CN111160610A - Intelligent security inspection method based on big data - Google Patents
Intelligent security inspection method based on big data Download PDFInfo
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- CN111160610A CN111160610A CN201911204655.XA CN201911204655A CN111160610A CN 111160610 A CN111160610 A CN 111160610A CN 201911204655 A CN201911204655 A CN 201911204655A CN 111160610 A CN111160610 A CN 111160610A
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Abstract
An intelligent security inspection method based on big data comprises the following steps: before taking the airplane, acquiring information of taking the airplane and airport information, wherein the information of taking the airplane comprises identity information and information of taking the airplane, and the airport information comprises traffic information around the airport and information of a security check station; step two: analyzing the information of the passenger, calculating a matching security inspection channel according to the arrival time of the passenger, the age of the passenger and other information, and feeding back a matching result; step three: after the safety inspection, the safety inspection is confirmed, and the database is reset with the robot information. By means of the method and the device, intelligent boarding is achieved through analysis of big data of boarding, different security inspection methods are configured, and the purpose of fast boarding is achieved.
Description
Technical Field
The invention belongs to the field of intelligent security inspection, and particularly relates to an intelligent security inspection method based on big data.
Background
With the rapid development of society, communication between cities is more frequent, and people hope to improve life quality and realize the demand as much as possible in the shortest time. For example, when passengers take planes, the passengers not only desire to use more self-service tools to complete the whole travel link from booking to luggage service, but also desire to provide more personalized information prompts to help manage the travel, such as check-in, security queuing time information, flight and gate information, destination traffic, weather, and the like.
Therefore, how to help passengers to pass through security inspection, the passenger congestion is effectively controlled, and the realization of intelligent security inspection allocation becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent security inspection method based on big data. In order to solve the above problems, the present invention provides the following technical solutions:
the method comprises the following steps: before taking the airplane, acquiring information of taking the airplane and airport information, wherein the information of taking the airplane comprises identity information and information of taking the airplane, and the airport information comprises traffic information around the airport and information of a security check station;
step two: analyzing the information of the passenger, calculating a matching security inspection channel according to the arrival time of the passenger, the age of the passenger and other information, and feeding back a matching result;
step three: after the safety inspection, the safety inspection is confirmed, and the database is reset with the robot information.
In conclusion, compared with the prior art, the technical scheme of the invention has obvious advantages and beneficial effects. By the technical scheme, the time required by the passenger to arrive at the airport is evaluated, the queuing condition of the security check station is obtained, the passenger can obtain personalized information prompt in the process of going to the airport and even getting on duty, meanwhile, the airport can rapidly allocate passenger security check rules according to the personal information and the actual condition of the passenger, the intelligent security check on duty is realized, the airport operation efficiency is improved, and the airport service quality is improved.
The above description will be described in detail by embodiments, and further explanation will be provided for the technical solution of the present invention.
Drawings
FIG. 1 is a flowchart of an implementation of an intelligent security inspection method based on big data according to an embodiment of the present invention
Detailed Description
The present invention will be explained below by way of examples. However, the embodiments of the present invention are not intended to limit the present invention to any environment, application, or manner in which the embodiments are implemented. Therefore, the description of the embodiments is for the purpose of illustration only, and is not intended to be limiting. It should be noted that, in the following embodiments and drawings, elements not directly related to the present invention are omitted and not shown.
The method comprises the following steps: before taking the airplane, acquiring information of taking the airplane and airport information, wherein the information of taking the airplane comprises identity information and information of taking the airplane, and the airport information comprises traffic information around the airport and information of a security check station;
furthermore, according to the relevant information of the passenger obtained when the passenger buys the air ticket, the personal information of the passenger and the flight travel time are collected. Further, the acquisition of the personal information comprises the acquisition of the identification card number, the date of birth, the sex, the validity period of the identification card, the face image, the home address, the mobile phone number and the flight information of the passenger who purchases the flight on the same day. The identity information is derived from an identity card information database of the public security department of the cloud data center.
Furthermore, the time of the passenger arriving at the airport is predicted according to the flight travel time and the real-time airport information. The airport information includes airport surrounding traffic information.
Further, the information of the security check desk comprises the queuing condition of passengers in the security check process. The passengers in the queuing area are tracked and identified through a sensor technology, so that the length and the throughput of a specific queuing area are measured, monitored data are processed by using a queuing model, waiting time required by security check queuing is predicted, and a security check channel and real-time waiting time can be provided for the passengers in the form of a mobile phone application program or a large airport screen.
