Large city marginal area town and village toughness evaluation method and system

Abstract

The invention discloses a method and a platform for evaluating the toughness of a town and a village in a marginal area of a large city, and the method comprises the steps: building a multi-level evaluation index system, obtaining and processing evaluation data, calculating an index weight and an evaluation result, and building the multi-level index system comprising economic toughness, social toughness, ecological toughness and infrastructure toughness; obtaining related literature data by using a crawler technology and a semantic analysis model, screening a final index through a TF-IDF algorithm, calculating an index weight by using an information entropy method, and finally obtaining an evaluation result by using a fuzzy comprehensive evaluation method; according to the method, the automation of the evaluation process is realized, the toughness level of the town and village in the marginal area of the large city can be quickly and accurately evaluated, and a scientific basis is provided for urban planning and management decision.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban planning and management, and particularly relates to a method for evaluating the resilience of towns and villages in the fringe area of big cities. Background Art

[0002] As the urban-rural transitional zone, the fringe area of big cities is faced with the pressure of urban expansion and undertakes the important mission of urban-rural integrated development; the traditional resilience evaluation methods have obvious limitations. The existing evaluation systems mostly focus on single-dimensional evaluation, unable to comprehensively reflect the comprehensive resilience level of towns and villages in multiple aspects such as economy, society, ecology and infrastructure. At the same time, the selection of evaluation indicators often has strong subjectivity and lacks a scientific screening mechanism.

[0003] The evaluation methods of the existing technologies mainly rely on manual collection and collation of data, which is not only time-consuming and laborious, but also prone to data omission and errors. The commonly used evaluation methods at present often ignore the fuzziness and uncertainty between indicators, and there are deviations between the evaluation results and the actual situation; at the same time, the determination of indicator weights mostly relies on expert experience and lacks objective quantitative basis; in addition, the existing evaluation work mainly focuses on scattered research, lacking a unified evaluation standard and an automated evaluation tool, making it difficult to support large-scale and regular resilience evaluation work, which seriously restricts the practical application of evaluation results in urban planning and management decisions.

[0004] The existence of these problems makes it difficult to effectively carry out the resilience evaluation work of towns and villages in the fringe area of big cities and unable to provide reliable decision-making support for regional sustainable development. Therefore, it is urgent to develop a set of scientific and efficient resilience evaluation methods and supporting platforms to improve the quality and efficiency of evaluation work. The traditional evaluation methods often lack systematicness and comprehensiveness. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and platform for evaluating the resilience of towns and villages in the fringe area of big cities. By constructing a multi-level evaluation index system, calculating the weights of each level of indicators based on the initial indicator values in the literature, constructing an indicator weight matrix, and using the fuzzy comprehensive evaluation method to calculate the evaluation results of the resilience of towns and villages, the rapid and accurate evaluation of the resilience of towns and villages in the target area is realized.

[0006] The specific scheme is as follows:

[0007] A method for evaluating the resilience of towns and villages in the fringe area of big cities, the method comprising the following steps:

[0008] Step S1, constructing a multi-level evaluation index system, the multi-level evaluation index system including an objective layer, a criterion layer and an index layer, wherein the objective layer is the evaluation result of the resilience of towns and villages, and the criterion layer includes four dimensions: economic resilience, social resilience, ecological resilience and infrastructure resilience.

[0009] Step S2: Obtain the literature and data related to the evaluation of the resilience of towns and villages based on web crawler technology and semantic analysis models, establish the initial index set at the index layer, and use the TF-IDF algorithm to screen the initial index set to determine the final index layer indicators.

[0010] Step S3: Calculate the weights of each level of indicators based on the initial indicator values in the literature, construct an index weight matrix, and use the fuzzy comprehensive evaluation method to calculate the evaluation results of the resilience of towns and villages.

[0011] Furthermore, the specific process of establishing the initial index sets at the criterion layer and the index layer in step S2 is as follows:

[0012] Obtain the publicly available literature related to resilience evaluation on the Internet based on web crawler technology to form a literature set L, where L = {L1, L2, L3, …, L n}, n > 200. Use the semantic analysis model to preprocess the literature set L and extract the initial index set I, where I = {I1, I2, I3, …, I m}, m > 16.

