CN111598749A - Optimization method for education development research - Google Patents
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Abstract
The invention relates to an optimization method for educational development research, in particular to the field of prediction or optimization. The method comprises the following steps: s1: acquiring and generating a decision information system according to an evaluation object set, an evaluation index set and observation values of n indexes of m regions, and then obtaining a weight omega according to the decision information system; s2: obtaining an evaluation matrix I according to the spatial associated particle matrix and the weight omegaij(ii) a S3: according to the evaluation matrix IijA green index is obtained. The technical problem of how to optimize the education space layout is solved, and the method is suitable for the optimization of education development research.
Description
Technical Field
The invention relates to the field of prediction or optimization, in particular to an optimization method for educational development research.
Background
Education is the standing book of the country, and the balanced and coordinated development of education is always the focus of concern for the country and people. The method has the advantages that the spatial distribution of the education development of China is researched, the education gaps of different areas are analyzed, the advantages and disadvantages of the education development of each area are recognized, the education spatial layout is optimized, and the balanced collaborative development of the regional education is further promoted. A mixed model based on fusion of a particle matrix and spatial autocorrelation is provided, 12 education indexes for education development are analyzed by using data of 'Chinese education index 2017' issued by Yangtze river education research institute, and education development levels of the whole country and each province (autonomous region, city in the direct jurisdiction) in 2017 are researched. Research results show that the education development of 31 provinces and cities in the country has strong spatial autocorrelation, and relatively similar spatial aggregation characteristics exist in spatial distribution. The education development index analysis result shows that the national distribution characteristics are obvious, the education development presents equilibrium, but the differences of innovation levels and green level areas still exist, the higher innovation level distribution is mainly concentrated in the southeast area, and the lower green level distribution is concentrated in the northwest area.
In the study of the problem of education spatial distribution, many experts and scholars have proposed their own unique perspectives and research methods in recent years. For example, the Gao Weidong (2018) discusses the spatial relation among the high education provinces of China based on a gravitation model, a complex network model and regional entropy analysis. Gouqian (2019), and the spatial distribution evolution and the spatial distribution characteristics of the 2013-2016 obligate education high-quality teachers are explored by utilizing the Wolfson polarization index and the spatial trend surface analysis. And (2016) establishing a panel data model by referring to the traditional Scobu-Douglas production function, and carrying out empirical test on economic development effect of the education investment in western regions since 2000. Zhang ya Smart (2019), based on the gravity model analysis of China land resource management professional Master points spatial distribution pattern. The ginger shines through the courage (2019), and the absolute distribution and the relative distribution of higher education resources at a provincial level and the absolute distribution at a ground level are respectively researched by making a thematic map, a Lorentz curve, a Kini coefficient, a first scale and a first ratio and utilizing the quantity of colleges and universities, the population quantity and the total value of local production. Pennieya (2019), and empirical analysis is carried out on the education investment and the income change of farmers in various income level areas in China based on a Kobub-Douglas production function model. The method comprises the steps of constructing a mathematical model for measuring the coordination degree of higher education development and regional economic growth, empowering evaluation indexes by adopting an entropy weight method, and analyzing the order degree of higher education and regional economic subsystems and the coordination degree of a composite system in the Guangdong province from 1996 to 2011 by empirical analysis.
The students respectively use different evaluation indexes to describe the balance and the synergy of regional education development, but the key of the comprehensive evaluation of the regional education development lies in the determination of the weight of each evaluation index, and the accuracy directly influences the objectivity of an evaluation result. The current method for determining the index weight mainly comprises a hierarchical analysis method, a fuzzy statistical method, a combined weighting method, an entropy weight method, a rank and operation method and the like. Although the evaluation methods fully consider the influence of each evaluation index on the education development, the evaluation process has great subjectivity, and the level of the education development of each area is difficult to objectively evaluate, so that the evaluation methods have obvious defects.
The rough set theory does not need to provide any prior information except a data set required by problem processing, and the importance of each attribute is determined completely by data driving, so that the rough set theory and the fuzzy set theory are applied to introduce particle matrixes, the self-correlation between the indexes is researched by using a particle matrix algorithm so as to research the weight vector of the indexes, and the spatial self-correlation between regions is combined to provide a mixed model based on the fusion of the particle matrixes and the spatial self-correlation, so that the comprehensive evaluation result of the regional education development research is provided.
