CN116820137B - Unmanned aerial vehicle power distribution network routing inspection route generation method - Google Patents
Unmanned aerial vehicle power distribution network routing inspection route generation method Download PDFInfo
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Abstract
The application discloses a method for generating a routing inspection route of an unmanned aerial vehicle power distribution network, which comprises the following steps: step 1: according to known fault points, according to a distribution network inspection rule, and by using an empirical model as a tower grading, predicting tower points which are important to be inspected; step 2: determining the effective flight distance of various types according to the pole tower grading; step 3: optimizing unmanned aerial vehicle take-off and landing points patrolling along power distribution network, and step 4: and (3) optimizing a power distribution network inspection route generation method, and generating an optimal flight route according to the inspection flight rule and the take-off and landing points obtained in the step (3). According to the method, the position of the take-off and landing point is calculated according to the position of the key tower and the parameters (energy consumption, time and range) of the unmanned aerial vehicle, the inspection route of the power distribution network is optimally constructed, the optimal machine type and the optimal inspection route are rapidly selected according to the fault prediction point in the inspection range, and the inspection efficiency is effectively improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle inspection of power systems, in particular to a method for generating an inspection route of an unmanned aerial vehicle power distribution network.
Background
With the continuous development and expansion of power systems, maintenance and management of power equipment has become one of the vital tasks in the power industry. The traditional power inspection method generally depends on manual inspection and equipment detection, has the problems of large workload, difficulty in finding abnormal conditions in time, high safety risk, low inspection efficiency and the like, and forms a great challenge for power equipment management and ensuring the reliability and stability of power supply. In recent years, with the continuous development and application of unmanned aerial vehicle technology, unmanned aerial vehicle power inspection is increasingly focused and valued by power system administrators, and is widely applied to the field of power inspection. Unmanned aerial vehicle utilizes camera equipment, sensor and the location technique of high accuracy, can realize that all-round, high-efficient, accurate power equipment patrols and examines, and unmanned aerial vehicle patrols and examines the technique and has important effect to improving power equipment's safe operation and management, has realized intelligent and the digital management of electric power trade, has promoted the innovation and the development of electric power trade.
Unmanned aerial vehicle inspection is in the aspect of power distribution network inspection application, still at the stage of taking off to try at present, and the problem that mainly runs into is because power distribution network grid structure is intensive, and is distributed in crowded region more, and building, trees, vehicle are great to unmanned aerial vehicle's flight interference, and distribution unmanned aerial vehicle inspection personnel is when controlling unmanned aerial vehicle, and the field of vision is not wide, the environment is complicated, the emergency is many, causes the condition of frying easily to take place. Unmanned aerial vehicle independently flies and can effectively reduce the error that manual control introduced, utilizes unmanned aerial vehicle's RTK real-time positioning information from taking, and unmanned aerial vehicle just can oneself fly according to the good route of patrolling and examining of personnel planning in advance, has effectively reduced the risk coefficient of patrolling and examining, has reduced the condition of frying, has improved the efficiency of patrolling and examining simultaneously. However, in the aspect of selecting a take-off and landing point and effectively utilizing the cruising ability of the unmanned aerial vehicle, the inspection personnel are always plagued. The landing point is selected at will, the inspection efficiency of the whole unmanned aerial vehicle is low, the extra consumption of a battery can be caused, and meanwhile, the driving time for switching the next landing point is increased.
Through public patent search, the following comparison documents were found:
the method comprises the following steps: s1, inputting transmission tower information and transmission line information into a patrol system, establishing a transmission tower and transmission line model through three-dimensional simulation, numbering each tower, and establishing a tower database; s2, marking a pole tower part, automatically generating a waypoint according to the marking part, and automatically generating an optimal tour route of the power transmission line according to the waypoint; s3, determining a patrol take-off and landing point of the unmanned aerial vehicle, calculating a take-off and landing route, and determining an actual patrol circulation route by combining an optimal patrol route of the power transmission line; s4, the unmanned aerial vehicle performs inspection according to an actual inspection circulating route, meanwhile, the route state is updated in real time through self-defined check parameters, route check, route intelligent correction and automatic combination of the inspection route and the waypoints are performed, route introduction, route editing and route check are provided through third-party route management, and the unmanned aerial vehicle inspection process is controlled in real time.
