CN110136041A - Artificial population synthetic method, system, device based on multiple social relationships constraint - Google Patents
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Abstract
The invention belongs to computer societies to emulate field, and in particular to a kind of artificial population synthetic method based on multiple social relationships constraint, system, device, it is intended to solve the problems, such as that the synthesis of artificial population can not handle distribution deviation and multiple social relationships constraint simultaneously.This system method includes obtaining the full attribute entities number of social entity；The full attribute entities number of social entity is converted into population number of individuals；The public attribute of full attribute entities number and population number of individuals based on social entity calculates original demographic's distribution, and remaining attribute obtains the population distribution of all properties to original demographic's distributed expansion；Calculate the corresponding social entity's distribution of each attribute in the population distribution of all properties；According to member composition relationship, the population distribution of all properties social entity's distribution corresponding with each organizational attribution is associated, individual data items collection and social entity's collection are obtained.The present invention can synthesize the virtual population of a variety of social relationships and will synthesize population and each social input constraint control errors to minimum.
Description
Technical field
The invention belongs to computer societies to emulate field, and in particular to a kind of artificial people based on multiple social relationships constraint
Mouth synthetic method, system, device.
Background technique
Computer Simulation has become traffic trip modeling and simulation, complicated economy behavioural analysis, city land plan, war
Strive evolution and the important assistant analysis means such as deduction, transmission and control, diffusion of information of fighting, and artificial population is then point
The basis of analysis.Artificial population is reference with reality, has built the virtual community taken out from reality, is extensive group
The analysis of body problem provides a real alternate version.The advantage of artificial population is with lower cost simulation people's
The feasibility and validity of the policies such as traffic control, social management are examined in trip and other social actions.Meanwhile individual behavior
Computation model can emulate to obtain quantitative evaluation result, provide reference for the quantity decision of administrative department.
Artificial population is constructed to need to generate corresponding population individual record collection first according to the statistical data of de facto population
It closes.When artificial population can accurately reflect attribute, structure and the characteristic distributions of real population, obtained trip simulates, is economical
The simulation results such as activity, urban evolution just have higher confidence level.Currently, there are two main classes for the synthetic method of artificial population, and
It only considered family relationship.The first kind is known as distribution method, and the conceptual data based on family and population has independently produced household entities
Set and population individual collections are chosen from population set then according to the feature-set allocation rule of each family and meet item
The population individual of part is assigned in family.Second class is known as fitting process, as a whole by the attribute variable of family and population, by two
Macroscopical conceptual data of a level is collectively as constraint condition, and optimization generates household entities set, then further according to each family
Feature generate its member, thus obtain population individual.The advantage of distribution method is to meet the forming process of social relationships, relatively more straight
It sees.But most important defect be not can guarantee population individual can " entirely accurate " be assigned in all families, lead to family
Entity (population individual) exhausts and population individual (household entities) is still a large amount of remaining, so that very large deviation occurs in result.Fitting process
The case where total deviation can be controlled, but a variety of social relationships constraints (such as family, enterprise, school, hospital) can not be handled.For existing
Methodical deficiency both can control total inclined The present invention gives a kind of artificial population generation method for considering multiple social relationships
Difference, and be avoided that between population individual and social entity and occur largely mismatching.
Summary of the invention
In order to solve the above problem in the prior art, can not be handled simultaneously point in order to solve artificial population synthetic method
The problem of constraining with deviation and multiple social relationships, first aspect present invention propose a kind of based on multiple social relationships constraint
Artificial population synthetic method, this method comprises:
Step S10 obtains the full attribute entities of each social entity in input data based on default social entity's type
Number；
Step S20, each member composition relationship based on social entity, each society that step S10 is obtained are real
The full attribute entities number of body is converted to the corresponding population number of individuals of personal attribute；
Step S30, the public attribute in the full attribute entities number based on social entity and population number of individuals, calculates all public affairs
Original demographic's distribution under attribute altogether；Will in the full attribute entities number and population number of individuals of social entity except the public attribute it
Outer other attributes as remaining attribute, and based on the remaining attribute to original demographic under all public attributes be distributed into
Row extension, obtains the population distribution comprising all properties；
Step S40, the member composition based on the population distribution comprising all properties, according to each social entity's type
Relationship calculates the corresponding social entity's distribution of each organizational attribution；
Step S50, according to the member composition relationship of social entity's type, by step S30 obtain described in comprising all categories
Property the distribution of population distribution corresponding with each organizational attribution that step S40 is obtained social entity be associated, obtain comprising society
The individual data items collection of relationship and each type of social entity's data set.
