CN109214573A - Transmission line of electricity arboreal growth or lodging dangerous point prediction technique and device - Google Patents
Transmission line of electricity arboreal growth or lodging dangerous point prediction technique and device Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30161—Wood; Lumber
Abstract
The present invention provides a kind of transmission line of electricity arboreal growth or lodging dangerous point prediction technique and devices, are related to the technical field of transmission line safety, comprising: obtain point cloud data, point cloud data scans region to be predicted by laser radar system and obtains；Point cloud data is pre-processed, pretreatment point cloud data is obtained；By preset sorting algorithm to pretreatment point cloud data classification, classification point cloud data is obtained；Point cloud data of classifying includes transmission line of electricity point cloud data and tree point cloud data；Single wood segmentation is carried out to tree point cloud data by preset single wooden partitioning algorithm, obtains segmentation point cloud data；Segmentation point cloud data is predicted by preset dangerous point prediction algorithm, obtains arboreal growth or lodging dangerous point.The convenience, timeliness and safety of transmission line safety inspection can be improved in the embodiment of the present invention, provides reference frame for screen of trees cleaning and Remedy work.
Description
Technical field
The present invention relates to transmission line safety technical fields, endanger more particularly, to a kind of transmission line of electricity arboreal growth or lodging
Dangerous point prediction method and apparatus.
Background technique
It is constantly promoted with the fast development of Chinese economy, people's lives are horizontal, electricity needs is also further increased,
The only safety and reliability of guaranteed Operation of Electric Systems is just able to achieve the raising of power supply quality.Transmission line of electricity is in entire electricity
It occupies an important position in Force system, the height of running quality decides the quality of power supply, and to power grid overall operation
Safety have extreme influence.Electric transmission line channel is an important ring for electric power netting safe running safely, in electric transmission line channel
Screen of trees it is excessively close with conductor spacing, sometimes result in trip accident, endanger the safe operation of route.According to " overhead transmission line
Operating standard " requirement, under 200kv nominal voltage, the vertical range between conducting wire and trees is 4.5m, between conducting wire and trees
Clearance be 4.0m.In order to guarantee the requirement of the distance between conducting wire and trees, it is often necessary to carry out manual patrol.It prevents from setting
Wood growth causes to cause a hidden trouble with the hypotelorism of conducting wire in transmission line of electricity, endanger line security.
However the method for this manual patrol, the effect maked an inspection tour while many manpowers of consumption and time is maked an inspection tour every time
Generally, cause convenience, timeliness and safety lower, can not be cleared up for screen of trees and Remedy work provides reference frame,
It can not predict in the several years in the case of variety classes arboreal growth with a distance from conducting wire.
It is lower for convenience, timeliness and safety in above-mentioned manual patrol in the prior art, it can not be cleared up for screen of trees
The problem of providing reference frame with Remedy work, not yet proposes effective solution at present.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of transmission line of electricity arboreal growth dangerous point prediction technique and dresses
It sets, the convenience, timeliness and safety of transmission line safety inspection can be improved, mentioned for screen of trees cleaning and Remedy work
Foundation for reference.
In a first aspect, the embodiment of the invention provides a kind of transmission line of electricity arboreal growth dangerous point prediction techniques, comprising: obtain
Point cloud data is taken, point cloud data scans region to be predicted by laser radar system and obtains；Point cloud data is pre-processed, is obtained
Pre-process point cloud data；By preset sorting algorithm to pretreatment point cloud data classification, classification point cloud data is obtained；Classification point
Cloud data include transmission line of electricity point cloud data and tree point cloud data；By preset single wooden partitioning algorithm to tree point cloud data
Single wood segmentation is carried out, segmentation point cloud data is obtained；Segmentation point cloud data is predicted by preset dangerous point prediction algorithm,
Obtain arboreal growth or lodging dangerous point.
With reference to first aspect, the embodiment of the invention provides the first possible embodiments of first aspect, wherein logical
The step of crossing preset dangerous point prediction algorithm to predict segmentation point cloud data, obtaining arboreal growth or lodging dangerous point,
Include: to be predicted by preset arboreal growth dangerous point prediction algorithm segmentation point cloud data, obtains arboreal growth danger
Point；Segmentation point cloud data is predicted by preset trees lodging dangerous point prediction algorithm, obtains trees lodging dangerous point.
The possible embodiment of with reference to first aspect the first, the embodiment of the invention provides second of first aspect
Possible embodiment, wherein segmentation point cloud data is predicted by preset arboreal growth dangerous point prediction algorithm, is obtained
The step of to arboreal growth dangerous point, comprising: receive the growth parameter(s) of input；Growth parameter(s) includes growth year and growth speed
Degree；Segmentation point cloud data is predicted according to preset arboreal growth dangerous point prediction algorithm, is judged in arboreal growth situation
Whether lower distance of the trees away from transmission line of electricity is less than secure threshold；If so, output segmentation point cloud data in location parameter with
And the trees cutting capacity of preset arboreal growth dangerous point the prediction algorithm growth year predicted and statistics.