Further, the traffic information around the airport can be acquired through a traffic network.
Step two: analyzing the information of the passenger, calculating a matching security inspection channel according to the arrival time of the passenger, the age of the passenger and other information, and feeding back a matching result;
reading the MAC address of the mobile equipment held by the passenger after the passenger arrives at the airport; the MAC address reading comprises the steps that the WIFI equipment acquires the held mobile equipment in a passive scanning or active scanning mode to establish association, and when the mobile equipment and the WIFI equipment perform a handshake protocol, the WIFI equipment is associated with the MAC address of the mobile equipment. And acquiring the number of the mobile equipment in an operator interface through the MAC address, and storing the number into a temporary statistical table. And storing the information in the temporary statistical table based on the online time of the MAC address. And acquiring the relevant information of the passenger in the step one according to the acquired mobile equipment number.
And distributing a security inspection channel for the passenger according to the flight information, the gender and the age of the passenger and the risk information of the passenger.
The passenger risk information includes: if the detected object has records of terrorist attacks or suspicion of terrorist attacks or participates in banned organizations, the risk level is a dark red level; if other crime records exist but no terrorist attack suspicion exists, or records or suspicions carrying prohibited articles exist in the past, the grade is red; if no criminal record exists, no relevant recording personnel or passengers taking the airplane for the first time belong to a yellow grade; if the passenger belongs to high-reliability and high-reliability occupation and the past security inspection history is normal, the passenger is in a green grade. For known passengers, different security inspection channels can be selected and matched for security inspection according to the security inspection scoring model during security inspection, so that the manpower resources of the airline company are saved, and finally, the inspection result is transmitted back to the database for storage for next classification.
The security inspection scoring model is as follows:
wherein C is the safety inspection score of the passenger, r is the passenger risk information grade score, w is the passenger risk proportion coefficient, α is the safety severity evaluation value of the endangered airport, and gamma1Is a human age offset factor, gamma2For the passenger to distinguish the offset factor, gamma3Is a flight time offset factor for an airplane, wherein r x W x α is a risk scoreThe part of the steel wire is broken off,
when the value of the risk assessment part is higher for the passenger individual demand part, judging that the risk coefficient of the passenger is high; and when the value of the risk assessment part is positioned at the normal level value, determining the channel matched with the passenger according to the personalized demand part of the passenger. Wherein, γ1、γ2Offset factors corresponding to the age and gender of the passenger, such as gamma when the passenger is older and the gender is female1、γ2Numerical value<0.4; when the time for the passenger to arrive at the airport and the time for the passenger to take off are less than 45 minutes, gamma is obtained3<0.3. Therefore, the security check score is comprehensively determined according to the numerical values of the two parts.
Further, when a passenger reaches the vicinity of an airport, the position of the passenger is obtained, flight information to be taken by the passenger is obtained, a boarding gate and a corresponding security inspection gate are obtained through the flight information, a path between the position of the passenger and the corresponding security inspection gate is used as a traveling route, the traveling route comprises the characteristics of traffic conditions around the current airport, pedestrian flow congestion conditions in the airport and the like, and the travel time required by the passenger to pass through the traveling route is calculated according to the traveling route and the historical traveling route. Wherein the historical travel route comprises: the travel route comprises a plurality of different historical travel paths, travel route characteristics corresponding to each historical travel path, and actual travel time of each historical travel path. Taking the current travel characteristic as the input of a path time calculation model, and calculating the predicted travel time of the target travel path by adopting the path time calculation model; the method comprises the steps of establishing a training sample set according to historical traveling data, and training the training samples by adopting a machine learning algorithm to obtain a path time calculation model. After the predicted travel time is obtained, a flight time offset factor gamma is determined according to the travel time matching3Setting Y, if the time taken for a passenger to travel the path is relatively long, resulting in a relatively short departure time from the aircraft3Less than a predetermined value.
The riding risk information level score may be a predetermined score, for example, a dark red level of 100 points and a red level of 80 points. It can also be calculated by r δ 1 × X1+ δ 2 × X2+ … + δ n × Xn, where δ i is a weighting coefficient and Xi is the score of the i-th type of illegal criminal record or bad act.
