CN104575075A - City road network vehicle coordinate correcting method and device based on plough satellite - Google Patents

City road network vehicle coordinate correcting method and device based on plough satellite Download PDF

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Publication number
CN104575075A
CN104575075A CN201510018927.2A CN201510018927A CN104575075A CN 104575075 A CN104575075 A CN 104575075A CN 201510018927 A CN201510018927 A CN 201510018927A CN 104575075 A CN104575075 A CN 104575075A
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big dipper
floating car
road network
coordinate
section
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CN104575075B (en
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高万宝
吴先会
张广林
邹娇
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Anhui Langqiu Intelligent Technology Co.,Ltd.
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HEFEI GELYU INFORMATION TECHNOLOGY Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
    • 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

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)

Abstract

The invention provides a city road network vehicle coordinate correcting method based on the plough satellite. The city road network vehicle coordinate correcting method comprises the steps of obtaining vehicle plough satellite information; processing a city road network in a meshing mode; reading vehicle coordinate information; determining a mesh where vehicle coordinates are located; determining the sudoku where the vehicle coordinates are located; determining a candidate road segment set; calculating distance and direction angle deviation; calculating matching indexes; determining matching road segments; updating the corrected vehicle coordinates. The invention further discloses a city road network vehicle coordinate correcting device based on the plough satellite. According to the city road network vehicle coordinate correcting method and device based on the plough satellite, by structuring the sudoku and a coordinate matching index model, vehicle coordinates in the city road network can be quickly and accurately positioned, the deviation of city network plough satellite positioning under the high-rise building traffic environment is reduced, the speed of matching massive data is increased, and the operation efficiency and the service level of a floating vehicle traffic information collecting system are increased.

