CN115908740A - Lightweight three-dimensional point cloud model and modeling method - Google Patents
Lightweight three-dimensional point cloud model and modeling method Download PDFInfo
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- CN115908740A CN115908740A CN202211319263.XA CN202211319263A CN115908740A CN 115908740 A CN115908740 A CN 115908740A CN 202211319263 A CN202211319263 A CN 202211319263A CN 115908740 A CN115908740 A CN 115908740A
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Abstract
The invention relates to a lightweight three-dimensional point cloud model and a modeling method, which are used for a transformer substation, wherein the modeling method comprises the following steps: carrying out surveying and mapping scanning on an area in a transformer substation to obtain original point cloud data; preprocessing the original point cloud data to obtain preprocessed point cloud data; marking the preprocessed point cloud data, marking out an operation area, a dangerous area, each charged body and a mobile carrier of the transformer substation, and forming marked point cloud data; and finally, training the marked point cloud data to obtain a lightweight three-dimensional point cloud model. According to the invention, a plurality of different laser radars are adopted to jointly acquire the point cloud data of the transformer substation, and the acquired point cloud data is subjected to denoising and optimization processing to obtain the high-precision lightweight three-dimensional point cloud model of the whole scene of the transformer substation, so that the position precision and the updating frequency of the transformer substation model are greatly improved, and the usability of operation safety control is improved.
Description
Technical Field
The invention belongs to the field of transformer substation control, and particularly relates to a lightweight three-dimensional point cloud model and a modeling method.
Background
In the prior art, with the gradual construction of the Beidou foundation enhancement platform, beidou positioning tags are arranged on transformer substation operators and vehicles, centimeter-level positioning accuracy can be realized, and the application prospect in power grid operation and inspection services is wide. However, real-time positioning needs to be combined with an accurate equal-proportion three-dimensional transformer substation model, and intelligent operation safety control can be achieved. However, the implementation method currently requires manual to on-site measurement to create the three-dimensional model, and the creation method has the following disadvantages: on one hand, due to measurement errors, the positions of the devices in the substation in the three-dimensional model do not conform to the actual positions, and on the other hand, manual modeling requires a long period and is high in cost. In addition, after infrastructure or equipment replacement is carried out in the transformer substation, the corresponding three-dimensional model is difficult to update in time in a short time, so that operation safety control depending on accurate position information is difficult to apply practically.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides a lightweight three-dimensional point cloud model and a modeling method, which are used for solving the problems in the prior art.
A modeling method of a lightweight three-dimensional point cloud model is used for a transformer substation, and comprises the following steps:
s1, surveying, mapping and scanning an area in a transformer substation to obtain original point cloud data;
s2, preprocessing the original point cloud data to obtain preprocessed point cloud data;
s3, marking the preprocessed point cloud data, marking out an operation area, a dangerous area, each charged body and a mobile carrier of the transformer substation, and forming marked point cloud data;
and S4, training the marked point cloud data to obtain a lightweight three-dimensional point cloud model.
The above-described aspect and any possible implementation manner further provide an implementation manner, where the raw point cloud data in S1 includes relative positions of devices in the substation, volume size data of the devices, and/or an area of a working area.
The above aspect and any possible implementation further provide an implementation, where each of the apparatuses includes a charging body, and the charging body includes: one or more of a grading ring, a high voltage shunt reactor and/or a bus.
In the above aspect and any possible implementation manner, an implementation manner is further provided, in S1, a radar pan-tilt control system provided on the mobile carrier is used for performing mapping and scanning.
The above aspects and any possible implementation manners further provide an implementation manner, and the mobile carrier includes one or more of a robot, a robot dog, or an unmanned aerial vehicle.
The above aspects and any possible implementation manners further provide an implementation manner, where the radar pan-tilt control system includes a console, a remote communication module, and a pan-tilt, where the console is configured to input a control instruction to the pan-tilt; the remote communication module is used for realizing communication between the console and the cloud deck, transmitting the command sent by the console to the console, and simultaneously feeding back data of radar mapping and scanning on the console to the console.
As to the above-mentioned aspect and any possible implementation manner, there is further provided an implementation manner, where the preprocessing in S2 includes removing noise points existing in the original point cloud data, specifically: and presetting the minimum point cloud number existing in the specified radius around the original point cloud data, and if the point cloud number around the given target point is less than the minimum point cloud number, determining the given target point as a noise point and removing the noise point.
In the foregoing aspect and any possible implementation manner, an implementation manner is further provided, and in S3, the point cloud data after being preprocessed is labeled by using a point cloud segmentation technology.
