CN108415048B - Large-scale network RTK positioning method and system based on spatial clustering - Google Patents
Large-scale network RTK positioning method and system based on spatial clustering Download PDFInfo
- Publication number
- CN108415048B CN108415048B CN201810065877.7A CN201810065877A CN108415048B CN 108415048 B CN108415048 B CN 108415048B CN 201810065877 A CN201810065877 A CN 201810065877A CN 108415048 B CN108415048 B CN 108415048B
- Authority
- CN
- China
- Prior art keywords
- users
- reference station
- virtual reference
- type
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention discloses a large-scale network RTK positioning method and a system based on spatial clustering, wherein the method comprises the following steps: (1) performing spatial clustering on the users according to the positions of the users; (2) respectively determining a virtual reference station position according to the area of each type of users, namely the position of the virtual reference station shared by each type of users; the position of a virtual reference station shared by each type of users is positioned in the area where each type of users is positioned; (3) calculating and generating a virtual reference station observation value according to the real-time observation data stream of the CORS reference station and the position of the virtual reference station, namely the virtual reference station observation value shared by each type of users; (4) each type of user employs a common virtual reference station observation for RTK positioning. The invention can support a large number of users to be simultaneously on line without being limited by server computing resources, and solves the contradiction between the high number of concurrent users and limited computing resources in the construction of the large-scale network RTK system.
Description
Technical Field
The invention belongs to the field of mapping and precise positioning, and particularly relates to a large-scale network RTK positioning method and system based on spatial clustering.
Background
Network RTK (real-time dynamic carrier phase differential positioning) has been widely used at home and abroad as an important GNSS precision positioning means. The network RTK is processed by utilizing real-time GNSS observation data of a continuously operating reference station system (CORS), and the GNSS observation correction number of a CORS network area is calculated and is broadcasted to a user. The user end utilizes the received correction number to improve the self positioning precision. The method belongs to a differential precision positioning technology and can realize precision positioning with the precision of 2 cm-3 cm on the plane and 5cm in elevation. Compared with a single base station RTK, the network RTK has the advantages of wide service range, high reliability, simplicity and convenience in user operation and the like, so that the network RTK is widely applied. Since the establishment of CORS in Shenzhen city in 1998, CORS networks of China are established in succession in each province and city in China, network RTK differential service is provided, and the requirements of precise measurement of surveying and mapping, surveying, engineering and the like are met. Conservative estimation currently built CORS stations in China reach thousands, and network RTK service based on CORS becomes a space infrastructure of national economic construction. In recent years, the foundation enhancement technology core technology of the Beidou system is also a network RTK positioning technology.
There are two main ways of implementing network RTK technology for a long time: one is a broadcast type area correction method (FKP); another is virtual reference station technology (VRS). The local correction method mainly uses the form of one-way communication to broadcast the local correction to the user, and the user end interpolates and processes the local correction after receiving the local correction. The virtual reference station technology adopts a two-way communication mode, a user reports the rough coordinate to the data center, and the data center generates an observed value of the virtual reference station through error interpolation and geometric correction according to the rough coordinate of the user and broadcasts the observed value to the user. And the user side determines the precise coordinates of the user side by double differences of the observed value of the user side and the received observed value of the virtual reference station. The virtual reference station technology is the mainstream technology at present, because the user end only needs to use the algorithm of the single-base-station RTK positioning to realize the network RTK, and does not need to make any modification on the receiver firmware.
However, the virtual reference station technology has a problem that the data center needs to calculate the virtual reference station correction number for each user accessing the network, and the network RTK system is a real-time system, so that the calculation pressure of the data center is large. At present, most network RTK software limits the number of concurrent users, or the price of the network RTK software is determined according to the number of the concurrent users. Therefore, the number of concurrent users supported by most provincial and urban CORS is only dozens or hundreds, and the number of concurrent users can be handled by a small number of professional users, but the increasing requirement of precise positioning service is difficult to meet, and on the other hand, certain resource waste is caused. The limitation on the number of concurrent users causes that the established CORS network cannot play the role to the maximum extent, and causes resource waste.
Disclosure of Invention
The invention aims to provide a large-scale network RTK positioning method and system based on spatial clustering, and the method and system can solve the problem of data center calculation pressure caused by the fact that a large-scale user accesses a CORS network.