In some preferred embodiments, " each member composition relationship based on social entity, will in step S20
The full attribute entities number for each social entity that step S10 is obtained is converted to the corresponding population number of individuals of personal attribute ",
Calculation method is as follows:
Wherein, Q(k)For the full attribute entities number of kLei social entity,For kLei social entity
The value of attribute variable, P(k)For the corresponding population number of individuals of kth class personal attribute, (x1,x2,…,xn) it is n population individual category
Property variable value, n be population variable value, mkFor attribute variable's numerical value of kZhong social entity.
In some preferred embodiments, " full attribute entities number and population individual based on social entity in step S30
Public attribute in number calculates original demographic's distribution under all public attributes ", calculation method is as follows:
P(x1,x2,…,xu)=λ0·P(0)(x1,x2,…,xu)+…+λK·P(K)(x1,x2,…,xu)
Wherein, P (x1,x2,…,xu) it is that original demographic is distributed, (x1,x2,…,xu) be u public attribute a kind of value
Combination, K is the type number of all social entities, (λ0,…,λK) it is weight coefficient.
In some preferred embodiments, " by the full attribute entities number and population number of individuals of social entity in step S30
In other attributes in addition to the public attribute as remaining attribute, and based on the remaining attribute to all public categories
Property under original demographic distribution be extended, obtain the population distribution comprising all properties ", method are as follows:
In remaining attribute, the public variable appeared in k kind social entity is chosen, if it does not exist, successively decreasing, it is public to find
Variable；Wherein, initial k value is K+1；
If k ≠ 1, calculate spreading coefficient, according to spreading coefficient extension original demographic distribution, extended original demographic distribution after
Continuous previous step finds public variable；
If k=1, remaining every attribute is extended, the population distribution comprising all properties is obtained.
In some preferred embodiments, " based on the population distribution comprising all properties, foundation in step S40
The member composition relationship of each social entity's type calculates the corresponding social entity's distribution of each organizational attribution ", calculation method
It is as follows:
Wherein,For the kLei social entity number under respective attributes value, μ is balance social entity's total number constraint
With the weight of population total number constraint.
In some preferred embodiments, it " according to the member composition relationship of social entity's type, will be walked in step S50
The population distribution comprising all properties social entity corresponding with each organizational attribution that step S40 is obtained that rapid S30 is obtained
Distribution is associated, and obtains the individual data items collection and each type of social entity's data set comprising social relationships ", method
Are as follows:
By step S30 obtain described in the population distribution comprising all properties and the obtained each organizational attribution pair of step S40
The social entity's distribution answered, generates population individual collections and social entity's set respectively；
It is associated from individual is chosen in population set with the entity in social entity's set；
It is successfully associated, the entity number that individual number is social entity is set.
The second aspect of the present invention proposes a kind of artificial population synthesis system based on multiple social relationships constraint, should
System includes synthesis module, conversion module, expansion module, reverse computing module, relating module；
The synthesis module is configured to default social entity's type, and it is real to obtain each society in input data
The full attribute entities number of body；
The conversion module is configured to each member composition relationship of social entity, synthesis module is obtained
Each social entity full attribute entities number, be converted to the corresponding population number of individuals of personal attribute；
The expansion module, the public category being configured in the full attribute entities number and population number of individuals of social entity
Property, calculate original demographic's distribution under all public attributes；It will be removed in the full attribute entities number and population number of individuals of social entity
Other attributes except the public attribute are based on the remaining attribute under all public attributes as remaining attribute
Original demographic's distribution is extended, and obtains the population distribution comprising all properties；
The reverse computing module is configured to the population distribution comprising all properties, according to each society
The member composition relationship of entity type calculates the corresponding social entity's distribution of each organizational attribution；
The relating module is configured to the member composition relationship according to social entity's type, expansion module is obtained
The population distribution comprising all properties social entity corresponding with each organizational attribution that reverse computing module obtains be distributed into
Row association, obtains the individual data items collection and each type of social entity's data set comprising social relationships.
The third aspect of the present invention proposes a kind of storage device, wherein be stored with a plurality of program, described program apply by
Processor loads and executes the above-mentioned artificial population synthetic method based on multiple social relationships constraint.
The fourth aspect of the present invention proposes a kind of processing setting, including processor, storage device；Processor is suitable for
Execute each program；Storage device is suitable for storing a plurality of program；Described program is suitable for being loaded by processor and being executed above-mentioned
Based on multiple social relationships constraint artificial population synthetic method.
Beneficial effects of the present invention:
The present invention can synthesize the virtual population comprising a variety of social relationships and input synthesis population and each society about
Beam control errors to minimum.The present invention gives the synthetic methods of a variety of social relationships constraint servant worker mouthful, and choose population
Starting distribution of the weighted mean of constraint and social relationships constraint as individual distribution, is conducive to reduce final result and each constraint
Between overall error.Meanwhile this method does not need sample input, reduces the requirement to data source.