The possible embodiment of with reference to first aspect the first, the embodiment of the invention provides the third of first aspect
Possible embodiment, wherein segmentation point cloud data is predicted by preset trees lodging dangerous point prediction algorithm, is obtained
The step of lodging dangerous point to trees, further includes: lodge dangerous point prediction algorithm to segmentation point cloud data according to preset trees
Predicted judge whether distance of the trees away from transmission line of electricity is less than secure threshold when trees lodge；If so, output
Divide the trees cutting capacity of the location parameter and preset trees lodging dangerous point prediction algorithm statistics in point cloud data.
With reference to first aspect, the embodiment of the invention provides the 4th kind of possible embodiments of first aspect, wherein also
Include: by preset Vectorization Algorithm to classification point cloud data in shaft tower and power line be fitted, obtain vector quantization
Shaft tower and power line；It is carried out by shaft tower and power line of the preset dangerous point prediction algorithm to segmentation point cloud data and vector quantization
Prediction obtains arboreal growth or lodging dangerous point.
The 4th kind of possible embodiment with reference to first aspect, the embodiment of the invention provides the 5th kind of first aspect
Possible embodiment, wherein by preset dangerous point prediction algorithm to the shaft tower and electricity of segmentation point cloud data and vector quantization
The step of line of force is predicted, obtains arboreal growth or lodging dangerous point, comprising: pass through preset arboreal growth danger point prediction
Algorithm predicts the shaft tower and power line of segmentation point cloud data and vector quantization, obtains arboreal growth dangerous point；By default
Trees lodging dangerous point prediction algorithm segmentation point cloud data and the shaft tower and power line of vector quantization are predicted, obtain trees
Lodge dangerous point.
With reference to first aspect, the embodiment of the invention provides the 6th kind of possible embodiments of first aspect, wherein also
It include: that danger area range is arranged according to arboreal growth or lodging dangerous point；The segmentation point cloud data extracted within the scope of danger area is made
For danger area point cloud data.
With reference to first aspect, the embodiment of the invention provides the 7th kind of possible embodiments of first aspect, wherein logical
Cross the step of preset single wooden partitioning algorithm carries out single wood segmentation, obtain segmentation point cloud data to tree point cloud data, comprising: logical
It crosses preset single wooden partitioning algorithm and initial partitioning is carried out to tree point cloud data, obtain the vertex position of trees；By vertex position
It is superimposed with classification point cloud data, obtains segmentation point cloud data.
The 7th kind of possible embodiment with reference to first aspect, the embodiment of the invention provides the 8th kind of first aspect
Possible embodiment, wherein further include: judge whether the location information of superposition is correct；If so, by superimposed cloud number
According to as segmentation point cloud data；If not, receiving artificial delete for the place of over-segmentation or less divided in segmentation point cloud data
Or the cut-point of addition, and divided again.
Second aspect, the embodiment of the present invention also provide a kind of transmission line of electricity arboreal growth or the dangerous point prediction meanss that lodge,
It include: acquisition module, for obtaining point cloud data, point cloud data scans region to be predicted by laser radar system and obtains；Pre- place
Module is managed, for pre-processing to point cloud data, obtains pretreatment point cloud data；Categorization module, for passing through preset point
Class algorithm obtains classification point cloud data to pretreatment point cloud data classification；Point cloud data of classifying includes transmission line of electricity point cloud data
And tree point cloud data；Divide module, for carrying out single wood segmentation to tree point cloud data by preset single wooden partitioning algorithm,
Obtain segmentation point cloud data；Prediction module, for being predicted by preset dangerous point prediction algorithm segmentation point cloud data,
Obtain arboreal growth or lodging dangerous point.
The embodiment of the present invention bring it is following the utility model has the advantages that
A kind of transmission line of electricity arboreal growth provided in an embodiment of the present invention or lodging dangerous point prediction technique and device, to by
The point cloud data that laser radar system scanning obtains successively pre-processed, is classified and single wood segmentation, later to arboreal growth or
It is predicted lodging dangerous point.The convenience, timeliness and safety of transmission line safety inspection can be improved, cleared up for screen of trees
It works with Remedy and reference frame is provided.
Other feature and advantage of the disclosure will illustrate in the following description, alternatively, Partial Feature and advantage can be with
Deduce from specification or unambiguously determine, or by implement the disclosure above-mentioned technology it can be learnt that.
To enable the above objects, features, and advantages of the disclosure to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the process of a kind of transmission line of electricity arboreal growth provided in an embodiment of the present invention or the dangerous point prediction technique that lodges
Figure；
Fig. 2 is provided in an embodiment of the present invention a kind of by arboreal growth dangerous point prediction algorithm prediction transmission line of electricity trees
Grow the flow chart of the method for dangerous point；
Fig. 3 is the stream of another transmission line of electricity arboreal growth provided in an embodiment of the present invention or the dangerous point prediction technique that lodges
Cheng Tu；
Fig. 4 is the structure of a kind of transmission line of electricity arboreal growth provided in an embodiment of the present invention or the dangerous point prediction meanss that lodge
Schematic diagram.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
It is constantly promoted with the fast development of Chinese economy, people's lives are horizontal, electricity needs is also further increased,
The only safety and reliability of guaranteed Operation of Electric Systems is just able to achieve the raising of power supply quality.Transmission line of electricity is in entire electricity
It occupies an important position in Force system, the height of running quality decides the quality of power supply, and to power grid overall operation
Safety have extreme influence.Electric transmission line channel is an important ring for electric power netting safe running safely, in electric transmission line channel
Screen of trees it is excessively close with conductor spacing, sometimes result in trip accident, endanger the safe operation of route.According to " overhead transmission line
Operating standard " requirement, under 200kv nominal voltage, the vertical range between conducting wire and trees is 4.5m, between conducting wire and trees
Clearance be 4.0m.In order to guarantee the requirement of the distance between conducting wire and trees, prevent arboreal growth from causing and transmission line of electricity
The hypotelorism of middle conducting wire, causes a hidden trouble, and endangers line security, needs often to carry out inspection to transmission line safety.