Description

A kind of city road network vehicle coordinate bearing calibration based on the Big Dipper and device
Technical field
The present invention relates to the city road network vehicle positioning technology field based on Big Dipper data, specifically a kind of city road network vehicle coordinate bearing calibration based on the Big Dipper and device.
Background technology
Floating car traffic information acquisition technique is by installing the Big Dipper on vehicle, the devices such as GPS, the dynamic position change information of vehicle is utilized to carry out the technology of real-time road extraction, based on Floating Car displacement data, seasonal effect in time series vehicle location coordinate is mated with map, calculate average velocity and the section travelling speed of all floating points, and then the traffic behavior of road can be extracted, this technology comprises data prediction, map match, path culculating and historical speed such as to supplement at the crucial handling procedure, the transaction module of each program is also diversified, precision also exists difference.
City road network environment is different from rural area and rural road, and due to the impact of a large amount of high-rise building thing, the location of the Big Dipper also exists deviation, and at present normal deviation is at about 10 meters, and the more local effect of buildings is poorer, can not carry out precise positioning to vehicle.In Floating Car information acquisition system, because the object scale of Floating Car map match is very huge, particularly towards metropolitan application, mating of up to ten thousand Floating Car and up to ten thousand sections to be completed in the short period of time, higher to the requirement of matching speed.
Summary of the invention
The object of the present invention is to provide a kind of city road network vehicle coordinate bearing calibration based on the Big Dipper and device, can under high buildings and large mansions traffic environment, realize the quick and precisely location of vehicle coordinate in city road network, promote operational efficiency and the service level of Floating Car information acquisition system.
Technical scheme of the present invention is:
Based on a city road network vehicle coordinate bearing calibration for the Big Dipper, comprise the following steps:
(1) obtain the dynamic parameter of all Big Dipper Floating Car in city road network, comprise time, coordinate and deflection data;
(2) gridding process is carried out to city road network map, all sections numbering in city road network is numbered to carry out associating with grid and binds;
(3) based on the changing coordinates of Big Dipper Floating Car, determine the grid at its place, centered by this grid, for radius, by the alternatively section, section be in nine grids within the scope of Big Dipper positioning error, obtain candidate road section collection with around nine grids;
(4) calculate the changing coordinates of Big Dipper Floating Car and candidate road section and concentrate distance between each candidate road section, the deviation between each candidate road section deflection is concentrated in conjunction with the current deflection of Big Dipper Floating Car and candidate road section, build city road network vehicle coordinate match index model, obtain the section matched with the changing coordinates of Big Dipper Floating Car;
(5) adopt distance between beeline and dot formula, calculate the intersection point coordinate of changing coordinates on the section matched with it of Big Dipper Floating Car, and using the calibration coordinate of this intersection point coordinate as the changing coordinates of Big Dipper Floating Car.
The described city road network vehicle coordinate bearing calibration based on the Big Dipper, described step (2) specifically comprises:
(21) on city road network basis, nerve of a covering is formatted processing layer, and determine numbering and the bounds of each grid, the base unit of sizing grid is 25 meters * 25 meters;
(22) based on starting point and the terminal point coordinate information in sections all in city road network, section is associated with grid and binds.
The described city road network vehicle coordinate bearing calibration based on the Big Dipper, in described step (3), based on the changing coordinates of Big Dipper Floating Car, determine the grid at its place, be specially: if in the scope that the changing coordinates of Big Dipper Floating Car falls into the top-left coordinates of certain grid, upper right coordinate, lower-left coordinate, lower right coordinate define, then judge that the changing coordinates of Big Dipper Floating Car belongs to this grid.
The described city road network vehicle coordinate bearing calibration based on the Big Dipper, described step (4) specifically comprises:
(41) obtain candidate road section and concentrate each candidate road section information, comprise candidate road section numbering, candidate road section starting point coordinate, candidate road section terminal point coordinate and candidate road section deflection;
