CN111160696A - Big data based detected person grading method - Google Patents
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Abstract
A big data based method and a big data based device for grading checked personnel are provided, the method comprises the following steps: step one, collecting historical data and establishing a database; secondly, establishing a risk assessment model based on the historical data; thirdly, acquiring passenger identity behavior information, evaluating the danger degree of related personnel according to the evaluation model, and dividing the danger degree of the related personnel according to the evaluation result; and fourthly, carrying out corresponding detection on the related personnel according to the division result. The invention can rapidly and effectively carry out personnel safety inspection, and effectively improve the speed and the effect of grading detection by combining the existing safety inspection means.
Description
Technical Field
The invention relates to the field of big data information processing, in particular to a case method for personnel classification based on big data statistics.
Background
In recent years, global aviation industry has been rapidly developed, passenger flow is increased, global civil aviation safety situation is severe, emergency happens sometimes, and in order to ensure civil aviation safety, various national airports adopt a plurality of modes for security inspection, for example: the security inspector can verify the identity of the person to be inspected by checking certificates such as an identity card and the like, and confirm whether the inspected person is in a related suspicious person list of a public security department. For example, the security inspector may scan the baggage of the inspected person using radioactive rays (e.g., X-rays) generated by a specific device (e.g., a security inspection machine), and inspect the baggage carried by the traveler for the presence of dangerous goods or prohibited goods based on the scanned images. Security examiners may also, for example, perform physical examinations on the suspicious passenger using a human examination instrument to check whether the suspicious passenger carries metal or other contraband with him or her. When the suspicious people are met, the security inspection strength is increased, and the time of the normal passenger in the same trip is influenced. In a word, the current security check process is complicated, the check time is long, and the experience of the security check of passengers is poor.
Therefore, to solve the above problems, it is an urgent need to divide passengers and adopt different security checks according to different levels to shorten the security check time and enhance the passenger security check experience.
The above information disclosed in this background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for classifying a person to be inspected in a case, so as to perform a differentiated inspection on the person to be inspected, thereby shortening a security inspection process and reducing an inspection time.
Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to an aspect of the present invention, a method for classifying detected persons based on big data is provided, which is characterized in that:
in step S101, history data is collected and a database is built. The historical data is from historical security check information of a security check system or third-party institution record information.
And S102, establishing a risk evaluation model of the detected personnel based on the historical database, and determining the danger degree of the personnel in the historical data.
And S103, acquiring network behavior data of the detected person to perform emotion analysis.
And S104, dividing the danger degree of the related personnel according to the secondary evaluation result.
And S105, carrying out corresponding detection on the related personnel according to the division result.
Further, the establishment of the risk assessment model comprises the following steps:
1. selecting a plurality of track scoring factors from each type of travel track information according to the travel track information of the personnel collected by the data acquisition and processing system of the ministry of public security, and establishing a travel track scoring factor system of each type of personnel;
2. and taking the trip track scoring factor as a variable used in the risk early warning model.
Further, the network behavior data collection may include the steps of:
the method comprises the following steps: acquiring Internet information such as microblogs, WeChat public numbers, post bar messages, used emoticons, copybacks, comments and the like of personnel by means of an Internet information base;
step two: processing the data collected in the first step, and dividing the data into different categories, such as videos, music, games and the like; analyzing the emotion of the personnel according to the category content and the semantic meaning, and marking emotion labels on corresponding emotion data; the emotion labels are happy, angry, sadness, fear, surprise, acceptance, mania, vigilance, hate and other emotions.
Step three: and carrying out statistical analysis on the personal historical network behavior by taking the week as a time scale aiming at the collected historical microblogs of the target, wherein the analysis content comprises emotional indexes.
Step four; an emotional tendency function is constructed, and the emotional change of the person in the last month is calculated through the function.
Further, analyzing positive, negative, neutral emotions may include the steps of:
⑴, acquiring network sample data with known emotional tendency;
and acquiring sample comment sentences with known emotional tendencies, wherein each sample comment sentence corresponds to one determined emotional tendency.
⑵, calculating a vector of the network sample data, and establishing an emotion classifier according to the vector;
after a sample comment sentence with known emotional tendency is obtained, a sentence vector of the sample comment sentence can be calculated.
⑶, predicting the emotional tendency of the network comment to be tested by using an emotional classifier.
According to another aspect of the present invention, a big data based classification device for a person to be examined is provided, which is characterized in that:
a database module: and collecting historical data and establishing a database. The historical data is from historical security check information of a security check system or third-party institution record information.
