CN110059727A - The recognition methods of the high-risk addicts based on communication network a kind of and device - Google Patents
The recognition methods of the high-risk addicts based on communication network a kind of and device Download PDFInfo
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- CN110059727A CN110059727A CN201910224232.8A CN201910224232A CN110059727A CN 110059727 A CN110059727 A CN 110059727A CN 201910224232 A CN201910224232 A CN 201910224232A CN 110059727 A CN110059727 A CN 110059727A
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
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Abstract
The recognition methods of this application provides a kind of high-risk addicts based on communication network and device, comprising: obtain the description information of each drug addict in addicts；According to the description information of each drug addict in addicts, the personal feature combination of each drug addict in addicts is determined；Identical drug addict personal feature is combined to be divided in identity set；According to the default evaluation coefficient of each drug addict included in each set, the characteristic coefficient of each set is determined；Selecting the characteristic coefficient for meeting preset condition is target signature coefficient；The included drug addict of the corresponding set of target signature coefficient is determined as high-risk addicts.By the above method, it can determine the high-risk addicts in addicts, and emphasis monitoring is carried out to high-risk addicts, improve the utilization rate of monitoring resource.
Description
Technical field
This application involves technical field of information processing, more particularly, to a kind of high-risk addicts's based on communication network
Recognition methods and device.
Background technique
It in the prior art, is to carry out same journey for each drug addict when to being monitored with drug addict
The monitoring of degree, and in fact, for the addicts with different characteristics, needs to carry out different degrees of monitoring, such as
The number for the supervisor that the different personnel of drug abuse length of time are distributed will be different.
In fact, when carrying out the monitoring of equal extent for each drug addict, since groups of people do not need to carry out
The monitoring of this kind of degree, therefore be easy for causing the waste of part monitoring resource, it is made full use of so that monitoring resource is unable to get,
High-risk addicts biggish for influence degree in addicts, it should key monitoring is carried out, and for shadow in addicts
Monitoring resource can suitably be distributed when being monitored resource allocation by ringing the lesser drug addict of degree.
Summary of the invention
In view of this, a kind of recognition methods for being designed to provide high-risk addicts based on communication network of the application
And device, to identify the high-risk addicts in communication network, and carry out reasonable resource allocation.
In a first aspect, the embodiment of the present application provides the recognition methods of high-risk addicts based on communication network a kind of,
Include:
Obtain the description information of each drug addict included in communication network, wherein the communication network is to retouch
State the network of relationship between drug addict；
According to the description information of each drug addict in the addicts, each in the addicts is determined
The personal feature of drug addict combines, wherein the personal feature combines the feature for describing the drug addict；
The personal feature combination of each drug addict in the addicts is input to the mathematical modulo put up in advance
In type, perform the following operations:
Identical drug addict personal feature is combined to be divided in identity set；
According to the default evaluation coefficient of each drug addict included in each set, each set is determined
Characteristic coefficient, wherein the default evaluation coefficient is used to describe influence degree of the drug addict in the addicts,
The characteristic coefficient is used for the influence degree for describing to be integrated into corresponding to the characteristic coefficient in the addicts；
Selecting the characteristic coefficient for meeting preset condition is target signature coefficient；
The included drug addict of the corresponding set of the target signature coefficient is determined as high-risk addicts.
With reference to first aspect, the embodiment of the present application provides the first possible embodiment of first aspect, wherein institute
The description information according to each drug addict in the addicts is stated, determines each drug abuse people in the addicts
The personal feature combination of member, comprising:
For each drug addict, following processing is executed:
According to the description information of drug addict, determine that the drug addict presets taking under personal feature type at each
Value；
The value under personal feature type is preset at each according to the drug addict, determines of the drug addict
Body characteristics combination.
With reference to first aspect, the embodiment of the present application provides second of possible embodiment of first aspect, wherein institute
The default evaluation coefficient for stating the drug addict according to included in each set determines the characteristic coefficient of each set, packet
It includes:
Determine the sum of default evaluation coefficient of each drug addict included in each set for each set
Characteristic coefficient.
With reference to first aspect, the embodiment of the present application provides the third possible embodiment of first aspect, wherein institute
Stating and selecting to meet the characteristic coefficient of preset condition is target signature coefficient, comprising:
The characteristic coefficient of all set is ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
With reference to first aspect, the embodiment of the present application provides the 4th kind of possible embodiment of first aspect, wherein institute
State the description information obtained to each drug addict in addicts, comprising:
From the description information to each drug addict in addicts is read in database, alternatively, receiving user
The description information to each drug addict in addicts of input.
