CN106411711B - A kind of modified determines system based on the temporary social network of computer big data - Google Patents
A kind of modified determines system based on the temporary social network of computer big data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/04—Real-time or near real-time messaging, e.g. instant messaging [IM]
- H04L51/043—Real-time or near real-time messaging, e.g. instant messaging [IM] using or handling presence information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/56—Unified messaging, e.g. interactions between e-mail, instant messaging or converged IP messaging [CPM]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0618—Block ciphers, i.e. encrypting groups of characters of a plain text message using fixed encryption transformation
- H04L9/0631—Substitution permutation network [SPN], i.e. cipher composed of a number of stages or rounds each involving linear and nonlinear transformations, e.g. AES algorithms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
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Abstract
Modified determines system based on the temporary social network of computer big data: information acquisition unit, for the dominant information of user, recessive information to be sent to information classifying unit；Information classifying unit, for by encrypted information and not needing encryption information and being sent to identity authenticating unit；Identity authenticating unit, for verifying subscriber identity information；Collection establishes unit, indicates the probability of user behavior for connecting each mapping relations for concentrating each element multi-to-multi；First array establishes unit, for establishing the probability array that above three relationship array in unit establishes user property by the first array；Second array establishes unit, for calculating the correlation and their respective activity trajectories of different user activity trajectory, to delimit User Activity region；Presumption units, for carrying out probabilistic forecasting to user behavior；Interim social activity task allocation unit, is allocated for the role to each user；Group's recommendation unit, for carrying out group recommendation.
Description
Technical field
The present invention relates to network social intercourse technical field, in particular to interim society of a kind of modified based on computer big data
Network is handed over to determine system.
Background technique
With the fast development of social networks, the various network structures to come in every shape are presented in social networks and network closes
System.Online social networks has been increasingly becoming connection disparate networks information and the indispensable tie of mankind's real world.To social activity
The depth profiling of network can help people better understand the construction mechanism of social networks, in network user behavior pattern and net
The evolutionary process of network structure.
Due in network social intercourse, the presence of various temporary relations, thus the intimate degree of customer relationship can not be referred to
Scalarization.
Summary of the invention
In view of this, the present invention proposes that a kind of modified determines system based on the temporary social network of computer big data.
A kind of modified determines system based on the temporary social network of computer big data comprising such as lower unit:
Information acquisition unit, for input information and instant messaging information, social network information based on each user
The dominant information and recessive information of user are obtained, and the dominant information of user, recessive information are sent to information classifying unit；
Information classifying unit needs encryption information and does not need to encrypt for dominant information and recessive information to be divided into
Information；By AES encryption algorithm to needing encryption information to encrypt, and by encrypted information and does not need encryption information and be sent to
Identity authenticating unit；
Identity authenticating unit, when verifying does not pass through, is terminated for verifying subscriber identity information；It, will when being verified
By encrypted information and does not need encryption information and be sent to that collection establishes unit, the first array establishes unit, second array is built
Vertical unit；
Collection establishes unit, for receiving the dominant information and recessive information of simultaneously decrypted user, and establishes user accordingly and exists
The space bit that future time divides collection in social networks, each period corresponds respectively to user behavior collection, user behavior occurs
Set the inducement collection that collection and user behavior occur；User is indicated by above-mentioned each mapping relations for concentrating each element multi-to-multi
Behavior it is probability；
First array establishes unit, for receive and decrypted user dominant information and recessive information, and accordingly to
Mapping relations array is established in family and its corresponding user role or occupation, is established user role or professional transformational relation array, is built
Subordinate relation array between vertical user role or occupation and user property, establishes above three relationship in unit by the first array
Array establishes the probability array of user property；
Second array establishes unit, for receiving the dominant information and recessive information of simultaneously decrypted user, and establishes accordingly
Mapping relations array between different user collection establishes user and position mapping relations array；Second array is established two in unit
A map array combines, and the correlation and their respective activity trajectories of different user activity trajectory is calculated, to delimit
User Activity region；
Presumption units carry out probabilistic forecasting to user behavior for establishing the probability of user behavior in unit according to collection；
The attribute of user is predicted according to the probability array that the first array establishes user property in unit, and to the role's of user
Migration is analyzed and predicted, and is inferred when user role is migrated to the occupation of user；
Interim social activity task allocation unit, for receiving different types of temporary social network building demand, and according to collection
The probability of user behavior in unit is established, the probability and second array that the first array establishes user property in unit is established
The mapping relations array between different user collection, user user corresponding with the selection of position mapping relations array in unit, which is constituted, to be faced
When social networks, and the role of each user is allocated；
Group's recommendation unit, for carrying out group according to the temporary social network established in interim social task allocation unit
Property recommend.
