CN112862973A - Real-time remote training method and system based on fault site - Google Patents
Real-time remote training method and system based on fault site Download PDFInfo
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Abstract
The invention discloses a real-time remote training method and a system based on a fault site, wherein the method utilizes AR equipment at a fault site end to collect fault site information and AR knowledge information in real time; generating a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information; constructing a fault site map by using a VSLAM algorithm; transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end; and the AR equipment of the remote training end displays the 3D digital twin image and the natural interaction information on the site of the remote training end in real time according to the sparse point cloud map. According to the method, the fault site 3D image and the natural interaction information constructed based on the AR are transmitted to the remote training personnel in real time, training contents with strong practical operability and more real and visual contents are provided for the training personnel, and the training effect is effectively improved.
Description
Technical Field
The invention relates to the technical field of power system training, in particular to a real-time remote training method and system based on a fault site.
Background
In the power industry, power equipment is numerous and widely distributed, and the problem of equipment faults in a plurality of places is often easy to occur, so that an enterprise generally needs to arrange a plurality of operation and maintenance personnel on duty to maintain good operation of a power system, and the enterprise is required to do emergency repair technical training work of a large number of operation and maintenance personnel to ensure that each operation and maintenance personnel can timely and efficiently process the equipment faults.
At present, most enterprises adopt a traditional training mode to carry out technical training on operation and maintenance personnel, including practice training of a face-to-face student, theoretical training of textbooks, theoretical training of videos and the like. However, emergency repair training relates to training contents with strong technical performance, the traditional training mode only focuses on theoretical guidance, the contents are not visual enough, actual operation training cannot be provided, and the training effect is limited.
Disclosure of Invention
Aiming at the technical problems, the invention provides a real-time remote training method and a real-time remote training system based on a fault site.
The embodiment of the invention provides a real-time remote training method based on a fault site, which comprises the following steps:
acquiring fault site information and AR knowledge information in real time by utilizing AR equipment at a fault site end;
generating a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information;
constructing a fault site map by using a VSLAM algorithm;
transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end;
and the AR equipment of the remote training end displays the 3D digital twin image and the natural interaction information on the site of the remote training end in real time according to the sparse point cloud map.
In one embodiment, a depth perception camera sensor and an RBG camera sensor of the AR equipment are used for scanning a fault site in real time to obtain fault site information, wherein the information comprises depth information and color information of a fault site environment; and recognizing the hand gesture and the voice content in real time by utilizing the AR equipment to obtain AR knowledge information.
In one embodiment, the AR knowledge information further includes AR spatial labeling based on the failure site environment, spatial anchor labeling based on the failure site environment, and loT device parameters.
In one embodiment, the 3D digital twin image, the natural interaction information and the sparse point cloud map are transmitted to AR equipment of a remote training end by using a 5G network technology.
In one embodiment, the remote training end can utilize the AR equipment to perform real-time voice interaction and AR knowledge information interaction with the fault site end.
In one embodiment, the 3D digital twin image, the natural interaction information, and the sparse point cloud map may be transmitted to a plurality of AR devices of a remote training terminal at the same time.
The embodiment of the invention also provides a real-time remote training system based on the fault site, which comprises the following components:
the acquisition unit is used for acquiring fault site information and AR knowledge information in real time by utilizing AR equipment at a fault site end;
the construction unit is used for generating a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information; constructing a fault site map by using a VSLAM algorithm;
the transmission unit is used for transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end;
and the training unit is used for displaying the 3D digital twin image and the natural interaction information on the scene of the remote training end in real time by the AR equipment of the remote training end according to the sparse point cloud map.
In one embodiment, the remote training end can utilize the AR equipment to perform real-time voice interaction and AR knowledge information interaction with the fault site end.
In one embodiment, the 3D digital twin image, the natural interaction information, and the sparse point cloud map may be transmitted to a plurality of AR devices of a remote training terminal at the same time.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method according to any of the above embodiments.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
according to the real-time remote training method and system based on the fault site, the fault site is used as a training base, the 3D images of the fault site constructed by using the AR technology and the natural interaction information of a training lecturer are transmitted to remote training personnel in real time, training contents with strong practical operation performance and more real and visual contents are provided for the training personnel, and the training effect is effectively improved.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a real-time remote training method based on a fault site according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of a real-time remote training system based on a fault site according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification 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.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, an embodiment of the present invention provides a real-time remote training method based on a fault site, which includes the following steps.