Second aspect, the embodiment of the present application also provide the identification device of high-risk addicts based on communication network a kind of,
Include:
Module is obtained, for obtaining the description information of each drug addict included in communication network, wherein described
Communication network is the network for describing relationship between drug addict；
First determining module, for the description information according to each drug addict in the addicts, determine described in
The personal feature combination of each drug addict in addicts, wherein personal feature combination is for describing the drug abuse
The feature of personnel；
Processing module is taken in advance for the personal feature combination of each drug addict in the addicts to be input to
In the mathematical model built up, perform the following operations, comprising:
Division unit is divided in identity set for personal feature combining identical drug addict；
Second determination unit, the default evaluation system for each drug addict according to included in each set
Number determines the characteristic coefficient of each set, wherein the default evaluation coefficient is for describing the drug addict in the suction
Influence degree in malicious crowd, the characteristic coefficient is for describing to be integrated into the addicts corresponding to the characteristic coefficient
In influence degree；
Selecting unit is target signature coefficient for selecting the characteristic coefficient for meeting preset condition；
Third determination unit, for the included drug addict of the corresponding set of the target signature coefficient to be determined as height
Endanger addicts.
In conjunction with second aspect, the embodiment of the present application provides the first possible embodiment of second aspect, wherein institute
The first determining module is stated, in the description information according to each drug addict in the addicts, determines the addicts
In each drug addict personal feature combination when, be specifically used for:
For each drug addict, following processing is executed:
According to the description information of drug addict, determine that the drug addict presets taking under personal feature type at each
Value；
The value under personal feature type is preset at each according to the drug addict, determines of the drug addict
Body characteristics combination.
In conjunction with second aspect, the embodiment of the present application provides second of possible embodiment of second aspect, wherein institute
The second determination unit is stated, in the default evaluation coefficient of the drug addict according to included in each set, determines each collection
When the characteristic coefficient of conjunction, it is specifically used for:
Determine the sum of default evaluation coefficient of each drug addict included in each set for each set
Characteristic coefficient.
In conjunction with the first possible embodiment or second of possible embodiment of second aspect, the embodiment of the present application
Provide the third possible embodiment of second aspect, wherein the selecting unit meets the spy of preset condition in selection
When sign coefficient is target signature coefficient, it is specifically used for:
The characteristic coefficient of all set is ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
In conjunction with second aspect, the embodiment of the present application provides the 4th kind of possible embodiment of second aspect, wherein institute
Acquisition module is stated, when obtaining to the description information of each drug addict in addicts, is specifically used for:
From the description information to each drug addict in addicts is read in database, alternatively, receiving user
The description information to each drug addict in addicts of input.
The third aspect, the embodiment of the present application also provide a kind of electronic equipment, comprising: processor, memory and bus, it is described
Memory is stored with the executable machine readable instructions of the processor, when electronic equipment operation, the processor with it is described
By bus communication between memory, the machine readable instructions executed when being executed by the processor it is above-mentioned in a first aspect, or
Step in any possible embodiment of first aspect.
Fourth aspect, the embodiment of the present application also provide a kind of computer readable storage medium, the computer-readable storage medium
Computer program is stored in matter, which executes above-mentioned in a first aspect, or first aspect times when being run by processor
A kind of step in possible embodiment.
The recognition methods of high-risk addicts provided by the embodiments of the present application based on communication network and device, by obtaining
The description information of each drug addict is analyzed in the addicts taken, and then determines each drug abuse people in addicts
The personal feature combination of member, then personal feature combines identical drug addict and is divided into identity set, and determination is each
The characteristic coefficient of a set, then target signature coefficient is filtered out from characteristic coefficient, and according to the target signature coefficient filtered out
High-risk addicts is filtered out from addicts.
By the above method, the biggish high-risk addicts of influence degree in addicts can be filtered out, as emphasis
Monitoring object distributes monitoring resource then according to the high-risk addicts filtered out, and rationally dividing for monitoring resource may be implemented
Match, avoids the waste of monitoring resource.
