A method and device for intelligent auxiliary decision-making in participatory planning of smart communities

Abstract

This application provides a method, apparatus, electronic device, and storage medium for intelligent auxiliary decision-making in participatory planning of smart communities, relating to the field of computer technology. The method utilizes hierarchical analysis of decision to establish an intelligent auxiliary decision-making model for participatory planning of smart communities, including a target layer, a criterion layer, and a scheme layer. It is highly interpretable. At the target layer, it sets the overall goal of smart community construction, considering multiple dimensions. Simultaneously, it uses multi-source urban big data fusion computing to construct multiple decision indicators reflecting relevant factors in urban renewal and smart community planning and construction. It performs comprehensive measurement and scoring calculations based on multiple decision indicators for one or more urban local spaces where smart community construction may be carried out. Furthermore, it fully utilizes the subjective opinions and user input of participating entities during the planning decision-making process, generating interactive experiences with decision-makers, thereby accurately and efficiently identifying local spatial units based on the overall goal of smart community construction involving multiple dimensions.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, electronic device and storage medium for intelligent auxiliary decision-making in participatory planning of smart communities. Background Technology

[0002] With my country's urban development gradually shifting towards stock renewal and high-quality development, optimizing the living circle by combining the renovation and upgrading of old residential areas, the smart upgrading of communities, and the improvement of surrounding service resources will be the key tasks of urban community planning and governance going forward. Against the backdrop of rapid urbanization and its maturation, the focus of urban development should gradually shift from new land acquisition to the renewal of existing land.

[0003] At the community level, identifying aging community spaces and service facilities, coordinating the interests of various stakeholders, and maintaining ongoing operations are current challenges. Accurate spatial identification, combined with the renovation and upgrading of old residential areas, the modernization of communities with smart technology, and the optimization of living environments with improved surrounding service resources, will be key tasks in future urban community planning and governance.

[0004] Currently, relevant planning-supported decision-making technologies mainly include two methods: urban health checks and questionnaire surveys. These methods have several problems, such as a lack of flexible and scalable model structures (meaning their models cannot be applied to different numbers of indicators and analytical objects); a lack of interpretable algorithmic principles (meaning that solutions using machine learning and deep learning principles often suffer from "black box algorithms," making it difficult to adjust relevant parameters); and traditional questionnaire surveys require significant manpower and resources, have long data collection periods, are difficult to control data quality, and have small sample sizes. Therefore, it is urgent to solve these technical problems. Summary of the Invention

[0005] In view of the above problems, this application is made to provide a method, apparatus, electronic device, and storage medium for intelligent auxiliary decision-making in participatory planning of smart communities that overcomes or at least partially solves the above problems. The technical solution is as follows:

[0006] Firstly, a method for intelligent decision-making support in participatory planning for smart communities is provided, including:

[0007] The map of the target city is spatially rasterized to obtain multiple spatial grids of the target city.

[0008] Acquire multi-source data from various spatial grids of the target city;

[0009] Based on multi-source data from various spatial grids of the target city, multiple decision indicators are constructed to reflect relevant factors in urban renewal and smart community planning and construction.

[0010] Based on multi-source data and multiple decision indicators from various spatial grids of the target city, a smart community participatory planning intelligent auxiliary decision-making model is established, comprising a target layer, a criterion layer, and a scheme layer. The target layer includes the overall goal of smart community construction involving multiple dimensions of consideration. The criterion layer includes multiple decision indicators. The scheme layer includes one or more potential smart communities. Each potential smart community is composed of one or more spatial grids, and the multi-source data of each potential smart community is obtained by fusing and calculating the multi-source data of the one or more spatial grids.

[0011] Based on the smart community participatory planning intelligent auxiliary decision-making model, a performance score reflecting the degree to which the overall goal is met is calculated for each proposed smart community.

[0012] An interactive interface is provided to support user information input, and the importance comparison relationship between various decision indicators for smart communities is received by the user through the interactive interface; then, based on the importance comparison relationship between various decision indicators for smart communities input by the user, the coefficient of subjective evaluation tendency of each decision indicator is obtained.

[0013] A comprehensive evaluation score is obtained by combining the performance scores of each potential smart community and a coefficient based on subjective evaluation bias.

[0014] Based on the comprehensive evaluation scores of each potential smart community, the potential smart communities that meet the set conditions are displayed on the map of the target city to assist decision-making and serve as key considerations for smart community development.

[0015] In one possible implementation, the multiple decision indicators include several of the following: age of residents, vulnerable population, job-to-residence ratio, economic level, walking quality, economic types, water and green environment, and recreational activities.

[0016] In one possible implementation, the method further includes:

[0017] Based on the multi-source data of each spatial grid of the target city and the quantitative definition of each decision indicator, the quantitative values ​​of each decision indicator of each spatial grid of the target city are calculated.

[0018] Based on the quantitative values ​​of various decision indicators for each spatial grid in the target city, spatial information is visualized using the color corresponding to the quantitative value for each decision indicator, in order to assist decision-making and to focus on the local spatial scope for carrying out smart community construction.

[0019] In one possible implementation, based on the intelligent auxiliary decision-making model for participatory planning of smart communities, a performance score reflecting the degree to which overall goals are met is calculated for each proposed smart community, including:

[0020] Based on the intelligent auxiliary decision-making model for participatory planning in smart communities, the importance of multiple decision indicators in the criteria layer relative to the target layer is compared pairwise, and a judgment comparison matrix of the criteria layer is generated by combining the pre-set comparison data information.

[0021] Based on the multi-source data of each potential smart community in the scheme layer, calculate the performance scores of each decision-making indicator for each potential smart community.