Currently, transmission line safety inspection is mainly carried out by way of manual patrol, however the side of this manual patrol
Method, the effect that tour is maked an inspection tour while many manpowers of consumption and time every time is general, leads to convenience, timeliness and safety
Property it is lower, can not for screen of trees clear up and Remedy work reference frame be provided.Based on this, one kind provided in an embodiment of the present invention
Transmission line of electricity arboreal growth or lodging dangerous point prediction technique and device, can be improved transmission line safety inspection convenience,
Timeliness and safety provide reference frame for screen of trees cleaning and Remedy work.
To be given birth to a kind of transmission line of electricity trees disclosed in the embodiment of the present invention first convenient for understanding the present embodiment
Long or lodging dangerous point prediction technique describes in detail.
Embodiment 1
The embodiment of the present invention 1 provides a kind of transmission line of electricity arboreal growth or the dangerous point prediction technique that lodges, referring to Fig. 1 institute
The flow chart of a kind of transmission line of electricity arboreal growth or the dangerous point prediction technique that lodges shown, includes the following steps:
Step S102 obtains point cloud data.
Point cloud data refers to that scanning data records in dots, each point includes three-dimensional coordinate, some may contain
There are colouring information or Reflection intensity information.Point cloud data scans region to be predicted by laser radar system and obtains, and laser radar is swept
System is retouched by lidar measurement equipment, global positioning system (Global Positioning System, GPS), inertia measurement
Unit (Inertial Measurement Unit, IMU) and storage control unit composition.Point cloud data includes: that base station GPS is seen
Measured data, IMU data and original point cloud data,
Step S104, pre-processes point cloud data, obtains pretreatment point cloud data.
Some junk information, invalid information, inconsistent information and duplicate message can be filtered by pretreatment, will put cloud
Data carry out calibration and adjustment.Pretreatment includes data downloading, data calculation and Data correction.Data downloading refers to downloading by swashing
The point cloud data that optical detection and ranging system scanning obtains, including base station GPS observation data, IMU data and original point cloud data.Data solution
Calculate includes the resolving of ground base station GPS static state, the resolving of Airborne GPS difference, GPS/IMU data aggregate resolving and coordinate transformation parameter meter
It calculates.Data correction refers to the calibration and adjustment of roll in point cloud data, pitching and course heading.
Step S106 obtains classification point cloud data by preset sorting algorithm to pretreatment point cloud data classification.
The step of preset sorting algorithm include: choose machine learning training sample, sample classification, establish training pattern,
Automatic classification, modification classification results and denoising based on model.By the atural object in point cloud data according to transmission line of electricity and trees into
Row classification, classification point cloud data includes transmission line of electricity point cloud data and tree point cloud data, wherein transmission line of electricity point cloud data packet
Shaft tower and power line are included, tree point cloud data is classified according to the different cultivars of trees, and tree point cloud data also includes vegetation.For
The case where preset sorting algorithm can not be classified can carry out manual sort, for example, the above method further include: recipient
The classification point cloud data of work input.
The artificial classification point cloud data obtain after point cloud data classification, can directly use.
Step S108 carries out single wood segmentation to tree point cloud data by preset single wooden partitioning algorithm, obtains cut-point
Cloud data.
Single wood segmentation, which refers to, is divided into monomer for the trees in tree point cloud data, and obtains each strain near transmission line of electricity
The parameter informations such as height, hat width, number and the position of trees.By the vertex of every trees after segmentation and classification point cloud data
Superposition, available segmentation point cloud data.The step of single wood partitioning algorithm, is mainly: by the height value of analysis site and and its
Distance between he puts obtains single wooden position, tree height, hat width diameter, crown area, hat width volume with the Dan Mu that determination is to be split
Equal attribute informations.Point cloud after segmentation can be demarcated and be shown according to tree number.
Step S110 predicts segmentation point cloud data by preset dangerous point prediction algorithm, obtains arboreal growth
Or lodging dangerous point.
Dangerous point prediction algorithm includes arboreal growth dangerous point prediction algorithm and trees lodging dangerous point prediction algorithm.Pass through
Preset arboreal growth dangerous point prediction algorithm predicts segmentation point cloud data, obtains arboreal growth dangerous point；By pre-
If trees lodging dangerous point prediction algorithm to segmentation point cloud data predict, obtain trees lodging dangerous point.