To enable the above objects, features, and advantages of the application to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows a kind of recognition methods of the high-risk addicts based on communication network provided by the embodiment of the present application
Flow diagram；
Fig. 2 shows the flow diagrams that a kind of emphasis monitoring object provided by the embodiment of the present application determines method；
Fig. 3 shows a kind of flow diagram of the determining method of personal feature combination provided by the embodiment of the present application；
Fig. 4 shows the process signal that another emphasis monitoring object provided by the embodiment of the present application determines method
Figure；
Fig. 5 shows a kind of identification device of the high-risk addicts based on communication network provided by the embodiment of the present application
500 configuration diagram；
Fig. 6 shows the structural schematic diagram of electronic equipment 600 provided by the embodiment of the present application.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
Middle attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
It is some embodiments of the present application, instead of all the embodiments.The application being usually described and illustrated herein in the accompanying drawings is real
The component for applying example can be arranged and be designed with a variety of different configurations.Therefore, below to the application's provided in the accompanying drawings
The detailed description of embodiment is not intended to limit claimed scope of the present application, but is merely representative of the selected reality of the application
Apply example.Based on embodiments herein, those skilled in the art institute obtained without making creative work
There are other embodiments, shall fall in the protection scope of this application.
In view of in the prior art, not can be carried out reasonable distribution for limited monitoring resource, it is based on this, the application
Embodiment provides recognition methods and the device of a kind of high-risk addicts based on communication network, carries out below by embodiment
Description.
For convenient for understanding the present embodiment, first to a kind of based on communication network disclosed in the embodiment of the present application
The recognition methods of high-risk addicts describes in detail.
Embodiment one
It is shown in Figure 1, it is a kind of identification of the high-risk addicts based on communication network provided by the embodiments of the present application
The flow diagram of method, comprising the following steps:
S101, the description information for obtaining each drug addict included in communication network.
Wherein, the communication network is the network for describing relationship between drug addict.
It, can be described to each drug addict in addicts from being read in database in a kind of possible embodiment
Description information, alternatively, receive user input the description information to each drug addict in addicts.
S102, according to the description information of each drug addict in addicts, determine in addicts that each is inhaled
The personal feature of malicious personnel combines.
Wherein, personal feature combines the feature for describing drug addict.
Specifically, being directed to each drug addict, following processing can be executed:
According to the description information of drug addict, determine that the drug addict presets taking under personal feature type at each
Value；
The value under personal feature type is preset at each according to the drug addict, determines of the drug addict
Body characteristics combination.
S103, the personal feature combination of each drug addict in addicts is input to the mathematical modulo put up in advance
In type, the high-risk addicts in addicts is determined.
Wherein, the personal feature combination of each drug addict in addicts is being input to the mathematics put up in advance
After in model, operation as shown in Figure 1 can be executed, comprising the following steps:
S1031, it personal feature combines identical drug addict and is divided in identity set.
S1032, according to each set included in each drug addict default evaluation coefficient, determine each
The characteristic coefficient of set.
Specifically, can determine that the sum of the default evaluation coefficient of each drug addict included in each set is
The characteristic coefficient of each set.
S1033, to select the characteristic coefficient for meeting preset condition be target signature coefficient.
Specifically, the characteristic coefficient of all set can be ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
S1034, the included drug addict of the corresponding set of target signature coefficient is determined as high-risk addicts.
The recognition methods of high-risk addicts provided by the embodiments of the present application based on communication network, passes through the suction to acquisition
The description information of each drug addict is analyzed in malicious crowd, and then determines of each drug addict in addicts
Body characteristics combination, then personal feature combines identical drug addict and is divided into identity set, and determines each set
Characteristic coefficient, then target signature coefficient is filtered out from characteristic coefficient, and according to the target signature coefficient filtered out from drug abuse
High-risk addicts is filtered out in crowd.
By the above method, the biggish high-risk addicts of influence degree in addicts can be filtered out, as emphasis
Monitoring object distributes monitoring resource then according to the high-risk addicts filtered out, and rationally dividing for monitoring resource may be implemented
Match, avoids the waste of monitoring resource.
Embodiment two
In practical application, it is not limited in the monitoring to drug addict, can also be the monitoring to remaining personnel to be monitored.
The embodiment of the present application provides a kind of emphasis monitoring object and determines method, by filtering out emphasis monitoring object, and then realizes and closes
Reason distribution monitoring resource.
It is shown in Figure 2, it is the flow diagram that a kind of emphasis monitoring object provided by the embodiments of the present application determines method,
The following steps are included:
S201, it obtains to the description information of each monitoring object in Monitoring Population.
