CN106530726B - highway vehicle management system based on cloud calculates - Google Patents
highway vehicle management system based on cloud calculates Download PDFInfo
- Publication number
- CN106530726B CN106530726B CN201710029139.2A CN201710029139A CN106530726B CN 106530726 B CN106530726 B CN 106530726B CN 201710029139 A CN201710029139 A CN 201710029139A CN 106530726 B CN106530726 B CN 106530726B
- Authority
- CN
- China
- Prior art keywords
- highway
- cloud computing
- vehicle
- traffic
- management center
- 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
- 230000006854 communication Effects 0.000 claims abstract description 27
- 238000004891 communication Methods 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims abstract description 10
- 230000005540 biological transmission Effects 0.000 claims description 23
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 238000005562 fading Methods 0.000 claims description 8
- 239000013307 optical fiber Substances 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 claims description 2
- 238000005457 optimization Methods 0.000 claims description 2
- 230000015556 catabolic process Effects 0.000 abstract description 3
- 238000006731 degradation reaction Methods 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
the invention provides a highway vehicle management system based on cloud computing, which comprises a highway communication system, a remote cloud computing server and a highway management center, wherein vehicles exchange information with the remote cloud computing server through the highway communication system, the cloud computing management system of the remote cloud computing server processes the vehicle information, the cloud computing management system collects all traffic flow data and feeds the traffic flow data back to the highway management center, and the highway management center adjusts the degradation of the line condition and the traffic jam of a highway at any time. The invention has the beneficial effects that: the pressure caused by free passing at high speed in holidays is obviously reduced, and the phenomenon of traffic jam on expressways is avoided.
Description
Technical Field
the invention relates to the technical field of vehicle management, in particular to a highway vehicle management system based on cloud computing.
Background
with the increasing number of closed type highways in China, the highways are provided with a plurality of bayonets, the traffic flow of the bayonets is large at ordinary times, particularly, the highway congestion phenomenon is particularly serious every holiday due to the fact that the highway is not charged in the current holiday minibus highways, and the scene of long traffic in front of a toll station becomes a holiday scene; therefore, the traffic flow detection is particularly important. At present, various traffic flow detectors and other devices exist, but the results counted by the devices have large errors, and the problems that vehicle types cannot be accurately judged, the vehicle flow detection data cannot be updated in real time and the like exist.
disclosure of Invention
In view of the above problems, the present invention aims to provide a cloud computing-based highway vehicle management system.
the purpose of the invention is realized by adopting the following technical scheme:
the highway vehicle management system based on cloud computing comprises a highway communication system, a remote cloud computing server and a highway management center, wherein vehicles exchange information with the remote cloud computing server through the highway communication system, the cloud computing management system of the remote cloud computing server processes the vehicle information, the cloud computing management system collects all traffic flow data and feeds the traffic flow data back to the highway management center, and the highway management center adjusts line condition degradation highway traffic jam at any time.
The invention has the beneficial effects that: the pressure caused by free passing at high speed in holidays is obviously reduced, and the phenomenon of traffic jam on expressways is avoided.
drawings
the invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic view of the structural connection of the present invention.
Reference numerals:
the system comprises an expressway communication system 1, a remote cloud computing server 2 and a high-speed management center 3.
Detailed Description
the invention is further described with reference to the following examples.
referring to fig. 1, the highway vehicle management system based on cloud computing comprises a highway communication system 1, a remote cloud computing server 2 and a highway management center 3, wherein vehicles exchange information with the remote cloud computing server through the highway communication system 1, the cloud computing management system of the remote cloud computing server 2 processes vehicle information, the cloud computing management system collects all traffic data and feeds the traffic data back to the highway management center 3, and the highway management center 3 adjusts degradation of highway traffic jam of line conditions at any time.
The embodiment obviously reduces the pressure caused by free passing at high speed in holidays, and avoids the phenomenon of traffic jam on expressways.
Preferably, the high-speed management center 3 comprises a gate management center, a traffic management center and a traffic guidance command center; the cloud computing management system gathers the traffic flow information of each gate and feeds the traffic flow information back to the traffic management center and the traffic guidance command center, and traffic managers release the traffic flow information to the LED traffic guidance screens of the high-speed inlets through the remote cloud computing server 2 to display the traffic flow information.
the preferred embodiment realizes the traffic flow adjustment at any time.