The specific implementation method and the technical effect of the unmanned aerial vehicle are different from those of the unmanned aerial vehicle, so that the novelty of the unmanned aerial vehicle is not affected.
In summary, how to generate an unmanned aerial vehicle power distribution network routing inspection route with strong adaptability, low energy consumption and high efficiency according to the tower and cable layout becomes a technical problem to be solved urgently by the person in the field.
Disclosure of Invention
The application aims to overcome the defects of the prior art and provides a method for generating an inspection route of an unmanned aerial vehicle power distribution network.
A method for generating a routing inspection route of an unmanned aerial vehicle power distribution network comprises the following steps:
step 1: according to known fault points, according to a distribution network inspection rule, and by using an empirical model as a tower grading, predicting tower points which are important to be inspected;
step 2: determining the effective flight distance of various types according to the pole tower grading;
step 3: optimize unmanned aerial vehicle take off and land point of patrolling along distribution network, specifically include:
step 3.1: selecting a patrol task, and collecting information of a tower G and a wire segment set in a task coverage area;
step 3.2: generating a tower topological connection relation tree table structure T according to the connection relation of the wire segments;
step 3.3: constructing an circumscribed circle C of the region set where the tower set G is positioned;
step 3.4: selecting any tower node on the external circle as a starting point, selecting different types of tower nodes, and calculating the number of layers of the covered parent-level tower nodes;
step 3.5: taking a tower where the farthest parent node is located as a first take-off and landing point H1, and putting the tower into the take-off and landing point set H;
step 3.6: calculating the coverage range of the take-off and landing point from h1, namely, a subset G1 of all the parent and child towers covered;
step 3.7: removing the tower subset G1 from the tower set G;
step 3.8: repeating the step 3.3 until the tower set G is empty, wherein the set H is a planned take-off and landing point;
step 4: and (3) optimizing a power distribution network inspection route generation method, and generating an optimal flight route according to the inspection flight rule and the take-off and landing points obtained in the step (3).
Preferably, the tower grading in step 1 comprises: a class A pole tower, a patrol tower head, an insulator, a cross arm and a small hardware fitting; b-stage pole tower, inspection tower head and cross arm; and C-stage pole tower, and inspection tower head.
Preferably, in step 2, the type of the existing machine type is counted first, and the endurance time and the flight speed of each machine type are collected; and then testing the time consumption of flying each model in each level tower, and finally calculating and comparing the optimal inspection tower level of each model, wherein the method comprises the following specific steps:
step 2.1: selecting a model of a patrol A-level tower, counting take-off time, patrol time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data A1; b, inspecting the B-stage tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data B1; c, inspecting the C-level tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data C1;
step 2.2: repeating the step 1 until all the machine types are inspected;
step 2.3: for different tower levels, selecting a model with short time and high residual electric quantity from the data set as an alternative model.
Preferably, in step 3.2, a topology connection relation tree table is constructed by adopting a space query method, which specifically comprises the following steps:
step 3.2.1: selecting any tower G1 in the tower set G, and analyzing a buffer zone with the radius of 0.5 m in the wire segment set, wherein the analysis result is a wire segment set L connected with the tower;
step 3.2.2: taking one section of wire segment L1 in the collection L, calculating the Euclidean distance between the end point of the wire segment and the tower, taking one end with a long distance, and analyzing a buffer zone with the radius of 0.5 meter in the wire segment collection, wherein the analysis result is the name of the superior connection relation, namely the number of the tower;
step 3.2.3: writing a tower set ID and a wire outlet point name, namely a tower number and a superior connection relation type acquired in the last step, namely a tower and a superior connection relation name, namely an analysis result and a connection relation type acquired in the last step, namely a tower and a connection relation name, namely a tower number selected in the step 3.2.1, into a tower topology connection relation tree table structure, and deleting L1 from the set L;
step 3.2.4: repeating step 3.2.2 until the set L is empty;
step 3.2.5: g1 is removed from set G and then step 1 is repeated until set G is empty.