Disclosure of Invention
The technical problem to be solved by the invention is how to optimize the spatial layout of education.
The technical scheme for solving the technical problems is as follows: a method of optimizing an educational development study, comprising the steps of:
s1: acquiring and generating a decision information system according to an evaluation object set, an evaluation index set and observation values of n indexes of m regions, and then obtaining a weight omega according to the decision information system;
s2: obtaining an evaluation matrix I according to the spatial associated particle matrix and the weight omegaij；
S3: according to the evaluation matrix IijA green index is obtained.
The invention has the beneficial effects that: the balance and the cooperativity of the educational development are greatly influenced by the spatial layout of geographic positions, the spatial autocorrelation is to study the correlation of variable indexes at different spatial positions, the spatial correlation matrix describes the correlation degree of the spatial correlation, the spatial correlation matrix R of m regions is defined by an adjacent matrix to obtain the regional global Moran index, wherein the larger the value of I is, the larger the difference is, the smaller the correlation degree is in the global space, and conversely, the smaller the value of I is, the smaller the difference is, the stronger the correlation is in the global space, so that the green index of regional education is obtained, wherein the green index comprises the health degree, the ecology degree and the law degree, and the input and the construction strength of a target region to the education are obtained according to the green index of the regional education.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, step S1 may specifically be:
s11: and (U, V, A) generating a decision information system S, and acquiring m regions to form an evaluation object set U: u ═ U1,…,um) N indexes form an evaluation index set V: v ═ V (V)1,…,vn) And an index matrix composed of n index observations of m regions, the index matrix being represented as a set of attributes
Wherein a isijRepresenting the jth index observed value of the ith area;
s12: dividing the object set U into M by using the attribute set A, and recording the M as { X1,X2,…,XMIn which X isiIs an information particle;
s13: the information particles XiExpressed by a binary vector with length m, X is obtainedi＝{xi1,xi2,…,xik,…,ximTherein of
Matrix array
As a matrix of information particles, i.e.
S14: order to
Get
The information particle differentiation metric is defined as
S15: according to said MLObtaining the importance of the information particle evaluation index
Wherein M isL-{v}Representing the classification capability of the attribute set object set after the index v is deleted in the index system;
s16: obtaining an evaluation index set V-V (V) according to the information particle evaluation index importance N (V)1,…,vn)；
S17: according to the evaluation index set V ═ V (V)1,…,vn) Obtaining the weight
Further, step S2 specifically includes:
s21: defining a spatial correlation matrix R of m regions:
wherein the content of the first and second substances,
s22: the j index value of the ith area is expressed as aijThe average of the j-th index is expressed as
Obtaining a Moire index matrix Iij：
S23: carrying out normalization transformation on the jth index of the ith area to obtain an evaluation matrix
Wherein IiFor each region, the index is evaluated comprehensively by
Obtained by obtaining, I is the regional global Moran index
Thus obtaining the product.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a method flow diagram of an embodiment of the present invention for optimizing a method for educational development research.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The embodiment is basically as shown in the attached figure 1:
the optimization method for the educational development research in the embodiment comprises the following steps:
s1: acquiring and generating a decision information system according to an evaluation object set, an evaluation index set and observation values of n indexes in m regions, and then obtaining a weight omega according to the decision information system;
s2: obtaining an evaluation matrix I according to the spatial associated particle matrix and the weight omegaij；
S3: according to the evaluation matrix IijA green index is obtained.
The invention has the beneficial effects that: the balance and the cooperativity of the educational development are greatly influenced by the spatial layout of geographic positions, the spatial autocorrelation is to study the correlation of variable indexes at different spatial positions, the spatial correlation matrix describes the correlation degree of the spatial correlation, the spatial correlation matrix R of m regions is defined by an adjacent matrix to obtain the regional global Moran index, wherein the larger the value of I is, the larger the difference is, the smaller the correlation degree is in the global space, and conversely, the smaller the value of I is, the smaller the difference is, the stronger the correlation is in the global space, so that the green index of regional education is obtained, wherein the green index comprises the health degree, the ecology degree and the law degree, and the input and the construction strength of a target region to the education are obtained according to the green index of the regional education.
