Municipal infrastructure information distribution and spatial visualization method, system and equipment

Abstract

The present invention provides a method, system and device for the distribution and spatial visualization of municipal infrastructure information, the method comprising: obtaining the maturity index and geographical location of each municipal infrastructure project in a target area; creating an irregular grid layer of the entire target area on a map, dividing the grid units based on the existing project plot data and block data; calculating the maturity index of the grid units that have completed the assignment by using a hybrid local polynomial weighted interpolation method (HLPWI), assigning values ​​to the unassigned target grid units, generating a maturity distribution model for the entire area, and finally generating a spatial distribution map that characterizes the density of the maturity distribution of the municipal infrastructure. The present invention realizes a comprehensive and efficient spatial visualization display of the distribution of municipal infrastructure maturity information, improves the estimation accuracy, reduces manual intervention, improves the evaluation efficiency, and has strong adaptability to complex geographical features and irregular data.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal infrastructure management, and in particular to a method, system and equipment for municipal infrastructure information distribution and spatial visualization. Background Art

[0002] Urban municipal infrastructure is an important guarantee for urban development and people's production and life. With the development of cities, the standards of municipal infrastructure construction are also constantly improving, so municipal infrastructure needs to be reasonably regulated and constructed. Challenges faced by existing technologies in the field of municipal infrastructure management include the lack of a comprehensive framework to guide digital transformation. Most existing studies focus on specific case studies or limited contexts, which limits the general applicability of the research results. Therefore, there is a need for broader and more systematic research to understand and build the process of municipal digital transformation more comprehensively and deeply. In particular, municipal departments lack effective digital tools and methods in managing the life cycle of their infrastructure assets, and cannot intuitively understand the spatial distribution of infrastructure maturity in various urban areas, and thus cannot accurately identify the key areas and priorities for infrastructure improvement in urban areas.

[0003] Therefore, how to provide a method and system for the distribution and spatial visualization of municipal infrastructure information to accurately identify the key areas and priorities for urban regional infrastructure improvement is of great significance to improving the management level and efficiency of municipal infrastructure supporting facilities projects. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a method, system and device for municipal infrastructure information distribution and spatial visualization that overcomes the above problems or at least partially solves the above problems.

[0005] In a first aspect, the present invention provides a method for the distribution and spatial visualization of municipal infrastructure information, the method comprising:

[0006] Obtain the maturity index of each municipal infrastructure project in the target area and the geographical location of each municipal infrastructure project;

[0007] Creating a grid layer covering the target area on the map, matching the maturity index of each municipal infrastructure project with the grid layer based on GIS technology, so as to assign the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project;

[0008] Grid cells are established for the block areas in the grid layer that have completed the maturity index assignment, and the remaining unassigned blank areas are divided into irregular grid cells according to blocks, and each grid cell in the grid layer is uniquely identified;

[0009] According to the maturity index of the assigned grid cells, the hybrid local polynomial weighted interpolation method is used to estimate the maturity index of other unassigned grid cells in the grid layer, and a spatial distribution map of the maturity distribution of municipal infrastructure is generated to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area.

[0010] Furthermore, the method of estimating the maturity index of other grid cells that are not assigned a value in the grid layer by applying a hybrid local polynomial weighted interpolation method (HLPWI) according to the maturity index of the assigned grid cell includes:

[0011] Step 1: Use the weight formula to calculate the weight of each known point. The formula is as follows:

[0012]

[0013] Where: w i represents the weight of the known point i;

[0014] d i Represents the distance between the position (x, y) and the known point i;

[0015] P represents the distance weight parameter, which is generally 2;

[0016] Z i Represents the maturity index value at a known point i, ranging from 0 to 100;

[0017] λ represents the influence coefficient of urbanization degree, a value between 0 and 1, that is, the influence of urbanization degree on weight;

[0018] u i represents the characteristic value of urbanization degree at a known point i, that is, building density;

[0019] σ u It represents the urbanization degree parameter, which controls the decay speed of the urbanization degree effect;

[0020] μ represents the time characteristic influence coefficient, a value between 0 and 1, that is, the degree of influence of the time characteristic on the weight;

[0021] T i It represents the time characteristic value at the known point i, that is, the characteristic value of the project completion time and the most recent maintenance time;

[0022] T max Indicates the maximum value of the time characteristic value, used to normalize the influence of the time characteristic;

[0023] Step 2: Use the mixed local polynomial weighted interpolation method to make a preliminary estimate of the known points to obtain the maturity density estimate. The formula is as follows:

[0024]

[0025] Where: Z(x,y) represents the estimated value of maturity density at the grid cell (x,y);

[0026] Z i represents the maturity index value at the known point i;

[0027] w i represents the weight of the known point i, which is calculated based on the distance and other relevant factors and is obtained by formula (1);

[0028] a kl represents the polynomial coefficients, obtained by least squares fitting;

[0029] n represents the highest order of the polynomial, i.e., the maturity stratification order;

[0030] N represents the number of known points involved in the calculation, which is determined by combining the diversity and complexity of maturity project data;

[0031] Step 3: Use the diffusion formula to radiate the influence of high-level points to low-level points. According to the distance attenuation and the different influence factors of high-level points, the maturity index value after diffusion is obtained. The formula is as follows:

[0032]

[0033] Where: Z'(x,y) represents the post-diffusion maturity index value at position (x,y);

[0034] α represents the weight coefficient of the preliminary estimate Z(x,y), which is a value between 0 and 1. The error of the interpolation result is minimized by adjusting the weight coefficient;

[0035] β represents the weight coefficient of the influence of surrounding high-level points, a value between 0 and 1, satisfying α+β=1;

[0036] Z(x,y) represents the preliminary estimated maturity index value;

[0037] K represents the number of high-level points around the position (x, y);

[0038] Z k Represents the maturity index value of the kth high-level point around the position (x, y);

[0039] d k Represents the distance between the position (x, y) and the high-level point k;

[0040] σ is the distance decay parameter, which controls the speed of distance decay;

[0041] φ k represents the influence factor of high-level point k, indicating the weight difference of different high-level points;

[0042] Step 4: Introduce nonlinear correction terms and make adaptive adjustments based on the initial estimation results and the differences between the surrounding grid cells to obtain the final maturity index value. The formula is as follows:

[0043]

[0044] Where: Z" (x, y) represents the final maturity index value at position (x, y);

[0045] Z(x,y) represents the preliminary estimated maturity index value;

[0046] Z'(x,y) represents the maturity index value after diffusion;

[0047] γ represents the adaptive adjustment coefficient, which indicates the difference between the corrected initial estimate and the surrounding average, with a value between 0 and 1;

[0048] δ represents the adaptive nonlinear correction coefficient, which represents the weight of the introduced nonlinear adjustment, a value between 0 and 1;

[0049] Z m Represents the average maturity index value of the grid cells around the location (x, y);

[0050] Z i Represents the maturity index value of the i-th grid cell around the position (x, y);

[0051] N represents the number of grid cells around the position (x, y);

[0052] Step 5: By running the result rationality judgment algorithm, the initial diffusion parameters (α, β, σ) and adaptive parameters (γ, δ) are used to perform preliminary maturity distribution calculations. After the calculation is completed, the predicted maturity index value is compared with the actual known maturity index value to obtain the error average value. The formula is as follows:

[0053]

[0054] Where: E is the error mean;

[0055] N is the number of known points;

[0056] Z i is the actual maturity index value of the i-th known point;

[0057] is the predicted maturity index value of the i-th known point;

[0058] The error mean value E is compared with the preset threshold value ε. When the error mean value E is less than the preset threshold value ε, the result is considered reasonable, the system stops iterating and generates the final maturity distribution model; when the error mean value E is greater than or equal to the threshold value ε, the diffusion parameters (α, β, σ) and adaptive parameters (γ, δ) are automatically updated;

[0059] Among them, the automatic update of diffusion parameters (α, β, σ) and adaptive parameters (γ, δ) specifically includes:

[0060] Calculate the partial derivatives of the error with respect to each parameter to obtain the gradient information. Based on the calculated gradient information, use the gradient descent method to update the parameter value. The formula is as follows:

[0061]

[0062] Where: f(p) is the specific parameter value, namely α, β, σ, γ, δ;

[0063] is the partial derivative of the error E with respect to the parameter f(p);

[0064] η is the learning rate, which is used to control the step size of each parameter adjustment;

[0065] Using the updated parameters, recalculate the maturity distribution and the new error mean E again, repeat steps 3 and 4, and re-judge the rationality until the error E is less than the threshold ε or the maximum number of iterations is reached, until the maturity index of all unit grids is obtained.

