CN110991763B - Navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS - Google Patents
Navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS Download PDFInfo
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Abstract
The invention discloses a navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS, which comprises the steps of firstly, constructing an index fuzzy partition model; secondly, calculating the membership degree value of the sample to be identified under each index and belonging to each grade, establishing a decision matrix represented by a membership function, solving the multi-attribute decision problem by using a TOPSIS method, and obtaining the weight grade of the sample to be identified; finally, sorting the case data weights to obtain corresponding navigation emergency rescue resource demand quantity grades, and converting the navigation emergency rescue resource demand prediction problem into a multi-attribute decision-making problem by taking navigation emergency rescue resource demand prediction as a starting point through ingenious design; and then, the number of the demand forecast of the navigation emergency rescue resources is reasonably searched by utilizing a TOPSIS multi-attribute decision method, a scientific method is provided for forecasting the quantity of the demand of the navigation emergency rescue resources, and the method has strong industrial practicability and is convenient to popularize and use.
Description
Technical Field
The invention relates to the technical field of emergency rescue and demand resources, in particular to a navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS.
Background
China is vast, geographical and climatic conditions are complex, natural disasters are various and frequent, and almost all natural disasters such as flood disasters, drought disasters, earthquakes, typhoons, hail disasters, snow disasters, landslide, debris flow, plant diseases and insect pests, forest fires and the like except disasters caused by modern volcanic activities occur every year.
After a natural disaster occurs, people suffering from the disaster must be rescued in time, but the natural disaster usually causes interruption of roads and communication, and casualties of people in the disaster area cannot be timely transmitted to the command department, which is not favorable for resource deployment and scheduling planning of the disaster relief department.
In recent years, the general aircraft plays an irreplaceable role in the aspects of transporting wounded persons, transporting rescue goods and materials, collecting disaster situation information and the like, and shows great superiority and timeliness.
From flood and snow disaster to earthquake, the treatment of the natural disasters has revealed the problem that the allocation of navigation emergency rescue resources is unreasonable. Therefore, it is necessary to develop a method for predicting navigation emergency resources such as the number of helicopters in time through the first information sent from the disaster area when a natural disaster occurs, and the method has important practical significance.
Disclosure of Invention
Aiming at the substantial defects and shortcomings in the background content, the invention provides a navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS, which is used for building an index fuzzy partition model based on the idea of a membership function in fuzzy control by using the idea of the membership function in fuzzy control, and can solve the problems pointed out in the background technology.
A navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS comprises the steps of firstly, constructing an index fuzzy partition model; secondly, calculating a membership value of a sample to be identified based on a constructed fuzzy partition function, establishing a decision matrix represented by the membership function, further converting a navigation emergency rescue resource prediction problem into a multi-attribute decision problem, solving the multi-attribute decision problem by using a TOPSIS method, and obtaining a weight level of the sample to be identified; and finally, sequencing the collected data weights to obtain the corresponding navigation emergency rescue resource demand quantity grade, wherein the method comprises the following specific steps of:
step Q1: taking the prediction quantity of the demand of the navigation emergency rescue resources as a research object, constructing an index fuzzy segmentation model, calculating a membership value of a sample to be tested based on a constructed fuzzy segmentation function, establishing a decision matrix represented by the membership function, and converting the prediction problem of the demand of the navigation emergency rescue resources into a multi-attribute decision problem;
step Q2: and solving the multi-attribute decision problem by using a TOPSIS method, obtaining the weight and the sequence of the samples to be tested, and determining the quantity of the demand of navigation emergency rescue resources.
