CN109615708B - AR-based pipe network visualization system and method - Google Patents
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Abstract
The application relates to the technical field of augmented reality, in particular to an AR-based pipe network visualization system, which comprises: the real object identification module is used for acquiring real scenery pictures, acquiring real-time positions of patrol personnel, and identifying real object pictures from the real scenery pictures according to the real-time positions; the data screening module is used for acquiring the pipe network big data and the real-time position of the patrol personnel, and screening the pipe network big data according to the real-time position to obtain real-time pipe network data; the real-time modeling module is used for acquiring real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing the real-time three-dimensional pipe network data into a real-time pipe network 3D model; the AR projection module is used for acquiring the real-time pipe network 3D model and the real-time pipe network picture, and projecting the real-time pipe network 3D model onto the real-time pipe network picture to obtain the augmented reality picture. The application can solve the problems of serious memory capacity waste and low inspection efficiency caused by pre-constructing a 3D model in the existing AR visualization system.
Description
Technical Field
The application relates to the technical field of augmented reality, in particular to an AR-based pipe network visualization system and method.
Background
The underground pipe network of city is an important part of city infrastructure, it is like the nerves and blood vessels in human body, it is the material foundation for life and development of city, called life line of city. In recent years, urban construction in China is rapidly developed, the original underground pipe network of the city cannot meet the modern development requirement, and the current situation of the underground pipe network needs to be accurately mastered in the process of updating the old pipe network, designing and constructing the new pipe network and planning the new pipe network.
The management of underground pipe networks mainly has the following two problems: on one hand, most pipelines are underground, buried deeply, widely distributed, various and complex in structure, the data volume of various data is large, and the data retention period is long, so that great difficulty is brought to the design and management of a pipe network; on the other hand, the urban underground pipe network management information system is mature in the technical aspect of pipeline plane graphics, and has strong functions in the aspects of data acquisition and input, space data analysis and processing, data output and the like, but the two-dimensional GIS has the defect that the two-dimensional GIS is difficult to overcome. Two-dimensional views are still important tools for people to recognize spatial information due to the advantages of macroscopicity, integrity, abstract property and the like. Although three-dimensional scenes have realistic visual effects and have the advantages of dynamics, interactivity, reality and the like, three-dimensional scenes have the defects that the three-dimensional scenes are easy to lose direction when roaming in the environment of the three-dimensional pipe network scene, as well as walking in the real world space.
To solve the above problems, chinese patent publication No. CN101630419a discloses a method for constructing a three-dimensional visualization system for urban integrated pipe network, which comprises: establishing a central database of an urban comprehensive pipe network; a three-dimensional texture database; a spatial data engine; reading and modeling a central database and a texture database through a spatial data engine so as to construct an overground and underground two-dimensional view and a three-dimensional visual scene; and establishing the correspondence between the two three-dimensional data layers and the visual layers, wherein the correspondence between the two three-dimensional data layers is that the two-dimensional view of the system corresponds to the geographic coordinates of the three-dimensional scene, and the correspondence between the visual layers is that various spatial ground object models in the three-dimensional scene correspond to map symbols of the two-dimensional view. The scheme realizes two-dimensional and three-dimensional linkage, so that functions which are difficult to realize or macroscopic in a three-dimensional scene are realized through a two-dimensional view, and functions which cannot be realized or are not true enough in two dimensions are realized through a three-dimensional scene, and the advantages of the two are complementary, so that the efficient management of the urban comprehensive pipe network can be realized.
According to the scheme, the pipe network is efficiently managed in a two-dimensional and three-dimensional linkage mode, but no matter a two-dimensional model or a three-dimensional model is constructed, the two-dimensional model or the three-dimensional model is a virtual environment, patrol personnel can hardly accurately combine the information such as the position, the proportion, the arrangement mode and the like of the pipeline in the virtual environment and the real environment, the problem that the position of the pipeline cannot be found easily exists, and the patrol efficiency of the pipe network is low.