In specific implementation, pre-stored each monitoring object in Monitoring Population can be retouched from being read in database
Information is stated, the description information to each monitoring object in Monitoring Population of user's input can also be received.
S202, according to the description information of each monitoring object in Monitoring Population, determine in Monitoring Population that each is supervised
Survey the personal feature combination of object.
In the description information according to each monitoring object in Monitoring Population, each monitoring pair in Monitoring Population is determined
After the personal feature combination of elephant, the personal feature combination of each monitoring object in Monitoring Population is input to and is put up in advance
Mathematical model in, execute step S103~S107 operation.
S203, it personal feature combines identical monitoring object and is divided in identity set.
The default evaluation coefficient of S204, each monitoring object according to included in each set, determine each
The characteristic coefficient of set.
S205, to select the characteristic coefficient for meeting preset condition be target signature coefficient.
S206, the included monitoring object of the corresponding set of target signature coefficient is determined as emphasis monitoring object.
In a kind of possible embodiment, in the description information according to each monitoring object in Monitoring Population, determine
In Monitoring Population when the personal feature combination of each monitoring object, for each monitoring object, it is possible to implement as shown in Figure 3
Personal feature combine determine method, comprising the following steps:
S301, the description information according to monitoring object determine that monitoring object is preset under personal feature type at each
Value.
It is addicts with Monitoring Population, monitoring object can be property for drug addict, to preset personal feature type
Not, for the first time drug abuse type, time span of taking drugs, number of giving up taking addictive drugs by force, mainly consume illegal drugs type etc., then according to the prison of acquisition
The description information for surveying object, determines that monitoring object presets the value under personal feature type at each.
Ginseng is shown in Table 1, and is a kind of possible default personal feature examples of types table:
Table 1
It should be noted that the dimension of different default personal feature types may be the same or different, each is pre-
If the value of personal feature type should include all possible value result.
It should be noted that should be independent from each other between each preset personal feature type, that is, determining in advance
If after the value of personal feature type A, will not influence the value of default personal feature type B.Illustratively, if drug abuse people
The gender of member is male, and the age that will not influence drug addict is 40 years old or more.
Illustratively, in actual drug abusing method, common drugs have heroin, methamphetamine, head-shaking pill, Sauteralgyl etc., this
A little different types of drugs often with specific a certain kind or certain some suck mode correlation.For example, heroin, generally takes
Tinfoil scalds the mode inhaled and injected；Methamphetamine to suck mode more special, need to use a kind of specific tool, curling stone is one
Kind is similar to the mode of water suction cigarette, equally takes the method without other drugs in addition to methamphetamine；Head-shaking pill can only be adopted as tablet
With oral mode；Sauteralgyl belongs to anesthesia class controlled drug, can only take the mode of injection.Above four kinds of drugs are common
Typical drugs, and respectively represent one kind and typically suck mode.Its excess-three kind only has one kind to suck mode in addition to heroin,
With certain specificity, and heroin is also only that there are two types of suck mode.Therefore, if presetting personal feature type both
Using drugs type, also using mode is sucked, then obtained feature composite type similitude is excessively high, can not make final true
There is apparent difference between fixed set.
In a kind of possible embodiment, can between personal feature type carry out correlation significance test,
And the personal feature type for meeting preset requirement is determined as default personal feature type.Wherein, between personal feature type
The method for carrying out the significance test of correlation can refer to confidence level and determine method, herein will not reinflated explanation.
S302, the value under personal feature type is preset at each according to monitoring object, determines the individual of monitoring object
Feature combination.
Wherein, personal feature combines the feature for describing monitoring object.
It should be noted that same monitoring object, which presets the value under personal feature type at each, to be had and only one
It is a.
In a kind of possible embodiment, finally determining personal feature combination can use irregular matrix mark, such as
Shown in lower:
Wherein, yiIndicate the personal feature combination of i-th of monitoring personnel, yiIn every a line indicate each personal feature class
Type, each column indicate a kind of value of each personal feature type.
Illustratively,Indicate taking for the first default personal feature type of monitoring personnel A
Value is the first, and the value of second of default personal feature type is the first, the value of the third default personal feature type
For the third, the value of the 4th kind of default personal feature type is the third.
In a kind of possible embodiment, if comprising M default personal feature types, every kind of default personal feature type
There is N number of value, then the possible personal feature combination of a total of M*N kind.By the individual of each monitoring object in Monitoring Population
After feature combination determines, the personal feature combination of each monitoring object is input in the mathematical model put up in advance, number
Model personal feature combines identical monitoring object and is divided in identity set, wherein number≤personal feature of set
Combined possibility number.