In the above technical solution, the establishment of the index fuzzy segmentation model in step Q1 is specifically as follows:
q11: the index fuzzy segmentation and TOPSIS navigation emergency rescue resource demand prediction model has the following preconditions:
at present, navigation resources applied to emergency rescue mainly comprise various navigation airplanes, different navigation airplanes can execute different emergency rescue tasks, when a navigation emergency rescue resource prediction model is drawn up by an application model, only a single task such as fire fighting, wounded personnel transportation, material transportation and the like is considered, the quantity required by a helicopter can be determined, and demand decisions are not made on the type and functions of the helicopter;
q12: variables and meanings used in the index fuzzy segmentation and the navigation emergency rescue resource demand prediction model of TOPSIS are as follows:
x i (i =1,2, \8230;, s) s samples were taken of the study object space X;
I j (j =1,2, \8230;, n) n is n characteristic indices per sample;
x ij is a sample x i In a characteristic index I j (ii) a measured value of;
{C 1 ,C 2 ,…,C m is an ordered partition class in attribute space F, where F is an attribute space of a certain class in X, { C 1 ,C 2 ,…,C m Is an arbitrary partition in attribute space F, and satisfies C 1 ＞C 2 ＞…＞C m Or C 1 ＜C 2 ＜…＜C m ；
[a jk ,a j(k+1) ]Is an index I j (j =1,2, \ 8230;, n) the k (k =1,2, \ 8230;, m) th division interval on the attribute space, and satisfies a j1 ＜a j2 ＜…＜a jm ＜a j(m+1) Or a j1 ＞a j2 ＞…＞a jm ＞a j(m+1) ；
Q13: the index fuzzy segmentation model is established as follows:
according to the known index classification standard, the attribute classification standard matrix is as follows:
considering that the lack of information may affect the rationality of the final evaluation result, the idea of membership function in the fuzzy control theory is used for reference, and the following improvement is made on the formula (1):
selecting trigonometric distribution function
The correlation calculation is performed as a function of membership. Wherein the parameter (a, c) determines the range of the membership function and the parameter b influences the position of the maximum of the membership function.
In the above technical solution, the solution of the TOPSIS model in step Q2 is specifically as follows:
q21: setting a decision matrix of a multi-attribute decision problem with m alternatives and n characteristic factors as follows:
wherein h is ij Represents the evaluation value of the ith alternative under the jth decision factor when I j When it is a benefit index, h ij The larger the value of (A) is, the better; when I j When it is a cost index, h ij The smaller the value of (A) is, the better;
q22: converting the decision matrix into a relative membership matrix R = (R) ij ) m×n For matrix R, the ideal solution X * = (1, \8230;, 1), negative ideal solution X - ＝(0,0,…,0)；
Q23: calculating the weight of the index, and setting the index G 1 ,G 2 ,…,G n Are respectively weighted as w 1 ,w 2 ,…,w n Scheme A i (i =1,2, \8230;, m) the sum of the squares of the weighted distances to the ideal solution and the negative ideal solution is
If a certain scheme is closest to the ideal solution and is far away from the negative ideal solution, the scheme is the best scheme in the scheme set, and f represents in a distance sense i The smaller (w) the better, thereby establishing a multi-objective planning model
Due to f i (w) ≧ 0, (i =1,2, \ 8230;, m), the aforementioned multi-target programming can be single-target programming;
q24: calculating weighted distances d of each solution to the ideal solution and the negative ideal solution i * 、d i - ；
Q25: calculating the closeness c of each scheme to the ideal solution i * ；
Q26: the goodness and badness of the scheme are sorted, c i * And sequencing from big to small to obtain a good and bad sequence.
The navigation emergency rescue resource demand prediction method based on the index fuzzy partition and the TOPSIS provided by the invention has the advantages that through ingenious design, the navigation emergency rescue resource demand prediction is taken as a starting point, the fuzzy characteristic that a traditional navigation emergency rescue resource demand prediction index system does not consider level division boundaries is considered, and an index fuzzy partition model is constructed based on the idea of a fuzzy membership function; by combining the model, the navigation emergency rescue resource demand prediction problem is converted into a multi-attribute decision problem; subsequently, the number of the demand forecast of the navigation emergency rescue resources is reasonably searched by using a TOPSIS multi-attribute decision-making method, a scientific method is provided for forecasting the number of the demand of the navigation emergency rescue resources, and the method has strong industrial practicability and is convenient to popularize and use.