Preferably, the highway communication system 1 includes a vehicle-mounted antenna of an automobile, a distributed base station subsystem and a wireless switching subsystem, each base station subsystem includes a baseband processing unit and a plurality of radio frequency processing units, the baseband processing unit and the radio frequency processing units are connected through optical fibers, a public route of the radio frequency processing units is arranged, signals reach the radio frequency processing units from the baseband processing units through the optical fibers, the vehicle-mounted antenna of the automobile and the radio frequency processing units perform wireless communication, and the wireless switching subsystem is used for realizing communication switching between the base stations.
The communication system suitable for the vehicle management system to communicate with the vehicle when the vehicle moves at a high speed is constructed in the preferred embodiment, wherein the baseband processing unit and the radio frequency processing unit are connected through the optical fiber, so that transmission errors of the vehicle management system in the vehicle communication process can be reduced, and the management efficiency is improved.
preferably, the wireless communication between the vehicle-mounted antenna and the radio frequency processing unit comprises the steps of establishing a channel model, calculating effective throughput and determining a link adaptive transmission mode;
The channel model is established in the following way: considering large-scale path fading and small-scale multipath fading in a channel, the probability density function f (γ) of the vehicle-ground link receiving signal-to-noise ratio can be expressed as:
In the above formula, γ is the receiving noise ratio of the vehicle-ground link, l is the small-scale multipath fading factor, l belongs to [5dB,7dB ], I0 [. cndot. ] is the first nth-order modified bessel function, P is the transmission power of the rf processing unit, vk (d) is the large-scale path loss, N is the noise power only considering the large-scale loss, P, VK (d) and N units are dB, where vk (d) is 150+22ln (d) +20ln (fc),
in the above equation, d is the distance between the vehicle-mounted antenna and the radio frequency processing unit, and has a unit of m, fc is the carrier frequency, and a unit of Hz.
The preferred embodiment considers the large-scale path fading and the small-scale multi-path fading in the channel of the highway vehicle management system in the communication process with the managed vehicle at the same time, obtains a more accurate channel model and improves the management efficiency of the vehicle management system.
preferably, the effective throughput is calculated by adopting the following method, the MIMO technology is adopted at two ends of the train-ground communication link, and assuming that the received signal-to-noise ratio of the train-ground link is γ, the effective transmission rate of the system is:
in the above equation, k1 is a constant, m is a multiplexing gain, Lt + w is the total length of a frame header and a frame tail of a link layer in a communication protocol, and Lz is the frame length of the link layer;
the corresponding frame error rate is:
in the above equation, Mf is the number of transmitting antennas, and Mj is the number of receiving antennas;
Assuming that the received snr γ 1 for the initial transmission of the system and the received snr γ n for the nth transmission, the expectation of the effective throughput of the system when the maximum allowed number of transmissions of the system is Nm can be expressed as:
in the above equation, it is the maximum goodput that can be obtained by the system after n transmissions; is the probability that a frame was not successfully transmitted the first n-1 times, but was successfully transmitted the nth time, wherein,
The optimal embodiment adopts the MIMO technology, greatly improves the overall performance of the highway vehicle management system, can effectively reduce the frame error rate of the vehicle management system in the management process by selecting the proper frame length, and improves the number of vehicles managed by the vehicle management system.
preferably, the adaptive transmission mode is determined by adopting the following mode, based on a partially observable Markov decision model, with effective throughput as an optimization target, and under a given target frame error rate (DGtar), selecting appropriate adaptive transmission parameters { m, Lz } to maximize the benefit of the system, and modeling the optimal link adaptive transmission problem as follows:
So that the user can easily and conveniently select the required position,
in the above equation, T is the total decision time, UA (m (T), lz (T)) is the instantaneous gain function at decision time T.
In the preferred embodiment, in the process of managing vehicles, the vehicle-mounted antenna of the automobile is in high-speed motion, the channel state of the vehicle-ground link is constantly changed, and in the adaptive transmission mode, the link adaptive transmission parameters can be constantly adjusted to adapt to the actual requirements of the management process.
preferably, the wireless handover subsystem is configured to implement a handover of communications between base stations in an improved handover manner. The improved switching mode comprises the following steps:
A. Measuring the received signal strength RSRP value and the channel quality RSRQ value of the current service cell and each adjacent cell;
B. selecting each adjacent cell meeting the judgment condition, wherein the judgment formula of the judgment condition is as follows:
min(RN)>0
wherein Δ RSRP (i) ψ D represents a difference between an RSRP value of a neighboring cell ψ at time i and an RSRP value of a current serving cell D, where qb (i) is a handover hysteresis threshold value at time i, RSRP (ψ) i is a received signal strength RSRP value of a neighboring cell satisfying the determination condition at time i, and RSRP (D) i is an RSRP value of a current serving cell D at time i;
C. And selecting the optimal adjacent cell from the adjacent cells meeting the judgment condition to trigger the switching.
