Urban agglomeration toughness driving mechanism analysis method and device

Abstract

The invention discloses an urban agglomeration toughness driving mechanism analysis method and device. The method comprises the steps that an evaluation index system used for evaluating the toughness of the urban agglomeration is determined, and the evaluation index system comprises a plurality of indexes used for reflecting the toughness level of the urban agglomeration; utilizing an interpretation structure model to construct a hierarchical structure of the urban agglomeration toughness on the basis of the transmission relationship among the indexes of the evaluation index system; and obtaining an urban agglomeration toughness driving mechanism based on the hierarchical structure of the urban agglomeration toughness. According to the method, the technical problems of high subjectivity, high workload and poor consistency of the existing method for constructing the adjacent matrix by depending on expert scoring are solved.

Description

Technical Field

[0001] The present invention relates to the field of urban system modeling and resilience analysis, and in particular to a method and device for analyzing the driving mechanism of urban agglomeration resilience. Background Art

[0002] As a classic system analysis tool, the Interpretive Structural Model (ISM) plays an important role in revealing the internal structure and operating mechanisms of complex systems. The core of this method is to identify the interrelationships between system elements and construct a hierarchical structural model based on these relationships, thereby visualizing the complex relationships between system elements.

[0003] Traditional ISM methods rely primarily on expert scoring to construct the initial adjacency matrix. This involves subjectively determining the binary relationships between system elements (e.g., the existence and direction of a relationship). While this approach can reflect system structural characteristics to a certain extent, it also has significant limitations. First, the objectivity of expert scoring cannot be guaranteed. Differences in expert knowledge, experience, and subjective judgment criteria can easily lead to biased scoring results. Second, this approach faces scalability issues when dealing with large-scale systems. For a system consisting of n elements, experts need to complete n(n-1) / 2 relationship judgments. As the system size (n) increases, the workload increases dramatically, and maintaining consistency in judgment results becomes difficult. More importantly, traditional methods rely primarily on subjective judgments and fail to effectively utilize objective quantitative data (such as historical data, operational data, and statistical relationships) in the system. This is particularly inadequate in the increasingly data-driven decision-making landscape.

[0004] With the increasing complexity of systems and the development of big data technologies, the limitations of traditional ISM methods have become increasingly apparent. For one thing, the relationships between system elements are often contained in vast amounts of objective data, and traditional methods are unable to effectively mine and utilize this valuable resource. Furthermore, traditional methods also have significant drawbacks when dealing with dynamic systems, making it difficult to capture and reflect the temporal and spatial evolution of relationships between system elements.

[0005] To address these issues, existing research focuses on improving the reliability of subjective judgments and enhancing the objectivity of models. The former involves introducing group decision-making mechanisms or optimizing expert scoring processes, while the latter attempts to combine ISM with other quantitative analysis methods (such as statistical analysis and fuzzy theory). However, these improvements are mostly local optimizations and fail to fundamentally overcome the inherent flaws of traditional ISM methods. Especially when dealing with large-scale, complex systems, traditional methods and their improved solutions still face core challenges such as high computational complexity, poor model stability, and insufficient utilization of objective data.

[0006] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0007] The embodiments of the present invention provide a method and device for analyzing the driving mechanism of resilience of urban agglomerations, so as to at least solve the technical problems of the existing method of relying on expert scoring to construct an adjacency matrix, which is highly subjective, has a large workload, and has poor consistency.

[0008] According to one aspect of an embodiment of the present invention, a method for analyzing the driving mechanism of urban agglomeration resilience is provided, comprising: determining an evaluation index system for evaluating the resilience of urban agglomerations, wherein the evaluation index system includes a plurality of indicators for reflecting the level of resilience of urban agglomerations; constructing a hierarchical structure of urban agglomeration resilience based on the transfer relationship between the indicators of the evaluation index system using an explanatory structural model; and obtaining the driving mechanism of urban agglomeration resilience based on the hierarchical structure of urban agglomeration resilience.

[0009] According to another aspect of an embodiment of the present invention, a device for analyzing the driving mechanism of resilience of urban agglomerations is also provided, including: a determination module, configured to determine an evaluation index system for evaluating the resilience of urban agglomerations, wherein the evaluation index system includes multiple indicators for reflecting the level of resilience of urban agglomerations; a construction module, configured to use an explanatory structural model to construct a hierarchical structure of urban agglomeration resilience based on the transfer relationship between the indicators of the evaluation index system; an ISM analysis module, configured to obtain the driving mechanism of urban agglomeration resilience based on the hierarchical structure of urban agglomeration resilience.

[0010] In an embodiment of the present invention, an evaluation index system for evaluating the resilience of urban agglomerations is determined, wherein the evaluation index system includes a plurality of indicators for reflecting the level of resilience of urban agglomerations; an explanatory structural model is used to construct a hierarchical structure of urban agglomeration resilience based on the transfer relationship between the indicators of the evaluation index system; and a driving mechanism for the resilience of urban agglomerations is obtained based on the hierarchical structure of urban agglomeration resilience, thereby solving the technical problems of the existing method of relying on expert scoring to construct an adjacency matrix, which is highly subjective, has a large workload, and has poor consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0012] Figure 1 is a flowchart of an optional method for analyzing the driving mechanism of urban agglomeration resilience according to an embodiment of the present invention;

[0013] Figure 2is a flowchart of another optional method for analyzing the driving mechanism of urban agglomeration resilience according to an embodiment of the present invention;

[0014] Figure 3 This is a flowchart of another optional method for analyzing the driving mechanism of urban agglomeration resilience according to an embodiment of the present invention;

[0015] Figure 4 Figure 1 is a multi-level hierarchical explanatory structural model of the driving mechanism of urban agglomeration resilience according to an embodiment of the present invention, where (a) is the overall hierarchical structure of the three major urban agglomerations, (b) is the hierarchical structure of the Beijing-Tianjin-Hebei urban agglomeration, (c) is the hierarchical structure of the Yangtze River Delta urban agglomeration, and (d) is the hierarchical structure of the Pearl River Delta urban agglomeration.

[0016] Figure 5 This is a structural diagram of an ISM-based urban agglomeration resilience mechanism analysis device according to an embodiment of the present invention. Figure 6 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] According to an embodiment of the present invention, a method embodiment for analyzing the driving mechanism of resilience of urban agglomerations is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0020] Example 1

[0021] Figure 1 This is a method for analyzing the driving mechanism of urban agglomeration resilience according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0022] Step S102: Determine an evaluation index system for evaluating the resilience of urban agglomerations, wherein the evaluation index system includes multiple indicators for reflecting the resilience level of urban agglomerations.

[0023] Step S104: using an explanatory structural model, constructing a hierarchical structure of urban agglomeration resilience based on the transfer relationship between the various indicators of the evaluation index system.

[0024] First, an adjacency matrix is ​​established. Based on the evaluation index system, the influence relationship between the quantitative indicators of the multivariate linear regression is determined to obtain the linear regression analysis results, and based on the linear regression analysis results, an adjacency matrix is ​​established; for example, a multivariate linear regression model is constructed for each indicator with the indicator as the dependent variable and the remaining indicators as independent variables, and the standardized coefficient and the regression coefficient significance test p-value of the multivariate linear regression model are obtained; for example, based on the multivariate linear regression model, a multivariate linear regression analysis is performed to obtain the standardized coefficient and the regression coefficient significance test p-value of each evaluation indicator. Thereafter, the adjacency matrix is ​​constructed based on the standardized coefficient and the regression coefficient significance test p-value. For example, for each regression coefficient significance test p-value in the multivariate linear regression analysis result, the Beta value passed by the t-test is extracted according to the statistical significance of the standardized coefficient, and the adjacency matrix is ​​constructed based on the Beta value.

[0025] Next, a hierarchical partitioning of urban agglomeration resilience is constructed. A reachability matrix is ​​calculated based on the adjacency matrix, and based on the reachability matrix, a hierarchical partitioning of urban agglomeration resilience is constructed. For example, based on the reachability matrix, a reachability set and antecedent set are constructed for each indicator. The intersection of the reachability set and the antecedent set is determined, and all indicators are divided into multiple levels according to the hierarchical partitioning rules.

[0026] Then, a hierarchical structure diagram of urban agglomeration resilience is established. Based on the division results of the hierarchical relationship of urban agglomeration resilience, a hierarchical structure diagram of urban agglomeration resilience is established.

[0027] Step S106: Obtain the driving mechanism of urban agglomeration resilience based on the hierarchical structure of urban agglomeration resilience.

[0028] In response to the shortcomings of the existing technology, an embodiment of the present invention provides an analysis method for the driving mechanism of urban agglomeration resilience based on the fusion of multiple linear regression and explanatory structural model. By replacing the method of constructing the adjacency matrix by expert scoring in the traditional ISM with multiple linear regression analysis, the standardized coefficient (Beta) is used to quantitatively evaluate the mutual influence relationship between indicators, thereby constructing a more objective and scientific adjacency matrix. The present invention is suitable for complex system analysis scenarios with multiple indicators. Through automated matrix iterative calculation and hierarchical partitioning algorithm, it solves the problem of traditional methods relying on manual experience and low efficiency. It can quickly identify key driving factors and generate visual models, significantly improving decision-making efficiency.

[0029] Example 2

[0030] This application also provides another method for analyzing the driving mechanism of urban agglomeration resilience based on an improved explanatory structural model of multiple linear regression, such as Figure 2 As shown, the following steps are included:

[0031] Step S202: Screen resilience driving factors and construct an evaluation index system.

[0032] After summarizing and combing through relevant literature on urban resilience, an evaluation index system for urban agglomeration resilience was constructed based on the influencing factors selected by previous researchers. This system includes 23 specific indicators in four dimensions: economic resilience, social resilience, ecological resilience, and infrastructure resilience, as shown in Table 1. 23}.

[0033]

[0034]

[0035] Table 1

[0036] Step S204: constructing a hierarchical structure using the explanatory structure model.

[0037] Using the explanatory structural model, we construct a hierarchical structure of urban agglomeration resilience based on the transfer relationships between various indicators, revealing the driving mechanisms of urban agglomeration resilience. The specific steps of model construction are as follows:

[0038] 1) The influence relationship between multiple linear regression quantitative indicators

[0039] Construct regression equation: Set urban agglomeration resilience evaluation indicators S1, S2, ..., S 23 is the dependent variable Y, and the other evaluation indicators are independent variables:

[0040] For example: S1=Y, the dependent variables are S2, S3, ... S23, Y=β0+β2S2+β3S3+…+β 23 S23 +∈

[0041] S2=Y, the dependent variables are S1, S3, ... S 23 , Y=β0+β1S1+β3S3+…+β 23 S 23 +∈

[0042] Obtain the linear regression analysis results.

[0043] 2) Establish the adjacency matrix A based on the results of multiple linear regression.

[0044] The present invention uses multiple linear regression analysis to replace traditional expert scoring to construct an adjacency matrix. The linear regression results are checked to see whether they pass the t-test (p<0.05), and the absolute value of the standardized coefficient Beta that passes the t-test is sorted out.

[0045] Define the direct impact coefficient c between indicators ij =|Beta|.

[0046] Set the adaptive threshold θ = μ + σ (μ is the mean, σ is the standard deviation), where Among them, 506 is the data volume (23 indicators are set as dependent variables Y, and each Y has 22 dependent variables), that is, 23x22=506.

[0047] If c ij ≥θ, then the adjacency matrix A ij =1, otherwise 0.

[0048] Input adjacency matrix (example data):

[0049]

[0050] 3) Calculate the reachability matrix M.

[0051] Add the adjacency matrix A to the identity matrix I to obtain the initial matrix M0=A+I.

[0052] Iterative calculation (Boolean operation), up to M k =M k-1 , at this time M k is the reachability matrix M, which represents all direct and indirect influence relationships.

[0053] Calculate the reachable matrix code:

[0054] reach_matrix=adj_matrix.copy()

[0055] for_in range(len(adj_matrix)):

[0056] reach_matrix=np.sign(reach_matrix+reach_matrix@adj_matrix)

[0057] 4) Construct a hierarchical relationship for the resilience of urban agglomerations.

[0058] For each indicator S i , calculate its reachable set R(S i ) and the antecedent set A(S i ).

[0059] If R(S i )∩A(S i )=R(S i ), then S i Incorporated into the current top-level set L1.

[0060] Remove the L1 element from the matrix M and repeat the above process to obtain L2, L3, ..., L m , until all elements are divided into levels.

[0061] Hierarchical division code:

[0062]

[0063]

[0064] 5) Establish a hierarchical diagram of urban agglomeration resilience.

[0065] Based on the hierarchical division results, a directed graph model is generated, with high-level elements at the top level and the direction of the arrow indicating the impact path.

[0066] Step S206: output the hierarchical structure diagram for driving path analysis and visualization.

[0067] To address the shortcomings of existing technologies, the present invention provides an analysis method for the driving mechanism of urban agglomeration resilience based on the fusion of multiple linear regression and explanatory structural models. This method replaces the traditional ISM method of constructing an adjacency matrix based on expert scoring with multiple linear regression analysis, and uses standardized coefficients (Beta) to quantitatively evaluate the mutual influence relationship between indicators, thereby constructing a more objective and scientific adjacency matrix. This method is suitable for complex system analysis scenarios with multiple indicators. Through automated matrix iterative calculation and hierarchical partitioning algorithms, it solves the problem of traditional methods relying on manual experience and low efficiency. It can quickly identify key driving factors and generate visual models, significantly improving decision-making efficiency.

[0068] Example 3

[0069] This paper also provides an improved method for analyzing the driving mechanisms of urban agglomeration resilience based on multiple linear regression. By replacing the traditional adjacency matrix constructed from expert scoring with multiple linear regression analysis, and using standardized regression coefficients (Beta) to quantitatively assess the inter-indicator influence, a more objective and scientific adjacency matrix is ​​constructed. This method is suitable for analyzing complex systems with multiple indicators and can effectively improve the accuracy and applicability of the model.

[0070] Specifically, if Figure 3 As shown, the following steps are included:

[0071] Step S302: Determine the evaluation index system.

[0072] This application collects 23 evaluation indicators for 49 cities in the three major urban agglomerations from 2013 to 2022, and refers to a large number of literature on resilience evaluation. The final evaluation index system constructed includes 4 first-level indicators and 23 second-level indicators. Let the indicator set be:

[0073] S={S1,S2,K,S 23}

[0074] Among them, S i It represents the i-th evaluation index, and the specific indicators are shown in Table 1 above.

[0075] Step S304: multiple linear regression analysis.

[0076] For each indicator S in the indicator set S i (i=1,2,K,23), take it as the dependent variable Y, and the remaining 22 indicators as independent variables X1,X2,...,X22, and perform multiple linear regression analysis (as shown in Table 2 below). The regression model is:

[0077] Y=β0+β1X1+β2X2+L+β 22 X 22 +ε

[0078] Among them, Y is the dependent variable, X1, X2, ..., X22 are independent variables, β0 is the intercept, β 1， β 2，L， β 22 is the regression coefficient, and ε is the random error term.

[0079] Linear regression analysis results (n=490)

[0080]

[0081]

[0082] Note: Dependent variable = GDP per capita (yuan)

[0083] *p<0.05 **p<0.01

[0084] Table 2

[0085] Repeat the above regression analysis for each indicator Si, and perform 23 regressions in total. Record the Beta value of each standardized coefficient in each regression and its significance test result (usually p value < 0.05 is used as the significance standard).

[0086] Step S306: Calculate the mean and standard deviation of the absolute values ​​of the normalization coefficients.

[0087] For each regression, extract the absolute value of the standardized coefficient β of all significant test indicators |β|. Sum up |β| in all regressions and calculate its average The formula is:

[0088]

[0089] Standard Deviation:

[0090] Among them, β ij Indicates index S j For indicator S i The standardized regression coefficient of N is the number of significant |β ij |Total number.

[0091] Step S308: Screen the influence relationships and construct an adjacency matrix.

[0092] Initialize a 23×23 adjacency matrix A (initial value is 0). For each pair of index S i and S j (i≠j): Check S i To S j The regression coefficient β ji and S j To S i The regression coefficient β ij Is p < 0.05 and greater than one standard deviation: If it is greater than one standard deviation, it is considered as the main influencing relationship. Assign values ​​according to the following rules: If S i Significantly affects S j (and the above conditions are met), then A ij =1,A ji =0; if S j Significantly affects S i , then A ji =1,A ij =0; if there is no significant effect, then A ij =0,A ji =0(if i=j, then Aij =0). Finally, the initial adjacency matrix A is obtained (Table 3).

[0093] S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S2 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 S3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S4 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 S5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 S7 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 S9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 S13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 S19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S21 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 S22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[0094] Table 3

[0095] Step S310: Perform ISM analysis.

[0096] Based on the constructed adjacency matrix A, the subsequent operations are performed according to the traditional ISM method:

[0097] 1) Calculate the reachability matrix R (Table 4):

[0098] R=(A+I) k

[0099] Where I is the identity matrix, k is the matrix that satisfies (A+I) k =(A+I) k+1 The smallest integer (Boolean operation).

[0100] S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 S2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 S3 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S4 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0 S5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 S7 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0 S8 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 S9 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S10 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 S11 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 S12 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 S13 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 S14 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 S15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 S16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 S17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 S18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 S19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 S20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 S21 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 S22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 S23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

[0101] Table 4

[0102] 2) Divide the hierarchy.

[0103] Through the analysis of reachable sets, predecessor sets and intersection sets, the hierarchical position of each indicator is determined. The reachable sets, predecessor sets and their intersection tables are shown in Table 5.

[0104] Reachable set, predecessor set and their intersection table

[0105] Reachable set, predecessor set and their intersection table

[0106]

[0107] Table 5

[0108] The numbers represent certain elements, for example, 2 represents the second element, and the number of iterations is 4. 3) Draw the explanatory structural model diagram.

[0109] Based on the hierarchical structure and adjacency relationship, the hierarchical structure of the system is drawn, as shown in Table 6:

[0110] Hierarchical decomposition

[0111]

[0112] Table 6

[0113] The present embodiment uses an explanatory structural model to deconstruct the index elements of urban agglomerations hierarchically, revealing their internal mechanisms and structural characteristics, such as Figure 4 As shown in the figure, the top-level indicators are direct influencing factors in the urban resilience evaluation system and are influenced by other factors. The bottom-level indicators are fundamental influencing factors, driving urban resilience and influencing other factors. The middle-level indicators are indirect influencing factors, through which the fundamental influencing factors influence the direct influencing factors at the top level.

[0114] The overall hierarchical structure of urban agglomerations described above demonstrates that total retail sales and local fiscal revenue are the fundamental drivers of urban agglomeration development. Total retail sales reflects the vitality of the domestic demand market and the foundation of consumption, while local fiscal revenue serves as the economic foundation for government resource allocation. Local fiscal revenue supports infrastructure and public services, shaping the industrial development environment. Growth in total retail sales drives industrial restructuring, providing the primary impetus for changes in upper-layer factors. The middle layer, encompassing factors such as per capita GDP, per capita disposable income of urban residents, and the proportion of the tertiary industry in GDP, serves as the transmission medium between the bottom and top layers. For example, total retail sales stimulate increases in residents' incomes, creating a "bottom-up drive-middle transmission" linkage effect. Top-layer indicators directly reflect urban operations. Local fiscal revenue is invested in universities and medical and health institutions, and economic growth enhances social resilience. Growth in total retail sales increases per capita disposable income and drives employment, thereby reducing unemployment. This recursive "bottom-middle-top" structure reveals the complete path of urban agglomerations, from fundamental drivers to external manifestations, providing a structured basis for analyzing urban development mechanisms and formulating policies.

[0115] Example 4

[0116] The embodiment of the present invention further provides another method for analyzing the driving mechanism of urban agglomeration resilience based on an improved explanatory structural model of multiple linear regression, which includes the following steps:

[0117] Step S502: Determine an evaluation index system for evaluating urban resilience.

[0118] The evaluation index system includes the 4 first-level indicators and 23 second-level indicators as described above.

[0119] Step S504: performing a multiple linear regression analysis on each evaluation indicator to obtain a standardized coefficient value of each evaluation indicator;

[0120] A multiple linear regression model is constructed by taking each evaluation indicator as a dependent variable and taking other evaluation indicators among the multiple evaluation indicators as independent variables; based on the multiple linear regression model, a multiple linear regression analysis is performed to obtain the standardized coefficient value and the regression coefficient significance test p value of each evaluation indicator.

[0121] Step S506, calculating the mean and standard deviation of the standardized coefficient based on the standardized coefficient value of each evaluation indicator, and constructing an adjacency matrix based on the mean and standard deviation of the standardized coefficient;

[0122] For each pair of indicators S in the secondary indicators i and S j , using the linear regression results, check S i To S j and S j To S i Whether it passes the significance test; calculate the absolute value |β| of the standardized coefficient β that passes the significance test, and calculate the mean of all absolute values ​​|β| and standard deviation σ, determine whether the absolute value |β| is greater than one standard deviation like Then take it as the main influence relationship; based on the main influence relationship, construct the adjacency matrix. For example, for each pair of indicators S in the secondary indicators i and S j , check S respectively i To S j and S j To S i Is the p value in the linear regression result less than 0.05? If p<0.05, it passes the significance test. Sort out the standardized coefficient β corresponding to the indicator with p<0.05 and take the absolute value |β|. Find the mean of all |β| And standard deviation σ. Determine whether the absolute value |β| is greater than one standard deviation like The main influencing relationship is taken as the main influencing relationship; based on the main influencing relationship, the adjacency matrix is ​​constructed, wherein the P value is used to indicate whether the significance test is passed.

[0123] Step S508: performing an explanatory structural model analysis based on the adjacency matrix to obtain the urban resilience;

[0124] A reachable set R is calculated based on the adjacency matrix; and the plurality of evaluation indicators are divided into a hierarchical structure based on the reachable set R to perform an ISM analysis. For example, the hierarchical positions of the plurality of evaluation indicators are determined based on the reachable set, the predecessor set, and the intersection set; and the plurality of evaluation indicators are divided into a hierarchical structure based on the hierarchical positions to perform the ISM analysis.

[0125] Example 5

[0126] This application also provides a device for analyzing the driving mechanism of urban agglomeration resilience, such as Figure 5As shown, it includes: a determination module 52, which is configured to determine an evaluation index system for evaluating the resilience of urban agglomerations, wherein the evaluation index system includes multiple indicators for reflecting the resilience level of urban agglomerations; a construction module 54, which is configured to use an explanatory structural model to construct a hierarchical structure of urban agglomeration resilience based on the transfer relationship between the indicators of the evaluation index system; an ISM analysis module 56, which is configured to obtain a driving mechanism for urban agglomeration resilience based on the hierarchical structure of urban agglomeration resilience.

[0127] It should be noted that the urban agglomeration resilience driving mechanism analysis device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the urban agglomeration resilience driving mechanism analysis device provided in the above embodiment and the urban agglomeration resilience driving mechanism analysis method embodiment are of the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0128] Example 6

[0129] Figure 6 Schematic diagram of the structure of an electronic device suitable for implementing the embodiment of the present disclosure is shown. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0130] like Figure 6 As shown, the electronic device includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 into the random access memory (RAM) 1003. Various programs and data required for system operation are also stored in the RAM 1003. The CPU 1001, ROM 1002 and RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0131] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1008 including a hard disk; and a communication section 1009 including a network interface card such as a LAN card or a modem. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1010 as needed, so that computer programs read therefrom can be installed into the storage section 1008 as needed.

[0132] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for analyzing the driving mechanism of urban agglomeration resilience, characterized by: include: Determining an evaluation index system for evaluating the resilience of urban agglomerations, wherein the evaluation index system includes multiple indicators for reflecting the resilience level of urban agglomerations; Using the explanatory structural model, a hierarchical structure of urban agglomeration resilience is constructed based on the transfer relationship between the various indicators in the evaluation index system; Based on the hierarchical structure of urban agglomeration resilience, the driving mechanism of urban agglomeration resilience is obtained.

2. The method according to claim 1, characterized in that The explanatory structural model was obtained by the following method: Based on the evaluation index system, determining the influence relationship between the multiple linear regression quantitative indicators, obtaining linear regression analysis results, and establishing an adjacency matrix based on the linear regression analysis results; Calculating a reachability matrix based on the adjacency matrix, and constructing a hierarchical relationship division of urban agglomeration resilience based on the reachability matrix; Based on the division results of the hierarchical relationship of urban agglomeration resilience, a hierarchical structure diagram of urban agglomeration resilience is established.

3. The method according to claim 2, characterized in that Based on the evaluation index system, the influence relationship between the multiple linear regression quantitative indicators is determined to obtain the linear regression analysis results, and based on the linear regression analysis results, an adjacency matrix is ​​established, including: For each indicator, a multiple linear regression model is constructed with the indicator as the dependent variable and the other indicators as independent variables, and the standardized coefficient and regression coefficient significance test p value of the multiple linear regression model are obtained; The adjacency matrix is ​​constructed based on the standardized coefficient and the regression coefficient significance test p-value.

4. The method according to claim 2, characterized in that Based on the reachability matrix, a hierarchical division of urban agglomeration resilience is constructed, including: According to the reachability matrix, the reachability set and antecedent set of each indicator are constructed respectively; The intersection of the reachable set and the antecedent set is determined, and all indicators are divided into multiple levels according to a level division rule.

5. The method according to claim 3, characterized in that Obtaining the standardized coefficient and the regression coefficient significance test p-value of the multiple linear regression model includes: performing multiple linear regression analysis based on the multiple linear regression model to obtain the standardized coefficient and the regression coefficient significance test p-value of each evaluation indicator.

6. The method according to claim 3, characterized in that Based on the standardized coefficient and the regression coefficient significance test p-value, the adjacency matrix is ​​constructed, including: for each regression coefficient significance test p-value in the multivariate linear regression analysis result, according to the statistical significance of the standardized coefficient, extracting the Beta value passed the t-test, and constructing the adjacency matrix based on the Beta value.

7. A device for analyzing the driving mechanism of urban agglomeration resilience, characterized by: include: a determination module configured to determine an evaluation index system for evaluating the resilience of an urban agglomeration, wherein the evaluation index system includes a plurality of indicators for reflecting the resilience level of an urban agglomeration; A construction module is configured to construct a hierarchical structure of urban agglomeration resilience based on the transfer relationship between the indicators of the evaluation indicator system using an explanatory structural model; The ISM analysis module is configured to obtain the driving mechanism of urban agglomeration resilience based on the hierarchical structure of urban agglomeration resilience.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, wherein the computer program enables the processor to execute the method according to any one of claims 1 to 6 when the computer program is executed.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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