[0022] Based on the judgment comparison matrix of the criterion layer and the performance scores of various decision indicators for each potential smart community, a performance score reflecting the degree to which the overall goal is met is calculated for each potential smart community.

[0023] In one possible implementation, based on the comprehensive evaluation scores of each potential smart community, the potential smart communities whose comprehensive evaluation scores meet the set conditions are displayed on a map of the target city. This serves as a key consideration for decision-making regarding the communities to be developed for smart community construction, including:

[0024] The proposed smart communities are sorted in descending order of their comprehensive evaluation scores, and a specified number of the top-ranked proposed smart communities are selected.

[0025] Display a specified number of potential smart communities on a map of the target city to support decision-making and identify key communities for smart community development.

[0026] Secondly, a device for intelligent auxiliary decision-making in participatory planning of smart communities is provided, comprising:

[0027] The raster processing module is used to perform spatial rasterization processing on the map of the target city, resulting in multiple spatial grids of the target city.

[0028] The data acquisition module is used to acquire multi-source data from various spatial grids of the target city;

[0029] The indicator construction module is used to construct multiple decision indicators based on multi-source data from various spatial grids of the target city to reflect relevant factors in urban renewal and smart community planning and construction.

[0030] The decision model building module is used to establish a smart community participatory planning intelligent auxiliary decision-making model based on multi-source data and multiple decision indicators from various spatial grids of the target city. The model includes a target layer, a criterion layer, and a scheme layer. The target layer includes the overall goal of smart community construction involving multiple dimensions of consideration. The criterion layer includes multiple decision indicators. The scheme layer includes one or more potential smart communities. Each potential smart community is composed of one or more spatial grids, and the multi-source data of each potential smart community is obtained by fusing and calculating the multi-source data of the one or more spatial grids.

[0031] The first calculation module is used to calculate, based on the smart community participatory planning intelligent auxiliary decision-making model, the performance score of each potential smart community, which reflects the degree to which the overall goal is met;

[0032] The interaction module is used to provide an interactive interface that supports user information input, receive the importance comparison relationship between various decision indicators for smart communities input by the user through the interactive interface; and then, based on the importance comparison relationship between various decision indicators for smart communities input by the user, obtain the coefficient of subjective evaluation tendency of each decision indicator.

[0033] The second calculation module is used to combine the performance scores of each potential smart community and the coefficients based on subjective evaluation tendencies to obtain a comprehensive evaluation score;

[0034] The display module is used to display potential smart communities that meet the set conditions on the map of the target city based on the comprehensive evaluation scores of each potential smart community, so as to assist decision-making and focus on the communities to be developed for smart construction.

[0035] In one possible implementation, the multiple decision indicators include several of the following: age of residents, vulnerable population, job-to-residence ratio, economic level, walking quality, economic types, water and green environment, and recreational activities.

[0036] In one possible implementation, the device further includes:

[0037] The third calculation module is used to calculate the quantitative values ​​of various decision indicators for each spatial grid of the target city based on the multi-source data and quantitative definitions of various decision indicators for each spatial grid of the target city.

[0038] The display module is also used to visualize spatial information based on the quantitative values ​​of various decision indicators of each spatial grid in the target city, using the colors corresponding to the quantitative values ​​of each decision indicator, in order to assist decision-making in focusing on the local spatial scope for carrying out smart community construction.

[0039] In one possible implementation, the first computing module is further configured to:

[0040] Based on the intelligent auxiliary decision-making model for participatory planning in smart communities, the importance of multiple decision indicators in the criteria layer relative to the target layer is compared pairwise, and a judgment comparison matrix of the criteria layer is generated by combining the pre-set comparison data information.

[0041] Based on the multi-source data of each potential smart community in the scheme layer, calculate the performance scores of each decision-making indicator for each potential smart community.

[0042] Based on the judgment comparison matrix of the criterion layer and the performance scores of various decision indicators for each potential smart community, a performance score reflecting the degree to which the overall goal is met is calculated for each potential smart community.

[0043] In one possible implementation, the display module is further used for:

[0044] The proposed smart communities are sorted in descending order of their comprehensive evaluation scores, and a specified number of the top-ranked proposed smart communities are selected.

[0045] Display a specified number of potential smart communities on a map of the target city to support decision-making and identify key communities for smart community development.

[0046] Thirdly, an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the intelligent auxiliary decision-making method for participatory planning of smart communities as described in any of the preceding claims.

[0047] Fourthly, a storage medium is provided that stores a computer program, wherein the computer program is configured to execute the intelligent auxiliary decision-making method for participatory planning of smart communities as described above during runtime.