(42) according to candidate road section starting point coordinate and candidate road section terminal point coordinate, candidate road section linear function is obtained;
(43) set the changing coordinates of Big Dipper Floating Car as (x 0, y 0, z 0), candidate road section P istraight-line equation be A ix+B iy+C iz+D i=0, then adopt following formulae discovery to go out (x 0, y 0, z 0) and P ibetween distance di:
d i = | A i x 0 + B i y 0 + C i z 0 + D i | A i 2 + B i 2 + C i 2
(44) city road network vehicle coordinate match index model is built:
MI i = 0.65 1 + d i / d + 0.35 1 + θ i / θ
Wherein, MI irepresent changing coordinates and the candidate road section P of Big Dipper Floating Car imatch index, d irepresent changing coordinates and candidate road section P ibetween distance, d represents Big Dipper data range deviation threshold value, θ irepresent current deflection and the candidate road section P of Big Dipper Floating Car ideflection between deviation, θ represents Big Dipper data direction angular displacement threshold value;
(45) section that the maximum candidate road section of match index matches as the changing coordinates with Big Dipper Floating Car is chosen.
The described city road network vehicle coordinate bearing calibration based on the Big Dipper, described step (5) specifically comprises:
(51) set the changing coordinates of Big Dipper Floating Car as (x 0, y 0, z 0), with (x 0, y 0, z 0) straight-line equation of section P that matches is Ax+By+Cz+D=0, then adopt following formulae discovery to go out (x 0, y 0, e0) intersection point coordinate on P (x ' 0, y ' 0, z ' 0):
x 0 ′ = x 0 - A ( Ax 0 + By 0 + Cz 0 + D ) A 2 + B 2 + C 2
y 0 ′ = y 0 - B ( Ax 0 + By 0 + Cz 0 + D ) A 2 + B 2 + C 2
z 0 ′ = z 0 - C ( Ax 0 + By 0 + Cz 0 + D ) A 2 + B 2 + C 2
(52) by the changing coordinates (x of Big Dipper Floating Car 0, y 0, z 0) be updated to (x ' 0, y ' 0, z ' 0).
A kind of city road network vehicle coordinate means for correcting based on the Big Dipper, comprise data communication and storage server, background process server, issue terminal and some Big Dipper Floating Car, the output terminal of described Big Dipper Floating Car is connected with the input end of storage server with background process server by data communication, and the described output terminal of background process server is connected with the input end of issue terminal.
The present invention is by the mining analysis to Big Dipper data, gridding process is carried out to city road network map, build nine grids search model and coordinate matching exponential model, by the coupling section of search vehicle coordinate, determine its calibration coordinate in city road network, realize the fast accurate location of vehicle, under reducing high buildings and large mansions traffic environment, the deviation of city road network Big Dipper location, improves the speed of mass data coupling, promotes operational efficiency and the service level of traffic information acquisition system.
Accompanying drawing explanation
Fig. 1 is method flow diagram of the present invention;
Fig. 2 is that vehicle coordinate of the present invention mates schematic diagram with section;
Fig. 3 is apparatus structure schematic diagram of the present invention.
Embodiment
Below, the present invention is further illustrated with specific embodiment by reference to the accompanying drawings.
As shown in Figure 1, a kind of city road network vehicle coordinate bearing calibration based on the Big Dipper, some Big Dipper Floating Car 1 that employing connects successively, data communication and storage server 2, background process server 3 and issue terminal 4 realize (as shown in Figure 3), and the method comprises the following steps:
S1, utilize floating car technology to detect the dynamic parameter of all Big Dipper Floating Car 1 in city road network, comprise time, coordinate and deflection data, the dynamic parameter detected through data communication and storage server 2 in real time stored in background process server 3.
S2, on city road network basis, nerve of a covering is formatted processing layer, and determine numbering and the bounds of each grid, the base unit of sizing grid is 25 meters * 25 meters, then based on starting point and the terminal point coordinate information in little sections all in city road network, little section is associated with grid and binds:
If section is encoded to P i, grid coding is Q j, grid coding Q jto encode P with section icarry out one-to-many association pairing:
Q j＝{P 1，P 2，…，P i}(i∈I，j∈J) (1)
I is the numbering of current road segment; J is the numbering of current grid; I is all sections number in current grid; J is total number of all grids in city road network.
S3, changing coordinates based on Big Dipper Floating Car, determine the grid at its place, centered by this grid, with around nine grids for radius, and the road section information in search target zone, determine candidate road section collection, specifically comprise:
If the changing coordinates of Big Dipper Floating Car is G=(x 0, y 0, z 0), grid Q j={ G 1, G 2, G 3, G 4, wherein, G 1for Q jtop-left coordinates, G 2for Q jupper right coordinate, G 3for Q jlower-left coordinate, G 4for Q jlower right coordinate, if G falls into G 1, G 2, G 3, G 4in the scope defined, then judge that G belongs to Q j:
Then with Q jcentered by, search nine grids around, utilize all road section informations in formula (1) extraction nine grids, alternatively section collection.
S4, acquisition candidate road section concentrate each candidate road section information, comprise candidate road section numbering, candidate road section starting point coordinate, candidate road section terminal point coordinate and candidate road section deflection; According to candidate road section starting point coordinate and candidate road section terminal point coordinate, obtain candidate road section linear function;
If the changing coordinates of Big Dipper Floating Car is (x 0, y 0, z 0), candidate road section P istraight-line equation be A ix+B iy+C iz+D i=0, then adopt following formulae discovery to go out (x 0, y 0, z 0) and P ibetween distance d i:
d i = | A i x 0 + B i y 0 + C i z 0 + D i | A i 2 + B i 2 + C i 2 - - - ( 2 )
Build city road network vehicle coordinate match index model:
MI i = 0.65 1 + d i / d + 0.35 1 + θ i / θ - - - ( 3 )
Wherein, MI irepresent changing coordinates and the candidate road section P of Big Dipper Floating Car imatch index, d irepresent changing coordinates and candidate road section P ibetween distance, d represents default Big Dipper data range deviation threshold constant, is traditionally arranged to be 10 meters, θ irepresent current deflection and the candidate road section P of Big Dipper Floating Car ideflection between deviation, θ represents default Big Dipper data direction angular displacement threshold constant, is traditionally arranged to be 30 degree;
MI ilarger, represent changing coordinates and the candidate road section P of Big Dipper Floating Car imatching degree higher, therefore, choose the section that the maximum candidate road section of match index matches as the changing coordinates with Big Dipper Floating Car.
Fig. 2 is city road network vehicle coordinate coupling schematic diagram, have some sections and three gps coordinate points P, Q, R, analyze for coordinate points Q, its place nine grids are as shown in Fig. 2 bend region, candidate road section comprises b, e, m, c, g, and coordinate points Q is d to the distance relation of each candidate road section m< d b=d c< d g< d e, the deflection deviation between section m and coordinate points Q is comparatively large, then according to the match index calculated, judges that coordinate points Q is on the b of section.In like manner obtain coordinate points P on the l of section.And coordinate points R is positioned near crossing, its deflection has relatively large deviation, and once coupling is difficult to ensure correctly, to determine its position, if next coordinate points is on a of section, then can judge that coordinate points R is on the b of section when route searching.
S5, establish the changing coordinates (x with Big Dipper Floating Car 0, y 0, z 0) straight-line equation of section P that matches is Ax+By+Cz+D=0, then adopt following formulae discovery to go out (x 0, y 0, z 0) intersection point coordinate on P (x ' 0, y ' 0, z ' 0):
x 0 &prime; = x 0 - A ( Ax 0 + By 0 + Cz 0 + D ) A 2 + B 2 + C 2 - - - ( 4 )
y 0 &prime; = y 0 - B ( Ax 0 + By 0 + Cz 0 + D ) A 2 + B 2 + C 2 - - - ( 5 )
z 0 &prime; = z 0 - C ( Ax 0 + By 0 + Cz 0 + D ) A 2 + B 2 + C 2 - - - ( 6 )
By the changing coordinates (x of Big Dipper Floating Car 0, y 0, z 0) be updated to (x ' 0, y ' 0, z ' 0) realize the correction of city road network vehicle coordinate.
Vehicle coordinate after S6, correction is stored in database, and issue terminal 4, by the calibration coordinate information of calling data bank interface service acquisition vehicle, realizes accurate location and the Information issued of city road network vehicle coordinate.
The present invention is by building nine grids and coordinate matching exponential model, the quick and precisely location of vehicle coordinate in city road network can be realized, under reducing high buildings and large mansions traffic environment, the deviation of city road network Big Dipper location, improve the speed of mass data coupling, promote operational efficiency and the service level of floating car traffic information acquisition system.
The above embodiment is only be described the preferred embodiment of the present invention; not scope of the present invention is limited; under not departing from the present invention and designing the prerequisite of spirit; the various distortion that those of ordinary skill in the art make technical scheme of the present invention and improvement, all should fall in protection domain that claims of the present invention determine.