Preset arboreal growth dangerous point prediction algorithm mainly predicts trees according to the safety distance threshold of user setting
It whether there is headroom dangerous point under growing state, the algorithm parameter being mainly arranged includes: minimum range (is not considered as less than the distance
Dangerous point), safe distance (be detected classification point apart from power line be less than or equal to the threshold value and be greater than minimum range, it is believed that
Be dangerous point, different safety distance thresholds be generally set according to voltage class), cluster threshold value (headroom dangerous point is gathered
Time-like space min cluster distance, the value are less than maximum cluster range), maximum cluster range (after the cluster of headroom dangerous point if
Be greater than the value along power line direction length, then cut), growth year (setting prediction growth time), the speed of growth it is (annual
Arboreal growth speed).
Preset trees lodging dangerous point prediction algorithm is mainly the safety distance threshold and Dan Mu according to user setting
The wooden position of the list that segmentation obtains, tree is high, hat width is straight, predicts to whether there is headroom dangerous point in the case of trees lodge, main to be arranged
Algorithm parameter include: minimum range (being not considered as dangerous point less than the distance), safe distance (be detected classification point distance electricity
The line of force is less than or equal to the threshold value and is greater than minimum range, it is believed that is dangerous point, different peaces is generally arranged according to voltage class
Full distance threshold value), cluster threshold value (poly- time-like space min cluster distance carried out to headroom dangerous point, which is less than maximum cluster
Range), maximum cluster range (if being greater than the value along power line direction length after the cluster of headroom dangerous point, being cut).
The above method provided in an embodiment of the present invention successively carries out the point cloud data obtained by laser radar system scanning
Pretreatment, classification and single wood segmentation, later predict arboreal growth or lodging dangerous point.Transmission line safety can be improved
Convenience, timeliness and the safety of inspection provide reference frame for screen of trees cleaning and Remedy work.
In the step of obtaining point cloud data, the mode taken the photograph that can navigate is acquired, for example, can execute according to the following steps:
(1) according to customer demand, the data in region to be predicted is collected, boat is carried out and takes the photograph conceptual design.According to region to be predicted
Landforms, weather, flying condition etc., design suitable course line and taken the photograph in boat of suitable time.
(2) ground base station is laid.Scheme is taken the photograph according to the boat of design, suitable ground point target is selected to lay ground base station, with
Increase the accuracy of the point cloud data of acquisition.
(3) calibration field flies.Calibration field is the place of uniformly distributed permanent surface mark for calibration aerial surveying camera, by
Calibration field flight, can be with calibration aerial surveying camera.
(4) boat takes the photograph acquisition point cloud data.After calibration field flight calibration aerial surveying camera, scheme is taken the photograph in the suitable time according to boat
It carries out boat to take the photograph, acquires point cloud data.
Point cloud data is acquired in such a way that boat is taken the photograph, preparation is sufficient, elapsed time is short and has higher accuracy.
Single wood segmentation is being carried out to tree point cloud data by preset single wooden partitioning algorithm, is obtaining segmentation point cloud data
In step, for example, can execute according to the following steps:
(1) initial partitioning is carried out to tree point cloud data by preset single wooden partitioning algorithm, obtains the vertex position of trees
It sets.Each trees in tree point cloud data are divided using single wooden partitioning algorithm, and obtain the vertex information of every trees.
(2) vertex position is superimposed with classification point cloud data, obtains segmentation point cloud data.The vertex position of every trees with
The classification superimposed point cloud data of point cloud data is segmentation point cloud data.Can by single wooden partitioning algorithm segmentation and with classification point
Cloud is superimposed to obtain segmentation point cloud data.
Superimposed segmentation point cloud data can be used to judge whether the location information of each tree is correct, for example, can be by
It is executed according to following steps:
(1) judge whether the location information of superposition is correct.Judge whether the location information of superposition is correct, exactly judges vertex
Whether position meets with classification point cloud data, if met, means that the location information of superposition is correct；If do not met, with regard to table
Show the location information mistake of superposition, there are over-segmentation or less divideds.
(2) if so, using superimposed point cloud data as segmentation point cloud data.If the location information of superposition is correct,
Using superimposed point cloud data as segmentation point cloud data.
(3) if not, for the place of over-segmentation or less divided in segmentation point cloud data, what reception was manually deleted or added
Cut-point, and divided again.If the location information mistake of superposition, for the place of segmentation or less divided, using artificial
It deletes or the method for the cut-point of addition is deleted or addition cut-point, divided again.
Single wood segmentation is carried out to tree point cloud data by preset single wooden partitioning algorithm, segmentation point cloud data is obtained, adopts
Based on single wooden partitioning algorithm, the artificial method deleted or add cut-point supplement, can effectively reduce single wooden partitioning algorithm can
A possibility that capable of leading to the location information mistake of each tree.