Preferably, in step 3.3, the circumscribed circle C is constructed by adopting a three-point method, and the specific steps are as follows:
step 3.3.1: selecting a tower with the largest longitude and the smallest longitude and a tower with the largest latitude from the tower set;
step 3.3.2: three towers are selected from the four towers at will, and a triangle is formed by taking the three towers as vertexes;
step 3.3.3: solving the circumscribed circle of the triangle;
step 3.3.4: judging whether the rest towers are out of the circumscribing circle, and repeating 3.3.2 and 3.3.3 if the rest towers are out of the circumscribing circle; if not, the circumscribed circle is the circumscribed circle C of the tower set.
Preferably, in step 3.4, the model with the largest number of nodes covering the parent level tower is selected as the candidate model M; the specific calculation steps are as follows:
step 3.4.1: selecting a model calculation, searching a next-stage tower g1 according to a tower topological connection relation tree table structure T, and calculating the tower patrol time and the flight time;
step 3.4.2: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 3.4.1 until all the machine types are calculated; if so, putting g1 into the subset O1;
step 3.4.3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower patrol time and the flight time;
step 3.4.4: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 3.4.1 until all the machine types are calculated; if so, putting g2 into the subset O1;
step 3.4.5: repeating the step 3.4.3 until all the model calculation is completed, wherein the set O1, O2 and O3 … On is the coverage range of the take-off and landing points of each model.
Preferably, the specific calculation step of step 3.6 is as follows:
step 3.6.1: searching a next-stage tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower inspection time and the flight time;
step 3.6.2: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 3.6.1 until all the machine types are calculated; if so, putting G1 into the subset G1;
step 3.6.3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower patrol time and the flight time;
step 3.6.4: judging whether the unmanned aerial vehicle is in a cruising state or not, and if not, repeating the step 3.6.1; if so, putting G2 into the subset G1;
step 3.6.5: repeating step 3.6.3, the collection G1, G2, G3 … Gn is the coverage area of each landing point.
Preferably, step 4 further comprises the sub-steps of:
step 4.1: determining an unmanned aerial vehicle distribution network inspection rule, wherein the inspection rule comprises flying inspection along a line trend or flying inspection of a key tower;
step 4.2: generating a flight route map of each take-off and landing point unmanned aerial vehicle; the specific generation steps are as follows:
step 4.2.1: from the lifting point set H, arbitrarily selecting one lifting point H1, and searching a next-stage tower g1 according to a tower topology connection relation tree table structure T;
step 4.2.2: judging whether G1 is in the subset G1, if not, repeating the step 4.2.1; if yes, recording the routes of the towers g1 to R1 in a collection;
step 4.2.3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T;
step 4.2.4: judging whether G2 is in the subset G1, if not, repeating the step 4.2.1; if yes, recording the routes of the towers g2 to R1 in a collection;
step 4.2.5: repeating the step 4.2.3 until the algorithm is finished, wherein the sets R1, R2 and R3 … Rn are flight routes of each take-off and landing point;
step 4.3: calculating an optimal driving route according to road network and traffic road conditions;
step 4.4: and generating a landing point switching roadmap.
The application has the advantages and technical effects that:
(1) The application provides a power distribution network unmanned aerial vehicle routing inspection line planning method, which can rapidly select an optimal machine type and an optimal routing inspection line according to fault prediction points in an inspection range, and effectively improve the unmanned aerial vehicle routing inspection efficiency.
