NL2031090B1 - Innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation - Google Patents

Innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation Download PDF

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NL2031090B1
NL2031090B1 NL2031090A NL2031090A NL2031090B1 NL 2031090 B1 NL2031090 B1 NL 2031090B1 NL 2031090 A NL2031090 A NL 2031090A NL 2031090 A NL2031090 A NL 2031090A NL 2031090 B1 NL2031090 B1 NL 2031090B1
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tourism
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Cui Hongyan
Zhou Xinbo
Nimer Abukhalifeh Alaa
Xu Bo
Gao Biao
Liu Lihua
Lu Xiaoling
Wu Yichao
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Baicheng Teachers College
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Abstract

The invention discloses the innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation, which comprises the following steps: S1, Collect the existing economic benefit data of rural tourism, and make the corresponding statistical map and based on the existing economic benefit of the village, build an innovative rural tourism efficiency evaluation index system; S2, Determine the weight of evaluation index based on fuzzy comprehensive evaluation method; SB‘ According to the statistical data chart made in Si, the development trend chart of the future tourism economic benefits of the village is simulated by using big data system. 10 By constructing an innovative rural tourism efficiency evaluation method, Using big data simulation and multi-level grey model, that is, by collecting rural data, simulating development trends and using a large number of experts' analysis and research to obtain objective and credible evaluation results, the weight of each index can be determined, and then the economic performance of rural tourism can be calculated, 15 which provides a basis for guiding the development of rural tourism.

Description

INNOVATIVE RURAL TOURISM EFFICIENCY EVALUATION METHOD BASED ON
FUZZY COMPREHENSIVE EVALUATION
FIELD OF THE INVENTION
[0001] The invention belongs to the technical field of innovative rural tourism efficiency evaluation, in particular to an innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation.
BACKGROUND OF THE RELATED ART
[0002] Rural tourism is a form of village tourism with the aim of tourism and vacation, taking the wild village as the space, no interference in humanities, no destruction in ecology, and the characteristics of wandering and wild behavior.
With the development of reform and opening up, China's tourism industry has experienced a process from zero to small to large, and the scale of tourism economy has been expanding. The prediction and evaluation of tourism economic performance has a very important impact on improving the tourism system and realizing the structural optimization and transformation and upgrading of tourism.
Therefore, it is of great theoretical and practical significance to evaluate the performance of China's tourism economy for understanding the development level of China's tourism economy and promoting the sustainable and coordinated development of China's tourism industry.
[0003] With the requirements of ecological civilization construction, The development of rural tourism industry has entered a new height. Rural tourism industry builds ecological civilization of tourism industry by improving the ecological efficiency of core departments and related departments, which is not only the basic requirement of building an ecological civilized society in China in the new period, but also the inevitable choice of realizing harmonious social development. However, there is a lack of evaluation methods for rural tourism efficiency in the market, which makes it more difficult for staff to quickly grasp the development and planning of rural tourism, and then easily affects the sustainable and coordinated development of rural tourism and reduces the economic benefits of rural tourism.
[0004] Therefore, in order to solve the existing problems, the invention provides an innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation.
SUMMARY OF THE INVENTION
[0005] The purpose of the invention is to provide an innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation, so as to solve the problem that rural tourism lacks rural tourism efficiency evaluation.
[0006] In order to achieve the above purpose, the technical proposal provided by an embodiment of the invention is as follows:
[0007] An innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation comprises the following steps:
[0008] S1, Collect the existing economic benefit data of rural tourism, and make the corresponding statistical map and based on the existing economic benefit of the village, build an innovative rural tourism efficiency evaluation index system;
[0009] S2, Determine the weight of evaluation index based on fuzzy comprehensive evaluation method;
[0010] S3, According to the statistical data chart made in S1, the development trend chart of the future tourism economic benefits of the village is simulated by using big data system.
[0011] S4, Randomly select the economic benefit data of a certain day in the village, and several experts evaluate and score the extracted data, record the evaluation data of experts, and construct a grey evaluation category matrix;
[0012] S5, According to the grey evaluation category matrix in S4, the economic performance of rural tourism is determined.
[0013] Further, the data collection in S1 includes rural tourism online platform collection and offline service organization collection, The rural tourism online platform mainly uses the principle and technology of cloud computing, takes the whole network data as the data source, takes a single rural tourist spot as the object, and statistically analyzes the rural tourism situation from six aspects:
tickets, homestays, specialties, restaurants, channels and evaluation. The offline service institutions include rural tourism terminal service points and distribution service centers.