[0066] Furthermore, the target area is a pre-designated geographic area covered by a specific development or renovation project, or a pre-designated administrative area, or a user-selected geographic area.

[0067] Furthermore, the obtaining of the maturity index of each municipal infrastructure project in the target area includes:

[0068] Determine the evaluation indicators of municipal infrastructure and establish a municipal infrastructure evaluation tree, wherein the evaluation tree includes a three-level evaluation structure, and the evaluation indicators of the lower level are the detailed evaluation indicators that affect the evaluation indicators of the higher level;

[0069] Generate an evaluation language for expressing the relative importance of the evaluation indicators in pairs for the evaluation indicators at all levels of municipal infrastructure, and configure corresponding fuzzy numerical values ​​for the evaluation languages ​​with different relative importance, wherein the fuzzy numerical values ​​are used to identify the relative importance represented by the current fuzzy numerical value through the size of each numerical value in the numerical string contained therein;

[0070] Based on the fuzzy numerical values ​​corresponding to the evaluation indicators at each level, a fuzzy judgment matrix is ​​established to compare the relative importance of each evaluation indicator at a lower level with the corresponding evaluation indicator at a higher level.

[0071] Based on the fuzzy judgment matrix, the maturity index of each municipal infrastructure project is calculated using the fuzzy analytic hierarchy process.

[0072] Furthermore, the maturity index of each municipal infrastructure project is calculated using the fuzzy analytic hierarchy process based on the fuzzy judgment matrix, including:

[0073] Performing row normalization processing on each fuzzy judgment matrix to convert the fuzzy numerical value corresponding to each evaluation index in each row into a normalized fuzzy number, and performing defuzzification processing on the normalized fuzzy number to obtain a local weight of each evaluation index;

[0074] Determine whether the fuzzy judgment matrices are consistent; if they are consistent, calculate the global weight of each evaluation index relative to the overall goal of municipal infrastructure; if they are not consistent, adjust the fuzzy values ​​in the fuzzy judgment matrix so that the corrected fuzzy judgment matrix meets the consistency, and then calculate the global weight of each evaluation index relative to the overall goal of municipal infrastructure;

[0075] The local weight of the evaluation indicator of the lower level is multiplied by the score corresponding to each evaluation indicator of the current level of each municipal infrastructure project to obtain the weighted score of the low-level evaluation indicator. The weighted score of the low-level evaluation indicator is multiplied by the weight of the evaluation indicator of the previous level, and the above process is recursively performed until the weighted score of the highest-level evaluation indicator is obtained, and it is used as the maturity index of the corresponding municipal infrastructure project.

[0076] Further, each fuzzy judgment matrix is ​​subjected to row normalization processing, including: dividing the sum of the fuzzy numerical values ​​corresponding to each element in the fuzzy judgment matrix by the sum of the fuzzy numerical values ​​corresponding to all elements in the row to obtain a corresponding normalized fuzzy number;

[0077] Defuzzification is performed on the normalized fuzzy number to obtain the local weight of each evaluation index, including: calculating the median or average value of the normalized fuzzy number corresponding to each evaluation index to obtain the local weight of each evaluation index.

[0078] Furthermore, judging whether each fuzzy judgment matrix is ​​consistent includes:

[0079] For each fuzzy judgment matrix, its fuzzy consistency index FCI is calculated by fuzzy algorithm;

[0080] The fuzzy consistency ratio FCR is calculated according to the fuzzy consistency index FCI of each fuzzy judgment matrix and the preset random consistency index. The fuzzy consistency ratio calculation formula is as follows:

[0081]

[0082] Where: max represents the maximum eigenvalue of the fuzzy judgment matrix, p represents the number of selected evaluation indicators in the fuzzy judgment matrix, and RCI represents the random consistency index of the fuzzy environment;

[0083] If the FCR is less than or equal to the preset verification threshold, the fuzzy judgment matrix is ​​determined to be consistent.

[0084] Furthermore, the global weight of each evaluation indicator relative to the overall goal of municipal infrastructure is calculated, including:

[0085] The local weight of each evaluation indicator is multiplied by the local weight of the evaluation indicator at the next level above it, and this is repeated recursively up to the highest level evaluation indicator to obtain the global weight of each indicator relative to the overall municipal infrastructure goal.

[0086] In a second aspect, the present invention further provides a municipal infrastructure information distribution and spatial visualization system, the system comprising functional modules for implementing the above municipal infrastructure information distribution and spatial visualization method.

[0087] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned municipal infrastructure information distribution and spatial visualization method when executing the computer program.

[0088] The municipal infrastructure information distribution and spatial visualization method, system and device provided by the embodiment of the present invention create a grid layer covering the entire target area on a map, match the maturity index of each municipal infrastructure project in the target area with the irregular grid layer covering the entire target area, so as to assign the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project, establish grid units for the block areas in the grid layer to which the maturity index has been assigned, and divide the remaining unassigned blank areas into irregular grid units according to blocks, and then apply the hybrid local polynomial weighted interpolation method (HLPWI) according to the maturity index of the grid unit that has been assigned, and generate a spatial distribution map that characterizes the density of the maturity distribution of municipal infrastructure, so as to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area. The present invention can utilize effective digital tools and methods in the life cycle management of municipal infrastructure to achieve a comprehensive and efficient spatial visualization of the distribution of municipal infrastructure maturity information, so as to intuitively understand the spatial distribution of infrastructure maturity in various urban areas, and then accurately identify the key areas and priorities for urban regional infrastructure improvement, providing a theoretical basis and practical guidance for the construction and development of municipal infrastructure construction projects.

[0089] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0091] Figure 1 A flow chart of a method for municipal infrastructure information distribution and spatial visualization provided by an embodiment of the present invention;

[0092] Figure 2 A schematic diagram of the implementation process of visualizing the spatial information of the maturity distribution of municipal infrastructure provided by an embodiment of the present invention;

[0093] Figure 3 A schematic diagram of an implementation process of building a maturity model provided by an embodiment of the present invention;

[0094] Figure 4A schematic diagram of the implementation process of the municipal infrastructure status warning provided by an embodiment of the present invention;

[0095] Figure 5 A schematic diagram of the implementation process of estimating project supporting fees provided by an embodiment of the present invention;

[0096] Figure 6 A structural block diagram of a municipal infrastructure information distribution and spatial visualization system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0097] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0098] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0099] Glossary: ​​HLPWI: The Chinese name is hybrid local polynomial weighted interpolation method. The present invention proposes HLPWI technology based on the characteristics of this field and combines the advantages of local polynomial regression and weighted interpolation. This technology can accurately capture the spatial variation characteristics of data by performing local fitting and weight adjustment at different levels.

[0100] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0101] Figure 1 The flowchart of the municipal infrastructure information distribution and spatial visualization method according to one embodiment of the present invention is schematically shown. Figure 1 The municipal infrastructure information distribution and spatial visualization method of the embodiment of the present invention specifically includes the following steps:

[0102] S11. Obtain the maturity index of each municipal infrastructure project in the target area and the geographical location of each municipal infrastructure project.