(2) The effective flight distance acquisition method provided by the application is obtained through a test flight test, and can be corrected according to the actual condition of post-inspection, so that the dynamic adjustability is ensured.
(3) The application provides a landing point selection method, which effectively reduces the time of driving a road wasted by switching landing points.
(4) The take-off and landing point switching algorithm provided by the application combines comprehensive factors of road network, traffic and power grid data, and is efficient and feasible.
Drawings
FIG. 1 is a flow chart of a method for selecting and calculating a pole and tower point position in the application;
FIG. 2 is a flowchart of a method for calculating the effective flight distance of an unmanned aerial vehicle according to the present application;
FIG. 3 is a flowchart of a take-off and landing point selection algorithm according to the present application;
FIG. 4 is a flow chart of a method for generating a flight path of an unmanned aerial vehicle according to the present application;
FIG. 5 is a schematic diagram of a tower topology connection relationship tree T of the present application;
FIG. 6 is a schematic illustration of a tower set of the present application;
FIG. 7 is a schematic illustration of the location of a tower set circle according to the present application;
FIG. 8 is a schematic diagram of the position of the most accessible inspection tower, with g1 selected as the starting point and g 3;
FIG. 9 is a schematic diagram showing the steps of selecting G3 as the landing point h1 and calculating the inspection range G1 according to the present application;
FIG. 10 is a schematic view of the locations of the remaining tower set G and the set H of take-off and landing points in the present application;
FIG. 11 is a schematic view of a circle circumscribed by the remaining tower set G in the present application;
FIG. 12 is a schematic diagram showing the positions of the most accessible inspection towers, wherein g14, g6 and g10 are selected as starting points, and g12, g6 and g9 are selected as starting points;
FIG. 13 is a schematic diagram showing the steps of selecting G12, G6, G9 as the landing points h2, h3, h4, and calculating the inspection ranges G2, G3, G4;
FIG. 14 is a schematic view of the locations of the tower set G and the set H of take-off and landing points that remain again in the present application;
FIG. 15 is a schematic view of the resulting landing gear position in the present application.
Description of the embodiments
For a further understanding of the nature, features, and efficacy of the present application, the following examples are set forth to illustrate, but are not limited to, the application. The present embodiments are to be considered as illustrative and not restrictive, and the scope of the application is not to be limited thereto.
The application relates to a method for generating a routing inspection route of an unmanned aerial vehicle power distribution network, which mainly comprises the following four steps:
the first step of the application is to provide a power distribution network inspection tower point position selection algorithm to realize optimal inspection tower point position selection, and according to known fault points, according to the inspection rule of a distribution network, the tower is rated according to an empirical model (A-level tower inspection tower head, insulator, cross arm and other small hardware fittings; B-level tower inspection tower head, cross arm; C-level tower, inspection tower head; …), and the tower point position which is important to be inspected is predicted.
Defining an empirical model as that a tower which has failed recently also fails;
the probability of the tower adjacent to the fault point is high;
the probability of failure of the towers near the pond after rain is high;
the probability of failure of the crossing line is high;
the failure probability of the tension pole tower is high;
the second step of the application is to provide an effective flight distance calculation method, so that the unmanned aerial vehicle can fly to achieve the highest inspection efficiency, and the effective flight distances of various types are calculated according to the pole tower grading.
Counting the types of the existing machine types, and collecting the endurance time and the flight speed of each machine type;
the time for flying each model in each level tower is spent;
calculating and comparing the optimal inspection pole tower level of each model, and specifically, the steps of: step 1: selecting a model (such as a Dajiang Royal 2 unmanned plane) for inspecting a class A tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data A1; b, inspecting the B-stage tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data B1; c, inspecting the C-level tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data C1; step 2: repeating the step 1 until all the machine types are inspected; step 3: for different tower levels (A, B, C), selecting a model with short time and high residual capacity from the data set as an alternative model.
