Community life circle planning evaluation method and system

Abstract

The invention relates to the technical field of community planning, and discloses a community life circle planning evaluation method and system, and the method comprises the steps: carrying out the real-time analysis of data based on a data collection end, extracting the core attribute of a community service facility, generating a unified semantic representation, and carrying out the distributed synchronization of the unified semantic representation to a central evaluation database through federal learning; based on standardized data and resident behavior portrait features, dynamically generating facility coverage conflict solving strategies, and performing classified execution by an edge node local preprocessing module; after the conflict is solved, selecting a target edge node in combination with a population age structure and dynamically configuring a cache evaluation index set in a collaborative architecture of the edge node and the central server; and obtaining an evaluation demand of a planner, analyzing a planning intention, generating a comprehensive evaluation feature in combination with the cache index, determining an evaluation level, outputting a planning optimization suggestion, and updating an evaluation index weight system and a planning strategy based on the feedback of the planner. The application can dynamically adapt to the change of the relationship between the resident demand and the facility supply and demand.

Description

Technical Field

[0001] This application relates to the technical field of community planning, and in particular to a method and system for evaluating community living circle planning. Background Technology

[0002] Community living circle planning evaluation is a crucial foundation for optimizing urban public service allocation and enhancing residents' quality of life. Its scientific validity relies on the effective utilization of multi-source spatiotemporal data and a precise understanding of the actual supply and demand relationship within the community. With the advancement of urbanization and the diversification of residents' needs, the data sources used for planning evaluation are becoming increasingly abundant, covering various information such as population distribution, facility attributes, spatial characteristics, and resident activity feedback. However, data from different sources suffers from format differences and semantic inconsistencies, making data integration challenging. Furthermore, the dynamic changes in the supply and demand imbalance of facilities within the community, as well as insufficient coverage and adaptability, pose challenges to accurate evaluation.

[0003] Existing planning evaluation methods have failed to adequately address the issue of efficient integration of multi-source heterogeneous data, and are also unable to dynamically adapt to changes in residents' needs and the supply and demand relationship of facilities. As a result, the evaluation process cannot comprehensively and accurately reflect the actual operation of the community living circle, and the evaluation results lack pertinence and reliability. Consequently, they cannot provide scientific and effective decision support for subsequent planning optimization, thus hindering the improvement of the quality of community living circle planning.

[0004] As can be seen from the above, how to dynamically adapt to changes in residents' needs and the supply and demand relationship of facilities still needs to be addressed. Summary of the Invention

[0005] In order to dynamically adapt to changes in residents' needs and the supply and demand of facilities, this application provides a method and system for evaluating community living circle planning.

[0006] Firstly, this application provides a method for evaluating community living circle planning, employing the following technical solution: A method for evaluating community living circle planning includes: At the data acquisition end, community population distribution data, population profile data, POI data, satellite remote sensing data, and social media text data from multi-source spatiotemporal big data platforms are analyzed in real time to extract the core attributes of community service facilities and generate a unified semantic representation. The core attributes include facility type, facility scale, and facility density. The semantic representation establishes a semantic mapping relationship between facility attributes across platforms based on a context-aware facility type mapping mechanism and spatial association rules. The standardized data is then transmitted to the central evaluation database through a federated learning distributed synchronization mechanism. Based on the facility attribute information and resident behavior profile features in the standardized data, a conflict resolution strategy is dynamically generated when a facility coverage conflict occurs. The conflict resolution strategy is executed by the local preprocessing module deployed on the edge node. The local preprocessing module responds quickly to sudden resident activity data and processes periodic community data in batches. After the conflict is resolved, the data content after the conflict is resolved is obtained. In the computing architecture composed of edge nodes and central servers, the target edge node is selected based on the data content and the age structure information of the community population, and the cache evaluation index set of the target edge node is dynamically configured. The system obtains evaluation requirement descriptions input by community planners, parses the corresponding planning intent information based on these descriptions, generates corresponding comprehensive evaluation features by combining the planning intent information with multi-dimensional evaluation indicators in the configured cache, determines the community living circle evaluation level based on the comprehensive evaluation features using a dynamic weighted aggregation algorithm, and outputs corresponding planning optimization suggestions based on the evaluation level. The system also obtains feedback information from community planners on the planning optimization suggestions and updates the evaluation indicator weight system and planning strategy based on the feedback information.

[0007] Optionally, the context-aware facility type mapping mechanism further includes the following method: The BERT model is used to perform semantic parsing on cross-platform facility names to generate semantic vectors for facility names; a spatial graph is constructed based on the geographical coordinates of the facilities, with facilities as nodes and the spatial distance between facilities as edge weights. Based on graph neural network processing of spatial graphs, spatial association rules between facilities are learned to form spatial association matrices of facility types; the semantic vectors and spatial association matrices are fused with the spatial association matrices through a weighted fusion mechanism to generate semantic mapping relationships between facility types across platforms.

[0008] Optionally, in the conflict resolution strategy, the method further includes: Community vulnerability indicators are calculated based on population distribution data. These indicators include the proportion of elderly and children, the proportion of disabled persons, and the proportion of owner-occupied housing. Community function robustness indicators are calculated based on POI data. These indicators include emergency shelter coverage, medical aid station density, and accessibility of fire-fighting facilities. Emergency management effectiveness indicators are calculated based on community management data. These indicators include the reserve of emergency response teams, the level of material reserves, and the degree of planning of emergency response plans. The community resilience index is obtained by weighting the community vulnerability index, the community functional robustness index, and the emergency management effectiveness index using the analytic hierarchy process. When the community resilience index is greater than the preset community resilience threshold, conflicts related to emergency facilities are resolved first, and the priority weight of emergency facility conflict resolution is dynamically adjusted based on the ratio of the elderly and children.

[0009] Optionally, in the set of cache evaluation metrics for dynamically configured target edge nodes, the method further includes: Based on demographic data, the community's population structure is divided into elderly, youth, and family groups; the current user's age group is identified through user device location information or user input. Based on the current user's age group, a subset of evaluation indicators for the corresponding group is extracted from the cache. The subset of evaluation indicators includes the elderly group's focus on medical facilities and the young group's focus on cultural facilities. By dynamically adjusting the weights of the evaluation indicator subset based on real-time population flow data, the cached evaluation indicator set is highly matched with the needs of the current user group.

[0010] Optionally, the method further includes the following in the multi-dimensional evaluation indicators: Define indicators for facility richness, including facility diversity, facility density, and facility scale ratio; define indicators for distribution balance, including facility spatial coverage blind spot rate and the matching degree between facility distribution and population distribution; define a community vitality dimension, including cultural vitality index and social connectivity index. The cultural vitality index is calculated using social media cultural activity check-in data and sentiment analysis, while the social connectivity index is calculated using mobile signaling data and spatial syntax analysis. The vitality index weight and the connectivity index weight of the cultural vitality index are dynamically adjusted according to the frequency of community cultural activities, so that the multi-dimensional evaluation index system can reflect changes in community cultural vitality in real time.

[0011] Optionally, in the dynamic weighted aggregation algorithm, the method further includes: The evaluation index weighting system is initialized based on historical data; the difference between the current evaluation level and the actual community improvement effect is calculated; the weights are adjusted according to the difference value using the gradient descent method to make the evaluation results closer to the actual improvement effect; The frequency of community cultural activities is introduced as a factor for population structure change, and the vitality index weight and the connectivity index weight are applied to re-aggregate multi-dimensional evaluation indicators.

[0012] Optionally, in the process of updating the evaluation index weight system and planning strategy based on feedback information, the method also includes: Collect feedback from community planners on planning optimization suggestions, including satisfaction scores and improvement suggestions; transform the feedback information into reward signals in reinforcement learning to evaluate the effectiveness of the current evaluation system; The evaluation index weight system is updated using the PPO reinforcement learning algorithm to make the weight system more in line with actual planning needs; the execution parameters of the planning strategy are dynamically adjusted in combination with the updated weight system and the feedback information.