Detailed description of the invention
By reading the detailed description done to non-limiting embodiment done referring to the following drawings, the application other
Feature, objects and advantages will become more apparent upon.
Fig. 1 is that the process of the artificial population synthetic method based on multiple social relationships constraint of an embodiment of the present invention is shown
It is intended to；
Fig. 2 is the detailed stream of the artificial population synthetic method based on multiple social relationships constraint of an embodiment of the present invention
Journey schematic diagram；
The frame signal of the artificial population synthesis system based on multiple social relationships constraint of Fig. 3 an embodiment of the present invention
Figure.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to the embodiment of the present invention
In technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, without
It is whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not before making creative work
Every other embodiment obtained is put, shall fall within the protection scope of the present invention.
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is only used for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to just
Part relevant to related invention is illustrated only in description, attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.
Artificial population synthetic method based on multiple social relationships constraint of the invention, as shown in Figure 1, including following step
It is rapid:
Step S10 obtains the full attribute entities of each social entity in input data based on default social entity's type
Number；
Step S20, each member composition relationship based on social entity, each society that step S10 is obtained are real
The full attribute entities number of body is converted to the corresponding population number of individuals of personal attribute；
Step S30, the public attribute in the full attribute entities number based on social entity and population number of individuals, calculates all public affairs
Original demographic's distribution under attribute altogether；Will in the full attribute entities number and population number of individuals of social entity except the public attribute it
Outer other attributes as remaining attribute, and based on the remaining attribute to original demographic under all public attributes be distributed into
Row extension, obtains the population distribution comprising all properties；
Step S40, the member composition based on the population distribution comprising all properties, according to each social entity's type
Relationship calculates the corresponding social entity's distribution of each organizational attribution；
Step S50, according to the member composition relationship of social entity's type, by step S30 obtain described in comprising all categories
Property the distribution of population distribution corresponding with each organizational attribution that step S40 is obtained social entity be associated, obtain comprising society
The individual data items collection of relationship and each type of social entity's data set.
In order to more clearly to the present invention is based on the artificial population synthetic methods that multiple social relationships constrain to be illustrated, under
Face carries out expansion detailed description to each step in a kind of embodiment of the method for the present invention in conjunction with attached drawing 1 and attached drawing 2.
Step S10 obtains the full attribute entities of each social entity in input data based on default social entity's type
Number.
In the present embodiment, the constrained edge distribution frequency and member composition for obtaining the every a kind of social entity of survey data are closed
System, including three parts: (1) the total population statistics frequency disribution (data can be issued using statistics bureau) that census obtains；
(2) social entity (such as family, enterprise, school) overall distribution frequency that various social investigations obtain (can use statistics bureau
Or authorities issue data)；(3) the member composition relationship of every a kind of social entity (can be issued using social investigation result
Or the data calculated).
Based on population and family, two class social relationships of enterprise, social entity's quantitative value K=2 at this time, ascribed characteristics of population variables set
Are as follows: family type, place province, residence type, age bracket, with capitalization (X1,X2,…,Xn) indicate the ascribed characteristics of population variable
Set.With corresponding lowercase (x1,x2,…,xn) indicating the value of variable, value collection is combined into (I1,I2,…,In), i.e.,
(x1∈I1,x2∈I2,…,xn∈In).Population variable numerical value n=4 at this time, ascribed characteristics of population variable and value are as shown in table 1:
Table 1
| Name variable | Corresponding variable symbol | Variable-value | Value number | Corresponding value set symbol |
| Family type | X1 | Family, the collective ownership of an enterprise | 2 | I1 |
| Place province | X2 | Beijing, Tianjin, the Inner Mongol ... | 31 | I2 |
| Residence type | X3 | City, town, rural area | 3 | I3 |
| Age bracket | X4 | 0-19,20-64, >=65 | 3 | I4 |
Similarly, with capitalizationIndicate the attribute variable of kth class (1≤k≤K) social entity, with
Corresponding lowercaseIndicate the value of variable, value collection is combined intoI.e.mkFor attribute variable's number of kZhong social entity.