A risk assessment judgment module: and establishing a risk evaluation model of the detected personnel based on the historical database, and determining the danger degree of the personnel in the historical data.
And an emotion analysis module: and collecting network behavior data of the detected person to perform emotion analysis.
A risk division module: and dividing the danger degree of the related personnel according to the secondary evaluation result.
A detection division module: and correspondingly detecting the related personnel according to the division result.
Further, the establishment of the risk assessment module comprises the following steps:
1. selecting a plurality of track scoring factors from each type of travel track information according to the travel track information of the personnel collected by the data acquisition and processing system of the ministry of public security, and establishing a travel track scoring factor system of each type of personnel;
2. and taking the trip track scoring factor as a variable used in the risk early warning model.
Further, the network behavior data collection may include the steps of:
a network information acquisition module: acquiring Internet information such as microblogs, WeChat public numbers, post bar messages, used emoticons, copybacks, comments and the like of personnel by means of an Internet information base;
the network information processing and analyzing module: processing the data collected in the first step, and dividing the data into different categories, such as videos, music, games and the like; analyzing the emotion of the personnel according to the category content and the semantic meaning, and marking emotion labels on corresponding emotion data; the emotion labels are happy, angry, sadness, fear, surprise, acceptance, mania, vigilance, hate and other emotions.
The network data emotion analysis module: and carrying out statistical analysis on the personal historical network behavior by taking the week as a time scale aiming at the collected historical microblogs of the target, wherein the analysis content comprises emotional indexes.
An emotional tendency function module: an emotional tendency function is constructed, and the emotional change of the person in the last month is calculated through the function.
Further, analyzing positive, negative, neutral emotions may include:
⑴, acquiring network sample data with known emotional tendency;
and acquiring sample comment sentences with known emotional tendencies, wherein each sample comment sentence corresponds to one determined emotional tendency.
⑵, calculating a vector of the network sample data, and establishing an emotion classifier according to the vector;
after a sample comment sentence with known emotional tendency is obtained, a sentence vector of the sample comment sentence can be calculated.
⑶, predicting the emotional tendency of the network comment to be tested by using an emotional classifier.
Through the technical scheme, the detected personnel can be differentially checked, so that the security check process is shortened, and the check time is shortened. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
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The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the invention and other drawings may be derived from those drawings by a person skilled in the art without inventive effort.
Fig. 1 is a flowchart illustrating a hierarchical security inspection method for inspected persons based on big data according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating a big data based examined person grading apparatus according to an exemplary embodiment.
Detailed Description
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below could be termed a second component without departing from the teachings of the present concepts. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be appreciated by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or flow charts in the drawings are not necessarily required to practice the present invention and are, therefore, not intended to limit the scope of the present invention.
FIG. 1 is a flow chart illustrating a big data based method for rating inspected persons according to an exemplary embodiment.
As shown in fig. 1, in step S101, history data is collected and a database is built. The historical data is from historical security check information of a security check system or third-party institution record information. The historical security check information may include: travel track information of the person.
The personnel travel track information is from a public security department and third-party data, and the third-party data comprises a ticket business company and a hotel check-in management system.
And S102, establishing a risk evaluation model of the detected personnel based on the historical database, and determining the risk degree of the personnel in the historical data.
Further, classifying the personnel, establishing a track clustering model according to the travel track information of the detected personnel, and acquiring track groups classified by different types of personnel and track scoring factors; the people categories may include a young category, a middle-aged category, an old category, a suspect category;
and associating each personnel travel track in the track group with the risk assessment information, and establishing a behavior track risk association model so as to obtain a track grading model comprising personnel classification and risk early warning models.
Further, the establishment of the human risk early warning model comprises the following steps:
the first step is as follows: selecting a plurality of track scoring factors from each type of travel track information according to the travel track information of the personnel collected by the data acquisition and processing system of the ministry of public security, and establishing a travel track scoring factor system of each type of personnel;
further, the travel track scoring factor may be as follows:
the second step is that: in a track scoring model based on a behavior track risk model, a trip track scoring factor is used as a variable used in a risk early warning model, wherein a correlation coefficient formula is as follows:
wherein p isa，bThe coefficient of the trip track scoring factor and the risk early warning model is represented, a represents the trip track scoring factor, b represents the risk early warning information, F (a) represents the mathematical expectation of the trip track scoring factor, F (ab) represents the mathematical expectation of the trip track scoring factor multiplied by the risk early warning information, F (b) represents the mathematical expectation of the risk early warning information, F2(a)、F2(b) Respectively representing the square of the mathematical expectation of the trip track scoring factor and the risk early warning information, F (a)2)、F(b2) Respectively representing the mathematical expectation of the trip track scoring factor and the risk early warning information square.