S11: and acquiring fault site information and AR knowledge information in real time by utilizing the AR equipment at the fault site end.
In this embodiment, the acquiring of the fault site information and the AR knowledge information in real time by using the AR device at the fault site end specifically includes: scanning a fault site in real time by using a depth perception camera sensor and an RBG camera sensor of the AR equipment to obtain fault site information, wherein the information comprises depth information and color information of a fault site environment; and recognizing the hand gesture and the voice content in real time by utilizing the AR equipment to obtain AR knowledge information.
In this embodiment, the AR knowledge information also includes AR spatial labeling based on the failed field environment, spatial anchor labeling based on the failed field environment, and loT device parameters.
In one particular embodiment, the AR device includes AR smart glasses.
S12: generating a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information; and constructing a fault site map by using a VSLAM algorithm.
S13: and transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end.
In one embodiment, the 3D digital twin imagery, the natural interaction information, and the sparse point cloud map may be transmitted to an AR device of a remote training end using 5G network technology.
In one embodiment, the 3D digital twin imagery, the natural interaction information, and the sparse point cloud map may be transmitted simultaneously to a plurality of AR devices of a remote training end.
S14: and the AR equipment of the remote training end displays the 3D digital twin image and the natural interaction information on the site of the remote training end in real time according to the sparse point cloud map.
In one embodiment, the remote training end can utilize the AR equipment to perform real-time voice interaction and AR knowledge information interaction with the fault site end.
The following provides a specific embodiment, which illustrates the application of the fault field-based real-time remote training method in the training of personnel in the power industry.
Maintenance personnel with field faults act as training instructors, wear AR (augmented reality) glasses at the fault field end, scan the fault field in real time by using a depth perception camera sensor (TOF) and an RGB (red, green and blue) camera sensor of the AR glasses, and construct a high-precision dense point cloud 3D digital twin image of the fault field.
The system comprises a depth perception camera sensor (TOF), a three-dimensional surface information processing module and a data processing module, wherein the TOF is used for acquiring depth information of a fault site, reconstructing 3D visual point cloud data of the fault site, and meshing the point cloud data through real-time calculation to further process the three-dimensional surface information; and the RGB camera sensor is used for acquiring color information of the fault site, and pasting textures and colors of the actual fault site on the 3D grid surface information in a map form after picture splicing and UV correction processing.
The 3D holographic digital twin image of the real fault scene with texture and geometric shape can be obtained by combining the information output by the depth perception camera sensor (TOF) and the RGB camera sensor, so that the training effect is more real.
Meanwhile, the training instructor acquires space environment perception capability (VSLAM) through binocular fisheye RGB lens sensors and inertial sensors of the AR glasses and constructs a sparse point cloud map in real time.
In a fault site, the AR glasses worn by a training instructor perform fault explanation through natural voice interaction and natural gesture interaction modes, and acquire AR knowledge information such as gestures, voice explanation, AR space labeling based on a site environment, space anchor point labeling based on the site environment, equipment IOT parameters and the like in real time.
The method comprises the steps that an AR space label based on a field environment is an AR space label of a training instructor circled in a fault field; the space anchor point label based on the site environment is the anchor point of the training lecturer in the fault site space, and the IOT parameter is the Internet of things equipment data pulled by the training lecturer through the cloud.
And then, calculating three-dimensional information of the AR knowledge information according to the position relation and the time sequence relation of the real space of the fault site, placing the AR knowledge information in a three-dimensional coordinate system of the space, and combining the two information together to form a fault site holographic multi-dimensional information collection.
The information collection comprises the information of the fault site and the knowledge information of the site fault maintenance personnel, the training effect is improved through the deep combination of the information,
in particular, real spatial information may be acquired in conjunction with TOF and fisherye RGB cameras of AR glasses.
The sparse point cloud map, the 3D digital twin image and the AR knowledge information generated by the fault site end can be transmitted to a remote student end in real time through a 5G network technology.
The 5G network technology has the characteristics of high bandwidth and low time delay, and can provide higher transmission efficiency in real time.
For a plurality of students at a remote training end, each student can wear AR glasses at different places simultaneously, receives 3D digital twin images and AR knowledge information sent by a training instructor at a fault site end, and presents the information in the real world of the student through the environment sensing and positioning capability (VSLAM) of the AR glasses by combining a sparse point cloud map, so that 1: 1 recurring fault site. Meanwhile, the student can check the digital 3D world content information of the fault site end from different angles and distances by moving the position of the student.