The third step of the application is to provide a power distribution network inspection take-off and landing point selection algorithm, and to realize the effective flight maximization of inspection by using the existing unmanned aerial vehicle resources, reduce the take-off and landing times of the unmanned aerial vehicle, and reduce the time spent in switching the take-off and landing points.
Selecting a patrol task, and collecting information of a tower G and a wire segment set in a task coverage area;
generating a tower topological connection relation tree table structure T according to the connection relation of the wire segments, wherein the tree table structure T is as follows:
the method for constructing the topological connection relation tree table is realized by adopting space inquiry, and comprises the following specific steps: step 1: selecting any tower G1 (G1 epsilon G) in the tower set G, and analyzing a buffer area with the radius of 0.5 m in the wire segment set, wherein the analysis result is a wire segment set L connected with the tower; step 2: taking one section of the lead L in the collection L 1 Calculate the Euclidean distance between the end point of the wire segment and the tower (++>) Taking one end far away, and analyzing a buffer zone with the radius of 0.5 meter in the wire segment set, wherein the analysis result is the name of the upper-level connection relation (the number of the pole tower); step 3: writing the tower set ID, the outlet point name (tower number selected in step 1), the upper connection relation type (tower), the upper connection relation name (analysis result in step 2), the connection relation type (tower) and the connection relation name (tower number selected in step 1) into a tower topology connection relation tree table structure, writing L 1 Removing from the collection L; step 4: repeating the step 2 until the set L is empty; step 5: will g 1 Remove from set G and then repeat step 1 until set G is empty.
The tower set G (g= { G1, G2, G3, …, gn }, n= {1,2,3, … }) circumscribes circle C is constructed. The method for constructing the circumscribed circle comprises the following steps: the method comprises the following specific steps: step 1: selecting a tower with the largest longitude and the smallest longitude and a tower with the largest latitude from the tower set; step 2: three towers are selected from the four towers at will, and a triangle is formed by taking the three towers as vertexes; step 3: solving the circumscribed circle of the triangle; step 4: judging whether the rest towers are outside the circumscribed circle, and repeating the step 2 and the step 3 if the rest towers are outside the circumscribed circle; if not, the circumscribed circle is the circumscribed circle C of the tower set.
Selecting any tower node on the external circle as a starting point, selecting different models, calculating the parent tower node covering several layers, and selecting the model with the largest parent tower node covering as an alternative model M; the specific calculation steps are as follows: step 1: selecting a model calculation, searching a next-stage tower G1 (G1 epsilon G) according to a tower topological connection relation tree table structure T, and calculating the tower inspection time and the flight time; step 2: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 1 until calculation of all the models is completed; if so, g1 is put into subset O1. Step 3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower patrol time and the flight time; step 4: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 1 until calculation of all the models is completed; if so, g2 is placed in subset O1. Step 5: repeating the step 3 until all the model calculation is completed, and collecting O1, O2 and O3 … On (n= {1,2,3, … }) is the coverage of the take-off and landing points of each model.
Taking a tower where the farthest parent node is located as a first take-off and landing point H1, and putting the tower into the take-off and landing point set H;
the coverage of this take-off and landing point, i.e. all parent and child level tower subsets G1 of the coverage, is calculated starting from h1. The specific calculation steps are as follows: step 1: searching a next-stage tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower inspection time and the flight time; step 2: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 1 until calculation of all the models is completed; if so, G1 is placed in subset G1. Step 3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower patrol time and the flight time; step 4: judging whether the unmanned aerial vehicle is in a course or not enough to return, and if not, repeating the step 1; if so, G2 is placed in subset G1. Step 5: repeating step 3, the set G1, G2, G3 … Gn (n= {1,2,3, … }) is the coverage of each landing point.
Removing the tower subset G1 from the tower set G;
and (3) repeating the step (3) until the tower set G is empty, wherein the set H is the planned take-off and landing point.