Risk early warning model
Y＝P1a1+P2a2+P3a3+…+Piai
Wherein Y is a risk early warning model, PiIs a linear coefficient, aiAnd (3) representing the personnel travel track scoring factors, and i represents the number of the track scoring factors.
And calculating a risk early warning value of the detected person through a risk early warning model based on the travel track of the detected person, and determining the risk index of the detected person according to a corresponding threshold value.
And S103, acquiring network behavior data of the detected person to perform emotion analysis.
After the risk index of the person to be detected is determined, the recent psychological fluctuation condition of the person needs to be further judged, and the judgment can be often carried out according to the network behavior of the person to be detected.
Network behavior data collection may include the steps of:
the method comprises the following steps: acquiring Internet information such as microblogs, WeChat public numbers, post bar messages, used emoticons, copybacks, comments and the like of personnel by means of an Internet information base;
step two: processing the data collected in the first step, and dividing the data into different categories, such as videos, music, games and the like; analyzing the emotion of the personnel according to the category content and the semantic meaning, and marking emotion labels on corresponding emotion data; the emotion labels are happy, angry, sadness, fear, surprise, acceptance, mania, vigilance, hate and other emotions.
Step three: and carrying out statistical analysis on the personal historical network behavior by taking the week as a time scale aiming at the collected historical microblogs of the target, wherein the analysis content comprises emotional indexes.
Step four; an emotional tendency function is constructed, and the emotional change of the person in the last month is calculated through the function. The method specifically comprises the following steps: the emotion labels of step three are divided into 3 trends: happiness, surprise, pleasure is positive tendency, peaceful, acceptance is neutral tendency, anger, sadness, fear, vigilance and hate are negative tendency, and then the psychological fluctuation of the people is judged according to the emotional tendency proportion of the people.
Wherein the emotion analysis in step three may include the following steps:
⑴, acquiring network sample data with known emotional tendency;
and acquiring sample comment sentences with known emotional tendencies, wherein each sample comment sentence corresponds to one determined emotional tendency.
⑵, calculating a vector of the network sample data, and establishing an emotion classifier according to the vector;
after obtaining the sample comment sentences with known emotional tendencies, a sentence vector of the sample comment sentences can be calculated, ⑶, and the emotional tendencies of the network comments to be tested are predicted by utilizing an emotion classifier.
And S104, dividing the danger degree of the related personnel according to the secondary evaluation result.
According to the evaluation result, the danger degree of the personnel is divided into: no danger, light danger, danger and the like in 3 grades.
And S105, carrying out corresponding detection on the related personnel according to the division result.
As described above, for example, the gate machine for verifying the personal identification card may acquire the information of the personal identification card, establish communication with the security check server, acquire the historical security check information of the person, and acquire the historical security check information set through data cleaning. And establishing a risk early warning model of the detected personnel according to the historical security inspection information, and calculating the risk level of the detected personnel in real time. Then, network data of the person is collected, and the emotional tendency of the detected person is judged through analysis, so that the possible risks of the person are judged. For example, the obtained grading result of security inspection can be combined with the actual situation of the security inspection field to perform differentiation detection on the detected personnel. Such as: the security level is fast passed through, the general security check of suspicion level, and key security check such as human detector is used to key inspection level, checks.
According to the method for grading the detected personnel in the security inspection, the related information of the detected personnel is obtained, and the related data analysis method is combined, so that the security inspection efficiency can be improved, and the detected personnel can be inspected in a differentiation mode.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Fig. 2 is a block diagram illustrating a big-data-based examined person classification apparatus according to an exemplary embodiment.
As shown in fig. 1, the database module: including gathering historical data and building a database. The historical data is from historical security check information of a security check system or third-party institution record information. The historical security check information may include: travel track information of the person.
The personnel travel track information is from a public security department and third-party data, and the third-party data comprises a ticket business company and a hotel check-in management system.
A risk assessment judgment module: and establishing a risk evaluation model of the detected personnel based on the historical database, and determining the risk degree of the personnel in the historical data.
Further, classifying the personnel, establishing a track clustering model according to the travel track information of the detected personnel, and acquiring track groups classified by different types of personnel and track scoring factors; the people categories may include a young category, a middle-aged category, an old category, a suspect category;
and associating each personnel travel track in the track group with the risk assessment information, and establishing a behavior track risk association model so as to obtain a track grading model comprising personnel classification and risk early warning models.