Family attribute variable are as follows: family type, place province, residence type, number of members, old man's number, teenage number, this
Shi Jiating variable value m1=6.Family attribute variable and value are as shown in table 2:
Table 2
Enterprise attributes variable are as follows: place province, the type of business, scope of the enterprise, at this time enterprise's variable value m2=3.Enterprise
Attribute variable and value are as shown in table 3:
Table 3
It is as shown in table 4 that three classes input edge distribution:
Table 4
Such as: the member composition relationship of family is indicated as shown in formula (1) (2) (3):
PerNum (age bracket=0-19 | family type, place province, residence type)=∑Number of members, old man's numberTeenage number
× HHNum (number of members, old man's number, teenage number | family type, place province, residence type) (1)
PerNum (age bracket=>=65 | family type, place province, residence type)=∑Number of members, teenage numberOld man's number ×
HHNum (number of members, old man's number, teenage number | family type, place province, residence type) (2)
PerNum (age bracket=20-64 | family type, place province, residence type)=∑Old man's number, teenage number(number of members-
Old man's number-teenage number) and × HHNum (number of members, old man's number, teenage number | family type, place province, residence class
Type) (3)
Wherein, PerNum represents population, and HHNum represents family's number.
Shown in employee's component relationship such as formula (4) of enterprise:
PerNum (age bracket=20-64 | place province)=∑The type of business, scope of the enterprise(staff number) × EnterNum (enterprise-class
Type, scope of the enterprise | place province) (4)
Wherein, EnterNum represents enterprise's number, staff number by under scope of the enterprise of all categories total population and total enterprise's number estimate
It calculates.
By only the population comprising part attribute, family, enterprise object frequency are respectively synthesized comprising entire population's variable in table 4
The population Joint Distribution frequency of (family type, place province, residence type, age bracket), the family comprising whole family's attributes
Joint Distribution frequency, and the cartel distribution frequency comprising enterprise's whole attribute.Such as: totally divided by the Partial Variable of population
Cloth frequency P(0){ residence=Haidian, gender=male, age=25-29 } and P(0){ gender=male, age=25-29, cultural journey
Degree=senior middle school } calculate its Joint Distribution frequency: P(0)Residence=Haidian, and gender=male, the age=25-29, schooling=
Senior middle school }.
It can thus be concluded that macroscopical population individual sum constraint: place province × residence type × family type × age bracket (P
(0)), as shown in formula (5):
P(0)=P(0)(x1,x2,…,xn) (5)
Macroscopical household entities sum constraint: place province × residence type × family type × number of members × old man's number × not
Adult number (Q (1))；Macroscopical business entity's sum constraint: the place province × type of business × scope of the enterprise (Q(2)).Therefore, often
A kind of full attribute entities number Q of social entity(k)As shown in formula (6):
Step S20, each member composition relationship based on social entity, each society that step S10 is obtained are real
The full attribute entities number of body is converted to the corresponding population number of individuals of personal attribute.
In the present embodiment, to every a kind of social entity, by each obtained social entity of member composition and step S10
Full attribute entities number Q(k)Calculate the Joint Distribution of corresponding population level, i.e. population number of individuals.As shown in formula (7):
Such as:
P(k)Represent populations be transformed by kLei social entity (being family here), under respective conditions, Q(k)Generation
Family's number of attribute value is corresponded in table bracket.Old man is defined as the age in the population of over-65s.Pay attention to P(k)May only include
The attribute variable of part population individual.
Macroscopic view constraint to family is converted into size of population distribution by member composition relationship, as shown in formula (8) (9) (10):
P(1)(family type, place province, residence type, age bracket=0-19)=∑{ number of members, old man's number, teenage number }c·Q(1)
(place province, residence type, family type, number of members, old man's number, teenage number=c) (8)
P(1)(family type, place province, residence type, age bracket=>=65)=∑{ number of members, old man's number, teenage number }e·Q(1)
(place province, residence type, family type, number of members, old man's number=e, teenage number) (9)
P(1)(family type, place province, residence type, age bracket=20-64)=∑{ number of members, old man's number, teenage number }(m-e-
c)·Q(1)(place province, residence type, family type, number of members=m, old man's number=e, teenage number=c) (10)
Similarly, the macroscopic view of enterprise is constrained, is converted into size of population distribution, as shown in formula (11):
P(2)(place province, age bracket=20-64)=∑{ type of business, scope of the enterprise }m·Q(2)(place province, the type of business, enterprise
Industry scale=m) (11)
Step S30, the public attribute in the full attribute entities number based on social entity and population number of individuals, calculates all public affairs
Original demographic's distribution under attribute altogether；Will in the full attribute entities number and population number of individuals of social entity except the public attribute it
Outer other attributes as remaining attribute, and based on the remaining attribute to original demographic under all public attributes be distributed into
Row extension, obtains the population distribution comprising all properties.
Specific step is as follows:
Step S31, if P(0),P(k)The public variable of (1≤k≤K) is (X1,X2,…,Xu), (u≤n), wherein u is number
Magnitude.New population frequency is calculated according to formula (12):
P(x1,x2,…,xu)=λ0·P(0)(x1,x2,…,xu)+…+λK·P(K)(x1,x2,…,xu) (12)
Wherein,Indicate each constraint Joint Distribution pair
(X1,X2,…,Xu) except variable summation result.
(λ0≥0,…,λK>=0) it is weight coefficient, by user according to data source reliability sets itself, meetsIt indicates to except (x1,x2,…,xu) outside every other attribute value summation.If P(0),P(k)(1
≤ k≤K) in be free of public variable, then choose and appear in the attributes of most social entity's types, calculate population distribution frequency.
Step S32, enables k=K+1, does following extension:
Step S321 in remaining attribute, chooses and occurs in addition to the attribute for having included in step S31 population distribution frequency
Public variable X in k kind social entity (including population individual)u+1.The public variable for meeting the condition if it does not exist, then enable k
=k-1, continually looks for public variable.If finally obtaining k=1, show any two constraint without public variable.If k=1, turn
Step S324；Otherwise, there is k > 1, calculate shown in spreading coefficient such as formula (13):
Wherein, (i1,…,ik) it is the corresponding k subscript constrained.P(i)(x1,…,xu,xu+1) it is still Joint Distribution to guarantor
The result for staying the attribute except attribute to sum.Note that according to P(i)(x1,…,xu,xu+1) and P(i)(x1,…,xu) relationship acquire
Shown in spreading coefficient such as formula (14):
Step S322 extends population frequency according to formula (15) spreading coefficient:
P(x1,…,xu,xu+1)=P (x1,x2,…,xu)·η(xu+1) (15)
S323 goes to step S324 if k=1；Otherwise, S321 is gone to step.
Step S324 extends remaining each variable, as shown in formula (16):
xu+iIt is P(k)It is in (population constraint) from kLei social entity number constraints conversion but be not step S322
The all properties in P (the new population distribution frequency being calculated using weighted average and spreading factor) being calculated.
Above step will finally obtain the overall Joint Distribution frequency of population and all social entity's attributes.Attribute is pressed into society
Meeting entity type arrangement is as shown in formula (17):
Wherein,It is the attribute of kLei social entity, x1,…,xnIt is the attribute of population individual.
In order to more clearly from understand the embodiment in step S30, citing description herein.
Step A31, P(0)、P(1)And P(2)Public variable be place province, new population frequency is calculated, such as formula (18) institute
Show:
P (place province)=λ0·P(0)(place province)+λ1·P(1)(place province)+λ2·P(2)(place province) (18)
Wherein,
P(0)(place province)=∑~(place province)P(0)(place province, residence type, family type, age bracket)
P(1)(place province)=∑~(place province)P(1)(place province, residence type, family type, age bracket)
P(2)(place province)=∑~(place province)P(2)(place province, age bracket=20-64)
(λ0>0,λ1>0,λ2> 0) it is weight coefficient, belongs to preset value, meet λ0+λ1+λ2=1.
Step A32 enables k=K+1=3, makees following extension.
Step A321 is removed in the calculated attribute of previous step (place province), and in remaining attribute, selection appears in k=3
Public attribute in kind social entity (including population individual).Obviously, the public attribute appeared in 3 kinds of entities (individual) only has
Age bracket=20-64 calculates shown in spreading coefficient such as formula (19) (20) (21):
Step A322 extends population frequency by formula (22) spreading coefficient:
P (place province, age bracket)=P (place province) η (age bracket) (22)
Step A323 goes to step A321 because of k=3, continually looks for appearing in the public attribute in three kinds of constraints.Residue belongs to
Requirement is not satisfied in property.Therefore k=k-1=2 is enabled, (residence type, family type) is met the requirements in remaining attribute.It is by extension
After number successively extends, population frequency P (place province, residence type, family type, age bracket) is obtained.At this point, again without appearing in
Public attribute in two kinds of constraints, k=1 go to step A324.
Step A324 extends remaining variable.First investigate P(0), woth no need to the variable of extension.P is investigated again(1), expand in order
Variable is opened up, as shown in formula (23) (24) (25):
Finally investigate P(2), expansion variable in order, as shown in formula (26) (27) (28):
P (place province, residence type, family type, number of members, old man's number, teenage number, age bracket ≠ 20-64,
The type of business, scope of the enterprise)=P (place province, residence type, family type, number of members, old man's number, teenage number, year
Age section ≠ 20-64, the type of business=nothing, scope of the enterprise=nothing) (28)
Step S40, the member composition based on the population distribution comprising all properties, according to each social entity's type
Relationship calculates the corresponding social entity's distribution of each organizational attribution.