[0013] Secondly, this application provides a community living circle planning evaluation system, which adopts the following technical solution: A community living circle planning evaluation system includes: The multi-source data fusion module performs real-time analysis on community population distribution data, population profile data, POI data, satellite remote sensing data, and social media text data from multi-source spatiotemporal big data platforms at the data acquisition end. It extracts the core attributes of community service facilities and generates a unified semantic representation. The core attributes include facility type, facility scale, and facility density. The semantic representation establishes semantic mapping relationships between facility attributes across platforms based on a context-aware facility type mapping mechanism and spatial association rules. The standardized data is then transmitted to the central evaluation database through a federated learning distributed synchronization mechanism. The dynamic conflict resolution module dynamically generates conflict resolution strategies when a conflict occurs, based on facility attribute information and resident behavior profile characteristics in the standardized data. The conflict resolution strategies are executed by the local preprocessing module deployed on the edge node. The local preprocessing module responds quickly to sudden resident activity data and performs batch processing on periodic community data. The intelligent indicator configuration module, after conflict resolution, obtains the data content after conflict resolution. In the computing architecture composed of edge nodes and central server collaboration, it selects target edge nodes based on the data content and community population age structure information, and dynamically configures the cached evaluation indicator set of the target edge nodes. The closed-loop optimization evaluation module obtains the evaluation requirement description input by community planners, parses the corresponding planning intent information based on the evaluation requirement description, and generates corresponding comprehensive evaluation features by combining the planning intent information with the multi-dimensional evaluation indicators in the configured cache. Based on the comprehensive evaluation features, the evaluation level of the community living circle is determined through a dynamic weighted aggregation algorithm, and corresponding planning optimization suggestions are output based on the evaluation level. The module also obtains feedback information from community planners on the planning optimization suggestions and updates the evaluation indicator weight system and planning strategy based on the feedback information.

[0014] Thirdly, this application provides a community living circle planning evaluation system, which adopts the following technical solution: A community living circle planning evaluation system includes a processor, wherein the processor runs a program of any one of the community living circle planning evaluation methods described above.

[0015] Fourthly, this application provides a storage medium, which adopts the following technical solution: A storage medium storing a program for the community living circle planning evaluation method described in any one of the above.

[0016] In summary, this application includes at least one of the following beneficial technical effects: Through refined processing and dynamic conflict adjustment of multi-source spatiotemporal data, precise adaptation to changes in resident needs and facility supply and demand has been achieved. On the one hand, heterogeneous population, POI, and remote sensing data are transformed into a unified semantic representation through context-aware semantic mapping mechanisms and spatial association rules. Combined with federated learning and distributed synchronization, data timeliness is ensured, laying a data foundation for capturing dynamic changes in supply and demand. On the other hand, conflict resolution strategies are dynamically generated based on resident behavior profiles and community resilience indices (integrating ontological vulnerability indicators such as the proportion of elderly and children and functional robustness indicators such as emergency facility coverage). The local preprocessing modules of edge nodes can quickly respond to sudden resident activity data and batch process periodic data, ensuring that facility coverage conflicts are resolved in a timely manner when supply and demand changes, achieving dynamic optimization of supply and demand matching.

[0017] Meanwhile, through dynamic configuration of indicators and iterative updates to the evaluation system, the adaptability to changes in demand and supply-demand relationships has been further strengthened. Based on the age structure of the community population, group types are categorized, and corresponding subsets of evaluation indicators are dynamically extracted and their weights adjusted in conjunction with real-time population flow, ensuring that the evaluation indicators accurately match the needs of different groups. Dynamic weight adjustments to cultural vitality and social connectivity indices within the multi-dimensional evaluation indicators, along with the dynamic weighted aggregation algorithm optimizing evaluation results through gradient descent and population structure change factors, ensure that the evaluation reflects supply-demand dynamics in real time. Furthermore, the PPO reinforcement learning algorithm transforms planner feedback into reward signals, iteratively updating indicator weights and planning strategies, forming a closed loop of "data perception – dynamic evaluation – feedback optimization," continuously adapting to the dynamic evolution of residents' needs and the supply-demand relationship of facilities. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a community living circle planning evaluation method according to an exemplary embodiment.

[0019] Figure 2 This is a structural block diagram of a community living circle planning evaluation system according to an exemplary embodiment. Detailed Implementation

[0020] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0021] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0022] This application discloses a method for evaluating community living circle planning, referring to... Figure 1 ,include: S100 performs real-time analysis on community population distribution data, population profile data, POI data, satellite remote sensing data, and social media text data from multi-source spatiotemporal big data platforms at the data acquisition end. It extracts the core attributes of community service facilities and generates a unified semantic representation. The core attributes include facility type, facility scale, and facility density. The semantic representation establishes a semantic mapping relationship between facility attributes across platforms based on a context-aware facility type mapping mechanism and spatial association rules. The standardized data is then transmitted to the central evaluation database through a federated learning distributed synchronization mechanism.

[0023] The S100 execution process is divided into three stages: The first stage involves real-time analysis of multi-source data and extraction of core attributes: The data acquisition platform first connects to a multi-source spatiotemporal big data platform to simultaneously acquire five key data sources: community population distribution data (reflecting the area's population size and spatial density), population profile data (including resident characteristics such as age, occupation, and lifestyle habits), POI data (covering the specific locations and basic information of various service facilities within the community, such as commercial, medical, and educational facilities), satellite remote sensing data (characterizing the community's spatial morphology and land use), and social media text data (containing unstructured information such as residents' feedback on facility usage and their needs). The acquisition platform uses real-time parsing algorithms to initially process this heterogeneous data, removing redundant and abnormal data. It then focuses on extracting the core attributes of community service facilities, explicitly retaining three core information categories: facility type (such as kindergartens, supermarkets, and community hospitals), facility scale (quantitative indicators such as building area and service capacity), and facility density (the distribution of similar / various types of facilities within a unit area). This lays the foundation for subsequent data standardization.

[0024] The second stage involves generating a unified semantic representation. Because different data platforms use different naming rules and attribute definitions for the same type of facility (e.g., "community health service station" on platform A and "community medical point" on platform B are essentially the same type of facility, but their descriptions and attribute fields differ), a semantic mapping mechanism is needed to unify cross-platform data. Specifically, on the one hand, a context-aware facility type mapping mechanism, combined with the platform characteristics of the data source and the data context (such as the region to which the facility belongs and the service recipients), eliminates naming ambiguities for facility types across different platforms; on the other hand, based on spatial association rules, combined with the geographic spatial relationships of facilities (such as the spatial distribution patterns of similar facilities and the functional relationships between different facilities), a cross-platform correspondence of facility attributes is established. Through the synergistic effect of these two mechanisms, facility attribute data from different platforms and in different formats are transformed into a unified semantic standard representation, solving the core problem of "semantic heterogeneity" in multi-source data.

[0025] The third stage involves the standardized distributed synchronous transmission of data. After completing the semantic unification and format standardization of the data, the processed data is transmitted to the central evaluation database through a federated learning distributed synchronization mechanism. This transmission mechanism adopts a distributed architecture, eliminating the need to centralize all raw data to a single node. Instead, after each data source node completes its local standardization processing, a synchronization protocol enables cross-node collaborative updates of the standardized data, which is then aggregated into the central evaluation database. This ensures both the efficiency of data transmission and the integrity and consistency of the data in the central database.

[0026] Through a full-process approach of "real-time parsing, semantic unification, and distributed synchronization," the key issues of heterogeneous formats, semantic inconsistencies, and low transmission efficiency of multi-source spatiotemporal data were successfully resolved, providing high-quality and standardized foundational data support for the entire planning and evaluation methodology. The generated unified semantic data not only eliminates data barriers in subsequent conflict resolution, indicator configuration, and evaluation calculation, but also ensures data timeliness and integrity through real-time parsing and federated learning distributed synchronization mechanisms. This ensures that the central evaluation database can dynamically receive the latest data reflecting community facilities and residents' needs, providing a reliable data prerequisite for subsequent data-driven solutions to facility coverage conflicts, dynamic indicator configuration, and accurate evaluation. This is the core foundation for realizing the entire methodology's goal of "dynamically adapting to changes in residents' needs and facility supply and demand."

[0027] S200, based on facility attribute information and resident behavior profile features in standardized data, dynamically generates conflict resolution strategies when facility coverage conflicts occur. The conflict resolution strategies are executed by the local preprocessing module deployed on the edge node. The local preprocessing module responds quickly to sudden resident activity data and performs batch processing on periodic community data.

[0028] The S200 execution process is divided into three key stages, and the details of each stage are as follows: The first phase involves dynamic identification of facility coverage conflicts (refined matching analysis and conflict determination): Using the standardized data output from S100 as the core input, the data is first further refined: quantitative indicators are extracted from facility attribute information, including facility service radius (a reasonable coverage range derived from scale), actual service capacity (the upper limit of reception / carrying capacity corresponding to facility type), and current usage load (real-time occupancy rate calculated by combining historical usage data); subdivided dimensions are extracted from resident behavior profile characteristics, covering residents' time-based activity trajectories (such as the path distribution of medical visits during the morning peak and shopping during the evening peak), facility usage frequency and preferences (such as the medical facility usage records of the elderly more than 3 times a week), and demand gap feedback (such as demands such as "a certain area lacks kindergartens" extracted from social media text data).

