constructed under the condition that only the geographical coordinates of the tower exist through a linear regression prediction algorithm, so that intelligent task planning is facilitated.
According to another embodiment of the present invention, step a specifically includes the following steps:
a1, dimension reduction treatment: carrying out dimension reduction processing on the geographic coordinates of the three-dimensional tower to convert the geographic coordinates into two-dimensional coordinates; setting the three-dimensional coordinate of the original A pole tower as (x)t,yt,zt),xtRepresenting the three-dimensional spatial longitude coordinate, y, of the towertRepresenting tower latitude coordinate, ztThe altitude of the tower is shown, and the coordinates of the tower A after the dimensionality reduction are (x)t,yt)；
A2, establishment of regression equation: the unitary linear regression prediction model formula applied to the power transmission line mission planning is as follows:
in the formula xtThe longitude coordinates of the tower at the moment t are shown,representing the estimated latitude coordinate at the time t;
a3, retrieving the prediction step size as N, and obtaining the solving equation of the parameters a and b in the regression equation as follows:
wherein,n is a predicted moving step length; because the distance between two base pole towers is different from dozens of meters to hundreds of meters, most of the cases are that the continuous multiple base poles and towers are composed and can approximate to a straight line segment.
According to another embodiment of the present invention, step N of step A3 is 5 m to 10 m.
According to another embodiment of the present invention, step a further comprises step a 4: and (4) performing optimal prediction fitting, and determining a curve determined by the tower coordinates through the optimal fitting curve. Since the curve can be approximated to a straight line within a certain distance except for special cases, a unary linear regression method is adopted in the invention. The curve fitting adopts a 'least square method', namely 'residual square sum minimum' to determine the position of a straight line, and in terms of formula (1), the estimation method of the 'least square method-OLS' should meet the condition that the smaller the difference ei between an actual observed value Yi and a predicted value, the better, the ei represents the formula as follows:
solving by using the claime rule to obtain an OLS estimation formula in the form of an observed value as follows:
fitting a final linear equation according to the processing result as follows:
according to another embodiment of the present invention, step a further comprises step a 5: and constructing a prediction interval. Because the curve equation determined by the actual tower and the prediction equation have a certain deviation, the Y value is subjected to interval prediction, namely a prediction interval of the average value is constructed. And according to the distribution condition of the towers, setting a significance level a, and calculating a prediction interval with the confidence coefficient of the Y average value being 1-a.
According to another embodiment of the present invention, step C specifically includes the following steps:
c1, establishing a tower matrix, wherein the row and column coordinates represent the tower number, and the matrix data are tower geographical position information; the towers are divided into two types according to the prediction result: a node tower and a non-node tower; the nodes comprise a line start-stop tower and a cross tower; the non-node tower is an internal tower only belonging to a single line;
c2, constructing a node tower adjacency list for the node tower;
and C3, for the non-node tower, storing a matrix structure, wherein the matrix row coordinates represent the line to which the non-node tower belongs, and the column coordinates represent the tower number.
In step C2, for the node tower, a chain storage structure-adjacency list is used in consideration of the algorithm storage space and the algorithm efficiency due to the large tower data size.
The construction process of the adjacency list for the node tower is as follows:
c21, sorting according to the geographic position coordinates of the towers and the reference direction;
c22, the data structure of each tower vertex has the following attributes:
a. subscripts: the method is used for marking the tower line;
b. front adjacent point: when the node tower is a cross tower, the previous adjacent line of the line where the front adjacent point is located;
c. rear adjacent point: when the node tower is a cross tower, the next adjacent line of the line where the rear adjacent point is located is adjacent to the line.
In step C3, matrix structure storage is performed on the non-node tower, the matrix row coordinates represent the line to which the non-node tower belongs, and the column coordinates represent the tower number. Note the following:
I. whether the node is a head node, wherein the head node represents one of the end points of one line;
II. Whether it is a cross node: the intersection point value is '1', and the common node is '0';
III, the circuit of the device: the common node has only one line, and the cross node has a plurality of lines.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can utilize the algorithm to carry out intelligent planning on the power transmission line, thereby improving the planning efficiency;
2. the invention provides a whole-course inspection scheme, which not only inspects the route, but also inspects the route, thereby greatly improving the efficiency of line inspection;
3. aiming at the task planning of a complex power transmission line network, the optimal patrol route is planned in the line network through an intelligent traversal algorithm, and the traversal of all towers in an area under the condition of the shortest patrol distance is met.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
Fig. 1 is an overall flowchart of the intelligent tower connectivity graph construction method of embodiment 1;
FIG. 2 is a flow chart of step A in example 1.