The above aspects and any possible implementation further provide an implementation in which the mobile carrier is a construction worker and/or a vehicle for substation field work.
The invention also provides a lightweight three-dimensional point cloud model, which is obtained by adopting the modeling method.
The invention has the advantages of
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a modeling method of a lightweight three-dimensional point cloud model, which is used for a transformer substation and comprises the following steps: carrying out surveying and mapping scanning on an area in a transformer substation to obtain original point cloud data; preprocessing the original point cloud data to obtain preprocessed point cloud data; marking the preprocessed point cloud data, marking out an operation area, a dangerous area, each charged body and a mobile carrier of the transformer substation, and forming marked point cloud data; and finally, training the marked point cloud data to obtain a lightweight three-dimensional point cloud model. According to the method, the point cloud data of the transformer substation are jointly acquired by adopting a plurality of different laser radars, and the acquired point cloud data are subjected to denoising and optimization processing to obtain the high-precision lightweight three-dimensional point cloud model of the whole scene of the transformer substation, so that the position precision and the updating frequency of the transformer substation model are greatly improved, and the availability of operation safety control is improved.
Drawings
FIG. 1 is a schematic flow chart of a method in an embodiment of the present invention;
FIG. 2 is a schematic diagram of TOF camera ranging in an embodiment of the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the present disclosure includes but is not limited to the following detailed description, and similar techniques and methods should be considered as within the scope of the present invention. In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
It should be understood that the described embodiments of the invention are only some of the described embodiments of the invention, and not all of the described embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
As shown in fig. 1, the overall framework of the present invention is shown, and the system oriented to the present invention does not limit the specific running hardware and programming language, and can be written in any language, and therefore, other working modes are not described again.
The invention discloses a modeling method of a lightweight three-dimensional point cloud model, which is used for a transformer substation and comprises the following steps:
(1) Mapping and scanning an area in the transformer substation to obtain original point cloud data;
(2) Preprocessing the original point cloud data to obtain preprocessed point cloud data;
(3) Marking the preprocessed point cloud data, marking out an operation area, a dangerous area, each charged body and a mobile carrier of the transformer substation, and forming marked point cloud data;
(4) Training the marked point cloud data to obtain a lightweight three-dimensional point cloud model.
Preferably, in the embodiment of the present invention, a radar pan-tilt control system disposed on a mobile vehicle is used for performing mapping scanning, where the mobile vehicle includes but is not limited to one or more of a robot, a robot dog, or an unmanned aerial vehicle, the video or laser radar-based pan-tilt control system includes a console, a remote communication module, and a pan-tilt, and the console is used for a user to input a control command for the pan-tilt, for example, to perform a traveling motion in up, down, left, and right directions. The console is a PC machine provided with a corresponding software system; the remote communication module is used for realizing the communication between the console and the cloud deck, and transmitting the instruction sent by the console to the cloud deck; on the other hand, the data of the radar survey and drawing scanning of the cloud deck is also fed back to the console, the cloud deck control system also comprises a cloud deck converter and two alternating current motors, the cloud deck converter is arranged on the cloud deck, and the received console instruction is decoded and converted into a control signal for controlling the operation of the motors; in addition, the motor on the holder is driven to perform corresponding actions according to the control signal, and the holder is an installation platform consisting of two alternating current motors and can move in the horizontal and vertical directions. The horizontal and vertical directions of the pan-tilt are driven by two different motors, so the rotational speed of the pan-tilt is also divided into horizontal rotational speed and vertical rotational speed. Due to the load, the torque of the vertical motor during starting and operation maintaining is larger than that of the horizontal direction, and the requirement on the horizontal rotating speed is higher than that of the vertical rotating speed when actual monitoring is added, so that the vertical rotating speed of the tripod head is lower than the horizontal rotating speed.
When the system is used, a tripod, a rotating tripod head and a laser radar of a tripod head control system are deployed at 2-3 points in a field area of a transformer substation, and a high-precision positioning module is used as an auxiliary, and the selection of the point positions requires that the position without shielding in a visual field is selected according to the range and the angle of the laser radar. And point cloud data of various devices of mobile personnel and transformer substations can be obtained in time within the coverage range of the laser radar.
Preferably, in the embodiment of the present invention, when the surveying and mapping is performed by using the aircraft, since the distance measurement is far, the present invention uses a point cloud acquisition technology based on a time of flight TOF, and uses a TOF depth camera, which belongs to one of solid-state laser radar TOF, and uses the TOF camera, which is based on the principle that the distance from an object to the camera is calculated by projecting a modulated light source onto the observed object and then observing the time difference between the reflected light and the incident light, as shown in fig. 2.