The invention provides a large-scale network RTK positioning method based on spatial clustering, which comprises the following steps:
(1) performing spatial clustering on the users according to the positions of the users;
(2) respectively determining a virtual reference station position according to the area of each type of users, namely the position of the virtual reference station shared by each type of users; the position of a virtual reference station shared by each type of users is positioned in the area where each type of users is positioned;
(3) calculating and generating a virtual reference station observation value according to the real-time observation data stream of the CORS reference station and the position of the virtual reference station, namely the virtual reference station observation value shared by each type of users;
(4) each type of user employs a common virtual reference station observation for RTK positioning.
Further, the spatial clustering includes, but is not limited to, a K-nearest neighbor method, a K-means method, a DBSCAN method, a supervised clustering method, an unsupervised clustering method, or a predefined clustering region method.
Further, in the step (1), the position of the virtual reference station is determined by adopting a test verification method, so that the actual positioning requirement can be met.
Further, the virtual reference station position is the geometric center of all user positions in each class, the position near the cluster core user of each class, or the gravity center of an outsourcing convex polygon of all user positions in each class.
Further, the large-scale network RTK positioning method based on spatial clustering further comprises the following steps:
establishing a user mapping table according to the spatial clustering result, and mapping the same type of users to the same virtual account;
and broadcasting the virtual reference station observation value common to each type of users to each corresponding type of users according to the user mapping table.
The large-scale network RTK positioning system based on spatial clustering comprises a CORS reference station, a server, a broadcasting center and a user terminal, wherein an intermediate server is arranged on a data link between the broadcasting center and the user terminal, and the broadcasting center and the user terminal are in two-way communication with the intermediate server;
the intermediary server is configured to:
performing spatial clustering on the users according to the positions of the users;
respectively determining a virtual reference station position according to the area of each type of users, namely the position of the virtual reference station shared by each type of users; and the position of the virtual reference station common to each type of users is positioned in the area where each type of users is positioned.
Further, the intermediate server is further configured to:
and establishing a user mapping table according to the spatial clustering result, and mapping the same type of users to the same virtual account.
In the current virtual reference station technology, a data center needs to respond to a positioning request of each user accessing a CORS network, and respectively calculate and generate a virtual reference station observation value for each user. Most network RTK software avoids increasing the computational pressure of the data center by limiting the number of concurrent users accessing the CORS network.
Aiming at the current situation, the invention carries out spatial clustering on all users accessing the CORS network, takes the users with concentrated spatial position distribution as a class of users, and the similar users share a virtual reference station observation value. For the same kind of users, the same virtual reference station observation value is used, and the virtual reference station observation values are respectively used, so that the positioning effects of the two virtual reference station observation values are not obviously different. Therefore, by adopting the method, for the same type of users in the same area, the data center does not need to generate a virtual reference station observation value for each user, and can carry out accurate positioning only by generating a common virtual reference station observation value, thereby reducing the calculation pressure of the data center. In addition, in principle, under the condition of limited computing resources, the method supports the concurrent operation of any number of users in the same area, thereby realizing the network RTK system accessed by large-scale users.
The characteristics and beneficial effects of the invention are summarized as follows:
(1) computing resources and concurrency limits are fully utilized, and virtual reference station observation values needing to be computed are determined according to active user operation areas; when the number of active users is small, the calculation amount of the data center is also reduced, and the waste of calculation resources is avoided; for the existing network RTK positioning system, the minimum number of concurrent users is not limited, namely, the network RTK positioning system only allows one concurrent user to use.
(2) The number of concurrent users of the network RTK positioning system can be increased without modifying a network RTK server software algorithm or modifying user receiver firmware, and large-scale concurrent user access is realized. And the communication format and the communication content between the data center and the user do not need to be changed, and the problem of incompatibility with the existing software and hardware is solved.
(3) The method is suitable for the reconstruction of the existing network RTK positioning system, can be realized in the form of system middleware, does not need to modify the existing network RTK system software, and has good adaptability and simple and easy operation.
(4) The virtual reference station mode of bidirectional communication is used, so that management, geo-fencing, charging and the like of online users of network RTK users are facilitated.
Drawings
FIG. 1 is a schematic diagram of a network RTK positioning system;
FIG. 2 is a schematic diagram of a logic structure according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of the spatial clustering method according to the embodiment of the present invention.
In the figure, 1-CORS reference station; 2-a data cable; 3-the user; 4-a virtual reference station; 5-a first data link; 6-a second data link; 7-data center.
Detailed Description
In order to more clearly illustrate the present invention and/or the technical solutions in the prior art, the following will describe embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
It should be noted that the CORS reference station, i.e. the continuously operating reference station, described below, refers in particular to an online network RTK user.