In mathematical model, gather for each, the default evaluation coefficient of the monitoring object according to included in set,
Determine the characteristic coefficient of each set, wherein default evaluation coefficient is for describing influence of the monitoring object in Monitoring Population
Degree, influence degree of the characteristic coefficient for being integrated into corresponding to Expressive Features coefficient in Monitoring Population.
It can be calculated according to following formula:
Specifically, by each gather included in the sum of the default evaluation coefficient of each monitoring object be determined as often
The characteristic coefficient of one set, wherein P indicates that the characteristic coefficient of any set H, n indicate the monitoring object that set H is included
Number, riIndicate the default evaluation coefficient of i-th of monitoring object.
In a kind of possible embodiment, after the evaluation coefficient for calculating each set, it can screen and meet
The characteristic coefficient of preset condition is determined as target signature coefficient, specifically, can be by the characteristic coefficient of all set according to from big
It is ranked up to small sequence, top n characteristic coefficient is then determined as target signature coefficient, wherein N is positive integer.
It is considered that influence degree of a certain monitoring object in Monitoring Population is higher, but with the individual of the monitoring object
It is less that feature combines identical monitoring object, therefore, to eliminate influence of the number to the evaluation coefficient of set, another can
Can embodiment in, calculate each set evaluation coefficient after, can by each set evaluation coefficient with
The number for the monitoring object that the set is included is divided by, and the average ratings coefficient of each set is calculated, then by all set
Average ratings coefficient be ranked up according to sequence from big to small, then by top n average ratings coefficient be determined as target spy
Coefficient is levied, N is positive integer.
In one example of the application, the value of N can be according to the number of monitoring resource by being artificially adjusted.
After determining the target signature coefficient of Monitoring Population, the corresponding set of target signature coefficient can also be determined as
Emphasis monitoring gathered included monitoring object and determined as emphasis monitoring object by emphasis monitoring set, and based on determining
Emphasis monitoring object distributes monitoring resource for Monitoring Population.
Specifically, be the higher people of influence degree in Monitoring Population because of the emphasis monitoring object filtered out, therefore,
More monitoring resource can be distributed into emphasis monitoring object, and then realize prison when distributing monitoring resource for Monitoring Population
Survey making full use of for resource.
Wherein, preparatory mathematical model has following constraint for the emphasis monitoring object determined:
Constraint one: Zi′≥Zgi
Constraint two:
Zi' indicate i-th of emphasis monitoring object, ZgiIndicate i-th of monitoring object in g-th of set, G indicates all heavy
The set of point monitoring object, g indicates Monitoring Population, when constraint one indicates that the set where monitoring object i is emphasis monitoring set,
Monitoring object i must be emphasis monitoring object, constraint two indicate when monitoring object i not emphasis monitor gather when, monitoring object i
It is not centainly emphasis monitoring object.
The embodiment of the present application also provides another emphasis monitoring objects to determine method, is the application referring to shown in Fig. 4
Another emphasis monitoring object that embodiment provides determines the flow diagram of method, comprising the following steps:
S401, it obtains to the description information of each monitoring object in Monitoring Population.
S402, basis preset the possibility value of personal feature type, determine the possibility type of personal feature combination, and be every
A kind of possible personal feature composite type progress label.
S403, according to the description information of each monitoring object in Monitoring Population, determine the individual of each monitoring object
Feature combination.
S404, the possibility type combined according to the combination of the personal feature of each monitoring object and personal feature, determine
The label of the personal feature composite type of each monitoring object.
Each possible type that the personal feature combination of monitoring object is combined with personal feature is matched, and general
Label with successful possible type is determined as the label of the personal feature composite type of monitoring object.
S405, by the monitoring object of same label, be divided in identity set, and according to each set included in
The default evaluation coefficient of each monitoring object determines the characteristic coefficient of each set.
S406, to select the characteristic coefficient for meeting preset condition be target signature coefficient.
S407, the included monitoring object of the corresponding set of target signature coefficient is determined as emphasis monitoring object.
After determining emphasis monitoring object, it can be distributed based on the emphasis monitoring object determined for Monitoring Population
Monitoring resource.