In the preferred embodiment, the continuity of vehicle management by the highway vehicle management system is ensured, the vehicles can realize the switching of the base stations in the process of managing the vehicles by the highway vehicle management system, specifically, the communication switching between the base stations is realized by adopting an improved switching mode, the adjacent cells meeting the judgment conditions are selected, and then the optimal adjacent cells are selected to trigger the switching, so that the switching times are reduced, the switching success rate is improved, and the continuity of the management process is ensured.
Preferably, the selecting an optimal neighboring cell from among neighboring cells meeting the determination condition to trigger handover includes:
A. Measuring the resource change rate of each adjacent cell which meets the judgment condition and the distance from each adjacent cell to the current service cell;
B. The handover reliability Γ (ψ) of a neighboring cell that meets the decision condition is calculated according to the following formula:
In the above equation, A, B is a set weight, a + B is 1, a resource change rate of a neighboring cell meeting the determination condition, a received signal strength RSRP value of the neighboring cell meeting the determination condition at time i, a distance from the neighboring cell meeting the determination condition to the current serving cell, B1 and B2 are set weights, and B1+ B2 is 1;
C. and selecting the adjacent cell with the maximum handover reliability gamma (psi) to trigger handover.
The optimal performance of the vehicle management system on the highway is ensured by the optimal embodiment, the optimal adjacent cell is selected through the calculation of the switching reliability to trigger the switching, and the cell resource change rate and the distance between the cell and the current service cell are considered, so that the optimal adjacent cell can be selected, the switching success rate is further improved, and the continuity and stability of the management process are ensured.
preferably, the calculation formula for setting the switching hysteresis threshold value qb (i) at time i is set as follows:
QB(i)＝max{α[LS],β[UP]}
in the above formula, α and β are upper and lower limits of qb (i), ν is RSRQ when qb (i) reaches the upper limit α, qb (i) starts to decrease when RSRQ is smaller than ν, and η and n are speed and trajectory parameters for adjusting qb (i) to decrease as RSRQ decreases.
The preferred embodiment improves the environmental adaptability of the highway vehicle management system, and particularly sets the switching hysteresis threshold value QB (i) at the moment i, so that the value QB (i) and the value RSRP (D) i are interconnected, QB (i) can be more flexibly configured according to different environments of each base station and hardware facilities of the base station, and the adaptability of each adjacent cell meeting the judgment condition to different environments is improved.
Statistical analysis is carried out on the management data of the vehicle management system for three months, and the effects generated by the vehicle management system are summarized as shown in the following table:
| Freeway average speed boost | average congestion time reduction percentage | |
| Holiday | 15km/h | 20％ |
| At ordinary times | 18km/h | 17％ |
finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (1)
1. A highway vehicle management system based on cloud computing is characterized by comprising a highway communication system, a remote cloud computing server and a highway management center, wherein vehicles exchange information with the remote cloud computing server through the highway communication system, the cloud computing management system of the remote cloud computing server processes the vehicle information, the cloud computing management system collects all traffic flow data and feeds the traffic flow data back to the highway management center, and the highway management center adjusts the line condition at any time to prevent highway traffic jam;
the high-speed management center comprises a gate management center, a traffic management center and a traffic guidance command center; the cloud computing management system gathers the traffic flow information of each gate and feeds the traffic flow information back to the traffic management center and the traffic guidance command center, and traffic managers release the traffic flow information to the LED traffic guidance screens of the high-speed inlets for display through the remote cloud computing server;