Divide the available tree parameters of point cloud data by trees.For example, with reference to a kind of tree parameters shown in table 1
Table, trees share 10 and are numbered since 1, and the parameter of every trees includes: trees abscissa, trees ordinate, tree
The wooden weight, crown diameter, tree crown area and Tree Crown Volume.Wherein the unit of trees weight is kg, and the unit of crown diameter is m,
The unit of tree crown area is m2, the unit of Tree Crown Volume is m3.For example, number be 1 trees, coordinate be (437943.050,
2564153.460), trees weight is 55.610kg, and crown diameter 3.321m, tree crown area is 8.662m2, Tree Crown Volume is
17.554m3。
| Trees abscissa | Trees ordinate | Trees weight | Crown diameter | Tree crown area | Tree Crown Volume | |
| 1 | 437943.050 | 2564153.460 | 55.610 | 3.321 | 8.662 | 17.554 |
| 2 | 437949.690 | 2564155.070 | 55.500 | 3.696 | 10.726 | 30.261 |
| 3 | 437933.110 | 2564152.830 | 53.780 | 6.905 | 37.444 | 75.573 |
| 4 | 437938.290 | 2564152.950 | 53.660 | 4.783 | 17.965 | 34.342 |
| 5 | 437944.740 | 2564157.330 | 53.060 | 6.261 | 30.791 | 76.216 |
| 6 | 437944.760 | 2564157.460 | 53.060 | 3.183 | 7.955 | 20.262 |
| 7 | 437921.450 | 2564149.580 | 52.090 | 2.659 | 5.554 | 2.783 |
| 8 | 437932.170 | 2564163.870 | 51.670 | 3.111 | 7.599 | 14.065 |
| 9 | 437932.310 | 2564162.450 | 51.670 | 7.231 | 41.065 | 72.023 |
| 10 | 437927.950 | 2564151.310 | 51.550 | 1.921 | 2.898 | 1.900 |
Table 1
It can be to arboreal growth danger by preset arboreal growth dangerous point prediction algorithm according to segmentation point cloud data
Point is predicted, provides reference frame for screen of trees cleaning and Remedy work, and predicts that variety classes trees are raw in the several years
In long situation with a distance from conducting wire.One kind shown in Figure 2 predicts transmission line of electricity by arboreal growth dangerous point prediction algorithm
The flow chart of the method for arboreal growth dangerous point, includes the following steps:
Step S202 receives the growth parameter(s) of input.
Growth parameter(s) includes growth year and the speed of growth.The speed of growth of different types of trees is different, same tree
The speed of growth may also be different under the different age of trees for wood.The speed of growth can be inputted by way of function.
Step S204 predicts segmentation point cloud data according to preset arboreal growth dangerous point prediction algorithm, judges
In arboreal growth, whether distance of the trees away from transmission line of electricity is less than secure threshold, if so, executing step S206；If
It is no, then terminate.
Available trees are raw to be predicted to segmentation point cloud data by preset arboreal growth dangerous point prediction algorithm
Distance of the trees away from transmission line of electricity in long situation.Secure threshold can be with manual setting, generally between 3-5m.According to " making somebody a mere figurehead defeated
Electric line operating standard ", under 200kv nominal voltage, the vertical range between conducting wire and trees is 4.5m, between conducting wire and trees
Clearance be 4.0m, vertical range refer to transmission line wire maximum hang down arc geometric dimension opposite with the space of trees,
Clearance refer to electric wire line conductor under maximum windage yaw state, geometric dimension opposite with the space of trees.Secure threshold is general
Refer to safe clearance distance threshold, is set as 4.0m.
The location parameter and preset arboreal growth dangerous point prediction algorithm in point cloud data are divided in step S206, output
The growth year of prediction and the trees cutting capacity of statistics.
If distance of the trees away from transmission line of electricity is less than preset secure threshold under trees growing state, corresponding tree is exported
Location parameter, the growth year of growth dangerous point prediction algorithm prediction and the trees felling of statistics in wood segmentation point cloud data
Amount.
By predicting arboreal growth dangerous point, the position ginseng in corresponding trees segmentation point cloud data can be exported
The trees cutting capacity of number, the growth year of growth dangerous point prediction algorithm prediction and statistics.
In order to further increase classify point cloud data in shaft tower and power line data accuracy, by shaft tower and electric power
Line carries out vectorized process, the above method further include:
(1) by preset Vectorization Algorithm to classification point cloud data in shaft tower and power line be fitted, sweared
The shaft tower and power line of quantization.Under normal circumstances, power line should be the continuously and power line in point cloud data of classifying
There may be acquire it is incomplete and cause interruption the case where, by preset Vectorization Algorithm to classification point cloud data in shaft tower and
Power line is fitted, and obtains the shaft tower and power line of vector quantization, can increase the standard of acquisition with the not collected power line of completion
True property.Include: mark insulator hanging point and shaft tower key point by the step of carrying out vectorized process to shaft tower and electric pole, criticizes
Amount individually fits shaft tower and power line, modifies vector quantization mistake.
(2) it is carried out by shaft tower and power line of the preset dangerous point prediction algorithm to segmentation point cloud data and vector quantization pre-
It surveys, obtains arboreal growth or lodging dangerous point.Arboreal growth or lodging dangerous point are carried out using preset dangerous point prediction algorithm
When prediction, in addition to that can also be predicted according to the shaft tower and power line of vector quantization, increase prediction according to other than segmentation point cloud data
Accuracy.For predicting arboreal growth dangerous point, for example, can execute according to the following steps:
(1) growth parameter(s) of input is received.Growth parameter(s) includes growth year and the speed of growth.