AR, augmented reality (Augmented Reality, abbreviated as AR), also called mixed reality, is a technique of applying virtual information to the real world by computer technology, and real environment and virtual objects are superimposed on the same screen or space in real time and exist at the same time. If the AR technology is adopted to project virtual pipe network data onto the mobile equipment in the form of a 3D model, the pipe network visualization can be realized, and the problem that the virtual and the reality cannot be accurately combined can be solved.
AR technology is the projection of virtual images, video, and 3D models into a real environment. The existing AR systems all store pre-built 3D models into a server, and when the existing AR systems are used, the 3D models of the server are downloaded to mobile equipment for projection, so that the existing AR systems have the following problems: 1) The pre-constructed 3D model occupies a large space, has extremely high memory requirements on a server and mobile equipment of patrol personnel, and causes serious memory capacity waste; 2) The 3D model has large occupation and high download bandwidth, not only depends on the network seriously, but also takes a great deal of time, and the inspection efficiency is low.
Disclosure of Invention
The application aims to provide an AR-based pipe network visualization system, which can avoid the problems of serious memory capacity occupation and low inspection efficiency caused by pre-constructing a 3D model in the existing AR visualization system.
The basic scheme provided by the application is as follows: an AR-based pipe network visualization system, comprising:
the real object identification module is used for acquiring real scenery pictures, acquiring real-time positions of patrol personnel, and identifying real object pictures from the real scenery pictures according to the real-time positions;
the data screening module is used for acquiring the pipe network big data and the real-time position of the patrol personnel, and screening the pipe network big data according to the real-time position to obtain real-time pipe network data;
the real-time modeling module is used for acquiring real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing the real-time three-dimensional pipe network data into a real-time pipe network 3D model;
the AR projection module is used for acquiring the real-time pipe network 3D model and the real-time pipe network picture, and projecting the real-time pipe network 3D model onto the real-time pipe network picture to obtain the augmented reality picture.
The application has the beneficial effects that: 1) Compared with the prior art that the 3D model is stored in the server in advance, the real-time pipe network 3D model is built in real time, and the method has the advantages that the waste of storage space and capacity does not exist, and the memory requirement on mobile equipment is lower. Compared with the prior art of downloading the 3D model from the server, the method only needs to download the real-time pipe network data, has small network bandwidth and higher speed. The application can solve the problems of serious memory capacity occupation and low inspection efficiency caused by pre-constructing a 3D model in the existing AR visualization system. 2) The application can identify the real object and screen the pipe network data before constructing the real-time pipe network 3D model, the constructed real-time pipe network 3D model can be highly matched with the real-time scenery picture, the superposition of the virtual 3D model and the real scenery picture can be more accurate, and compared with the prior art of pre-storing and downloading, the pipe network visualization effect of the application can be better.
Further, the method further comprises the following steps: and the real-time position positioning module is used for positioning the patrol personnel in real time and acquiring the real-time position of the patrol personnel.
The beneficial effects are that: according to the scheme, a centimeter-level GNSS positioning system is adopted and is used for assisting in positioning by SLAM, and a composite fixed solution algorithm based on GPS, GLONASS and a Beidou positioning system is adopted, so that the real geographic space can be positioned, and the positioning accuracy can reach the centimeter level.
Further, the method further comprises the following steps: the data acquisition module is used for acquiring pipe network big data, wherein the pipe network big data comprises pipe network digital information and pipe network monitoring data.
The beneficial effects are that: the three-dimensional pipe network data conforming to the real attribute is generated by massive pipe network data in real time, so that a large amount of unnecessary modeling workload can be avoided, and meanwhile, the equipment with the same performance can process more data.
Further, the pipe network digital information comprises pipeline data, a pipe network building design blueprint and a two-dimensional plane electronic pipeline map; the pipeline data comprises buried trend, buried depth, longitude and latitude coordinates, pipe diameter size and pipeline information, and the pipeline information comprises pipeline purposes, streets and departments.
Further, the pipe network monitoring data includes current air pressure data, water pressure data, permeability data, and leakage data.