It is determined in system in modified of the present invention based on the temporary social network of computer big data, the supposition
Unit further includes carrying out the recommendation of similar users to user according to the probability array that the first array establishes user property in unit.
It is determined in system in modified of the present invention based on the temporary social network of computer big data, collection is established single
Member includes: to establish future time division collection T={ t in the social networks where user collects U1,t2,t3,...tn}；
Each period corresponds respectively to user behavior collectionWhereinIndicate t1Moment is used
The event that family is engaged in, and sequentially analogize；
The set of spatial locations of user behavior generation is defined respectivelyWith inducement collectionWhereinWithRespectively indicate t1Moment user behavior occur when spatial position and
The inducement for causing user behavior to occur；
The probability of user behavior is expressed as follows: U → T → L → E → W, wherein each concentration each element is followed successively by multi-to-multi
Mapping relations.
It is determined in system in modified of the present invention based on the temporary social network of computer big data, described second
Array establishes unit
Mapping relations array M is established to user and its corresponding user role or occupationU→R, line identifier sequence { MU1,MU2,
MU3..., MUnAnd column mark sequence { MR1, MR2, MR3..., MRmRespectively indicate user collect corresponding with each user user role or
Occupation collection；Numerical value corresponding to the intersection of row and column indicates that mapping relations array is one therebetween with the presence or absence of mapping relations
The Boolean array of a n*m rank, ranks crossover values are that there are mapping relations for both 1 expressions, indicate no mapping relations for 0；
It establishes to user role or professional transformational relation array RF→B, line identifier sequence { RF1,RF2,RF3…,RFnHe Liebiao
Know sequence { RB1,RB2,RB3…,RBnRespectively indicate the user role for converting front and back or occupation；Number corresponding to the intersection of row and column
Probability of the value to indicate user role or occupation conversion；
Array RF→BIt is as follows:
For,Its closed should all be kept after showing user role or occupation before switching；In
After user role or occupation before switching in one closed space, existShow user role or occupation
When remaining unchanged, constant transformational relation probability is 0；
Establish the subordinate relation array M between user role or occupation and user propertyR→A, line identifier sequence { MR1,MR2,
MR3…,MRnAnd column mark sequence { MA1,MA2,MA3…,MAnRespectively correspond user role or occupational category and user property class
Not, numerical value corresponding to the intersection of row and column is put to the proof to indicate between user role or occupation and user property with the presence or absence of subordinate
Relationship；
Array MR→AIt is as follows:
Subordinate relation array MR→ACorresponding is a Boolean array, show be between user role or occupation and user property
No there are subordinate relation, otherwise it is 0 that ranks crossover probability, which is 1, if it exists；
According to array MU→R、RF→B、MR→AArray operation relationship is established to the probability of user property:
Uproperty=MU→R×MR→A×RF→B
Wherein UpropertyIndicate the probability array of user property.
It is determined in system in modified of the present invention based on the temporary social network of computer big data, second array
Establishing unit includes:
Establish the mapping relations array M between different user collectionU→U', wherein line identifier sequence { U1,U2,U3…,UnAnd column
Identify sequence { U '1,U’2,U’3…,U’nRespectively indicate the customer relationship that user collects foundation or rupture between U and U '；Row and column
Intersection corresponding to numerical value to indicate whether establish mapping relations between different user's collection；
Array MU→U'It is as follows:
Array MU→U'In, corresponding mapping relations array between different user collection is a Boolean array, for indicating
Social relationships or the relationship whether are established between user to be caused to rupture for some reason, if opening relationships between user
Ranks crossover probability is 1, and ranks crossover probability is 0 if not setting up relationship between user, if the relationship between user is due to certain original
Thus leading to rupture, then crossover probability is -1；
Establish user and position mapping relations array MU→L
Array MU→LEach position corresponding to middle User Activity is one there are certain probability, the value of the probability
Numerical value between 0-1, numerical value show that more greatly the movable frequency of the user in the position is bigger, to illustrate that the position is to use
The movable common position in family.