The invention starts from the distribution characteristics of network RTK users to solve the problem of data center calculation pressure caused by concurrency of a large number of users. The locations of online users are not spatially distributed continuously, but are distributed in a cluster according to their work areas. For example, a project is built on a site, and a plurality of measurement workers can perform precise measurement operation simultaneously near the site. The spatial distribution of the measurement workers of the same engineering construction project is relatively centralized, and the distribution of the measurement workers of different engineering construction projects is relatively dispersed. The existing network RTK positioning system does not consider the spatial correlation among users, but accesses all online users into the network RTK positioning system as independent individuals to respectively calculate the observation value of the virtual reference station for each online user, so that the number of concurrent online users is bound to be limited by limited calculation resources. The invention fully considers the correlation of the spatial distribution of the users in the measurement operation, automatically gathers the users with close spatial position distribution into one class by carrying out spatial clustering on the online users, determines the optimal virtual reference station position of the similar users, and reports the optimal virtual reference station position to the server. The server only needs to calculate and generate a virtual reference station observation value for the same type of users, and the requirement of differential precision positioning of all the users of the same type can be met.
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
Referring to FIG. 1, a typical network RTK positioning system includes a CORS reference station 1, a user 3, and a data center 7; the data center 7 comprises a server and network RTK positioning software running on the server; the CORS reference station 1 and the data center 7 communicate through a data cable 2; the user 3 sends the authentication information and the position information of the user to the data center 7 through the first data link 5; the data center 7 transmits the virtual reference station observation value and the virtual reference station location information, which are collectively referred to as virtual reference station information, through the second data link 6. The working principle of the network RTK positioning system is as follows: and the data center 7 generates virtual reference station information by utilizing the real-time observation data stream of the CORS reference station 1 according to the request of the user 3 and broadcasts the virtual reference station information to the user 3, so that the precision positioning requirement of the user 3 is met. The network RTK precision positioning is a differential positioning mode, and when the distance between a user and a reference station is short, the differential effect is good, and the positioning precision is high. The virtual reference station 4 generated by the data center 7 is a virtual reference station located near the user 3, and can meet the requirement of short-baseline differential positioning of the user 3. The virtual reference station 4 is not changed immediately following the change in location of the user 3 but is fixed near the initial location of the user 3. A new virtual reference station 4 is not newly generated near the user 3 until the distance between the user 3 and the virtual reference station 4 is beyond a certain range. It is generally believed that the baseline length does not significantly affect the positioning results for short baseline positioning within kilometers. Therefore, it can be considered that the same virtual reference station is used by all users with close distances and each user uses a respective virtual reference station, so that the positioning effect is not obviously different.
The network RTK positioning system shown in fig. 1 has 5 online concurrent users, and according to the existing network RTK system, virtual reference stations need to be generated for the 5 concurrent users respectively according to concurrent user requests and broadcast to the users. The invention firstly groups 5 users into two types according to the spatial positions of concurrent users. A common virtual reference station is then generated for each type of user. In this way, the server of the data center only needs to calculate and generate 2 virtual reference stations, and the concurrent user data increase in the same spatial region does not significantly increase the calculation amount of the server of the data center.
Fig. 2 shows a schematic logical structure of the present invention, and in this embodiment, the data transmission protocol uses the Ntrip protocol. In fig. 2, Ntrip clients are online users, and Ntrip clients 1, Ntrip clients 2, Ntrip clients 3, Ntrip clients 4, and Ntrip clients 5 are 5 online users; the Ntrip case is a broadcasting center, and the Ntrip Server is a Server of the data center; the Ntrip Source, the data Source, is shown to include the real-time observation data streams for each CORS reference station. The Ntrip Client, the Ntrip case, the Ntrip Server and the Ntrip Source are also components of a typical network RTK positioning system. In the specific embodiment, an intermediate server is added to a data link between an Ntrip Client and an Ntrip case, and the added intermediate server is used for performing online user spatial clustering, online user management and online user mapping; the Ntrip Client directly carries out two-way communication with the intermediate server and carries out two-way communication with the Ntrip case in a forwarding mode of the intermediate server. The method can be realized only by slightly modifying the existing network RTK positioning system and without modifying software and hardware of the existing network RTK positioning system.
The work flow of the specific embodiment is as follows:
s100: and the Ntrip Client sends the authentication information and the position information of the user to the intermediate server.
S200: and the intermediate server performs spatial clustering on all concurrent users according to the position information of the users.