the highway communication system comprises automobile vehicle-mounted antennas, distributed base station subsystems and wireless switching subsystems, wherein each base station subsystem comprises a baseband processing unit and a plurality of radio frequency processing units, the baseband processing units are connected with the radio frequency processing units through optical fibers, the radio frequency processing units are arranged along a public line, signals reach the radio frequency processing units from the baseband processing units through the optical fibers, the automobile vehicle-mounted antennas are in wireless communication with the radio frequency processing units, and the wireless switching subsystems are used for realizing communication switching among the base stations;
The wireless communication between the vehicle-mounted antenna and the radio frequency processing unit comprises the steps of establishing a channel model, calculating effective throughput and determining a link self-adaptive transmission mode;
the channel model is established in the following way: considering large-scale path fading and small-scale multipath fading in a channel, the probability density function f (γ) of the vehicle-ground link receiving signal-to-noise ratio can be expressed as:
in the above formula, γ is the receiving noise ratio of the vehicle-ground link, l is the small-scale multipath fading factor, l belongs to [5dB,7dB ], I0 [. cndot. ] is the first nth-order modified bessel function, P is the transmission power of the rf processing unit, vk (d) is the large-scale path loss, N is the noise power only considering the large-scale loss, P, VK (d) and N units are dB, where vk (d) is 150+22ln (d) +20ln (fc),
In the formula, d is the distance between the vehicle-mounted antenna and the radio frequency processing unit, the unit is m, fc is carrier frequency, and the unit is Hz;
And calculating effective throughput by adopting the following method, wherein MIMO technology is adopted at two ends of the train-ground communication link, and the effective transmission rate of the system is as follows assuming that the receiving signal-to-noise ratio of the train-ground link is gamma:
In the above equation, k1 is a constant, m is a multiplexing gain, Lt + w is the total length of a frame header and a frame tail of a link layer in a communication protocol, and Lz is the frame length of the link layer;
The corresponding frame error rate is:
in the above equation, Mf is the number of transmitting antennas, and Mj is the number of receiving antennas;
Assuming that the received snr γ 1 for the initial transmission of the system and the received snr γ n for the nth transmission, the expectation of the effective throughput of the system when the maximum allowed number of transmissions of the system is Nm can be expressed as:
In the above equation, it is the maximum goodput that can be obtained by the system after r transmissions;
is the probability that a frame was not successfully transmitted the first n-1 times, but was successfully transmitted the nth time, wherein,
the determination of the adaptive transmission mode comprises the following steps: based on a partially observable Markov decision model, with effective throughput as an optimization target, selecting appropriate adaptive transmission parameters { m, Lz } to maximize the benefit of the system under a given target frame error rate (DGtar), and modeling the optimal link adaptive transmission problem as follows:
so that the user can easily and conveniently select the required position,
In the above equation, T is the total decision time, UA (m (T), lz (T)) is the instantaneous profit function at decision time T;
The wireless switching subsystem is used for realizing communication switching between base stations by adopting an improved switching mode, wherein the improved switching mode comprises the following steps:
A. measuring the received signal strength RSRP value and the channel quality RSRQ value of the current service cell and each adjacent cell;
B. selecting each adjacent cell meeting the judgment condition, wherein the judgment formula of the judgment condition is as follows:
min(RN)>0
Wherein Δ RSRP (i) ψ D represents a difference between an RSRP value of a neighboring cell ψ and an RSRP value of a current serving cell D at time i, where qb (i) is a handover hysteresis threshold value at time i, RSRP (ψ) i is a received signal strength RSRP value of the neighboring cell ψ which meets the determination condition at time i, and RSRP (D) i is an RSRP value of the current serving cell D at time i;
C. and selecting the optimal adjacent cell from the adjacent cells meeting the judgment condition to trigger the switching.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710029139.2A CN106530726B (en) | 2017-01-16 | 2017-01-16 | highway vehicle management system based on cloud calculates |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710029139.2A CN106530726B (en) | 2017-01-16 | 2017-01-16 | highway vehicle management system based on cloud calculates |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106530726A CN106530726A (en) | 2017-03-22 |
| CN106530726B true CN106530726B (en) | 2019-12-06 |
Family
ID=58335495