[0103] In this embodiment, the target area is a geographic area covered by a pre-specified specific development or renovation project, or a pre-specified administrative area, or a custom selected geographic area. Specifically, when determining the scope of visualization of spatial information on the maturity distribution of municipal infrastructure, you can select the project area scope, district boundary scope, or custom selection scope. The project area scope targets the geographic area covered by a specific development or renovation project, and focuses on evaluating the impact of the project on the maturity of surrounding infrastructure. The district boundary scope is determined based on administrative divisions and is suitable for comprehensively displaying the maturity level of infrastructure in a certain urban area or specific administrative district. The custom selection range provides flexibility, allowing planners to freely set the scope of analysis according to research needs or management objectives, whether it is a comprehensive area spanning multiple projects or district boundaries or a key area around a specific infrastructure. The realization of the visualization of spatial information on the maturity distribution of municipal infrastructure mainly includes the steps of determining the spatial distribution range, constructing a grid, reading the results of the maturity comprehensive index, mixed local polynomial weighted interpolation analysis, and visualization distribution map of the maturity distribution spatial information. The implementation process is as follows: Figure 2 shown.

[0104] In this embodiment, the maturity index of the municipal infrastructure project is used to evaluate the development level of the municipal infrastructure.

[0105] S12. Create a grid layer covering the target area on the map, and match the maturity index of each municipal infrastructure project with the grid layer based on GIS technology to assign the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project.

[0106] In this embodiment, based on GIS technology, through accurate geocoding and spatial analysis functions, the maturity index of each infrastructure project or field is matched with the previously constructed grid system to ensure that the maturity data in each grid unit reflects the actual maturity level of the infrastructure at the geographical location. Specifically, the present invention geocodes the infrastructure project or field. This step is based on the detailed address information of the infrastructure project or field, which is converted into geographic coordinate information using the geographic information system map service.

[0107] S13. Grid cells are established for the block areas in the grid layer that have completed the assignment of maturity index, and the remaining unassigned blank areas are divided into irregular grid cells according to blocks, and each grid cell in the grid layer is uniquely identified.

[0108] In the specific implementation process of constructing the grid in this embodiment, the map and grid layers are first initialized according to the specified range, and the assigned block areas are identified and removed. Then, the remaining blank areas are divided into blocks, and the blank areas are subdivided into multiple irregularly shaped grid cells by identifying block boundaries and geographical features. Next, it is verified whether all grids are completely within a block polygon. For grid cells that span multiple block polygons, the block boundaries are used to clip the grid cells. Finally, the integrity of the irregular grid is verified, the grid division is optimized, the same block grids are merged, and the uniquely identified grid cells are marked.

[0109] S14. Based on the maturity index of the assigned grid cells, the mixed local polynomial weighted interpolation method is used to estimate the maturity index of other unassigned grid cells in the grid layer, and a spatial distribution map of the maturity distribution of municipal infrastructure is generated to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area.

[0110] Furthermore, the maturity value is only supported by definite data within the scope of known projects, and the maturity data outside the scope of the project is missing. In order to fill these missing values ​​and fully understand the distribution of infrastructure maturity in the region, the present invention adopts a hybrid local polynomial weighted interpolation method (HLPWI) to analyze irregular grids divided by blocks as the smallest unit. Specifically, the present invention matches the maturity data of known projects with the grid system, and uses the HLPWI analysis technology to estimate the maturity value in each grid unit according to the density of surrounding projects. According to the characteristics of this field, the present invention combines the advantages of local polynomial regression and weighted interpolation to propose the HLPWI technology, which can accurately capture the spatial variation characteristics of the data by performing local fitting and weight adjustment at different levels. HLPWI first processes the data in layers, builds a polynomial model based on the local data characteristics at each layer, and then dynamically adjusts the weights according to the fitting effect of the model to improve the interpolation accuracy. Through this hierarchical and progressive approach, HLPWI can achieve high-precision interpolation results in complex terrain and irregular grids, and realize high-precision spatial interpolation of the municipal infrastructure maturity index.

[0111] In an embodiment of the present invention, in step S14, based on the maturity index of the assigned grid cell, the hybrid local polynomial weighted interpolation method (HLPWI) is used to estimate the maturity index of other unassigned grid cells in the grid layer, specifically including: calculating the weight of each known point through a weight formula, making a preliminary estimate of the maturity index data of the municipal infrastructure project, radiating the influence of high-level points to low-level points, performing hierarchical diffusion according to distance attenuation and different influence factors of high-level points, introducing nonlinear correction terms, making adaptive adjustments according to the differences between the preliminary estimation results and the surrounding grid cells, and finally obtaining the adjustment results through layer-by-layer iteration.

[0112] In step S14, the weight of each known point is calculated using the following weight algorithm, and the formula is as follows:

[0113]

[0114] Where: w i represents the weight of the known point i, that is, the influence of the known point i on the maturity index of the target point; d i Indicates the distance between the position (x, y) and the known point i, and is used to indicate the spatial distance between the target point and the known point. That is, the distance between the center point of the grid unit and the center point of the target grid unit; P represents the distance weight parameter, which is generally 2, indicating the decay speed of the distance and controlling the degree of influence of the distance on the weight; Z i represents the maturity index value at a known point i, ranging from 0 to 100, that is, the maturity of the municipal infrastructure project at that location; λ represents the influence coefficient of urbanization, a value between 0 and 1, that is, the influence of urbanization on the weight; u i represents the characteristic value of urbanization degree at known point i, that is, building density; σ u represents the urbanization degree parameter, which controls the attenuation rate of the urbanization degree; μ represents the time characteristic influence coefficient, a value between 0 and 1, which represents the influence of the time characteristic on the weight; T i represents the time characteristic value at the known point i, that is, the characteristic value of the project completion time and the most recent maintenance time; T max Indicates the maximum value of the time characteristic value, which is used to normalize the influence of the time characteristic.

[0115] In step S14, the following hybrid local polynomial weighted interpolation method (HLPWI) is used to make a preliminary estimate of the known points, and the formula is as follows:

[0116]

[0117] Where: Z(x,y) represents the estimated value of maturity density at the grid cell (x,y); Z irepresents the maturity index value at a known point i, representing the maturity score of a municipal infrastructure project (0-100); w i represents the weight, which combines the correlation between distance and maturity index and is calculated based on distance and other relevant factors, refer to formula (1); a kl represents the polynomial coefficient, which is obtained by fitting the least squares method; n represents the highest order of the polynomial, that is, the maturity stratification order; N represents the number of known points involved in the calculation, which is determined by combining the diversity and complexity of the maturity project data.

[0118] In step S14, the following hierarchical diffusion algorithm is used to radiate the influence of high-level points to low-level points, taking into account distance attenuation and different influence factors of high-level points. The formula is as follows:

[0119]

[0120] Where: Z'(x,y) represents the maturity index value of the municipal infrastructure after diffusion at the location (x,y), which represents the maturity of the location after diffusion and adjustment; α represents the weight coefficient of the preliminary estimated value Z(x,y), which is between 0 and 1. This value is adjusted according to the reliability of the preliminary estimated result to minimize the error of the interpolation result; β represents the weight coefficient of the influence of the surrounding high-level points, which is between 0 and 1. It is determined by evaluating the influence of the surrounding infrastructure projects on the target area, satisfying α+β=1; Z(x,y) represents the preliminary estimated maturity index value, which is calculated based on the local polynomial and weighted interpolation method; K represents the number of high-level points around the location (x,y), that is, several important project points in the vicinity are regarded as high-level points; Z k represents the municipal infrastructure maturity index value of the kth high-level point around the location (x, y), indicating the maturity of the high-level point; d k Indicates the distance between the position (x, y) and the high-level point k; σ is the distance decay parameter, which controls the speed of distance decay and is determined based on the spatial distribution characteristics and influence range of the project; φ k Represents the influence factor of high-level point k, which indicates the difference in weights of different high-level points, determined based on the scale, importance or other relevant attributes of the project.

