CN107633082A - The method of work of trajectory track and track differentiation is carried out using magnanimity action trail data - Google Patents

The method of work of trajectory track and track differentiation is carried out using magnanimity action trail data Download PDF

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Publication number
CN107633082A
CN107633082A CN201710884473.6A CN201710884473A CN107633082A CN 107633082 A CN107633082 A CN 107633082A CN 201710884473 A CN201710884473 A CN 201710884473A CN 107633082 A CN107633082 A CN 107633082A
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msub
travel track
track
data
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刘玉蓉
杨晓凡
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Chongqing City Intellectual Property Road Science And Technology Co Ltd
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Chongqing City Intellectual Property Road Science And Technology Co Ltd
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Abstract

The present invention proposes a kind of method of work that trajectory track and track differentiation are carried out using magnanimity action trail data, comprises the following steps：S1, high in the clouds data are pushed by sampling of data mode to user, the travel track time data obtained in travel track historical data, time prediction data and the air speed data of departure place and place of arrival, temperature record and precipitation data are extracted, S2, the corresponding air speed data of travel track after extraction, temperature record and precipitation data are carried out to feed back to user after convergence estimate.

Description

The work of trajectory track and track differentiation is carried out using magnanimity action trail data Method
Technical field
Travel field the present invention relates to intelligence, more particularly to it is a kind of using magnanimity action trail data progress trajectory track with And the method for work that track differentiates.
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, it is unable to carry out outdoor activity, so as to Medical Transport equipment and the Intelligent medical equipment of having arisen at the historic moment, example Such as power assisted wheelchair or electric wheelchair, and the product such as manual balance car, although finished product is market-oriented.But due to user It is slower to electronic equipment manipulation understanding, it is unable to carry out people's car mutual well, this automatic Pilot wheelchair that just arisen at the historic moment, But the problem that automatic Pilot wheelchair, which is exactly the route walked for user, can not plan judgement well, road is saved Efficiency reduction driving time is improved in footpath.
The content of the invention
It is contemplated that at least solving technical problem present in prior art, especially innovatively propose a kind of using extra large Measure the method for work that action trail data carry out trajectory track and track differentiates.
In order to realize the above-mentioned purpose of the present invention, track is carried out using magnanimity action trail data the invention provides a kind of The method of work that tracking and track differentiate, comprises the following steps：
S1, high in the clouds data are pushed by sampling of data mode to user, will be obtained in travel track historical data Travel track time data, time prediction data and the air speed data of departure place and place of arrival, temperature record and drop Water data are extracted,
Travel track end point location information is obtained from user terminal, path judgement is carried out according to path cruise constraints；
By the travel track data transfer after screening to user terminal, the geographical position letter that user terminal is sent in real time is obtained Breath, judges travel track end point location information；
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.
S2, the corresponding air speed data of travel track after extraction, temperature record and precipitation data are subjected to convergence estimate User is fed back to afterwards.
The described method of work that trajectory track and track differentiation are carried out using magnanimity action trail data, it is preferred that The S1 includes：
S1-1, extract the time consumption value of each travel track
Wherein, EγFor the time intensity of travel track, η is parameter undetermined, when Γ (n) is n-th track in travel track Between trend Γ distribution, T (t) be travel track time consumption in geographical location information texture, t >=0；
S1-2, extract the predicted value of the time consumption of each travel track
Nj(t)=2 [Eγ(T(t)+T(t+1))-μpT (t)],
Wherein, μpFor geographical position accumu-late parameter, T (t+1) is travel track subsequent time period in geographical location information Time consumption texture,
S1-3, extract the wind speed judgment value of each travel track
Wherein,For wind speed shock response component,Component is adjusted for wind speed dynamic change,For the dry of wind speed change Disturb component.For the random disturbances component in wind speed dynamic change,For the timing node component of wind speed dynamic change,For the periodic component of t wind speed dynamic change,
S1-4, extract the judgment value of the temperature of each travel track
Wherein,For temperature independent sample average, I1And I (t)2(t) it is temperature independent sample,For I1And I (t)2 (t) the reference coefficient of temperature independent sample, IhighFor maximum temperature value sample, I is temperature history reference value in travel track；
S1-5, extract the judgment value of the precipitation of each travel track
Wherein, d1、d2、d3、d4And d5For the sample parameter of precipitation in travel track, σ is the interference coefficient of precipitation,For the Gaussian component in precipitation dynamic change.
The described method of work that trajectory track and track differentiation are carried out using magnanimity action trail data, it is preferred that The S2 includes：
The cost function that user is sampled to travel track data is,
Wherein, Ni(t) it is the time consumption value of each travel track, Nj(t) for each travel track time consumption it is pre- Measured value, Nk(t) it is the wind speed judgment value of each travel track, Nl(t) it is the temperature judgment value of each travel track, Nm(t) it is every The precipitation judgment value of individual travel track, M are track numbers whole in statistics travel track；P be whole travel track information to Amount.
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, user selects the model estimation of travel track, when estimating for history traveling Between, precipitation, the data such as wind speed and temperature Change determine travel track time, precipitation, wind speed as model data attribute And the overall functional operation of temperature Change attribute, 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 overview flow chart 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, sentenced the invention provides a kind of using magnanimity action trail data progress trajectory track and track Other method of work, comprises the following steps：
S1, high in the clouds data are pushed by sampling of data mode to user, will be obtained in travel track historical data Travel track time data, time prediction data and the air speed data of departure place and place of arrival, temperature record and drop Water data are extracted,
Travel track end point location information is obtained from user terminal, path judgement is carried out according to path cruise constraints；
By the travel track data transfer after screening to user terminal, the geographical position letter that user terminal is sent in real time is obtained Breath, judges travel track end point location information；
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.
S2, the corresponding air speed data of travel track after extraction, temperature record and precipitation data are subjected to convergence estimate User is fed back to afterwards.
The described method of work that trajectory track and track differentiation are carried out using magnanimity action trail data, it is preferred that The S1 includes：
S1-1, extract the time consumption value of each travel track
Wherein, EγFor the time intensity of travel track, η is parameter undetermined, when Γ (n) is n-th track in travel track Between trend Γ distribution, T (t) be travel track time consumption in geographical location information texture, t >=0；
S1-2, extract the predicted value of the time consumption of each travel track
Nj(t)=2 [Eγ(T(t)+T(t+1))-μpT (t)],
Wherein, μpFor geographical position accumu-late parameter, T (t+1) is travel track subsequent time period in geographical location information Time consumption texture,
S1-3, extract the wind speed judgment value of each travel track
Wherein,For wind speed shock response component,Component is adjusted for wind speed dynamic change,For the interference of wind speed change Component.For the random disturbances component in wind speed dynamic change,For the timing node component of wind speed dynamic change,For the periodic component of t wind speed dynamic change,
S1-4, extract the judgment value of the temperature of each travel track
Wherein,For temperature independent sample average, I1And I (t)2(t) it is temperature independent sample,For I1And I (t)2 (t) the reference coefficient of temperature independent sample, IhighFor maximum temperature value sample, I is temperature history reference value in travel track；
S1-5, extract the judgment value of the precipitation of each travel track
Wherein, d1、d2、d3、d4And d5For the sample parameter of precipitation in travel track, σ is the interference coefficient of precipitation,For the Gaussian component in precipitation dynamic change.
The described method of work that trajectory track and track differentiation are carried out using magnanimity action trail data, it is preferred that The S2 includes：
The cost function that user is sampled to travel track data is,
Wherein, Ni(t) it is the time consumption value of each travel track, Nj(t) for each travel track time consumption it is pre- Measured value, Nk(t) it is the wind speed judgment value of each travel track, Nl(t) it is the temperature judgment value of each travel track, Nm(t) it is every The precipitation judgment value of individual travel track, M are track numbers whole in statistics travel track；P be whole travel track information to Amount.
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, user selects the model estimation of travel track, when estimating for history traveling Between, precipitation, the data such as wind speed and temperature Change determine travel track time, precipitation, wind speed as model data attribute And the overall functional operation of temperature Change attribute, ensure that user is safe to use.
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 (3)

  1. A kind of 1. method of work that trajectory track and track differentiation are carried out using magnanimity action trail data, it is characterised in that Comprise the following steps：
    S1, high in the clouds data are pushed by sampling of data mode to user, the row that will be obtained in travel track historical data Enter trajectory time data, time prediction data and the air speed data of departure place and place of arrival, temperature record and precipitation Data are extracted,
    Travel track end point location information is obtained from user terminal, path judgement is carried out according to path cruise constraints；
    By the travel track data transfer after screening to user terminal, the geographical location information that user terminal is sent in real time is obtained. Judge travel track end point location information；
    Some trace informations for being obtained from travel track carry out path cruise constraint, and the constraint formulations are,
    <mrow> <mi>W</mi> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> <mo>-</mo> <mfrac> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>n</mi> <mi>g</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>w</mi> <mrow> <mi>s</mi> <mi>h</mi> <mi>o</mi> <mi>r</mi> <mi>t</mi> </mrow> </msub> <mo>)</mo> <mi>z</mi> </mrow> <msub> <mi>Z</mi> <mi>max</mi> </msub> </mfrac> <mo>)</mo> <mo>&amp;CenterDot;</mo> <mi>Q</mi> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> </mfrac> <mo>,</mo> <mi>&amp;tau;</mi> <mo>&gt;</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <mi>&amp;tau;</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
    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.
    S2, after the corresponding air speed data of travel track after extraction, temperature record and precipitation data progress convergence estimate Feed back to user.
  2. 2. the work side according to claim 1 that trajectory track and track differentiation are carried out using magnanimity action trail data Method, it is characterised in that the S1 includes：
    S1-1, extract the time consumption value of each travel track
    <mrow> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>E</mi> <mi>&amp;gamma;</mi> </msub> <mfrac> <mrow> <mn>4</mn> <mrow> <mo>(</mo> <msqrt> <mrow> <mi>&amp;eta;</mi> <mi>n</mi> </mrow> </msqrt> <mo>)</mo> </mrow> </mrow> <mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>+</mo> <msub> <mi>E</mi> <mi>&amp;gamma;</mi> </msub> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mrow> <mo>(</mo> <mi>&amp;eta;</mi> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <mi>T</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
    Wherein, EγFor the time intensity of travel track, η is parameter undetermined, and Γ (n) is that nth bar trajectory time becomes in travel track Gesture Γ distribution, T (t) be travel track time consumption in geographical location information texture, t >=0；
    S1-2, extract the predicted value of the time consumption of each travel track
    Nj(t)=2 [Eγ(T(t)+T(t+1))-μpT (t)],
    Wherein, μpFor geographical position accumu-late parameter, T (t+1) is the time of travel track subsequent time period in geographical location information The texture of consuming,
    S1-3, extract the wind speed judgment value of each travel track
    <mrow> <msub> <mi>N</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mn>1</mn> </msubsup> <mo>+</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mfrac> <mi>t</mi> <msubsup> <mi>C</mi> <mi>k</mi> <mn>3</mn> </msubsup> </mfrac> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msup> <mi>t</mi> <mn>2</mn> </msup> <msup> <mrow> <mo>(</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mn>3</mn> </msubsup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mfrac> </mrow> </msup> <mo>+</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mn>4</mn> </msubsup> <mo>&amp;CenterDot;</mo> <msub> <mi>E</mi> <mi>&amp;gamma;</mi> </msub> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mrow> <mo>(</mo> <msubsup> <mi>C</mi> <mi>k</mi> <mn>4</mn> </msubsup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
    Wherein,For wind speed shock response component,Component is adjusted for wind speed dynamic change,For the interference point of wind speed change Amount.For the random disturbances component in wind speed dynamic change,For the timing node component of wind speed dynamic change,For the periodic component of t wind speed dynamic change,
    S1-4, extract the judgment value of the temperature of each travel track
    <mrow> <msub> <mi>N</mi> <mi>l</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mover> <mi>I</mi> <mo>&amp;OverBar;</mo> </mover> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mrow> <msub> <mi>I</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>I</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>H</mi> <mrow> <msub> <mi>I</mi> <mn>1</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <msub> <mi>I</mi> <mrow> <mi>h</mi> <mi>i</mi> <mi>g</mi> <mi>h</mi> </mrow> </msub> </mfrac> </mrow> <mi>I</mi> </mfrac> <mo>,</mo> </mrow>
    Wherein,For temperature independent sample average, I1And I (t)2(t) it is temperature independent sample,For I1And I (t)2(t) gas The reference coefficient of warm independent sample, IhighFor maximum temperature value sample, I is temperature history reference value in travel track；
    S1-5, extract the judgment value of the precipitation of each travel track
    <mrow> <msub> <mi>N</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>d</mi> <mn>1</mn> </msub> <msup> <mi>e</mi> <mrow> <mo>&amp;lsqb;</mo> <mfrac> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> <msubsup> <mi>d</mi> <mn>3</mn> <mn>2</mn> </msubsup> </mfrac> <mo>&amp;rsqb;</mo> </mrow> </msup> <mo>+</mo> <msub> <mi>d</mi> <mn>4</mn> </msub> <mi>&amp;sigma;</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <msub> <mi>d</mi> <mn>5</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> </mrow>
    Wherein, d1、d2、d3、d4And d5For the sample parameter of precipitation in travel track, σ is the interference coefficient of precipitation,For the Gaussian component in precipitation dynamic change.
  3. 3. the work side according to claim 1 that trajectory track and track differentiation are carried out using magnanimity action trail data Method, it is characterised in that the S2 includes：
    The cost function that user is sampled to travel track data is,
    <mrow> <mi>F</mi> <mrow> <mo>(</mo> <mi>p</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>&amp;lsqb;</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msup> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <msub> <mi>N</mi> <mi>j</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <msub> <mi>N</mi> <mi>k</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <msub> <mi>N</mi> <mi>l</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <msub> <mi>N</mi> <mi>m</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mi>P</mi> <mo>&amp;rsqb;</mo> </mrow> <mn>2</mn> </msup> </mrow>
    Wherein, Ni(t) it is the time consumption value of each travel track, Nj(t) for each travel track time consumption prediction Value, Nk(t) it is the wind speed judgment value of each travel track.Nl(t) it is the temperature judgment value of each travel track, Nm(t) it is each The precipitation judgment value of travel track, M are track numbers whole in statistics travel track；P is whole travel track information vectors.
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CN108169824A (en) * 2018-02-12 2018-06-15 安徽千云度信息技术有限公司 Rainfall distribution forecasting system based on big data

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