CN107631731A - Intelligent medical equipment safety driving path plans extracting method - Google Patents
Intelligent medical equipment safety driving path plans extracting method Download PDFInfo
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- CN107631731A CN107631731A CN201710885277.0A CN201710885277A CN107631731A CN 107631731 A CN107631731 A CN 107631731A CN 201710885277 A CN201710885277 A CN 201710885277A CN 107631731 A CN107631731 A CN 107631731A
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Abstract
The present invention proposes a kind of Intelligent medical equipment safety driving path planning extracting method, comprise the following steps by sampling of data mode to be pushed high in the clouds data 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, feed back to user.
Description
Technical Field
The invention relates to the field of intelligent driving, in particular to a method for planning and extracting a safe driving path of intelligent medical equipment.
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, 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 the outgoing activities due to the inconvenient actions, so that medical transportation equipment and intelligent medical equipment, such as power-assisted wheelchairs or electric wheelchairs, hand-controlled balance cars and other products are produced, although the finished products are already on the market. However, since the user has a slow understanding of the operation and the control of the electronic device, and the human-vehicle interaction cannot be well performed, the automatic driving wheelchair is produced at the right moment, but the problem of the automatic driving wheelchair is that the walking route of the user cannot be well refined, so that 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 creatively provides an intelligent medical equipment safe driving path planning and extracting method.
In order to achieve the above object, the present invention provides an intelligent medical device safe driving path planning and extracting method, which includes the following steps:
pushing the cloud data to a user in a data sampling mode, extracting travel track time data and time prediction data acquired from travel track historical data, and wind speed data, air temperature data and precipitation data of a departure place and an arrival place,
s1, extracting the time consumption value of each travel track
Wherein E isγthe time intensity of the advancing track is shown, eta is an undetermined parameter, gamma (n) is gamma distribution of the time trend of the nth track in the advancing track, T (T) is texture of the advancing track in the time consumption of the geographical position information, and T is more than or equal to 0;
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,
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,
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, IhighAs a sample of the maximum air temperature, IThe historical reference value of the air temperature in the advancing track is obtained;
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,is a Gaussian component in the dynamic change of the precipitation;
after the travel track historical data is extracted, more excellent travel track information can be extracted from massive data.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
by the method, the optimization judgment of the travel track selected by the user is realized, the data such as historical travel estimated time, precipitation, wind speed and air temperature change are used as judgment attributes, the total judgment information of the travel track time, precipitation, wind speed and air temperature change attributes is determined, and 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 flow diagram 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 invention provides an intelligent medical device safe driving path planning and extracting method, which comprises the following steps:
pushing the cloud data to a user in a data sampling mode, extracting travel track time data and time prediction data acquired from travel track historical data, and wind speed data, air temperature data and precipitation data of a departure place and an arrival place,
s1, extracting the time consumption value of each travel track
Wherein E isγthe time intensity of the advancing track is shown, eta is an undetermined parameter, gamma (n) is gamma distribution of the time trend of the nth track in the advancing track, T (T) is texture of the advancing track in the time consumption of the geographical position information, and T is more than or equal to 0;
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,
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,for dynamic wind speedThe random interference component in the variation,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,
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;
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,is a Gaussian component in the dynamic change of the precipitation;
after the travel track historical data is extracted, more excellent travel track information can be extracted from massive data.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
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. An intelligent medical equipment safe driving path planning and extracting method is characterized by comprising the following steps:
pushing the cloud data to a user in a data sampling mode, extracting travel track time data and time prediction data acquired from travel track historical data, and wind speed data, air temperature data and precipitation data of a departure place and an arrival place,
s1, extracting the time consumption value of each travel track
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Wherein E isγthe time intensity of the advancing track is shown, eta is an undetermined parameter, gamma (n) is gamma distribution of the time trend of the nth track in the advancing track, T (T) is texture of the advancing track in the time consumption of the geographical position information, and T is more than or equal to 0;
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,
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,for random disturbance in dynamic variation of wind speedThe amount of the compound (A) is,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,
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;
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,is a Gaussian component in the dynamic change of the precipitation;
after the travel track historical data is extracted, more excellent travel track information can be extracted from massive data.
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| US20110066303A1 (en) * | 2009-09-16 | 2011-03-17 | Hitachi, Ltd. | Determining system for localization methods combination |
| CN103838846A (en) * | 2014-03-06 | 2014-06-04 | 中国科学院软件研究所 | Emergency guiding method and emergency guiding system for individual on basis of big data |
| CN104200660A (en) * | 2014-08-29 | 2014-12-10 | 百度在线网络技术（北京）有限公司 | Method and device for updating road condition information |
| CN104931041A (en) * | 2015-05-03 | 2015-09-23 | 西北工业大学 | Method for predicting place sequence based on user track data |
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