On the basis of the technical scheme, the invention can be further improved as follows.
Optionally, in some other embodiments, step S1 may specifically be:
s11: and (U, V, A) generating a decision information system S, and acquiring m regions to form an evaluation object set U: u ═ U1,…,um) N indexes form an evaluation index set V: v ═ V (V)1,…,vn) And an index matrix composed of n index observations of the m regions, the index matrix being represented as a set of attributes
Wherein a isijRepresents the J-th index observed value of the ith area;
the decision information system S is shown in table 1:
| v1 | v2 | …… | vn | |
| u1 | a11 | a12 | …… | a1n |
| u2 | a21 | a22 | …… | a2n |
| …… | …… | …… | …… | …… |
| um | am1 | am2 | …… | amn |
table 1
In this embodiment, first, rounding is performed on the obtained evaluation object set, the evaluation index set, and the observed values of the n indexes of the m regions by a rounding method to obtain a system table of the decision information system S, as shown in table 2:
table 2
S12: the attribute set A is used to divide the object set U into M, and is marked as { X1,X2,…,XMIn which X isiIs an information particle;
s13: information particle XiExpressed by a binary vector with length m, X is obtainedi＝{xi1,xi2,…,xik,…,ximTherein of
Matrix array
As a matrix of information particles, i.e.
The information particle matrix obtained in this embodiment is
Wherein xii1, (i-1, …,29), and x1，14＝1，x11，17＝1；
S14: order to
Get
The information particle differentiation metric is defined as
In this embodiment, the following data are obtained from table 2:
removing the scale degree v1Attribute set A is divided into 26 classes, where u1、u3、u14As one group, u7、u10As one group, u11、u17As one group, u18、u24As one class, the remaining individual objects are grouped into one class, which results in:
s15: according to MLObtaining the importance of the information particle evaluation index
Wherein M isL-{v}Representing the classification capability of the attribute set object set after the index v is deleted in the index system;
in the present example, the information particle evaluation index scale degree v can be obtained from table 21Degree of importance of
The same reasonable input degree v2Of importance is
Quality measure v3Of importance is
Information degree v4Of importance is
Fairness degree v5Of importance is
Degree of contribution v6Of importance is
S16: obtaining an evaluation index set V (V) according to the information particle evaluation index importance N (V)1,…,vn)；
S17: according to the evaluation index set V ═ V (V)1,…,vn) Obtaining the weight
According to the above data, the weight in this embodiment is:
ω＝(ω1,…,ωn)＝(0.2457，0.1991，0.0960，0，0.0960，0.3631)。
optionally, in some other embodiments, step S2 specifically includes:
s21: defining a spatial correlation matrix R of m regions:
wherein the content of the first and second substances,
s22: the j index value of the ith area is expressed as aijThe average of the j-th index is expressed as
Obtaining a Moire index matrix Iij：
In this embodiment, the spatial distribution autocorrelation and the spatial correlation table of 31 provinces and cities in table 3 are combined to obtain a Moran matrix I of 31 province and city development indexesijAccording to the Moire matrix IijObtaining table 4;
| an Badge | North China Jing made of Chinese medicinal materials | Heavy load Celebration | Good fortune Building (2) | Sweet taste Su Zhi | All-grass of Longtube Fang East | All-grass of Longtube Fang Western medicine | Noble State of the year | Sea water South China | River with water-collecting device North China | Black colour Dragon with water storage device River (Jiang) | River with water-collecting device South China | Lake North China | Lake South China | River (Jiang) Su (Chinese character of 'su') | River (Jiang) Western medicine | Lucky toy Forest (forest) | Liao (Chinese character of 'Liao') Ning (medicine for curing rheumatism) | Inner part Covering for window Ancient times | Ning (medicine for curing rheumatism) Summer (summer) | Green leaf of Chinese cabbage Sea water | Mountain East | On the upper part Sea water | Shaanxi Western medicine | Mountain Western medicine | Fourthly Sichuan style food | Sky Jin-jin | New Jiang (Chinese character of' Jiang | Western medicine Tibetan medicine | Cloud South China | Zhejiang river | |
| An Badge | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| North China Jing made of Chinese medicinal materials | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Heavy load Celebration | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Good fortune Building (2) | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Sweet taste Su Zhi | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| All-grass of Longtube Fang East | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| All-grass of Longtube Fang Western medicine | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Noble State of the year | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
| Sea water South China | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| River with water-collecting device North China | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