The application also discloses an AR-based pipe network visualization method, which comprises the following steps:
a step of acquiring a real scenery, namely acquiring a real scenery picture, acquiring a real-time position of a patrol personnel, and identifying a real-object picture in the real scenery picture according to the real-time position;
a real-time modeling step, namely acquiring pipe network big data, screening the pipe network big data according to the real-time position of a patrol personnel to obtain real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing the real-time three-dimensional pipe network data into a real-time pipe network 3D model;
and AR projection, namely projecting the real-time pipe network 3D model onto a real-object picture to obtain an augmented reality picture.
Further, before the step of obtaining the real scenery, the method further comprises the following steps: and a real-time positioning step, namely positioning the position of the patrol personnel in real time to obtain the real-time position of the patrol personnel.
Drawings
FIG. 1 is a block diagram of a tube network visualization system according to an embodiment of the present application;
fig. 2 is a flow chart of a method for visualizing a pipe network according to an embodiment of the present application.
Detailed Description
The following is a further detailed description of the embodiments:
embodiment one:
as shown in fig. 1: a visual system of a pipe network based on AR comprises a server and a mobile terminal worn by patrol personnel, wherein the mobile terminal and the server are connected through an existing wireless communication module network.
1. A server, comprising:
the data acquisition module is used for acquiring pipe network big data, wherein the pipe network big data comprises pipe network digital information and pipe network monitoring data.
The pipe network digital information comprises pipeline data, a pipe network building design blueprint and a two-dimensional plane electronic pipeline map, wherein the pipeline data comprises buried trend, buried depth, longitude and latitude coordinates, pipe diameter size and pipeline information, and the pipeline information comprises information such as purposes (such as water pipes and natural gas pipes), streets, departments and the like; the two-dimensional plane electronic pipeline map is drawn by centimeter-level GNSS positioning knot pipeline data.
The pipe network monitoring data comprise current pressure data, air pressure data, water pressure data, permeability data and leakage data. The existing internet of things detector, pressure sensor and leakage sensor are adopted to collect monitoring data.
And the storage module is used for storing the pipe network data and all system data. The storage module is an existing computer hard disk.
2. A mobile terminal, comprising:
and the real-time position positioning module is used for positioning the patrol personnel in real time and acquiring the real-time position of the patrol personnel.
When the positioning is carried out for the patrol personnel, the error of the positioning accuracy of the patrol personnel can be controlled within 1 cm by adopting a composite fixed solution algorithm of a GPS, GLONASS and a Beidou positioning system.
And the real object identification module is used for acquiring the real scene picture, acquiring the real-time position of the patrol personnel, and identifying the real object picture from the real scene picture according to the real-time position.
The scenery acquisition module is a camera on the mobile equipment of the patrol personnel, and acquires the picture of the real scenery. And identifying the object picture from the real scene picture by adopting an artificial intelligence learning technology of fusion Net. Fusion net is a mix of three neural networks, V-CNN I, V-CNN II and MV-CNN (the last neural network is built based on AlexNet structure and pre-trained by ImageNet dataset), which are fused at the scoring layer, and the final predicted classification is found by calculating the linear combination of scores. The first two networks use the voxelized CAD model and the last uses 2D projection as input. A standard pre-training neural network model (AlexNet) is adopted as a basis of a 2D network MV-CNN, and a warm-start (warm-start) pre-training is carried out on a network of 2D projection of a 3D object, and the model is based on a large-scale 2D pixel picture data set ImageNet.
And the data acquisition module is used for downloading and acquiring pipe network big data from the server.
The automatic modeling subsystem comprises a data screening module and a real-time modeling module. And the data screening module is used for acquiring the pipe network big data and the real-time position of the patrol personnel, and screening the pipe network big data according to the real-time position to obtain the real-time pipe network data. The real-time modeling module is used for acquiring real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing the real-time three-dimensional pipe network data into a real-time pipe network 3D model. The data screening can be realized BY adopting a keyword extraction mode, and the related functions can be realized BY using a GROUP BY clause in SQL language; the automatic modeling adopts the existing three-dimensional modeling mode.
The AR projection module is used for acquiring the real-time pipe network 3D model and the real-time pipe network picture, and projecting the real-time pipe network 3D model onto the real-time pipe network picture to obtain the augmented reality picture. In order to ensure the accuracy of the projection of the 3D model of the real-time pipe network, in this embodiment, centimeter-level GNSS positioning is adopted, and in consideration of the fact that the accuracy of GNSS positioning is easily affected by a building and the like, SLAM assisted GNSS is adopted for positioning.
The embodiment also discloses an AR-based pipe network visualization method, as shown in fig. 2, which comprises the following steps:
s1: a real-time positioning step, namely positioning the position of the patrol personnel in real time to obtain the real-time position of the patrol personnel;
s2: a step of acquiring a real scenery, namely acquiring a real scenery picture, acquiring a real-time position of a patrol personnel, and identifying a real-object picture in the real scenery picture according to the real-time position;
s3: a real-time modeling step, namely acquiring pipe network big data, screening the pipe network big data according to the real-time position of a patrol personnel to obtain real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing a real-time pipe network 3D model by using the real-time three-dimensional pipe network data;
s4: and AR projection, namely projecting the real-time pipe network 3D model onto a real-object picture to obtain an augmented reality picture.
Embodiment two:
the difference between this embodiment and the first embodiment is that the disaster estimation subsystem is further included in this embodiment.
In recent years, earthquake disasters frequently occur, and the normal operation of a pipe network can be seriously affected by the earthquake. The pipe network is mostly buried underground deeply, so that the working state of the pipe network is difficult to acquire, and the state of the pipe network is difficult to judge only according to the change on the ground surface. In the prior art, various data in the pipe network work are monitored in real time by adopting an Internet of things detector, a leakage sensor and the like, and when the data are abnormal, the pipe network fault is judged. Although the method is accurate in judgment, pertinence and predictability are lacking: firstly, a patrol personnel cannot see the actual condition of a pipe network, can not determine an important investigation region, can only carry out 'carpet' -type investigation, and is not enough in time for the investigation of the pipe network; secondly, the condition can be known only after the pipe network has failed, and the sudden failure can lead the inspection personnel to be overwhelmed, so that large-area pipe network lines are easily paralyzed because no preventive measures are taken.
In this embodiment, the disaster estimation subsystem includes:
the disaster position calculation module is used for obtaining disaster information, calculating a disaster receiving area according to the disaster grade and the disaster source position in the disaster information, and calculating a key disaster receiving position in the disaster receiving area according to the damage information in the disaster information.
In this embodiment, an earthquake is taken as an example: disaster information comprises information such as magnitude, intensity, earthquake focus, earthquake median and the like, and can be crawled from the national earthquake bureau through a web crawler.
And the investigation position evaluation module is used for acquiring real-time sensor data of the pipe network, calculating the real-time sensor data and the original sensor data to obtain a post-disaster change value, and evaluating the key investigation position in the key disaster-affected position by using the post-disaster change value.
When a pipe network is installed, a pressure sensor, an Internet of things detector, a leakage sensor, an air pressure sensor, a water pressure sensor and the like are installed on a pipeline of the pipe network, real-time sensor data of the sensors are obtained in real time through a server, the original sensor data refer to the numerical value of each sensor when the pipe network is installed, and the post-disaster variation refers to the difference value between the real-time sensor data and the original sensor data. And (3) calculating the key investigation position: if the post-disaster change value of the position a is 5 and the post-disaster change value of the position B is 10, the position B is an important investigation region compared with the position a.
The pipe network data pre-estimating module is used for acquiring pipe network big data, and extracting pre-estimated pipe network data from the pipe network big data by combining the key investigation position with the real object picture in the real object identification module.
The estimated pipe network data can be realized BY a keyword extraction mode, and the related functions can be realized BY a GROUP BY clause in SQL language.
The disaster model construction module is used for acquiring estimated pipe network data, generating an estimated three-dimensional model from the estimated pipe network data, and constructing an estimated pipe network 3D model by using the estimated three-dimensional model.
The 3D model optimization module is used for acquiring real-time pressure data in real-time sensor data of the pipe network, comparing the real-time pressure data with the original pressure data, calculating to obtain the pipe network offset direction and offset, and adjusting and optimizing the position of the estimated pipe network 3D model according to the pipe network offset direction and offset.
The beneficial effects of this embodiment are:
1) The embodiment can evaluate and obtain the key investigation position, and the patrol personnel can carry out investigation on the key investigation position, thereby being beneficial to timely investigating the potential risk of the pipe network, preparing preventive measures and solving the problem of paralysis of the large-area pipe network line caused by sudden failure of the pipe network; 2) According to the embodiment, the post-disaster pipe network 3D model is estimated through the real object image, the pipe network position is optimized through the real-time sensor data, and the finally obtained pipe network 3D model is closer to the post-disaster actual pipe network position, so that the inspection effect of inspection personnel is improved.
The foregoing is merely an embodiment of the present application, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application date or before the priority date, can know all the prior art in the field, and has the capability of applying the conventional experimental means before the date, and a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (6)
1. An AR-based pipe network visualization system, characterized in that: comprising the following steps:
the real object identification module is used for acquiring real scenery pictures, acquiring real-time positions of patrol personnel, and identifying real object pictures from the real scenery pictures according to the real-time positions;
the data acquisition module is used for acquiring pipe network big data, wherein the pipe network big data comprises pipe network digital information and pipe network monitoring data;
the data screening module is used for acquiring the pipe network big data and the real-time position of the patrol personnel, and screening the pipe network big data according to the real-time position to obtain real-time pipe network data;
the real-time modeling module is used for acquiring real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing the real-time three-dimensional pipe network data into a real-time pipe network 3D model;
the AR projection module is used for acquiring the real-time pipe network 3D model and the real-time pipe network picture, and projecting the real-time pipe network 3D model onto the real-time pipe network picture to obtain an augmented reality picture;
the disaster prediction subsystem is also included; the disaster estimation subsystem comprises:
the disaster position calculation module is used for acquiring disaster information, calculating a disaster receiving area according to the disaster grade and the disaster source position in the disaster information, and calculating a key disaster receiving position in the disaster receiving area according to the damage information in the disaster information;
the investigation position evaluation module is used for acquiring real-time sensor data of the pipe network, calculating the real-time sensor data and the original sensor data to obtain a post-disaster change value, and evaluating an important investigation position in the important disaster-affected position by using the post-disaster change value; the position with the post-disaster change value of 10 is an important investigation region;
the pipe network data pre-estimating module is used for acquiring pipe network big data, and extracting pre-estimated pipe network data from the pipe network big data by combining the key investigation position with the real object picture in the real object identification module;
the disaster model construction module is used for acquiring estimated pipe network data, generating an estimated three-dimensional model from the estimated pipe network data, and constructing an estimated pipe network 3D model by using the estimated three-dimensional model;
the 3D model optimization module is used for acquiring real-time pressure data in real-time sensor data of the pipe network, comparing the real-time pressure data with the original pressure data, calculating to obtain the pipe network offset direction and offset, and adjusting and optimizing the position of the estimated pipe network 3D model according to the pipe network offset direction and offset.
2. The AR-based pipe network visualization system of claim 1, wherein: further comprises:
and the real-time position positioning module is used for positioning the patrol personnel in real time and acquiring the real-time position of the patrol personnel.
3. The AR-based pipe network visualization system of claim 1, wherein: the pipe network digital information comprises pipeline data, a pipe network building design blueprint and a two-dimensional plane electronic pipeline map; the pipeline data comprises buried trend, buried depth, longitude and latitude coordinates, pipe diameter size and pipeline information, and the pipeline information comprises pipeline purposes, streets and departments.
4. The AR-based pipe network visualization system of claim 3, wherein: the pipe network monitoring data comprise current air pressure data, water pressure data, permeability data and leakage data.