The fourth object of the application is to provide a power distribution network patrol route generation algorithm, which generates an optimal flight route according to planned take-off and landing points and patrol flight rules.
Determining unmanned aerial vehicle distribution network inspection rules, for example: optionally, flying along the line;
generating a flight route map of each take-off and landing point unmanned aerial vehicle; the specific generation steps are as follows: step 1: from the lifting point set H, arbitrarily selecting one lifting point H1, and searching a next-stage tower g1 according to a tower topology connection relation tree table structure T; step 2: judging whether G1 is in the subset G1, if not, repeating the step 1. If yes, recording the routes of the towers g1 to R1 in a collection; step 3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T; step 4: judging whether G2 is in the subset G1, if not, repeating the step 1. If yes, recording the routes of the towers g2 to R1 in a collection; step 5: step 3 is repeated until the algorithm is finished, and the sets R1, R2, R3 … Rn (n= {1,2,3, … }) are both flight routes of each take-off and landing point.
Calculating an optimal driving route according to road network and traffic road conditions;
and generating a landing point switching roadmap.
Finally, the following description of the embodiments of the application refers to the accompanying drawings:
in the power distribution network inspection tower point position selection process, optimal inspection tower point position selection is realized, according to known fault points and the inspection rule of a distribution network, the tower is ranked according to an empirical model, and the tower point positions which are important to inspect are predicted, wherein a specific flow is shown in fig. 1.
In the effective flight distance calculation process, the unmanned aerial vehicle is enabled to fly to achieve the highest inspection efficiency, the effective flight distances of various types are calculated according to the pole tower grading, and a specific flow is shown in fig. 2.
In the process of selecting the power distribution network inspection take-off and landing points, the existing unmanned aerial vehicle resources are utilized to realize effective flight maximization of inspection, take-off and landing times of the unmanned aerial vehicle are reduced, the time spent for switching the take-off and landing points is reduced, and a specific flow is shown in fig. 3.
In the power distribution network inspection route generation process, according to planned take-off and landing points and inspection flight rules, an optimal flight route is generated, and the specific flow is shown in fig. 4.
In addition, the application relates to a method for generating the inspection route of the unmanned aerial vehicle power distribution network, which has the following operation principle:
1. in general, the unmanned aerial vehicle inspection task comprises a plurality of pole towers, and is calculated according to the method:
(1) coverage area of each type of take-off and landing point: the sets O1, O2, O3 … On (n= {1,2,3, … }).
(2) Calculating to obtain a planned take-off and landing point according to one common model M (coverage area O3): set H, coverage of each landing point: the sets G1, G2, G3 … Gn (n= {1,2,3, … }).
(3) Tower topology connection relation tree table structure: t.
(4) And calculating a flight route of each take-off and landing point according to the coverage area G of each take-off and landing point and the topological connection relation tree table structure T of the pole tower: the sets R1, R2, R3 … Rn (n= {1,2,3, … }).
When the patrol task is executed, a take-off and landing point H1 is selected from the set H, then the unmanned aerial vehicle is driven (or otherwise) to be transported to H1, the unmanned aerial vehicle is flown from H1, patrol is carried out according to a flight route R1 of the take-off and landing point H1, then routes R2 and … and finally routes Rn, and the patrol of the take-off and landing point is completed. B. And (C) repeating the step A until all take-off and landing points are inspected.
2. In special cases, when the planned take-off and landing point h1 cannot be reached due to environmental factors such as rainfall, ponding, construction and the like, the take-off and landing point h1 needs to be adjusted. To generate minimal impact, a merging adjustment of the routing paths, such as merging the flight paths R1 and R2 for routing, is required. At this time, the original model M (O3) cannot complete the inspection of the combined flight path due to factors such as electric quantity endurance, and a new model M (O1) needs to be selected again. Therefore, the machine type can be flexibly selected according to the coverage range of the take-off and landing points of each machine type, and the inspection condition of local fine adjustment of the take-off and landing points can be met.