[0013] Perform word segmentation on the literature set L, remove stop words and filtering words to form a word-segmented literature set L'. Calculate the term frequency TF and inverse document frequency IDF of each index I i in the initial index set I in the word-segmented literature set L as follows:

[0014]

[0015] where N{I i ∈L'} is the number of times the index I i appears in the word-segmented literature set L', and T L' is the number of word segments in the word-segmented literature set; M{I i ∈L'} is the number of documents containing the index I i .

[0016] Calculate the TF-IDF value of the index I i : TF-IDF = TF × IDF; Sort the indexes in the initial index set I according to the TF-IDF values from high to low, select the top 15 items as the final indexes at the index layer, and classify the final indexes at the index layer according to the four dimensions of economic resilience, social resilience, ecological resilience, and infrastructure resilience to complete the correspondence between the index layer and the criterion layer.

[0017] Furthermore, the correspondence between the index layer and the criterion layer specifically includes:

[0018] Economic resilience: GDP per capita, fixed asset investment, and fiscal revenue;

[0019] Social resilience: population density, number of hospital beds, coverage rate of educational facilities, social security coverage rate;

[0020] Ecological resilience: green space coverage rate, air quality index, water quality compliance rate, waste treatment rate;

[0021] Infrastructure resilience: road network density, water supply network density, communication network coverage rate, power facility density.

[0022] Furthermore, calculating the weights of each level of indicators based on the initial indicator values in the literature and constructing an indicator weight matrix includes:

[0023] Collecting the original data of each indicator and constructing an evaluation matrix R: where rij is the value of the jth initial indicator in the ith literature, 1 ≤ i ≤ n, 1 ≤ j ≤ m, n is the number of literatures, and m is the number of initial indicators; normalizing the evaluation matrix R to obtain a standardized matrix P, where the element p ij is calculated as: Calculating the information entropy ej of the jth indicator j : where k is the entropy value coefficient, the element p ij is the value of the jth initial indicator in the ith literature after standardization.

[0024] Calculating the difference coefficient dj of the jth indicator j : dj j = 1 - ej j ; then, based on the final indicators at the indicator layer, constructing an indicator weight vector W: W = (w1, w2,..., w 15 ), where the weight wj of the jth indicator j is calculated as:

[0025] Furthermore, the steps of calculating the evaluation results of the resilience of towns and villages by using the fuzzy comprehensive evaluation method include:

[0026] Establishing an evaluation grade set V: V = {V1, V2, V3, V4, V5}, where V1 represents high resilience, V2 represents relatively high resilience, V3 represents medium resilience, V4 represents relatively low resilience, and V5 represents low resilience.

[0027] Constructing a relationship matrix Q:

[0028] where: q sk represents the membership degree of the sth final indicator to the kth evaluation grade V k , 1 ≤ s ≤ 15, 1 ≤ k ≤ 5, calculating the comprehensive evaluation result B: B = WQ = (b1, b2, b3, b4, b5), bk , where \(1\leq k\leq5\), is the membership value of the \(k\)th evaluation level.

[0029] Furthermore, the normalized interval division of the evaluation level set \(V\) is: \(V1 = [0.8, 1.0]\), \(V2 = [0.6, 0.8]\), \(V3 = [0.4, 0.6]\), \(V4 = [0.2, 0.4]\), \(V5 = [0, 0.2]\).

[0030] Furthermore, the element \(q\) in the relationship matrix \(Q\) sk is calculated as follows:

[0031] Calculate the average value for each column \(j\) of the standardized matrix \(P\) Then the average value of the column corresponding to the final index is expressed as Element \(q\) sk includes: \(q\) s1 , \(q\) s2 , \(q\) s3 , \(q\) s4 , \(q\) s5 , according to the value of and the corresponding relationship of the evaluation level, obtain the value of element \(q\) sk ;

[0032]

[0033] Furthermore, the fuzzy comprehensive evaluation method includes:

[0034] Calculate the membership value of the \(k\)th evaluation level:

[0035] where \(b\) k is the \(k\)th component in the evaluation result \(B\); after obtaining all 5 evaluation result components, calculate the maximum value of the evaluation result components:

[0036] \(b\) max = max{\(b1\), \(b2\), \(b3\), \(b4\), \(b5\)}, then the evaluation level corresponding to the maximum value is the evaluation result of the rural and town resilience.

