CN107607121A - Travel line and travel line recommended work method are obtained by geographical location information - Google Patents
Travel line and travel line recommended work method are obtained by geographical location information Download PDFInfo
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- CN107607121A CN107607121A CN201710884486.3A CN201710884486A CN107607121A CN 107607121 A CN107607121 A CN 107607121A CN 201710884486 A CN201710884486 A CN 201710884486A CN 107607121 A CN107607121 A CN 107607121A
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Abstract
The present invention proposes one kind and obtains travel line and travel line recommended work method by geographical location information, comprises the following steps：S1, according to the history travel track of high in the clouds data acquisition mass users, speckle noise reduction model discrimination is carried out for history travel track；S2, by the data transfer after screening to user terminal, travel track end point location information is obtained from user terminal, path judgement is carried out according to path cruise constraints；S3, some results after will determine that carry out data display by sampling of data mode；S4, the travel track data after displaying are subjected to Model Matching.
Description
Technical field
The present invention relates to big data analysis field, more particularly to it is a kind of by geographical location information obtain travel line and
Travel line recommended work method.
Background technology
Because the aging of population gradually highlights, its quality of life and health status need to obtain the care of society with
Look after, and handicapped personnel also are intended to absorb some fresh airs and interact communication with society, but due to row
The reason for dynamic inconvenient, be unable to carry out outdoor activity, so as to the Medical Transport equipment that arisen at the historic moment, such as power assisted wheelchair or
Electric wheelchair, and the product such as manual balance car, although finished product is market-oriented.But because user manipulates to electronic equipment
Understanding is slower, is unable to carry out people's car mutual well, this automatic Pilot wheelchair that just arisen at the historic moment, but automatic Pilot wheel
The problem that chair is exactly that the route walked for user can not plan judgement well, saves path or improves efficiency
Shorten driving time.
The content of the invention
It is contemplated that at least solving technical problem present in prior art, especially innovatively propose one kind and pass through ground
Manage positional information and obtain travel line and travel line recommended work method.
In order to realize the above-mentioned purpose of the present invention, travel line is obtained by geographical location information the invention provides one kind
And travel line recommended work method, comprise the following steps：
S1, according to the history travel track of high in the clouds data acquisition mass users, speckle drop is carried out for history travel track
Make an uproar model discrimination；
S2, by the data transfer after screening to user terminal, travel track end point location information, root are obtained from user terminal
Path judgement is carried out according to path cruise constraints；
S3, some results after will determine that carry out data display by sampling of data mode；
S4, the travel track data after displaying are subjected to Model Matching.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S1 includes：
S1-1, preliminary screening, the ground according to residing for user's navigation information determines the user are carried out for cloud massive data
Position is managed, obtains the travel track data similar with geographic position data from mass data in the geographical position；
S1-2, screened by equation below for travel track data, adopted according to identified geographical location information
With mean square error product algorithm,
K (p)=R (| r (p)2|)·n(p)·[|r(p)2|/q(p)]·[|r(p)2|·u(p)]
Wherein, r (p)2For the intensity of travel track data, p is geographical location information, and n (p) is to form track at the p of position
Preceding intensity, q (p) are to form the intensity behind track at the p of position, and u (p) is the intensity that front and rear overall track is formed at the p of position, R
(|r(p)2|) it is geographical position track model of place；
S1-3, after product algorithm computing, convergence data screening is carried out, it is close by probability from travel track data
Degree computing is screened,
Wherein, μpFor geographical position accumu-late parameter, TsiFor i-th of time component of s-th of travel track,For s-th
I-th of point of interest component of travel track, c1And c2For interest factor, x1And x2For the random number of geographical position coordinates, pgiTo be complete