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710029139.2A Active CN106530726B (en) | 2017-01-16 | 2017-01-16 | highway vehicle management system based on cloud calculates |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106530726B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118366319B (en) * | 2024-06-19 | 2024-08-20 | 贵州桥梁建设集团有限责任公司 | Intelligent acquisition method and system for traffic flow of expressway |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103680136A (en) * | 2012-09-14 | 2014-03-26 | 宋怀淳 | Vehicle-mounted card type highway traffic flow statistics and traffic jam forecasting method |
| CN204045022U (en) * | 2014-08-28 | 2014-12-24 | 浪潮集团有限公司 | A kind of RFID vehicle on highway management system based on cloud computing |
| CN105679039A (en) * | 2016-04-13 | 2016-06-15 | 成都锐奕信息技术有限公司 | Monitoring system for obtaining traffic state of highway |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8723690B2 (en) * | 2011-01-26 | 2014-05-13 | International Business Machines Corporation | Systems and methods for road acoustics and road video-feed based traffic estimation and prediction |
| US9435654B2 (en) * | 2013-06-01 | 2016-09-06 | Savari, Inc. | System and method for creating, storing, and updating local dynamic MAP database with safety attribute |
-
2017
- 2017-01-16 CN CN201710029139.2A patent/CN106530726B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103680136A (en) * | 2012-09-14 | 2014-03-26 | 宋怀淳 | Vehicle-mounted card type highway traffic flow statistics and traffic jam forecasting method |
| CN204045022U (en) * | 2014-08-28 | 2014-12-24 | 浪潮集团有限公司 | A kind of RFID vehicle on highway management system based on cloud computing |
| CN105679039A (en) * | 2016-04-13 | 2016-06-15 | 成都锐奕信息技术有限公司 | Monitoring system for obtaining traffic state of highway |
Non-Patent Citations (1)
| Title |
|---|
| 高速移动列车无线组网方案;郭军伟;《中兴通讯技术》;20100430;第16卷(第2期);第53-55页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106530726A (en) | 2017-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Maurer et al. | Narrow-band measurement and analysis of the inter-vehicle transmission channel at 5.2 GHz | |
| Dong et al. | An edge computing empowered radio access network with UAV-mounted FSO fronthaul and backhaul: Key challenges and approaches | |
| CN101977097B (en) | Method for combined adjustment of adaptive rate and power of remote WiFi link | |
| CN110166100B (en) | Millimeter-wave high-speed rail train-ground communication stable beam forming method based on position fairness | |
| CN109347525A (en) | Adaptive multi-beamforming and beam switching method for millimeter-wave high-speed rail vehicle-ground communication | |
| CN110636523B (en) | Millimeter wave mobile backhaul link energy efficiency stabilization scheme based on Q learning | |
| EP3292726B1 (en) | Method and apparatus for connectivity adjustment in a wireless communication network | |
| CN111132075B (en) | A relay selection method based on state transition probability for air-ground integrated vehicle networking | |
| Arbabi et al. | Monitoring free flow traffic using vehicular networks | |
| CN114050855B (en) | Channel information self-adaption-oriented intelligent cooperative transmission method between low-orbit satellites | |
| Tchouankem et al. | Impact of buildings on vehicle-to-vehicle communication at urban intersections | |
| Charalampopoulos et al. | V2I applications in highways: How RSU dimensioning can improve service delivery | |
| CN106530726B (en) | highway vehicle management system based on cloud calculates | |
| WO2023050993A1 (en) | Terminal communication mode adjustment method and apparatus, terminal and storage medium | |
| CN112188497B (en) | Air-ground integrated vehicle network spectrum resource optimization method | |
| CN106774432B (en) | A kind of unmanned vehicle | |
| CN111132298B (en) | A power distribution method and device | |
| Chen et al. | How cars talk louder, clearer and fairer: Optimizing the communication performance of connected vehicles via online synchronous control | |
| CN109041009B (en) | Internet of vehicles uplink power distribution method and device | |
| CN106686110A (en) | Highway integrated event processing and emergency dispatching system | |
| CN106686689B (en) | Method for rapidly accessing wireless network on train | |
| CN106656337B (en) | High-speed railway communication system | |
| CN106672021B (en) | A kind of track traffic automated system | |
| CN106708052A (en) | Multi-sensor fusion intelligent vehicle based on magnetic navigation | |
| CN106683212B (en) | A kind of highway big data parameter state list system-wide net sending system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20191112 Address after: 264000, 11, Mingda West Road, Laishan District, Shandong, Yantai Applicant after: Yantai Huitong Network Technology Co., Ltd. Address before: Elite Building No. 1024 Nanshan Nanshan District Guangdong street, 518000 Avenue in Shenzhen city in Guangdong province 206 Applicant before: Shenzhen Huitong Intelligent Technology Co., Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |