CN111031487A - Method for real-time detection of tourist trip risk by using artificial intelligence - Google Patents

Method for real-time detection of tourist trip risk by using artificial intelligence Download PDF

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CN111031487A
CN111031487A CN201911299564.9A CN201911299564A CN111031487A CN 111031487 A CN111031487 A CN 111031487A CN 201911299564 A CN201911299564 A CN 201911299564A CN 111031487 A CN111031487 A CN 111031487A
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段吉民
梁善廷
孙建华
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Shandong Hengyun Information Technology Co ltd
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Abstract

The method for detecting the trip risk of the tourists in real time by using the artificial intelligence can quickly and effectively extract the behavior characteristics of a plurality of tourists by analyzing the behavior track of the tourists in the trip process, and predict the risk probability of the tourists in the trip process according to the behavior characteristics. The channel neural network calculates periodic behavior characteristics on a plurality of time dimensions, and combines positioning information to convert the information into risk probability. The high-precision real-time prediction model can be constructed for the data sequences of multiple tourists and different behavior tracks in a low training cost mode. Because the time dimension and the geographical positioning option are introduced into the risk assessment model as additional input, the time step length for training the neural network risk assessment model can be greatly shortened, the training cost can be effectively reduced, and the prediction precision is improved.

Description

Method for real-time detection of tourist trip risk by using artificial intelligence
Technical Field
The invention relates to the technical field of data security and protection of information technology, in particular to a method for detecting trip risks of tourists in real time by using artificial intelligence.
Background
At present, the turnover of the tourism industry is rapidly increased by 10% every year, and effective means for evaluating the trip risk of tourists is lacked while the tourism industry is greatly developed. The problems of unauthorized group separation, illegal tourism and the like are frequently prohibited during traveling. For tourists with potential high-risk behaviors, the method can discover and take preventive measures in time, and is the most effective means for reducing risks.
Generally, tourists, who are prone to high-risk activities, have the following activities:
1, higher voluntary action
2, often act separately from the bolus.
In the traditional mode, distinguishing, early warning prompting and the like are carried out by depending on the personal work experience of the tour guide, so as to control the generation of risks. The invention hopes to automatically give early warning and prompt to the guide and the tourist through the artificial intelligence technology and the analysis of the behavior track, thereby strengthening the safety consciousness and preventing the generation of risks.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a method for detecting the trip risk of the tourist in real time by using artificial intelligence, which utilizes the artificial intelligence neural network to combine geographical positioning and network communication to send early warning information to the possible risk behavior of the tourist.
The technical scheme adopted by the invention for overcoming the technical problems is as follows:
a method for real-time detection of tourist travel risks by using artificial intelligence comprises the following steps:
a) collecting coordinate positions of N tourists in a tourist group at fixed time intervals, uploading the obtained coordinate positions to a server, and obtaining a circular range capable of containing all the tourists according to the coordinate positions of all the tourists, wherein the center coordinate of the circular range is OiThe center of a circle coordinate OiAn uploading server, t is a timestamp when the circle center coordinates are reported to the server, i is more than or equal to 1 and less than or equal to t, wherein the circle center OiIs expressed as (x)middle,i＝t,ymiddle,i＝t) X is a longitude value and y is a latitude value, wherein:
Figure BDA0002318645770000021