Emphasis monitoring object provided by the embodiments of the present application determines method, is supervised by each in the Monitoring Population to acquisition
The description information for surveying object is analyzed, and then determines the personal feature combination of each monitoring object in Monitoring Population, then
It personal feature combines identical monitoring object to be divided into identity set, and determines the characteristic coefficient of each set, then from
Target signature coefficient is filtered out in characteristic coefficient, and emphasis is filtered out from Monitoring Population according to the target signature coefficient filtered out
Monitoring object distributes monitoring resource then according to the emphasis monitoring object filtered out for Monitoring Population.
By the above method, the biggish people of influence degree in Monitoring Population can be filtered out, as emphasis monitoring object, so
Afterwards according to the emphasis monitoring object filtered out, monitoring resource is distributed, the reasonable distribution of monitoring resource may be implemented, monitoring is avoided to provide
The waste in source.
Embodiment two
The embodiment of the present application provides the identification device of high-risk addicts based on communication network a kind of, referring to Fig. 5 institute
Show, for a kind of framework signal of the identification device 500 of the high-risk addicts based on communication network provided by the embodiments of the present application
Figure, including module 501, the first determining module 502, processing module 503 are obtained, wherein processing module 503 includes division unit
5031, the second determination unit 5032, selecting unit 5033, third determination unit 5034, specific:
Module 501 is obtained, for obtaining the description information of each drug addict in addicts；
First determining module 502 determines institute for the description information according to each drug addict in the addicts
The personal feature combination of each drug addict in addicts is stated, wherein personal feature combination is for describing the suction
The feature of malicious personnel；
Processing module 503, for for the personal feature of each drug addict in the addicts combining input
Into the mathematical model put up in advance, perform the following operations, comprising:
Division unit 5031, for being divided in identity set for personal feature combining identical drug addict；
Second determination unit 5032, the default evaluation for each drug addict according to included in each set
Coefficient determines the characteristic coefficient of each set, wherein the default evaluation coefficient is for describing the drug addict described
Influence degree in addicts, the characteristic coefficient is for describing to be integrated into the drug abuse people corresponding to the characteristic coefficient
Influence degree in group；
Selecting unit 5033 is target signature coefficient for selecting the characteristic coefficient for meeting preset condition；
Third determination unit 5034, for determining the included drug addict of the corresponding set of the target signature coefficient
For high-risk addicts.
In a kind of possible embodiment, first determining module 502 is inhaled according to each in the addicts
The description information of malicious personnel is specifically used for when determining the personal feature combination of each drug addict in the addicts:
For each drug addict, following processing is executed:
According to the description information of drug addict, determine that the drug addict presets taking under personal feature type at each
Value；
The value under personal feature type is preset at each according to the drug addict, determines of the drug addict
Body characteristics combination.
In a kind of possible embodiment, second determination unit 5032, according to included in each set
The default evaluation coefficient of drug addict is specifically used for when determining the characteristic coefficient of each set:
Determine the sum of default evaluation coefficient of each drug addict included in each set for each set
Characteristic coefficient.
In a kind of possible embodiment, the selecting unit 5033, the characteristic coefficient for selecting to meet preset condition for
When target signature coefficient, it is specifically used for:
The characteristic coefficient of all set is ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
In a kind of possible embodiment, the acquisition module 501 is being obtained to each drug addict in addicts
Description information when, be specifically used for:
From the description information to each drug addict in addicts is read in database, alternatively, receiving user
The description information to each drug addict in addicts of input.
The identification device of high-risk addicts provided by the embodiments of the present application based on communication network, passes through the suction to acquisition
The description information of each drug addict is analyzed in malicious crowd, and then determines of each drug addict in addicts
Body characteristics combination, then personal feature combines identical drug addict and is divided into identity set, and determines each set
Characteristic coefficient, then target signature coefficient is filtered out from characteristic coefficient, and according to the target signature coefficient filtered out from drug abuse
High-risk addicts is filtered out in crowd.
By above-mentioned apparatus, the biggish people of influence degree in addicts can be filtered out, as emphasis monitoring object, so
Afterwards according to the emphasis monitoring object filtered out, monitoring resource is distributed, the reasonable distribution of monitoring resource may be implemented, monitoring is avoided to provide
The waste in source.