Drawings
Fig. 1 is a schematic flow diagram of a navigation emergency rescue resource demand prediction method based on index fuzzy partition and toposis provided by the present invention.
Detailed Description
One embodiment of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the invention is not limited to the embodiment.
Examples
As shown in fig. 1, a navigation emergency rescue resource demand prediction method based on index fuzzy partition and toposis specifically includes the following steps:
step 1: taking the prediction quantity of the demand of the navigation emergency rescue resources as a research object, constructing an index fuzzy segmentation model by considering the fuzzy characteristic that an index system of the traditional navigation emergency rescue demand prediction does not consider a grade division boundary, calculating the membership value of a sample to be tested which belongs to each grade under each index based on the constructed fuzzy segmentation function, establishing a decision matrix represented by a membership function, and converting the prediction problem of the demand of the navigation emergency rescue resources into a multi-attribute decision problem;
step 2: and solving the multi-attribute decision problem by using a TOPSIS method, obtaining the weight and the sequence of the samples to be tested, and determining the required quantity of the navigation emergency rescue resources according to the index classification standard of the required quantity of the navigation emergency rescue resources.
The establishment of the index fuzzy segmentation model in the step 1 is as follows:
(1) The index fuzzy segmentation and TOPSIS navigation emergency rescue resource demand prediction model has the following preconditions:
at present, navigation resources applied to emergency rescue are mainly various navigation airplanes, different navigation airplanes can execute different emergency rescue tasks, when a model is applied to drawing up a navigation emergency rescue resource prediction model, only a single task is considered, such as fire fighting, wounded personnel transportation, material transportation and the like, the quantity required by helicopters can be determined, and demand decisions on the helicopter type and functions are not made.
(2) Variables and meanings used in the index fuzzy segmentation and the navigation emergency rescue resource demand prediction model of TOPSIS are as follows:
x i (i =1,2, \8230;, s) s samples were taken of the study object space X;
I j (j =1,2, \8230;, n) has n characteristic indexes per sample;
x ij is a sample x i In a characteristic index I j (ii) a measured value of;
{C 1 ,C 2 ,…,C m is an ordered partition class in attribute space F, where F is an attribute space of a certain class in X, { C 1 ,C 2 ,…,C m Is an arbitrary partition in attribute space F, and satisfies C 1 ＞C 2 ＞…＞C m Or C 1 ＜C 2 ＜…＜C m ；
[a jk ,a j(k+1) ]Is an index I j (j =1,2, \ 8230;, n) the k (k =1,2, \ 8230;, m) th division interval on the attribute space, and satisfies a j1 ＜a j2 ＜…＜a jm ＜a j(m+1) Or a j1 ＞a j2 ＞…＞a jm ＞a j(m+1) ；
(3) The index fuzzy segmentation model is established as follows:
according to the known index classification standard, the attribute classification standard matrix is as follows:
considering that the lack of information may affect the rationality of the final evaluation result, the idea of membership function in the fuzzy control theory is used for reference, and the following improvement is made on the formula (1):
selecting trigonometric distribution functions
The correlation calculation is performed as a function of membership. Wherein the parameter (a, c) determines the range of the membership function and the parameter b influences the position of the maximum of the membership function.
Based on the formula (1) and the formula (2), the index I can be obtained j (j =1,2, \8230;, n) is determined, and the maximum value of the membership function is determined according to the five rules of the membership functionThen, let b be b jk B is to be jk By substituting into the membership function, each partition { C is obtained 1 ,C 2 ,…,C m The specific membership function corresponding to.
The TOPSIS model in step 2 is specifically solved as follows:
(1) Setting a decision matrix of a multi-attribute decision problem with m alternatives and n characteristic factors as follows:
wherein h is ij Represents the evaluation value of the ith alternative under the jth decision factor. When I is j When it is a benefit index, h ij The larger the value of (A) is, the better; when I is j When it is a cost index, h ij The smaller the value of (A) is, the better.