S300: the intermediate server respectively determines a virtual reference station position according to the area of each type of users, namely the position of the virtual reference station shared by each type of users; and the position of the virtual reference station common to each type of users is positioned in the area where each type of users is positioned.
There are various methods for determining the position of the virtual reference station, and particularly, the optimal position of the virtual reference station can be determined by adopting a test verification method according to actual conditions. In general, the virtual reference station location may be the geometric center of all user locations in each class, or near the cluster kernel of each class, or the center of gravity of the outsourced convex polygon of all user locations in each class.
S400, the intermediate server distributes a virtual Ntrip account number to each type respectively and establishes a user mapping table.
Specifically, the intermediate server establishes a user mapping table according to the spatial clustering result, and maps the same type of users to the same virtual Ntrip account.
S500, the intermediate Server broadcasts the virtual reference station position shared by each class of users and the virtual Ntrip account number corresponding to each class of users to the Ntrip case by the Ntrip protocol, and the Ntrip case broadcasts the virtual reference station position and the virtual Ntrip account number to the Ntrip Server.
S600Ntrip Server calculates and generates a virtual reference station observation value according to the real-time observation data stream of each CORS reference station and the position of the virtual reference station, namely the virtual reference station observation value common to each type of users; and forwarding the virtual reference station observation value common to each type of users and the corresponding virtual Ntrip account number to an intermediate server through the Ntrip case.
S700, the intermediate server broadcasts the virtual reference station observation value common to each type of users to each corresponding type of users at the same time according to the user mapping table and the virtual Ntrip account corresponding to the virtual reference station observation value.
In specific implementation, the intermediate server needs to dynamically maintain the user mapping table and dynamically manage the online states of concurrent users. In this way, the network RTK service system of the high-capacity concurrent user is realized.
Of course, the network RTK positioning system of the present invention is not limited to the one embodiment shown in fig. 2, and an intermediate server may be combined with or replace the Ntrip case.
The spatial clustering method of the invention can adopt a K nearest neighbor method, a K mean value method, a DBSCAN method, a supervised or unsupervised clustering method, a predefined clustering region method and the like, but is not limited to the methods. Fig. 3 is a flowchart of the spatial clustering method according to the present embodiment, which only shows a preferred spatial clustering method, but the spatial clustering method according to the present invention is not limited thereto.
The preferable spatial clustering method comprises the following specific steps:
s210: initializing an alternative set, namely putting the positions of all online users into the alternative set; the cluster set is initialized to an empty set and distance tolerance is initialized.
S220: and creating a new cluster set, arbitrarily selecting the position of an online user from the current candidate set to be set as a cluster core, and adding the new cluster set.
S230: checking whether the distance between each online user position in the current alternative set and the current clustering core meets a distance limit difference; if yes, the current detected online user and the online user corresponding to the current clustering core belong to the same class, and S240 is executed; if not, the current detected online user and the online user corresponding to the current clustering core are not in the same class, and S260 is executed.
S240: and adding the positions of the current checked online users meeting the distance tolerance into the current cluster set, and deleting the positions from the alternative set.
S250: checking whether the current alternative set is an empty set, when the alternative set is the empty set, indicating that the spatial clustering is finished, and turning to S280; when the alternative set is a non-empty set, go to S260.
S260: checking whether the current alternative set is traversed or not, and executing S220 to start the next round of clustering when the alternative set is traversed; when the alternative set is not traversed to completion, then execution proceeds to S270, where the location of the next online user is checked.
S270, any online user position is selected from the alternative set for checking, and the process returns to the step S230.
S280: and when the current alternative set is an empty set, all the online users are clustered, and clustering is finished.
After spatial clustering, the clustering number is less than or equal to the number of concurrent users, so that the purpose of capacity expansion of the concurrent users is achieved. And according to the clustering result, distributing a virtual Ntrip account number to each class, and establishing a user mapping table between the online users and the virtual users, so that the purpose of capacity expansion of concurrent users can be realized. Considering the motion characteristics and the online status characteristics of online users, the spatial clustering method needs to be repeatedly executed at certain time intervals to update the user mapping table. The spatial clustering method provided by the specific embodiment is simple, small in calculated amount and memory consumption, and suitable for large-scale user spatial clustering.