Claims (6)

1., based on a city road network vehicle coordinate bearing calibration for the Big Dipper, it is characterized in that, comprise the following steps:
(1) obtain the dynamic parameter of all Big Dipper Floating Car in city road network, comprise time, coordinate and deflection data;
(2) gridding process is carried out to city road network map, all sections numbering in city road network is numbered to carry out associating with grid and binds;
(3) based on the changing coordinates of Big Dipper Floating Car, determine the grid at its place, centered by this grid, for radius, by the alternatively section, section be in nine grids within the scope of Big Dipper positioning error, obtain candidate road section collection with around nine grids;
(4) calculate the changing coordinates of Big Dipper Floating Car and candidate road section and concentrate distance between each candidate road section, the deviation between each candidate road section deflection is concentrated in conjunction with the current deflection of Big Dipper Floating Car and candidate road section, build city road network vehicle coordinate match index model, obtain the section matched with the changing coordinates of Big Dipper Floating Car;
(5) adopt distance between beeline and dot formula, calculate the intersection point coordinate of changing coordinates on the section matched with it of Big Dipper Floating Car, and using the calibration coordinate of this intersection point coordinate as the changing coordinates of Big Dipper Floating Car.
2. the city road network vehicle coordinate bearing calibration based on the Big Dipper according to claim 1, it is characterized in that, described step (2) specifically comprises:
(21) on city road network basis, nerve of a covering is formatted processing layer, and determine numbering and the bounds of each grid, the base unit of sizing grid is 25 meters * 25 meters;
(22) based on starting point and the terminal point coordinate information in sections all in city road network, section is associated with grid and binds.
3. the city road network vehicle coordinate bearing calibration based on the Big Dipper according to claim 1, it is characterized in that, in described step (3), based on the changing coordinates of Big Dipper Floating Car, determine the grid at its place, be specially: if in the scope that the changing coordinates of Big Dipper Floating Car falls into the top-left coordinates of certain grid, upper right coordinate, lower-left coordinate, lower right coordinate define, then judge that the changing coordinates of Big Dipper Floating Car belongs to this grid.
4. the city road network vehicle coordinate bearing calibration based on the Big Dipper according to claim 1, it is characterized in that, described step (4) specifically comprises:
(41) obtain candidate road section and concentrate each candidate road section information, comprise candidate road section numbering, candidate road section starting point coordinate, candidate road section terminal point coordinate and candidate road section deflection;
(42) according to candidate road section starting point coordinate and candidate road section terminal point coordinate, candidate road section linear function is obtained;
(43) set the changing coordinates of Big Dipper Floating Car as (x 0, y 0, z 0), candidate road section P istraight-line equation be A ix+B iy+C iz+D i=0, then adopt following formulae discovery to go out (x 0, y 0, z 0) and P ibetween distance d i:
d i = | A i x 0 + B i y 0 + C i z 0 + D i | A i 2 + B i 2 + C i 2
(44) city road network vehicle coordinate match index model is built:
MI i = 0.65 1 + d i / d + 0.35 1 + &theta; i / &theta;
Wherein, MI irepresent changing coordinates and the candidate road section P of Big Dipper Floating Car imatch index, d irepresent changing coordinates and candidate road section P ibetween distance, d represents Big Dipper data range deviation threshold value, θ irepresent current deflection and the candidate road section P of Big Dipper Floating Car ideflection between deviation, θ represents Big Dipper data direction angular displacement threshold value;
(45) section that the maximum candidate road section of match index matches as the changing coordinates with Big Dipper Floating Car is chosen.
5. the city road network vehicle coordinate bearing calibration based on the Big Dipper according to claim 1, it is characterized in that, described step (5) specifically comprises:
(51) set the changing coordinates of Big Dipper Floating Car as (x 0, y 0, z 0), with (x 0, y 0, z 0) straight-line equation of section P that matches is Ax+By+Cz+D=0, then adopt following formulae discovery to go out (x 0, y 0, z 0) intersection point coordinate on P (x ' 0, y ' 0, z ' 0):
x 0 &prime; = x 0 - A ( A x 0 + B y 0 + C z 0 + D ) A 2 + B 2 + C 2
y 0 &prime; = y 0 - B ( A x 0 + B y 0 + C z 0 + D ) A 2 + B 2 + C 2
z 0 &prime; = z 0 - C ( A x 0 + B y 0 + C z 0 + D ) A 2 + B 2 + C 2
(52) by the changing coordinates (x of Big Dipper Floating Car 0, y 0, z 0) be updated to (x ' 0, y ' 0, z ' 0).
6. one kind realizes the city road network vehicle coordinate means for correcting based on the Big Dipper of method described in any one of claim 1 to 5, it is characterized in that: comprise data communication and storage server, background process server, issue terminal and some Big Dipper Floating Car, the output terminal of described Big Dipper Floating Car is connected with the input end of storage server with background process server by data communication, and the described output terminal of background process server is connected with the input end of issue terminal.
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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105261210A (en) * 2015-07-23 2016-01-20 合肥革绿信息科技有限公司 Beidou-equipment-based calculating method of traffic congestion index of road
CN106469505A (en) * 2015-08-20 2017-03-01 方正国际软件(北京)有限公司 A kind of floating wheel paths method for correcting error and device
CN106980029A (en) * 2016-01-15 2017-07-25 厦门雅迅网络股份有限公司 Overspeed of vehicle determination methods and its system
CN106997419A (en) * 2016-01-22 2017-08-01 北京四维图新科技股份有限公司 The relative offset deviation polarization state of road collection divides equally correcting method and device
CN107284477A (en) * 2017-07-17 2017-10-24 中车株洲电力机车有限公司 A kind of anti-control method and the anti-mobile unit that advances rashly of line end of advancing rashly of line end
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050049783A1 (en) * 2003-08-29 2005-03-03 Hunzinger Jason F. Iterative logical renewal of navigable map database
CN101409011A (en) * 2008-10-28 2009-04-15 北京世纪高通科技有限公司 Method, apparatus and system for matching map and conferring route
CN102280031A (en) * 2011-07-22 2011-12-14 南京莱斯信息技术股份有限公司 Crossing traffic state recognition method based on floating car data
CN103149576A (en) * 2013-01-29 2013-06-12 武汉大学 Map matching method of floating car data
CN103927873A (en) * 2014-04-28 2014-07-16 中国航天系统工程有限公司 Matching method for probe car and road section and method for obtaining real-time traffic status in parallel