5. An AR-based pipe network visualization method, suitable for use in an AR-based pipe network visualization system according to any one of claims 1-4, wherein: comprising the following steps:
a step of acquiring a real scenery, namely acquiring a real scenery picture, acquiring a real-time position of a patrol personnel, and identifying a real-object picture in the real scenery picture according to the real-time position;
a real-time modeling step, namely acquiring pipe network big data, screening the pipe network big data according to the real-time position of a patrol personnel to obtain real-time pipe network data, generating real-time three-dimensional pipe network data from the real-time pipe network data, and constructing the real-time three-dimensional pipe network data into a real-time pipe network 3D model;
AR projection, namely projecting the real-time pipe network 3D model onto a real-object picture to obtain an augmented reality picture;
the method also comprises a disaster estimation sub-step; the disaster estimation substep includes:
calculating disaster positions, namely acquiring disaster information, calculating disaster receiving areas according to disaster grades and disaster source positions in the disaster information, and calculating key disaster receiving positions in the disaster receiving areas according to damaged information in the disaster information;
the method comprises the steps of checking position evaluation, obtaining real-time sensor data of a pipe network, calculating the real-time sensor data and original sensor data to obtain post-disaster change values, and using the post-disaster change values to evaluate key checking positions in key disaster-affected positions; the position with the post-disaster change value of 10 is an important investigation region;
the pipe network data prediction is used for acquiring pipe network big data, and the important investigation position is combined with the real object picture in the real object identification module to extract the predicted pipe network data from the pipe network big data;
the disaster model construction is used for acquiring estimated pipe network data, generating an estimated three-dimensional model from the estimated pipe network data, and constructing an estimated pipe network 3D model by using the estimated three-dimensional model;
and 3D model optimization, which is used for acquiring real-time pressure data in real-time sensor data of the pipe network, comparing the real-time pressure data with original pressure data, calculating to obtain the pipe network offset direction and offset, and adjusting and optimizing the position of the estimated pipe network 3D model according to the pipe network offset direction and offset.
6. The AR-based pipe network visualization method of claim 5, wherein: the step of acquiring the real scenery further comprises the following steps: and a real-time positioning step, namely positioning the position of the patrol personnel in real time to obtain the real-time position of the patrol personnel.
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| CN201910071311.XA CN109615708B (en) | 2019-01-25 | 2019-01-25 | AR-based pipe network visualization system and method |
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| CN201910071311.XA CN109615708B (en) | 2019-01-25 | 2019-01-25 | AR-based pipe network visualization system and method |
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| CN111753384A (en) * | 2020-06-24 | 2020-10-09 | 杭州深地信息科技有限公司 | AR pipeline display method and readable storage medium |
| CN112669484A (en) * | 2020-12-15 | 2021-04-16 | 北京无线电计量测试研究所 | Underground pipe network monitoring system and method |
| CN113239447B (en) * | 2021-06-11 | 2023-08-15 | 重庆电子工程职业学院 | Indoor pipeline abnormality detection system and method |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205827452U (en) * | 2016-07-14 | 2016-12-21 | 南京市城市地下管线数字化管理中心 | A kind of pipeline inspection system possessing real-time positioning and augmented reality ability |
| WO2018221842A1 (en) * | 2017-05-30 | 2018-12-06 | 주식회사 차후 | Mobile terminal, underground facilities management server, and 3d spatial information-based underground facilities management system comprising same |
| CN109246195A (en) * | 2018-08-13 | 2019-01-18 | 孙琤 | A kind of pipe network intelligence management-control method and system merging augmented reality, virtual reality |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101876419B1 (en) * | 2016-01-07 | 2018-07-10 | 한국전자통신연구원 | Apparatus for providing augmented reality based on projection mapping and method thereof |
-
2019
- 2019-01-25 CN CN201910071311.XA patent/CN109615708B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205827452U (en) * | 2016-07-14 | 2016-12-21 | 南京市城市地下管线数字化管理中心 | A kind of pipeline inspection system possessing real-time positioning and augmented reality ability |
| WO2018221842A1 (en) * | 2017-05-30 | 2018-12-06 | 주식회사 차후 | Mobile terminal, underground facilities management server, and 3d spatial information-based underground facilities management system comprising same |
| CN109246195A (en) * | 2018-08-13 | 2019-01-18 | 孙琤 | A kind of pipe network intelligence management-control method and system merging augmented reality, virtual reality |
Non-Patent Citations (1)
| Title |
|---|
| 城市地下管网三维可视化实现技术研究;陈子辉等;《工程图学学报》;20101215(第06期);全文 * |
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