[0121] In step S14, the following adaptive adjustment algorithm is used to perform adaptive adjustment based on the initial estimation result and the difference between the surrounding grid cells to obtain the final maturity index value. The formula is as follows:

[0122]

[0123] Where: Z" (x, y) represents the final maturity index value at the position (x, y); Z (x, y) represents the preliminary estimated maturity index value, which is calculated based on the local polynomial and weighted interpolation method; Z' (x, y) represents the maturity index value after diffusion, which is calculated based on the hierarchical diffusion algorithm; γ represents the adaptive adjustment coefficient, which represents the difference between the corrected preliminary estimate and the surrounding average value, and is a value between 0 and 1; δ represents the adaptive nonlinear correction coefficient, which represents the weight of the introduced nonlinear adjustment, and is a value between 0 and 1; Z m Represents the average maturity index value of the grid cells around the location (x, y); Z i It represents the maturity index value of the i-th grid cell around the position (x, y), which is obtained through actual observation or existing data; N represents the number of grid cells around the position (x, y).

[0124] In step S14, by running the result rationality judgment algorithm, the system will use the initially set diffusion parameters (α, β, σ) and adaptive parameters (γ, δ) to perform preliminary maturity distribution calculations. After the calculation is completed, the system will compare the predicted maturity index value with the actual known maturity index value. The formula is as follows:

[0125]

[0126] Where: E is the error mean, N is the number of known points, Z i is the actual maturity index value of the i-th known point, is the predicted maturity index value of the i-th known point.

[0127] When the average error value E is less than the preset threshold value ε, the result is considered reasonable, the system stops iterating and generates the final maturity distribution model. When the average error value E is greater than or equal to the threshold value ε, parameter adjustment and recalculation are required.

[0128] In step S14, the diffusion parameter and adaptive parameter update algorithm is adopted. When the system detects that the error E is greater than or equal to the threshold ε, the diffusion parameters (α, β, σ) and adaptive parameters (γ, δ) are automatically adjusted. The specific contents include:

[0129] The system first calculates the partial derivatives of the error with respect to each parameter to obtain the gradient information. This gradient information reflects the direction and magnitude of the impact of each parameter on the error. Based on the calculated gradient information, the system uses the gradient descent method to update the parameter value. The update formula is as follows:

[0130]

[0131] Where: f(p) is the specific parameter value, namely α, β, σ, γ, δ, is the partial derivative of the error E with respect to the parameter f(p), and η is the learning rate, which is used to control the step size of each parameter adjustment.

[0132] Using the updated parameters, the system recalculates the maturity distribution and calculates the new error average E again. The system repeats formula (3) and formula (4) until the error E is less than the threshold ε or the maximum number of iterations is reached, until the maturity index of all unit grids is obtained.

[0133] The visualization distribution map of the maturity distribution spatial information is generated through local polynomial and weighted interpolation analysis, which intuitively shows the spatial distribution of infrastructure maturity in various areas of the city. In the embodiment of the present invention, the distribution map can also use color gradients to indicate the level of maturity index, where hot tones indicate areas with higher maturity, and cold tones indicate areas with lower maturity, so as to intuitively and quickly identify key areas and priorities for infrastructure improvement.

[0134] The municipal infrastructure information distribution and spatial visualization method provided by the embodiment of the present invention creates a grid layer covering the entire target area on the map, matches the maturity index of each municipal infrastructure project in the target area with the grid layer covering the entire target area, assigns the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project, establishes grid units for the block areas in the grid layer where the maturity index assignment is completed, and divides the remaining unassigned blank areas into irregular grid units according to blocks, and then estimates the maturity index of other unassigned grid units in the grid layer according to the maturity index of the grid unit that has completed the assignment, and generates a spatial distribution map that characterizes the density of the maturity distribution of the municipal infrastructure, so as to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area. The present invention can use effective digital tools and methods when performing life cycle management of municipal infrastructure to achieve comprehensive and efficient spatial visualization of the distribution of municipal infrastructure maturity information, so as to intuitively understand the spatial distribution of the maturity of infrastructure in various urban areas, and then accurately identify the key areas and priorities for improving urban regional infrastructure, and provide theoretical basis and practical guidance for the construction and development of municipal infrastructure construction projects.

[0135] In the municipal infrastructure maturity evaluation, the maturity levels are specifically divided into excellent (A), good (B), average (C), poor (D) and insufficient (E). Specifically, excellent (A), urban infrastructure at the excellent level shows comprehensive development and efficient operation, advanced technology and wide coverage of services, not only meeting current needs but also foreseeing future challenges. Intelligent network systems ensure the high reliability and response capabilities of facilities, such as smart city projects and comprehensive public transportation systems, ensuring efficient management of energy and water resources and sufficient green space. Good (B), in cities at the good level, infrastructure coverage is extensive, technology and service quality have reached a high level, basically meeting residents' needs and some long-term needs, although there is room for improvement. Demonstration projects include a complete public transportation network and a stable energy supply system, as well as good environmental quality and a high greening rate. Average (C), the city's infrastructure at the average level can meet basic residents' needs, but there are deficiencies in many key areas such as service quality and technology application. More investment and improvement efforts are needed to improve service efficiency. Typical status quo includes basic public transportation services and basic energy and water supply, but may be accompanied by service interruptions and limited green space. Poor (D) Cities in the poor category have lagging infrastructure development, which is difficult to meet the extensive needs of residents, poor service quality, lack of technology application, resulting in greatly reduced sustainability and resilience. These cities often face problems such as insufficient public transportation services, energy and water shortages, and low greening rates, which are in urgent need of attention and improvement. Insufficient (E) Cities in the insufficient category face serious lack or aging infrastructure, backward technology, extremely limited service coverage, and can hardly meet the basic living and development needs of residents. They are in urgent need of comprehensive transformation and improvement. These cities may not have an effective public transportation system, often experience energy and water supply interruptions, lack of green and leisure space, which seriously affects the city's image and the quality of life of residents.

[0136] In order to achieve efficient and accurate management of urban planning and infrastructure construction, the present invention overcomes the limitations of traditional management methods in data processing accuracy, resource allocation efficiency and project economic benefits through comprehensive data processing and spatial information analysis. By introducing the calculation of the maturity comprehensive index and combining the steps of visualization of maturity distribution spatial information, the management process is optimized, the scientificity and benefits of planning and implementation are improved, and strong technical support is provided for promoting the sustainable development of urban infrastructure.

[0137] Based on the fuzzy hierarchical analysis method, the calculation steps of the maturity comprehensive index involve clarifying the goal of evaluating the maturity of municipal infrastructure, building an indicator system covering all evaluation indicators, and creating a fuzzy judgment matrix to represent the relationship and weight between indicators. The process includes the separate sorting and consistency test of indicators at each level, and then the total sorting of the levels and the overall consistency test. The specific implementation process is as follows: Figure 3 In the embodiment of the present invention, the step S11 of obtaining the maturity index of each municipal infrastructure project in the target area specifically includes the following subdivision steps not shown in the figure:

[0138] S111. Determine evaluation indicators of municipal infrastructure and establish a municipal infrastructure evaluation tree, wherein the evaluation tree includes a three-level evaluation structure, and the evaluation indicators of a lower level are detailed evaluation indicators that affect each evaluation indicator of a higher level.

[0139] S112. Generate evaluation language for expressing the relative importance of the evaluation indicators in pairwise comparison for the evaluation indicators at all levels of municipal infrastructure, and configure corresponding fuzzy numerical values ​​for the evaluation languages ​​with different relative importance, and the fuzzy numerical value is used to identify the relative importance represented by the current fuzzy numerical value through the size of each numerical value in the contained numerical string. In this embodiment, when constructing the fuzzy judgment matrix of the maturity of municipal infrastructure, it is necessary to set an automatically generated fuzzy comparison scale, which will be used to quantify the relative importance between the indicators. The fuzzy comparison scale usually includes language evaluation and corresponding fuzzy numerical values. When setting the fuzzy comparison scale, a series of language evaluations are used, ranging from "equally important", "slightly more important", "obviously more important", "very more important" to "absolutely more important", and these evaluations are paired with increasing fuzzy numerical values ​​to quantify the relative importance between the evaluation indicators. Through the automatic generation system, the following pairings can be made: "equally important" is assigned a fuzzy value (1,1,1), indicating that the two indicators are completely equal in importance; "slightly more important" is expressed through fuzzy values ​​(1,2,3) to express subtle differences in importance; "significantly more important" is shown by fuzzy values ​​(3,4,5) and (3,5,7) respectively, indicating the increasing level of importance; "very more important" is emphasized by fuzzy values ​​(5,7,9) to emphasize the significant superiority of one indicator over another; and finally, "absolutely more important" uses the highest fuzzy value (7,9,9) to highlight the overwhelming advantage of one indicator. Through the above automatically generated fuzzy comparison scale, the fuzzy judgment matrix of municipal infrastructure maturity can be effectively constructed to achieve accurate quantification of the relative importance of each evaluation indicator.

[0140] S113, based on the fuzzy numerical values ​​corresponding to the evaluation indicators at each level, a fuzzy judgment matrix is ​​established to compare the relative importance of each evaluation indicator at a lower level with respect to the corresponding evaluation indicator at a higher level.

[0141] S114. Calculate the maturity index of each municipal infrastructure project using the fuzzy analytic hierarchy process based on the fuzzy judgment matrix.

[0142] Furthermore, step S114 specifically includes the following implementation steps:

[0143] First, each fuzzy judgment matrix is ​​subjected to row normalization processing to convert the fuzzy numerical value corresponding to each evaluation index in each row into a normalized fuzzy number, and the normalized fuzzy number is defuzzified to obtain the local weight of each evaluation index. The defuzzification of the normalized fuzzy number to obtain the local weight of each evaluation index includes: calculating the median or average value of the normalized fuzzy number corresponding to each evaluation index to obtain the local weight of each evaluation index. In the hierarchical single sorting process of the municipal infrastructure maturity model, extracting normalized fuzzy numbers and calculating local weights are two key processes, which ensure that the relative importance of the evaluation index can be accurately quantified. Extracting normalized fuzzy numbers involves row normalization processing of the constructed fuzzy judgment matrix. In this process, the fuzzy number in each row (representing the relative importance evaluation between the indicators) is converted into a normalized fuzzy number by dividing the sum of the fuzzy numerical values ​​corresponding to each element in the fuzzy judgment matrix by the sum of the fuzzy numerical values ​​corresponding to all elements in the row to obtain the corresponding normalized fuzzy number. The fuzzy numbers processed in this way reflect the importance of each indicator relative to other indicators under the same criterion, and the sum of the fuzzy numbers in each row is 1, ensuring the consistency and relativity of the evaluation.

[0144] In the step of calculating the local weight, based on the extracted normalized fuzzy number, the local weight of each evaluation index is obtained by calculating the median or average value of the normalized fuzzy number corresponding to each evaluation index. In addition, the denormalization of the normalized fuzzy number can be achieved by other applicable defuzzification methods, which are not specifically limited in the present invention. The local weight represents the relative importance of each index in the criterion or value layer to which it belongs. The key to this step is to convert the fuzzy logic into an operational numerical weight.

[0145] Then, it is determined whether each fuzzy judgment matrix is ​​consistent; if it is consistent, the global weight of each evaluation index relative to the overall goal of municipal infrastructure is calculated; if it is not consistent, the fuzzy values ​​in the fuzzy judgment matrix are adjusted so that the corrected fuzzy judgment matrix meets the consistency, and then the global weight of each evaluation index relative to the overall goal of municipal infrastructure is calculated. Further, it is determined whether each fuzzy judgment matrix is ​​consistent, including: calculating the fuzzy consistency index FCI of each fuzzy judgment matrix through a fuzzy algorithm; calculating the fuzzy consistency ratio FCR according to the fuzzy consistency index FCI of each fuzzy judgment matrix and the preset random consistency index, and the fuzzy consistency ratio calculation formula is as follows: Where: max represents the maximum eigenvalue of the fuzzy judgment matrix, p represents the number of selected evaluation indicators in the fuzzy judgment matrix, and RCI represents the random consistency index of the fuzzy environment; if FCR is less than or equal to the preset verification threshold, the fuzzy judgment matrix is ​​judged to be consistent.

[0146] In the application of fuzzy analytic hierarchy process in the maturity evaluation of municipal infrastructure, the process of implementing consistency verification needs to adapt to the characteristics of fuzzy environment. A similar logical framework is used for consistency evaluation. The specific implementation process is as follows: for each constructed fuzzy judgment matrix, its fuzzy consistency index (FCI) is calculated by fuzzy algorithm, which involves evaluating the consistency between the fuzzy numerical values ​​of each element in the matrix. Subsequently, a random consistency index (RCI) suitable for the fuzzy environment is determined, which is based on a predefined fuzzy numerical set or by simulating a large number of random fuzzy judgment matrices. In the embodiment of the present invention, the RCI value can be set to 1. After obtaining FCI and RCI, the fuzzy consistency ratio (FCR) is calculated to evaluate the consistency level of the judgment matrix. For the result, if FCR is less than or equal to the preset verification threshold, the fuzzy judgment matrix is ​​considered to have good consistency and its result is suitable for subsequent analysis. This preset verification threshold can be selected as 0.1, which is based on common standards in experience and practice, which means that a certain degree of subjective judgment difference is allowed, while ensuring the overall reliability and consistency of the evaluation results. The final result output is "passed" and "failed". If it is "passed", the hierarchical total sorting step is performed, and if it is "failed", parameter optimization is performed. After parameter optimization, the fuzzy judgment matrix is ​​reconstructed using the adjusted fuzzy comparison scale, and the consistency test is performed again to calculate the new FCR value. Until the condition of FCR <= 0.1 is met, the rigor of the evaluation process and the credibility of the results are ensured. Further, the global weight of each evaluation indicator relative to the overall goal of municipal infrastructure is calculated, including: multiplying the local weight of each evaluation indicator by the local weight of the evaluation indicator of the upper level, and recursively upward to the highest level of evaluation indicators in this way, so as to obtain the global weight of each indicator relative to the overall goal of municipal infrastructure. The hierarchical total ranking is to calculate the global importance of each indicator in the entire evaluation system by integrating the results of single rankings at each level. The specific implementation process includes multiplying the local weight of each indicator or criterion (i.e., the hierarchical single ranking result) by the weight of its upper criterion, and recursively upward to the value layer in this way, so as to obtain the global weight of each indicator relative to the overall goal. The fuzzy comprehensive evaluation results, the output results show the maturity level of various infrastructure indicators in current urban management and planning, and are then stored in the database.

[0147] Finally, the local weights of the evaluation indicators at the lower level are multiplied by the scores of the evaluation indicators at the current level of each municipal infrastructure project to obtain the weighted scores of the evaluation indicators at the lower level. The weighted scores of the evaluation indicators at the lower level are multiplied by the weights of the evaluation indicators at the upper level, and the above process is performed recursively until the weighted scores of the evaluation indicators at the highest level are obtained, which are used as the maturity index of the corresponding municipal infrastructure project. The maturity index calculation involves calculating the scores of each indicator based on the indicator system, that is, evaluating the current actual performance or achievement level of each indicator. Using the full-stage weights previously obtained through the fuzzy analytic hierarchy process (FAHP), that is, the relative importance of each indicator or criterion in its level, these local weights are multiplied by the scores of the corresponding indicators. The score of each indicator is weighted according to its relative importance to reflect the size of its role in improving maturity. This weighted score is multiplied by the weight of its upper criterion, and the process is continued recursively until the top value layer. At each level, the weighted scores of the lower-level indicators are combined with the weights of the upper-level criteria according to their contribution to the goals of the upper level, ensuring that the contribution of each indicator is appropriately considered and accumulated. Ultimately, this method can calculate the global maturity index of each indicator relative to the overall goal, that is, an indicator that comprehensively reflects the maturity level of urban infrastructure.

[0148] The present invention uses fuzzy analytic hierarchy process and GIS (Geographic Information System) technology to accurately calculate the maturity comprehensive index of municipal infrastructure and intuitively display the maturity distribution, effectively optimizing the management process. The maturity distribution and spatial information visualization steps enhance the application of spatial information and make resource allocation and land use more reasonable.

[0149] The municipal infrastructure information distribution and spatial visualization method proposed in the embodiment of the present invention can also realize the municipal infrastructure status warning, including maturity index threshold setting, data monitoring and analysis, warning information generation, warning notification, response and adjustment, and warning effect evaluation. The implementation process is as follows: Figure 4 As shown, the specific steps are as follows:

[0150] Maturity index threshold setting involves determining the critical maturity level of each infrastructure field or indicator based on historical data and urban development goals. Based on the maturity index hierarchy, the specific thresholds are as follows:

[0151] Excellent (Grade A), with an index range of 80 to 100. The city's infrastructure performs well, not only meeting current needs but also effectively responding to future challenges.

[0152] Good (B), with an index range of 60 to 79. The quality of infrastructure is good, basically meeting the needs of residents, with a small amount of room for improvement.

[0153] Average (C), with an index range of 44 to 59. Infrastructure is at a medium level, meeting basic needs but with room for improvement in several key areas.

[0154] Poor (Grade D), with an index range of 20 to 39. Infrastructure at this level fails to adequately meet residents' needs, has significant problems and risks, and requires immediate attention and improvement measures.

[0155] Insufficient (E level), the index range is 0 to 19. The infrastructure is seriously insufficient or aging, and urgently needs comprehensive transformation and upgrading, which belongs to the high-risk warning range.

[0156] The maturity levels of D and E are included in the risk warning range. Once the maturity index of urban infrastructure falls into these two levels, a risk warning is triggered, prompting managers to take immediate action to improve the maturity of infrastructure through specific project improvements, capital investment or other emergency measures to avoid potential service failures or greater social and economic losses.

[0157] Data monitoring and analysis, the implementation process ensures that the maturity index data of urban infrastructure can be obtained regularly through the update mechanism of regularly updating the status data, and by comparing the real-time or regularly updated maturity index with the preset warning threshold, identify those infrastructure areas whose maturity index is below the threshold and have potential risks or problems. Based on the comprehensive maturity index, by comparing with the preset risk warning threshold, the system can automatically identify which infrastructure areas have a maturity index below this threshold. When the maturity index falls into the range of excellent (A level), good (B level) and general (C level), it enters the step of "recording warning log". When the maturity index falls into the range of poor (D level) and insufficient (E level), it indicates that the risk warning has been triggered, and it will enter the step of "generating warning information".

[0158] Warning information generation, data monitoring and analysis steps Identifying that the maturity index of urban infrastructure is below the set risk warning threshold will automatically trigger the generation of warning information. The process includes a detailed overview of the affected infrastructure areas, the current maturity index, the specific difference from the warning threshold, and the initial assessment of potential risks or issues. Each warning message clearly identifies the affected infrastructure area, the current maturity index, the gap with the threshold, and the specific issues that need attention. This information is compiled into a clear and easy-to-understand report format to ensure that all key data and analysis results are included.

[0159] Early warning notifications, generated warning information is automatically sent to relevant city managers, including but not limited to urban planning departments, infrastructure operation teams, and emergency response teams through a customized communication system. This process utilizes a multi-channel communication strategy, including email and a dedicated city management information system to ensure that information can be delivered quickly and accurately.

[0160] Response and adjustment are key links in the emergency response process of urban management departments. Once an early warning message is received, the relevant departments will take immediate action to conduct a detailed assessment and on-site investigation of the issues and risks involved in the early warning information. This process ensures the accuracy of the early warning information and a comprehensive understanding of the actual situation. Based on these investigations and assessments, the urban management department will develop targeted improvement measures or emergency intervention plans, focusing on the infrastructure areas involved in the early warning to ensure that these problems are effectively resolved and risks are properly managed. After completing these necessary infrastructure construction and improvement measures, the relevant data will be updated to reflect the latest infrastructure status and submitted through appropriate channels to ensure that the data in the urban infrastructure information system is kept up to date.

[0161] Early warning effect evaluation: urban management departments will systematically collect and analyze data after response and adjustment measures to evaluate the accuracy of the early warning system, the effectiveness of response measures, and the actual impact of infrastructure improvements. This includes comparing the changes in the infrastructure maturity index before and after the warning, monitoring the difference between the issuance time of early warning information and the actual response time, and evaluating the satisfaction and feedback of residents and relevant stakeholders. In addition, special attention will be paid to those infrastructure areas that have triggered early warnings to check whether the improvement measures have effectively solved the problems pointed out in the early warning. Based on these evaluation results, the management department will further optimize the early warning threshold setting, improve the monitoring and response process, and adjust the infrastructure planning and management strategy to improve the overall performance of the early warning system and the resilience of urban infrastructure. After this step is completed, re-enter data monitoring and analysis.

[0162] Record the warning log. This step will record the detailed information of the warning event, including the time of the warning, the infrastructure area that triggered the warning, the specific maturity index, the warning threshold, and the specific content of the warning and the recommended response measures.

[0163] The municipal infrastructure information distribution and spatial visualization method proposed in the embodiment of the present invention can also realize the estimation of project supporting fees, including selecting the scope of new projects, whether there is historical project data within the threshold range, reading the maturity comprehensive index results and historical project data, estimating supporting fees, and outputting the estimation results. The specific implementation process of the method is as follows: Figure 5 As shown, the following steps are included:

[0164] Selecting the scope of a new project involves comprehensive consideration of the project's location, area, expected functions and objectives. Through GIS technology, the specific location and natural conditions of the project are determined, their impact on project implementation is assessed, and the required area is accurately measured and defined according to the nature and objectives of the project.

[0165] Whether there is historical project data within the threshold range, this step is to evaluate whether there is historical project data within the threshold range of 10 kilometers outside the project boundary. If there is, "read the maturity comprehensive index results and historical project data" will be performed. If not, it will prompt "insufficient data samples, unable to estimate" and end. The key to this evaluation process is to collect and analyze relevant infrastructure project information implemented in the past within the threshold range, including but not limited to key data such as the specific location, area, cost, and maturity index of the project. Using GIS technology and project databases, systematically query and summarize historical project information, determine the availability and relevance of these data, and confirm whether there is enough historical data to support the estimation of supporting fees for new projects.

[0166] Read the maturity index results and historical project data, and extract the current maturity index of the new project area based on the maturity index calculation results. At the same time, read the project database to read the maturity index planned to be achieved, as well as detailed information on historical projects within the threshold range, including the project's area, cost, and maturity index before and after implementation.

[0167] Estimation of supporting fees: This step uses the historical project supporting fee data (including project area, historical maturity index, and post-construction maturity index), target area area, target area maturity index, and planned maturity index data to estimate the supporting fees required for the new project using a linear analysis model. The specific model calculation formula is as follows:

[0168] C=α+β 1 *A+β 2 *ΔI+ε

[0169] Where: C represents supporting costs, i.e. the estimated total supporting costs of the project;

[0170] α is the intercept term in the model, representing the baseline package cost when all other variables are zero;

[0171] β 1 and β 2 They represent the impact coefficients of changes in project land area and maturity index on supporting costs;

[0172] A represents the project area, which may be in square meters or hectares;

[0173] ΔI represents the change in maturity index, i.e., the difference in maturity index before and after project implementation;

[0174] ε represents the error term, which reflects the random variation that is not explained by the model.

[0175] During the application, R 2 The value measures the goodness of fit of the model. 2 The value ranges from 0 to 1, indicating the proportion of the variation explained by the model to the total variation. The closer the value is to 1, the better the model fits the data and the stronger its predictive ability. 2 If the value is not ideal, reconsider the variable selection in the model to improve R 2 values ​​and the predictive accuracy of the model.

[0176] After completing the linear regression analysis of supporting costs based on historical data and new project parameters, and after necessary tuning and verification of the model, the detailed information of the estimated supporting costs is compiled into a report. This report will list the expected supporting costs of the new project in detail, including but not limited to direct construction costs, indirect costs, and reserved costs after considering potential risks. In addition, the report will also provide information on model selection, parameter estimation, R 2 Analysis of key statistical indicators such as value to reflect the reliability and accuracy of the estimation results.

[0177] In a specific example, considering the analysis of urban management and residents' needs, the evaluation targets of water supply, drainage, transportation, public safety and other evaluation targets in the field of municipal infrastructure can be expanded with optional functions, and the optional evaluation indicators include the total mileage and density of main and secondary roads, road quality and road maintenance level, public transportation coverage and service frequency, tap water supply coverage and water quality safety, drainage system coverage and drainage efficiency, water resource utilization and water resource management, sewage treatment rate and compliance with emission standards, electricity supply coverage and grid stability, renewable energy proportion and energy conservation, heating facility coverage and heating efficiency, greening and environmental protection facilities, garbage disposal and resource recycling rate, fire station and police station coverage, the incidence and disposal efficiency of public safety incidents, the integrity and implementation effect of the city master plan, the standardization and sustainability of urban construction projects, and the scientific nature and efficiency of urban management.

[0178] Constructing a hierarchical structure, when constructing the urban infrastructure maturity index system, it is composed of three main levels, which ensures the comprehensiveness of the evaluation system and a well-defined logical structure. The highest level is the value layer, which represents the ultimate goal of maturity evaluation, that is, to measure the overall maturity and development level of urban infrastructure. The middle layer, as a bridge between the value layer and the bottom layer, usually includes the strategy layer and the criteria layer. This level involves breaking down the ultimate goal into specific evaluation criteria and strategies to guide the implementation and evaluation of the lower layers. The bottom layer, the program layer, concretizes the practical means and methods, and includes specific indicators and measures to achieve the middle layer criteria and strategies. The indicators at this level are directly related to the specific operation and management practices of urban infrastructure. The specific hierarchical relationship is as follows:

[0179] The value layer, which is the highest level of the model, includes five core areas: road traffic facilities, water supply and drainage facilities, electricity and heating facilities, greening and environmental protection facilities, and safety and management facilities. The value layer determines the main areas of maturity evaluation and reflects the overall development goals of urban infrastructure.

[0180] Criteria layer: Specific evaluation criteria are detailed for each value layer area. For example, road traffic facilities are divided into road construction level and road service level; water supply and drainage facilities are divided into water supply service capacity and drainage treatment capacity; power and heating facilities include power facility level and functional service level; greening and environmental protection facilities cover greening facility level and environmental protection facility level; safety and management facilities include safety emergency response capacity, urban planning facility level and urban management capacity. The criteria layer is the intermediate bridge connecting the value layer and the program layer, and clarifies the evaluation focus within each field.

[0181] Program level: Specific implementation indicators are listed for the evaluation criteria of each criterion layer. In the field of road traffic facilities, the road construction level includes the total mileage and density of main roads and secondary roads, road surface quality and road maintenance level; the road service level is composed of road capacity and congestion, public transportation coverage and service frequency. The water supply service capacity of water supply and drainage facilities is evaluated by tap water supply coverage and water quality safety, and the drainage treatment capacity is evaluated by water resource utilization and water resource management, sewage treatment rate and compliance with emission standards. The power facility level of power and heating facilities is measured by power supply coverage and grid stability, renewable energy proportion and energy conservation, and the functional service level is determined by heating facility coverage and heating efficiency. The greening facility level and environmental protection facility level of greening and environmental protection facilities are directly related to the construction and maintenance of greening and environmental protection facilities. The emergency response capabilities of safety and management facilities are evaluated by the coverage rate of fire stations and police stations, the incidence rate of public safety incidents, and the efficiency of handling. The level of urban planning facilities and urban management capabilities are measured by the integrity and implementation effect of the city's overall plan, the standardization and sustainability of urban construction projects, and the scientificity and efficiency of urban management. The indicators at the program level are directly related to the operation and management practices of urban infrastructure, providing a specific basis for evaluation and optimization.

[0182] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0183] In addition, an embodiment of the present invention further provides a municipal infrastructure information distribution and spatial visualization system, the system comprising a functional module for implementing the above municipal infrastructure information distribution and spatial visualization method. Figure 6 The structure block diagram of the municipal infrastructure information distribution and spatial visualization system according to one embodiment of the present invention is schematically shown. Figure 6 The municipal infrastructure information distribution and spatial visualization system of the embodiment of the present invention specifically includes an acquisition module 201, a matching module 202, a grid unit generation module 203 and a HLPWI estimation module 204, wherein:

[0184] An acquisition module 201 is used to acquire the maturity index of each municipal infrastructure project in the target area and the geographical location of each municipal infrastructure project;

[0185] A matching module 202 is used to create a grid layer covering the target area on the map, and match the maturity index of each municipal infrastructure project with the grid layer based on GIS technology to assign the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project;

[0186] The grid unit generation module 203 is used to establish grid units for the block areas in the grid layer that have completed the maturity index assignment, and divide the remaining unassigned blank areas into irregular grid units according to blocks, and uniquely identify each grid unit in the grid layer;

[0187] The HLPWI estimation module 204 is used to estimate the maturity index of other unassigned grid cells in the grid layer using a hybrid local polynomial weighted interpolation method based on the maturity index of the assigned grid cells, and generate a spatial distribution map of the maturity distribution of municipal infrastructure to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area.

[0188] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment, and it has corresponding technical effects.

[0189] In addition, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above municipal infrastructure information distribution and spatial visualization method when executing the computer program. Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned municipal infrastructure information distribution and spatial visualization system embodiment are realized, for example Figure 6 The illustrated acquisition module 201 , matching module 202 , grid unit generation module 203 and HLPWI estimation module 204 .

[0190] The municipal infrastructure information distribution and spatial visualization method, system and device provided by the embodiment of the present invention create a grid layer covering the entire target area on a map, match the maturity index of each municipal infrastructure project in the target area with the grid layer covering the entire target area, so as to assign the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project, establish grid cells for the block areas in the grid layer to which the maturity index has been assigned, and divide the remaining unassigned blank areas into irregular grid cells according to blocks, and then apply the hybrid local polynomial weighted interpolation method (HLPWI) according to the maturity index of the grid cell that has been assigned, and generate a spatial distribution map that characterizes the density of the maturity distribution of municipal infrastructure, so as to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area. The present invention can utilize effective digital tools and methods in the life cycle management of municipal infrastructure to achieve a comprehensive and efficient spatial visualization of the distribution of municipal infrastructure maturity information, so as to intuitively understand the spatial distribution of infrastructure maturity in various urban areas, and then accurately identify the key areas and priorities for urban regional infrastructure improvement, providing a theoretical basis and practical guidance for the construction and development of municipal infrastructure construction projects.

[0191] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, any one of the claimed embodiments may be used in any combination.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for visualizing spatial information of maturity distribution of municipal infrastructure, characterized in that: The method comprises: Obtain the maturity index of each municipal infrastructure project in the target area and the geographical location of each municipal infrastructure project; Creating a grid layer covering the target area on the map, matching the maturity index of each municipal infrastructure project with the grid layer based on GIS technology, so as to assign the maturity index of each municipal infrastructure project to the area corresponding to the geographical location of the current municipal infrastructure project; Grid cells are established for the block areas in the grid layer that have completed the maturity index assignment, and the remaining unassigned blank areas are divided into irregular grid cells according to blocks, and each grid cell in the grid layer is uniquely identified; According to the maturity index of the assigned grid cells, the mixed local polynomial weighted interpolation method is used to estimate the maturity index of other grid cells that are not assigned in the grid layer, and a spatial distribution map of the maturity distribution of municipal infrastructure is generated to visualize the spatial distribution of the maturity of each municipal infrastructure project in the target area; The method of estimating the maturity index of other grid cells that are not assigned a value in the grid layer by using a mixed local polynomial weighted interpolation method according to the maturity index of the assigned grid cell includes: Step 1: Use the weight formula to calculate the weight of each known point. The formula is as follows: （1） Where: Represents a known point The weight of Indicates location With known points distance; Represents the distance weight parameter, generally 2; Represents a known point The maturity index value at the location ranges from 0 to 100; It represents the influence coefficient of urbanization degree, a value between 0 and 1, that is, the influence of urbanization degree on weight; Represents a known point The characteristic value of urbanization degree, that is, building density; It represents the urbanization degree parameter, which controls the decay speed of the urbanization degree effect; Indicates the time characteristic influence coefficient, a value between 0 and 1, that is, the degree of influence of the time characteristic on the weight; Represents a known point The time characteristic value at, that is, the characteristic value of the project completion time and the most recent maintenance time; Indicates the maximum value of the time characteristic value, used to normalize the influence of the time characteristic; Step 2: Use the mixed local polynomial weighted interpolation method to make a preliminary estimate of the known points to obtain the maturity density estimate. The formula is as follows: （2） Where: Represented in grid cells The estimated value of maturity density at ; Represents a known point Maturity index value at Represents a known point The weight of is calculated based on the distance and other relevant factors, and is obtained by formula (1); represents the polynomial coefficients, obtained by least squares fitting; It represents the highest order of the polynomial, i.e. the maturity stratification order; It indicates the number of known points involved in the calculation, which is determined by combining the diversity and complexity of the maturity project data; Step 3: Use the diffusion formula to radiate the influence of high-level points to low-level points. According to the distance attenuation and the different influence factors of high-level points, the maturity index value after diffusion is obtained. The formula is as follows: （3） Where: Indicates location The post-diffusion maturity index value at ; Indicates a preliminary estimate The weight coefficient is a value between 0 and 1. The error of the interpolation result is minimized by adjusting the weight coefficient. Represents the weight coefficient of the influence of the surrounding high-level points, a value between 0 and 1, satisfying ; Indicates the preliminary estimated maturity index value; Indicates location The number of surrounding high-level points; Indicates location Around Maturity index value of each high-level point; Indicates location With high-level points distance; Distance attenuation parameter, which controls the speed of distance attenuation; Represents a high-level point The impact factor indicates the difference in weights of different high-level points; Step 4: Introduce nonlinear correction terms and make adaptive adjustments based on the initial estimation results and the differences between the surrounding grid cells to obtain the final maturity index value. The formula is as follows: （4） Where: Indicates location The final maturity index value at Indicates the preliminary estimated maturity index value; represents the maturity index value after diffusion; represents the adaptive adjustment coefficient, which indicates the difference between the corrected initial estimate and the surrounding average, a value between 0 and 1; Represents the adaptive nonlinear correction coefficient, which represents the weight of the introduced nonlinear adjustment, a value between 0 and 1; Indicates location the average maturity index value of the surrounding grid cells; Indicates location Around Maturity index value of each grid cell; Indicates location The number of surrounding grid cells; Step 5, by running the result rationality judgment algorithm, using the initially set diffusion parameters ( ) and adaptive parameters ( ) to perform preliminary maturity distribution calculations. After the calculation is completed, the predicted maturity index value is compared with the actual known maturity index value to obtain the average error value. The formula is as follows: （5） Where: is the error mean; is the number of known points; It is The actual maturity index value of a known point; It is The predicted maturity index value of a known point; The average error With the preset threshold When comparing the mean error Less than the preset threshold The result is considered reasonable, the system stops iterating and generates the final maturity distribution model; when the error average value is Greater than or equal to threshold , then the diffusion parameters are automatically updated ( ) and adaptive parameters ( ); Among them, the diffusion parameters are automatically updated ( ) and adaptive parameters ( ) specifically include: Calculate the partial derivatives of the error with respect to each parameter to obtain the gradient information. Based on the calculated gradient information, use the gradient descent method to update the parameter value. The formula is as follows: （6） Where: is the specific parameter value, that is ; Error Parameters The partial derivative of is the learning rate, which is used to control the step size of each parameter adjustment; Using the updated parameters, recalculate the maturity distribution and calculate the new error mean again , repeat steps 3 and 4, and re-judge the rationality until the error Less than threshold Or until the maximum number of iterations is reached, until the maturity index of all unit grids is obtained; The maturity index of each municipal infrastructure project in the target area is obtained, including: Determine the evaluation indicators of municipal infrastructure and establish a municipal infrastructure evaluation tree, wherein the evaluation tree includes a three-level evaluation structure, and the evaluation indicators of the lower level are the detailed evaluation indicators that affect the evaluation indicators of the higher level; Generate an evaluation language for expressing the relative importance of the evaluation indicators in pairs for the evaluation indicators at all levels of municipal infrastructure, and configure corresponding fuzzy numerical values ​​for the evaluation languages ​​with different relative importance, wherein the fuzzy numerical values ​​are used to identify the relative importance represented by the current fuzzy numerical value through the size of each numerical value in the numerical string contained therein; Based on the fuzzy numerical values ​​corresponding to the evaluation indicators at each level, a fuzzy judgment matrix is ​​established to compare the relative importance of each evaluation indicator at a lower level with the corresponding evaluation indicator at a higher level. The maturity index of each municipal infrastructure project is calculated using the fuzzy analytic hierarchy process based on the fuzzy judgment matrix; The maturity index of each municipal infrastructure project is calculated by using the fuzzy analytic hierarchy process according to the fuzzy judgment matrix, including: Performing row normalization processing on each fuzzy judgment matrix to convert the fuzzy numerical value corresponding to each evaluation index in each row into a normalized fuzzy number, and performing defuzzification processing on the normalized fuzzy number to obtain a local weight of each evaluation index; Determine whether the fuzzy judgment matrices are consistent; if they are consistent, calculate the global weight of each evaluation index relative to the overall goal of municipal infrastructure; if they are not consistent, adjust the fuzzy values ​​in the fuzzy judgment matrix so that the corrected fuzzy judgment matrix meets the consistency, and then calculate the global weight of each evaluation index relative to the overall goal of municipal infrastructure; The local weight of the evaluation index of the lower level is multiplied by the score corresponding to each evaluation index of the current level of each municipal infrastructure project to obtain the weighted score of the lower level evaluation index, the weighted score of the lower level evaluation index is multiplied by the weight of the evaluation index of the previous level, and the current step is recursively executed until the weighted score of the highest level evaluation index is obtained, and it is used as the maturity index of the corresponding municipal infrastructure project; The determination of whether each fuzzy judgment matrix is ​​consistent includes: calculating the fuzzy consistency index FCI of each fuzzy judgment matrix by a fuzzy algorithm; calculating the fuzzy consistency ratio FCR according to the fuzzy consistency index FCI of each fuzzy judgment matrix and a preset random consistency index. The fuzzy consistency ratio calculation formula is as follows: （7） Where: represents the maximum eigenvalue of the fuzzy judgment matrix, and p represents the number of selected evaluation indicators in the fuzzy judgment matrix; if FCR is less than or equal to the preset verification threshold, the fuzzy judgment matrix is ​​judged to be consistent; The calculation of the global weight of each evaluation indicator relative to the overall goal of municipal infrastructure includes: multiplying the local weight of each evaluation indicator by the local weight of the evaluation indicator of the previous level, and recursively upward to the highest level evaluation indicator, so as to obtain the global weight of each indicator relative to the overall goal of municipal infrastructure.

2. The method according to claim 1, characterized in that The target area is a pre-designated geographic area covered by a specific development or renovation project, or a pre-designated administrative area, or a user-selected geographic area.

3. The method according to claim 1, characterized in that Normalizing each fuzzy judgment matrix, including: dividing the sum of the fuzzy values ​​corresponding to each element in the fuzzy judgment matrix by the sum of the fuzzy values ​​corresponding to all elements in the row to obtain a corresponding normalized fuzzy number; Defuzzification is performed on the normalized fuzzy number to obtain the local weight of each evaluation index, including: calculating the median or average value of the normalized fuzzy number corresponding to each evaluation index to obtain the local weight of each evaluation index.

4. A municipal infrastructure maturity distribution spatial information visualization system, characterized in that: The system comprises a functional module for implementing the method according to any one of claims 1 to 3.

5. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 3 when executing the computer program.

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