The invention utilizes the spatial distribution correlation of the network RTK online users to perform spatial clustering on the online users, maps the same type of users into the same virtual user, calculates the virtual reference station observation value for the same virtual user only once by the network RTK server, and then broadcasts the virtual reference station observation value to all the online users simultaneously. Therefore, the user differential positioning precision is ensured, the calculation amount of the network RTK server is reduced, a large number of users are supported to be simultaneously on line, and the network RTK server is not limited by the calculation resources of the server. The method and the device do not need to change the existing network RTK software and hardware, have low implementation cost, can greatly improve the number of the concurrent users of the network RTK platform, and simultaneously solve the contradiction between the high number of the concurrent users and the limited computing resources in the large-scale network RTK system construction.
The specific embodiments described herein are merely illustrative of the patent spirit of the invention. Various modifications or additions may be made or substituted in a similar manner to the specific embodiments described herein by those skilled in the art without departing from the spirit of the invention or exceeding the scope thereof as defined in the appended claims.
Claims (4)
1. A large-scale network RTK positioning method based on spatial clustering is characterized by comprising the following steps:
(1) performing spatial clustering on the users according to the positions of the users;
(2) respectively determining a virtual reference station position according to the area of each type of users, namely the position of the virtual reference station shared by each type of users; the position of a virtual reference station shared by each type of users is positioned in the area where each type of users is positioned; the position of the virtual reference station is determined by adopting a test verification method, so that the actual positioning requirement can be met;
(3) calculating and generating a virtual reference station observation value according to the real-time observation data stream of the CORS reference station and the position of the virtual reference station, namely the virtual reference station observation value shared by each type of users;
(4) each type of user adopts a common virtual reference station observation value to carry out RTK positioning;
the large-scale network RTK positioning method is realized by adopting a large-scale network RTK positioning system based on spatial clustering, the large-scale network RTK positioning system comprises a CORS reference station, a server, a broadcasting center and a user terminal, an intermediate server is arranged on a data link between the broadcasting center and the user terminal, and the broadcasting center and the user terminal are in two-way communication with the intermediate server;
the intermediary server is configured to:
performing spatial clustering on the users according to the positions of the users;
and respectively determining a virtual reference station position according to the area of each type of users, namely the virtual reference station position common to each type of users.
2. The large-scale network RTK positioning method based on spatial clustering as claimed in claim 1, wherein:
the spatial clustering is a K nearest neighbor method, a K mean value method, a DBSCAN method, a supervision clustering method, an unsupervised clustering method or a predefined clustering region method.
3. The large-scale network RTK positioning method based on spatial clustering as claimed in claim 1, wherein:
the virtual reference station position is the geometric center of all the user positions in each class, the position near the clustering core of each class, or the gravity center of the outsourcing convex polygon of all the user positions in each class.
4. The large-scale network RTK positioning method based on spatial clustering as claimed in claim 1, wherein:
further comprising:
establishing a user mapping table according to the spatial clustering result, and mapping the same type of users to the same virtual account;
and broadcasting the virtual reference station observation value common to each type of users to each corresponding type of users according to the user mapping table.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810065877.7A CN108415048B (en) | 2018-01-23 | 2018-01-23 | Large-scale network RTK positioning method and system based on spatial clustering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810065877.7A CN108415048B (en) | 2018-01-23 | 2018-01-23 | Large-scale network RTK positioning method and system based on spatial clustering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108415048A CN108415048A (en) | 2018-08-17 |
| CN108415048B true CN108415048B (en) | 2021-10-22 |
Family
ID=63126230
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810065877.7A Active CN108415048B (en) | 2018-01-23 | 2018-01-23 | Large-scale network RTK positioning method and system based on spatial clustering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108415048B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109688535B (en) * | 2018-12-13 | 2021-01-26 | 大唐软件技术股份有限公司 | Method, device, electronic equipment and medium for determining geographic position of wireless hotspot |