In this example, social entity is distributed shown in number calculating method such as formula (29):
Wherein,It is the kLei social entity distribution number under the respective attributes value being calculated,It is the kLei social entity number that step S20 is calculated according to survey data,It is Q(k)
The population number of individuals converted according to social entity's component relationship,It is that step S30 to step S32 is calculated
Population under the corresponding value arrived, 0 < μ < 1 are the power for balancing the constraint of social entity's total number and the constraint of population total number
Weight.
The meaning of formula (29) is, for given social entity's attribute valued combinationsAccording to its accordingly at
Member's component relationship, inversely seeks social entity's number, so that being transformed by social entity's total number deviation and by social entity
Population deviation reach minimum.
In order to more clearly from understand the embodiment in step S40, citing description herein.
It calculates shown in family's number such as formula (30):
Above formula is indicated to each (place province, residence type, family type, number of members, old man's number, teenage number)
Attribute valued combinations calculate family's volume deviation and are converted into the volume deviation of population individual by family.When two deviations weight
When (0 < μ < 1 is weight) averagely obtains minimum value, family's number at this time saves as a result.Pay attention to P(1)Value by optimized variableIt influences, the method which generally can be used alternative optimization solves.Similarly, enterprise's number is calculated, as shown in formula (31):
Step S50, according to the member composition relationship of social entity's type, by step S30 obtain described in comprising all categories
Property the distribution of population distribution corresponding with each organizational attribution that step S40 is obtained social entity be associated, obtain comprising society
The individual data items collection of relationship and each type of social entity's data set.
In the present embodiment, individual is randomly selected from population concentration first, as shown in formula (32):
Wherein,For each attribute value of the individual.
Successively investigate kth (1≤k≤K) class social entity collection.If social entity concentrates containing social with individual Ind kth class
The entity of the identical value of attribute, i.e.,Then randomly select such social entityIndividual is associated in selected social entity, individual kLei social entity subjected entity is set
Number；Otherwise, the kth class social property that the individual is arranged is onrelevant tissue.Individual Ind is saved to database, and by Ind from
Population concentration is deleted.
It examinesAssociation member whether reach its number of members upper limit.If so, willIt saves and is concentrated from kLei social entity and removed.
All individuals of population concentration are repeated up to all to be assigned and remove.
In order to more clearly from understand the embodiment in step S50, citing description herein.
According to total population Joint Distribution number (P), the familial distribution of cases number being calculatedEnterprise object numberIt generates
Corresponding population individual collection, household entities collection and business entity.
Step A511 randomly selects individual from population collection P, as shown in formula (33):
Ind (place province, residence type, family type, number of members, old man's number, teenage number, age bracket, enterprise-class
Type, scope of the enterprise) (33)
Step A512 first distributes family's attribute.IfIt is middle to there is (place province, residence type, family type, member
Number, old man's number, teenage number) variable-value household entities identical with Ind, then such household entities are randomly selected,
Ind is added in the members list of this family, family's number of Ind is set as the selected family got and is numbered；Otherwise, it is arranged
Family's attribute of Ind is " no family ".
Reallocation enterprise attributes.IfIt is middle to there is (place province, the type of business, scope of the enterprise) variable-value and Ind phase
Same business entity, then randomly select such business entity, Ind be added in the members list of this enterprise, by Ind's
Enterprise's number is set as the selected enterprise's number got；Otherwise, the enterprise attributes that Ind is arranged are " unemployed ".Ind is saved to data
Library, and Ind is deleted from population concentration.
Whether associated number of members reaches its upper limit for family selected by step A513, checking step A512 and enterprise.If so,
The family or enterprise are then saved, and removes it corresponding entity data set.
Step A514 repeats step A511 to step A513 until all individual are all removed finish in P.
A kind of artificial population synthesis system based on multiple social relationships constraint of second embodiment of the invention, such as Fig. 3 institute
Show, comprising: synthesis module 100, conversion module 200, expansion module 300, reverse computing module 400, relating module 500；
Synthesis module 100 is configured to default social entity's type, obtains each social entity in input data
Full attribute entities number；
Conversion module 200 is configured to each member composition relationship of social entity, synthesis module 100 is obtained
Each social entity full attribute entities number, be converted to the corresponding population number of individuals of personal attribute；
Expansion module 300, the public attribute being configured in the full attribute entities number and population number of individuals of social entity,
Calculate original demographic's distribution under all public attributes；It is described by being removed in the full attribute entities number and population number of individuals of social entity
Other attributes except public attribute are based on the remaining attribute to initial under all public attributes as remaining attribute
Population distribution is extended, and obtains the population distribution comprising all properties；
Reverse computing module 400 is configured to the population distribution comprising all properties, according to each social entity
The member composition relationship of type calculates the corresponding social entity's distribution of each organizational attribution；
Relating module 500 is configured to the member composition relationship according to social entity's type, expansion module 300 is obtained
The population distribution comprising all properties social entity's distribution corresponding with each organizational attribution that reverse computing module 400 obtains
It is associated, obtains the individual data items collection and each type of social entity's data set comprising social relationships.