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050049783A1 (en) * 2003-08-29 2005-03-03 Hunzinger Jason F. Iterative logical renewal of navigable map database
CN101409011A (en) * 2008-10-28 2009-04-15 北京世纪高通科技有限公司 Method, apparatus and system for matching map and conferring route
CN102280031A (en) * 2011-07-22 2011-12-14 南京莱斯信息技术股份有限公司 Crossing traffic state recognition method based on floating car data
CN103149576A (en) * 2013-01-29 2013-06-12 武汉大学 Map matching method of floating car data
CN103927873A (en) * 2014-04-28 2014-07-16 中国航天系统工程有限公司 Matching method for probe car and road section and method for obtaining real-time traffic status in parallel

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105261210B (en) * 2015-07-23 2017-11-10 合肥革绿信息科技有限公司 A kind of road section traffic volume congestion index computational methods based on Big Dipper equipment
CN105261210A (en) * 2015-07-23 2016-01-20 合肥革绿信息科技有限公司 Beidou-equipment-based calculating method of traffic congestion index of road
CN106469505A (en) * 2015-08-20 2017-03-01 方正国际软件(北京)有限公司 A kind of floating wheel paths method for correcting error and device
CN106980029B (en) * 2016-01-15 2021-11-02 厦门雅迅网络股份有限公司 Vehicle overspeed judgment method and system
CN106980029A (en) * 2016-01-15 2017-07-25 厦门雅迅网络股份有限公司 Overspeed of vehicle determination methods and its system
CN106997419A (en) * 2016-01-22 2017-08-01 北京四维图新科技股份有限公司 The relative offset deviation polarization state of road collection divides equally correcting method and device
CN106997419B (en) * 2016-01-22 2019-11-15 北京四维图新科技股份有限公司 Road acquires opposite offset deviation polarization state and divides equally correcting method and device
CN107284477A (en) * 2017-07-17 2017-10-24 中车株洲电力机车有限公司 A kind of anti-control method and the anti-mobile unit that advances rashly of line end of advancing rashly of line end
CN107284477B (en) * 2017-07-17 2019-01-22 中车株洲电力机车有限公司 A kind of anti-control method and the anti-mobile unit that advances rashly of line end of advancing rashly of line end
CN107911786A (en) * 2017-10-24 2018-04-13 星际空间（天津）科技发展有限公司 A kind of mixing indoor orientation method based on road network correction
CN109164799A (en) * 2018-07-24 2019-01-08 江苏大学 A kind of driving Active collision avoidance system and method
CN111552759A (en) * 2020-05-06 2020-08-18 深圳市丰巢科技有限公司 Method, device, equipment and medium for acquiring action track related data
CN111552759B (en) * 2020-05-06 2023-08-01 深圳市丰巢科技有限公司 Method, device, equipment and medium for acquiring action track related data
CN112288550A (en) * 2020-11-19 2021-01-29 食亨(上海)科技服务有限公司 Regional order analysis method, system and computer readable medium
CN112433211A (en) * 2020-11-27 2021-03-02 浙江商汤科技开发有限公司 Pose determination method and device, electronic equipment and storage medium
CN112433211B (en) * 2020-11-27 2022-11-29 浙江商汤科技开发有限公司 Pose determination method and device, electronic equipment and storage medium
CN113176599A (en) * 2021-05-20 2021-07-27 中国第一汽车股份有限公司 Geographical position determining method, device, equipment and storage medium
CN113176599B (en) * 2021-05-20 2024-03-15 中国第一汽车股份有限公司 Geographic position determining method, device, equipment and storage medium
CN113722728A (en) * 2021-08-13 2021-11-30 刘应森 Intelligent government affair information management method based on block chain
CN113722728B (en) * 2021-08-13 2023-09-15 深圳市法自然信息科技有限公司 Intelligent government affair information management method based on block chain
CN116228188A (en) * 2022-12-19 2023-06-06 西南交通大学 Railway passenger station equipment management method, device, equipment and readable storage medium
CN116228188B (en) * 2022-12-19 2023-11-24 西南交通大学 Railway passenger station equipment management method, device, equipment and readable storage medium

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