(2) according to preset arboreal growth dangerous point prediction algorithm to the shaft tower and electric power of segmentation point cloud data and vector quantization
Line is predicted judge whether distance of the trees away from transmission line of electricity is less than secure threshold in arboreal growth.It is predicted
When, not only in accordance with cut-point cloud data, it is contemplated that the shaft tower and power line of vector quantization, it is accurate to be predicted with increase
Property.
(3) if so, location parameter and preset arboreal growth danger point prediction in output segmentation point cloud data are calculated
The growth year of method prediction and the trees cutting capacity of statistics.If trees are less than away from the distance of transmission line of electricity under trees growing state
Secure threshold then exports the location parameter in corresponding trees segmentation point cloud data, the growth that growth dangerous point prediction algorithm is predicted
The trees cutting capacity of the time limit and statistics.
The above method provided in an embodiment of the present invention can be increased by carrying out vectorized process to shaft tower and power line
The accuracy of shaft tower and power line data in classification point cloud data.
Danger area range can be arranged to arboreal growth or lodging dangerous point, extract the cut-point cloud number within the scope of danger area
According to simultaneously key monitoring, the above method further include:
(1) according to arboreal growth or lodging dangerous point, danger area range is set.Danger area range is with arboreal growth or to fall
Volt dangerous point is the center of circle, and the danger zone threshold value of manual setting is the circle of radius.Danger zone threshold value can be with manual setting, generally
For 3-10m.
(2) the segmentation point cloud data within the scope of danger area is extracted as danger area point cloud data.It extracts within the scope of danger area
All segmentation point cloud datas as danger area point cloud data, key monitoring can be carried out.
The above method provided in an embodiment of the present invention can be extracted within the scope of danger area by the way that danger area range is arranged
Divide point cloud data and key monitoring.
A kind of transmission line of electricity arboreal growth provided in this embodiment or lodging dangerous point prediction technique, to by laser radar system
The point cloud data that system scanning obtains successively pre-processed, is classified and single wood segmentation, is carried out at vector quantization to shaft tower and power line
Reason is later predicted arboreal growth or lodging dangerous point, from conducting wire in the case of variety classes trees in the prediction several years
Simultaneously danger area range is arranged in distance.The convenience, timeliness and safety of transmission line safety inspection can be improved, be that screen of trees is clear
Reason and Remedy work provide reference frame, and predict in the several years to increase in the case of variety classes trees with a distance from conducting wire
The accuracy of shaft tower and power line data in bonus point class point cloud data extracts segmentation point cloud data and emphasis within the scope of danger area
Monitoring.
Embodiment 2
The embodiment of the present invention 2 provides a kind of transmission line of electricity arboreal growth or the dangerous point prediction technique that lodges, shown in Figure 3
Another transmission line of electricity arboreal growth or lodge dangerous point prediction technique flow chart, include the following steps:
Step S302 collects the data in region to be predicted according to customer demand, and design boat takes the photograph scheme, lays ground base station,
Calibration field flight is carried out, acquisition point cloud data is taken the photograph by boat.
The data such as landforms, weather, the flying condition in region to be predicted according to collection design suitable course line suitable
Time boat is taken the photograph.Scheme is taken the photograph according to the boat of design, suitable ground point target is selected to lay ground base station, to increase the point cloud number of acquisition
According to accuracy.It, can be with calibration aerial surveying camera by flying in calibration field.The point in region to be measured is acquired by way of aeroplane photography
Cloud data.
Step S304, pre-processes point cloud data, obtains pretreatment point cloud data.
Pretreatment includes data downloading, data calculation and Data correction.
Step S306 obtains classification point cloud data by preset sorting algorithm to pretreatment point cloud data classification.
Atural object in point cloud data is classified according to transmission line of electricity and trees, classification point cloud data includes transmission line of electricity
Point cloud data and tree point cloud data, wherein transmission line of electricity point cloud data includes shaft tower and power line, tree point cloud data according to
The different cultivars of trees is classified, and tree point cloud data also includes vegetation.The feelings that can not classify for preset sorting algorithm
Condition can carry out manual sort, for example, the above method further include: receive the classification point cloud data being manually entered.
Step S308 carries out initial partitioning to tree point cloud data by preset single wooden partitioning algorithm, obtains trees
Vertex position.
Each trees in tree point cloud data are divided using single wooden partitioning algorithm, and obtain the vertex of every trees
Information.
Vertex position is superimposed with classification point cloud data, judges whether the location information of superposition is correct by step S310, if
It is to execute step S312；If not, executing step S314.
Judge whether the location information of superposition is correct, exactly judge whether vertex position meets with classification point cloud data, such as
Fruit meets, and means that the location information of superposition is correct；If do not met, the location information mistake of superposition is meant that, exist excessively
It cuts or less divided.
Step S312, using superimposed point cloud data as segmentation point cloud data.
If the location information of superposition is correct, using superimposed point cloud data as segmentation point cloud data.
