CN109447337A - Smart cloud conference data sharing exchange platform path optimization method - Google Patents
Smart cloud conference data sharing exchange platform path optimization method Download PDFInfo
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- CN109447337A CN109447337A CN201811233070.6A CN201811233070A CN109447337A CN 109447337 A CN109447337 A CN 109447337A CN 201811233070 A CN201811233070 A CN 201811233070A CN 109447337 A CN109447337 A CN 109447337A
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Abstract
The invention discloses a path optimization method for a smart cloud conference data sharing exchange platform, which comprises the following steps: s1, acquiring the identity information of the pedestrian and positioning the geographic position of the pedestrian in real time; s2, acquiring the crowding degree on the pedestrian movement path, and establishing a crowding degree model; s3, acquiring the vision definition on the pedestrian movement path, and establishing a vision definition model; and S4, extracting the optimal path of the pedestrian according to the models established in the steps S2 and S3. The invention can provide optimal walking path information for pedestrians, can effectively save the time of the pedestrians and improve the safety factor of the pedestrians.
Description
Technical field
The present invention relates to path optimization's technical fields, share switching plane road more particularly to a kind of smart cloud conferencing data
Diameter optimization method.
Background technique
Common path planning algorithm often successively plans trip according to the distance between present position and terminal
The next step motion path of people, does not account for environmental factor, such as thunderstorm weather, flow of the people etc., and path planning does not have the overall situation
Property.The guidance path being easy to appear in this way there is a problem of because congestion, the visual field are fuzzy, cause the guidance path quality cooked up compared with
It is low.
Summary of the invention
The present invention is directed at least solve the technical problems existing in the prior art, a kind of smart cloud is especially innovatively proposed
Conferencing data shares switching plane method for optimizing route.
In order to realize above-mentioned purpose of the invention, the invention discloses a kind of smart cloud conferencing datas to share switching plane road
Diameter optimization method, comprising the following steps:
S1 obtains the identity information of pedestrian, is positioned in real time to the geographical location of pedestrian；
S2 obtains the crowding on pedestrian movement path, establishes crowding model；
S3 obtains the degree of getting a clear view on pedestrian movement path, establishes degree of getting a clear view model；
S4 extracts the optimal path of pedestrian according to the model that step S2 and step S3 is established.
In the preferred embodiment of the present invention, crowding in step s 2 are as follows:
Wherein, Υt′It (s) is the closeness of the pedestrian sample s in moment t '；
Υt′(s ') is the closeness of the vehicle sample s ' in moment t '；
Us(t ') is pedestrian sample s used time on motion path when reaching preset geographical position；
Us′(t ') is vehicle sample s ' used time on motion path when reaching preset geographical position；
Ks,s′(t ') is pedestrian sample s and vehicle sample s ' crowding in moment t '；
snFor total sample number amount, i.e. the sum of vehicle sample s ' total quantity and pedestrian sample s total quantity；
η is sample factor parameter；That is the sum of vehicle sample s ' factor parameter and pedestrian sample s factor parameter；
Tt′For the temperature value in moment t '；
Crowding model in step s 2 are as follows:
Wherein, Ks,s′' (t ') is pedestrian sample s and vehicle sample s ' crowding, K in moment t 's,s′' (t '+1) is row
This s of proper manners and vehicle sample s ' crowding at+1 t ' of subsequent time；s′nFor vehicle sample s ' total quantity；snIt " is pedestrian sample s
Total quantity；ηs′For vehicle sample s ' factor parameter, ηsFor pedestrian sample s factor parameter；Sample crowding when K (t ') is moment t '
Model；η is sample factor parameter；snFor total sample number amount.
In the preferred embodiment of the present invention, degree of getting a clear view in step s3 are as follows:
Wherein, ΨθRainfall on the θ articles motion path when (t ') is moment t '；tθContinue for rainfall on θ paths
Duration；Tt′For the temperature value in moment t '；Tt′+1For the temperature value at -1 t ' of last moment；δ is rainfall undetermined multipliers；Uθ
(t ' -1) is the air quantity changing value at -1 t ' of last moment on θ paths；Uθ(t ') is in moment t ' on θ paths
Air quantity changing value；ΓθPath degree of getting a clear view when (t ') is moment t ' on the θ articles motion path；
Degree of getting a clear view model in step s3 are as follows:
Wherein, τ is the dust concentration factor；Motion path degree of getting a clear view model when Γ (t ') is moment t '；Γθ(t ') is
Path degree of getting a clear view when moment t ' on the θ articles motion path；ΓθThe θ articles movement road when (t ' -1) is -1 t ' of last moment
Path degree of getting a clear view on diameter.
In the preferred embodiment of the present invention, the refinement method of optimal path in step s 4 are as follows:
Wherein, Q is map panoramic information, at the time of t " is that pedestrian reaches preset geographical position, sample when K (t ') is moment t '
This crowding model；Motion path clarity model when Γ (t ') is moment t '；T is current time.
In the preferred embodiment of the present invention, step S1 the following steps are included:
It is preset with unique authentication account and authentication password corresponding with the authentication account in server end, judges visitor
Authentication account, authentication password and the identifying code of the input of family end and the authentication account and authentication password on server end and server are sent out
Whether the identifying code sent is consistent；
If client input authentication account, authentication password and identifying code on server end authentication account and authenticate it is close
The identifying code that code and server are sent is consistent, then logins successfully；
If the authentication account and the preset all authentication accounts of server end of client input are inconsistent, prompt to input
Authentication account be not present, re-enter authentication account, authentication password and identifying code；
If authentication password corresponding to preset authentication account is inconsistent on the authentication password and server of client input,
The authentication password mistake for then prompting input re-enters authentication account, authentication password and identifying code；
If the identifying code of client input and the identifying code that server end is sent are inconsistent, prompt the identifying code of input wrong
Accidentally, server end retransmits new identifying code, re-enters authentication account, authentication password and identifying code.
In the preferred embodiment of the present invention, identifying code is one of number, capitalization or lowercase or appoints
Meaning combination.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are: the present invention can mention for pedestrian
For most ideal walking path information, it can be effectively saved pedestrian's time, improve the safety coefficient of pedestrian.
Detailed description of the invention
Fig. 1 is flow diagram of the present invention.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not considered as limiting the invention.
The invention discloses a kind of smart cloud conferencing datas to share switching plane method for optimizing route, as shown in Figure 1, including
Following steps:
S1 obtains the identity information of pedestrian, is positioned in real time to the geographical location of pedestrian；
S2 obtains the crowding on pedestrian movement path, establishes crowding model；
S3 obtains the degree of getting a clear view on pedestrian movement path, establishes degree of getting a clear view model；
S4 extracts the optimal path of pedestrian according to the model that step S2 and step S3 is established.
In the preferred embodiment of the present invention, crowding in step s 2 are as follows:
Wherein, Υt′It (s) is the closeness of the pedestrian sample s in moment t '；
Υt′(s ') is the closeness of the vehicle sample s ' in moment t '；
Us(t ') is pedestrian sample s used time on motion path when reaching preset geographical position；
Us′(t ') is vehicle sample s ' used time on motion path when reaching preset geographical position；
Ks,s′(t ') is pedestrian sample s and vehicle sample s ' crowding in moment t '；
snFor total sample number amount, i.e. the sum of vehicle sample s ' total quantity and pedestrian sample s total quantity；
η is sample factor parameter；That is the sum of vehicle sample s ' factor parameter and pedestrian sample s factor parameter；
Tt′For the temperature value in moment t '；
Crowding model in step s 2 are as follows:
Wherein, Ks,s′' (t ') is pedestrian sample s and vehicle sample s ' crowding, K in moment t 's,s′' (t '+1) is row
This s of proper manners and vehicle sample s ' crowding at+1 t ' of subsequent time；s′nFor vehicle sample s ' total quantity；snIt " is pedestrian sample s
Total quantity；ηs′For vehicle sample s ' factor parameter, ηsFor pedestrian sample s factor parameter；Sample crowding when K (t ') is moment t '
Model；η is sample factor parameter；snFor total sample number amount.
In the preferred embodiment of the present invention, degree of getting a clear view in step s3 are as follows:
Wherein, ΨθRainfall on the θ articles motion path when (t ') is moment t '；tθContinue for rainfall on θ paths
Duration；Tt′For the temperature value in moment t '；Tt′+1For the temperature value at -1 t ' of last moment；δ is rainfall undetermined multipliers；Uθ
(t ' -1) is the air quantity changing value at -1 t ' of last moment on θ paths；Uθ(t ') is in moment t ' on θ paths
Air quantity changing value；ΓθPath degree of getting a clear view when (t ') is moment t ' on the θ articles motion path；
Degree of getting a clear view model in step s3 are as follows:
Wherein, τ is the dust concentration factor；Motion path degree of getting a clear view model when Γ (t ') is moment t '；Γθ(t ') is
Path degree of getting a clear view when moment t ' on the θ articles motion path；ΓθThe θ articles movement road when (t ' -1) is -1 t ' of last moment
Path degree of getting a clear view on diameter.
In the preferred embodiment of the present invention, the refinement method of optimal path in step s 4 are as follows:
Wherein, Q is map panoramic information, at the time of t " is that pedestrian reaches preset geographical position, sample when K (t ') is moment t '
This crowding model；Motion path clarity model when Γ (t ') is moment t '；T is current time.
In the preferred embodiment of the present invention, step S1 the following steps are included:
It is preset with unique authentication account and authentication password corresponding with the authentication account in server end, judges visitor
Authentication account, authentication password and the identifying code of the input of family end and the authentication account and authentication password on server end and server are sent out
Whether the identifying code sent is consistent；
If client input authentication account, authentication password and identifying code on server end authentication account and authenticate it is close
The identifying code that code and server are sent is consistent, then logins successfully；
If the authentication account and the preset all authentication accounts of server end of client input are inconsistent, prompt to input
Authentication account be not present, re-enter authentication account, authentication password and identifying code；
If authentication password corresponding to preset authentication account is inconsistent on the authentication password and server of client input,
The authentication password mistake for then prompting input re-enters authentication account, authentication password and identifying code；
If the identifying code of client input and the identifying code that server end is sent are inconsistent, prompt the identifying code of input wrong
Accidentally, server end retransmits new identifying code, re-enters authentication account, authentication password and identifying code.
In the preferred embodiment of the present invention, identifying code is one of number, capitalization or lowercase or appoints
Meaning combination.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that: not
A variety of change, modification, replacement and modification can be carried out to these embodiments in the case where being detached from the principle of the present invention and objective, this
The range of invention is defined by the claims and their equivalents.
Claims (6)
1. a kind of smart cloud conferencing data shares switching plane method for optimizing route, which comprises the following steps:
S1 obtains the identity information of pedestrian, is positioned in real time to the geographical location of pedestrian；
S2 obtains the crowding on pedestrian movement path, establishes crowding model；
S3 obtains the degree of getting a clear view on pedestrian movement path, establishes degree of getting a clear view model；
S4 extracts the optimal path of pedestrian according to the model that step S2 and step S3 is established.
2. smart cloud conferencing data according to claim 1 shares switching plane method for optimizing route, which is characterized in that
Crowding in step S2 are as follows:
Wherein, Υt′It (s) is the closeness of the pedestrian sample s in moment t '；
Υt′(s ') is the closeness of the vehicle sample s ' in moment t '；
Us(t ') is pedestrian sample s used time on motion path when reaching preset geographical position；
Us′(t ') is vehicle sample s ' used time on motion path when reaching preset geographical position；
Ks,s′(t ') is pedestrian sample s and vehicle sample s ' crowding in moment t '；
snFor total sample number amount, i.e. the sum of vehicle sample s ' total quantity and pedestrian sample s total quantity；
η is sample factor parameter；That is the sum of vehicle sample s ' factor parameter and pedestrian sample s factor parameter；
Tt′For the temperature value in moment t '；
Crowding model in step s 2 are as follows:
Wherein, Ks,s′' (t ') is pedestrian sample s and vehicle sample s ' crowding, K in moment t 's,s′' (t '+1) is pedestrian's sample
This s and vehicle sample s ' crowding at+1 t ' of subsequent time；sn' it is vehicle sample s ' total quantity；sn" for pedestrian sample s sum
Amount；ηs′For vehicle sample s ' factor parameter, ηsFor pedestrian sample s factor parameter；Sample crowding mould when K (t ') is moment t '
Type；η is sample factor parameter；snFor total sample number amount.
3. smart cloud conferencing data according to claim 1 shares switching plane method for optimizing route, which is characterized in that
Degree of getting a clear view in step S3 are as follows:
Wherein, ΨθRainfall on the θ articles motion path when (t ') is moment t '；tθFor rainfall duration on θ paths；
Tt′For the temperature value in moment t '；Tt′+1For the temperature value at -1 t ' of last moment；δ is rainfall undetermined multipliers；Uθ(t′-
It 1) is the air quantity changing value at -1 t ' of last moment on θ paths；Uθ(t ') is the wind in moment t ' on θ paths
Measure changing value；ΓθPath degree of getting a clear view when (t ') is moment t ' on the θ articles motion path；
Degree of getting a clear view model in step s3 are as follows:
Wherein, τ is the dust concentration factor；Motion path degree of getting a clear view model when Γ (t ') is moment t '；Γθ(t ') is the moment
Path degree of getting a clear view when t ' on the θ articles motion path；ΓθWhen (t ' -1) is -1 t ' of last moment on the θ articles motion path
Path degree of getting a clear view.
4. smart cloud conferencing data according to claim 1 shares switching plane method for optimizing route, which is characterized in that
The refinement method of optimal path in step S4 are as follows:
Wherein, Q is map panoramic information, and at the time of t " is that pedestrian reaches preset geographical position, sample is gathered around when K (t ') is moment t '
Squeeze degree model；Motion path clarity model when Γ (t ') is moment t '；T is current time.
5. smart cloud conferencing data according to claim 1 shares switching plane method for optimizing route, which is characterized in that step
Rapid S1 the following steps are included:
It is preset with unique authentication account and authentication password corresponding with the authentication account in server end, judges client
What authentication account, authentication password and the identifying code of input and the authentication account and authentication password on server end and server were sent
Whether identifying code is consistent；
If authentication account and authentication password on authentication account, authentication password and identifying code and the server end of client input and
The identifying code that server is sent is consistent, then logins successfully；
If the authentication account and the preset all authentication accounts of server end of client input are inconsistent, the mirror of input is prompted
Power account is not present, and re-enters authentication account, authentication password and identifying code；
If authentication password corresponding to preset authentication account is inconsistent on the authentication password and server of client input, mention
The authentication password mistake for showing input re-enters authentication account, authentication password and identifying code；
If the identifying code of client input and the identifying code that server end is sent are inconsistent, the identifying code mistake of input is prompted,
Server end retransmits new identifying code, re-enters authentication account, authentication password and identifying code.
