CN109523130A - The evaluation model and construction method of private higher learning institution's sustainable development - Google Patents
The evaluation model and construction method of private higher learning institution's sustainable development Download PDFInfo
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Abstract
The invention belongs to educational development fields, disclose the evaluation model and construction method of a kind of private higher learning institution's sustainable development；The evaluation model of private higher learning institution's sustainable development, including sustainable development identification layer, development process modification level and model construction layer；The sustainable development identification layer is under dimensioning, and the private higher learning institution in Study of recognition region leads to the problem of in the process of sustainable development in the research period；Process modification level is the target of sustainable development based on these private higher learning institutions, in process of sustainable development, is constantly modified to the problem；Model construction layer is to determine evaluation index, then by frequency analysis, give evaluation criterion by carrying out regression analysis to revised process of sustainable development.The present invention realizes the process of sustainable development evaluation under variation scale, preferably the sustainable development rule of image study region private higher learning institution, is allowed to be able to satisfy the sustainable development needs of private higher learning institution.
Description
Technical field
The invention belongs to the evaluation models and building of educational development field more particularly to a kind of private higher learning institution's sustainable development
Method.
Background technique
Private higher learning institution refers to that enterprises and institutions, public organization and other social organizations and individual citizens utilize non-country
The fiscal expenditure of education, the institution of higher education held towards the society and other educational institutions.Sustainable development is to run by the community efficiently must
By road.The sustainable development of current private higher learning institution is influenced by many internal and external factors, such as Characteristics of Running, the sources of funds, body
Making mechanism etc., these factors affect the sustainable development of these private higher learning institutions, bring some predicaments to these private higher learning institutions.
In conclusion problem of the existing technology is: the sustainable development of current private higher learning institution is by many internal and external factors
Influence, such as Characteristics of Running, the sources of funds, institutional mechanisms etc., these factors affect the sustainable development of these private higher learning institutions
Exhibition, brings some predicaments to these private higher learning institutions.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of evaluation model of private higher learning institution's sustainable development and
Construction method.
The invention is realized in this way the present invention provides a kind of evaluation model of private higher learning institution's sustainable development, including can
Sustainable development problem identification layer, development process modification level and model construction layer；
The sustainable development identification layer is under dimensioning, and the private higher learning institution in Study of recognition region is in the research period
It is led to the problem of in interior process of sustainable development；
The process modification level is the target of sustainable development based on these private higher learning institutions, in process of sustainable development,
Constantly the problem is modified；
The model construction layer is to determine that evaluation refers to by carrying out regression analysis to revised process of sustainable development
Mark, then by frequency analysis, give evaluation criterion.
The energy consumption model of the frequency analysis, sensor node energy consumption are divided into transmitting data energy consumption, receive data energy
The distance of consumption and aggregated data energy consumption, node to receiving point is less than threshold value d0, then free space model is used, otherwise, using more
Path attenuation model, so that the energy consumption for emitting the receiving point that bit data is to distance is as follows:
Wherein EelecFor transmit circuit energy consumption, εfsFor energy needed for power amplification circuit under free space model, εmp
For energy needed for power amplification circuit under multipath attenuation model, bit data energy consumption is received:
ERx(l)=l × Eelec；
It polymerize the energy consumption of bit data:
EA=l × EDA；
Wherein EDAIndicate the energy consumption of 1 bit data of polymerization.
Further, the development problem identification layer includes evaluation unit and recognition unit；
The evaluation unit is to construct the sustainable development under dimensioning under the premise of generating the spatio-temporal difference of development problem
Open up evaluation index；
The recognition unit is given Sustainable Development Evaluation standard, to identify that problem generates process；
The step of data aggregation method of the recognition unit, is as follows:
Step 1, in the deployment region that area is S=LL, the wireless sensor node of the N number of isomorphism of random distribution, sink
Node is located at except deployment region, the data being collected into the entire wireless sensor network of node processing；
Step 2, non-homogeneous cluster
Sink node is located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have phase
Same width w, and the equal length of the length of each swimming lane and deployment region；Use the ID from 1 to s as swimming lane, left end
Swimming lane ID be 1, then each swimming lane is divided into multiple rectangular mesh along y-axis, each grid in each swimming lane by
A level is defined, the level of the lowermost grid is 1, and each grid and each swimming lane have identical width w；In each swimming lane
Distance dependent of number, length and the swimming lane of grid to sink；The size of grid is adjusted by the way that the length of grid is arranged；For
Different swimming lanes, the lattice number that distance sink remoter swimming lane contains are smaller；For same swimming lane, distance sink remoter net
The length of lattice is bigger；Contain S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid is with one
A array (i, j) is used as ID, indicates that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of array Hv
Indicate the length of grid in v-th of swimming lane, and HvW-th of element hvwIndicate the length of grid (v, w)；Grid (i, j)
Boundary are as follows:
O_x+ (i-1) × w < x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to carry out, and chooses in each round each
The maximum node of dump energy is added cluster according to nearby principle, is then counted again as cluster head node, remaining node in grid
According to polymerization；
Step 3, Grubbs pretreatment
Sensor node needs pre-process the data of collection, then transmit data to cluster head node again；Using lattice
This pre- criterion of granny rag carries out pretreatment to the collected data of sensor node institute and assumes that some cluster head node contains a sensor
Node, the data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set:
vi=xi-x0,
According to order statistics principle, Grubbs statistic is calculated:
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, surveys
Magnitude participates in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, be not involved in down
The data aggregate of one level；
Step 4, adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, seeks the measurement data of each sensor node
Euclidean distance between value and estimated value, using normalized Euclidean distance as adaptive weighted warm weight；It selects in cluster
The collected data of sensor node maxima and minima average value centered on data；
There is a sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) indicate respective nodes measured value,
Euclidean distance by calculating each node data and centre data reacts the deviation between different node datas and centre data
Size, wherein liCalculation formula are as follows:
According to the corresponding weight size of Euclidean distance adaptive setting, the bigger weight of distance is smaller, gets over apart from smaller weight
Greatly；
WhereinwiFor corresponding weight.
Further, the process modification level includes analogy unit and amending unit；
The analogy unit is to carry out analogy according to Sustainable Development Evaluation standard according to development process；
The amending unit is modified to the process for deviateing Sustainable Development Evaluation standard, is commented to be returned to again
Price card is quasi-.
Further, the analogy unit includes the first decision-making module and the second decision-making module；
In first decision-making module, indicated by ternary formula MF=(E, X, U) in environmental parameter E=(e1, e2..., ep) under,
The satisfactory solution of system is X=(x1, x2..., xn), and matrix U=(μij)p×nIt indicates to work as environmental parameter ejChanges delta ejWhen, by because
Fruit relationship and experience can speculate xiAdjustment amount be about μijxi, wherein μijIt is empirical coefficient；
If
MFs=< Es, Xs, Rs> s=1,2
For two first kind decision-making modes, wherein ExAnd XsComponent suitably sorted after:
With With
Corresponding similar element, similitude are divided into state similitude and relationship similitude, state respectively in two-mode
Similitude is divided into difference similitude and ratio similitude again, by difference state similarity is defined as:
Wherein wi(I=1 ..., n1) it is weighted number about each environmental parameter,For the characteristic function of respective element,
It is easily defined as the form that value is non-negative rating fraction or limited nonnegative real number, and than state of value similarity is defined as:
Wherein:
Wherein k is certain similar specific ray constant；
The relationship similarity of first quasi-mode is defined as:
Wherein bi(i=1 ..., n1), dj(j=1 ..., n2) it is weighting about each environmental parameter and decision variable respectively
Value, and the similarity of the first quasi-mode is defined as:
Q3=w1Q1+w2Q2
Wherein Q1ForOrW1With W1It is weighted value；
In second decision-making module, if two the second class decision-making modes are as follows:
MSs=< IRs, IAs, Rs> (s=1,2)
Wherein:
With WithRespectively two moulds
Corresponding similar element in formula；
The difference state similarity of second quasi-mode is defined as:
Wherein wi(i=1 ..., n1) it is weighted value about each goal constraint Improvement requirement,For respective element
Characteristic function.
Further, the model construction layer includes regression analysis unit and frequency analysis unit；
The regression analysis unit be by analyzing revised process of sustainable development, calculate develop benefit and
The relationship lasted is solved the problems, such as, to construct Sustainable Development Evaluation index；
The frequency analysis unit is to carry out frequency analysis to the Sustainable Development Evaluation index of mutative scale, so that given can
The evaluation criterion of sustainable development.
Further, the regression analysis unit includes analysis module, for counting to modified process of sustainable development
According to analysis；
It is connected with analysis module, the computing module for being calculated data develops benefit reconciliation to calculate
The certainly relationship that problem lasts；
It is connected with computing module, the analog module for being simulated to calculated result；
It is connected with analog module, for planning, constructing the constructing module of Sustainable Development Evaluation index again.
The present invention provides a kind of construction method of private higher learning institution's sustainable development, comprising the following steps:
Step 1: the sustainable development identification under dimensioning according to the sustainable development identification layer, is based on
The spatio-temporal difference of Variable Fuzzy theory and sustainable development criterion of identification, constructs adaptive fuzzy matrix, then adopts
Quantify sustainable development with variable fuzzy assessment method, sustainable development knowledge is finally carried out based on unity of opposites theorem
Not, the sustainable development obtained in research sequence generates process；
Step 2: development process amendment, according to the development process modification level, the sustainable development based on these private higher learning institutions
Exhibition target is constantly modified the problem in process of sustainable development；
Step 3: the building of the evaluation model of sustainable development, according to the model construction layer, by it is revised can
Sustainable development process carries out regression analysis, determines evaluation index, then by frequency analysis, give evaluation criterion.
Advantages of the present invention and good effect are as follows: mutative scale Sustainable Development Evaluation Model proposed by the present invention, compared to
Dimensioning Arid Evaluation model realizes the process of sustainable development evaluation under variation scale, and avoids dimensioning model and make
The case where isolating at process of sustainable development, preferably the sustainable development rule of image study region private higher learning institution, make it
Meet the sustainable development needs of private higher learning institution.
Detailed description of the invention
Fig. 1 is the evaluation model structural block diagram that the present invention implements the private higher learning institution's sustainable development provided；
Fig. 2 is the evaluation model construction method flow diagram that the present invention implements the private higher learning institution's sustainable development provided；
Fig. 3 is the structural block diagram for the regression analysis unit that the present invention implements the private higher learning institution's sustainable development provided；
In figure: 1, sustainable development identification layer；2, development process modification level；3, model construction layer；4, evaluation unit；
5, recognition unit；6, regression analysis unit；7, frequency analysis unit；8, analogy unit；9, amending unit；10, analysis module；
11, computing module；12, analog module；13, constructing module.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
With reference to the accompanying drawing and specific embodiment is further described application principle of the invention.
As shown in Figure 1, the evaluation model of private higher learning institution's sustainable development provided in an embodiment of the present invention, including sustainable development
Open up problem identification layer 1, development process modification level 2 and model construction layer 3；
The sustainable development identification layer 1 is under dimensioning, and the private higher learning institution in Study of recognition region is in research
It is led to the problem of in process of sustainable development in section；
The process modification level 2 is the target of sustainable development based on these private higher learning institutions, in process of sustainable development,
Constantly the problem is modified；
The model construction layer 3 is to determine that evaluation refers to by carrying out regression analysis to revised process of sustainable development
Mark, then by frequency analysis, give evaluation criterion.
As the preferred embodiment of the present invention, the development problem identification layer includes evaluation unit 4 and recognition unit 5；
The evaluation unit 4 is constructed sustainable under dimensioning under the premise of generating the spatio-temporal difference of development problem
Development Assessment index；
The recognition unit 5 is given Sustainable Development Evaluation standard, to identify that problem generates process.
The step of data aggregation method of the recognition unit 5, is as follows:
Step 1, in the deployment region that area is S=LL, the wireless sensor node of the N number of isomorphism of random distribution, sink
Node is located at except deployment region, the data being collected into the entire wireless sensor network of node processing；
Step 2, non-homogeneous cluster
Sink node is located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have phase
Same width w, and the equal length of the length of each swimming lane and deployment region；Use the ID from 1 to s as swimming lane, left end
Swimming lane ID be 1, then each swimming lane is divided into multiple rectangular mesh along y-axis, each grid in each swimming lane by
A level is defined, the level of the lowermost grid is 1, and each grid and each swimming lane have identical width w；In each swimming lane
Distance dependent of number, length and the swimming lane of grid to sink；The size of grid is adjusted by the way that the length of grid is arranged；For
Different swimming lanes, the lattice number that distance sink remoter swimming lane contains are smaller；For same swimming lane, distance sink remoter net
The length of lattice is bigger；Contain S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid is with one
A array (i, j) is used as ID, indicates that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of array Hv
Indicate the length of grid in v-th of swimming lane, and HvW-th of element hvwIndicate the length of grid (v, w)；Grid (i, j)
Boundary are as follows:
O_x+ (i-1) × w < x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to carry out, and chooses in each round each
The maximum node of dump energy is added cluster according to nearby principle, is then counted again as cluster head node, remaining node in grid
According to polymerization；
Step 3, Grubbs pretreatment
Sensor node needs pre-process the data of collection, then transmit data to cluster head node again；Using lattice
This pre- criterion of granny rag carries out pretreatment to the collected data of sensor node institute and assumes that some cluster head node contains a sensor
Node, the data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set:
vi=xi-x0,
According to order statistics principle, Grubbs statistic is calculated:
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, surveys
Magnitude participates in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, be not involved in down
The data aggregate of one level；
Step 4, adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, seeks the measurement data of each sensor node
Euclidean distance between value and estimated value, using normalized Euclidean distance as adaptive weighted warm weight；It selects in cluster
The collected data of sensor node maxima and minima average value centered on data；
There is a sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) indicate respective nodes measured value,
Euclidean distance by calculating each node data and centre data reacts the deviation between different node datas and centre data
Size, wherein liCalculation formula are as follows:
According to the corresponding weight size of Euclidean distance adaptive setting, the bigger weight of distance is smaller, gets over apart from smaller weight
Greatly；
WhereinwiFor corresponding weight.
As the preferred embodiment of the present invention, the process modification level 2 includes analogy unit 8 and amending unit 9；
The analogy unit 8 is to carry out analogy according to Sustainable Development Evaluation standard according to development process；
The amending unit 9 is modified to the process for deviateing Sustainable Development Evaluation standard, to be returned to again
Evaluation criterion.
The analogy unit includes the first decision-making module and the second decision-making module；
In first decision-making module, indicated by ternary formula MF=(E, X, U) in environmental parameter E=(e1, e2..., ep) under,
The satisfactory solution of system is X=(x1, x2..., xn), and matrix U=(μij)p×nIt indicates to work as environmental parameter ejChanges delta ejWhen, by because
Fruit relationship and experience can speculate xiAdjustment amount be about μijxi, wherein μijIt is empirical coefficient；
If
MFs=< Es, Xs, Rs> s=1,2
For two first kind decision-making modes, wherein ExAnd XsComponent suitably sorted after:
With With
Corresponding similar element, similitude are divided into state similitude and relationship similitude, state respectively in two-mode
Similitude is divided into difference similitude and ratio similitude again, by difference state similarity is defined as:
Wherein wi(I=1 ..., n1) it is weighted number about each environmental parameter,For the characteristic function of respective element,
It is easily defined as the form that value is non-negative rating fraction or limited nonnegative real number, and than state of value similarity is defined as:
Wherein:
Wherein k is certain similar specific ray constant；
The relationship similarity of first quasi-mode is defined as:
Wherein bi(i=1 ..., n1), dj(j=1 ..., n2) it is weighting about each environmental parameter and decision variable respectively
Value, and the similarity of the first quasi-mode is defined as:
Q3=w1Q1+w2Q2
Wherein Q1ForOrW1With W1It is weighted value；
In second decision-making module, if two the second class decision-making modes are as follows:
MSs=< IRs, IAs, Rs> (s=1,2)
Wherein:
With WithRespectively two moulds
Corresponding similar element in formula；
The difference state similarity of second quasi-mode is defined as:
Wherein wi(i=1 ..., n1) it is weighted value about each goal constraint Improvement requirement,For respective element
Characteristic function.
As the preferred embodiment of the present invention, the model construction layer includes regression analysis unit 6 and frequency analysis unit
7；
The regression analysis unit 6 is to calculate by analyzing revised process of sustainable development and develop benefit
With the relationship for solving the problems, such as to last, to construct Sustainable Development Evaluation index；
The frequency analysis unit 7 is to carry out frequency analysis to the Sustainable Development Evaluation index of mutative scale, thus given
The evaluation criterion of sustainable development.
The energy consumption model of the frequency analysis, sensor node energy consumption are divided into transmitting data energy consumption, receive data energy
The distance of consumption and aggregated data energy consumption, node to receiving point is less than threshold value d0, then free space model is used, otherwise, using more
Path attenuation model, so that the energy consumption for emitting the receiving point that bit data is to distance is as follows:
Wherein EelecFor transmit circuit energy consumption, εfsFor energy needed for power amplification circuit under free space model, εmp
For energy needed for power amplification circuit under multipath attenuation model, bit data energy consumption is received:
ERx(l)=l × Eelec；
It polymerize the energy consumption of bit data:
EA=l × EDA；
Wherein EDAIndicate the energy consumption of 1 bit data of polymerization.
As shown in figure 3, the regression analysis unit 6 includes analysis module 10 as the preferred embodiment of the present invention, it is used for
Data analysis is carried out to modified process of sustainable development；
It is connected with analysis module 10, the computing module 11 for being calculated data, to calculate development benefit
With the relationship for solving the problems, such as to last；
It is connected with computing module 11, the analog module 12 for being simulated to calculated result；
It is connected with analog module 12, for planning, constructing the constructing module 13 of Sustainable Development Evaluation index again.
As shown in Fig. 2, the present invention provides a kind of construction method of private higher learning institution's sustainable development, comprising the following steps:
Step S101: the sustainable development identification under dimensioning, according to the sustainable development identification layer, base
In Variable Fuzzy theory and the spatio-temporal difference of sustainable development criterion of identification, adaptive fuzzy matrix is constructed, then
Sustainable development is quantified using variable fuzzy assessment method, sustainable development is finally carried out based on unity of opposites theorem
Identification, the sustainable development obtained in research sequence generate process；
Step S102: development process amendment, according to the development process modification level, based on the sustainable of these private higher learning institutions
Developing goal is constantly modified the problem in process of sustainable development；
Step S103: the building of the evaluation model of sustainable development, according to the model construction layer, by revised
Process of sustainable development carries out regression analysis, determines evaluation index, then by frequency analysis, give evaluation criterion.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (7)
1. a kind of evaluation model of private higher learning institution's sustainable development, which is characterized in that the evaluation of private higher learning institution's sustainable development
Model includes sustainable development identification layer, development process modification level and model construction layer；
The sustainable development identification layer is under dimensioning, and the private higher learning institution in Study of recognition region is within the research period
It is led to the problem of in process of sustainable development；
The process modification level is the target of sustainable development based on these private higher learning institutions, in process of sustainable development, constantly
The problem is modified；
The model construction layer be by carrying out regression analysis, determine evaluation index to revised process of sustainable development, then
By frequency analysis, evaluation criterion is given；
The energy consumption model of the frequency analysis, sensor node energy consumption be divided into transmitting data energy consumption, receive data energy consumption and
The distance of aggregated data energy consumption, node to receiving point is less than threshold value d0, then free space model is used, otherwise, using multipath
Attenuation model, so that the energy consumption for emitting the receiving point that bit data is to distance is as follows:
Wherein EelecFor transmit circuit energy consumption, εfsFor energy needed for power amplification circuit under free space model, εmpIt is more
Energy needed for power amplification circuit under path attenuation model receives bit data energy consumption:
ERx(l)=l × Eelec；
It polymerize the energy consumption of bit data:
EA=l × EDA；
Wherein EDAIndicate the energy consumption of 1 bit data of polymerization.
2. the evaluation model of private higher learning institution's sustainable development as described in claim 1, which is characterized in that the development problem is known
Other layer includes evaluation unit and recognition unit；
The evaluation unit is under the premise of generating the spatio-temporal difference of development problem, and the sustainable development constructed under dimensioning is evaluated by exhibit
Valence index；
The recognition unit is given Sustainable Development Evaluation standard, to identify that problem generates process；
The step of data aggregation method of the recognition unit, is as follows:
Step 1, in the deployment region that area is S=LL, the wireless sensor node of the N number of isomorphism of random distribution, sink node
Except deployment region, the data that are collected into the entire wireless sensor network of node processing；
Step 2, non-homogeneous cluster:
Sink node is located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have identical
Width w, and the equal length of the length of each swimming lane and deployment region；Use the ID from 1 to s as swimming lane, the swimming of left end
The ID in road is 1, and then each swimming lane is divided into multiple rectangular mesh along y-axis, and each grid in each swimming lane is defined
One level, the level of the lowermost grid are 1, and each grid and each swimming lane have identical width w；Grid in each swimming lane
Number, length and swimming lane to sink distance dependent；The size of grid is adjusted by the way that the length of grid is arranged；For difference
Swimming lane, the lattice number that distance sink remoter swimming lane contains is smaller；For same swimming lane, distance sink remoter grid
Length is bigger；Contain S element, the number of k-th of element representation grid in k-th of swimming lane in A；One number of each grid
Group (i, j) is used as ID, indicates that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of array HvIt indicates
The length of grid in v-th of swimming lane, and HvW-th of element hvwIndicate the length of grid (v, w)；The boundary of grid (i, j)
Are as follows:
O_x+ (i-1) × w < x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to carry out, and chooses each grid in each round
Cluster is added according to nearby principle as cluster head node, remaining node in the middle maximum node of dump energy, and it is poly- then to carry out data again
It closes；
Step 3, Grubbs pretreatment:
Sensor node needs pre-process the data of collection, then transmit data to cluster head node again；Using Ge Labu
This pre- criterion carries out pretreatment to the collected data of sensor node institute and assumes that some cluster head node contains a sensor node,
The data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set:
vi=xi-x0,
According to order statistics principle, Grubbs statistic is calculated:
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, measured value
Participate in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, be not involved in next layer
Secondary data aggregate；
Step 4, adaptive aggregating algorithm:
Obtain the unbiased estimator of each node measurement data by iteration, seek the measured data values of each sensor node with
Euclidean distance between estimated value, using normalized Euclidean distance as adaptive weighted warm weight；Select the biography in cluster
Data centered on the average value of the maxima and minima of the collected data of sensor node；
There is a sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) indicate respective nodes measured value, pass through
The Euclidean distance for calculating each node data and centre data reacts deviation size between different node datas and centre data,
Wherein liCalculation formula are as follows:
According to the corresponding weight size of Euclidean distance adaptive setting, the bigger weight of distance is smaller, bigger apart from smaller weight；
WhereinwiFor corresponding weight.
3. the evaluation model of private higher learning institution's sustainable development as described in claim 1, which is characterized in that the development process is repaired
Positive layer includes analogy unit and amending unit；
The analogy unit is to carry out analogy according to Sustainable Development Evaluation standard according to development process；
The amending unit is modified to the process for deviateing Sustainable Development Evaluation standard, to be returned to evaluation mark again
It is quasi-.
4. the evaluation model of private higher learning institution's sustainable development as claimed in claim 3, which is characterized in that the analogy unit packet
Include the first decision-making module and the second decision-making module；
In first decision-making module, indicated by ternary formula MF=(E, X, U) in environmental parameter E=(e1, e2..., ep) under, system
Satisfactory solution be X=(x1, x2..., xn), and matrix U=(μij)p×nIt indicates to work as environmental parameter ejChanges delta ejWhen, it is closed by cause and effect
System and experience can speculate xiAdjustment amount be about μijxi, wherein μijIt is empirical coefficient；
If
MFs=< Es, Xs, Rs> s=1,2
For two first kind decision-making modes, wherein ExAnd XsComponent suitably sorted after:
With WithPoint
Corresponding similar element, similitude state similitude and relationship similitude Wei not be divided into two-mode, state similitude is divided again
For difference similitude and ratio similitude, by difference state similarity is defined as:
Wherein wi(I=1 ..., n1) it is weighted number about each environmental parameter,For the characteristic function of respective element, Yi Ding
Justice is the form of non-negative rating fraction or limited nonnegative real number at value, and than state of value similarity is defined as:
Wherein:
Wherein k is certain similar specific ray constant；
The relationship similarity of first quasi-mode is defined as:
Wherein bi(i=1 ..., n1), dj(j=1 ..., n2) it is weighted value about each environmental parameter and decision variable respectively, and
The similarity of first quasi-mode is defined as:
Q3=w1Q1+w2Q2
Wherein Q1ForOrW1With W1It is weighted value；
In second decision-making module, if two the second class decision-making modes are as follows:
MSs=< IRs, IAs, Rs> (s=1,2)
Wherein:
With WithRespectively in two-mode
Corresponding similar element；
The difference state similarity of second quasi-mode is defined as:
Wherein wi(i=1 ..., n1) it is weighted value about each goal constraint Improvement requirement,For the feature of respective element
Function.
5. the evaluation model of private higher learning institution's sustainable development as described in claim 1, which is characterized in that the model construction layer
Including regression analysis unit and frequency analysis unit；
The regression analysis unit is to calculate by analyzing revised process of sustainable development and develop benefit and solution
The relationship that problem lasts, to construct Sustainable Development Evaluation index；
The frequency analysis unit is to carry out frequency analysis to the Sustainable Development Evaluation index of mutative scale, to give sustainable
The evaluation criterion of development.
6. the evaluation model of private higher learning institution's sustainable development as claimed in claim 4, which is characterized in that the regression analysis list
Member includes analysis module, for carrying out data analysis to modified process of sustainable development；
It is connected with analysis module, the computing module for being calculated data is asked to calculate development benefit and solution
Inscribe the relationship lasted；
It is connected with computing module, the analog module for being simulated to calculated result；
It is connected with analog module, for planning, constructing the constructing module of Sustainable Development Evaluation index again.
7. a kind of construction method of private higher learning institution's sustainable development, which comprises the following steps:
Step 1: the sustainable development identification under dimensioning, according to the sustainable development identification layer, based on variable
The spatio-temporal difference of fuzzy theory and sustainable development criterion of identification constructs adaptive fuzzy matrix, and then using can
The evaluation method that fogs quantifies sustainable development, finally carries out sustainable development identification based on unity of opposites theorem,
The sustainable development obtained in research sequence generates process；
Step 2: development process amendment, according to the development process modification level, the sustainable development mesh based on these private higher learning institutions
Mark, in process of sustainable development, is constantly modified the problem；
Step 3: the building of the evaluation model of sustainable development, according to the model construction layer, by revised sustainable
Development process carries out regression analysis, determines evaluation index, then by frequency analysis, give evaluation criterion.
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