CN109472299A - A kind of Impoverished University Students recognition methods based on smart card big data - Google Patents

A kind of Impoverished University Students recognition methods based on smart card big data Download PDF

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CN109472299A
CN109472299A CN201811221651.8A CN201811221651A CN109472299A CN 109472299 A CN109472299 A CN 109472299A CN 201811221651 A CN201811221651 A CN 201811221651A CN 109472299 A CN109472299 A CN 109472299A
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impoverished
data
smart card
student
university students
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袁玉波
叶宣佐
刘智海
张万军
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ZHEJIANG ZHENGYUAN ZHIHUI TECHNOLOGY Co Ltd
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ZHEJIANG ZHENGYUAN ZHIHUI TECHNOLOGY Co Ltd
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    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
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    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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Abstract

The present invention mainly utilizes campus big data to realize accurate poverty alleviation, and in particular to a kind of Impoverished University Students recognition methods based on smart card big data includes: to establish cloud campus data centre；Establish feature extraction model；The campus smart card consumption feature data of university student are extracted from the cloud campus data centre according to the feature extraction model；Establish Impoverished University Students identification model based on support vector machines, using quartic polynomial as smooth function；According to the Impoverished University Students characteristic data set training Impoverished University Students identification model obtained in advance: identifying poor student according to the Impoverished University Students identification model after the campus smart card consumption feature data and the training.The present invention can provide aid decision to the judge of Impoverished University Students for major colleges and universities and regulatory authorities, and the selected fairness of Impoverished University Students can be improved, promote the validity of Impoverished University Students management work.

Description

A kind of Impoverished University Students recognition methods based on smart card big data
Technical field
The present invention mainly utilizes campus big data to realize accurate poverty alleviation, and in particular to a kind of based on the poor of smart card big data Tired university student's recognition methods.
Background technique
According to the higher education statistical data that the Ministry of Education announces, about 28,930,000 people of national institution of higher learning student at school's quantity in 2016 (not including adult and Web education), wherein 20% student is poor student, 8% student is extremely poor student.Poor student's goes to school With problem concerning life, concerning social equity and harmonious, and the test to state education resource reasonable distribution.Although education department is It has been formulated a series of system and method goes identification poor student, but these systems and method also can in the actual operation process The problem of being faced with the validity of identification, that is, be identified as student that whether student of poor student is not identified than those with greater need for The help of state and society.How more efficiently to identify poor student, has become each colleges and universities and learn urgently to be resolved one of Ministry of worker's door A problem.
With the determination of the first batch of Experiment site school of Ministry of Education's " Intelligent campus ", large data center construction has been included in colleges and universities basis and has set Construction plan is applied, and smart card big data is always the most direct and important resource of campus big data.Gradually with data volume Increase, using historical data, Impoverished University Students are identified using machine learning method, become the common recognition of students managing worker. Wherein, campus big data how is effectively utilized, innovative identification Impoverished University Students accomplish accurately poverty alleviation, mitigate its life Living and study trouble and worry, is the major tasks that colleges and universities' big data utilizes.
In practice, since a few years ago, data research project verification is less in the school to research for colleges and universities, cause theoretical research deficient. Although existing certain methods still can be used for the identification of the poor student based on campus big data, specific data feelings are considered Border, there are still larger problems for the validity of these methods.It is main reason is that the data volume in database is huge and data Dimension is complicated, and existing research fails to solve following two critical issues under such data characteristics: (1) from data center Which type of data is extracted as input feature vector, which type of model (2) construct to complete identification process.
Summary of the invention
To solve the above problems, the present invention provides a kind of Impoverished University Students recognition methods based on smart card big data, Include:
Establish cloud campus data centre B；
Establish feature extraction model；
The campus smart card consumption of university student is extracted from the cloud campus data centre B according to the feature extraction model Characteristic；
Establish Impoverished University Students identification model based on support vector machines, using quartic polynomial as smooth function；
According to the Impoverished University Students characteristic data set training Impoverished University Students identification model obtained in advance:
It is poor according to the Impoverished University Students identification model identification after the campus smart card consumption feature data and the training Tired life.
Optionally, the feature after the extraction includes: that the consumption in the morning, afternoon and evening monthly average of smart card is flat according to, the single moon in water Equal data, Campus Shopping summation, smart card consume moon summation.
Optionally, the feature after the extraction further includes that class poor student assesses data.
Optionally, the Feature Selection Model of establishing includes:
1) obtains Impoverished University Students characteristic data set by samples selection:
Wherein m indicates the sum of database university student, xjIndicate the feature of j-th of university student, zj∈ { 0,1 } is indicated j-th The poor classification of university student.
During modeling, training set is denoted as D1D is denoted as with test set2, i.e. D=D1∪D2。
2) it is to find an optimal model f that, which establishes the process of poor student's identification model,*∈ H, H be some to Fixed abstract model space.
It is as follows to define identification model:
f(xi) indicate to use training dataset xiThe identification model of foundation, f*It indicates so that f (xi) classification results and reality The smallest optimal models of border classification results error.
If it is as follows to define risk function
Risk function ε (f) is used to metric averaging meaning drag f (xi) the fine or not degree predicted in test data set.
The identification model is defined as:
Given training set D1, then the supporting vector machine model Jing Guo kernel function φ coring is defined as:
ω indicates that the normal vector of segmentation hyperplane, γ indicate the intercept of segmentation hyperplane, Rk+1Indicate the European sky of value Between.(ω*,γ*) indicate the value so that when supporting vector machine model least risk.
It is as follows to define decision function:
Sign (f (x)) indicates that jump function, decision function d (x) indicate different classifications corresponding to each example, work as mould Type ω*φ(x)T+γ*The example is indicated when >=0 above support hyperplane, decision function d (x) value is 1；As model ω*φ (x)T+γ*The example is indicated when < 0 below support hyperplane, decision function d (x) value is -1.
The learning model of use is as follows:
Wherein, I=(1,1 ..., 1) ∈ Rk, φ: Rn→Rk, φ (A)T=(φ (x1)T,Learning model is the amendment to standard supporting vector machine model, can The strong convexity of objective function is exported, and obtains optimal ω with this*And γ*Value.
The learning model is abbreviated as
Wherein,
The objective function of unconstrained optimization in the i.e. above-mentioned learning model of F (ω, γ).
Optionally, using quadrinomial smooth functionTo the objective function F of Non-smooth surface (ω, Approximate substitution γ) is carried out, wherein quadrinomial smooth function
Wherein, coring function phi is defined as:
Wherein,
Indicate the mean value of each example in input consumption feature data, L1For the number of example.
Optionally, according to the Impoverished University Students identification model after the campus smart card consumption feature data and the training Identification poor student includes: that the campus smart card consumption feature data are inputted identification model, is identified according to decision function d (x) Poor student ,+1 is expressed as non-poor student, and -1 is expressed as poor student.
The beneficial effects of the present invention are: the present invention can comment Impoverished University Students for major colleges and universities and regulatory authorities Offer aid decision is provided, the selected fairness of Impoverished University Students can be improved, promote the validity of Impoverished University Students management work； In addition the Impoverished University Students status early warning that the present invention can be online according to consumption dynamic constitution and implementation, improves Impoverished University Students Process trace.
Detailed description of the invention
Fig. 1 is the flow diagram of the embodiment of the present invention；
Fig. 2 is the Technology Roadmap of the embodiment of the present invention；
Fig. 3 is the smartcard features data sample table of 9 university students.
Specific embodiment
The present invention is described further below with reference to embodiment.
As shown in Figure 1, a kind of Impoverished University Students recognition methods based on smart card big data of the embodiment of the present invention includes Following steps:
SO1 establishes cloud campus data centre B；
SO3 establishes feature extraction model；
SO5 extracts the campus smart card of university student according to the feature extraction model from the cloud campus data centre B Consumption feature data；
SO7, establish based on support vector machines, using quartic polynomial as the Impoverished University Students of smooth function identification mould Type；
SO9, according to the Impoverished University Students characteristic data set training Impoverished University Students identification model obtained in advance；
S11 knows according to the Impoverished University Students identification model after the campus smart card consumption feature data and the training Other poor student.
Fig. 2 is the Technology Roadmap of the embodiment of the present invention, and wherein F1 (x) indicates the recognition function of Joe college, F2 (x) table Show that the recognition function of collegegirl, PSSVM algorithm are recognizer proposed by the present invention.
In conjunction with whole technical route figure as shown in Fig. 2, one kind of the embodiment of the present invention is based on smart card big data Impoverished University Students recognition methods be accomplished by
Step 1, cloud campus data centre B is established.
Cloud campus large data center is established after data improvement according to the actual conditions of oneself first in campus.
Step 2, on large data center basis, several feature extraction models are established, extract the campus intelligence of university student Card consumption feature data obtain the characteristic data set of all students as input data source；
Extracted feature includes: the monthly average of consumption in the morning, afternoon and evening evidence, the single moon in water average data, campus of smart card 6 dimensions such as summation, smart card consumption moon summation of doing shopping also extract class poor student and assess number to promote recognition accuracy According to as feature, add up to 7 dimensions.
The feature extraction that the present invention defines is as follows:
1) consumption statistic c (t), statistical time t ∈ [t1,t2],
WhereinFor reference standard, k (t) is the consumption frequency in statistical time section, cj(t) it indicates to disappear in the t period The number taken,Indicate that student's same day consumes number cusum.Reference standardIt provides there are two types of modes, a kind of foundation The average data of normal students consumption, is given by student-directed expert.Rule of judgment usesAs threshold value, it is Because there is abnormal behavior of swiping the card in some students, for example help band meal etc., it may appear that spending amount is excessive to be more thanAt this time Data are calculated according to the average consumption of student.
2) with the presence or absence of consumption n (t), daily statistical time t ∈ [t in the .t period1,t2],
K (t) is the consumption frequency in statistical time section.K (t) >=1 indicates that student had consumption within the period on same day t Behavior, then n (t)=1；K (t)=0 indicates that consumer behavior was not present in student within the period on same day t, then n (t)=0.
3) monthly average consumption statistics x (t):
Wherein cj(t) it is counted according to (1) formula, nj(t) it is counted according to (2) formula, N is the number of days monthly counted, monthly average consumption x (t) indicate that monthly t period overall consumption number consumes accumulative total degree divided by monthly.
4) campus smart card consumption feature data are constituted as follows:
Wherein,
xi1For i-th of monthly early dinner cost: time 6:00~8:00；
xi2Take for i-th of monthly lunch: time 11:00~13:00；
xi3For i-th of monthly dinner expense: time 16:30~19:30；
xi4For water rate average outgo in i-th month campus；
xi5For i-th month total cost of doing shopping in campus；
xi6For i-th month class's assessment data, data were by 0 or 1；
xi7For effectively consumption integrates in i-th month campus:
I=1,2 ..., 9 indicate the statistics month in school；It is three months total according to summer vacation and winter vacation in principle, it is remaining Month.xi7Indicate i-th of monthly morning, the noon, dinner, water rate average outgo and shopping total cost summation.
Illustratively, Fig. 3 is the smartcard features data sample table of 9 university students.
Step 3, establish based on support vector machines, using quartic polynomial as the Impoverished University Students of smooth function identification mould Type separates characteristic data set according to men and women, later, training data is selected to establish recognition methods；
Since there are significant differences in the consumer behavior of school by boy student and schoolgirl, so needing characteristic data set according to male Female separates.Later, selection training data establishes identification model.
1) obtains Impoverished University Students characteristic data set by samples selection:
Wherein m indicates the sum of database university student, xjIndicate the feature of j-th of university student, zj∈ { 0,1 } is indicated j-th The poor classification of university student.
During modeling, training set is denoted as D1D is denoted as with test set2, i.e. D=D1∪D2。
2) it is to find an optimal model f that, which establishes the process of poor student's identification model,*∈ H, H be some to Fixed abstract model space.
It is as follows to define identification model:
f(xi) indicate to use training dataset xiThe identification model of foundation, f*It indicates so that f (xi) classification results and reality The smallest optimal models of border classification results error.
If it is as follows to define risk function
Risk function ε (f) is used to metric averaging meaning drag f (xi) the fine or not degree predicted in test data set.
The identification model is defined as:
Given training set D1, then the supporting vector machine model Jing Guo kernel function φ coring is defined as:
ω indicates that the normal vector of segmentation hyperplane, γ indicate the intercept of segmentation hyperplane, Rk+1Indicate the European sky of value Between.(ω*,γ*) indicate the value so that when supporting vector machine model least risk.
It is as follows to define decision function:
Sign (f (x)) indicates that jump function, decision function d (x) indicate different classifications corresponding to each example, work as mould Type ω*φ(x)T+γ*The example is indicated when >=0 above support hyperplane, decision function d (x) value is 1；As model ω*φ (x)T+γ*The example is indicated when < 0 below support hyperplane, decision function d (x) value is -1.
The learning model of use is as follows:
Wherein, I=(1,1 ..., 1) ∈ Rk, φ: Rn→Rk, φ (A)T=(φ (x1)T,Learning model is the amendment to standard supporting vector machine model, can The strong convexity of objective function is exported, and obtains optimal ω with this*And γ*Value.
The learning model is abbreviated as
Wherein,
The objective function of unconstrained optimization in the i.e. above-mentioned learning model of F (ω, γ).
In identification model of the present invention, using quadrinomial smooth functionTo the target of Non-smooth surface Function F (ω, γ) carries out approximate substitution, wherein quadrinomial smooth function
Is defined as:
Wherein coring function phi is selected as
Wherein,
Indicate the mean value of each example in input consumption feature data, L1For the number of example.
Step 4, identification model according to the present invention determines whether university student belongs to poor student.
The campus smart card consumption feature data are inputted into identification model, poor student is identified according to decision function d (x) ,+ 1 is expressed as non-poor student, and -1 is expressed as poor student.
Described in the present invention specific embodiments are merely illustrative of the spirit of the present invention, technology belonging to the present invention The technical staff in field can make various modifications or additions to the described embodiments or by a similar method Substitution, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.

Claims (7)

1. a kind of Impoverished University Students recognition methods based on smart card big data characterized by comprising
Establish cloud campus data centre B；
Establish feature extraction model；
The campus smart card consumption feature of university student is extracted from the cloud campus data centre B according to the feature extraction model Data；
Establish Impoverished University Students identification model based on support vector machines, using quartic polynomial as smooth function；
According to the Impoverished University Students characteristic data set training Impoverished University Students identification model obtained in advance:
Poor student is identified according to the Impoverished University Students identification model after the campus smart card consumption feature data and the training.
2. a kind of Impoverished University Students recognition methods based on smart card big data according to claim 1, which is characterized in that Feature after the extraction includes: the monthly average of consumption in the morning, afternoon and evening evidence, the single moon in water average data, Campus Shopping of smart card Summation, smart card consume moon summation.
3. a kind of Impoverished University Students recognition methods method based on smart card big data according to claim 2, feature It is, the feature after the extraction further includes that class poor student assesses data.
4. a kind of Impoverished University Students recognition methods based on smart card big data according to claim 2, which is characterized in that The Feature Selection Model of establishing includes:
1) consumption statistic c (t), daily statistical time t ∈ [t1,t2],
WhereinFor reference standard, k (t) is the consumption frequency in statistical time section, cj(t) consumption in the t period is indicated Number,Indicate that student's same day consumes number cusum.Reference standardIt provides there are two types of modes, a kind of foundation is just The average data of normal college student's consumption, is given by student-directed expert.Rule of judgment usesAs threshold value, be because Abnormal behavior of swiping the card occur for some students, for example help band meal etc., it may appear that spending amount is excessive to be more thanIt presses at this time Data are calculated according to the average consumption of student.
2) with the presence or absence of consumption n (t), daily statistical time t ∈ [t in the .t period1,t2],
K (t) is the consumption frequency in statistical time section.K (t) >=1 indicates that student had consumption row within the period on same day t For then n (t)=1；K (t)=0 indicates that consumer behavior was not present in student within the period on same day t, then n (t)=0.
3) monthly average consumption statistics x (t):
Wherein cj(t) it is counted according to (1) formula, nj(t) it is counted according to (2) formula, N is the number of days monthly counted, monthly average consumption x (t) table Show that monthly t period overall consumption number consumes accumulative total degree divided by monthly.
4) campus smart card consumption feature data are constituted as follows:
Wherein,
xi1For i-th of monthly early dinner cost: time 6:00~8:00；
xi2Take for i-th of monthly lunch: time 11:00~13:00；
xi3For i-th of monthly dinner expense: time 16:30~19:30；
xi4For water rate average outgo in i-th month campus；
xi5For i-th month total cost of doing shopping in campus；
xi6For i-th month class's assessment data, data were by 0 or 1；
xi7For effectively consumption integrates in i-th month campus:
I=1,2 ..., 9 indicate the statistics month in school；It is three months total according to summer vacation and winter vacation in principle, the remaining moon Part.xi7Indicate i-th of monthly morning, the noon, dinner, water rate average outgo and shopping total cost summation.
5. a kind of Impoverished University Students recognition methods based on smart card big data according to claim 4, which is characterized in that Establishing based on support vector machines, the Impoverished University Students identification model using quartic polynomial as smooth function includes:
1) obtains Impoverished University Students characteristic data set by samples selection:
Wherein m indicates the sum of database university student, xjIndicate the feature of j-th of university student, zj∈ { 0,1 } indicates j-th of university Raw poor classification.
During modeling, training set is denoted as D1D is denoted as with test set2, i.e. D=D1∪D2。
2) it is to find an optimal model f that, which establishes the process of poor student's identification model,*∈ H, H are some given pumpings As the model space.
It is as follows to define identification model:
f(xi) indicate to use training dataset xiThe identification model of foundation, f*It indicates so that f (xi) classification results and actual classification The smallest optimal models of resultant error.
If it is as follows to define risk function
Risk function ε (f) is used to metric averaging meaning drag f (xi) the fine or not degree predicted in test data set.
The identification model is defined as:
Given training set D1, then the supporting vector machine model Jing Guo kernel function φ coring is defined as:
ω indicates that the normal vector of segmentation hyperplane, γ indicate the intercept of segmentation hyperplane, Rk+1Indicate the theorem in Euclid space of value. (ω*,γ*) indicate the value so that when supporting vector machine model least risk.
It is as follows to define decision function:
Sign (f (x)) indicates jump function, and decision function d (x) indicates different classifications corresponding to each example, as model ω* φ(x)T+γ*The example is indicated when >=0 above support hyperplane, decision function d (x) value is 1；As model ω*φ(x)T+ γ*The example is indicated when < 0 below support hyperplane, decision function d (x) value is -1.
The learning model of use is as follows:
Wherein, I=(1,1 ..., 1) ∈ Rk, φ: Rn→Rk, φ (A)T=(φ (x1)T,Learning model is the amendment to standard supporting vector machine model, can The strong convexity of objective function is exported, and obtains optimal ω with this*And γ*Value.
The learning model is abbreviated as
Wherein,
The objective function of unconstrained optimization in the i.e. above-mentioned learning model of F (ω, γ).
6. a kind of Impoverished University Students recognition methods based on smart card big data according to claim 5, which is characterized in that Using quadrinomial smooth functionApproximate substitution is carried out to the objective function F (ω, γ) of Non-smooth surface, Wherein quadrinomial smooth functionIs defined as:
Wherein, coring function phi is defined as:
Wherein,
Indicate the mean value of each example in input consumption feature data, L1For the number of example.
7. a kind of Impoverished University Students recognition methods based on smart card big data according to claim 6, which is characterized in that Include: according to the Impoverished University Students identification model identification poor student after the campus smart card consumption feature data and the training
The campus smart card consumption feature data are inputted into identification model, poor student ,+1 table are identified according to decision function d (x) It is shown as non-poor student, -1 is expressed as poor student.
CN201811221651.8A 2018-10-19 2018-10-19 A kind of Impoverished University Students recognition methods based on smart card big data Pending CN109472299A (en)

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CN109992592A (en) * 2019-04-10 2019-07-09 哈尔滨工业大学 Impoverished College Studentss recognition methods based on campus consumption card pipelined data
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CN112416914A (en) * 2020-10-15 2021-02-26 三峡大学 Difficult student identification and early warning method and system based on big data analysis
CN112416914B (en) * 2020-10-15 2023-07-11 三峡大学 Difficult student identification and early warning method and system based on big data analysis

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Application publication date: 20190315