(2) Converting the decision matrix into a relative membership matrix R = (R) ij ) m×n For matrix R, the ideal solution X * = (1, \8230;, 1), negative ideal solution X - ＝(0,0,…,0)。
(3) Calculating the weight of the index, and setting the index G 1 ,G 2 ,…,G n Are respectively weighted by w 1 ,w 2 ,…,w n Scheme A i (i =1,2, \8230;, m) the sum of the squares of the weighted distances to the ideal solution and the negative ideal solution is
If a certain scheme is closest to the ideal solution and is far away from the negative ideal solution, the scheme is the best scheme in the scheme set, and f represents in a distance sense i The smaller (w) the better, thereby establishing a multi-objective planning model
Due to f i (w) ≧ 0, (i =1,2, \ 8230;, m), the aforementioned multi-target planning can be single-target planning
(4) Calculating weighted distances d of each solution to the ideal solution and the negative ideal solution i * 、d i - ；
(5) Calculating the closeness c of each scheme to the ideal solution i * ；
(6) The goodness and badness of the scheme are sorted, c i * And sequencing from big to small to obtain a good and bad sequence.
Example (c): the method comprises the steps of taking application of navigation emergency rescue resources to forest fires as a background, predicting the number of helicopters applied to fire extinguishment, establishing a fire-fighting helicopter demand prediction index system on the basis of emergency incident attributes, and establishing the fire-fighting helicopter demand prediction index system from the three aspects of emergency incident factors, forest fire factors and aviation fire factors, wherein the specific demand prediction index system is shown in table 1.
According to the 20 indexes shown in the table 1, 6 forest fire history data are collected, and the data under the index system are shown in the table 2.
Step 1: grade standard of forest fire helicopter demand prediction index system
The grade standard of the demand forecasting index of the forest fire-fighting helicopter must be established on the premise of science and reasonability. The rule used is the Sturges rule, which determines the ranking criteria: according to the requirements of the fire-fighting helicopters in forest fires, the requirement indexes of the fire-fighting helicopters are divided into four levels: i, II, III and IV. Indexes having the same value under all samples were ignored, and then the grade standard of each index is shown in table 3. In conjunction with table 2, the predicted demand ratings for the five test samples of the fire helicopter are therefore I, IV, I and IV, respectively.
Step 2: index fuzzy segmentation model
The values of the index fuzzy partitioning parameter b corresponding to each fire helicopter demand index are listed in table 4 according to the index fuzzy partitioning model.
And step 3: identification by weighted TOPSIS
The closeness and ranking of each alternative to the ideal solution according to the TOPSIS model is listed in table 5.
The results show that: the predicted demand levels for the five sample fire helicopter are I, I, IV, III and IV. Thus, the predicted demand level of the test sample sum is the same as the actual demand level, which indicates the rationality of the method presented herein.
It can also be seen from table 5 that the forecast demand level of the forecast sample is I, i.e. 2-5 fire helicopters are needed for forest fire in great khingan. In practice, 5 fire-fighting helicopters were assigned for forest fires in great khingan. This is similar to the calculation result. The feasibility and rationality of the method is demonstrated.