Examples of illegal criminal records are shown in the following table:
and prompting the user to go to a specific security check channel according to the score obtained by the security check scoring model and in combination with the information of the security check platform in the step one so as to accelerate the passing speed.
The prompt can be performed in a mobile terminal APP mode.
The security inspection channels can comprise normal channels, risk channels, VIP channels and special channels; the normal passage is used for the simple scanning and inspection of the whole body by passing the luggage through a security inspection machine and passing an individual through a security inspection door; the risk channel is a channel with a large risk value for suspicious personnel, such as distrusted personnel and president personnel; the VIP channel is a passage channel provided for soldiers, government workers, foreign guests and the like; the special passage is a passage for people with special conditions, such as old, weak, sick and disabled people, pregnant women, passengers carrying children and the like.
And determining the security check channel matched with the passenger according to the matching result.
Step three: after the safety inspection, the safety inspection is confirmed, and the database is reset with the robot information.
After the passenger takes the airplane for security check, the database records the passenger taking information and the security check information of the passenger taking the airplane.
By the method and the system, passengers can know the time consumed by the process treatment at the airport in advance, and can automatically judge the arrival time at the airport or avoid the airport check-in and security peak periods, so that seamless butt joint at the airport is realized, and the pressure of the passengers is relieved. The airport can set a queuing early warning threshold value according to the queuing condition, and when the real-time queuing length or the waiting time exceeds the early warning threshold value, managers of the airport need to make timely response measures, such as opening another queuing channel, allocating personnel to shorten the queuing time, and improving the operation efficiency and the service quality of the airport.
The above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. An intelligent security inspection method based on big data comprises the following steps: before taking the airplane, acquiring information of taking the airplane and airport information, wherein the information of taking the airplane comprises identity information and information of taking the airplane, and the airport information comprises traffic information around the airport and information of a security check station; step two: analyzing the information of the passenger, calculating a matching security inspection channel according to the time of arrival at the airport and the age information of the passenger, and feeding back a matching result; step three: after the safety inspection, the safety inspection is confirmed, and the database is reset with the robot information.
2. The method of claim 1, wherein the passenger information comprises obtaining an identification number, a date of birth, a sex, an expiration date, a facial image, a home address, a cell phone number, and flight information of a passenger purchasing an airline flight.
3. The method of claim 2, predicting the arrival time of the passenger at the airport based on flight travel time and real-time airport information.
4. The method of claim 1, wherein the information of the security check-in desk comprises the queuing condition of passengers in the security check-in process, the passengers in the queuing area are tracked and identified by sensor technology to measure the length and the throughput of a specific queuing queue, and the monitored data is processed by using a queuing model to predict the waiting time of the security check-in queue.
5. The method according to claim 1, wherein in the second step, a matching security inspection channel is selected according to a security inspection scoring model during security inspection, and the security inspection scoring model is as follows:
6. The method of claim 5, wherein r is calculated by the formula r δ 1X 1+ δ 2X 2+ … + δ n Xn, where δ i is a weighting factor and Xi is a score of the ith type of criminal record or offense.
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| CN201911204655.XA CN111160610A (en) | 2019-11-29 | 2019-11-29 | Intelligent security inspection method based on big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230186216A1 (en) * | 2021-05-25 | 2023-06-15 | The Government of the United States of America, as represented by the Secretary of Homeland Security | Anonymous screening with chain of custody sensor information |
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| CN103745519A (en) * | 2014-01-03 | 2014-04-23 | 深圳市劲凯科技有限公司 | Intelligent self-service method of airport and system |
| CN107958435A (en) * | 2016-10-17 | 2018-04-24 | 同方威视技术股份有限公司 | Safe examination system and the method for configuring rays safety detection apparatus |
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- 2019-11-29 CN CN201911204655.XA patent/CN111160610A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103745519A (en) * | 2014-01-03 | 2014-04-23 | 深圳市劲凯科技有限公司 | Intelligent self-service method of airport and system |
| CN107958435A (en) * | 2016-10-17 | 2018-04-24 | 同方威视技术股份有限公司 | Safe examination system and the method for configuring rays safety detection apparatus |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230186216A1 (en) * | 2021-05-25 | 2023-06-15 | The Government of the United States of America, as represented by the Secretary of Homeland Security | Anonymous screening with chain of custody sensor information |
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Application publication date: 20200515 |