Detailed Description
Example 1
The embodiment provides a method for constructing an intelligent tower connectivity graph for planning an unmanned aerial vehicle inspection track, which comprises the following steps of:
A. and predicting the distribution of the power transmission line towers by adopting a unitary nonlinear regression prediction method to generate a plurality of tower lines, wherein each tower line covers a plurality of towers. As shown in fig. 2, step a specifically includes the following steps:
a1, dimension reduction treatment: carrying out dimension reduction processing on the geographic coordinates of the three-dimensional tower to convert the geographic coordinates into two-dimensional coordinates; setting the three-dimensional coordinate of the original A pole tower as (x)t,yt,zt),xtRepresenting the three-dimensional spatial longitude coordinate, y, of the towertRepresenting tower latitude coordinate, ztIndicating the altitude of the towerAnd the coordinates of the A pole tower after dimension reduction are (x)t,yt) (ii) a But the storage structure still keeps the tower elevation information for planning the flight path.
A2, establishment of regression equation: the unitary linear regression prediction model formula applied to the power transmission line mission planning is as follows:
in the formula xtThe longitude coordinates of the tower at the moment t are shown,representing the estimated latitude coordinate at the time t;
a3, retrieving the prediction step size as N, and obtaining the solving equation of the parameters a and b in the regression equation as follows:
wherein,n is a predicted moving step length; because the distance between two base pole towers is different from dozens of meters to hundreds of meters, most of the cases are that the continuous multiple base poles and towers are composed and can approximate to a straight line segment. According to the tower distribution condition of a specific area and the accuracy and rapidity of prediction are guaranteed, the step length N can be set to be 5-10 m.
And A4, best prediction fitting, and determining a curve determined by the tower coordinates through the best fitting curve. Since the curve can be approximated to a straight line within a certain distance except for special cases, a unary linear regression method is adopted in the invention. The curve fitting adopts a 'least square method', namely 'residual square sum minimum' to determine the position of a straight line, and in terms of formula (1), the estimation method of the 'least square method-OLS' should meet the condition that the smaller the difference ei between an actual observed value Yi and a predicted value, the better, the ei represents the formula as follows:
solving by using the claime rule to obtain an OLS estimation formula in the form of an observed value as follows:
fitting a final linear equation according to the processing result as follows:
and A5, constructing a prediction interval. Because the curve equation determined by the actual tower and the prediction equation have a certain deviation, the Y value is subjected to interval prediction, namely a prediction interval of the average value is constructed. And according to the distribution condition of the towers, setting a significance level a, and calculating a prediction interval with the confidence coefficient of the Y average value being 1-a.
B. Connecting a plurality of tower lines to form a line connection diagram by using the critical condition of tower distribution; wherein the critical conditions include: the method comprises the following steps of (1) crossing conditions, parallel distribution conditions of a plurality of lines at close distances, tower steering conditions and tower branch conditions;
in the step, if the pole tower distribution reaches a critical condition due to the occurrence of special conditions, critical type judgment is carried out, and processing is carried out; the critical types of tower distribution are divided into the following types:
cross-over situation: predicting that a plurality of points are generated in the interval, the number of the points exceeds the set number of the towers, and intersection points exist;
the parallel distribution condition that the distance of a plurality of lines is short: predicting that a plurality of points appear in the interval, exceed the set number of towers and have no intersection points;
the tower turns to the condition: compared with a prediction equation, a unique inflection point appears in a prediction interval;
the tower branch situation: in contrast to the prediction equation, a plurality of inflection points occur within the prediction interval.
C. And constructing a storage structure of the line connection diagram. The step C specifically comprises the following steps:
c1, establishing a tower matrix, wherein the row and column coordinates represent the tower number, and the matrix data are tower geographical position information; the towers are divided into two types according to the prediction result: a node tower and a non-node tower; the nodes comprise a line start-stop tower and a cross tower; the non-node tower is an internal tower only belonging to a single line;
c2, constructing a node tower adjacency list for the node tower;
and C3, for the non-node tower, storing a matrix structure, wherein the matrix row coordinates represent the line to which the non-node tower belongs, and the column coordinates represent the tower number.
In step C2, for the node tower, a chain storage structure-adjacency list is used in consideration of the algorithm storage space and the algorithm efficiency due to the large tower data size.
The construction process of the adjacency list for the node tower is as follows:
c21, sorting according to the geographic position coordinates of the towers and the reference direction;
c22, the data structure of each tower vertex has the following attributes:
a. subscripts: the method is used for marking the tower line;
b. front adjacent point: when the node tower is a cross tower, the previous adjacent line of the line where the front adjacent point is located;
c. rear adjacent point: when the node tower is a cross tower, the next adjacent line of the line where the rear adjacent point is located is adjacent to the line.