Step S314, for dividing the place of over-segmentation or less divided in point cloud data, what reception was manually deleted or added
Cut-point, and divided again, obtain segmentation point cloud data.
If the location information mistake of superposition, for the place of segmentation or less divided, using artificial point deleted or added
The method of cutpoint is deleted or addition cut-point, is divided again, and the point cloud data after dividing again is segmentation point cloud data.
Step S316, by preset Vectorization Algorithm to classification point cloud data in shaft tower and power line be fitted,
Obtain the shaft tower and power line of vector quantization.
By preset Vectorization Algorithm to classification point cloud data in shaft tower and power line be fitted, obtain vector quantization
Shaft tower and power line, the accuracy of acquisition can be increased with the not collected power line of completion.
Step S318 receives the growth parameter(s) of input.
Growth parameter(s) includes growth year and the speed of growth.
Step S320, according to preset arboreal growth dangerous point prediction algorithm to the shaft tower of segmentation point cloud data and vector quantization
Predicted judge whether distance of the trees away from transmission line of electricity is less than secure threshold in arboreal growth with power line.Such as
Fruit is to execute step S322；If it is not, then terminating.
By preset arboreal growth dangerous point prediction algorithm to the shaft tower and power line of segmentation point cloud data and vector quantization
It is predicted, distance of the trees away from transmission line of electricity and judges the trees in arboreal growth in the case of available arboreal growth
Whether the distance away from transmission line of electricity is less than secure threshold, and secure threshold is generally 4.0m.
The location parameter and preset arboreal growth dangerous point prediction algorithm in point cloud data are divided in step S322, output
The growth year of prediction and the trees cutting capacity of statistics.
If distance of the trees away from transmission line of electricity is less than preset secure threshold under trees growing state, corresponding tree is exported
Location parameter, the growth year of growth dangerous point prediction algorithm prediction and the trees felling of statistics in wood segmentation point cloud data
Amount.
Step S324 lodges dangerous point prediction algorithm to the shaft tower for dividing point cloud data and vector quantization according to preset trees
Predicted judge whether distance of the trees away from transmission line of electricity is less than secure threshold when trees lodge with power line.Such as
Fruit is to execute step S326；If it is not, then terminating.
Location parameter and preset trees lodging dangerous point prediction algorithm in point cloud data are divided in step S326, output
The trees cutting capacity of statistics.
Danger area range is arranged according to arboreal growth or lodging dangerous point in step S328.
Step S330 extracts the segmentation point cloud data within the scope of danger area as danger area point cloud data.
A kind of transmission line of electricity arboreal growth provided in this embodiment or lodging dangerous point prediction technique, to by laser radar system
The point cloud data that system scanning obtains successively pre-processed, is classified and single wood segmentation, is carried out at vector quantization to shaft tower and power line
Reason later predicts arboreal growth or lodging dangerous point, and danger area range is arranged.Transmission line safety can be improved to patrol
Convenience, timeliness and the safety of inspection provide reference frame for screen of trees cleaning and Remedy work, and predict in the several years
In the case of variety classes trees with a distance from conducting wire, increases the accuracy of shaft tower and power line data in classification point cloud data, mention
Take the segmentation point cloud data and key monitoring within the scope of danger area.
Embodiment 3
The embodiment of the present invention 3 provides a kind of transmission line of electricity arboreal growth or the dangerous point prediction meanss that lodge, shown in Figure 4
A kind of transmission line of electricity arboreal growth or the dangerous point prediction meanss that lodge structural schematic diagram, including obtain module 41, pretreatment
Module 42, categorization module 43, segmentation module 44 and prediction module 45, the function of above-mentioned each module are as follows:
Module 41 is obtained, for obtaining point cloud data, point cloud data scans region to be predicted by laser radar system and obtains；
Preprocessing module 42 obtains pretreatment point cloud data for pre-processing to point cloud data；
Categorization module 43, for, to pretreatment point cloud data classification, obtaining classification point cloud number by preset sorting algorithm
According to；Point cloud data of classifying includes transmission line of electricity point cloud data and tree point cloud data；
Divide module 44, for carrying out single wood segmentation to tree point cloud data by preset single wooden partitioning algorithm, obtains
Divide point cloud data；
Prediction module 45 is set for being predicted by preset dangerous point prediction algorithm segmentation point cloud data
Wood growth or lodging dangerous point.
It is apparent to those skilled in the art that for convenience and simplicity of description, the transmission of electricity of foregoing description
The specific work process of route arboreal growth or the dangerous point prediction meanss that lodge, can be with reference to the correspondence in preceding method embodiment
Process, details are not described herein.
Transmission line of electricity arboreal growth provided in an embodiment of the present invention or lodging dangerous point prediction meanss, mention with above-described embodiment
The transmission line of electricity arboreal growth of confession or lodging dangerous point prediction technique technical characteristic having the same, so also can solve identical
Technical problem reaches identical technical effect.