Further, the establishment of the human risk early warning model comprises the following steps:
the first step is as follows: selecting a plurality of track scoring factors from each type of travel track information according to the travel track information of the personnel collected by the data acquisition and processing system of the ministry of public security, and establishing a travel track scoring factor system of each type of personnel;
further, the travel track scoring factor may be as follows:
the second step is that: taking the trip track scoring factor as a variable used in a risk early warning model, wherein a correlation coefficient formula is as follows:
wherein p isa，bThe coefficient of the trip track scoring factor and the risk early warning model is represented, a represents the trip track scoring factor, b represents the risk early warning information, F (a) represents the mathematical expectation of the trip track scoring factor, F (ab) represents the mathematical expectation of the trip track scoring factor multiplied by the risk early warning information, and F (b) representsMathematical expectation of risk pre-warning information, F2(a)、F2(b) Respectively representing the square of the mathematical expectation of the trip track scoring factor and the risk early warning information, F (a)2)、F(b2) Respectively representing the mathematical expectation of the trip track scoring factor and the risk early warning information square.
Risk early warning model
Y＝P1a1+P2a2+P3a3+…+Piai
Wherein Y is a risk early warning model, PiIs a linear coefficient, aiAnd (3) representing the personnel travel track scoring factors, and i represents the number of the track scoring factors.
And calculating a risk early warning value of the detected person through a risk early warning model based on the travel track of the detected person, and determining the risk index of the detected person according to a corresponding threshold value.
And an emotion analysis module: and collecting network behavior data of the detected person to perform emotion analysis.
After the risk index of the person to be detected is determined, the recent psychological fluctuation condition of the person needs to be further judged, and the judgment can be often carried out according to the network behavior of the person to be detected.
Network behavior data collection may include the steps of:
a network information acquisition module: acquiring Internet information such as microblogs, WeChat public numbers, post bar messages, used emoticons, copybacks, comments and the like of personnel by means of an Internet information base;
the network information processing and analyzing module: processing the data collected in the first step, and dividing the data into different categories, such as videos, music, games and the like; analyzing the emotion of the personnel according to the category content and the semantic meaning, and marking emotion labels on corresponding emotion data; the emotion labels are happy, angry, sadness, fear, surprise, acceptance, mania, vigilance, hate and other emotions.
The network data emotion analysis module: and carrying out statistical analysis on the personal historical network behavior by taking the week as a time scale aiming at the collected historical microblogs of the target, wherein the analysis content comprises emotional indexes.
An emotional tendency function module: an emotional tendency function is constructed, and the emotional change of the person in the last month is calculated through the function. The method specifically comprises the following steps: the emotion labels of step three are divided into 3 trends: happiness, surprise, pleasure is positive tendency, peaceful, acceptance is neutral tendency, anger, sadness, fear, vigilance and hate are negative tendency, and then the psychological fluctuation of the people is judged according to the emotional tendency proportion of the people.
The emotional tendency function module executes the following steps:
⑴, acquiring network sample data with known emotional tendency;
and acquiring sample comment sentences with known emotional tendencies, wherein each sample comment sentence corresponds to one determined emotional tendency.
⑵, calculating a vector of the network sample data, and establishing an emotion classifier according to the vector;
after obtaining the sample comment sentences with known emotional tendencies, a sentence vector of the sample comment sentences can be calculated, ⑶, and the emotional tendencies of the network comments to be tested are predicted by utilizing an emotion classifier.
And dividing the danger degree of the related personnel according to the secondary evaluation result.
According to the evaluation result, the danger degree of the personnel is divided into: no danger, light danger, danger and the like in 3 grades.
A detection division module: and correspondingly detecting the related personnel according to the division result.
As described above, for example, the gate machine for verifying the personal identification card may acquire the information of the personal identification card, establish communication with the security check server, acquire the historical security check information of the person, and acquire the historical security check information set through data cleaning. And establishing a risk early warning model of the detected personnel according to the historical security inspection information, and calculating the risk level of the detected personnel in real time. Then, network data of the person is collected, and the emotional tendency of the detected person is judged through analysis, so that the possible risks of the person are judged. For example, the obtained grading result of security inspection can be combined with the actual situation of the security inspection field to perform differentiation detection on the detected personnel. Such as: the security level is fast passed through, the general security check of suspicion level, and key security check such as human detector is used to key inspection level, checks.