TABLE 1 demand forecast index system
TABLE 2 forest fire History data
TABLE 3 grading Standard of demand indexes of fire-fighting helicopters
TABLE 4 fuzzy index partitioning parameter b corresponding to each fire helicopter demand index
TABLE 5 proximity and rating of 6 samples
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any modifications that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (1)
1. A navigation emergency rescue resource demand prediction method based on index fuzzy partition and TOPSIS is characterized in that an index fuzzy partition model is constructed firstly; secondly, calculating a membership value of a sample to be identified based on a constructed fuzzy partition function, establishing a decision matrix represented by the membership function, further converting a navigation emergency rescue resource prediction problem into a multi-attribute decision problem, solving the multi-attribute decision problem by using a TOPSIS method, and obtaining a weight level of the sample to be identified; and finally, sequencing the collected data weights to obtain the corresponding navigation emergency rescue resource demand quantity grade, wherein the method comprises the following specific steps of:
step Q1: constructing an index fuzzy segmentation model by taking the prediction quantity of the demand of the navigation emergency rescue resources as a research object, calculating a membership value of a sample to be tested based on a constructed fuzzy segmentation function, establishing a decision matrix represented by a membership function, and converting the prediction problem of the demand of the navigation emergency rescue resources into a multi-attribute decision problem;
step Q2: solving the multi-attribute decision problem by using a TOPSIS method, obtaining the weight and the sequence of the samples to be tested, and determining the required quantity of navigation emergency rescue resources according to the required quantity grade of the navigation emergency rescue resources;
the establishment of the index fuzzy segmentation model in the step Q1 specifically includes:
q11: the index fuzzy segmentation and TOPSIS navigation emergency rescue resource demand prediction model has the following preconditions:
at present, navigation resources applied to emergency rescue are mainly various navigation airplanes, different navigation airplanes can execute different emergency rescue tasks, when a navigation emergency rescue resource prediction model is drawn up by an application model, only a single task is considered, the quantity required by helicopters can be determined, and demand decisions are not made on helicopter models and functions;
q12: variables used in the index fuzzy segmentation and the navigation emergency rescue resource demand prediction model of TOPSIS and meanings thereof are as follows:
x i (i =1,2, \8230;, s) s samples were taken of the study object space X;
I j (j =1,2, \8230;, n) n is n characteristic indices per sample;
x ij is a sample x i In a characteristic index I j (ii) a measured value of;
{C 1 ，C 2 ，…，C m is an ordered partition class in attribute space F, where F is an attribute space of some class in X, { C 1 ，C 2 ，…，C m Is an arbitrary partition in attribute space F, and satisfies C 1 ＞C 2 ＞…＞C m Or C 1 ＜C 2 ＜…＜C m ；
[a jk ，a j(k+1) ]Is an index I j (j =1,2, \ 8230;, n) the k (k =1,2, \ 8230;, m) th division interval on the attribute space, and satisfies a j1 ＜a j2 ＜…＜a jm ＜a j(m+1) Or a j1 ＞a j2 ＞…＞a jm ＞a j(m+1) ；
Q13: the index fuzzy segmentation model is established as follows:
according to the known index classification standard, the attribute classification standard matrix is as follows:
the idea of membership function in fuzzy control theory is used for reference, and the following improvement is made on the formula (1):
selecting a trigonometric distribution function mu A (x)：
Performing correlation calculation as a membership function; based on the formula (1) and the formula (2), an index can be obtained
Assuming b is b, the membership function corresponding to each division of (1) jk B is mixing jk By substituting into the membership function, each partition { C is obtained 1 ，C 2 ，…，C m The specific membership function to which parameter (a, c) determines the range of the membership function and parameter b affects the position of the maximum of the membership function;
the TOPSIS method is utilized in the step Q2 to solve the multi-attribute decision problem, and the method specifically comprises the following steps:
q21: setting a decision matrix of a multi-attribute decision problem with m alternatives and n characteristic factors as follows:
wherein h is ij Represents the evaluation value of the ith alternative under the jth decision factor when I j When it is a benefit index, h ij The larger the value of (A) is, the better; when I j When it is a cost index, h ij The smaller the value of (A) is, the better;
q22: converting the decision matrix into a relative membership matrix R = (R) ij ) m×n For matrix R, the ideal solution X * = (1, \8230;, 1), negative ideal solution X - ＝(0，0，…，0)；
Q23: calculating the weight of the index, and setting the index G 1 ，G 2 ，…，G n Are respectively weighted as w 1 ，w 2 ，…，w n Scheme A i (i =1,2, \8230;, m) the sum of the squares of the weighted distances to the ideal solution and the negative ideal solution is
If a certain scheme is closest to the ideal solution and is far away from the negative ideal solution, the scheme is the best scheme in the scheme set, and f represents in a distance sense i The smaller (w) the better, thereby establishing a multi-objective planning model
Due to f i (w) ≧ 0, (i =1,2, \8230;, m), the aforementioned multi-objective planning can be a single-objective planning;
q24: calculating weighted distances d of each solution to the ideal solution and the negative ideal solution i * 、d i - ；
Q25: calculating the closeness c of each scheme to the ideal solution i * ；
Q26: the goodness and badness of the scheme are sorted, c i * And sequencing from big to small to obtain a good and bad sequence.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504544A (en) * | 2014-11-26 | 2015-04-08 | 沈阳建筑大学 | Decision-making method of uncertain emergency material distribution |
| CN105137418A (en) * | 2015-07-28 | 2015-12-09 | 中国人民解放军海军航空工程学院 | Multi-object tracking and data interconnection method based on whole neighborhood fuzzy clustering |
| CN105979553A (en) * | 2016-05-25 | 2016-09-28 | 重庆邮电大学 | ayered network handover decision-making method based on fuzzy logic and TOPSIS algorithms |
| CN106997509A (en) * | 2017-03-28 | 2017-08-01 | 南京航空航天大学 | A kind of emergency materials distributed needs Forecasting Methodology of uncertain information fusion |
| CN109539886A (en) * | 2018-03-25 | 2019-03-29 | 哈尔滨工程大学 | A kind of stealthy aid decision-making system of submarine and decision-making technique |
| CN109829636A (en) * | 2019-01-22 | 2019-05-31 | 中国人民解放军国防科技大学 | emergency task scheduling planning method based on dynamic priority |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7792321B2 (en) * | 2004-07-28 | 2010-09-07 | The Aerospace Corporation | Hypersensor-based anomaly resistant detection and identification (HARDI) system and method |
| US8874477B2 (en) * | 2005-10-04 | 2014-10-28 | Steven Mark Hoffberg | Multifactorial optimization system and method |
-
2019
- 2019-12-13 CN CN201911289208.9A patent/CN110991763B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504544A (en) * | 2014-11-26 | 2015-04-08 | 沈阳建筑大学 | Decision-making method of uncertain emergency material distribution |
| CN105137418A (en) * | 2015-07-28 | 2015-12-09 | 中国人民解放军海军航空工程学院 | Multi-object tracking and data interconnection method based on whole neighborhood fuzzy clustering |
| CN105979553A (en) * | 2016-05-25 | 2016-09-28 | 重庆邮电大学 | ayered network handover decision-making method based on fuzzy logic and TOPSIS algorithms |
| CN106997509A (en) * | 2017-03-28 | 2017-08-01 | 南京航空航天大学 | A kind of emergency materials distributed needs Forecasting Methodology of uncertain information fusion |
| CN109539886A (en) * | 2018-03-25 | 2019-03-29 | 哈尔滨工程大学 | A kind of stealthy aid decision-making system of submarine and decision-making technique |
| CN109829636A (en) * | 2019-01-22 | 2019-05-31 | 中国人民解放军国防科技大学 | emergency task scheduling planning method based on dynamic priority |
Non-Patent Citations (6)
| Title |
|---|
| Dynamic Emergency Decision-Making Method With Probabilistic Hesitant Fuzzy Information Based on GM(1,1) and TOPSIS;Jian Wu等;《IEEE Access》;20181228;第7卷;第7054-7066页，全文 * |
| Model for civil aviation emergency plan evaluation based on hybrid information representations;Xiong Shenghua等;《Computer Integrated Manufacturing Systems》;20190701;第25卷(第7期);第1784-94页，全文 * |
| 基于指标模糊分割和MULTIMOORA的航空公司机队可靠性识别模型;熊升华等;《计算机集成制造系统》;20190228;第25卷(第2期);第431-438页，全文 * |
| 基于模糊熵权法和TOPSIS 法的应急物资协议代储企业选择研究;蒋丽等;《第十三届（2018）中国管理学年会论文集》;20181130;第162-171页，全文 * |
| 大规模地质灾害救援队伍调配优化决策研究;张淑文;《中国优秀博硕士学位论文全文数据库(博士)社会科学Ⅰ辑》;20180715(第7期);第G110-2页，全文 * |
| 改进的TOPSIS法在突发事件应急决策中的应用;翟丹妮等;《中国公共安全(学术版)》;20131231(第4期);第14-18页，全文 * |
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