Implement modified provided by the invention and system and existing skill are determined based on the temporary social network of computer big data
Art, which is compared, has the advantages that matrix by establishing various set, can be by the presence of various temporary relations, thus nothing
Method carries out quantification of targets to the intimate degree of customer relationship；And the dominant information of above-mentioned user and recessive information are added
It is close and be sent to that collection establishes unit, the first array establishes unit, second array establishes unit, information can be greatly improved and transmitted
Safety in the process；In addition, by group's recommendation unit according to the interim social activity established in interim social task allocation unit
Network carry out group recommendation, be different from personalized recommendation, can targetedly carry out group recommendation, improve interest,
Like the efficiency recommended and covering surface.Further, by setting information taxon, by dominant information and recessive information
Being divided into needs encryption information and does not need encryption information；By AES encryption algorithm to needing encryption information to encrypt, and will be after encryption
Information and do not need encryption information and be sent to identity authenticating unit；By identity authenticating unit, subscriber identity information is verified,
When verifying does not pass through, terminate；When being verified, by encrypted information and encryption information will not needed it is sent to collection to build
Vertical unit, the first array establishes unit, second array establishes unit.The data volume of encryption and decryption is not only reduced, while simultaneous
The safety of data transmission is cared for.
Detailed description of the invention
Fig. 1 is that the modified of the embodiment of the present invention determines system structure frame based on the temporary social network of computer big data
Figure.
Specific embodiment
As shown in Figure 1, in view of the drawbacks of the prior art, the invention proposes a kind of modifieds based on computer big data
Temporary social network determines system comprising such as lower unit:
Information acquisition unit, for input information and instant messaging information, social network information based on each user
The dominant information and recessive information of user are obtained, and the dominant information of user, recessive information are sent to information classifying unit.
Information classifying unit needs encryption information and does not need to encrypt for dominant information and recessive information to be divided into
Information；By AES encryption algorithm to needing encryption information to encrypt, and by encrypted information and does not need encryption information and be sent to
Identity authenticating unit.
Identity authenticating unit, when verifying does not pass through, is terminated for verifying subscriber identity information；It, will when being verified
By encrypted information and does not need encryption information and be sent to that collection establishes unit, the first array establishes unit, second array is built
Vertical unit.
Collection establishes unit, for receiving the dominant information and recessive information of simultaneously decrypted user, and establishes user accordingly and exists
The space bit that future time divides collection in social networks, each period corresponds respectively to user behavior collection, user behavior occurs
Set the inducement collection that collection and user behavior occur；User is indicated by above-mentioned each mapping relations for concentrating each element multi-to-multi
Behavior it is probability；
First array establishes unit, for receive and decrypted user dominant information and recessive information, and accordingly to
Mapping relations array is established in family and its corresponding user role or occupation, is established user role or professional transformational relation array, is built
Subordinate relation array between vertical user role or occupation and user property, establishes above three relationship in unit by the first array
Array establishes the probability array of user property；
Second array establishes unit, for receiving the dominant information and recessive information of simultaneously decrypted user, and establishes accordingly
Mapping relations array between different user collection establishes user and position mapping relations array；Second array is established two in unit
A map array combines, and the correlation and their respective activity trajectories of different user activity trajectory is calculated, to delimit
User Activity region；
Presumption units carry out probabilistic forecasting to user behavior for establishing the probability of user behavior in unit according to collection；
The attribute of user is predicted according to the probability array that the first array establishes user property in unit, and to the role's of user
Migration is analyzed and predicted, and is inferred when user role is migrated to the occupation of user；
Interim social activity task allocation unit, for receiving different types of temporary social network building demand, and according to collection
The probability of user behavior in unit is established, the probability and second array that the first array establishes user property in unit is established
The mapping relations array between different user collection, user user corresponding with the selection of position mapping relations array in unit, which is constituted, to be faced
When social networks, and the role of each user is allocated；
Group's recommendation unit, for carrying out group according to the temporary social network established in interim social task allocation unit
Property recommend.