Finally, the application adopts the mature products and the mature technical means in the prior art.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (7)
1. The unmanned aerial vehicle power distribution network routing inspection route generation method is characterized by comprising the following steps of:
step 1: according to known fault points, according to a distribution network inspection rule, and by using an empirical model as a tower grading, predicting tower points which are important to be inspected;
step 2: determining the effective flight distance of various types according to the pole tower grading;
step 3: optimize unmanned aerial vehicle take off and land point of patrolling along distribution network, specifically include:
step 3.1: selecting a patrol task, and collecting information of a tower set G and a wire segment set in a task coverage area;
step 3.2: generating a tower topological connection relation tree table structure T according to the connection relation of the wire segments;
step 3.3: constructing an circumscribed circle C of the region set where the tower set G is positioned;
step 3.4: selecting any tower node on the external circle as a starting point, selecting different types of tower nodes, and calculating the number of layers of the covered parent-level tower nodes; in the step 3.4, the model with the most nodes covering the parent level pole tower is selected as an alternative model M; the specific calculation steps are as follows:
step 3.4.1: selecting a model calculation, searching a next-stage tower g1 according to a tower topological connection relation tree table structure T, and calculating the tower patrol time and the flight time;
step 3.4.2: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 3.4.1 until all the machine types are calculated; if so, putting g1 into the subset O1;
step 3.4.3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower patrol time and the flight time;
step 3.4.4: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 3.4.1 until all the machine types are calculated; if so, putting g2 into the subset O1;
step 3.4.5: repeating the step 3.4.3 until all the model calculation is completed, wherein the set O1, O2 and O3 … On is the coverage range of the take-off and landing points of each model;
step 3.5: taking a tower where the farthest parent node is located as a first take-off and landing point H1, and putting the tower into the take-off and landing point set H;
step 3.6: calculating the coverage range of the take-off and landing point from h1, namely, a subset G1 of all the parent and child towers covered;
step 3.7: removing the tower subset G1 from the tower set G;
step 3.8: repeating the step 3.3 until the tower set G is empty, wherein the set H is a planned take-off and landing point;
step 4: and (3) optimizing a power distribution network inspection route generation method, and generating an optimal flight route according to the inspection flight rule and the take-off and landing points obtained in the step (3).
2. The method for generating the inspection route of the power distribution network of the unmanned aerial vehicle according to claim 1, wherein the method comprises the following steps: the tower grading in the step 1 comprises the following steps: a class A pole tower, a patrol tower head, an insulator, a cross arm and a small hardware fitting; b-stage pole tower, inspection tower head and cross arm; and C-stage pole tower, and inspection tower head.
3. The method for generating the inspection route of the power distribution network of the unmanned aerial vehicle according to claim 1, wherein the method comprises the following steps: in the step 2, the types of the existing machine types are counted first, and the endurance time and the flying speed of each machine type are collected; and then testing the time consumption of flying each model in each level tower, and finally calculating and comparing the optimal inspection tower level of each model, wherein the method comprises the following specific steps:
step 2.1: selecting a model of a patrol A-level tower, counting take-off time, patrol time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data A1; b, inspecting the B-stage tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data B1; c, inspecting the C-level tower, counting take-off time, inspection time, return time, residual electric quantity and residual electric quantity percentage, and recording the calculated values as a group of data C1;
step 2.2: repeating the step 1 until all the machine types are inspected;
step 2.3: for different tower levels, selecting a model with short time and high residual electric quantity from the data set as an alternative model.