[0014] Further, the data collection in S1 further comprises a visitor browsing system comprising a processor for processing data codes and cooperating with the memory for data storage, a memory for storing data, a search rule and a server.
[0015] Further, the fuzzy comprehensive evaluation in S2 is a multi-level grey model, and the obtaining method of the evaluation index weight comprises an AHP method, a coefficient of variation method and a comprehensive weight, wherein, the AHP method comprises the weight of an index layer to a factor layer, specifically comprising:
[0016] The judgment matrix is constructed by 9 scalar method, and the weight value is obtained by eigenvector method, then the weight vk of the k-th index under each criterion layer is: wr ead ML)
[0017] Factor vx is the weight of the k-th index in the factor layer; Factor vx’ is the k-th eigenvector corresponding to the maximum eigenvalue of the judgment matrix; Factor m is the number of factor layer indicators. if the results pass the consistency test, the weights are reasonably distributed; otherwise, the judgment matrix is reconstructed to find the weights.
[0018] Further, the AHP method further comprises the weight of the index layer to the target layer, specifically comprising:
[0019] Factor pk is the weight of the k-th index in the j-th factor layer to the target layer; Factor vk is the weight of the k-th index in the j-th factor layer to the j-th factor layer; Factor v; is the weight of the J-th factor layer to the target layer (J=1, 2,..6; k=1,2, ..M) :
Pi = Up XU;
[0020] Further, the big data system simulation in S3 comprises the following steps:
[0021] S301, Classify and import the collected rural tourism economic benefit data into the big data simulation system program, and store the imported data in the database;
[0022] S302, The big data simulation system program obtains data simulation tasks, which carry multiple scene types, call simulation scripts according to the data simulation tasks, and find configuration files corresponding to each scene type through simulation scripts;
[0023] S303, Extracting scene data corresponding to each scene type from the configuration file through a simulation script, and determining the scene data corresponding to a plurality of scene types as simulation data obtained by simulation;
[0024] S304, The simulation message is generated according to the simulation data through the simulation script, and the generated simulation message is sent to the responder server through the simulation script, so that the responder server analyzes the simulation message and obtains the simulation data.
[0025] Further, the scene types in S302 include tourism resource conditions, tourism market capacity, tourism development benefits, socio-economic support, development condition support and environmental bearing support.
[0026] Further, the random sampling method in S4 comprises the following steps:
[0027] S401, Define random sampling rules, select sampling range and define random sampling ratio;
[0028] S402, Writing the random extraction rules into the extraction record database;
[0029] S403, According to the rule of random sampling, the sampling object is found out from the sample library, and the sampling ID number of the sampling object is obtained as a spot check sample.
[0030] Further, the specific operation steps of the grey evaluation category matrix in 34 are as follows:
[0031] The number of evaluation experts is k, k=1, 2, 3, … N, that is, there are N evaluation experts, and each evaluation expert evaluates the innovative rural tourism efficiency evaluation index system (if only one object is evaluated, a matrix is established for each first-level index; If you evaluate multiple objects, A matrix is created for each set of metrics, When there are multiple targets, An evaluation matrix 5 is created, It contains a group of indicators corresponding to different targets, that is, it can evaluate one target or multiple targets.) Here, taking multiple evaluation targets as an example, X scores the evaluation index Uij of the first project with the evaluation scale dij (x), and fills in the expert evaluation table. According to the evaluation table, the evaluation sample matrix D (x) of the first project is obtained: ad Gi 0 {= 1. dif; diy diy dy Un © GG ah ld dim dz dn [U
DS lj G0 0) at Ji, di dig dj oo dig |U 300 40 a |, diy dip dj oo diy Vis °
[0032] Further, the specific operation steps for determining the economic performance of rural tourism in S5 are as follows:
[0033] S501, Firstly, according to the division of evaluation grey categories, the whitening weight function of each grey category is calculated
[0034] S502, Then calculate the total grey evaluation coefficient of tourism economic performance under each evaluation grey category;
[0035] S503, According to the weight of each index and the weight vector of each performance, the evaluation vector of each sub-performance is calculated;
[0036] S504, Finally, according to the performance level and the weight vector of each performance, the evaluation value of tourism economic performance and the total performance are calculated.
[0037] Compared with the prior art, the invention has the following advantages:
[0038] By constructing an innovative rural tourism efficiency evaluation method,
Using big data simulation and multi-level grey model, that is, by collecting rural data, simulating development trends and using a large number of experts’ analysis and research to obtain objective and credible evaluation results, the weight of each index can be determined, and then the economic performance of rural tourism can be calculated, which provides a basis for guiding the development of rural tourism.