xmax,i＝tmaximum in longitude, x, of the set of coordinates of all guests at time tmin,i＝tThe minimum value in longitude in the coordinate set of all the tourists;
Figure BDA0002318645770000022
ymax,i＝tis the maximum value in latitude, y, in the coordinate set of all the tourists at the moment tmin,i＝tThe minimum value in latitude in the coordinate set of all the tourists;
b) the coordinate series from the departure to the time t of the N tourists is
[(xm,j＝1ym,j＝1),(xm,j＝2ym,j＝2),...(xm,j＝tym,j＝t)]J is a timestamp serial number, j is more than or equal to 1 and less than or equal to t, m is any visitor, m is more than or equal to 1 and less than or equal to N, and the coordinate sequence of each visitor is uploaded to the server;
c) through formula ∪ O'i,m＝[O′1,m,O′2,m,...,O′,tm]Calculating the center coordinates o 'of a circular range which can contain the remaining visitors after the visitor m is removed'i,mCollecting m is more than or equal to 1 and less than or equal to N, and uploading the collection to a server;
d) the server follows the formula | Oi-O′i,mI calculating the coordinate of the center of a circle as OiAnd a circle center coordinate o 'of a circular range in which the guest m is removed and the remaining guests can be contained'i,mAbsolute value of difference, | Oi-O′i,mThe larger the value of | indicates the larger the probability that the tourist m leaves the group, when | Oi-O′i,mAnd after the value of | exceeds a set threshold value, the server sends the alarm information to the mobile phone for guiding.
Further, step d) is performed by the formula
Figure BDA0002318645770000031
Calculating Oi,mWherein α is the group bulk, 1 is equal to or more than α is equal to or less than 99, Oi,mA larger value of (a) indicates a higher possibility that the guest m alone gets out of the group,
Figure BDA0002318645770000032
the risk weight is taken according to the risk prompt of chapter three of the national travel administration No. 41 'travel safety management method', when the risk prompt is no risk,
Figure BDA0002318645770000033
when the risk cue is a four-level risk,
Figure BDA0002318645770000034
when the risk cue is a tertiary risk,
Figure BDA0002318645770000035
when the risk cue is a secondary risk,
Figure BDA0002318645770000036
when the risk cue is a first-degree risk,
Figure BDA0002318645770000037
the invention has the beneficial effects that: by analyzing the behavior track of the tourists in the trip process, the behavior characteristics of a plurality of tourists can be extracted quickly and effectively, and the risk probability of the tourists in the trip process is predicted according to the behavior characteristics. The channel neural network calculates periodic behavior characteristics in multiple time dimensions (such as year, month, week and day) and combines positioning information to convert the information into risk probability. The high-precision real-time prediction model can be constructed for the data sequences of multiple tourists and different behavior tracks in a low training cost mode. Because the method introduces the options of time dimension and geographical positioning into the risk assessment model as additional input, the time step length for training the neural network risk assessment model can be greatly shortened, the training cost can be effectively reduced, and the prediction precision is improved.
Detailed Description
The present invention is further explained below.
A method for real-time detection of tourist travel risks by using artificial intelligence comprises the following steps:
a) collecting coordinate positions of N tourists in a tourist group at fixed time intervals, uploading the obtained coordinate positions to a server, and obtaining a circular range capable of containing all the tourists according to the coordinate positions of all the tourists, wherein the center coordinate of the circular range is OiThe center of a circle coordinate OiAn uploading server, t is a timestamp when the circle center coordinates are reported to the server, i is more than or equal to 1 and less than or equal to t, wherein the circle center OiIs expressed as (x)middle,i＝t,ymiddle,i＝t) X is a longitude value, y is a latitude value, the latitude takes the equator as a boundary line, and north is a positive direction; the longitude is defined by the meridian of this initial meridian and the eastern direction is defined as the positive direction. The steps of installing the server environment are: a server ServerA is installed, which has an international IP address and can be accessed by other internet devices according to the IP address or domain name. And opening a specific service port to respond to a message request sent by the client or the mobile terminal. And then, service software is built on a server, identity authentication, line registration and geographic position verification can be provided, and a platform environment required by the operation of the artificial intelligent neural network system is built, wherein the platform environment comprises Ubuntu 18.0.464 bit operating system, Python3 and other software. Wherein:
Figure BDA0002318645770000041
xmax,i＝tmaximum in longitude, x, of the set of coordinates of all guests at time tmin,i＝tThe minimum value in longitude in the coordinate set of all the tourists;
Figure BDA0002318645770000042
ymax,i＝tis the maximum value in latitude, y, in the coordinate set of all the tourists at the moment tmin,i＝tThe minimum value in latitude in the coordinate set of all the tourists;
b) the coordinate series from the departure to the time t of the N tourists is
[(xm,j＝1ym,j＝1),(xm,j＝2ym,j＝2),...(xm,j＝tym,j＝t)]J is a timestamp serial number, j is more than or equal to 1 and less than or equal to t, m is any visitor, m is more than or equal to 1 and less than or equal to N, and the coordinate sequence of each visitor is uploaded to the server;
c) through formula ∪ O'i,m＝[O′1,m,O′2,m,...,O′,tm]Calculating the center coordinates o 'of a circular range which can contain the remaining visitors after the visitor m is removed'i,mCollecting m is more than or equal to 1 and less than or equal to N, and uploading the collection to a server;
d) the server follows the formula | Oi-O′i,mI calculating the coordinate of the center of a circle as OiAnd a circle center coordinate o 'of a circular range in which the guest m is removed and the remaining guests can be contained'i,mAbsolute value of difference, | Oi-O′i,mThe larger the value of | indicates that the more distant the tourist m is from other people in the tourist party, the greater the probability that the tourist is in danger of leaving the party alone, when | Oi-O′i,mAnd after the value of | exceeds a set threshold value, the server sends the alarm information to the mobile phone for guiding.
By analyzing the behavior track of the tourists in the trip process, the behavior characteristics of a plurality of tourists can be extracted quickly and effectively, and the risk probability of the tourists in the trip process is predicted according to the behavior characteristics. The channel neural network calculates periodic behavior characteristics in multiple time dimensions (such as year, month, week and day) and combines positioning information to convert the information into risk probability. The high-precision real-time prediction model can be constructed for the data sequences of multiple tourists and different behavior tracks in a low training cost mode. Because the method introduces the options of time dimension and geographical positioning into the risk assessment model as additional input, the time step length for training the neural network risk assessment model can be greatly shortened, the training cost can be effectively reduced, and the prediction precision is improved.
Further, step d) is performed by the formula
Figure BDA0002318645770000051
Calculating Oi,mα is the looseness of the team, 1 is equal to or less than α is equal to or less than 99, the larger the parameter α is set, the larger the radius of the travel group activity is allowed to be, the less early warning is generated, the smaller the setting is, the more compact the travel group is required to be, and O isi,mA larger value of (a) indicates a higher possibility that the guest m alone gets out of the group,
Figure BDA0002318645770000052
the risk weight is taken according to the risk prompt of chapter three of the national travel administration No. 41 'travel safety management method', when the risk prompt is no risk,
Figure BDA0002318645770000061
when the risk cue is a four-level risk,
Figure BDA0002318645770000062
when the risk cue is a tertiary risk,
Figure BDA0002318645770000063
when the risk cue is a secondary risk,
Figure BDA0002318645770000064
when the risk cue is a first-degree risk,
Figure BDA0002318645770000065
setting a risk weight according to risk prompts in a grading manner, referring to the position information of the visitor and obtaining an accurate output result value Oi,m，Oi,mA larger value indicates a higher probability of risk for the guest m to leave the group alone.

Claims (2)

1. A method for real-time detection of tourist travel risks by using artificial intelligence is characterized by comprising the following steps:
a) collecting coordinate positions of N tourists in a tourist group according to a fixed time interval, uploading the obtained coordinate positions to a server, obtaining a circular range capable of containing all the tourists according to the coordinate positions of all the tourists, and obtaining the coordinate of the center of a circle of the circular rangeIs OiThe center of a circle coordinate OiAn uploading server, t is a timestamp when the circle center coordinates are reported to the server, i is more than or equal to 1 and less than or equal to t, wherein the circle center OiIs expressed as (x)middle,i＝t,ymiddle,i＝t) X is a longitude value and y is a latitude value, wherein:
Figure FDA0002318645760000011
xmax,i＝tmaximum in longitude, x, of the set of coordinates of all guests at time tmin,i＝tThe minimum value in longitude in the coordinate set of all the tourists;
Figure FDA0002318645760000012
ymax,i＝tis the maximum value in latitude, y, in the coordinate set of all the tourists at the moment tmin,i＝tThe minimum value in latitude in the coordinate set of all the tourists;
b) the coordinate series from the departure to the time t of the N tourists is
[(xm,j＝1ym,j＝1),(xm,j＝2ym,j＝2),...(xm,j＝tym,j＝t)]J is a timestamp serial number, j is more than or equal to 1 and less than or equal to t, m is any visitor, m is more than or equal to 1 and less than or equal to N, and the coordinate sequence of each visitor is uploaded to the server;
c) by the formula
Figure FDA0002318645760000013
Calculating the center coordinates o 'of a circular range which can contain the remaining visitors after the visitor m is removed'i,mCollecting m is more than or equal to 1 and less than or equal to N, and uploading the collection to a server;
d) the server follows the formula | Oi-O’i，mI calculating the coordinate of the center of a circle as OiAnd a circle center coordinate o 'of a circular range in which the guest m is removed and the remaining guests can be contained'i,mAbsolute value of difference, | Oi-O’i,mThe larger the value of | indicates the larger the probability that the tourist m leaves the group, when | Oi-O’i,mOfAnd after the value exceeds the set threshold value, the server sends the alarm information to the mobile phone of the guide.
2. The method for real-time detection of visitor travel risk using artificial intelligence as claimed in claim 1, wherein: in step d) by the formula
Figure FDA0002318645760000021
Calculating Oi,mWherein α is the group bulk, 1 is equal to or more than α is equal to or less than 99, Oi,mA larger value of (a) indicates a higher possibility that the guest m alone gets out of the group,
Figure FDA0002318645760000022
the risk weight is taken according to the risk prompt of chapter three of the national travel administration No. 41 'travel safety management method', when the risk prompt is no risk,
Figure FDA0002318645760000023
when the risk cue is a four-level risk,
Figure FDA0002318645760000024
when the risk cue is a tertiary risk,
Figure FDA0002318645760000025
when the risk cue is a secondary risk,
Figure FDA0002318645760000026
when the risk cue is a first-degree risk,
Figure FDA0002318645760000027
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102421060A (en) * 2011-11-28 2012-04-18 苏州迈普信息技术有限公司 GIS (Geographic Information System)-based emergency condition management system for tour group
CN102522044A (en) * 2011-11-28 2012-06-27 常熟南师大发展研究院有限公司 Method for judging dispersion degree of team and application thereof in tour guide field
CN102521486A (en) * 2011-11-28 2012-06-27 苏州迈普信息技术有限公司 Method for automatically judging whether tourists fall behind
CN103813450A (en) * 2014-03-07 2014-05-21 山东大学 Optimized mobile wireless sensor network node positioning method
CN108966115A (en) * 2017-05-23 2018-12-07 陕西胜慧源信息科技有限公司 Using the travelling group team management system of GPS and GIS
KR20190074532A (en) * 2017-12-20 2019-06-28 경희대학교 산학협력단 Method and system for user's location context inference

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102421060A (en) * 2011-11-28 2012-04-18 苏州迈普信息技术有限公司 GIS (Geographic Information System)-based emergency condition management system for tour group
CN102522044A (en) * 2011-11-28 2012-06-27 常熟南师大发展研究院有限公司 Method for judging dispersion degree of team and application thereof in tour guide field
CN102521486A (en) * 2011-11-28 2012-06-27 苏州迈普信息技术有限公司 Method for automatically judging whether tourists fall behind
CN103813450A (en) * 2014-03-07 2014-05-21 山东大学 Optimized mobile wireless sensor network node positioning method
CN108966115A (en) * 2017-05-23 2018-12-07 陕西胜慧源信息科技有限公司 Using the travelling group team management system of GPS and GIS
KR20190074532A (en) * 2017-12-20 2019-06-28 경희대학교 산학협력단 Method and system for user's location context inference

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