The technical personnel in the technical field can be clearly understood that, for convenience and simplicity of description, foregoing description
The specific course of work of system and related explanation, can be no longer superfluous herein with reference to the corresponding process in signature embodiment of the method
It states.
It should be noted that the artificial population synthesis system provided by the above embodiment based on multiple social relationships constraint,
Only the example of the division of the above functional modules, in practical applications, it can according to need and divide above-mentioned function
With being completed by different functional modules, i.e., by the embodiment of the present invention module or step decompose or combine again, for example,
The module of above-described embodiment can be merged into a module, can also be further split into multiple submodule, to complete above retouch
The all or part of function of stating.For module involved in the embodiment of the present invention, the title of step, it is only for distinguish each
A module or step, are not intended as inappropriate limitation of the present invention.
A kind of storage device of third embodiment of the invention, wherein be stored with a plurality of program, described program be suitable for by
Reason device loads and realizes the above-mentioned artificial population synthetic method based on multiple social relationships constraint.
A kind of processing unit of fourth embodiment of the invention, including processor, storage device；Processor is adapted for carrying out each
Program；Storage device is suitable for storing a plurality of program；Described program is suitable for being loaded by processor and being executed to realize above-mentioned base
In the artificial population synthetic method of multiple social relationships constraint.
The technical personnel in the technical field can be clearly understood that is do not described is convenienct and succinct, foregoing description
The specific work process and related explanation of storage device, processing unit can refer to the corresponding process in signature method example,
This is repeated no more.
Those skilled in the art should be able to recognize that, mould described in conjunction with the examples disclosed in the embodiments of the present disclosure
Block, method and step, can be realized with electronic hardware, computer software, or a combination of the two, software module, method and step pair
The program answered can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electric erasable and can compile
Any other form of storage well known in journey ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field is situated between
In matter.In order to clearly demonstrate the interchangeability of electronic hardware and software, in the above description according to function generally
Describe each exemplary composition and step.These functions are executed actually with electronic hardware or software mode, depend on technology
The specific application and design constraint of scheme.Those skilled in the art can carry out using distinct methods each specific application
Realize described function, but such implementation should not be considered as beyond the scope of the present invention.
Term " first ", " second " etc. are to be used to distinguish similar objects, rather than be used to describe or indicate specific suitable
Sequence or precedence.
Term " includes " or any other like term are intended to cover non-exclusive inclusion, so that including a system
Process, method, article or equipment/device of column element not only includes those elements, but also including being not explicitly listed
Other elements, or further include the intrinsic element of these process, method, article or equipment/devices.
So far, it has been combined preferred embodiment shown in the drawings and describes technical solution of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement will fall within the scope of protection of the present invention.
Claims (9)
1. a kind of artificial population synthetic method based on multiple social relationships constraint, which is characterized in that this method comprises:
Step S10 obtains the full attribute entities number of each social entity in input data based on default social entity's type；
Step S20, each member composition relationship based on social entity, by each obtained social entity of step S10
Full attribute entities number, is converted to the corresponding population number of individuals of personal attribute；
Step S30, the public attribute in the full attribute entities number based on social entity and population number of individuals calculate all public categories
Property under original demographic distribution；By in the full attribute entities number and population number of individuals of social entity in addition to the public attribute
Other attributes expand original demographic's distribution under all public attributes as remaining attribute, and based on the remaining attribute
Exhibition, obtains the population distribution comprising all properties；
Step S40, based on the population distribution comprising all properties, the member composition according to each social entity's type is closed
System calculates the corresponding social entity's distribution of each organizational attribution；
Step S50, according to the member composition relationship of social entity's type, by step S30 obtain described in comprising all properties
Population distribution social entity's distribution corresponding with each organizational attribution that step S40 is obtained is associated, and is obtained comprising social relationships
Individual data items collection and each type of social entity's data set.