I-th of trajectory components, u in portion's travel tracksiFor i-th of place component of s-th of travel track, t >=0.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S2 includes：
S2-1, by the travel track data transfer after screening to user terminal, obtain the geography that user terminal is sent in real time
Positional information, judge travel track end point location information；
S2-2, some trace informations for being obtained from travel track carry out path cruise constraint, and the constraint formulations are,
Wherein, Rp(τ) is preferable traveling scene at the p of position, Rs(τ) is the preferable traveling scene at s-th of track,
wlongFor the longest distance value of trail weight, wshortFor the shortest distance values of trail weight, z is current iteration number, ZmaxFor
Maximum iteration, Q (τ) are whole trace informations vector.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S3 includes：
S3-1, enter track data displaying, the travel track through the travel time in sampling process by calculating laggard every trade
The point of interest action trail data that data, the travel track data in place of going on a journey, and user often arrive at are predicted, for
Air speed data, temperature record and the precipitation data in the place that user obtains are compared judgement in history cycle, calculate and advance
The probable value of the corresponding air speed data in track, temperature record and precipitation data, so as to feed back to user.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S4 includes：
S4-1, generate time consumption model
Wherein, αtFor the threshold value of time consumption value, Ni(t) it is the time consumption value of each travel track, Ni(t+1) under being
The time consumption value of one period travel track,
S4-2, generate the forecast model of time consumption
Wherein, βtFor the threshold value of time consumption predicted value, Nj(t) for each travel track time consumption predicted value, Nj
(t+1) it is the time consumption predicted value of subsequent period travel track,
S4-3, generate the forecast model of wind speed
Wherein, χtFor the threshold value of wind speed judgment value, Nk(t) it is the wind speed judgment value of each travel track, Nk(t+1) under being
The wind speed judgment value of one period travel track,
S4-4, generate the forecast model of temperature
Wherein, δtFor the threshold value of temperature judgment value, Nl(t) it is the temperature judgment value of each travel track, Nl(t+1) under being
The temperature judgment value of one period travel track,
S4-5, generate precipitation forecast model
Wherein, εtFor the threshold value of precipitation judgment value, Nm(t) it is the precipitation judgment value of each travel track, Nm(t+1)
For the precipitation judgment value of subsequent period travel track.
In summary, by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
Realizing user by the above method selects the optimization of travel track to judge, provides the user with the choosing of a variety of trips
Select, for the precipitation of generation, the data such as wind speed and temperature Change determine precipitation, wind speed and gas as attribute is judged
The overall sampling model of warm change to attributes, so as to obtain the most preferable trip trace information of user, medical treatment can be effectively improved
Safety traffic probability of the equipment on complex road condition, ensure that user is safe to use.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the present invention.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein：
Fig. 1 is workflow diagram of the present invention.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, is only used for explaining the present invention, and is not considered as limiting the invention.
In the description of the invention, it is to be understood that term " longitudinal direction ", " transverse direction ", " on ", " under ", "front", "rear",
The orientation or position relationship of the instruction such as "left", "right", " vertical ", " level ", " top ", " bottom ", " interior ", " outer " is based on accompanying drawing institutes
The orientation or position relationship shown, it is for only for ease of the description present invention and simplifies description, rather than instruction or the dress for implying meaning
Put or element there must be specific orientation, with specific azimuth configuration and operation, therefore it is not intended that to limit of the invention
System.
In the description of the invention, unless otherwise prescribed with limit, it is necessary to explanation, term " installation ", " connected ",
" connection " should be interpreted broadly, for example, it may be mechanical connection or electrical connection or the connection of two element internals, can
To be to be joined directly together, can also be indirectly connected by intermediary, for the ordinary skill in the art, can basis
Concrete condition understands the concrete meaning of above-mentioned term.