| Black colour Dragon with water storage device River (Jiang) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| River with water-collecting device South China | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Lake North China | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Lake South China | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| River (Jiang) Su (Chinese character of 'su') | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| River (Jiang) Western medicine | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Lucky toy Forest (forest) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Liao (Chinese character of 'Liao') Ning (medicine for curing rheumatism) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Inner part Covering for window Ancient times | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Ning (medicine for curing rheumatism) Summer (summer) | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Green leaf of Chinese cabbage Sea water | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| Mountain East | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| On the upper part Sea water | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Shaanxi Western medicine | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Mountain Western medicine | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Fourthly Sichuan style food | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 |
| Sky Jin-jin | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| New Jiang (Chinese character of' Jiang | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| Western medicine Tibetan medicine | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 |
| Cloud South China | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 |
| Zhejiang province River (Jiang) | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Table 3
Table 4
S23: carrying out normalization transformation on the jth index of the ith area to obtain an evaluation matrix
Wherein IiFor each region, the index is evaluated comprehensively by
Obtained by obtaining, I is the regional global Moran index
Thus obtaining the product.
In this embodiment, the green indices of 31 provinces are obtained according to the above data, as shown in table 5:
table 5
It should be noted that the above embodiments are product embodiments corresponding to the above method embodiments, and for the description of each structural device and the optional implementation in this embodiment, reference may be made to the corresponding description in the above method embodiments, and details are not repeated herein.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. An optimization method for educational development research, comprising the steps of:
s1: acquiring and generating a decision information system according to an evaluation object set, an evaluation index set and observation values of n indexes of m regions, and then obtaining a weight omega according to the decision information system;
s2: obtaining an evaluation matrix I according to the spatial associated particle matrix and the weight omegaij；
S3: according to the evaluation matrix IijA green index is obtained.
2. A method of optimizing educational development research in accordance with claim 1, wherein:
step S1 may specifically be:
s11: and (U, V, A) generating a decision information system S, and acquiring m regions to form an evaluation object set U: u ═ U1,…,um) N indexes form an evaluation index set V: v ═ V (V)1,…,vn) And an index matrix composed of n index observations of m regions, the index matrix being represented as a set of attributes
Wherein a isijRepresenting the jth index observed value of the ith area;
s12: dividing the object set U into M by using the attribute set A, and recording the M as { X1,X2,…,XMIn which X isiIs an information particle;
s13: the information particles XiExpressed by a binary vector with length m, X is obtainedi＝{xi1,xi2,…,xik,…,ximTherein of
Matrix array
As a matrix of information particles, i.e.
S14: order to
Get
The information particle differentiation metric is defined as
S15: according to said MLObtaining the importance of the information particle evaluation index
Wherein M isL-{v}Representing the classification capability of the attribute set object set after the index v is deleted in the index system;
s16: obtaining an evaluation index set V-V (V) according to the information particle evaluation index importance N (V)1,…,vn)；
S17: according to the evaluation index set V ═ V (V)1,…,vn) Obtaining the weight
3. A method of optimizing educational development research in accordance with claim 1, wherein:
step S2 specifically includes:
s21: defining a spatial correlation matrix R of m regions:
wherein the content of the first and second substances,
s22: the j index value of the ith area is expressed as aijThe average value of the j-th index is recorded as
Obtaining a Moire index matrix Iij：
S23: carrying out normalization transformation on the jth index of the ith area to obtain an evaluation matrix
Wherein IiFor each region, the index is evaluated comprehensively by
Obtained by obtaining, I is the regional global Moran index
Thus obtaining the product.
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