6. smart cloud conferencing data according to claim 5 shares switching plane method for optimizing route, which is characterized in that test
Demonstrate,proving code is one of number, capitalization or lowercase or any combination.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070208492A1 (en) * | 2006-03-03 | 2007-09-06 | Inrix, Inc. | Dynamic time series prediction of future traffic conditions |
| CN102509470A (en) * | 2011-10-14 | 2012-06-20 | 北京掌城科技有限公司 | System and method for realizing energy conservation and emission reduction of vehicle based on dynamic path planning |
| CN104571113A (en) * | 2015-01-20 | 2015-04-29 | 博康智能网络科技股份有限公司 | Route planning method for mobile robot |
| CN105242669A (en) * | 2015-10-20 | 2016-01-13 | 安徽工程大学 | Mobile robot path planning method based on weighted average distance vision fish school algorithm |
| CN105425795A (en) * | 2015-11-26 | 2016-03-23 | 纳恩博（北京）科技有限公司 | Method for planning optimal following path and apparatus |
| CN105469201A (en) * | 2015-07-20 | 2016-04-06 | 浙江工业大学 | Method for logistics dispensing center work task processing and scheduling |
| CN205177328U (en) * | 2015-12-16 | 2016-04-20 | 重庆扬升信息技术有限公司 | LED large screen intelligent control display device |
| CN106203830A (en) * | 2016-07-12 | 2016-12-07 | 国网江西省电力公司南昌供电分公司 | Promote Distribution Network Failure response and the electric service system of repairing ability |
| KR101771347B1 (en) * | 2016-02-29 | 2017-08-24 | 현대오토에버 주식회사 | Tollgate base station for providing tollgate congestion information and vehicle for guiding tollgate optimal path |
| CN107657345A (en) * | 2017-09-28 | 2018-02-02 | 北京交通大学 | A kind of pedestrian's walking behavior prediction method based on Markovian state's saltus step |
-
2018
- 2018-10-23 CN CN201811233070.6A patent/CN109447337B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070208492A1 (en) * | 2006-03-03 | 2007-09-06 | Inrix, Inc. | Dynamic time series prediction of future traffic conditions |
| CN102509470A (en) * | 2011-10-14 | 2012-06-20 | 北京掌城科技有限公司 | System and method for realizing energy conservation and emission reduction of vehicle based on dynamic path planning |
| CN104571113A (en) * | 2015-01-20 | 2015-04-29 | 博康智能网络科技股份有限公司 | Route planning method for mobile robot |
| CN105469201A (en) * | 2015-07-20 | 2016-04-06 | 浙江工业大学 | Method for logistics dispensing center work task processing and scheduling |
| CN105242669A (en) * | 2015-10-20 | 2016-01-13 | 安徽工程大学 | Mobile robot path planning method based on weighted average distance vision fish school algorithm |
| CN105425795A (en) * | 2015-11-26 | 2016-03-23 | 纳恩博（北京）科技有限公司 | Method for planning optimal following path and apparatus |
| CN205177328U (en) * | 2015-12-16 | 2016-04-20 | 重庆扬升信息技术有限公司 | LED large screen intelligent control display device |
| KR101771347B1 (en) * | 2016-02-29 | 2017-08-24 | 현대오토에버 주식회사 | Tollgate base station for providing tollgate congestion information and vehicle for guiding tollgate optimal path |
| CN106203830A (en) * | 2016-07-12 | 2016-12-07 | 国网江西省电力公司南昌供电分公司 | Promote Distribution Network Failure response and the electric service system of repairing ability |
| CN107657345A (en) * | 2017-09-28 | 2018-02-02 | 北京交通大学 | A kind of pedestrian's walking behavior prediction method based on Markovian state's saltus step |
Non-Patent Citations (5)
| Title |
|---|
| EVANGELOS BAMPAS 等: "On a Noncooperative Model for Wavelength Assignment in Multifiber Optical Networks", 《 IEEE/ACM TRANSACTIONS ON NETWORKING》 * |
| PIAZZI, R. 等: "Destinations and the web: a network analysis view", 《INFORMATION TECHNOLOGY AND TOURISM》 * |
| WANG, KAI 等: "Optimal routing strategy based on the minimum information path", 《PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS》 * |
| 周利坤 等: "自适应人工鱼群算法在清罐移动机器人路径规划中的应用", 《机械科学与技术》 * |
| 徐斌 等: "融合社会力与人工蜂群的人群疏散仿真方法", 《小型微型计算机系统》 * |
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