BRIEF DESCRIPTION OF THE DRAWINGS
[0039] Fig. 1 is a step diagram of an innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation in an embodiment of the present invention;
[0040] Fig. 2 is a block diagram of a method for obtaining an evaluation index weight in an embodiment of the present invention;
[0041] Fig. 3 is a simulation step diagram of a big data system in an embodiment of the present invention;
[0042] Fig. 4 is a step diagram of a random sampling method in an embodiment of the present invention;
[0043] Fig. 5 is a step diagram for determining the economic performance of rural tourism in an embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0044] The invention discloses the innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation, which comprises the following steps:
[0045] S1, Collect the existing economic benefit data of rural tourism, and make the corresponding statistical map and based on the existing economic benefit of the village, build an innovative rural tourism efficiency evaluation index system;
[0046] S2, Determine the weight of evaluation index based on fuzzy comprehensive evaluation method;
[0047] S3, According to the statistical data chart made in S1, the development trend chart of the future tourism economic benefits of the village is simulated by using big data system.
[0048] S4, Randomly select the economic benefit data of a certain day in the village, and several experts evaluate and score the extracted data, record the evaluation data of experts, and construct a grey evaluation category matrix;
[0049] S5, According to the grey evaluation category matrix in S4, the economic performance of rural tourism is determined.
[0050] By constructing an innovative rural tourism efficiency evaluation method, the invention evaluates the innovative rural tourism efficiency, which not only facilitates workers to grasp the relevant data of rural tourism efficiency in time, but also contributes to promoting the sustainable development of rural tourism and points out the direction for the development planning of rural tourism.
[0051] In this embodiment, the innovative rural tourism efficiency evaluation index system in S1 should follow the general scientific paradigm and follow the principles of systematicness, scientificity, comparability and feasibility.
[0052] Wherein, the data collection in S1 mainly includes online platform collection and offline service organization collection of rural tourism. Because of collecting the current economic efficiency of rural tourism, experts can evaluate it.
[0053] In addition, the online platform of rural tourism mainly uses the principle and technology of cloud computing, takes the whole network data as the data source, and takes a single rural tourist spot as the object, and statistically analyzes the rural tourism situation from six aspects: tickets, homestays, specialties, restaurants, channels and evaluation.
[0054] Specifically, offline service organizations include rural tourism terminal service points and distribution service centers. Through data collection, it is convenient to obtain the current tourism economic efficiency of the village for experts to evaluate.
[0055] In addition, data collection also includes visitor browsing system, The visitor browsing system includes a processor, Memory, Search for rules and servers, The processor is used for processing data codes and cooperating with the memory for data storage, The memory is used to store data, The data includes the code stored in it,
And the code can be executed by the processor, The code configures the processor,
memory and server as a browsing system. Through the setting of the tourist browsing system, it is convenient for the staff to obtain the records of tourists browsing the village and make statistics on the records of tourists browsing, so that the staff can grasp the development potential of the village in time and point out the direction for the development rules of the village.
[0056] In this embodiment, the fuzzy comprehensive evaluation in S2 is a multi-level grey model, and the method for obtaining the evaluation index weight includes an AHP method, a coefficient of variation method and a comprehensive weight.
[0057] Wherein, the AHP method includes the weight of the index layer to the factor layer, specifically including: The judgment matrix is constructed by 9 scalar method, and the weight value is obtained by eigenvector method, then the weight vk of the k-th index under each criterion layer is: wr ead ML)
[0058] Factor vx is the weight of the k-th index in the factor layer; Factor vx’ is the k-th eigenvector corresponding to the maximum eigenvalue of the judgment matrix; Factor m is the number of factor layer indicators. if the results pass the consistency test, the weights are reasonably distributed; otherwise, the judgment matrix is reconstructed to find the weights.
[0059] In addition, the AHP method also includes the weight of the index layer to the target layer, specifically including: Factor pk is the weight of the k-th index in the j-th factor layer to the target layer; Factor v« is the weight of the k-th index in the j-th factor layer to the j-th factor layer; Factor v; is the weight of the J-th factor layer to the target layer (J=1, 2, ... 6; k=1, 2, ...m):
Di ZD, X U;
[0060] Specifically, the coefficient of variation method includes: Let ux be the weight of the k-th index obtained by the coefficient of variation method, m be the number of evaluation indexes and n be the number of evaluation experts. The formula for calculating the weight according to the coefficient of variation is as follows:
Lo a FL
Tint Wii) Inf ha (ViVi) In te [7 & wal ¥ &
Ve
[0061] Factor vi is the value of the i-th expert on the k-th index, and ~ % is the average of the values of all experts on the k-th index.