As shown in figure 1, pushed away the invention provides one kind by geographical location information acquisition travel line and travel line
Method of work is recommended, is comprised the following steps：
S1, according to the history travel track of high in the clouds data acquisition mass users, speckle drop is carried out for history travel track
Make an uproar model discrimination；
S2, by the data transfer after screening to user terminal, travel track end point location information, root are obtained from user terminal
Path judgement is carried out according to path cruise constraints；
S3, some results after will determine that carry out data display by sampling of data mode；
S4, the travel track data after displaying are subjected to Model Matching.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S1 includes：
S1-1, preliminary screening, the ground according to residing for user's navigation information determines the user are carried out for cloud massive data
Position is managed, obtains the travel track data similar with geographic position data from mass data in the geographical position；
S1-2, screened by equation below for travel track data, adopted according to identified geographical location information
With mean square error product algorithm,
K (p)=R (| r (p)2|)·n(p)·[|r(p)2|/q(p)]·[|r(p)2|·u(p)]
Wherein, r (p)2For the intensity of travel track data, p is geographical location information, and n (p) is to form track at the p of position
Preceding intensity, q (p) are to form the intensity behind track at the p of position, and u (p) is the intensity that front and rear overall track is formed at the p of position, R
(|r(p)2|) it is geographical position track model of place；
S1-3, after product algorithm computing, convergence data screening is carried out, it is close by probability from travel track data
Degree computing is screened,
Wherein, μpFor geographical position accumu-late parameter, TsiFor i-th of time component of s-th of travel track,For s-th
I-th of point of interest component of travel track, c1And c2For interest factor, x1And x2For the random number of geographical position coordinates, pgiTo be complete
I-th of trajectory components, u in portion's travel tracksiFor i-th of place component of s-th of travel track, t >=0.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S2 includes：
S2-1, by the travel track data transfer after screening to user terminal, obtain the geography that user terminal is sent in real time
Positional information, judge travel track end point location information；
S2-2, some trace informations for being obtained from travel track carry out path cruise constraint, and the constraint formulations are,
Wherein, Rp(τ) is preferable traveling scene at the p of position, Rs(τ) is the preferable traveling scene at s-th of track,
wlongFor the longest distance value of trail weight, wshortFor the shortest distance values of trail weight, z is current iteration number, ZmaxFor
Maximum iteration, Q (τ) are whole trace informations vector.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S3 includes：
S3-1, enter track data displaying, the travel track through the travel time in sampling process by calculating laggard every trade
The point of interest action trail data that data, the travel track data in place of going on a journey, and user often arrive at are predicted, for
Air speed data, temperature record and the precipitation data in the place that user obtains are compared judgement in history cycle, calculate and advance
The probable value of the corresponding air speed data in track, temperature record and precipitation data.
Described obtains travel line and travel line recommended work method by geographical location information, it is preferred that institute
Stating S4 includes：
S4-1, generate time consumption model
Wherein, αtFor the threshold value of time consumption value, Ni(t) it is the time consumption value of each travel track, Ni(t+1) under being
The time consumption value of one period travel track,
S4-2, generate the forecast model of time consumption
Wherein, βtFor the threshold value of time consumption predicted value, Nj(t) for each travel track time consumption predicted value, Nj
(t+1) it is the time consumption predicted value of subsequent period travel track,
S4-3, generate the forecast model of wind speed
Wherein, χtFor the threshold value of wind speed judgment value, Nk(t) it is the wind speed judgment value of each travel track, Nk(t+1) under being
The wind speed judgment value of one period travel track,
S4-4, generate the forecast model of temperature
Wherein, δtFor the threshold value of temperature judgment value, Nl(t) it is the temperature judgment value of each travel track, Nl(t+1) under being
The temperature judgment value of one period travel track,
S4-5, generate precipitation forecast model
Wherein, εtFor the threshold value of precipitation judgment value, Nm(t) it is the precipitation judgment value of each travel track, Nm(t+1)
For the precipitation judgment value of subsequent period travel track.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment or example of the present invention.In this manual, to the schematic representation of above-mentioned term not
Necessarily refer to identical embodiment or example.Moreover, specific features, structure, material or the feature of description can be any
One or more embodiments or example in combine in an appropriate manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle and objective of the present invention a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (5)
1. a kind of obtain travel line and travel line recommended work method by geographical location information, it is characterised in that bag
Include following steps：
S1, according to the history travel track of high in the clouds data acquisition mass users, speckle noise reduction mould is carried out for history travel track
Type screens；
S2, by the data transfer after screening to user terminal, travel track end point location information is obtained from user terminal, according to road
Footpath cruise constraints carries out path judgement；
S3, some results after will determine that carry out data display by sampling of data mode；
S4, the travel track data after displaying are subjected to Model Matching.
2. according to claim 1 obtain travel line and travel line recommended work side by geographical location information
Method, it is characterised in that the S1 includes：
S1-1, preliminary screening, the geographical position according to residing for user's navigation information determines the user are carried out for cloud massive data
Put, obtain the travel track data similar with geographic position data from mass data in the geographical position；
S1-2, screened for travel track data by equation below, according to identified geographical location information using equal
Square error product algorithm,
K (p)=R (| r (p)2|)·n(p)·[|r(p)2|/q(p)]·[|r(p)2|·u(p)]
Wherein, r (p)2For the intensity of travel track data, p is geographical location information, and n (p) is strong before track to be formed at the p of position
Degree, q (p) they are to form the intensity behind track at the p of position, and u (p) is the intensity that front and rear overall track is formed at the p of position, R (| r (p)2
|) it is geographical position track model of place；
S1-3, after product algorithm computing, convergence data screening is carried out, is transported from travel track data by probability density
Calculation is screened,
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Enter i-th of trajectory components in track, usiFor i-th of place component of s-th of travel track, t >=0.
3. according to claim 1 obtain travel line and travel line recommended work side by geographical location information
Method, it is characterised in that the S2 includes：
S2-1, by the travel track data transfer after screening to user terminal, obtain the geographical position that user terminal is sent in real time
Information, judge travel track end point location information；
S2-2, some trace informations for being obtained from travel track carry out path cruise constraint, and the constraint formulations are,
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Wherein, Rp(τ) is preferable traveling scene at the p of position, Rs(τ) is the preferable traveling scene at s-th track, wlong
For the longest distance value of trail weight, wshortFor the shortest distance values of trail weight, z is current iteration number, ZmaxChanged for maximum
Generation number, Q (τ) are whole trace informations vector.
4. according to claim 1 obtain travel line and travel line recommended work side by geographical location information
Method, it is characterised in that the S3 includes：
S3-1, by calculate laggard every trade enter track data show, the travel track data through the travel time in sampling process,
The travel track data in trip place, and the point of interest action trail data that user often arrives at are predicted, for history
Air speed data, temperature record and the precipitation data in the place that user obtains are compared judgement in cycle, calculate travel track
The probable value of corresponding air speed data, temperature record and precipitation data, so as to feed back to user.
5. according to claim 1 obtain travel line and travel line recommended work side by geographical location information
Method, it is characterised in that the S4 includes：
S4-1, generate time consumption model
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Wherein, αtFor the threshold value of time consumption value, Ni(t) it is the time consumption value of each travel track, Ni(t+1) it is lower a period of time
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S4-2, generate the forecast model of time consumption
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Wherein, βtFor the threshold value of time consumption predicted value, Nj(t) for each travel track time consumption predicted value, Nj(t+
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S4-3, generate the forecast model of wind speed
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Wherein, χtFor the threshold value of wind speed judgment value, Nk(t) it is the wind speed judgment value of each travel track, Nk(t+1) it is lower a period of time
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S4-4, generate the forecast model of temperature
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<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>N</mi>
<mi>l</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>&delta;</mi>
<mi>t</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>N</mi>