[0037] The present invention also provides a rural and town resilience evaluation platform for the urban-rural fringe area, and the platform is used to execute the method of the present invention. The platform includes: A rural and town resilience evaluation platform for the urban-rural fringe area, characterized in that the platform includes a data acquisition module, a preprocessing module, an index screening module, a weight calculation module, an evaluation result calculation module, and a result display module that are connected in sequence.

[0038] Furthermore, the data acquisition module is used to obtain publicly available literature related to resilience evaluation on the network based on web crawler technology and form a literature collection.

[0039] Further, the preprocessing module is used to perform semantic analysis and word segmentation on the literature collection to extract an initial index set.

[0040] Further, the index screening module is used to calculate the TF-IDF values of each index in the initial index set through the TF-IDF algorithm, and select the final indexes according to the TF-IDF value ranking.

[0041] Further, the weight calculation module is used to construct an evaluation matrix and calculate the weights of each index by the information entropy method.

[0042] Further, the evaluation result calculation module is used to establish an evaluation grade set, construct a relationship matrix, and calculate the evaluation result of the resilience of towns and villages by the fuzzy comprehensive evaluation method.

[0043] Further, the result display module is used to display the final rating of the resilience evaluation of towns and villages.

[0044] The present invention also provides a computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement the method for evaluating the resilience of towns and villages in the fringe area of a big city of the present invention.

[0045] The beneficial effects of the present invention are as follows:

[0046] The present invention establishes a multi-level evaluation index system, comprehensively evaluates the resilience of towns and villages from four dimensions of economy, society, ecology and infrastructure, and improves the scientificity and systematicness of the evaluation; uses web crawler technology and semantic analysis model to automatically obtain and process relevant literature data, combines the TF-IDF algorithm to screen key indexes, and improves the data acquisition and processing efficiency; uses the information entropy method to determine the index weights, combines the fuzzy comprehensive evaluation method for evaluation, and realizes the objectification and standardization of the evaluation process; develops a supporting evaluation platform, realizes the full-process automation from data collection to result display, and improves the efficiency and accuracy of the evaluation work. Description of the Drawings

[0047] Figure 1 is a flowchart of the method for evaluating the resilience of towns and villages in the fringe area of a big city of the present invention;

[0048] Figure 2 is a schematic diagram of the composition of the evaluation platform for the resilience of towns and villages in the fringe area of a big city of the present invention. Detailed Embodiments

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be described clearly and completely below. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the scope of protection of the present invention.

[0050] It should be noted that a large city refers to a city that meets the following conditions: (1) Scale condition: The permanent urban population is more than 3 million; (2) Economic condition: The gross regional product (GDP) reaches more than 300 billion yuan; (3) Administrative condition: It is a provincial capital city, a municipality directly under the Central Government, a city specifically designated in the state plan, or a sub-provincial city; (4) Spatial condition: The built-up area reaches more than 200 square kilometers; (5) Functional condition: It has a complete urban infrastructure system, has multiple urban sub-centers, forms a metropolitan regional economic center, has a significant industrial agglomeration effect, and has regional comprehensive service functions; (6) Radiation condition: It has a strong economic, social, and cultural radiation force on the surrounding areas and can drive regional coordinated development.

[0051] The towns and villages in the fringe area of a large city refer to the townships and their administrative villages within the administrative region of a large city and within a range of 15 - 50 kilometers from the central urban area, and have the following characteristics: (1) Spatial characteristics: It is in the urban-rural fringe area, showing continuity with the central urban area in space, but the construction land density is significantly lower than that of the central urban area; (2) Functional characteristics: It undertakes the node function of urban-rural element flow, including population flow, material transportation, information exchange, etc.; (3) Industrial characteristics: It has a composite industrial structure with the coexistence of the agglomeration of secondary and tertiary industries and agricultural functions; (4) Social characteristics: The permanent population composition includes local agricultural population, non-agricultural employed population, and urban spillover population; (5) Facility characteristics: The infrastructure and public service facilities are between urban and rural areas, showing obvious transitional characteristics.

[0052] Example 1

[0053] As Figure 1 shown, it is a method for evaluating the resilience of towns and villages in the fringe area of a large city of the present invention, and the method includes the following steps:

[0054] Step S1, constructing a multi-level evaluation index system, the multi-level evaluation index system includes an objective layer, a criterion layer, and an index layer, where the objective layer is the evaluation result of the resilience of towns and villages, and the criterion layer includes four dimensions: economic resilience, social resilience, ecological resilience, and infrastructure resilience.

[0055] This multi-level evaluation system draws on the idea of the Analytic Hierarchy Process (AHP). For example, when evaluating the resilience of a town in the fringe area of a large city, economic resilience can be examined by the degree of industrial structure diversification, social resilience can be evaluated by the allocation of medical and educational resources, ecological resilience focuses on the environmental quality status, and infrastructure resilience examines the perfection degree of municipal supporting facilities. This multi-dimensional evaluation can comprehensively reflect the development status of towns and villages.

[0056] Step S2: Based on web crawler technology and semantic analysis models, obtain relevant literature and data on the resilience evaluation of towns and villages, establish an initial index set for the index layer, and use the TF-IDF algorithm to screen the initial index set to determine the final index layer indicators.

[0057] In specific implementation, the Scrapy framework of Python can be used for web crawler development to capture relevant literature in academic databases such as CNKI and Wanfang. For example, taking "urban resilience", "rural resilience", etc. as keywords, more than 200 core journal papers published in the past 5 years can be obtained. Using the natural language processing toolkit NLTK for semantic analysis, evaluation indicators including "industrial diversity index", "medical resource density", etc. can be extracted from these literatures.

[0058] Based on web crawler technology, obtain publicly available resilience evaluation-related literature on the Internet to form a literature set L, L = {L1, L2, L3, …, L n}, n > 200. Use the semantic analysis model to preprocess the literature set L and extract the initial index set I, I = {I1, I2, I3, …, I m}, m > 16.

[0059] Perform word segmentation on the literature set L, remove stop words and filtering words to form a word-segmented literature set L'. Calculate the term frequency TF and inverse document frequency IDF of each index I i in the initial index set I in the word-segmented literature set:

[0060]

[0061] where N{I i ∈L'} is the number of times the index I i appears in the word-segmented literature set L', and T L' is the number of word segments in the word-segmented literature set; M{I i ∈L'} is the number of documents containing the index I i .

[0062] Calculate the index I iThe TF-IDF value: TF-IDF = TF × IDF; Sort the indicators in the initial indicator set I from high to low according to the TF-IDF value, select the top 15 as the final indicators of the indicator layer, and classify the final indicators of the indicator layer into four dimensions: economic resilience, social resilience, ecological resilience, and infrastructure resilience, to complete the correspondence between the indicator layer and the criterion layer.

[0063] The correspondence between the indicator layer and the criterion layer specifically includes:

[0064] Economic resilience: GDP per capita, fixed asset investment, and fiscal revenue;

[0065] Social resilience: population density, number of hospital beds, education facility coverage rate, and social security coverage rate;

[0066] Ecological resilience: green space coverage rate, air quality index, water quality compliance rate, and waste treatment rate;

[0067] Infrastructure resilience: road network density, water supply network density, communication network coverage rate, and power facility density.

[0068] These indicators are highly operable in practical applications. Taking a town in the fringe area of a large city as an example: In terms of economic resilience, the GDP per capita of the town reached 85,000 yuan in 2023, the fixed asset investment was 1.5 billion yuan, and the annual fiscal revenue was 280 million yuan, showing strong economic development potential. In terms of social resilience, the population density was 2,800 people per square kilometer, there were 3 community health service centers with a total of 120 hospital beds, 15 primary and secondary schools and kindergartens covered 95% of school-age population, and the social security coverage rate reached 92%. In terms of ecological resilience, the green space coverage rate was 35%, the proportion of days with good air quality was 78%, the water quality compliance rate was 85%, and the harmless treatment rate of domestic waste was 99%. In terms of infrastructure resilience, the road network density was 4.2 kilometers per square kilometer, the water supply network density was 3.8 kilometers per square kilometer, the 5G network coverage rate was 95%, and the distribution facility density was 2.5 substations per square kilometer.

[0069] Step S3: Calculate the weights of each level of indicators based on the initial indicator values in the literature, construct an indicator weight matrix, and use the fuzzy comprehensive evaluation method to calculate the evaluation results of the resilience of towns and villages.

[0070] Collect the original data of each indicator to construct an evaluation matrix R: where rij is the value of the jth initial indicator in the ith literature, 1 ≤ i ≤ n, 1 ≤ j ≤ m, n is the number of literatures, and m is the number of initial indicators; normalize the evaluation matrix R to obtain a standardized matrix P, where the element p ij The calculation formula is: Calculate the information entropy ej of the jth indicator j: where k is the entropy value coefficient, element p ij is the value of the j-th initial index in the i-th literature after standardization.

[0071] Calculate the difference coefficient d of the j-th index j : d j = 1 - e j ; Then, based on the final index of the index layer, construct the index weight vector W: W = (w1, w2,..., w 15 ), where the weight w j of the j-th index is calculated as follows:

[0072] The steps of calculating the evaluation result of the resilience of towns and villages by using the fuzzy comprehensive evaluation method include:

[0073] Establish the evaluation grade set V: V = {V1, V2, V3, V4, V5}, where V1 represents high resilience, V2 represents relatively high resilience, V3 represents medium resilience, V4 represents relatively low resilience, and V5 represents low resilience.

[0074] Construct the relationship matrix Q:

[0075] where: q sk represents the membership degree of the s-th final index to the k-th evaluation grade V k , 1 ≤ s ≤ 15, 1 ≤ k ≤ 5, calculate the comprehensive evaluation result B: B = WQ = (b1, b2, b3, b4, b5), b k , 1 ≤ k ≤ 5, is the membership degree value of the k-th evaluation grade.

[0076] The normalized interval division of the evaluation grade set V is: V1 = [0.8, 1.0], V2 = [0.6, 0.8], V3 = [0.4, 0.6], V4 = [0.2, 0.4], V5 = [0, 0.2].

[0077] The calculation method of the element q sk in the relationship matrix Q is:

[0078] Calculate the average value of each column j of the standardized matrix P Then the average value of the column corresponding to the final index is expressed as Suppose the standardized values in 5 literatures for the index of "fixed asset investment amount" are 0.75, 0.82, 0.68, 0.71, and 0.79 respectively, calculate the average value Since 0.75 falls within the interval [0.6, 0.8], it indicates that this indicator belongs to a relatively high toughness level. This method of calculating the average value can eliminate the influence of individual extreme values and more objectively reflect the overall level of the indicator.

[0079] Element q sk Includes: q s1 , q s2 , q s3 , q s4 , q s5 , according to the corresponding relationship between the value of and the evaluation level, the value of element q sk is obtained;

[0080]

[0081]

[0082] The fuzzy comprehensive evaluation method includes calculating the membership degree value of the k-th evaluation level:

[0083] wherein, b k is the k-th component in the evaluation result B; after obtaining all 5 evaluation result components, calculate the maximum value of the evaluation result components:

[0084] b max = max{b1, b2, b3, b4, b5}, then the evaluation level corresponding to the maximum value is the evaluation result of the resilience of the town and village.

[0085] Embodiment 2

[0086] As Figure 2 shown, it is a schematic diagram of the composition of the evaluation platform for the resilience of towns and villages in the fringe area of a big city of the present invention. The platform is used to execute the method of Embodiment 1. The platform includes: An evaluation platform for the resilience of towns and villages in the fringe area of a big city, characterized in that the platform includes a data acquisition module, a preprocessing module, an index screening module, a weight calculation module, an evaluation result calculation module, and a result display module that are connected in sequence.

[0087] The data acquisition module is used to obtain the publicly available literature related to resilience evaluation on the network based on web crawler technology and form a literature collection.

[0088] The preprocessing module is used to perform semantic analysis and word segmentation processing on the literature collection and extract the initial index set.

[0089] The index screening module is used to calculate the TF-IDF values of each index in the initial index set through the TF-IDF algorithm and select the final indexes according to the TF-IDF values for sorting.

[0090] The weight calculation module is used to construct an evaluation matrix and calculate the weights of each index by means of the information entropy method.

[0091] The evaluation result calculation module is used to establish an evaluation grade set, construct a relationship matrix, and calculate the evaluation results of the resilience of towns and villages by using the fuzzy comprehensive evaluation method.

[0092] The result display module is used to display the final rating of the resilience evaluation of towns and villages.

[0093] This platform can be deployed on a local server or a cloud platform. Taking the actual application as an example: Data acquisition module: Developed based on the Scrapy framework of Python, it can obtain information from multiple data sources simultaneously; Preprocessing module: Use natural language processing tools such as NLTK for text cleaning and word segmentation; Index screening module: Implement the TF-IDF algorithm using the scikit-learn library; Weight calculation module: Use NumPy for matrix operations; Evaluation result calculation module: Implement fuzzy comprehensive evaluation based on Pandas; Result display module: Use the Django framework to build a Web interface and combine ECharts for visual display.

[0094] Embodiment 3

[0095] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for evaluating the resilience of towns and villages in the fringe area of big cities in Embodiment 1.

[0096] This computer program can be implemented in various forms: Stand-alone software package form: The Windows version is packaged as an.exe executable file, the Linux version is compiled as a.bin file, and the Mac version is encapsulated as an.app application; Web application form: The front end uses the Vue.js framework to develop an interactive interface, the back end uses the Spring Boot framework to provide RESTful APIs, and the database uses MongoDB to store literature data and evaluation results; Mobile application form: The Android version is developed using Java / Kotlin, the iOS version is developed using Swift, and the cross-platform version is developed using the Flutter framework.

[0097] The deployment method can be deployed on a local server, support cloud server deployment (such as Alibaba Cloud, Tencent Cloud), and support containerized deployment (encapsulated using Docker).

[0098] The specific embodiments described above further elaborate on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. The resilience assessment method for towns and villages in the fringe areas of large cities is characterized by: The method comprises the following steps: Step S1, constructing a multi-level evaluation index system, which includes a target layer, a criterion layer and an indicator layer, wherein the target layer is the evaluation result of town and village resilience, and the criterion layer includes four dimensions: economic resilience, social resilience, ecological resilience and infrastructure resilience; Step S2, based on crawler technology and semantic analysis model, obtain relevant literature and data on town and village resilience evaluation, establish an initial indicator set of the indicator layer, and use the TF-IDF algorithm to screen the initial indicator set to determine the final indicator layer indicators; Step S3, calculate the weights of indicators at each level based on the initial indicator values ​​in the literature, construct an indicator weight matrix, and use the fuzzy comprehensive evaluation method to calculate the resilience evaluation results of towns and villages.

2. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 1 is characterized in that: The initial indicator set for establishing the criterion layer and the indicator layer in step S2 is specifically: Based on the crawler technology, we obtain the relevant documents related to resilience evaluation published on the Internet and form a document set L, where L = {L1, L2, L3, …, L n },n>200, use the semantic analysis model to preprocess the document set L and extract the initial indicator set I, I={I1,I2,I3,…,I m },m>16; Perform word segmentation on the document set L, remove stop words and filter words, and form a document word segmentation set L'. Use the TF-IDF algorithm to calculate each index I in the initial index set I i The term frequency TF and inverse document frequency IDF in the document segmentation set: Among them, N{I i ∈L'} is the index I in the document segmentation set L' i The number of occurrences, T L' is the number of segmented words in the document segmentation set; M{I i ∈L'} is the index I i The number of documents; Calculation Index I i TF-IDF value: TF-IDF = TF × IDF; sort the indicators in the initial indicator set I from high to low according to the TF-IDF value, select the top 15 items as the final indicators of the indicator layer, and classify the final indicators of the indicator layer according to the four dimensions of economic resilience, social resilience, ecological resilience and infrastructure resilience to complete the correspondence between the indicator layer and the criterion layer.

3. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 2 is characterized in that: The correspondence between the indicator layer and the criterion layer specifically includes: Economic resilience: GDP per capita, fixed asset investment, and fiscal revenue; Social resilience: population density, number of medical beds, coverage of educational facilities, and coverage of social security; Ecological resilience: green space coverage, air quality index, water quality compliance rate, and garbage disposal rate; Infrastructure resilience: road network density, water supply network density, communication network coverage, and power facility density.

4. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 3 is characterized in that: The calculation of the indicator weights of each level based on the initial indicator values ​​in the literature and the construction of the indicator weight matrix include: Collect the original data of each indicator and construct the evaluation matrix R: Where rij is the value of the jth initial indicator in the ith document, 1≤i≤n, 1≤j≤m, n is the number of documents, and m is the number of initial indicators; the evaluation matrix R is normalized to obtain the standardized matrix P, in which the element p ij The calculation formula is: Calculate the information entropy e of the jth indicator j : Where k is the entropy coefficient, Element p ij is the value of the jth initial indicator in the i-th document after standardization; Calculate the coefficient of difference d of the jth indicator j :d j =1-e j ; Then, based on the final indicator of the indicator layer, construct the indicator weight vector W: W = (w1, w2, ..., w 15 ), where the weight w of the jth indicator is j The calculation formula is:

5. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 4 is characterized in that: The step of calculating the town and village resilience evaluation results by using the fuzzy comprehensive evaluation method includes: Establish an evaluation level set V: V = {V1, V2, V3, V4, V5}, where V1 represents high toughness, V2 represents high toughness, V3 represents medium toughness, V4 represents low toughness, and V5 represents low toughness; Construct the relationship matrix Q: Where: q sk Represents the kth evaluation level V for the sth final indicator k The membership degree is 1≤s≤15, 1≤k≤5, and the comprehensive evaluation result B is calculated as: B=WQ=(b1,b2,b3,b4,b5), b k , 1≤k≤5, is the membership value of the kth evaluation level.

6. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 5 is characterized in that: The normalized intervals of the evaluation level set V are divided into: V1 = [0.8, 1.0], V2 = [0.6, 0.8], V3 = [0.4, 0.6], V4 = [0.2, 0.4], V5 = [0, 0.2].

7. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 6 is characterized in that: The element q in the relationship matrix Q sk The calculation method is: Calculate the mean value for each column j of the normalized matrix P The average value of the column corresponding to the final indicator is expressed as element q sk Includes: s1 ,q s2 ,q s3 ,q s4 ,q s5 ,according to The corresponding relationship between the value and the evaluation level is obtained, and the element q sk The value of 8. The method for evaluating the resilience of towns and villages in the fringe areas of large cities according to claim 7 is characterized in that: The fuzzy comprehensive evaluation method comprises: Calculate the membership value of the kth evaluation level: Among them, b k is the kth component in the evaluation result B; after obtaining all 5 evaluation result components, calculate the maximum value of the evaluation result components: b max =max{b1,b2,b3,b4,b5}, the evaluation level corresponding to the maximum value is the town and village resilience evaluation result.

9. A platform for evaluating the resilience of towns and villages in the fringe areas of large cities, the platform being used to implement the methods of claims 1-8, characterized in that: The platform includes: a resilience evaluation platform for towns and villages in the fringe areas of large cities, characterized in that the platform includes a data acquisition module, a preprocessing module, an indicator screening module, a weight calculation module, an evaluation result calculation module and a result display module connected in sequence: The data acquisition module is used to obtain relevant documents on resilience evaluation published on the Internet based on crawler technology to form a document collection; The preprocessing module is used to perform semantic analysis and word segmentation on the document set and extract an initial indicator set; The indicator screening module is used to calculate the TF-IDF value of each indicator in the initial indicator set through the TF-IDF algorithm, and select the final indicator according to the TF-IDF value sorting; The weight calculation module is used to construct an evaluation matrix and calculate the weight of each indicator by using the information entropy method; The evaluation result calculation module is used to establish an evaluation level set, construct a relationship matrix, and calculate the town and village resilience evaluation results using a fuzzy comprehensive evaluation method; The result display module is used to display the final rating of the town and village resilience evaluation.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating the resilience of towns and villages in the fringe areas of large cities as described in any one of claims 1 to 8 is implemented.

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