Optionally, the modified described in the embodiment of the present invention determines system based on the temporary social network of computer big data
In system, the presumption units further include carrying out phase to user according to the probability array that the first array establishes user property in unit
Like the recommendation of user.
Optionally, the modified described in the embodiment of the present invention determines system based on the temporary social network of computer big data
In system, it includes: to establish future time division collection T={ t in the social networks where user collects U that collection, which establishes unit,1,t2,t3,
...tn}；
Each period corresponds respectively to user behavior collectionWhereinIndicate t1Moment is used
The event that family is engaged in, and sequentially analogize；
The set of spatial locations of user behavior generation is defined respectivelyWith inducement collectionWhereinWithRespectively indicate t1Moment user behavior occur when spatial position and lead
The inducement for causing user behavior to occur；
The probability of user behavior is expressed as follows: U → T → L → E → W, wherein each concentration each element is followed successively by multi-to-multi
Mapping relations.
Optionally, the modified described in the embodiment of the present invention determines system based on the temporary social network of computer big data
In system, the second array establishes unit and includes:
Mapping relations array M is established to user and its corresponding user role or occupationU→R, line identifier sequence { MU1,MU2,
MU3..., MUnAnd column mark sequence { MR1, MR2, MR3..., MRmRespectively indicate user collect corresponding with each user user role or
Occupation collection；Numerical value corresponding to the intersection of row and column indicates that mapping relations array is one therebetween with the presence or absence of mapping relations
The Boolean array of a n*m rank, ranks crossover values are that there are mapping relations for both 1 expressions, indicate no mapping relations for 0；
It establishes to user role or professional transformational relation array RF→B, line identifier sequence { RF1,RF2,RF3…,RFnHe Liebiao
Know sequence { RB1,RB2,RB3…,RBnRespectively indicate the user role for converting front and back or occupation；Number corresponding to the intersection of row and column
Probability of the value to indicate user role or occupation conversion；
Array RF→BIt is as follows:
For,Its closed should all be kept after showing user role or occupation before switching；In
After user role or occupation before switching in one closed space p, existShow user role or duty
When industry remains unchanged, constant transformational relation probability is 0；
Establish the subordinate relation array M between user role or occupation and user propertyR→A, line identifier sequence { MR1,MR2,
MR3…,MRnAnd column mark sequence { MA1,MA2,MA3…,MAnRespectively correspond user role or occupational category and user property class
Not, numerical value corresponding to the intersection of row and column is put to the proof to indicate between user role or occupation and user property with the presence or absence of subordinate
Relationship；
Array MR→AIt is as follows:
Subordinate relation array MR→ACorresponding is a Boolean array, show be between user role or occupation and user property
No there are subordinate relation, otherwise it is 0 that ranks crossover probability, which is 1, if it exists；
According to array MU→R、RF→B、MR→AArray operation relationship is established to the probability of user property:
Uproperty=MU→R×MR→A×RF→B
Wherein UpropertyIndicate the probability array of user property.
The influence factor for influencing the variation of user property probability of occurrence can be concluded: the time, user's social relationships, is used position
It is engaged in work etc. in family.User can change in the user property of different times, such as user usually completes to learn in 6-22 one full year of life
Industry, therefore have every attribute of student, but after 22 one full year of life, user can enter society and be engaged in different occupation, play the part of different society
Role, therefore have the user property of different society role.User can change in the user property of different location, such as with
Family is taught in classroom, he has the user property of teacher, but he buys commodity in market, and the user that he then has consumer belongs to
Property.The social relationships of user have an impact the transformation of user property, for example, teacher-student, supplier-consumer, doctor-
There is a possibility that mutually converting between the various social relationships such as patient.Therefore, when social relationships change, user belongs to
Property changes therewith.