[0029] Based on the aforementioned detailed data, a dual-dimensional matching analysis model of "facility supply - resident demand" is constructed. The model identifies conflicts through two core algorithms: first, a spatial overlay analysis algorithm, which overlays the facility service radius with a resident distribution heatmap to calculate the percentage of overlapping coverage area (over 30% is considered a coverage overlap conflict) and the area of ​​uncovered demand areas (over 15% is considered a coverage insufficiency conflict); second, a supply-demand gap quantification algorithm, which calculates the supply-demand matching coefficient by dividing the actual service capacity of a certain type of facility by the number of corresponding demand groups. When the coefficient is below 0.8 and the demand frequency of this group is higher than the industry average, it is considered a coverage mismatch conflict (e.g., a medical facility matching coefficient of 0.6 in a densely populated elderly area, with the elderly group's medical needs occurring more than 2.5 times per week). Through the synergy of these two algorithms, the model accurately locates the conflict type, conflict area, and core affected groups, generating a conflict identification report.

[0030] The second stage involves the dynamic generation of conflict resolution strategies (differentiated strategy derivation and parameter quantification): For specific conflict scenarios identified in the report, based on the principles of "supply and demand balance, precise adaptation, and efficient resource utilization," a dynamic and actionable strategy is generated by combining detailed data, and the strategy includes clear quantitative parameters. To address coverage overlap conflicts: First, redundant facilities with a load below 50% are identified by sorting facility usage load data, and a "functional optimization strategy" is generated. For example, a supermarket with a load of 40% in the overlapping area is adjusted to a community fresh food convenience store, and the service radius of the adjusted facility is reduced to 60% of the original radius to meet the daily fresh food needs of three surrounding communities. If the load of facilities in the overlapping area is higher than 70%, a "layout fine-tuning strategy" is generated. Based on the principle of the shortest path of residents' activity trajectories, the service range of one of the facilities is shifted to the adjacent demand gap area, and the shift distance (such as shifting 500 meters to the east) and the coordinates of the adjusted coverage boundary are quantified.

[0031] To address the conflict of insufficient coverage: Based on the hot spots of residents' high-frequency activity trajectories (such as around subway entrances and within 500 meters of the entrances and exits of large residential communities), a "facility supplementation strategy" is generated, which specifies the type of supplementary facilities (such as adding shared bicycle parking spots around subway entrances), the scale (such as setting up 20 parking spaces), and the specific location coordinates; if it is not possible to supplement due to land resource constraints, a "service expansion strategy" is generated, which quantifies the expansion parameters of existing facilities (such as adding 2 clinics in a community hospital, increasing service capacity by 40%) and the adjustment of service hours after expansion (such as extending evening clinics to 8 pm).

[0032] To address coverage mismatch conflicts: Based on the group demand weights in the resident profile (e.g., the weight of medical needs for the elderly is 0.7, and the weight of cultural needs is 0.3), generate a "facility type replacement strategy"—for example, replacing a gym with only 20% usage in a densely populated elderly area with a community nursing station, specifying the number of beds (10) and service items (basic care, chronic disease management) of the nursing station; or generate a "functional upgrade strategy," such as upgrading an ordinary convenience store into a convenient service point with a counter for collecting medicines and elderly supplies, specifying the proportion of functional modules after the upgrade.

[0033] The third phase involves the efficient execution of conflict resolution strategies at edge nodes (implementation of classification and processing mechanisms and procedures): The generated conflict resolution strategy is executed by a local preprocessing module deployed at the community edge node. This module adopts a "hardware + software" collaborative architecture: the hardware layer is equipped with a local server, edge computing nodes, and data caching units to ensure computing power support for local data processing; the software layer includes a data receiving unit, a classification and scheduling unit, a strategy execution unit, and a feedback unit to achieve fully automated processing. The specific execution process is divided into two categories: Data processing flow for sudden resident activities: When a sudden scenario such as a large-scale temporary event or a public emergency occurs in the community, the data receiving unit receives data in real time, including the number of participants, the activity area, and the type of sudden demand. The classification and scheduling unit marks it as "high priority" and triggers a rapid response mechanism. The strategy execution unit adjusts the facility configuration in real time based on the quantitative parameters of the conflict resolution strategy. At the same time, the feedback unit sends the execution results (such as the usage load after the facility adjustment and resident feedback) back to the central server in real time to ensure that the strategy adapts to the sudden demand.

[0034] Periodic community data processing workflow: For periodic data such as residents' commuting peak hours, holiday shopping, and monthly fluctuations in medical demand, the data receiving unit summarizes conflicting data on a daily / weekly / monthly basis (such as the shortage of shared bicycles at subway entrances during Monday morning rush hour, and the excessive load on medical facilities caused by the peak medical visits of the elderly from the 5th to the 10th of each month). The classification and scheduling unit marks it as "regular priority" and initiates a batch processing mechanism. The strategy execution unit performs cluster analysis on the conflicting data within the period (such as grouping the morning rush hour shopping demand of multiple communities into one category), uniformly optimizes strategy parameters (such as adjusting the replenishment frequency of supermarkets during morning rush hour, increasing the number of temporary cash registers), and synchronizes the optimized strategy to the central database, forming a periodic strategy update mechanism to ensure long-term supply and demand balance.

[0035] Through a closed-loop process of "refined conflict identification – quantitative strategy generation – efficient execution," this approach deeply addresses the core pain points of traditional planning, such as ambiguous conflict identification, insufficient strategy targeting, and delayed execution response. It refines quantitative indicators of facility supply and resident demand, combining dual algorithms to achieve precise conflict localization, providing a clear basis for strategy generation. Quantitative parameters in differentiated strategies ensure operability and avoid "one-size-fits-all" planning problems. The classification processing mechanism of the local preprocessing module at edge nodes not only meets the dynamic adaptation to sudden demands through a rapid response mechanism but also optimizes the cyclical supply and demand relationship through a batch processing mechanism, significantly improving the efficiency and accuracy of conflict resolution. This step not only eliminates the interference of facility coverage conflicts on subsequent evaluations but also achieves real-time matching of facility supply and demand with resident needs through dynamic strategy adjustments. It provides a high-quality data environment for the S300 evaluation indicator configuration, characterized by "supply and demand adaptation and dynamic updates," serving as a crucial bridge between data standardization and evaluation precision in the entire methodology, and is the core execution vehicle for achieving "dynamic adaptation to changes in resident needs and facility supply and demand."

[0036] S300, after conflict resolution, acquires the data content after conflict resolution. In the computing architecture composed of edge nodes and central server, the target edge node is selected based on the data content and the age structure information of the community population, and the cache evaluation index set of the target edge node is dynamically configured.

[0037] S30, relying on an edge-center collaborative architecture, achieves precise transformation from conflict-resolved data to evaluation metric caching. The specific execution process is divided into three key stages: The first phase involves post-conflict data acquisition and refined preprocessing: First, the full dataset after the S200 conflict resolution is received. This dataset contains three core types of information: 1) Facility status update data, which records changes in the attributes of community service facilities after the conflict resolution strategy is implemented, including adjusted facility types, service radii, capacity limits, spatial coordinates, and functional adaptation directions; 2) Supply-demand matching verification data, covering quantitative results such as the compatibility coefficients of various facilities with corresponding demand groups, the elimination of blind spots in regional facility coverage, and resident satisfaction with demand response; 3) Basic characteristic data, focusing on extracting community population age structure information standardized by S100 and verified by S200, clearly classifying groups such as the elderly, youth, and minors, and simultaneously acquiring detailed data such as the population proportion, spatial distribution heat map, and core demand tags (e.g., the elderly focusing on medical care and elderly care, and the youth focusing on employment and commuting). Subsequently, the above data is preprocessed: outliers generated during strategy execution (such as facility capacity data that exceeds a reasonable range) are removed by data cleaning algorithms, and local missing information is filled in by data completion mechanisms. Finally, a preprocessed dataset with a well-structured and accurate dataset is formed, laying the foundation for subsequent node selection and indicator configuration.

[0038] The second stage involves collaborative screening of target edge nodes: Based on a distributed collaborative architecture consisting of edge nodes and a central server, the division of labor between the two is clearly defined: the central server is responsible for overall data coordination and edge node status monitoring (including real-time computing power, remaining cache capacity, current task load, and geographical coverage of each node), while the edge nodes are responsible for local data computation and metric caching. The selection process focuses on "data adaptability + resource availability." First, the central server performs feature analysis on the preprocessed dataset, extracting core dimensions of data association (such as the type of facilities involved, the corresponding demand groups, and the real-time requirements of the data). Combined with the spatial distribution characteristics of the community's population age structure (such as areas with concentrated elderly populations and areas with dense minor populations), candidate edge nodes with matching geographical coverage and suitable data processing types are selected from the node attribute library. Second, the central server collects resource status data of candidate nodes in real time, eliminating nodes with a computing power occupancy rate exceeding 80%, a remaining cache capacity of less than 300MB, or fault warnings. Finally, a weighted scoring mechanism (data suitability weight 0.5, node computing power weight 0.2, geographical proximity weight 0.2, historical processing efficiency weight 0.1) is used to calculate the comprehensive score of candidate nodes, and the node with the highest score is selected as the target edge node to ensure the efficiency of subsequent indicator configuration and calculation.

[0039] The third stage involves the dynamic configuration of the cache evaluation metric set: Using the target edge node as a carrier, and combining the preprocessed dataset with population age structure characteristics, the evaluation indicators are selected, weighted, and cached: First, the basic indicator system for community living circle planning evaluation is retrieved from the central server's multi-dimensional evaluation indicator library, covering core dimensions such as facility supply, balanced distribution, resident suitability, and service efficiency; Second, based on the population age structure and demand characteristics, sub-group indicator subsets are selected; for the elderly, indicators include accessibility to medical facilities, density of elderly care services, and coverage of barrier-free facilities; for the youth, indicators include the types of cultural and entertainment facilities, commuting convenience, and scale of sports and fitness facilities; and for minors, indicators include school facilities, etc. The evaluation process includes indicators such as the density of preschool education facilities, the safety of children's activity areas, and the completeness of after-school services. Subsequently, the weights of these indicators are dynamically adjusted based on the supply-demand matching results after conflict resolution. If the suitability of facilities for a particular group's core needs still requires close attention (e.g., the matching coefficient of elderly care facilities for the elderly group is not optimal), the weight of the corresponding indicator is increased (e.g., the weight of elderly care service density is increased from 0.18 to 0.25). Finally, the selected subset of indicators for each group and the adjusted weight parameters are encapsulated according to the caching format requirements of the target edge node, stored in the local cache unit of the target node through an edge-center collaborative synchronization mechanism, and a classification index is established to ensure rapid retrieval during subsequent evaluation processes.

[0040] By seamlessly integrating the entire process of "data preprocessing – node selection – indicator configuration," a highly efficient bridge has been successfully built between conflict resolution results and evaluation calculations. This not only ensures the reliability of data sources for indicator configuration through refined data preprocessing, avoiding interference from residual conflict data on evaluation accuracy; but also achieves precise selection of target edge nodes based on an edge-center collaborative architecture. This reduces the load on the central server and improves indicator retrieval efficiency through geographical proximity and data adaptability. Furthermore, the dynamic configuration of indicators based on population age structure solves the problem of the traditional "one-size-fits-all" approach to evaluation indicators, achieving precise adaptation of evaluation indicators to the needs of different groups.

[0041] S400: Obtain the evaluation requirement description input by the community planner, parse the corresponding planning intent information based on the evaluation requirement description, combine the planning intent information with the multi-dimensional evaluation indicators in the configured cache to generate the corresponding comprehensive evaluation features, determine the community living circle evaluation level based on the comprehensive evaluation features through a dynamic weighted aggregation algorithm, and output the corresponding planning optimization suggestions based on the evaluation level; obtain the feedback information of the community planner on the planning optimization suggestions, and update the evaluation indicator weight system and planning strategy based on the feedback information.

[0042] As the core evaluation and iterative closed-loop link of the community living circle planning evaluation method, S400's core is to accurately respond to the planners' needs, generate targeted evaluation results and optimization suggestions, and achieve dynamic updates of the system through feedback. The specific implementation process is divided into three key stages: The first stage involves evaluating the acquisition of needs and accurately analyzing planning intentions. First, the evaluation needs descriptions input by community planners are obtained through various methods such as visual interactive interfaces and API interfaces. These descriptions can include natural language text (e.g., "Evaluate the suitability of XX community for an age-friendly living environment"), structured filtering conditions (e.g., specifying evaluation dimensions as "equilibrium of facility distribution + service accessibility"), and goal-oriented requests (e.g., "Optimize the configuration of cultural facilities for youth groups"). Then, Natural Language Processing (NLP) technology is used to perform layered analysis of the needs descriptions: the first step involves extracting core elements using keyword extraction algorithms (e.g., TF-IDF), including the target evaluation object (specific community / area), core concern groups (elderly / youth / minors), and key evaluation dimensions (facility supply / equilibrium of distribution / community vitality, etc.). The second step involves using an intent classification model (combined with BERT semantic understanding) to determine the type of planning intent, which is divided into three categories: "current situation diagnosis" (such as assessing the current suitability of facilities), "optimization-oriented" (such as identifying weak links that need to be improved), and "specialized focus" (such as a specialized evaluation of a certain type of facility or group). Finally, the analysis results are integrated into structured planning intent information, clarifying the core evaluation objectives, priority ranking, and key areas of focus, providing guidance for the subsequent generation of evaluation features.

[0043] The second stage involves generating comprehensive evaluation features, determining the level, and outputting planning optimization suggestions: First, based on the parsed planning intent information, a subset of matching multi-dimensional evaluation indicators is selected from the target edge node cache configured in S300. For example, if the planning intent is "elderly-friendly adaptability evaluation", core indicators such as accessibility of medical facilities, density of elderly care services, and coverage of barrier-free facilities corresponding to the elderly group are retrieved first, while auxiliary indicators such as the balance of facility distribution and service duration adaptability are associated.

[0044] Subsequently, feature fusion was performed on the selected indicators: the quantitative data of each indicator (such as the accessibility score of medical facilities of 85 points and the density score of elderly care services of 78 points) were normalized according to the planning intention weight allocation rules (such as the weight of core indicators of 0.7 and the weight of auxiliary indicators of 0.3) to generate a comprehensive evaluation feature vector with unified dimensions and appropriate weights.

[0045] Next, the dynamic weighted aggregation algorithm is activated: based on the indicator weight system configured in S300, the weights are dynamically adjusted in combination with the planning intent (e.g., when specifically evaluating the elderly population, the weight of elderly care indicators is increased by 20%), and the comprehensive evaluation score is calculated by weighted summation, and the evaluation level is divided according to the preset threshold (e.g., 90 points and above is "excellent", 80-89 points is "good", 60-79 points is "average", and below 60 points is "needs optimization").

[0046] Finally, based on the shortcomings in the evaluation level and comprehensive evaluation characteristics (such as the "to be optimized" level corresponding to a medical facility accessibility score of 55), targeted planning optimization suggestions are generated. The suggestions should specify the optimization direction (such as "supplementing medical facilities for the elderly"), specific plans (such as "adding one community nursing station in the northwest area of ​​the community, with a service radius of 500 meters and 15 nursing beds"), implementation priority (such as "high priority, to be implemented within 3 months"), and expected results (such as "the medical facility accessibility score for the elderly is expected to increase to above 80 points").

[0047] The third stage involves the dynamic iteration and updating of the feedback information acquisition and evaluation system. First, feedback information from community planners on planning optimization suggestions is obtained through the feedback collection module. Feedback forms include satisfaction ratings (such as a 1-5 point rating), written improvement suggestions (such as "suggest adding chronic disease management functions to nursing stations"), and demand adjustment requests (such as "supplementing the convenience of travel for the elderly").

[0048] Subsequently, the feedback information was quantified and structured: the satisfaction score was converted into a quantitative reward value in the range of 0-1, and key adjustment elements (such as new indicator requirements, suggestions for adjusting the weight of existing indicators, and practical optimization points of the solution) were extracted from the text feedback.

[0049] Based on the processed feedback, a two-dimensional update mechanism is initiated: First, the evaluation indicator weighting system is updated; using gradient descent combined with a reinforcement learning reward mechanism, the original indicator weights are fine-tuned (e.g., if planners emphasize chronic disease management functions, the weight of the "functional completeness of elderly care service facilities" indicator is increased from 0.15 to 0.22). If feedback proposes new indicator requirements, these indicators are included in the multi-dimensional evaluation indicator library and assigned initial weights. Second, the planning strategy is updated; combining the optimization points in the feedback, conflict resolution strategies and facility configuration parameters are adjusted (e.g., expanding the functional modules of nursing stations from "basic nursing" to "basic nursing + chronic disease management"), and the planning strategy templates in the central database are updated simultaneously to ensure that subsequent similar evaluation needs can output suggestions that are more practical. Ultimately, a complete closed loop of "demand-evaluation-feedback-optimization" is formed, achieving continuous iteration of the evaluation system and planning strategy.

[0050] Through a closed-loop process of "demand analysis - precise evaluation - feedback iteration," a bridge is built to accurately connect planners' needs with the actual situation of the community. Its core role is reflected in three aspects: First, by using NLP technology to analyze the planning intent and combining it with the dynamically configured indicator set of S300 to generate targeted evaluation features, the problem of "fuzzy demand response and low indicator matching" in traditional evaluation is solved, ensuring that the evaluation results are in line with the core requirements of the plan. Second, based on the dynamic weighted aggregation algorithm and the analysis of the shortcomings indicators, feasible planning optimization suggestions are output, which transforms the abstract evaluation results into specific practical solutions and provides direct support for planning decisions. Third, by iteratively updating the evaluation indicator weight system and planning strategies through feedback information, a closed-loop mechanism for continuous optimization is formed, enabling the entire evaluation method to dynamically adapt to changes in planners' needs, the evolution of residents' needs, and adjustments in the supply and demand relationship of facilities. Ultimately, this ensures the scientific nature, relevance, and long-term adaptability of the community living circle planning evaluation, and completes the full-link implementation of "data support - evaluation decision-making - practical optimization".

[0051] Taking an "old community" as a case study, this community has an elderly population of 35% and a youth population of 45%. It suffers from insufficient medical facilities, low suitability of cultural facilities, and fluctuating demand due to occasional large-scale community events and seasonal peaks in medical visits. The solution first connects to a multi-source data platform via the S100 interface to analyze in real time community population distribution data (the elderly population is concentrated in the northwest area), POI data (only one community health service station), and social media text data (frequently mentioning requests for "difficulty in accessing medical care" and "lack of activity venues for youth"). Through a context-aware semantic mapping mechanism, heterogeneous data such as "community health service station" and "community medical point" from different platforms are unified semantically. Then, through federated learning, the data is distributed and synchronized to the central database, accurately capturing the core supply and demand characteristics of "concentrated medical needs of the elderly + gaps in cultural needs of youth," laying the data foundation for dynamic adaptation. When there are sudden resident activities or monthly peaks in medical treatment for elderly people with chronic diseases in the community, the S200 process identifies two types of conflicts based on standardized data and through spatial overlay analysis algorithms: "insufficient medical facility coverage" and "service capacity gap". Combined with the community resilience index (the high proportion of elderly and children makes the community vulnerable), it dynamically generates a long-term strategy of "adding one community nursing station (service radius of 500 meters) in the northwest area" and an emergency response strategy of "temporarily deploying two mobile testing points". These strategies are quickly executed by the local preprocessing module of the edge node to achieve dynamic resolution of supply and demand conflicts.

[0052] After the addition of nursing stations in the community, the demand from young people for shared office space and sports facilities in the surrounding area has become increasingly prominent. The S300 stage takes over the updated data (nursing station operation data and youth demand feedback data) after the conflict resolution in S200. Combined with population age structure information (young people account for 45%), target edge nodes with sufficient computing power and coverage of concentrated youth areas are selected through an edge-center collaborative architecture. Subsequently, the set of evaluation indicators is dynamically configured and cached: core indicators such as "accessibility to medical facilities and density of elderly care services" are retained for the elderly and their weights are increased (the weight of elderly care service density is increased from 0.18 to 0.25). Indicators such as "coverage rate of shared office facilities and scale of sports and fitness facilities" are added for the young people. At the same time, the indicator weights are adjusted in conjunction with real-time population flow data (concentration of youth commuting on weekdays), so as to achieve accurate adaptation of evaluation indicators to the dynamic needs of different groups and avoid the drawbacks of the "one-size-fits-all" approach of traditional indicators.

[0053] When community planners input their evaluation requirement of "optimizing the balance between elderly friendliness and youth suitability," the S400 stage uses NLP technology to analyze the planning intent, retrieves sub-group indicator subsets from the target edge node cache, generates comprehensive evaluation features, and uses a dynamic weighted aggregation algorithm to derive a "good" rating. It identifies the weakest indicator as "youth cultural facility coverage" and outputs an optimization suggestion: "Add one shared office + sports complex in the southeast area of ​​the community." After planners provide feedback suggesting "increasing the facility's nighttime opening hours," the solution incorporates "facility service time suitability" into the evaluation indicator system through a feedback iteration mechanism, assigning an initial weight of 0.12. Simultaneously, it updates the facility configuration parameters in the conflict resolution strategy (complex facilities open until 10 PM). Subsequently, as the proportion of the community's youth population increases annually to 52%, the solution continuously optimizes facility configuration and the evaluation system through S100 real-time data updates, S200 conflict re-identification, S300 indicator weight adjustments, and S400 strategy iterations, forming a closed-loop process to achieve long-term dynamic adaptation to changes in residents' needs and facility supply and demand.

[0054] In this embodiment of the application, the specific execution process of the context-aware facility type mapping mechanism is divided into three key stages: The first stage involves generating semantic vectors for cross-platform facility names: To address the naming differences of facilities across different data platforms (e.g., "Community Health Service Station" on Platform A, "Community Medical Point" on Platform B, and "Regional Health Service Center" on Platform C), these heterogeneous facility names are used as input data and fed into a pre-trained BERT model for deep semantic parsing. The BERT model, through understanding the contextual understanding of facility names (e.g., the semantic association between "health services," "medical services," and "health care services"), uncovers the core functional connotations behind the names, ultimately outputting a unified semantic vector for the facility names, thus quantifying the semantic features of facilities with different expressions.

[0055] The second stage involves constructing a facility space map and learning spatial association rules. First, precise geographic coordinate data (latitude and longitude information) of each facility is extracted. Each facility is treated as an independent node in the spatial map. The actual spatial distance (e.g., straight-line distance, road network distance) between any two facility nodes is calculated, and this distance is used as the weight of the edge between nodes (the closer the distance, the higher the weight), thus constructing a complete facility spatial map. Then, the spatial map is input into a graph neural network (GNN). Through graph convolution operations, the spatial relationship patterns between facilities are learned (e.g., similar medical facilities are often concentrated in one area, commercial facilities are spatially adjacent to residential facilities, etc.). These relationship patterns are then transformed into a quantified spatial relationship matrix of facility types, achieving a structured expression of spatial features.

[0056] The third stage involves weighted fusion of semantic and spatial features to generate semantic mapping relationships: A weighted fusion mechanism is used to fuse the semantic vectors and spatial association matrices generated in the first two stages. During the fusion process, weights are assigned according to data characteristics (0.6 for semantic vectors to highlight the consistency of functional connotations; 0.4 for spatial association matrices to strengthen spatial distribution correlations). The two types of features are deeply fused through matrix operations to generate a semantic mapping relationship table for cross-platform facility types, clarifying the unified semantic labels corresponding to the names of heterogeneous facilities on different platforms (e.g., mapping "community health service station", "community medical point", and "area health service center" to "community basic medical facilities").

[0057] By combining semantic parsing of the BERT model with spatial association learning of graph neural networks, the problems of semantic ambiguity and inconsistent attributes of facility names across platforms were accurately solved. The generated semantic mapping relationship ensured the consistency of semantics at the multi-source data level, providing key technical support for the standardization of multi-source data in S100 and further guaranteeing the accuracy of subsequent data processing and evaluation.

[0058] In this embodiment of the application, the conflict resolution strategy dynamically determines the priority of conflict resolution by constructing a community resilience evaluation system. The specific execution process is divided into three key stages: The first phase involves the quantitative calculation of three core indicators: Using S100-standardized multi-source data as input, key evaluation indicators are calculated across dimensions: Community vulnerability index calculation: Based on population distribution data, basic data such as the number of elderly and children, the number of disabled persons, the number of self-owned households, the total population and total number of households in the community are extracted. The data is quantified by the formula "target group number / corresponding total base number", that is, the proportion of elderly and children = (number of people aged 60 and above + number of people aged 0-17) / total population of the community, the proportion of disabled persons = number of registered disabled persons / total population of the community, and the proportion of self-owned households = number of self-owned households / total number of households in the community, forming a quantitative indicator reflecting the vulnerability of the community's population structure. Community functional robustness index calculation: Based on emergency-related facility information in POI data, spatial analysis and density statistics are used to quantify the following: emergency shelter coverage rate = actual area covered by emergency shelters / total area of ​​the community; medical aid station density = number of medical aid stations in the community / area under the jurisdiction of the community (unit: stations / square kilometer); fire-fighting facility accessibility = area within a 3-minute walk of fire-fighting facilities / total area of ​​the community, reflecting the community's emergency service supply capacity. Calculation of emergency management effectiveness indicators: Based on specific data provided by community management departments, the ratio of "actual state / standard state" is used for quantitative or graded scoring (0-100 points). Emergency team reserve = actual number of emergency team members / standard number of community personnel. Material reserve level = actual reserve of emergency items types and quantities / prescribed demand types and quantities. The planning level of the emergency plan system is graded and scored according to dimensions such as completeness and operability, reflecting the basic emergency management capabilities of the community.

[0059] The second stage involves a weighted calculation of the community resilience index: The Analytic Hierarchy Process (AHP) was used to determine the weights of the three types of indicators: First, a hierarchical structure was constructed (the target layer is the community resilience index, the criteria layer is the three core indicators, and the indicator layer is the sub-indicators). Experts in community planning and emergency management were invited to conduct pairwise comparisons of the criteria layer and the indicator layer to form a judgment matrix. After verifying the rationality of the judgment matrix through consistency checks, the weights of each indicator were calculated (for example, in the criteria layer, the weight of community vulnerability is 0.4, the weight of community functional robustness is 0.3, and the weight of emergency management effectiveness is 0.3). Finally, the community resilience index was obtained by weighted summation according to the formula "Community Resilience Index = Vulnerability Indicator × Corresponding Weight + Functional Robustness Indicator × Corresponding Weight + Emergency Management Effectiveness Indicator × Corresponding Weight", and a community resilience threshold was preset (e.g., 80 points, which can be dynamically adjusted according to city level and community type).

[0060] The third stage involves determining the priority of conflict resolution and dynamically adjusting its weights. The calculated community resilience index is compared with a preset threshold. If the resilience index is greater than the preset threshold (indicating that the community is generally resilient and has a certain foundation for emergency preparedness), then conflicts related to emergency facilities (such as overlapping coverage of emergency shelters and insufficient accessibility of fire-fighting facilities) are listed as priority categories for resolution. At the same time, the priority weight of this type of conflict is dynamically adjusted based on the proportion of the elderly and children calculated in the first phase. For example, if the proportion of the elderly and children exceeds 40% (highly vulnerable groups are concentrated), the priority weight for resolving conflicts related to emergency facilities is increased from the base value of 0.5 to 0.7. If the proportion of the elderly and children is less than 20%, the priority weight is reduced to 0.3, ensuring that conflict resolution strategies are tilted towards high-risk groups and key emergency needs.

[0061] By quantifying and weighting the vulnerability, functional robustness, and emergency management effectiveness of the community, a resilience index is obtained, enabling dynamic adaptation of conflict resolution priorities. This ensures that the core needs related to emergency facilities are met first, while adjusting the weights based on the proportion of the elderly and children reflects attention to highly vulnerable groups, making conflict resolution strategies more aligned with the actual risk situation of the community.

[0062] In this embodiment of the application, the specific execution process of dynamically configuring the cache evaluation index set of target edge nodes is divided into three key stages: The first phase involves segmenting the community population and identifying the current user group: First, based on the S100 standardized population profile data, clear group division rules are formulated: the elderly group is defined as people aged 60 and above, the youth group is defined as people aged 18-59, and the family group is defined as a family unit containing minor children aged 0-17 or couples living together (determined through family household registration information and residence registration information), thus completing the three-category division of the community population structure.

[0063] Subsequently, the user's current group is accurately identified in two ways: First, the user's real-time location is matched with the spatial distribution heat map of various groups in the community based on the user's device location information (e.g., if the user's location is in an area with a high concentration of elderly people, it is initially determined to be an elderly group); Second, a user input entry is provided, allowing users to directly select their group (e.g., "elderly", "youth", "family with children"). The two methods are cross-validated to ensure the accuracy of the current user group identification.

[0064] The second stage involves extracting a subset of evaluation indicators for the corresponding group: Based on the identified user group, a subset of targeted metrics is extracted from the multi-dimensional evaluation metric library synchronized from the central server to the edge node cache: The corresponding subset of indicators for the elderly population focuses on core needs indicators such as accessibility of medical facilities, density of elderly care service facilities, coverage of barrier-free facilities, and suitability of facility service duration (such as elderly-friendly hours in community hospitals); The corresponding subset of indicators for the youth group focuses on matching indicators such as the types of cultural and entertainment facilities, the convenience of employment and commuting, the scale of sports and fitness facilities, and the quality of network service coverage. The family group corresponds to a subset of indicators, which mainly include indicators such as the density of preschool education facilities, the service radius coverage of primary and secondary schools, the safety of children's activity venues, the completeness of after-school service facilities, and the convenience of family shopping, to ensure that the subset of indicators is directly linked to the core needs of the group.

[0065] The third stage involves dynamically adjusting the indicator weights based on real-time population flow data. First, real-time access to community population flow data (such as people returning home during holidays, commuters entering on weekdays, and temporary visitor data) is used to calculate the real-time percentage changes of each group through data analysis (such as a 20% increase in the inflow of young people on weekdays and a 15% increase in the proportion of families temporarily going out). Subsequently, the weights of the corresponding indicator subsets are dynamically adjusted based on changes in the proportion: if real-time data shows a temporary increase in the proportion of the youth population in the current area, the weights of "types of cultural and entertainment facilities" and "commuting convenience" in the youth population indicator subset are increased from the basic values ​​of 0.2 and 0.18 to 0.25 and 0.22, respectively; if families return to school in a concentrated manner due to the start of the school season, the weights of "density of preschool education facilities" and "safety of children's activity venues" are increased by 15%. Through dynamic adjustment of weights, the cached evaluation indicator set is always highly matched with the actual user needs of the current area.

[0066] By accurately segmenting three population groups, identifying the current user group from multiple dimensions, and dynamically adjusting the indicator weights in conjunction with real-time population flow data, the indicator configuration logic of S300 has been further refined. This solves the problem that fixed indicator weights cannot adapt to the real-time changes in group needs, making the cached evaluation indicator set more in line with the actual needs of the current users.

[0067] In this embodiment of the application, the specific execution process of the multi-dimensional evaluation indicators is divided into three key stages: The first stage involves the clear definition of multi-dimensional evaluation indicators: Based on the core requirements of community living circle planning and evaluation, the system defines three core evaluation dimensions and their sub-indicators: The facility richness dimension focuses on the scale and diversity of community service facilities supply, including facility type diversity (statistics on the total number of facilities of different functional types such as commerce, medical care, education, and culture in the community), facility density (the average number of various types of facilities per unit area, unit: facilities / square kilometer), and facility size ratio (the proportion of facilities of different size levels, such as the proportion of large supermarkets and community convenience stores). The distribution balance dimension focuses on the rationality of facility spatial layout, including facility spatial coverage blind spot rate (the proportion of the area not covered by any facility service radius to the total area of ​​the community) and the matching degree between facility distribution and population distribution (the correlation coefficient between the number of facilities and population density in each area is calculated through spatial overlay analysis, and the closer the coefficient is to 1, the higher the matching degree). The community vitality dimension reflects the community's social interaction and cultural atmosphere, including the cultural vitality index (quantifying the activity level of community cultural activities and resident participation) and the social connectivity index (characterizing the flow of people and the strength of social connections within and outside the community).

[0068] The second stage involves the quantitative calculation of the core indices: For the two key indices of community vitality, a multi-source data fusion calculation method is adopted: The calculation of the cultural vitality index involves: first, collecting check-in data for community cultural activities on social media platforms (such as the number of participants and check-in frequency for exhibitions, lectures, and festivals), and then calculating the total number of cultural activities and total number of participants per unit time (e.g., monthly). Simultaneously, sentiment analysis is performed on text data related to community cultural activities on social media (using NLP technology to extract positive, neutral, and negative sentiment tendencies and calculate the proportion of positive sentiment). Finally, a weighted sum is calculated using the formula: "Cultural activity frequency weight 0.4 + Participation weight 0.3 + Positive sentiment proportion weight 0.3," to obtain a quantified cultural vitality index (range 0-100 points). Social Connectivity Index Calculation: Based on mobile signaling data analysis, the community residents' travel trajectories and cross-regional mobility frequency (such as the number of round trips for commuting, shopping, and social travel) are analyzed, and the average interaction frequency of residents within the community is statistically analyzed; combined with spatial syntax analysis, the connectivity and accessibility of the community road network are analyzed (parameters such as road network integration and through traffic are calculated); the social connectivity index (range 0-100 points) is obtained by merging "resident mobility frequency weight 0.5 + road network connectivity parameter weight 0.5".

[0069] The third stage involves the dynamic adjustment of the index weights: The frequency of community cultural activities is used as the core adjustment criterion. Real-time data on the frequency of community cultural activities is collected (including registration data of officially organized activities, resident-initiated activities, and social media check-in statistics). Weight adjustment rules are established as follows: If the frequency of community cultural activities within a unit of time increases by more than 20% compared to the historical average (e.g., the number of monthly activities increases from 10 to more than 12), the weight of the vitality index of the cultural vitality index will be increased from the base value of 0.5 to 0.6, while the weight of the connectivity index of the social connectivity index will be decreased from 0.5 to 0.4. If the frequency of cultural activities decreases by more than 20% compared to the historical average, the weights will be adjusted in the opposite direction (the weight of the cultural vitality index will be decreased to 0.4, and the weight of the social connectivity index will be increased to 0.6). If the frequency of activities fluctuates within ±20%, the base weights will remain unchanged, ensuring that the multi-dimensional evaluation index system can respond to the dynamic changes in community cultural vitality in real time.

[0070] By clarifying the specific composition of multi-dimensional evaluation indicators, quantifying key indices, and dynamically adjusting weights, the logic of the S300 indicator system has been refined and improved. This not only enriches the coverage of evaluation dimensions but also allows the evaluation indicator system to accurately capture real-time changes in community cultural vitality through data fusion calculation and dynamic weight adjustment.

[0071] In this embodiment of the application, the execution process of the dynamic weighted aggregation algorithm is divided into three key stages: The first stage is the initialization of the evaluation index weighting system: Using historical data from community living circle planning and evaluation as the core input, this data includes indicator scores from previous rounds of evaluation, corresponding feedback on actual community improvement effects, and population structure and facility supply and demand data from different periods. Based on this data, statistical analysis methods (such as a combination of analytic hierarchy process and entropy weighting) are used to determine the initial weights of each evaluation indicator: for example, a weight of 0.3 for facility richness, 0.25 for distribution balance, 0.25 for community vitality, and 0.2 for service suitability. This forms an initial weight system for the evaluation indicators, ensuring that the weight allocation conforms to historical evaluation patterns and the actual needs of community planning, providing a benchmark for subsequent weight adjustments.

[0072] The second stage involves calculating the difference value and optimizing the weight gradient. First, clarify the quantitative standard for "actual community improvement effect". By collecting facility usage data after the implementation of the plan (such as the increase in facility utilization rate), resident satisfaction survey data, and supply and demand matching coefficient change data, a quantitative value of actual improvement effect is generated. Then, calculate the predicted improvement effect corresponding to the current evaluation level based on the initial weight, and compare it with the above quantitative value of actual improvement effect to obtain the difference value between the two (e.g., if the predicted improvement effect score is 80 points and the actual improvement effect score is 72 points, the difference value is 8 points). Finally, the gradient descent method was used to iteratively optimize the difference value. With the goal of "minimizing the difference value", the weight of each evaluation indicator was adjusted (for example, since the predicted value of the medical facility indicator differed the most from the actual value, its weight was adjusted from 0.15 to 0.18). The process was iterated repeatedly until the difference value dropped to the preset threshold (e.g., ≤3 points) to ensure that the evaluation results closely matched the actual improvement effect in the community.

[0073] The third stage involves introducing population structure change factors to re-aggregate indicators: The frequency of community cultural activities (statistical values ​​of the number of activities and participants per unit time extracted from social media check-in data and community activity registration data) can be used as a factor for population structure change. Since the frequency of community cultural activities is highly correlated with the proportion of youth groups and the activity level of family groups (for example, the frequency of community cultural activities is usually higher in communities where youth groups are concentrated), it can indirectly reflect the dynamic changes in population structure. Subsequently, the dynamic adjustment weights of the vitality index and connectivity index in the previous steps are invoked, and combined with the optimized weights of each dimension, the multi-dimensional evaluation indicators such as facility richness, distribution balance, and community vitality are re-weighted and aggregated to calculate the final comprehensive evaluation feature vector, ensuring that the aggregation result can respond in real time to the impact of population structure changes on the quality of community life circle.

[0074] By initializing historical data, optimizing differential values ​​through gradients, and introducing population structure change factors, the logic of the dynamic weighted aggregation algorithm has been further improved. This solves the problems of fixed weights, disconnect between evaluation results and actual effects, and difficulty in adapting to population structure changes in traditional weighted algorithms, making the comprehensive evaluation features more accurate and more in line with the actual situation of the community.

[0075] In this embodiment of the application, the process of updating the evaluation index weight system and planning strategy based on feedback information is divided into three key stages: The first stage involves collecting feedback information and transforming reinforcement learning reward signals: First, feedback from community planners was comprehensively collected through multiple channels, including visual feedback interfaces, offline surveys and questionnaires, and API feedback. The feedback included: 1) a satisfaction rating (out of 1 to 5 points, with 5 being the highest), which directly reflects the relevance of the planning optimization suggestions; 2) written improvement suggestions, including feedback on indicator suitability (e.g., "the need to add supporting indicators for family group education facilities"), suggestions on the practicality of the plan (e.g., "the service radius of the nursing station should be reduced to 300 meters for better results"), and requests for supplementary needs (e.g., "consider the suitability of barrier-free facilities for the disabled in the community").

[0076] Subsequently, the feedback information is quantified and signal transformed: the satisfaction score is mapped to a basic reward value in the range of 0-1 (5 points corresponds to 1.0, 4 points to 0.8, 3 points to 0.5, 2 points to 0.2, and 1 point to 0.0); the text-based improvement suggestions are semantically parsed to extract "positive optimization suggestions" (such as adding reasonable indicators) and "negative correction suggestions" (such as unreasonable parameters in the existing scheme), and additional reward values ​​or penalty values ​​are assigned to them respectively. Finally, they are integrated to form a comprehensive reward signal in reinforcement learning, which is used to quantitatively evaluate the effectiveness of the current evaluation indicator weight system and planning strategy.

[0077] The second stage involves updating the evaluation metric weighting system based on the PPO reinforcement learning algorithm: Using the current evaluation index weight system as the initial strategy of the reinforcement learning agent, and with the optimization objective of "maximizing the comprehensive reward signal", the PPO (proximal policy optimization) reinforcement learning algorithm is launched: First, the algorithm calls historical evaluation data, feedback reward signals and actual planning effect data of the community to construct a training sample set; Secondly, the weight parameters are adjusted through multiple iterations of training. In each iteration, an evaluation result is generated based on the current weight system, and the strategy loss value is calculated by comparing it with the reward signal. The weight adjustment range is limited by the clip function (to avoid parameter mutations) to ensure the stability of the update process. Finally, when the loss value of iterative training drops to a preset threshold (e.g., ≤0.05) and the reward signal corresponding to the updated weight system reaches the historical best value, training is stopped, and the optimized evaluation index weight system is output (e.g., the weight of "family group education facility matching index" is set to 0.18, and the weight of "accessibility facility adaptability" index is set to 0.12), so that the weight allocation is more in line with the actual needs of planners and community planning practices.

[0078] The third stage involves dynamically adjusting the execution parameters of the planning strategy by combining feedback and updated weights. Guided by the updated evaluation indicator weighting system, and by simultaneously integrating written improvement suggestions from planners, the core execution parameters of the original planning strategy were refined: First, the facility configuration parameters were adjusted (e.g., based on the suggestion of "reducing the service radius of nursing stations", the service radius was adjusted from 500 meters to 300 meters, and the bed configuration was simultaneously optimized from 15 beds to 10 beds). Second, the parameters of the conflict resolution strategy are adjusted (for example, after adding the indicator of "accessibility of facilities for people with disabilities", the judgment rule of "prioritizing the addition of accessibility facilities in areas with missing facilities" is added to the conflict resolution strategy for coverage mismatch). Third, the implementation priority and timeliness parameters were adjusted (e.g., based on the planner's suggestion that "educational facilities should be implemented first," the implementation priority of the preschool education facility supplementation plan was raised from "medium" to "high," and the implementation timeliness was shortened from 6 months to 4 months). The adjusted planning strategy was simultaneously updated to the strategy template library in the central database.

[0079] By transforming feedback information into reinforcement learning reward signals and combining it with the PPO algorithm to scientifically update the evaluation index weight system, while dynamically adjusting the execution parameters of the planning strategy, this method solves the problems of traditional feedback updates relying on manual adjustment, weight optimization lacking algorithmic support, and strategy adjustment lacking specificity. It forms a closed loop of "feedback - algorithm optimization - strategy iteration," ensuring that the evaluation system and planning strategy continuously align with actual planning needs, and further enhancing the long-term adaptability and practical value of the entire method.

[0080] This application discloses a method for evaluating community living circle planning, referring to... Figure 1 ,include: The multi-source data fusion module 001 performs real-time analysis on community population distribution data, population profile data, POI data, satellite remote sensing data, and social media text data from a multi-source spatiotemporal big data platform at the data acquisition end. It extracts the core attributes of community service facilities and generates a unified semantic representation. The core attributes include facility type, facility scale, and facility density. The semantic representation establishes a semantic mapping relationship between facility attributes across platforms based on a context-aware facility type mapping mechanism and spatial association rules. The standardized data is then transmitted to the central evaluation database through a federated learning distributed synchronization mechanism. The dynamic conflict resolution module 002 dynamically generates conflict resolution strategies when a facility coverage conflict occurs, based on facility attribute information and resident behavior profile characteristics in the standardized data. The conflict resolution strategies are executed by the local preprocessing module deployed on the edge node. The local preprocessing module responds quickly to sudden resident activity data and performs batch processing on periodic community data. The intelligent indicator configuration module 003, after conflict resolution, obtains the data content after conflict resolution. In the computing architecture composed of edge nodes and central server collaboration, it selects target edge nodes based on data content and community population age structure information, and dynamically configures the cached evaluation indicator set of target edge nodes. The closed-loop optimization evaluation module 004 obtains the evaluation requirement description input by the community planner, parses the corresponding planning intent information based on the evaluation requirement description, combines the planning intent information with the multi-dimensional evaluation indicators in the configured cache to generate corresponding comprehensive evaluation features, determines the community living circle evaluation level based on the comprehensive evaluation features through a dynamic weighted aggregation algorithm, and outputs corresponding planning optimization suggestions based on the evaluation level; it also obtains feedback information from the community planner on the planning optimization suggestions, and updates the evaluation indicator weight system and planning strategy based on the feedback information.

[0081] This application also discloses a community living circle planning evaluation method, including a processor, wherein the processor runs a program of any one of the above-described community living circle planning evaluation methods.

[0082] This application also discloses a storage medium storing a program for the community living circle planning evaluation method described in any one of the above embodiments.

[0083] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating community living circle planning, characterized in that, include: At the data acquisition end, community population distribution data, population profile data, POI data, satellite remote sensing data, and social media text data from multi-source spatiotemporal big data platforms are analyzed in real time to extract the core attributes of community service facilities and generate a unified semantic representation. The core attributes include facility type, facility scale, and facility density. The semantic representation establishes a semantic mapping relationship between facility attributes across platforms based on a context-aware facility type mapping mechanism and spatial association rules. The standardized data is then transmitted to the central evaluation database through a federated learning distributed synchronization mechanism. Based on the facility attribute information and resident behavior profile features in the standardized data, a conflict resolution strategy is dynamically generated when a facility coverage conflict occurs. The conflict resolution strategy is executed by the local preprocessing module deployed on the edge node. The local preprocessing module responds quickly to sudden resident activity data and processes periodic community data in batches. After the conflict is resolved, the data content after the conflict is resolved is obtained. In the computing architecture composed of edge nodes and central servers, the target edge node is selected based on the data content and the age structure information of the community population, and the cache evaluation index set of the target edge node is dynamically configured. The evaluation requirement description input by the community planner is obtained, the corresponding planning intention information is parsed based on the evaluation requirement description, the planning intention information is combined with the multi-dimensional evaluation indicators in the configured cache to generate the corresponding comprehensive evaluation features, the community living circle evaluation level is determined by the dynamic weighted aggregation algorithm based on the comprehensive evaluation features, and the corresponding planning optimization suggestions are output based on the evaluation level. Obtain feedback from community planners on their suggestions for planning optimization, and update the evaluation indicator weighting system and planning strategies based on the feedback.

2. The community living circle planning evaluation method according to claim 1, characterized in that, The context-aware facility type mapping mechanism further includes the following methods: The BERT model is used to perform semantic parsing on cross-platform facility names to generate semantic vectors for facility names; a spatial graph is constructed based on the geographical coordinates of the facilities, with facilities as nodes and the spatial distance between facilities as edge weights. Based on graph neural network processing of spatial graphs, spatial association rules between facilities are learned to form spatial association matrices of facility types; the semantic vectors and spatial association matrices are fused with the spatial association matrices through a weighted fusion mechanism to generate semantic mapping relationships between facility types across platforms.

3. The community living circle planning evaluation method according to claim 2, characterized in that, The conflict resolution strategy further includes the following methods: Community vulnerability indicators are calculated based on population distribution data. These indicators include the proportion of elderly and children, the proportion of disabled persons, and the proportion of owner-occupied housing. Community function robustness indicators are calculated based on POI data. These indicators include emergency shelter coverage, medical aid station density, and accessibility of fire-fighting facilities. Emergency management effectiveness indicators are calculated based on community management data. These indicators include the reserve of emergency response teams, the level of material reserves, and the degree of planning of emergency response plans. The community resilience index is obtained by weighting the community vulnerability index, the community functional robustness index, and the emergency management effectiveness index using the analytic hierarchy process. When the community resilience index is greater than the preset community resilience threshold, conflicts related to emergency facilities are resolved first, and the priority weight of emergency facility conflict resolution is dynamically adjusted based on the ratio of the elderly and children.

4. The community living circle planning evaluation method according to claim 1, characterized in that, In the set of cache evaluation metrics for dynamically configured target edge nodes, the method further includes: Based on demographic data, the community's population structure is divided into elderly, youth, and family groups; the current user's age group is identified through user device location information or user input. Based on the current user's age group, a subset of evaluation indicators for that group is extracted from the cache. The subset of evaluation indicators includes the elderly group's focus on medical facilities and the young group's focus on cultural facilities. By dynamically adjusting the weights of the evaluation indicator subset based on real-time population flow data, the cached evaluation indicator set is highly matched with the needs of the current user group.

5. The community living circle planning evaluation method according to claim 1, characterized in that, The method also includes the following in the multi-dimensional evaluation indicators: Define indicators for facility richness, including facility diversity, facility density, and facility scale ratio; define indicators for distribution balance, including facility spatial coverage blind spot rate and the matching degree between facility distribution and population distribution; define a community vitality dimension, including cultural vitality index and social connectivity index. The cultural vitality index is calculated using social media cultural activity check-in data and sentiment analysis, while the social connectivity index is calculated using mobile signaling data and spatial syntax analysis. The vitality index weight and the connectivity index weight of the cultural vitality index are dynamically adjusted according to the frequency of community cultural activities, so that the multi-dimensional evaluation index system can reflect changes in community cultural vitality in real time.

6. The community living circle planning evaluation method according to claim 5, characterized in that, In dynamic weighted aggregation algorithms, methods also include: The evaluation index weighting system is initialized based on historical data; the difference between the current evaluation level and the actual community improvement effect is calculated; the weights are adjusted according to the difference value using the gradient descent method to make the evaluation results closer to the actual improvement effect; The frequency of community cultural activities is introduced as a factor for population structure change, and the vitality index weight and the connectivity index weight are applied to re-aggregate multi-dimensional evaluation indicators.

7. The community living circle planning evaluation method according to claim 6, characterized in that, In the process of updating the evaluation index weight system and planning strategy based on feedback information, the methods also include: Collect feedback from community planners on planning optimization suggestions, including satisfaction scores and improvement suggestions; transform the feedback information into reward signals in reinforcement learning to evaluate the effectiveness of the current evaluation system; The evaluation index weight system is updated using the PPO reinforcement learning algorithm to make the weight system more in line with actual planning needs; the execution parameters of the planning strategy are dynamically adjusted in combination with the updated weight system and the feedback information.

8. A community living circle planning evaluation system, characterized in that, include: The multi-source data fusion module performs real-time analysis on community population distribution data, population profile data, POI data, satellite remote sensing data, and social media text data from multi-source spatiotemporal big data platforms at the data acquisition end. It extracts the core attributes of community service facilities and generates a unified semantic representation. The core attributes include facility type, facility scale, and facility density. The semantic representation establishes semantic mapping relationships between facility attributes across platforms based on a context-aware facility type mapping mechanism and spatial association rules. The standardized data is then transmitted to the central evaluation database through a federated learning distributed synchronization mechanism. The dynamic conflict resolution module dynamically generates conflict resolution strategies when a conflict occurs, based on facility attribute information and resident behavior profile characteristics in the standardized data. The conflict resolution strategies are executed by the local preprocessing module deployed on the edge node. The local preprocessing module responds quickly to sudden resident activity data and performs batch processing on periodic community data. The intelligent indicator configuration module, after conflict resolution, obtains the data content after conflict resolution. In the computing architecture composed of edge nodes and central server collaboration, it selects target edge nodes based on the data content and community population age structure information, and dynamically configures the cached evaluation indicator set of the target edge nodes. The closed-loop optimization evaluation module obtains the evaluation requirement description input by the community planner, parses the corresponding planning intent information based on the evaluation requirement description, combines the planning intent information with the multi-dimensional evaluation indicators in the configured cache to generate the corresponding comprehensive evaluation features, determines the community living circle evaluation level based on the comprehensive evaluation features through a dynamic weighted aggregation algorithm, and outputs the corresponding planning optimization suggestions based on the evaluation level. Obtain feedback from community planners on their suggestions for planning optimization, and update the evaluation indicator weighting system and planning strategies based on the feedback.

9. A community living circle planning evaluation system, characterized in that, Includes a processor, wherein the processor runs a program of the community living circle planning evaluation method as described in any one of claims 1-7.

10. A storage medium, characterized in that, The program stores the community living circle planning evaluation method as described in any one of claims 1-7.

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