The TOF camera comprises a laser generator and a photosensitive unit consisting of a photosensitive laser or an avalanche diode, and can be selected from a sine wave pulse camera or a square wave pulse camera. After a laser generator (Scanner) emits laser, the laser meets an obstacle (Target) and is reflected back, a photosensitive unit in a camera senses reflected light, the time required by the laser from emitting to receiving the reflected light is calculated, and then the distance between the obstacle and the camera is obtained according to the fact that the flight time is multiplied by the speed of light:
where d is the distance, et is the time required for the laser to emit light until the reflected light is received, and c is the speed of light.
In order to measure the time difference between the incident light and the reflected light, the light source is modulated into a continuous pulse light source, and a sine wave pulse TOF camera transmits a sine wave signal with a certain modulation frequency to a substation scene, wherein the incident signal is reflected by the surface of the substation scene and absorbed. When the signal arrives and is received, the amplitude of the signal is attenuated due to energy attenuation, a phase delay is generated, and according to the phase delay, the time difference between the incident signal and the reflected signal can be calculated, so that the depth value of the environment can be calculated.
The sine wave pulse method measures time difference based on the phase length of a brightness modulation light source, the light source is turned on and then turned off on a camera to form a beam of light pulse when the sine wave pulse method works, the electronic shutter is turned on by the control unit at the same time, and charges generated by the light pulse are stored on a chip and marked as Q0; then, at the moment the light source is turned off, shutter S1 is opened and the charge generated by the light pulse is also stored on the chip, denoted Q1; then, the delay time of the incident light and the reflected light can be calculated according to the values of Q0 and Q1, and further the depth value of the environment can be calculated.
The TOF camera using square-wave pulses to measure distance based on the propagation time of a single pulse is a very fast and accurate electronic component, but can also achieve high accuracy and resolution, and has very high requirements for the accuracy of the time measurement.
Preferably, the data volume of the original point cloud image acquired by the method is huge and contains more noise, and the direct operation on the initial point cloud data can reduce the precision of subsequent radar tracking and point cloud registration and influence the effect of a final model, so that the pretreatment on the initial depth data is very necessary, and the pretreatment comprises point cloud denoising, point cloud registration, point cloud segmentation, point cloud merging, point cloud rarefaction, point cloud sequencing, derivative point cloud and the like. The scanning data of the laser radar is stored in the form of point cloud (point cloud), that is, each scanning data packet is stored in the form of a plurality of scanning points, and each scanning point contains the three-dimensional coordinates, color information or reflectivity information and the like of the point. Due to the interference of environmental noise and the defect of accuracy, misrecognized points and stray points exist in original point cloud data, and the points are collectively called noise. The preprocessing of the original point cloud data comprises the removal of noise points in the original point cloud data, and specifically comprises the following steps: and (3) eliminating by adopting a radius-based filtering method, namely, presetting the minimum point cloud number in a specified radius around certain point cloud data, and if the point cloud number around a given target point is less than a set number, determining the given target point as a noise point and removing. The method can be used for preliminarily eliminating the noise points in the original point cloud data.
Preferably, in the step of the present invention, the preprocessed point cloud data is labeled to label each charged body of the substation, and the present invention can label more than 20 types of charged bodies with different shapes, such as: the equalizing ring, the high-voltage shunt reactor, the bus and the like automatically generate a three-dimensional electrified area according to the voltage grade and the shape and by combining the GB/T311 standard, and fine adjustment can be performed.
Preferably, the embodiment of the present invention further provides a ground fitting based on point cloud data, which is implemented by using a least square method, in an operation vehicle at a substation site, in an initial state, ground data of an area right in front of the vehicle is selected for performing three-dimensional fitting, and a fitting equation of a spatial three-dimensional plane may be expressed as ax + by + cz = d, where: and a, b and c are unit normal vectors of an x plane, a y plane and a z plane respectively, d is the distance from the coordinate origin to the plane, and the values of the parameters a, b, c and d can be calculated by using a least square method.
For arbitrary point cloud data x i ,y i ,z i Calculating the plane value d of the point according to the fitting parameters a, b and c i ：ax i +by i +cz i ＝d i Setting a plane threshold Δ d, if |/d i And d < delta d, judging that the point belongs to the ground.
The fitting method is also applied to the following situations: in the region needing near-electricity operation in the transformer substation, people are prevented from entering the electrified interval by mistake; when the field worker carries the materials or equipment beyond the large body size range and the crane boom grabs the materials or equipment beyond the large crane bucket range in the transformer substation or on the power transmission and distribution line.