Optionally, the modified described in the embodiment of the present invention determines system based on the temporary social network of computer big data
In system, second array establishes unit and includes:
Establish the mapping relations array M between different user collectionU→U', wherein line identifier sequence { U1,U2,U3…,UnAnd column
Identify sequence { U '1,U’2,U’3…,U’nRespectively indicate the customer relationship that user collects foundation or rupture between U and U '；Row and column
Intersection corresponding to numerical value to indicate whether establish mapping relations between different user's collection；
Array MU→U'It is as follows:
Array MU→U'In, corresponding mapping relations array between different user collection is a Boolean array, for indicating
Social relationships or the relationship whether are established between user to be caused to rupture for some reason, if opening relationships between user
Ranks crossover probability is 1, and ranks crossover probability is 0 if not setting up relationship between user, if the relationship between user is due to certain original
Thus leading to rupture, then crossover probability is -1；
Establish user and position mapping relations array MU→L
Array MU→LEach position corresponding to middle User Activity is one there are certain probability, the value of the probability
Numerical value between 0-1, numerical value show that more greatly the movable frequency of the user in the position is bigger, to illustrate that the position is to use
The movable common position in family.
Customer relationship it is probability, refer to the constantly changed uncertainty of association between different user.User is closed
The foundation and conversion of system, by the driving of the various inducements such as social demand, user's subjective desire, while also by mutual between user
The restriction of effect.Inducement driving can not be predicted, but it causes the multiple historical behavior of user in advance before customer incident generation
It repeats, but creates opportunity for the probabilistic forecasting of customer relationship.In addition, what the interaction, user's speech between user were propagated
The factors such as influence power can impact the probability of customer relationship.For example, mutual between the self-recommendation or user that pass through user
Recommend, customer relationship can lead to by report of the communication media (newspaper, internet, broadcast etc.) to related person or event
It establishes or ruptures.
It is understood that for those of ordinary skill in the art, can make in accordance with the technical idea of the present invention
Various other changes and modifications, and all these changes and deformation all should belong to the protection scope of the claims in the present invention.
Claims (5)
1. a kind of modified determines system based on the temporary social network of computer big data, which is characterized in that it includes as follows
Unit:
Information acquisition unit, for input information and instant messaging information, social network information acquisition based on each user
The dominant information and recessive information of user, and the dominant information of user, recessive information are sent to information classifying unit；
Information classifying unit needs encryption information and does not need encryption to believe for dominant information and recessive information to be divided into
Breath；By Advanced Encryption Standardalgorithm AES to needing encryption information to encrypt, and by encrypted information and encryption information is not needed
It is sent to identity authenticating unit；
Identity authenticating unit, when verifying does not pass through, is terminated for verifying subscriber identity information；When being verified, will add
Information after close and do not need encryption information be sent to collection establishes unit, the first array establishes unit, second array establish it is single
Member；
Collection establishes unit, for receiving the dominant information and recessive information of simultaneously decrypted user, and establishes user accordingly in social activity
The set of spatial locations that future time divides collection in network, each period corresponds respectively to user behavior collection, user behavior occurs
And the inducement collection that user behavior occurs；User behavior is indicated by above-mentioned each mapping relations for concentrating each element multi-to-multi
It is probability；
First array establishes unit, for receiving and the dominant information and recessive information of decrypted user, and accordingly to user and
Mapping relations array is established in its corresponding user role or occupation, is established user role or professional transformational relation array, is established and use
Subordinate relation array between family role or occupation and user property establishes above three relationship array in unit by the first array
Establish the probability array of user property；
Second array establishes unit, for receiving the dominant information and recessive information of simultaneously decrypted user, and establishes accordingly different
Mapping relations array between user's collection, establishes user and position mapping relations array；Second array is established in unit two to reflect
It penetrates array to combine, the correlation and their respective activity trajectories of different user activity trajectory is calculated, to delimit user
Zone of action；
Presumption units carry out probabilistic forecasting to user behavior for establishing the probability of user behavior in unit according to collection；According to
The probability array that first array establishes user property in unit predicts the attribute of user, and the migration of the role to user
It is analyzed and predicted, the occupation of user is inferred when user role migrates；
Interim social activity task allocation unit for receiving different types of temporary social network building demand, and is established according to collection
User behavior is probability in unit, and the probability and second array that the first array establishes user property in unit establishes unit
In different user collection between mapping relations array, corresponding with the selection of the position mapping relations array user of user constitute interim society
Network is handed over, and the role of each user is allocated；
Group's recommendation unit is pushed away for carrying out group according to the temporary social network established in interim social task allocation unit
It recommends.