Embodiment three
Based on the same technical idea, the embodiment of the present application also provides a kind of electronic equipment.Referring to shown in Fig. 6, for this
Apply for the structural schematic diagram for the electronic equipment 600 that embodiment provides, including processor 601, memory 602 and bus 603.Its
In, memory 602 is executed instruction for storing, including memory 6021 and external memory 6022；Here memory 6021 is also referred to as
Built-in storage, for temporarily storing the operational data in processor 601, and the number exchanged with external memories 6022 such as hard disks
According to, processor 601 carries out data exchange by memory 6021 and external memory 6022, when electronic equipment 600 is run, processing
It is communicated between device 601 and memory 602 by bus 603, so that processor 601 is being executed to give an order:
Obtain the description information of each drug addict in addicts；
According to the description information of each drug addict in the addicts, each in the addicts is determined
The personal feature of drug addict combines, wherein the personal feature combines the feature for describing the drug addict；
The personal feature combination of each drug addict in the addicts is input to the mathematical modulo put up in advance
In type, perform the following operations:
Identical drug addict personal feature is combined to be divided in identity set；
According to the default evaluation coefficient of each drug addict included in each set, each set is determined
Characteristic coefficient, wherein the default evaluation coefficient is used to describe influence degree of the drug addict in the addicts,
The characteristic coefficient is used for the influence degree for describing to be integrated into corresponding to the characteristic coefficient in the addicts；
Selecting the characteristic coefficient for meeting preset condition is target signature coefficient；
The included drug addict of the corresponding set of the target signature coefficient is determined as high-risk addicts.
In a kind of possible design, the description information according to each drug addict in the addicts is determined
The personal feature combination of each drug addict in the addicts, comprising:
For each drug addict, following processing is executed:
According to the description information of drug addict, determine that the drug addict presets taking under personal feature type at each
Value；
The value under personal feature type is preset at each according to the drug addict, determines of the drug addict
Body characteristics combination.
In a kind of possible design, the default evaluation coefficient of the drug addict according to included in each set,
Determine the characteristic coefficient of each set, comprising:
Determine the sum of default evaluation coefficient of each drug addict included in each set for each set
Characteristic coefficient.
In a kind of possible design, described to select the characteristic coefficient for meeting preset condition be target signature coefficient, comprising:
The characteristic coefficient of all set is ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
In a kind of possible design, the description information obtained to each drug addict in addicts, comprising:
From the description information to each drug addict in addicts is read in database, alternatively, receiving user
The description information to each drug addict in addicts of input.
The embodiment of the present application also provides a kind of computer readable storage medium, stored on the computer readable storage medium
There is computer program, the identification side of the high-risk addicts based on communication network is executed when which is run by processor
The step of method.
Specifically, which can be general storage medium, such as mobile disk, hard disk, on the storage medium
Computer program when being run, the recognition methods of the above-mentioned high-risk addicts based on communication network is able to carry out, to mention
The utilization rate of high monitoring resource.
The computer program of the recognition methods of high-risk addicts based on communication network provided by the embodiment of the present application
Product, the computer readable storage medium including storing program code, the instruction that program code includes can be used for executing front
Method in embodiment of the method, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.The apparatus embodiments described above are merely exemplary, for example, the division of the unit,
Only a kind of logical function partition, there may be another division manner in actual implementation, in another example, multiple units or components can
To combine or be desirably integrated into another system, or some features can be ignored or not executed.Another point, it is shown or beg for
The mutual coupling, direct-coupling or communication connection of opinion can be through some communication interfaces, device or unit it is indirect
Coupling or communication connection can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in the executable non-volatile computer-readable storage medium of a processor.Based on this understanding, the application
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, server or the network equipment etc.) executes each embodiment institute of the application
State all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-Only
Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk etc. is various to deposit
Store up the medium of program code.
Finally, it should be noted that embodiment described above, the only specific embodiment of the application, to illustrate the application
Technical solution, rather than its limitations, the protection scope of the application is not limited thereto, although with reference to the foregoing embodiments to this Shen
It please be described in detail, those skilled in the art should understand that: anyone skilled in the art
Within the technical scope of the present application, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of the embodiment of the present application technical solution, should all cover the protection in the application
Within the scope of.Therefore, the protection scope of the application shall be subject to the protection scope of the claim.