4. The method for generating the inspection route of the power distribution network of the unmanned aerial vehicle according to claim 1, wherein the method comprises the following steps: in the step 3.2, a topological connection relation tree table is constructed by adopting a space query method, and the method specifically comprises the following steps:
step 3.2.1: selecting any tower G1 in the tower set G, and analyzing a buffer zone with the radius of 0.5 m in the wire segment set, wherein the analysis result is a wire segment set L connected with the tower;
step 3.2.2: taking one section of wire segment L1 in the collection L, calculating the Euclidean distance between the end point of the wire segment and the tower, taking one end with a long distance, and analyzing a buffer zone with the radius of 0.5 meter in the wire segment collection, wherein the analysis result is the name of the superior connection relation, namely the number of the tower;
step 3.2.3: writing a tower set ID and a wire outlet point name, namely a tower number and a superior connection relation type acquired in the last step, namely a tower and a superior connection relation name, namely an analysis result and a connection relation type acquired in the last step, namely a tower and a connection relation name, namely a tower number selected in the step 3.2.1, into a tower topology connection relation tree table structure, and deleting L1 from the set L;
step 3.2.4: repeating step 3.2.2 until the set L is empty;
step 3.2.5: g1 is removed from set G and then step 1 is repeated until set G is empty.
5. The method for generating the inspection route of the power distribution network of the unmanned aerial vehicle according to claim 1, wherein the method comprises the following steps: in the step 3.3, a three-point method is adopted to construct the circumcircle C, and the specific steps are as follows:
step 3.3.1: selecting a tower with the largest longitude and the smallest longitude and a tower with the largest latitude from the tower set;
step 3.3.2: three towers are selected from the four towers at will, and a triangle is formed by taking the three towers as vertexes;
step 3.3.3: solving the circumscribed circle of the triangle;
step 3.3.4: judging whether the rest towers are outside the circumscribed circle, and repeating the step 2 and the step 3 if the rest towers are outside the circumscribed circle; if not, the circumscribed circle is the circumscribed circle C of the tower set.
6. The method for generating the inspection route of the power distribution network of the unmanned aerial vehicle according to claim 1, wherein the method comprises the following steps: the specific calculation steps of the step 3.6 are as follows:
step 3.6.1: searching a next-stage tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower inspection time and the flight time;
step 3.6.2: judging whether the unmanned aerial vehicle is in a cruising state or not, if not, repeating the step 3.6.1 until all the machine types are calculated; if so, putting G1 into the subset G1;
step 3.6.3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T, and calculating the tower patrol time and the flight time;
step 3.6.4: judging whether the unmanned aerial vehicle is in a cruising state or not, and if not, repeating the step 3.6.1; if so, putting G2 into the subset G1;
step 3.6.5: repeating step 3.6.3, the collection G1, G2, G3 … Gn is the coverage area of each landing point.
7. The method for generating the inspection route of the power distribution network of the unmanned aerial vehicle according to claim 1, wherein the method comprises the following steps: the step 4 further comprises the following sub-steps:
step 4.1: determining an unmanned aerial vehicle distribution network inspection rule, wherein the inspection rule comprises flying inspection along a line trend or flying inspection of a key tower;
step 4.2: generating a flight route map of each take-off and landing point unmanned aerial vehicle; the specific generation steps are as follows:
step 4.2.1: from the lifting point set H, arbitrarily selecting one lifting point H1, and searching a next-stage tower g1 according to a tower topology connection relation tree table structure T;
step 4.2.2: judging whether G1 is in the subset G1, if not, repeating the step 4.2.1; if yes, recording the routes of the towers g1 to R1 in a collection;
step 4.2.3: searching a next-stage tower g2 by using the tower g1 according to the tower topology connection relation tree table structure T;
step 4.2.4: judging whether G2 is in the subset G1, if not, repeating the step 4.2.1; if yes, recording the routes of the towers g2 to R1 in a collection;
step 4.2.5: repeating the step 4.2.3 until the algorithm is finished, wherein the sets R1, R2 and R3 … Rn are flight routes of each take-off and landing point;
step 4.3: calculating an optimal driving route according to road network and traffic road conditions;
step 4.4: and generating a landing point switching roadmap.
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