In step C3, matrix structure storage is performed on the non-node tower, the matrix row coordinates represent the line to which the non-node tower belongs, and the column coordinates represent the tower number. Note the following:
I. whether the node is a head node, wherein the head node represents one of the end points of one line;
II. Whether it is a cross node: the intersection point value is '1', and the common node is '0';
III, the circuit of the device: the common node has only one line, and the cross node has a plurality of lines.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that changes may be made without departing from the scope of the invention, and it is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.
Claims (5)
1. An intelligent tower connected graph construction method for planning an unmanned aerial vehicle inspection track is characterized by comprising the following steps:
A. predicting the distribution of the power transmission line towers by adopting a unitary nonlinear regression prediction method to generate a plurality of tower lines, wherein each tower line covers a plurality of towers;
B. connecting the plurality of tower lines to form a line connection diagram by using the critical condition of tower distribution; wherein the critical conditions include: the method comprises the following steps of (1) crossing conditions, parallel distribution conditions of a plurality of lines at close distances, tower steering conditions and tower branch conditions;
C. and constructing a storage structure of the line connection graph.
2. The connectivity graph construction method according to claim 1, wherein the step a specifically includes the steps of:
a1, dimension reduction treatment: carrying out dimension reduction processing on the geographic coordinates of the three-dimensional tower to convert the geographic coordinates into two-dimensional coordinates;
a2, establishment of regression equation: the unitary linear regression prediction model formula applied to the power transmission line mission planning is as follows:
in the formula xtThe longitude coordinates of the tower at the moment t are shown,representing the estimated latitude coordinate at the time t;
a3, retrieving the prediction step size as N, and obtaining the solving equation of the parameters a and b in the regression equation as follows:
wherein, <math>
<mrow>
<mi>Σ</mi>
<mo>=</mo>
<munderover>
<mi>Σ</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mo>,</mo>
</mrow>
</math> and N is the predicted moving step.
3. The method for constructing a connectivity graph according to claim 2, wherein in the step a3, the step length N is 5 m to 10 m.
4. The connectivity graph building method according to claim 2, wherein the step a further comprises a step a 4: and (4) performing optimal prediction fitting, and determining a curve determined by the tower coordinates through the optimal fitting curve.
5. The connectivity graph constructing method according to claim 1, wherein the step C specifically includes the steps of:
c1, establishing a tower matrix, wherein the row and column coordinates represent the tower number, and the matrix data are tower geographical position information; the towers are divided into two types according to the prediction result: a node tower and a non-node tower; the nodes comprise a line start-stop tower and a cross tower; the non-node tower is an internal tower only belonging to a single line;
c2, constructing a node tower adjacency list for the node tower;
and C3, for the non-node tower, storing a matrix structure, wherein the matrix row coordinates represent the line to which the non-node tower belongs, and the column coordinates represent the tower number.
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| CN111609855A (en) * | 2019-12-27 | 2020-09-01 | 北京数字绿土科技有限公司 | Method for generating refined routing inspection routes in batch based on tower shapes |
| CN111609855B (en) * | 2019-12-27 | 2021-05-18 | 北京数字绿土科技有限公司 | Method for generating refined routing inspection routes in batch based on tower shapes |
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| CN111443730B (en) * | 2020-05-12 | 2021-02-12 | 江苏方天电力技术有限公司 | Unmanned aerial vehicle track automatic generation method and device for power transmission line inspection |
| CN112414409A (en) * | 2020-11-16 | 2021-02-26 | 天津航天中为数据系统科技有限公司 | Autonomous inspection path planning method based on string structure and aircraft |
| CN113009929A (en) * | 2021-03-01 | 2021-06-22 | 中国南方电网有限责任公司超高压输电公司贵阳局 | Unmanned aerial vehicle power grid line patrol control method |
| CN113052339A (en) * | 2021-03-31 | 2021-06-29 | 广东电网有限责任公司 | Unmanned aerial vehicle inspection method and device based on line crossing and storage medium |
| CN113110577A (en) * | 2021-04-15 | 2021-07-13 | 中国南方电网有限责任公司超高压输电公司柳州局 | Unmanned aerial vehicle flight route planning management system is patrolled and examined to electric wire netting |
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| CN115686070A (en) * | 2022-11-18 | 2023-02-03 | 国网江苏省电力有限公司苏州供电分公司 | Unmanned aerial vehicle autonomous line patrol path planning method for power distribution/power transmission line |
| CN115686070B (en) * | 2022-11-18 | 2024-01-23 | 国网江苏省电力有限公司苏州供电分公司 | Autonomous line patrol path planning method for unmanned aerial vehicle of power distribution/transmission line |
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