Claims (10)
1. a kind of recognition methods of the high-risk addicts based on communication network characterized by comprising
Obtain the description information of each drug addict included in communication network, wherein the communication network is that description is inhaled
The network of relationship between malicious personnel；
According to the description information of each drug addict in the addicts, determine that each in the addicts is taken drugs
The personal feature of personnel combines, wherein the personal feature combines the feature for describing the drug addict；
The personal feature combination of each drug addict in the addicts is input in the mathematical model put up in advance,
It performs the following operations:
Identical drug addict personal feature is combined to be divided in identity set；
According to the default evaluation coefficient of each drug addict included in each set, the feature of each set is determined
Coefficient, wherein the default evaluation coefficient is described for describing influence degree of the drug addict in the addicts
Characteristic coefficient is used for the influence degree for describing to be integrated into corresponding to the characteristic coefficient in the addicts；
Selecting the characteristic coefficient for meeting preset condition is target signature coefficient；
The included drug addict of the corresponding set of the target signature coefficient is determined as high-risk addicts.
2. the method according to claim 1, wherein described according to each drug addict in the addicts
Description information, determine each drug addict in the addicts personal feature combination, comprising:
For each drug addict, following processing is executed:
According to the description information of drug addict, determine that the drug addict presets the value under personal feature type at each；
The value under personal feature type is preset at each according to the drug addict, determines that the individual of the drug addict is special
Sign combination.
3. the method according to claim 1, wherein the drug addict according to included in each set
Default evaluation coefficient, determine each set characteristic coefficient, comprising:
Determine the sum of default evaluation coefficient of each drug addict included in each set for the spy of each set
Levy coefficient.
4. the method according to claim 1, wherein described, to select the characteristic coefficient for meeting preset condition be target
Characteristic coefficient, comprising:
The characteristic coefficient of all set is ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
5. the method according to claim 1, wherein the acquisition is to each drug addict in addicts
Description information, comprising:
From the description information to each drug addict in addicts is read in database, alternatively, receiving user's input
The description information to each drug addict in addicts.
6. a kind of identification device of the high-risk addicts based on communication network characterized by comprising
Module is obtained, for obtaining the description information of each drug addict included in communication network, wherein the propagation
Network is the network for describing relationship between drug addict；
First determining module determines the drug abuse for the description information according to each drug addict in the addicts
The personal feature combination of each drug addict in crowd, wherein personal feature combination is for describing the drug addict
Feature；
Processing module is put up in advance for the personal feature combination of each drug addict in the addicts to be input to
Mathematical model in, perform the following operations, comprising:
Division unit is divided in identity set for personal feature combining identical drug addict；
Second determination unit, for the default evaluation coefficient of each drug addict according to included in each set, really
The characteristic coefficient of each fixed set, wherein the default evaluation coefficient is for describing the drug addict in the drug abuse people
Influence degree in group, the characteristic coefficient is for describing to be integrated into corresponding to the characteristic coefficient in the addicts
Influence degree；
Selecting unit is target signature coefficient for selecting the characteristic coefficient for meeting preset condition；
Third determination unit, for the included drug addict of the corresponding set of the target signature coefficient to be determined as high-risk suction
Malicious crowd.
7. device according to claim 6, which is characterized in that first determining module, according to the addicts
In each drug addict description information, determine each drug addict in the addicts personal feature combination
When, it is specifically used for:
For each drug addict, following processing is executed:
According to the description information of drug addict, determine that the drug addict presets the value under personal feature type at each；
The value under personal feature type is preset at each according to the drug addict, determines that the individual of the drug addict is special
Sign combination.
8. device according to claim 6, which is characterized in that second determination unit, according in each set
The default evaluation coefficient for the drug addict for being included is specifically used for when determining the characteristic coefficient of each set:
Determine the sum of default evaluation coefficient of each drug addict included in each set for the spy of each set
Levy coefficient.
9. device according to claim 6, which is characterized in that the selecting unit meets the spy of preset condition in selection
When sign coefficient is target signature coefficient, it is specifically used for:
The characteristic coefficient of all set is ranked up according to sequence from big to small；
Top n characteristic coefficient is determined as target signature coefficient, wherein N is positive integer.
10. device according to claim 6, which is characterized in that the acquisition module is being obtained to each in addicts
When the description information of a drug addict, it is specifically used for:
From the description information to each drug addict in addicts is read in database, alternatively, receiving user's input
The description information to each drug addict in addicts.
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| CN108510117A (en) * | 2018-03-29 | 2018-09-07 | 北京航空航天大学 | Drug abuse propagation prediction method, device and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108510117A (en) * | 2018-03-29 | 2018-09-07 | 北京航空航天大学 | Drug abuse propagation prediction method, device and electronic equipment |
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Application publication date: 20190726 |