[0048] Using the above technical solutions, the present application provides a method, apparatus, electronic device, and storage medium for intelligent auxiliary decision-making in participatory planning of smart communities. The method involves spatially rasterizing a map of a target city to obtain multiple spatial grids; acquiring multi-source data from each spatial grid of the target city; constructing multiple decision indicators reflecting relevant factors in urban renewal and smart community planning and construction based on the multi-source data from each spatial grid of the target city; and establishing an intelligent auxiliary decision-making model for participatory planning of smart communities, including a target layer, a criterion layer, and a scheme layer, based on the multi-source data and multiple decision indicators from each spatial grid of the target city. The target layer includes the overall goal of smart community construction involving multi-dimensional considerations; the criterion layer includes multiple decision indicators; and the scheme layer includes one or more potential smart communities, each potential smart community consisting of one or more spatial grids. Multi-source data is obtained by fusing multi-source data from one or more spatial grids. Based on the intelligent auxiliary decision-making model for participatory planning of smart communities, performance scores are calculated for each potential smart community to reflect the degree to which overall goals are met. An interactive interface is provided to support user information input, receiving the importance comparison relationships between various decision indicators for smart communities input by the user. Then, based on the importance comparison relationships between various decision indicators for smart communities input by the user, the coefficients of subjective evaluation tendencies for each decision indicator are obtained. Combining the performance scores of each potential smart community and the coefficients based on subjective evaluation tendencies, a comprehensive evaluation score is obtained. Based on the comprehensive evaluation scores of each potential smart community, potential smart communities that meet the set conditions are displayed on the map of the target city, using the auxiliary decision-making as a key focus for examining the local spatial scope for smart community construction. As can be seen, the embodiments of this application utilize hierarchical analysis to establish a smart community participatory planning intelligent auxiliary decision-making model, including a target layer, a criterion layer, and a scheme layer. This model is highly interpretable. At the target layer, it sets the overall goals for smart community construction, considering multiple dimensions. Simultaneously, it uses multi-source urban big data fusion computing to construct multiple decision indicators reflecting relevant factors in urban renewal and smart community planning and construction. It performs comprehensive measurement and scoring calculations based on multiple decision indicators for one or more urban local spaces where smart community construction may be carried out. Furthermore, it fully utilizes the subjective opinions and user inputs of participating entities during the planning decision-making process, generating interactive experiences with decision-makers. This accurately and efficiently identifies local spatial units based on the overall goals of smart community construction involving multiple dimensions, providing technical support for conducting smart community participatory planning based on multi-source urban data measurement and multi-level analysis. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0050] Figure 1 A flowchart is shown below illustrating the intelligent auxiliary decision-making method for participatory planning in smart communities provided in an embodiment of this application.

[0051] Figure 2 This paper illustrates the overall technical flowchart of the auxiliary decision-making model based on the analytic hierarchy process provided in an embodiment of this application.

[0052] Figure 3 This paper shows a structural diagram of the intelligent auxiliary decision-making model for participatory planning of smart communities provided in an embodiment of this application;

[0053] Figure 4 This diagram illustrates the structure of the smart community participatory planning intelligent auxiliary decision-making device provided in an embodiment of this application;

[0054] Figure 5 This invention provides a structural diagram of an intelligent auxiliary decision-making device for participatory planning in smart communities, according to another embodiment of this application.

[0055] Figure 6 A structural diagram of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0056] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0057] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0058] As mentioned earlier, combining the renovation and upgrading of old residential areas, the smart upgrade of communities, and the optimization of living circles with improved surrounding service resources will be the key tasks for urban community planning and governance going forward. When cities need to decide which communities in a certain spatial area to carry out smart construction, there is an urgent need to provide intelligent decision-making support solutions. On the one hand, these solutions need to be consistent with the goals of smart community construction, and on the other hand, they need to support the decision-making process of communities and local residents in specific urban spaces.

[0059] To address the aforementioned technical problems, embodiments of this application provide a method for intelligent auxiliary decision-making in participatory planning for smart communities, such as... Figure 1 As shown, the intelligent decision-making assistance method for participatory planning in smart communities may include the following steps S101 to S108:

[0060] Step S101: Perform spatial rasterization processing on the map of the target city to obtain multiple spatial grids of the target city.

[0061] In this step, information such as the urban building space, administrative divisions, and road network of the target city can be used, along with the minimum analyzable geographic area standard, to perform spatial rasterization processing on the target city's map, obtaining the grid boundary information of multiple spatial grids for the target city. The minimum analyzable geographic area standard can be flexibly configured according to the needs of spatial rasterization processing of the target city's map. For example, after spatial rasterization processing of the target city's map using the minimum analyzable geographic area standard, the area of ​​each spatial grid needs to be greater than or equal to a set threshold, etc.

[0062] In addition, the grid boundary information of each of the multiple spatial grids of the target city can be the grid's coding information and the latitude and longitude information of the grid boundary. In this way, the coding information of the grid can be used to determine which spatial grid it is, and the latitude and longitude information of the grid boundary can be used to determine the grid boundary of the spatial grid.

[0063] Step S102: Obtain multi-source data of each spatial grid of the target city.

[0064] In this step, the multi-source data can be basic geographic data, POI (Points of Interest) data, 3D city data, pedestrian quality assessment data, demographic data, mobile application preference data, etc., and this embodiment is not limited to these. Here, points of interest can be locations in a geographic information system or electronic map that relate to different functions such as life services, commerce, and entertainment.

[0065] Step S103: Based on multi-source data from various spatial grids of the target city, construct multiple decision indicators to reflect relevant factors in urban renewal and smart community planning and construction.

[0066] In this step, multiple decision-making indicators can be selected from factors such as the age of residents, vulnerable population, work-to-residence ratio, economic level, walking quality, economic types, water and green environment, and recreational activities.

[0067] Step S104: Based on multi-source data and multiple decision indicators from various spatial grids of the target city, establish a smart community participatory planning intelligent auxiliary decision-making model, including a target layer, a criterion layer, and a scheme layer. The target layer includes the overall goal of smart community construction involving multiple dimensions of consideration. The criterion layer includes multiple decision indicators. The scheme layer includes one or more potential smart communities. Each potential smart community is composed of one or more spatial grids, and the multi-source data of each potential smart community is obtained by fusing and calculating the multi-source data of the one or more spatial grids.

[0068] This step involves the potential development of smart communities, which refers to communities where a decision needs to be made regarding whether to undertake smart community construction.

[0069] Step S105: Based on the smart community participatory planning intelligent auxiliary decision-making model, calculate the performance score of each proposed smart community to reflect the degree to which the overall goal is met.

[0070] Step S106: Provide an interactive interface that supports user information input, receive the importance comparison relationship between various decision indicators for smart communities input by the user through the interactive interface; then, based on the importance comparison relationship between various decision indicators for smart communities input by the user, obtain the coefficient of subjective evaluation tendency of each decision indicator.

[0071] Step S107: Combine the performance scores of each potential smart community with the coefficients based on subjective evaluation tendencies to obtain a comprehensive evaluation score.

[0072] In this step, based on the coefficient of subjective evaluation tendency and the multi-source data of each potential smart community, the subjective evaluation score of each potential smart community can be obtained; then the performance score and the subjective evaluation score of each potential smart community are calculated to obtain the comprehensive evaluation score of each potential smart community.

[0073] Step S108: Based on the comprehensive evaluation scores of each potential smart community, display the potential smart communities that meet the set conditions on the map of the target city to assist decision-making and focus on the communities to be developed for smart construction.

[0074] This application's embodiments utilize hierarchical analysis to establish a smart community participatory planning intelligent auxiliary decision-making model, including a target layer, a criterion layer, and a scheme layer. It boasts strong interpretability. At the target layer, it sets overall goals for smart community construction involving multi-dimensional considerations. Simultaneously, it uses multi-source urban big data fusion computing to construct multiple decision indicators reflecting relevant factors in urban renewal and smart community planning and construction. It performs comprehensive measurement and scoring calculations based on multiple decision indicators for one or more urban local spaces where smart community construction may be carried out. Furthermore, it fully utilizes the subjective opinions and user inputs of participating entities during the planning decision-making process, generating interactive experiences with decision-makers. This accurately and efficiently identifies local spatial units based on the overall goals of smart community construction involving multi-dimensional considerations, providing technical support for conducting smart community participatory planning based on multi-source urban data measurement and multi-level analysis.

[0075] This application provides one possible implementation method. The multiple decision indicators mentioned above can be multiple factors such as the age of residents, vulnerable population, work-residence ratio, economic level, walking quality, economic types, water and green environment, and recreational activities. This embodiment does not impose any limitations on these factors. These decision indicators are consistent with the goals of smart community construction, such as building density, population characteristics, environmental quality, economic potential, social factors, transportation accessibility, and other factors related to smart community construction in a local space.

[0076] This application embodiment provides a possible implementation method. After step S103 above, which constructs multiple decision indicators based on multi-source data from various spatial grids of the target city to reflect relevant factors in urban renewal and smart community planning and construction, the following steps A1 and A2 may also be included:

[0077] Step A1: Based on the multi-source data of each spatial grid of the target city and the quantitative definition of each decision indicator, calculate the quantitative value of each decision indicator of each spatial grid of the target city.

[0078] Table 1. Names and Quantitative Definitions of Smart Community Planning Indicators

[0079]

[0080] Table 1 shows the various decision-making indicators for smart community planning and their quantitative definitions. It should be noted that the examples in Table 1 are merely illustrative and do not limit the scope of this embodiment.

[0081] Step A2: Based on the quantitative values ​​of various decision indicators for each spatial grid in the target city, the spatial information of each decision indicator is visualized using the color corresponding to the quantitative value, so as to assist decision-making and focus on the local spatial scope for carrying out smart community construction.

[0082] In this step, spatial information is visualized using the color corresponding to the quantitative value of each decision indicator. This is used to support decision-making and to focus on the development of smart communities. For example, when spatially displaying each decision indicator, the larger the quantitative value, the darker the color. The darker colored spatial grid can be used as the local spatial range for focusing on the development of smart communities.

[0083] This embodiment can combine the quantification of decision indicators with the visualization of spatial information to intuitively and efficiently assist decision-making in the local spatial scope of key investigations for the construction of smart communities.

[0084] This application embodiment provides a possible implementation method. Step S105 above is based on the smart community participatory planning intelligent auxiliary decision-making model to calculate the performance score of each proposed smart community, which reflects the degree to which the overall goal is met. Specifically, it may include the following steps B1 to B3:

[0085] Step B1: Based on the smart community participatory planning intelligent auxiliary decision-making model, the importance of multiple decision indicators in the criterion layer relative to the target layer is compared pairwise, and combined with pre-set comparison data information, a judgment comparison matrix of the criterion layer is generated.

[0086] In this step, the pre-set comparison data information can be found in Table 2 below. The importance scale and scale interpretation in Table 2 are based on the fundamental principles of the Analytic Hierarchy Process (AHP). It should be noted that the examples in Table 2 are merely illustrative and do not limit this embodiment.

[0087] Step B2: Based on the multi-source data of each potential smart community in the scheme layer, calculate the performance scores of each decision indicator for each potential smart community.

[0088] In this step, the multi-source data of each potential smart community in the scheme layer can be normalized, and the performance scores of each decision indicator of each potential smart community can be calculated.

[0089] Step B3: Based on the judgment comparison matrix of the criteria layer and the performance scores of various decision indicators for each potential smart community, calculate the performance score of each potential smart community to reflect the degree to which the overall goal is met.

[0090] Table 2 Pre-set comparison data information

[0091]

[0092] This embodiment employs hierarchical analysis, based on multi-objective and multi-scheme decision-making, combining qualitative and quantitative methods to obtain performance scores for each potential smart community, reflecting the degree to which overall objectives are met.

[0093] This application embodiment provides a possible implementation method. Step S108 above displays, on the map of the target city, potential smart communities whose comprehensive evaluation scores meet the set conditions, based on the comprehensive evaluation scores of each potential smart community, in order to assist decision-making and focus on the communities to be developed for smart construction. Specifically, it may include the following steps C1 and C2:

[0094] Step C1: Sort the potential smart communities in descending order of their comprehensive evaluation scores, and select a specified number of the top-ranked potential smart communities.

[0095] Step C2: Display a specified number of potential smart communities on the map of the target city, prioritizing them to support decision-making and highlight key communities for smart community development.

[0096] In this step, the specified quantity can be 50 or 10, etc. This embodiment does not limit this, and the specific quantity can be determined according to actual needs.

[0097] This embodiment can intuitively and accurately display a specified number of potential smart communities on a map of the target city, in order of priority, to assist decision-making in prioritizing the development of smart communities.

[0098] The above introduces Figure 1 The embodiments shown have various implementation methods for each stage. The following will further explain the intelligent auxiliary decision-making method for participatory planning of smart communities in this application through specific embodiments.

[0099] First, the following technical terms will be explained in detail.

[0100] AHP: Analytic Hierarchy Process, a decision-making method based on multiple objectives and multiple options. It is a decision analysis method that combines qualitative and quantitative approaches and does not rely entirely on data.

[0101] OSM data: Open Street Map data.

[0102] Python: A programming language widely used in web applications, software development, data science, and machine learning.

[0103] Pandas: Python Data Analysis Library, an open-source toolkit for data science analysis.

[0104] GeoPandas: An open-source toolkit for supporting Python in processing geospatial data.

[0105] NumPy: An open-source Python library primarily used for data analysis and scientific computing.

[0106] CARTO Builder: An open-source visualization tool based on geographic location information that can generate visual maps and filter and analyze geographic data.

[0107] Kepler.gl: An open-source geographic data analysis tool for visual analysis and exploration of large-scale geographic datasets.

[0108] This embodiment provides an urban planning decision support solution, which will help understand the smart living interaction scenarios of future urban communities and the planning strategies for living circles that can serve related behavioral needs. It aims to provide intelligent auxiliary algorithms and decision support models for participatory planning, data-enhanced design, implementation and operation management of community living circles in future cities. It also effectively solves the typical problems that are common in the multi-objective collaboration process of current urban smart community planning and can be promoted and used in multiple cities.

[0109] This embodiment focuses on how to utilize multi-source spatiotemporal urban big data in participatory planning of smart community living circles. By integrating multi-source urban big data, it calculates and analyzes the spatial layout, resource allocation, and behavioral interaction characteristics of different communities, and conducts multi-scale spatiotemporal statistical analysis, multivariate analysis, and benchmark measurement analysis on various types of communities in the city.

[0110] The Analytic Hierarchy Process (AHP) is a decision-making method based on multiple objectives and scenarios. It combines qualitative and quantitative methods and does not rely entirely on data. Addressing the practical needs of participatory planning and community governance in future communities, the output of the AHP model can be used to envision the participatory planning information interface and user experience of future smart communities. In this way, cities can utilize this model to provide intelligent auxiliary algorithms and decision support for participatory planning, data-enhanced design, implementation, operation, and maintenance management of future smart communities.

[0111] This embodiment mainly includes three core technologies: smart community planning and decision-making indicator measurement technology, multi-objective decision-making hierarchical analysis technology, and participatory planning information interaction technology. For example... Figure 2The diagram shows the overall technical flowchart of the decision support model based on the analytic hierarchy process (AHP), including the methodology, key technologies, and model architecture. The model architecture includes smart community planning and decision indicators, a multi-objective decision hierarchy model, and participatory planning information interaction, which will be described in detail below.

[0112] (1) Measurement technology for smart community planning and decision-making indicators

[0113] The measurement of decision-making indicators for smart community planning is a crucial criterion for selecting smart communities. These criteria are consistent with the goals of smart community construction, such as building density, population characteristics, environmental quality, economic potential, social factors, transportation accessibility, and other factors related to smart community construction within a local space. The data preprocessing process mainly includes steps such as rasterization of urban space, multi-source data fusion calculation, multi-scale spatial data association, statistical variable standardization, and quantitative indicator construction. Key data preprocessing technologies involved include grid generation based on geometric center points, extraction of raw geographic information from local spatial areas, construction of decision-making indicators, and standard quantification based on normalization methods. The preprocessed data structure is a dataset of length N and width M, where N equals the number of spatiotemporal behavioral statistical units (e.g., 800 meters by 800 meters) based on mobile data within the urban space, and M equals the variables based on statistical results of relevant spatial or spatiotemporal behavioral characteristics.

[0114] Based on the indicator definitions and quantification methods in Table 1 above, indicators are calculated for the spatial grid within the research scope to quantify the characteristics of residential areas, population, environment, POIs, and other aspects related to urban renewal and smart community construction in a local space (composed of one or more spatial grids). Typically, the quantification of each indicator requires data cleaning, fusion, calculation, and annotation. The grid-based system can quantify and visualize spatial information regarding aspects such as resident age, housing prices, water and green environment, economic activities, vulnerable populations, population density, job-housing ratio, and recreational activities in a local space.

[0115] (2) Multi-objective decision hierarchy analysis technique

[0116] Multi-objective decision hierarchy analysis (AHP) is a planning decision support technology based on the AHP principle. It can identify local spaces in a city where smart community construction should be prioritized. This technology can include the following steps:

[0117] 1) A preliminary multi-objective decision-making hierarchy model has been constructed. The basic framework of the AHP-based smart community planning decision-making model includes a three-layer structure: objective layer, criterion layer, and solution layer. Figure 3 As shown, it should be noted that Figure 3 The numerical values ​​shown are merely examples and are not intended to limit the scope of this embodiment.

[0118] When a city (such as Beijing) needs to select spatial units (based on the previous spatial grid division) within a certain spatial area (such as Haidian District) as the scope of smart community construction, that is, when a city needs to decide which communities in a certain spatial area to carry out smart construction, the target layer summarizes the overall target composition of smart communities, which may include multi-dimensional and multi-faceted considerations such as smart elderly-friendly, child-friendly, green ecology, and economic and commercial aspects.

[0119] 2) Construction of Criterion-Level Decision Indicators: The purpose of constructing the criterion-level indicator system is to define the relevant considerations and standards for selecting smart communities. These standards should be consistent with the overall goals of smart communities, such as building density, population characteristics, environmental quality, economic potential, social factors, transportation accessibility, and other factors related to smart community construction in a localized area.

[0120] 3) Judgment Comparison Matrix: By comparing the relative importance of each pair of decision indicators in the quantitative definition, and combining this with the pre-set comparison data information provided in Table 2, a judgment comparison matrix is ​​generated at the criterion level. The elements in the judgment comparison matrix are priority weights, representing the relative importance of the decision indicators. Based on the multi-source data of each potential smart community in the scheme layer, the performance scores of each decision indicator for each potential smart community are calculated.

[0121] 4) Performance score calculation: Based on the judgment comparison matrix of the criterion layer and the performance scores of various decision indicators of each potential smart community, the performance score of each potential smart community is calculated to reflect the degree to which the overall goal is met. The statistical distribution of the performance score and its various sub-indicators is then described and presented in a visual representation.

[0122] (3) Participatory planning information interaction technology

[0123] Participatory planning information interaction technology is a Python-based data computing environment that comprehensively utilizes various related data science toolkits. Specifically, it includes using Pandas to read, clean, integrate, and analyze multiple structured data files; using GeoPandas for the related reading, cleaning, spatial correlation, statistical analysis, and visualization of data containing geospatial information; and using NumPy for the construction and calculation of coefficient matrices. Based on the data preprocessing, computation, and analysis, CARTO Builder and Kepler.gl are used to visualize the analysis results. Simultaneously, an interactive interface supporting user information input is provided, receiving the importance comparison relationships between various decision indicators for smart communities input by the user through the interface. Then, based on the importance comparison relationships between the various decision indicators for smart communities input by the user, the coefficients of subjective evaluation tendencies for each decision indicator are obtained. Based on the coefficients of subjective evaluation tendencies and multi-source data for each potential smart community, a subjective evaluation score for each potential smart community can be obtained. Then, the performance score and subjective evaluation score of each potential smart community are calculated to obtain a comprehensive evaluation score for each potential smart community. The potential smart communities are then sorted in descending order of their comprehensive evaluation scores, and a specified number of the top-ranked potential smart communities are selected. These top-ranked potential smart communities are then displayed on a map of the target city to assist in decision-making and to prioritize communities for smart community development.

[0124] This embodiment addresses the future needs of smart community planning in Chinese cities with two main application scenarios: First, top-down urban decision support, which is a city-wide decision support application scenario for urban decision-makers, involving multi-indicator evaluation and consideration. Examples include using this model for assessments of urban health, community health, urban grid spatial type classification, and the priority of smart transformation. Second, bottom-up community participatory planning, which provides data analysis models for smart community participatory planning to support the decision-making process of communities and local residents in specific urban spaces.

[0125] This embodiment utilizes the hierarchical analysis decision-making principle to set multiple objectives for future smart community planning. Simultaneously, it employs multi-source urban big data fusion calculations to construct objective quantitative indicators reflecting these objectives. Through communication with urban functional departments, planners, and decision-makers, it constructs a weighted judgment and comparison matrix based on these indicators. Furthermore, it conducts comprehensive measurement and scoring calculations based on these indicators for local urban spaces to identify local spatial units based on various smart community construction objectives. This provides technical support for future participatory planning of smart communities based on multi-source urban big data measurement and multi-level analysis.

[0126] It should be noted that the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In practical applications, all the above possible implementation methods can be arbitrarily combined in a combined manner to form possible embodiments of this application, which will not be described in detail here.

[0127] Based on the methods for intelligent auxiliary decision-making in participatory planning of smart communities provided in the above embodiments, and based on the same inventive concept, this application also provides an apparatus for intelligent auxiliary decision-making in participatory planning of smart communities.

[0128] Figure 4 This is a structural diagram of the smart community participatory planning intelligent auxiliary decision-making device provided in the embodiments of this application. Figure 4 As shown, the smart community participatory planning intelligent auxiliary decision-making device may specifically include a grid processing module 410, a data acquisition module 420, an indicator construction module 430, a decision model establishment module 440, a first calculation module 450, an interaction module 460, a second calculation module 470, and a display module 480.

[0129] The raster processing module 410 is used to perform spatial rasterization processing on the map of the target city to obtain multiple spatial grids of the target city.

[0130] Data acquisition module 420 is used to acquire multi-source data from various spatial grids of the target city;

[0131] The indicator construction module 430 is used to construct multiple decision indicators based on multi-source data from various spatial grids of the target city to reflect relevant factors in urban renewal and smart community planning and construction.

[0132] The decision model building module 440 is used to build a smart community participatory planning intelligent auxiliary decision-making model based on multi-source data and multiple decision indicators of various spatial grids in the target city. The model includes a target layer, a criterion layer, and a scheme layer. The target layer includes the overall goal of smart community construction involving multiple dimensions of consideration. The criterion layer includes multiple decision indicators. The scheme layer includes one or more potential smart communities. Each potential smart community is composed of one or more spatial grids. The multi-source data of each potential smart community is obtained by fusing and calculating the multi-source data of the one or more spatial grids.

[0133] The first calculation module 450 is used to calculate, based on the smart community participatory planning intelligent auxiliary decision-making model, the performance score of each potential smart community that reflects the degree to which the overall goal is met.

[0134] The interaction module 460 is used to provide an interactive interface that supports user information input, receive the importance comparison relationship between various decision indicators for smart communities input by the user through the interactive interface; and then, based on the importance comparison relationship between various decision indicators for smart communities input by the user, obtain the coefficient of subjective evaluation tendency of each decision indicator.

[0135] The second calculation module 470 is used to combine the performance scores of each potential smart community and the coefficients based on subjective evaluation tendencies to obtain a comprehensive evaluation score.

[0136] The display module 480 is used to display, on the map of the target city, the communities that meet the set conditions for developing smart communities based on their comprehensive evaluation scores, so as to assist decision-making in focusing on the communities to be developed for smart community construction.

[0137] This application provides a possible implementation method, in which the multiple decision indicators include multiple factors such as the age of residents, vulnerable population, work-residence ratio, economic level, walking quality, economic formats, water and green environment, and recreational activities.

[0138] This application provides one possible implementation method, such as... Figure 5 As shown above, Figure 4 The device on display may also include a third computing module 510, which is used to calculate the quantitative values ​​of various decision indicators of various spatial grids of the target city based on multi-source data of various spatial grids and the quantitative definition of various decision indicators.

[0139] The display module 480 is also used to visualize spatial information based on the quantitative values ​​of various decision indicators of each spatial grid of the target city, using the colors corresponding to the quantitative values ​​of each decision indicator, so as to assist decision-making in focusing on the local spatial range for carrying out smart community construction.

[0140] This application embodiment provides a possible implementation, wherein the first computing module 450 is further configured to:

[0141] Based on the intelligent auxiliary decision-making model for participatory planning in smart communities, the importance of multiple decision indicators in the criteria layer relative to the target layer is compared pairwise, and a judgment comparison matrix of the criteria layer is generated by combining the pre-set comparison data information.

[0142] Based on the multi-source data of each potential smart community in the scheme layer, calculate the performance scores of each decision-making indicator for each potential smart community.

[0143] Based on the judgment comparison matrix of the criterion layer and the performance scores of various decision indicators for each potential smart community, a performance score reflecting the degree to which the overall goal is met is calculated for each potential smart community.

[0144] This application embodiment provides a possible implementation, wherein the display module 480 is further configured to:

[0145] The proposed smart communities are sorted in descending order of their comprehensive evaluation scores, and a specified number of the top-ranked proposed smart communities are selected.

[0146] Display a specified number of potential smart communities on a map of the target city to support decision-making and identify key communities for smart community development.

[0147] Based on the same inventive concept, this application also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the smart community participatory planning intelligent auxiliary decision-making method of any of the above embodiments.

[0148] In an exemplary embodiment, an electronic device is provided, such as Figure 6 As shown, Figure 6 The illustrated electronic device 600 includes a processor 601 and a memory 603. The processor 601 and the memory 603 are connected, for example, via a bus 602. Optionally, the electronic device 600 may also include a transceiver 604. It should be noted that in practical applications, the transceiver 604 is not limited to one type, and the structure of this electronic device 600 does not constitute a limitation on the embodiments of this application.

[0149] Processor 601 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 601 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0150] Bus 602 may include a pathway for transmitting information between the aforementioned components. Bus 602 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 602 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0151] The memory 603 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0152] The memory 603 stores computer program code that executes the scheme of this application, and its execution is controlled by the processor 601. The processor 601 executes the computer program code stored in the memory 603 to implement the content shown in the foregoing method embodiments.

[0153] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0154] Based on the same inventive concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute the smart community participatory planning intelligent auxiliary decision-making method of any of the above embodiments when running.

[0155] Those skilled in the art will clearly understand that the specific working process of the systems, devices, and modules described above can be referred to the corresponding process in the foregoing method embodiments. For the sake of brevity, it will not be repeated here.

[0156] Those skilled in the art will understand that the technical solution of this application, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several program instructions to cause an electronic device (e.g., a personal computer, server, or network device) to execute all or part of the steps of the methods described in the embodiments of this application when running the program instructions. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0157] Alternatively, all or part of the steps of the foregoing method embodiments can be implemented by hardware (such as electronic devices like personal computers, servers, or network devices) associated with program instructions. The program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0158] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principles of this application, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the corresponding technical solutions to leave the protection scope of this application.

Claims

1. A method for intelligent auxiliary decision-making in participatory planning of smart communities, characterized in that, include: The map of the target city is spatially rasterized to obtain multiple spatial grids of the target city. Acquire multi-source data from various spatial grids of the target city; Based on multi-source data from various spatial grids of the target city, multiple decision indicators are constructed to reflect relevant factors in urban renewal and smart community planning and construction. The data structure of the preprocessed results is a dataset with a length of N and a width of M, where N equals the number of spatiotemporal behavior statistical units based on mobile data within the urban spatial range, and M equals the variables based on statistical results of relevant spatial features or spatiotemporal behavior features. Based on multi-source data and multiple decision indicators from various spatial grids of the target city, a smart community participatory planning intelligent auxiliary decision-making model is established, including target layer, criterion layer and scheme layer. The target layer includes the overall goal of smart community construction involving multiple dimensions of consideration; the criteria layer includes multiple decision indicators; the scheme layer includes one or more potential smart communities; each potential smart community is composed of one or more spatial grids; and the multi-source data of each potential smart community is obtained by fusing and calculating the multi-source data of the one or more spatial grids. Based on the intelligent auxiliary decision-making model for participatory planning of smart communities, a performance score reflecting the degree to which the overall goal is met is calculated for each potential smart community. By comparing the relative importance between each pair of decision indicators in a quantitative definition and combining it with pre-set comparison data information, a judgment comparison matrix at the criterion layer is generated. The elements in the judgment comparison matrix are priority weights. Based on the multi-source data of each potential smart community at the scheme layer, the performance scores of each decision indicator of each potential smart community are calculated. Based on the judgment comparison matrix at the criterion layer and the performance scores of each decision indicator of each potential smart community, a performance score reflecting the degree to which the overall goal is met is calculated for each potential smart community. An interactive interface is provided to support user information input, and the importance comparison relationship between various decision indicators for smart communities is received by the user through the interactive interface; then, based on the importance comparison relationship between various decision indicators for smart communities input by the user, the coefficient of subjective evaluation tendency of each decision indicator is obtained. A comprehensive evaluation score is obtained by combining the performance scores of each potential smart community and a coefficient based on subjective evaluation bias. Based on the comprehensive evaluation scores of each potential smart community, the potential smart communities that meet the set conditions are displayed on the map of the target city. The communities that are selected for smart construction are used as the focus of decision support. The communities are sorted in descending order of their comprehensive evaluation scores, and a specified number of the top-ranked potential smart communities are selected and displayed on the map of the target city.

2. The method according to claim 1, characterized in that, The aforementioned multiple decision-making indicators include several factors such as the age of residents, vulnerable population, job-to-residence ratio, economic level, walking quality, economic types, water and green environment, and recreational activities.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Based on the multi-source data of each spatial grid of the target city and the quantitative definition of each decision indicator, the quantitative values ​​of each decision indicator of each spatial grid of the target city are calculated. Based on the quantitative values ​​of various decision indicators for each spatial grid in the target city, spatial information is visualized using the color corresponding to the quantitative value for each decision indicator, in order to assist decision-making and to focus on the local spatial scope for carrying out smart community construction.

4. A device for intelligent auxiliary decision-making in participatory planning of smart communities, characterized in that, include: The raster processing module is used to perform spatial rasterization processing on the map of the target city, resulting in multiple spatial grids of the target city. The data acquisition module is used to acquire multi-source data from various spatial grids of the target city; The indicator construction module is used to construct multiple decision indicators based on multi-source data from various spatial grids of the target city to reflect relevant factors in urban renewal and smart community planning and construction. The data structure of the preprocessed result is a dataset with a length of N and a width of M, where N equals the number of spatiotemporal behavior statistical units based on mobile data within the urban spatial range, and M equals the variables based on statistical results of relevant spatial features or spatiotemporal behavior features. The decision model building module is used to build a smart community participatory planning intelligent auxiliary decision-making model, including target layer, criterion layer and scheme layer, based on multi-source data and multiple decision indicators from various spatial grids of the target city. The target layer includes the overall goal of smart community construction involving multiple dimensions of consideration; the criteria layer includes multiple decision indicators; the scheme layer includes one or more potential smart communities; each potential smart community is composed of one or more spatial grids; and the multi-source data of each potential smart community is obtained by fusing and calculating the multi-source data of the one or more spatial grids. The first calculation module is used to calculate the performance score of each potential smart community, reflecting the degree to which the overall goal is met, based on the smart community participatory planning intelligent auxiliary decision-making model. By comparing the relative importance between each pair of decision indicators in a quantitative definition and combining it with pre-set comparison data information, a judgment comparison matrix of the criterion layer is generated. The elements in the judgment comparison matrix are priority weights. Based on the multi-source data of each potential smart community in the scheme layer, the performance score of each decision indicator of each potential smart community is calculated. Based on the judgment comparison matrix of the criterion layer and the performance scores of each decision indicator of each potential smart community, the performance score of each potential smart community, reflecting the degree to which the overall goal is met, is calculated. The interaction module is used to provide an interactive interface that supports user information input, receive the importance comparison relationship between various decision indicators for smart communities input by the user through the interactive interface; and then, based on the importance comparison relationship between various decision indicators for smart communities input by the user, obtain the coefficient of subjective evaluation tendency of each decision indicator. The second calculation module is used to combine the performance scores of each potential smart community and the coefficients based on subjective evaluation tendencies to obtain a comprehensive evaluation score; The display module is used to display potential smart communities that meet the set conditions on the map of the target city based on the comprehensive evaluation scores of each potential smart community. This module is used to assist decision-making by focusing on communities that are to be considered for smart construction. The modules sort the potential smart communities in descending order of their comprehensive evaluation scores and select a specified number of the top-ranked potential smart communities to display on the map of the target city.

5. The apparatus according to claim 4, characterized in that, The aforementioned multiple decision-making indicators include several factors such as the age of residents, vulnerable population, job-to-residence ratio, economic level, walking quality, economic types, water and green environment, and recreational activities.

6. The apparatus according to claim 4 or 5, characterized in that, The device further includes: The third calculation module is used to calculate the quantitative values ​​of various decision indicators for each spatial grid of the target city based on the multi-source data and quantitative definitions of various decision indicators for each spatial grid of the target city. The display module is also used to visualize spatial information based on the quantitative values ​​of various decision indicators of each spatial grid in the target city, using the colors corresponding to the quantitative values ​​of each decision indicator, in order to assist decision-making in focusing on the local spatial scope for carrying out smart community construction.

7. An electronic device, characterized in that, The system includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the method for intelligent auxiliary decision-making in participatory planning of smart communities as described in any one of claims 1 to 3.

8. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method for intelligent auxiliary decision-making in participatory planning of smart communities as described in any one of claims 1 to 3 when running.

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