| CN110095797B (en) * | 2019-05-28 | 2020-06-02 | 北京讯腾智慧科技股份有限公司 | Grid-based accurate positioning service method and system based on satellite navigation system |
| CN111045051B (en) * | 2019-07-10 | 2022-02-08 | 广东星舆科技有限公司 | VRS information generation method, positioning service providing method and positioning service device |
| CN110618435B (en) * | 2019-11-07 | 2021-11-23 | 广东星舆科技有限公司 | Observation data generation method and text data generation method for improving VRS stability |
| CN111447676B (en) * | 2020-03-16 | 2021-10-01 | 苏州星恒通导航技术有限公司 | System based on differential data source matching |
| CN113031011B (en) * | 2021-03-19 | 2023-09-29 | 交信北斗科技有限公司 | Beidou high-precision satellite navigation and position service system |
| CN113050138A (en) * | 2021-03-19 | 2021-06-29 | 北京中交创新投资发展有限公司 | Grid-based high-precision satellite navigation positioning service method |
| CN113596722A (en) * | 2021-08-04 | 2021-11-02 | 武汉攀达时空科技有限公司 | Method for generating and serving adjacent user data product sharing MCBI |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2795873Y (en) * | 2005-03-08 | 2006-07-12 | 中国农业大学 | Global positioning system receiver based on virtual difference technology |
| WO2008141320A1 (en) * | 2007-05-16 | 2008-11-20 | Trimble Navigation Limited | Post-mission high accuracy position and orientation system |
| CN104935462A (en) * | 2015-06-02 | 2015-09-23 | 清华大学 | Deploying method and device for terminal servers |
| CN106569239A (en) * | 2015-10-09 | 2017-04-19 | 唐颖哲 | Broadcast-type network RTK positioning technology |
| CN107422351A (en) * | 2017-08-02 | 2017-12-01 | 湖南省测绘科技研究所 | A kind of GNSS decimeter grade Differential positioning methods based on virtual grid |
-
2018
- 2018-01-23 CN CN201810065877.7A patent/CN108415048B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2795873Y (en) * | 2005-03-08 | 2006-07-12 | 中国农业大学 | Global positioning system receiver based on virtual difference technology |
| WO2008141320A1 (en) * | 2007-05-16 | 2008-11-20 | Trimble Navigation Limited | Post-mission high accuracy position and orientation system |
| CN104935462A (en) * | 2015-06-02 | 2015-09-23 | 清华大学 | Deploying method and device for terminal servers |
| CN106569239A (en) * | 2015-10-09 | 2017-04-19 | 唐颖哲 | Broadcast-type network RTK positioning technology |
| CN107422351A (en) * | 2017-08-02 | 2017-12-01 | 湖南省测绘科技研究所 | A kind of GNSS decimeter grade Differential positioning methods based on virtual grid |
Non-Patent Citations (1)
| Title |
|---|
| 虚拟参考站(VRS)技术及其精度评定;杨洋;《中国优秀硕士学位论文全文数据库 基础科学辑》;20071015(第04期);正文第1-57页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108415048A (en) | 2018-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108415048B (en) | Large-scale network RTK positioning method and system based on spatial clustering | |
| Xia et al. | Cost-effective app data distribution in edge computing | |
| Peng et al. | Mobility-aware and migration-enabled online edge user allocation in mobile edge computing | |
| CN110191148B (en) | Statistical function distributed execution method and system for edge calculation | |
| CN110095797B (en) | Grid-based accurate positioning service method and system based on satellite navigation system | |
| CN101133622B (en) | Splitting a workload of a node | |
| Zhang et al. | Toward a unified elastic computing platform for smartphones with cloud support | |
| Tang et al. | An offloading method using decentralized P2P-enabled mobile edge servers in edge computing | |
| CN107734558A (en) | A kind of control of mobile edge calculations and resource regulating method based on multiserver | |
| CN109862592A (en) | Resource management and dispatching method under a kind of mobile edge calculations environment based on multi-base station cooperative | |
| CN110461037B (en) | Dynamic gridding network RTK positioning method and system | |
| CN106991008B (en) | Resource lock management method, related equipment and system | |
| KR100931260B1 (en) | Method and system for seamless server access and service connectivity for supporting user terminal's mobility on sbc environments | |
| Meneguette et al. | Vehicular clouds leveraging mobile urban computing through resource discovery | |
| CN103457870B (en) | For the load balancing of distributed system and high availability subsystem and method | |
| Panigrahi et al. | Transmission in mobile cloudlet systems with intermittent connectivity in emergency areas | |
| CN110809317A (en) | Multi-source dynamic grid network RTK positioning method, system, terminal and storage medium | |
| Zhao et al. | Joint coverage-reliability for budgeted edge application deployment in mobile edge computing environment | |
| Huang et al. | Enabling DNN acceleration with data and model parallelization over ubiquitous end devices | |
| Cui et al. | Trading off between multi-tenancy and interference: A service user allocation game | |
| Hao et al. | Cognitive-caching: Cognitive wireless mobile caching by learning fine-grained caching-aware indicators | |
| Zhang et al. | Enhanced adaptive cloudlet placement approach for mobile application on spark | |
| CN111935205A (en) | Distributed resource allocation method based on alternative direction multiplier method in fog computing network | |
| CN103209102A (en) | Web quality of service distributed measurement system and method | |
| Jiang et al. | Economics of peer-to-peer mobile crowdsensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |