CN107506875A - The model generating method of the path of intelligent medical traveling instrument is obtained based on historical data - Google Patents
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Abstract
The present invention proposes a kind of model generating method for the path that intelligent medical traveling instrument is obtained based on historical data, including：User behavior track demand information is obtained, the action trail information provided according to high in the clouds data, by corresponding model, determines optimal user behavior path.
Description
Technical Field
The invention relates to the field of big data intelligent driving control, in particular to a model generation method for acquiring a track route of an intelligent medical advancing instrument based on historical data.
Background
The aging of population is gradually remarkable, the quality of life and health condition of people need to be concerned and cared by society, and people with inconvenient actions also want to absorb some fresh air and interactively communicate with the society, but the people with inconvenient actions can not carry out outgoing activities, so that medical transportation equipment such as a power-assisted wheelchair or an electric wheelchair, a manual balance car and the like is produced at the end, although the finished products are already marketed. However, since the user has a slow understanding of the operation and the control of the electronic device, and cannot perform human-vehicle interaction well, the automatic driving wheelchair is produced at the right moment, but the problem of the automatic driving wheelchair is that the route where the user walks cannot be well planned and judged, the route is saved, or the efficiency is improved, and the driving time is shortened.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly provides a model generation method for acquiring a track route of an intelligent medical advancing instrument based on historical data.
In order to achieve the above object of the present invention, the present invention provides a model generation method for obtaining a trajectory route of an intelligent medical traveling apparatus based on historical data, comprising:
acquiring user behavior track demand information, and determining an optimal user behavior track route through a corresponding model according to the behavior track information provided by the cloud data.
S1, extracting the time consumption value of each travel track
Wherein E isγη is a time intensity of the advancing track, is an undetermined parameter, (n) is the distribution of the time trend of the nth track in the advancing track, T (t) is the texture of the advancing track in the time consumption of the geographical position information, and t is more than or equal to 0;
generating a time-consuming model
Wherein, αtThreshold value for time consumption, Ni(t +1) is the time elapsed value for the next period of travel trajectory,
s2, extracting a predicted value of time consumption of each travel track
Nj(t)＝2[Eγ(T(t)+T(t+1))-μp·T(t)]，
Wherein, mupAccumulating the parameters for the geographic position, T (T +1) is the texture of the travel trajectory's time consumption for the next time period in the geographic position information,
generating a time-consuming predictive model
Wherein, βtThreshold value for time consuming prediction, Nj(t +1) isThe time to travel the trajectory for the next time period consumes the predicted value,
s3, extracting wind speed judgment value of each travel track
Wherein,as a component of the wind speed impulse response,the component is adjusted for the dynamic variation of the wind speed,as a disturbing component of the variation of the wind speed,being a random disturbance component in the wind speed dynamics,being the time node component of the dynamic variation of the wind speed,for the periodic component of the wind speed dynamics at time t,
generating a predictive model of wind speed
Wherein, χtIs a threshold value of a wind speed determination value, Nk(t +1) is a wind speed judgment value of the traveling locus in the next period,
s4, extracting judgment value of air temperature of each travel track
Wherein,is the mean value of independent samples of air temperature, I1(t) and I2(t) is a sample of the independent air temperature,is I1(t) and I2(t) reference coefficient of air temperature independent sample, IhighThe sample is the highest value of the air temperature, and I is the historical reference value of the air temperature in the advancing track;
generating a predictive model of air temperature
Wherein,tis a threshold value of the air temperature judgment value, Nl(t +1) is the air temperature judgment value of the traveling locus in the next period,
s5, extracting the precipitation judgment value of each travel track
Wherein d is1、d2、d3、d4And d5Is a sample parameter of the precipitation in the travel track, sigma is an interference coefficient of the precipitation,as gaussian component in the precipitation dynamics,
generating precipitation prediction models
Wherein,tthreshold value for the precipitation determination value, NmAnd (t +1) is a precipitation amount judgment value of the travel track in the next period.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
by the method, model estimation of the travel track selected by the user is realized, and the total function operation of the travel track time, the precipitation, the wind speed and the air temperature change attribute is determined by taking data such as historical travel estimation time, precipitation, wind speed and air temperature change as model data attributes, so that the safe travel probability of the medical equipment on the complex road condition can be effectively improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a general schematic of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, the present invention provides a model generation method for obtaining a trajectory route of an intelligent medical traveling instrument based on historical data, comprising:
acquiring user behavior track demand information, and determining an optimal user behavior track route through a corresponding model according to the behavior track information provided by the cloud data.
S1, extracting the time consumption value of each travel track
Wherein E isγη is a time intensity of the advancing track, is an undetermined parameter, (n) is the distribution of the time trend of the nth track in the advancing track, T (t) is the texture of the advancing track in the time consumption of the geographical position information, and t is more than or equal to 0;
generating a time-consuming model
Wherein, αtThreshold value for time consumption, Ni(t +1) is the time elapsed value for the next period of travel trajectory,
s2, extracting a predicted value of time consumption of each travel track
Nj(t)＝2[Eγ(T(t)+T(t+1))-μp·T(t)]，
Wherein, mupAccumulating the parameters for the geographic position, T (T +1) is the texture of the travel trajectory's time consumption for the next time period in the geographic position information,
generating a time-consuming predictive model
Wherein, βtThreshold value for time consuming prediction, Nj(t +1) consumes a predicted value for the time of the next period travel trajectory,
s3, extracting wind speed judgment value of each travel track
Wherein,as a component of the wind speed impulse response,the component is adjusted for the dynamic variation of the wind speed,as a disturbing component of the variation of the wind speed,being a random disturbance component in the wind speed dynamics,being the time node component of the dynamic variation of the wind speed,for the periodic component of the wind speed dynamics at time t,
generating a predictive model of wind speed
Wherein, χtIs a threshold value of a wind speed determination value, Nk(t +1) is a wind speed judgment value of the traveling locus in the next period,
s4, extracting judgment value of air temperature of each travel track
Wherein,is the mean value of independent samples of air temperature, I1(t) and I2(t) is a sample of the independent air temperature,is I1(t) and I2(t) reference coefficient of air temperature independent sample, IhighThe sample is the highest value of the air temperature, and I is the historical reference value of the air temperature in the advancing track;
generating a predictive model of air temperature
Wherein,tis a threshold value of the air temperature judgment value, Nl(t +1) is the air temperature judgment value of the traveling locus in the next period,
s5, extracting the precipitation judgment value of each travel track
Wherein d is1、d2、d3、d4And d5Is a sample parameter of the precipitation in the travel track, sigma is an interference coefficient of the precipitation,as gaussian component in the precipitation dynamics,
generating precipitation prediction models
Wherein,tthreshold value for the precipitation determination value, NmAnd (t +1) is a precipitation amount judgment value of the travel track in the next period.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (1)
1. A model generation method for obtaining a trajectory route of an intelligent medical advancing instrument based on historical data is characterized by comprising the following steps:
acquiring user behavior track demand information, and determining an optimal user behavior track route through a corresponding model according to the behavior track information provided by the cloud data.
S1, extracting 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>&gamma;</mi> </msub> <mfrac> <mrow> <mn>4</mn> <mrow> <mo>(</mo> <msqrt> <mrow> <mi>&eta;</mi> <mi>n</mi> </mrow> </msqrt> <mo>)</mo> </mrow> </mrow> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>+</mo> <msub> <mi>E</mi> <mi>&gamma;</mi> </msub> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mrow> <mo>(</mo> <mi>&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 E isγη is a time intensity of the advancing track, is an undetermined parameter, (n) is the distribution of the time trend of the nth track in the advancing track, T (t) is the texture of the advancing track in the time consumption of the geographical position information, and t is more than or equal to 0;
generating a time-consuming model
<mrow> <mi>A</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&alpha;</mi> <mi>t</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <msub> <mi>&alpha;</mi> <mi>t</mi> </msub> </mfrac> <mo>,</mo> </mrow>
Wherein, αtThreshold value for time consumption, Ni(t +1) is the time elapsed value for the next period of travel trajectory,
s2, extracting a predicted value of time consumption of each travel track
Nj(t)＝2[Eγ(T(t)+T(t+1))-μp·T(t)]，
Wherein, mupAccumulating the parameters for the geographic position, T (T +1) is the texture of the travel trajectory's time consumption for the next time period in the geographic position information,
generating a time-consuming predictive model
<mrow> <mi>B</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>N</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&beta;</mi> <mi>t</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>N</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <msub> <mi>&beta;</mi> <mi>t</mi> </msub> </mfrac> <mo>,</mo> </mrow>
Wherein, βtThreshold value for time consuming prediction, Nj(t +1) consumes a predicted value for the time of the next period travel trajectory,
s3, extracting 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>&CenterDot;</mo> <msub> <mi>E</mi> <mi>&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,as a component of the wind speed impulse response,the component is adjusted for the dynamic variation of the wind speed,as a disturbing component of the variation of the wind speed,being a random disturbance component in the wind speed dynamics,being the time node component of the dynamic variation of the wind speed,for the periodic component of the wind speed dynamics at time t,
generating a predictive model of wind speed
<mrow> <mi>C</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>N</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&chi;</mi> <mi>t</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>N</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> <msub> <mi>&chi;</mi> <mi>t</mi> </msub> </mfrac> <mo>,</mo> </mrow>
Wherein, χtIs a threshold value of a wind speed determination value, Nk(t +1) is a wind speed judgment value of the traveling locus in the next period,
s4, extracting judgment value of air 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>&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,is the mean value of independent samples of air temperature, I1(t) and I2(t) is a sample of the independent air temperature,is I1(t) and I2(t) reference coefficient of air temperature independent sample, IhighThe sample is the highest value of the air temperature, and I is the historical reference value of the air temperature in the advancing track;
generating a predictive model of air temperature
<mrow> <mi>D</mi> <mrow> <mo>(</mo> <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,tis a threshold value of the air temperature judgment value, Nl(t +1) is the air temperature judgment value of the traveling locus in the next period,
s5, extracting the precipitation judgment value 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>&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>&rsqb;</mo> </mrow> </msup> <mo>+</mo> <msub> <mi>d</mi> <mn>4</mn> </msub> <mi>&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 d is1、d2、d3、d4And d5Is a sample parameter of the precipitation in the travel track, sigma is an interference coefficient of the precipitation,as gaussian component in the precipitation dynamics,
generating precipitation prediction models
<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,tthreshold value for the precipitation determination value, NmAnd (t +1) is a precipitation amount judgment value of the travel track in the next period.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 CN201710884474.0A patent/CN107506875A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
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Application publication date: 20171222 |