2. modified as described in claim 1 determines that system, feature exist based on the temporary social network of computer big data
In the presumption units further include being carried out according to the probability array that the first array establishes user property in unit to user similar
The recommendation of user.
3. modified as claimed in claim 2 determines that system, feature exist based on the temporary social network of computer big data
In it includes: to establish future time division collection T={ t in the social networks where user collects U that collection, which establishes unit,1,t2,t3,
...tn}；
Each period corresponds respectively to user behavior collectionWhereinIndicate t1Moment user from
The event of thing, and sequentially analogize；
The set of spatial locations of user behavior generation is defined respectivelyWith inducement collectionWhereinWithRespectively indicate t1Moment user behavior occur when spatial position and lead
The inducement for causing user behavior to occur；
The probability of user behavior is expressed as follows: U → T → L → E → W, wherein each concentration each element is followed successively by reflecting for multi-to-multi
Penetrate relationship.
4. modified as claimed in claim 2 determines that system, feature exist based on the temporary social network of computer big data
In first array establishes unit and includes:
Mapping relations array M is established to user and its corresponding user role or occupationU→R, line identifier sequence { MU1,MU2,
MU3..., MUnAnd column mark sequence { MR1, MR2, MR3..., MRmRespectively indicate user collect corresponding with each user user role or
Occupation collection；Numerical value corresponding to the intersection of row and column indicates that mapping relations array is one therebetween with the presence or absence of mapping relations
The Boolean array of a n*m rank, ranks crossover values are that there are mapping relations for both 1 expressions, indicate no mapping relations for 0；
It establishes to user role or professional transformational relation array RF→B, line identifier sequence { RF1,RF2,RF3…,RFnAnd column mark sequence
Arrange { RB1,RB2,RB3…,RBnRespectively indicate the user role for converting front and back or occupation；Numerical value corresponding to the intersection of row and column is used
To indicate the probability of user role or occupation conversion；
Array RF→BIt is as follows:
For,Its closed should all be kept after showing user role or occupation before switching；At one
After user role or occupation before switching in closed space, existShow that user role or occupation are kept
When constant, constant transformational relation probability is 0；
Establish the subordinate relation array M between user role or occupation and user propertyR→A, line identifier sequence { MR1,MR2,MR3…,
MRnAnd column mark sequence { MA1,MA2,MA3…,MAnUser role or occupational category and user property classification are respectively corresponded, it puts to the proof
Numerical value corresponding to the intersection of row and column is to indicate between user role or occupation and user property with the presence or absence of subordinate relation；
Array MR→AIt is as follows:
Subordinate relation array MR→ACorresponding is a Boolean array, shows whether deposit between user role or occupation and user property
In subordinate relation, otherwise it is 0 that ranks crossover probability, which is 1, if it exists；
According to array MU→R、RF→B、MR→AArray operation relationship is established to the probability of user property:
Uproperty=MU→R×MR→A×RF→B
Wherein UpropertyIndicate the probability array of user property.
5. modified as claimed in claim 4 determines that system, feature exist based on the temporary social network of computer big data
In second array establishes unit and includes:
Establish the mapping relations array M between different user collectionU→U', wherein line identifier sequence { U1,U2,U3…,UnAnd column mark
Sequence { U '1,U’2,U’3…,U’nRespectively indicate the customer relationship that user collects foundation or rupture between U and U '；The friendship of row and column
The corresponding numerical value of fork is to indicate whether different users establishes mapping relations between collecting；
Array MU→U'It is as follows:
Array MU→U'In, corresponding mapping relations array between different user collection is a Boolean array, for indicating user
Between whether establish social relationships or the relationship and cause to rupture for some reason, the ranks if opening relationships between user
Crossover probability is 1, if not setting up relationship between user ranks crossover probability be 0, if the relationship between user for some reason and
Leading to rupture, then crossover probability is -1；
Establish user and position mapping relations array MU→L
Array MU→LEach position corresponding to middle User Activity there are certain probability, the value of the probability be one between
Numerical value between 0-1, numerical value show that more greatly the movable frequency of the user in the position is bigger, to illustrate that the position is that user is living
Dynamic common position.
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