The VSLAM (visual Simultaneous Localization and mapping) environment perception positioning capability can be used for establishing a three-dimensional model of a scene around a target object, and simultaneously restoring a motion track of a camera by positioning the spatial position of the camera.
Because the real world where the student is located corresponds to the virtual world space of the fault site end one to one, the student can also carry out real-time voice interaction and AR knowledge information editing interaction with site fault maintenance personnel through AR glasses, real-time remote interactive training is realized, and the intuitive and efficient training effect is achieved.
As shown in fig. 2, another embodiment of the present invention provides a real-time remote training system based on a fault site, which includes an acquisition unit 101, a construction unit 102, a transmission unit 103, and a training unit 104.
The acquisition unit 101 is configured to acquire the fault site information and the AR knowledge information in real time by using the AR device at the fault site.
The construction unit 102 is configured to generate a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information; and constructing a fault site map by using a VSLAM algorithm.
The transmission unit 103 is configured to transmit the 3D digital twin image, the natural interaction information, and the sparse point cloud map to an AR device of a remote training end.
The training unit 104 is used for displaying the 3D digital twin image and the natural interaction information on the scene of the remote training end in real time by the AR equipment of the remote training end according to the sparse point cloud map.
Because the information interaction, execution process and other contents between the units in the system are based on the same concept as the method embodiment of the present invention, specific contents can be referred to the description in the method embodiment of the present invention, and are not described herein again.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method according to any of the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and may include the processes of the embodiments of the methods when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A real-time remote training method based on a fault site is characterized by comprising the following steps:
acquiring fault site information and AR knowledge information in real time by utilizing AR equipment at a fault site end;
generating a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information;
constructing a fault site map by using a VSLAM algorithm;
transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end;
and the AR equipment of the remote training end displays the 3D digital twin image and the natural interaction information on the site of the remote training end in real time according to the sparse point cloud map.
2. The real-time remote training method based on the fault site as claimed in claim 1, wherein the fault site information and the AR knowledge information are collected in real time by using an AR device at the fault site end, specifically:
scanning a fault site in real time by using a depth perception camera sensor and an RBG camera sensor of the AR equipment to obtain fault site information, wherein the information comprises depth information and color information of a fault site environment;
and recognizing the hand gesture and the voice content in real time by utilizing the AR equipment to obtain AR knowledge information.
3. The fail-site based real-time remote training method according to claim 2, wherein the AR knowledge information further comprises AR spatial labeling based on a fail-site environment, spatial anchor labeling based on a fail-site environment, and loT device parameters.
4. The fail-site based real-time remote training method according to claim 2, further comprising:
and transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end by utilizing a 5G network technology.
5. The fail-site based real-time remote training method according to claim 2, further comprising:
the remote training end can utilize the AR equipment to perform real-time voice interaction and AR knowledge information interaction with the fault site end.
6. The fail-site based real-time remote training method according to claim 2, further comprising:
the 3D digital twin image, the natural interaction information and the sparse point cloud map can be simultaneously transmitted to a plurality of AR devices of a remote training end.
7. A real-time remote training system based on a fault site is characterized by comprising:
the acquisition unit is used for acquiring fault site information and AR knowledge information in real time by utilizing AR equipment at a fault site end;
the construction unit is used for generating a 3D digital twin image according to the fault site information; combining the AR knowledge information with the fault site information in real time to obtain natural interaction information; constructing a fault site map by using a VSLAM algorithm;
the transmission unit is used for transmitting the 3D digital twin image, the natural interaction information and the sparse point cloud map to AR equipment of a remote training end;
and the training unit is used for displaying the 3D digital twin image and the natural interaction information on the scene of the remote training end in real time by the AR equipment of the remote training end according to the sparse point cloud map.
8. The fail-site based real-time remote training system according to claim 7, further comprising:
the remote training end can utilize the AR equipment to perform real-time voice interaction and AR knowledge information interaction with the fault site end.
9. The fail-site based real-time remote training system according to claim 7, further comprising:
the 3D digital twin image, the natural interaction information and the sparse point cloud map can be simultaneously transmitted to a plurality of AR devices of a remote training end.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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