2. the artificial population synthetic method according to claim 1 based on multiple social relationships constraint, which is characterized in that step
" each member composition relationship based on social entity, by the full category of each obtained social entity of step S10 in rapid S20
Property entity number, is converted to the corresponding population number of individuals of personal attribute ", calculation method is as follows:
Wherein, Q(k)For the full attribute entities number of kLei social entity,For the attribute of kLei social entity
The value of variable, P(k)For the corresponding population number of individuals of kth class personal attribute, (x1,x2,…,xn) it is that n population individual attribute becomes
The value of amount, n are population variable value, mkFor attribute variable's number of kZhong social entity.
3. the artificial population synthetic method according to claim 2 based on multiple social relationships constraint, which is characterized in that step
" public attribute in the full attribute entities number based on social entity and population number of individuals, calculates under all public attributes in rapid S30
Original demographic distribution ", calculation method is as follows:
P(x1,x2,…,xu)=λ0·P(0)(x1,x2,…,xu)+…+λK·P(K)(x1,x2,…,xu)
Wherein, P (x1,x2,…,xu) it is that original demographic is distributed, (x1,x2,…,xu) be u public attribute a kind of valued combinations,
K is the type number of all social entities, (λ0,…,λK) it is weight coefficient.
4. the artificial population synthetic method according to claim 3 based on multiple social relationships constraint, which is characterized in that step
" other attributes in the full attribute entities number and population number of individuals of social entity in addition to the public attribute are made in rapid S30
For remaining attribute, and original demographic's distribution under all public attributes is extended based on the remaining attribute, is wrapped
Population distribution containing all properties ", method are as follows:
In remaining attribute, chooses the public variable appeared in k kind social entity and successively decrease if it does not exist and find public variable；
Wherein, initial k value is K+1；
If k ≠ 1, calculate spreading coefficient, according to spreading coefficient extension original demographic distribution, extended original demographic distribution continue on
One step finds public variable；
If k=1, remaining every attribute is extended, the population distribution comprising all properties is obtained.
5. the artificial population synthetic method according to claim 1 based on multiple social relationships constraint, which is characterized in that step
" based on the population distribution comprising all properties, according to the member composition relationship of each social entity's type, meter in rapid S40
Calculate the corresponding social entity's distribution of each organizational attribution ", calculation method is as follows:
Wherein,For the kLei social entity number under respective attributes value, μ is the constraint of balance social entity's total number and people
The weight of mouth total number constraint.
6. the artificial population synthetic method according to claim 1 based on multiple social relationships constraint, which is characterized in that step
In rapid S50 " according to the member composition relationship of social entity's type, by step S30 obtain described in include all properties population
It is distributed social entity's distribution corresponding with each organizational attribution that step S40 is obtained to be associated, obtains comprising social relationships
Volumetric data set and each type of social entity's data set ", method are as follows:
By step S30 obtain described in comprising all properties population distribution it is corresponding with each organizational attribution that step S40 is obtained
Social entity's distribution generates population individual collections and social entity's set respectively；
It is associated from individual is chosen in population set with the entity in social entity's set；
It is successfully associated, the entity number that individual number is social entity is set.
7. a kind of artificial population synthesis system for considering multiple social relationships constraint, which is characterized in that the system includes synthesis mould
Block, conversion module, expansion module, reverse computing module, relating module；
The synthesis module is configured to default social entity's type, obtains each social entity in input data
Full attribute entities number；
The conversion module is configured to each member composition relationship of social entity, synthesis module is obtained every
A kind of full attribute entities number of social entity, is converted to the corresponding population number of individuals of personal attribute；
The expansion module, the public attribute being configured in the full attribute entities number and population number of individuals of social entity,
Calculate original demographic's distribution under all public attributes；It is described by being removed in the full attribute entities number and population number of individuals of social entity
Other attributes except public attribute are based on the remaining attribute to initial under all public attributes as remaining attribute
Population distribution is extended, and obtains the population distribution comprising all properties；
The reverse computing module is configured to base based on the population distribution comprising all properties, in fact according to each society
The member composition relationship of body type calculates the corresponding social entity's distribution of each organizational attribution；
The relating module is configured to the member composition relationship according to social entity's type, expansion module is obtained described in
Population distribution comprising all properties social entity's distribution corresponding with each organizational attribution that reverse computing module obtains is closed
Connection, obtains the individual data items collection and each type of social entity's data set comprising social relationships.
8. a kind of storage device, wherein being stored with a plurality of program, which is characterized in that described program is applied and loaded and held by processor
Row is to realize the artificial population synthetic method described in any one of claims 1-6 based on multiple social relationships constraint.
9. a kind of processing setting, including processor, storage device；Processor is adapted for carrying out each program；Storage device is fitted
For storing a plurality of program；It is characterized in that, described program is suitable for being loaded by processor and being executed to realize claim 1-6
Described in any item artificial population synthetic methods based on multiple social relationships constraint.
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