[0062] In addition, the comprehensive weights include: Calculate the objective weight of the i-th index wsi and woi, and the calculation formula is as follows: a Wg XWay
Wi = Vins Woy XW
[0063] In this embodiment, the big data system simulation in S3 includes the following steps:
[0064] S301, Classify and import the collected rural tourism economic benefit data into the big data simulation system program, and store the imported data in the database;
[0065] S302, The big data simulation system program obtains data simulation tasks, which carry multiple scene types, call simulation scripts according to the data simulation tasks, and find configuration files corresponding to each scene type through simulation scripts;
[0066] S303, Extracting scene data corresponding to each scene type from the configuration file through a simulation script, and determining the scene data corresponding to a plurality of scene types as simulation data obtained by simulation;
[0067] S304, The simulation message is generated according to the simulation data through the simulation script, and the generated simulation message is sent to the responder server through the simulation script, so that the responder server analyzes the simulation message and obtains the simulation data.
[0068] Wherein, the scene types in S302 include tourism resource conditions, tourism market capacity, tourism development benefits, social and economic support, development condition support and environmental bearing support.
[0069] Through the setting of big data system simulation, it is convenient to predict the future development trend of rural tourism efficiency, so that the staff can make reasonable rural development plans according to the development trend, and at the same time, it also provides certain guiding significance for experts to evaluate rural tourism efficiency.
[0070] In this embodiment, the specific operation steps of the grey evaluation category matrix in S4 are as follows: The number of evaluation experts is k, k=1, 2, 3, ... n, that is, there are n evaluation experts, and each evaluation expert evaluates the innovative rural tourism efficiency evaluation index system (if only one object is evaluated, a matrix is established for each first-level index; If you evaluate multiple objects, a matrix is created for each set of metrics, when there are multiple targets, an evaluation matrix is created, it contains a group of indicators corresponding to different targets, that is, it can evaluate one target or multiple targets.) and here, taking multiple evaluation targets as an example, X scores the evaluation index Uij of the first project with the evaluation scale dij (x), and fills in the expert evaluation table. According to the evaluation table, the evaluation sample matrix D (x) of the first project is obtained: © 4) jz 369 7, di Ore dis oo dn U
J G00 x di dps dps oo di |Ug
DW = 46) 46 1 ly. d 131 di diy diz, U 13
G40 4 a
Gt dp dj oe diy [Va
[0071] Wherein, experts in S3 evaluate the innovative rural tourism efficiency evaluation index system, including evaluating grey categories, determining grey evaluation coefficients, constructing grey evaluation weight vectors and weight matrices, comprehensive evaluation and determining comprehensive evaluation value.
[0072] According to the evaluation gray category: Due to the influence of human factors, experts can only give a grey number of whiteness values. In order to truly reflect the degree of belonging to a certain category, it is necessary to determine the grade of grey category, grey number of grey category and whitening weight function of grey number. Let the grey number of evaluation be e, e = 1, 2, 3, ... , m, that is, there are m evaluation grey classes. Usually, according to the actual needs of the research content, the evaluation grey class is divided into five grades (from high to low), and m=5. In this case, it is necessary to determine the whitening weight function of the evaluation grey class to characterize the above grey class.
[0073] For example: The first grayscale category: For high level (e=1), let the grey = degree number be VI € [0, 1, 2] and the whitening weight function be f:
Ee rae ee Fo sin fF {8} ora o fd) 4 (2d) / 1 dy €112] ijk € | 32]
[0074] The second grayscale category: For a very high order (e=2), let the grey <p number be ©! € [O, 2, 4] and the whitening weight function be fz ry} 1 a oe $y 100 = rp a { di € 10,4] $
[0075] The third grayscale category: High level (e=3), let the grey degree number ° € [O, 3, 6], whitening weight function f3 da / 3 dn € 10,3) hadt aj dY €13,68]
Jk ijk (Grt) 1 Hk Ne Na
Ong 0 di € 10,6)
[0076] The fourth grayscale category: For normal level (e=4), let grey degree number
SLE [O, 4, 8], whitening weight function be f4 : Ee of 383 5 . a [4 dg) € [04] ty (dig) (Bd; 3 / 4 di € [4,8]
[0077] The fifth grayscale category: For very low grade (e=5), let the grey degree number be ‘lg [0, 5, 10] and the whitening weight function be fs diy / 5 di € [0,5] fsd) (10-d5)/5 die € [5.10] {x} ev EY 0 diy; € [0,10]
[0078] According to the grey evaluation coefficient: For the evaluation index Uj, the grey evaluation coefficient of item x belonging to the grey category of item e is recorded as Mie ®
Al ot pop ain
Mie = dt feld) 10 .