Preferably, in the embodiment of the present invention, the moving carrier in S3 includes field workers and/or vehicles for performing work in a substation field, so that a moving target segmentation and positioning technology is adopted in the present invention, the preprocessed point cloud data includes areas such as a foreground and a background, a target object to be labeled needs to be extracted from the areas, that is, a constructor, a construction vehicle and other equipment which are in operation are regarded as a moving target, and because dynamic changes of a background image, such as influences of weather, illumination, shadows and other interferences, and the acquired point cloud data is a high redundancy and non-uniform data structure, and a surface shape of the point cloud data may be arbitrary and does not have data statistical distribution, the target object to be labeled in the point cloud data is labeled by using a segmentation and positioning technology, so that shapes and spatial positions of electrical equipment such as a ground area, field workers, vehicles, and various charged bodies are accurately identified from three-dimensional point cloud data, and a segmentation and positioning technology in the present invention is specifically adopted and is not repeated.
Preferably, the embodiment of the invention also provides a lightweight three-dimensional point cloud model, and the model is obtained by adopting the modeling method provided by the invention. The three-dimensional point cloud model greatly improves the position precision and the updating frequency of the transformer substation model, improves the usability of operation safety control, and can be used as a basic model for constructing the three-dimensional virtual model of the transformer substation.
The foregoing description shows and describes several preferred embodiments of the invention, but as aforementioned, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A modeling method of a lightweight three-dimensional point cloud model is used for a transformer substation, and is characterized by comprising the following steps:
s1, surveying and mapping scanning is carried out on an area in a transformer substation to obtain original point cloud data;
s2, preprocessing the original point cloud data to obtain preprocessed point cloud data;
s3, marking the preprocessed point cloud data, marking out an operation area, a dangerous area, each charged body and a mobile carrier of the transformer substation, and forming marked point cloud data;
and S4, training the marked point cloud data to obtain a lightweight three-dimensional point cloud model.
2. The modeling method of the lightweight three-dimensional point cloud model according to claim 1, wherein the raw point cloud data in S1 comprises relative positions of devices in a substation, volume size data of itself, and/or an area of a working area.
3. The method of modeling a lightweight three-dimensional point cloud model of claim 2, wherein each of said devices comprises an electrically charged body comprising: one or more of a grading ring, a high voltage shunt reactor and/or a busbar.
4. The modeling method of a lightweight three-dimensional point cloud model according to claim 1, wherein in S1, a radar pan-tilt control system provided on a mobile vehicle is used for mapping and scanning.
5. The modeling method of the lightweight three-dimensional point cloud model according to claim 4, wherein the mobile vehicle comprises one or more of a robot, a robot dog, or an unmanned aerial vehicle.
6. The modeling method of the lightweight three-dimensional point cloud model according to claim 4, wherein the radar pan-tilt control system comprises a console, a remote communication module and a pan-tilt, the console is used for inputting control instructions to the pan-tilt; the remote communication module is used for realizing communication between the console and the cloud deck, transmitting the command sent by the console to the console, and feeding back data of radar surveying and mapping scanning on the console to the console.
7. The modeling method of the lightweight three-dimensional point cloud model according to claim 1, wherein the preprocessing in S2 includes removing noise points existing in the original point cloud data, specifically: and setting a minimum point cloud number existing in a specified radius around the original point cloud data in advance, and if the point cloud number around the given target point is less than the minimum point cloud number, taking the given target point as a noise point and removing the noise point.
8. The modeling method of the lightweight three-dimensional point cloud model according to claim 1, wherein in S3, the preprocessed point cloud data is labeled by using a point cloud segmentation technique.
9. The modeling method of a lightweight three-dimensional point cloud model according to claim 1, wherein the mobile carrier is a constructor and/or a vehicle of a substation field operation.
10. A lightweight three-dimensional point cloud model, characterized in that the model is obtained using the modeling method of any one of claims 1-9.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116993923A (en) * | 2023-09-22 | 2023-11-03 | 长沙能川信息科技有限公司 | Three-dimensional model making method, system, computer equipment and storage medium for converter station |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116993923A (en) * | 2023-09-22 | 2023-11-03 | 长沙能川信息科技有限公司 | Three-dimensional model making method, system, computer equipment and storage medium for converter station |
| CN116993923B (en) * | 2023-09-22 | 2023-12-26 | 长沙能川信息科技有限公司 | Three-dimensional model making method, system, computer equipment and storage medium for converter station |
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