<mi>l</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<msub>
<mi>&delta;</mi>
<mi>t</mi>
</msub>
</mfrac>
<mo>,</mo>
</mrow>
Wherein, δtFor the threshold value of temperature judgment value, Nl(t) it is the temperature judgment value of each travel track, Nl(t+1) it is lower a period of time
The temperature judgment value of section travel track,
S4-5, generate precipitation forecast model
<mrow>
<mi>E</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>N</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>&epsiv;</mi>
<mi>t</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>N</mi>
<mi>m</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<msub>
<mi>&epsiv;</mi>
<mi>t</mi>
</msub>
</mfrac>
<mo>,</mo>
</mrow>
Wherein, εtFor the threshold value of precipitation judgment value, Nm(t) it is the precipitation judgment value of each travel track, Nm(t+1) under being
The precipitation judgment value of one period travel track.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108955693A (en) * | 2018-08-02 | 2018-12-07 | 吉林大学 | A kind of method and system of road network |
| CN109492799A (en) * | 2018-10-23 | 2019-03-19 | 重庆扬升信息技术有限公司 | Path optimization method under data environment of mass sharing exchange platform |
| CN109742681A (en) * | 2019-01-15 | 2019-05-10 | 北京拓维思科技有限公司 | The processing method and processing device of track of marching into the arena based on swing method |
| CN109861131A (en) * | 2018-11-20 | 2019-06-07 | 北京拓维思科技有限公司 | The determination method and device of combinational gap, storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201055497Y (en) * | 2007-05-28 | 2008-05-07 | 罗来安 | Safety auxiliary device for visually impaired people walking |
| CN103808326A (en) * | 2012-11-07 | 2014-05-21 | 腾讯科技（深圳）有限公司 | Navigation method and navigation system |
| CN105342769A (en) * | 2015-11-20 | 2016-02-24 | 宁波大业产品造型艺术设计有限公司 | Intelligent electric wheelchair |
| CN105455979A (en) * | 2015-11-20 | 2016-04-06 | 合肥工业大学 | Control system and method of intelligent wheelchair |
| US9310211B1 (en) * | 2010-12-10 | 2016-04-12 | Google Inc. | Extracting patterns from location history |
| CN106444750A (en) * | 2016-09-13 | 2017-02-22 | 哈尔滨工业大学深圳研究生院 | Two-dimensional code positioning-based intelligent warehousing mobile robot system |
| CN106767871A (en) * | 2016-12-27 | 2017-05-31 | 上海斐讯数据通信技术有限公司 | A kind of navigation system and its application method of the preset mode based on high in the clouds |
-
2017
- 2017-09-26 CN CN201710884486.3A patent/CN107607121A/en not_active Withdrawn
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201055497Y (en) * | 2007-05-28 | 2008-05-07 | 罗来安 | Safety auxiliary device for visually impaired people walking |
| US9310211B1 (en) * | 2010-12-10 | 2016-04-12 | Google Inc. | Extracting patterns from location history |
| CN103808326A (en) * | 2012-11-07 | 2014-05-21 | 腾讯科技（深圳）有限公司 | Navigation method and navigation system |
| CN105342769A (en) * | 2015-11-20 | 2016-02-24 | 宁波大业产品造型艺术设计有限公司 | Intelligent electric wheelchair |
| CN105455979A (en) * | 2015-11-20 | 2016-04-06 | 合肥工业大学 | Control system and method of intelligent wheelchair |
| CN106444750A (en) * | 2016-09-13 | 2017-02-22 | 哈尔滨工业大学深圳研究生院 | Two-dimensional code positioning-based intelligent warehousing mobile robot system |
| CN106767871A (en) * | 2016-12-27 | 2017-05-31 | 上海斐讯数据通信技术有限公司 | A kind of navigation system and its application method of the preset mode based on high in the clouds |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108955693A (en) * | 2018-08-02 | 2018-12-07 | 吉林大学 | A kind of method and system of road network |
| CN108955693B (en) * | 2018-08-02 | 2020-06-16 | 吉林大学 | Road network matching method and system |
| CN109492799A (en) * | 2018-10-23 | 2019-03-19 | 重庆扬升信息技术有限公司 | Path optimization method under data environment of mass sharing exchange platform |
| CN109492799B (en) * | 2018-10-23 | 2021-12-14 | 重庆扬升信息技术有限公司 | Path optimization method under data environment of mass sharing exchange platform |
| CN109861131A (en) * | 2018-11-20 | 2019-06-07 | 北京拓维思科技有限公司 | The determination method and device of combinational gap, storage medium |
| CN109742681A (en) * | 2019-01-15 | 2019-05-10 | 北京拓维思科技有限公司 | The processing method and processing device of track of marching into the arena based on swing method |
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