[0079] For the evaluation index Uij, the grey coefficient of item x under each evaluation grey category is recorded as Mie ©
O3 vm paix)
M; = Qt Mi
[0080] For constructing grey evaluation weight vector and weight matrix needle: For the evaluation index Uj of all evaluation experts, the evaluation weight of the grey category of item E of the X item is recorded as rie ©, then Tije 9 = Mie ®/M; ®-
Considering that there are five grey categories, i.e. e=1, 2, 3, 4 and 5, for the evaluation index Uij of the X item to be evaluated, each grey category has a grey evaluation weight vector ry 99
A) pl ER) x) x) (x)
EE
[0081] For the index U; of the x-th evaluated item, the grey evaluation weight matrix
Ri 9 of each evaluation grey category is given
OT Le a a 0 0]
Ty Tar Tar Daa Tas Tas x} Sapo a x my whe For Tae Ton Tos Tis
Rv == & 6 & 0 13 131 132 133 i34 125
RES Ax) xy (x) =) Ax) ig Fist Isa Disa Tse Tiss
[0082] For comprehensive evaluation: Comprehensive evaluation of the evaluation index Uj of the X project, the result is recorded as Bi% po = AL % Rp —_ (pt Ro bh {x} B® bi pT ERY TT AM a jg a Vg oe Mig a Mis J ° 0083] From the comprehensive evaluation result B of Ui, the grey evaluation weight i matrix R® of U; index of each evaluation grey category of the x-th evaluation item is obtained pix} hl) 00 BE OE) 0
B; biy Die ba Das bis (x) GO pi) p00 8
B bar bz boz bas bos
Roll (0 0 0 ©
B; bai biz Dis Dga big (0) Gp 16 DE op
Bs | [bs Do Diss biss This
[0084] Therefore, for the X-th evaluation item, the comprehensive evaluation index U; is recorded as B%
Rx = AX RO) = (} {x} Rix) pe |x t {x} nj 5 = (by, by", by a Mg be)
[0085] For comprehensive evaluation value: If “grey grade” is assigned to each evaluation grey grade, then each evaluation grey grade assigns a vector C= (1, 2, 3, 4.5). Therefore, the calculation formula of the comprehensive evaluation value Z (x) of the x-th evaluated item is as follows:
ZE) = RE 5 CF
[0086] In this embodiment, the specific operation steps for determining the economic performance of rural tourism in S5 are as follows:
[0087] S501, Firstly, according to the division of evaluation grey categories, the whitening weight function of each grey category is calculated
When e=1
MD ob J ph CD jz (1 {1} Jy
Mii = Dopey Tir = (Tyg Hyp Hog Tije Fis) = 0
[0088] S502, Then calculate the total grey evaluation coefficient of tourism economic performance under each evaluation grey category;
Mis ‘1 is for
MD oS aff eat ail) {1 ACD ae
My = Dice Mi = (My; +My + Min + My + Mis)
[0089] S503, According to the weight of each index and the weight vector of each performance, the evaluation vector of each sub-performance is calculated;
[0090] Wherein, according to the grey weight vector formula, the weight vector of tourism economic performance can be obtained as follows:
LI rt) REY KE INNES. Ijs yp = lin Fl +13 + Tyg + 0s)
[0091] S504, Finally, according to the performance level and the weight vector of each performance, the evaluation value of tourism economic performance and the total performance are calculated.
[0092]Based on the multi-level grey model, the invention evaluates the rural tourism efficiency from the aspects of tourism resource conditions, tourism market capacity, tourism development benefit, tourism economic support, social economic support, social economic support and environmental bearing support, etc., which points out the direction for rural tourism development and has certain guiding significance for the development of tourism in China.

Claims (10)

CONCLUSIESCONCLUSIONS 1. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie, die wordt zich gekenmerkt doordat de methode de volgende stappen omvat: S1, Verzamel de bestaande gegevens over de economische voordelen van plattelandstoerisme en maak de bijbehorende statistische kaart en bouw op basis van het bestaande economische voordeel van het dorp een innovatief evaluatie- indexsysteem voor efficiëntie van het plattelandstoerisme; S2, Bepaal het gewicht van de evaluatie-index op basis van een vage uitgebreide evaluatiemethode; S3, Volgens de statistische gegevenskaart gemaakt in S1 wordt de ontwikkelingstrendkaart van de toekomstige toeristische economische voordelen van het dorp gesimuleerd met behulp van het big data-systeem; S4, Selecteer willekeurig de economische voordeelgegevens van een bepaalde dag in het dorp, en verschillende deskuntigen evalueren en scoren de geëxtraheerde gegevens, registreren de evaluatiegegevens van deskuntigen en construeren een grijze evaluatiecategoriematrix; S5, Volgens de grijze evaluatiecategoriematrix in S4 worden de economische prestaties van plattelandstoerisme bepaald.1. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation, which is characterized in that the method includes the following steps: S1, Collect the existing data on the economic benefits of rural tourism and prepare the corresponding statistical map and build based on the existing economic advantage of the village, an innovative evaluation index system for rural tourism efficiency; S2, Determine the weight of the evaluation index based on fuzzy comprehensive evaluation method; S3, According to the statistical data map made in S1, the development trend map of the future tourism economic benefits of the village is simulated using the big data system; S4, Randomly select the economic benefit data of a certain day in the village, and different experts evaluate and score the extracted data, record experts' evaluation data, and construct a gray evaluation category matrix; S5, According to the gray evaluation category matrix in S4, the economic performance of rural tourism is determined. 2. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de gegevensverzameling in S1 het verzamelen van online platformen voor plattelandstoerisme en het verzamelen van offline serviceorganisaties omvat, waarbij de cloud computing, de hele netwerkgegevens als gegevensbron neemt, een enkele landelijke toeristische plek als object en analyseert statistisch de situatie van het plattelandstoerisme vanuit zes aspecten neemt: tickets, gastgezinnen, specialiteiten, restaurants, kanalen en evaluatie en de offline service-instellingen omvatten servicepunten voor terminals voor plattelandstoerisme en distributieservicecentra.2. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation according to Conclusion 1, which is characterized in that the data collection in S1 includes the collection of online rural tourism platforms and the collection of offline service organizations, where the cloud computing, the takes whole network data as the data source, takes a single rural tourist spot as the object and statistically analyzes the situation of rural tourism from six aspects: tickets, homestays, specialties, restaurants, channels and evaluation and the offline service institutions include service points for rural tourism terminals and distribution service centers. 3. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de gegevensverzameling in S1 verder een systeem voor het doorbladeren van bezoekers omvat, het systeem voor het doorbladeren van bezoekers een processor, een geheugen, een zoekregel en een server, wordt de processor gebruikt voor het verwerken van gegevenscodes en het samenwerken met het geheugen voor gegevensopslag, en het geheugen wordt gebruikt voor het opslaan van gegevens.3. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation according to Claim 1, which is characterized in that the data collection in S1 further includes a visitor browsing system, the visitor browsing system a processor, a memory, a search line, and a server, the processor is used to process data codes and interact with the memory for data storage, and the memory is used to store data. 4. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de vage uitgebreide evaluatie in S2 een grijsmodel met meerdere niveaus is, en de methode voor het verkrijgen van het gewicht van de evaluatie-index een AHP omvat. methode, een variatiecoëfficiëntmethode en een alomvattend gewicht, waarbij de AHP-methode het gewicht van een indexlaag op een factorlaag omvat, met name omvattende: De beoordelingsmatrix wordt geconstrueerd met de 9 scalaire methode en de gewichtswaarde wordt verkregen door de eigenvectormethode, dan is het gewicht Vk van de k-de index onder elke criteriumlaag: U = VL / Yi Uy Factor vk is het gewicht van de k-de index in de factorlaag; Factor vr is de k-de eigenvector die overeenkomt met de maximale eigenwaarde van de beoordelingsmatrix; factor m is het aantal factorlaagindicatoren. als de resultaten de consistentietest doorstaan, zijn de gewichten redelijk verdeeld; anders wordt de beoordelingsmatrix gereconstrueerd om de gewichten te vinden.4. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation according to Claim 1, which is characterized in that the fuzzy comprehensive evaluation in S2 is a multi-level gray model, and the method of obtaining the weight of the evaluation index includes an AHP. method, a coefficient of variation method and a comprehensive weight, where the AHP method includes the weight of an index layer on a factor layer, specifically including: The rating matrix is constructed by the 9 scalar method and the weight value is obtained by the eigenvector method, then the weight is Vk of the kth index under each criterion layer: U = VL / Yi Uy Factor vk is the weight of the kth index in the factor layer; Factor vr is the kth eigenvector corresponding to the maximum eigenvalue of the rating matrix; factor m is the number of factor layer indicators. if the results pass the consistency test, the weights are fairly distributed; otherwise, the rating matrix is reconstructed to find the weights. 5. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 4, die wordt zich gekenmerkt doordat de AHP-methode verder het gewicht van een indexlaag op een doellaag omvat, in het bijzonder omvattende: Factor pk is het gewicht van de k-de index in de j-de factorlaag ten opzichte van de doellaag; Factor vk is het gewicht van de k-de index in de j-de factorlaag ten opzichte van de j-de factorlaag; Factor v; is het gewicht van de J-de factorlaag ten opzichte van de doellaag (J=1, 2, … 6; k=1, 2, ...M):5. The innovative efficiency evaluation method for rural tourism based on a fuzzy comprehensive evaluation according to Claim 4, which is characterized in that the AHP method further includes the weight of an index layer on a target layer, in particular including: Factor pk is the weight of the k-th index in the j-th factor layer relative to the target layer; Factor vk is the weight of the k-th index in the j-th factor layer relative to the j-th factor layer; Factor v; is the weight of the Jth factor layer with respect to the target layer (J=1, 2, … 6; k=1, 2, …M): De =U XU;The =U XU; 6. De innovatieve efficiéntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de big data-systeemsimulatie in S3 de volgende stappen omvat: S301, Classificeer en importeer de verzamelde economische voordelen van het plattelandstoerisme in het big data-simulatiesysteemprogramma en sla de geïmporteerde gegevens op in de database; S302, Het big data-simulatiesysteemprogramma verkrijgt gegevenssimulatietaken, die meerdere scenetypen bevatten, roept simulatiescripts op volgens de gegevenssimulatietaken en vindt configuratiebestanden die overeenkomen met elk scènetype via simulatiescripts; S303, Het extraheren van scénegegevens die overeenkomen met elk scènetype uit het configuratiebestand via een simulatiescript, en het bepalen van de scènegegevens die overeenkomen met een aantal scènetypes als simulatiegegevens die zijn verkregen door simulatie; S304, Het simulatiebericht wordt gegenereerd volgens de simulatiegegevens via het simulatiescript en het gegenereerde simulatiebericht wordt via het simulatiescript naar de responderserver gestuurd, zodat de responderserver het simulatiebericht analyseert en de simulatiegegevens verkrijgt.6. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation according to Conclusion 1, which is characterized in that the big data system simulation in S3 includes the following steps: S301, Classify and import the collected economic benefits of rural tourism into the big data simulation system program and save the imported data into the database; S302, The big data simulation system program obtains data simulation tasks, which contain multiple scene types, calls simulation scripts according to the data simulation tasks, and finds configuration files corresponding to each scene type through simulation scripts; S303, Extracting scene data corresponding to each scene type from the configuration file through a simulation script, and determining the scene data corresponding to a plurality of scene types as simulation data obtained by simulation; S304, The simulation message is generated according to the simulation data through the simulation script, and the generated simulation message is sent to the responder server through the simulation script, so that the responder server analyzes the simulation message and obtains the simulation data. 7. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 6, die wordt zich gekenmerkt doordat de scènetypes in S302 toeristische hulpbronnen, toeristische marktcapaciteit, voordelen voor toeristische ontwikkeling, sociale en economische ondersteuning, ondersteuning van ontwikkelingscondities en ondersteuning van het milieu omvatten. .7. The innovative rural tourism efficiency evaluation method based on vague comprehensive evaluation according to Conclusion 6, which is characterized by the scene types in S302 tourism resources, tourism market capacity, tourism development benefits, social and economic support, development condition support and support of the environment. . 8. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de willekeurige steekproefmethode in S4 de volgende stappen omvat: S401, Definieer willekeurige steekproefregels, selecteer het steekproefbereik en definieer de willekeurige steekproefverhouding;8. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation according to Conclusion 1, which is characterized in that the random sampling method in S4 includes the following steps: S401, Define random sampling rules, select the sampling range and define the random sampling ratio; S402, Schrijven van de regels voor willekeurige extractie in de database met extractierecords; S403, Volgens de regel van willekeurige bemonstering wordt het bemonsteringsobject gevonden uit de monsterbibliotheek en wordt het bemonsterings- ID-nummer van het bemonsteringsobject verkregen als een steekproef voor steekproeven.S402, Writing the random extraction rules in the extraction record database; S403, According to the rule of random sampling, the sampling object is found from the sample library, and the sampling ID number of the sampling object is obtained as a sample for sampling. 9. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de specifieke bewerkingsstappen van de grijze evaluatiecategoriematrix in S4 als volgt zijn: Het aantal evaluatiedeskundigen is k, k=1, 2, 3, ... , n dat wil zeggen, er zijn n evaluatiedeskundigen en elke evaluatiedeskundige evalueert het innovatieve evaluatie-indexsysteem voor efficiëntie van het plattelandstoerisme (als er slechts één object wordt geëvalueerd, wordt een matrix opgesteld voor elke index op het eerste niveau; Als u meerdere objecten evalueert, wordt er een matrix gemaakt voor elke set metrieken, Als er meerdere doelen zijn, wordt er een evaluatiematrix gemaakt. Het bevat een groep indicatoren die overeenkomen met verschillende doelen, dat wil zeggen dat het één doel of meerdere doelen kan evalueren.), waarbij meerdere evaluatiedoelen als voorbeeld worden genomen, scoort X de evaluatie-index Uij van het eerste project met de evaluatieschaal dij (x), en vult hij de deskuntigen evaluatietabel in. Volgens de evaluatietabel wordt de evaluatievoorbeeldmatrix D (x) van het eerste project verkregen: dt diy d A ne diy, il 11 dy, dy, diy oc di |U DO za af, df df |U dy dt; a en di Us9. The innovative rural tourism efficiency evaluation method based on fuzzy comprehensive evaluation according to Claim 1, which is characterized in that the specific processing steps of the gray evaluation category matrix in S4 are as follows: The number of evaluation experts is k, k=1, 2, 3, ... , n that is, there are n evaluation experts and each evaluation expert evaluates the innovative rural tourism efficiency evaluation index system (if only one object is evaluated, a matrix is established for each index at the first level; If you evaluate multiple objects, a matrix is created for each set of metrics, If there are multiple goals, an evaluation matrix is created. It contains a group of indicators that correspond to different goals, that is, it can be one goal or multiple goals evaluate.), taking several evaluation objectives as an example, X scores the evaluation index Uij of the first project with the evaluation scale dij (x), and completes the expert evaluation table. According to the evaluation table, the evaluation sample matrix D (x) of the first project is obtained: dt diy d A ne diy, il 11 dy, dy, diy oc di |U DO za af, df df |U dy dt; a and di Us 10. De innovatieve efficiëntie-evaluatiemethode voor plattelandstoerisme op basis van een vage uitgebreide evaluatie volgens Conclusie 1, die wordt zich gekenmerkt doordat de specifieke bewerkingsstappen voor het bepalen van de economische prestaties van het plattelandstoerisme in S5 als volgt zijn: S501, Ten eerste wordt, op basis van de verdeling van evaluatiegrijze categorieën, de functie voor het bleken van gewicht van elke grijze categorie berekend; S502, Daarna wordt de totale grijze evaluatiecoéfficiént van de economische prestaties van het toerisme onder elke grijze evaluatiecategorie berekend; S503, En dan wordt het gewicht van elke index en de gewichtsvector van elke prestatie, de evaluatievector van elke subprestatie berekend; S504, Ten slotte worden, op basis van het prestatieniveau en de gewichtsvector van elke prestatie, de evaluatiewaarde van de toeristische economische prestatie en de totale prestatie berekend.10. The innovative rural tourism efficiency evaluation method based on vague comprehensive evaluation according to Claim 1, which is characterized in that the specific processing steps for determining the economic performance of rural tourism in S5 are as follows: S501, Firstly, based on the distribution of evaluation gray categories, the weight whitening function of each gray category calculated; S502, Then the total gray evaluation coefficient of tourism economic performance under each gray evaluation category is calculated; S503, And then the weight of each index and the weight vector of each performance, the evaluation vector of each sub-performance is calculated; S504, Finally, based on the performance level and the weight vector of each performance, the evaluation value of tourism economic performance and the total performance are calculated.
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YUE ZHANG: "The Evaluation of AHP-GRA", 2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL AND INFORMATION SCIENCES, IEEE, 21 June 2013 (2013-06-21), pages 1823 - 1826, XP032514768, DOI: 10.1109/ICCIS.2013.477 *
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