Finally, it should be noted that embodiment described above, only a specific embodiment of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, those skilled in the art should understand that: anyone skilled in the art
In the technical scope disclosed by the present invention, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover in protection of the invention
Within the scope of.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (10)
1. a kind of transmission line of electricity arboreal growth or lodging dangerous point prediction technique characterized by comprising
Point cloud data is obtained, the point cloud data scans region to be predicted by laser radar system and obtains；
The point cloud data is pre-processed, pretreatment point cloud data is obtained；
Classified by preset sorting algorithm to the pretreatment point cloud data, obtains classification point cloud data；The classification point cloud
Data include transmission line of electricity point cloud data and tree point cloud data；
Single wood segmentation is carried out to the tree point cloud data by preset single wooden partitioning algorithm, obtains segmentation point cloud data；
The segmentation point cloud data is predicted by preset dangerous point prediction algorithm, arboreal growth is obtained or lodging is dangerous
Point.
2. the method according to claim 1, wherein it is described by preset dangerous point prediction algorithm to described point
The step of cutting point cloud data to be predicted, obtaining arboreal growth or lodging dangerous point, comprising:
The segmentation point cloud data is predicted by preset arboreal growth dangerous point prediction algorithm, obtains arboreal growth danger
Dangerous point；
The segmentation point cloud data is predicted by preset trees lodging dangerous point prediction algorithm, obtains trees lodging danger
Dangerous point.
3. according to the method described in claim 2, it is characterized in that, described pass through preset arboreal growth dangerous point prediction algorithm
The step of segmentation point cloud data is predicted, obtains arboreal growth dangerous point, comprising:
Receive the growth parameter(s) of input；The growth parameter(s) includes growth year and the speed of growth；
The segmentation point cloud data is predicted according to preset arboreal growth dangerous point prediction algorithm, is judged in arboreal growth
In the case of distance of the trees away from transmission line of electricity whether be less than secure threshold；
If so, the location parameter and the preset arboreal growth danger point prediction in the output segmentation point cloud data are calculated
The growth year of method prediction and the trees cutting capacity of statistics.
4. according to the method described in claim 2, it is characterized in that, described pass through preset trees lodging dangerous point prediction algorithm
The step of segmentation point cloud data is predicted, trees lodging dangerous point is obtained, further includes:
The segmentation point cloud data is predicted according to preset trees lodging dangerous point prediction algorithm, judges to lodge in trees
In the case of distance of the trees away from transmission line of electricity whether be less than secure threshold；
If so, the location parameter and the preset trees in the output segmentation point cloud data lodge, dangerous point prediction is calculated
The trees cutting capacity of method statistics.
5. the method according to claim 1, wherein further include:
By preset Vectorization Algorithm to it is described classification point cloud data in shaft tower and power line be fitted, obtain vector quantization
Shaft tower and power line；
By the preset dangerous point prediction algorithm to the shaft tower and power line of the segmentation point cloud data and the vector quantization
It is predicted, obtains the arboreal growth or lodging dangerous point.
6. according to the method described in claim 5, it is characterized in that, it is described by the preset dangerous point prediction algorithm to institute
The shaft tower and power line for stating segmentation point cloud data and the vector quantization are predicted, the arboreal growth or lodging dangerous point are obtained
The step of, comprising:
By preset arboreal growth dangerous point prediction algorithm to the shaft tower and electricity of the segmentation point cloud data and the vector quantization
The line of force is predicted, arboreal growth dangerous point is obtained；
Dangerous point prediction algorithm is lodged to the shaft tower and electricity for dividing point cloud data and the vector quantization by preset trees
The line of force is predicted, trees lodging dangerous point is obtained.
7. the method according to claim 1, wherein further include:
According to the arboreal growth or lodging dangerous point, danger area range is set；
The segmentation point cloud data within the scope of the danger area is extracted as danger area point cloud data.
8. the method according to claim 1, wherein it is described by preset single wooden partitioning algorithm to the trees
The step of point cloud data carries out single wood segmentation, obtains segmentation point cloud data, comprising:
Initial partitioning is carried out to the tree point cloud data by preset single wooden partitioning algorithm, obtains the vertex position of the trees
It sets；
The vertex position is superimposed with the classification point cloud data, obtains segmentation point cloud data.
9. according to the method described in claim 8, it is characterized by further comprising:
Judge whether the location information of superposition is correct；
If so, using superimposed point cloud data as the segmentation point cloud data；
If not, receiving artificial point deleted or added for the place of over-segmentation or less divided in the segmentation point cloud data
Cutpoint, and divided again.
10. a kind of transmission line of electricity arboreal growth or lodging dangerous point prediction meanss characterized by comprising
Module is obtained, for obtaining point cloud data, the point cloud data scans region to be predicted by laser radar system and obtains；
Preprocessing module obtains pretreatment point cloud data for pre-processing to the point cloud data；
Categorization module obtains classification point cloud data for classifying by preset sorting algorithm to the pretreatment point cloud data；
The classification point cloud data includes transmission line of electricity point cloud data and tree point cloud data；
Divide module, for carrying out single wood segmentation to the tree point cloud data by preset single wooden partitioning algorithm, is divided
Cut point cloud data；
Prediction module obtains trees for predicting by preset dangerous point prediction algorithm the segmentation point cloud data
Growth or lodging dangerous point.
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