The foregoing is directed to embodiments of the present application and it is noted that numerous modifications and adaptations may be made by those skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
Claims (8)
1. A big data-based method for grading detected personnel is characterized in that:
in step S101, historical data is collected and a database is established, wherein the historical data is from historical security check information of a security check system or third-party organization record information;
s102, establishing a risk assessment model of the detected personnel based on the historical database, and determining the danger degree of the personnel in the historical data;
s103, acquiring network behavior data of the detected person to perform emotion analysis;
s104, dividing the danger degree of the related personnel according to the secondary evaluation result;
and S105, carrying out corresponding detection on the related personnel according to the division result.
2. The method of claim 1, wherein step S102 comprises: selecting a plurality of track scoring factors from each type of travel track information according to the travel track information of the personnel collected by the data acquisition and processing system of the ministry of public security, and establishing a travel track scoring factor system of each type of personnel; and taking the trip track scoring factor as a variable used in the risk early warning model.
3. The method of claim 2, wherein step S103 comprises:
the method comprises the following steps: acquiring microblogs, WeChat public numbers, post-bar messages, used emoticons, replying and commenting internet information of personnel by means of an internet information base;
step two: processing the data collected in the step one, and dividing the data into different categories;
step three: aiming at the collected historical microblog of the target, carrying out statistical analysis on the personal historical network behavior by taking the week as a time scale, wherein the analysis content comprises emotion indexes;
step four; an emotional tendency function is constructed, and the emotional change of the person in the last month is calculated through the function.
4. The method of claim 3, comprising in step three:
⑴, acquiring network sample data with known emotional tendency, and acquiring sample comment sentences with known emotional tendency, wherein each sample comment sentence corresponds to a determined emotional tendency;
⑵, calculating a vector of the network sample data, and establishing an emotion classifier according to the vector;
after obtaining a sample comment statement with known emotional tendency, calculating a statement vector of the sample comment statement;
⑶, predicting the network comments to be tested from three emotional tendencies of positive, negative and neutral by using the emotional classifier.
5. The utility model provides a personnel grading plant examined based on big data which characterized in that:
a database module: collecting historical data and establishing a database, wherein the historical data is from historical security inspection information of a security inspection system or third-party organization record information;
a risk assessment judgment module: establishing a risk evaluation model of the detected personnel based on the historical database, and determining the danger degree of the personnel in the historical data;
and an emotion analysis module: collecting network behavior data of detected personnel to perform emotion analysis;
a risk division module: dividing the danger degree of related personnel according to the secondary evaluation result;
a detection division module: and correspondingly detecting the related personnel according to the division result.
6. The apparatus of claim 5, the risk assessment module comprising: selecting a plurality of track scoring factors from each type of travel track information according to the travel track information of the personnel collected by the data acquisition and processing system of the ministry of public security, and establishing a travel track scoring factor system of each type of personnel; and taking the trip track scoring factor as a variable used in the risk early warning model.
7. The apparatus of claim 6, the sentiment analysis module comprising:
a network information acquisition module: acquiring microblogs, WeChat public numbers, post-bar messages, used emoticons, replying and commenting internet information of personnel by means of an internet information base;
the network information processing and analyzing module: processing the data collected in the step one, and dividing the data into different categories;
the network data emotion analysis module: aiming at the collected historical microblog of the target, carrying out statistical analysis on the personal historical network behavior by taking the week as a time scale, wherein the analysis content comprises emotion indexes;
an emotional tendency function module; an emotional tendency function is constructed, and the emotional change of the person in the last month is calculated through the function.
8. The apparatus of claim 7, the sentiment analysis module comprising:
⑴, acquiring network sample data with known emotional tendency, and acquiring sample comment sentences with known emotional tendency, wherein each sample comment sentence corresponds to a determined emotional tendency;
⑵, calculating a vector of the network sample data, and establishing an emotion classifier according to the vector;
after obtaining a sample comment statement with known emotional tendency, calculating a statement vector of the sample comment statement;
⑶, predicting the network comments to be tested from three emotional tendencies of positive, negative and neutral by using the emotional classifier.
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| CN112215451A (en) * | 2020-07-21 | 2021-01-12 | 中国人民公安大学 | Differentiation security check method and system based on civil aviation passenger classification |
| CN112035507A (en) * | 2020-08-06 | 2020-12-04 | 杭州安恒信息技术股份有限公司 | Abnormal inquiry person early warning method and device, electronic equipment and readable storage medium |
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