A smart city service management method and system

Abstract

The application provides a kind of wisdom city service management method and system, it is related to city service management technical field.The method includes the collection multidimensional operation data of target city;The function area of city is divided;The basic service type of each area and the layout density benchmark value are calculated;The spatial position correlation degree and the function complementary correlation degree between each function area are quantified;The optimal service management layout scheme is generated;Dynamic adaptation and closed-loop optimization of service management are realized.The application adjusts service layout dynamically, responds to demand fluctuations caused by population tide, sudden events, etc., and improves the resilience of city services.At the same time, resource optimization reduces invalid investment and reduces the cost of city management, providing strong support for sustainable development.

Description

Technical Field

[0001] This invention relates to the field of urban service management technology, and in particular to a smart city service management method and a smart city service management system. Background Technology

[0002] Rapid urbanization has transformed cities into complex mega-systems, and the high concentration of population, resources, and the environment poses systemic challenges to service management systems. Currently, global urban service management generally suffers from an overemphasis on construction and a neglect of operation and coordination. Existing technological systems are ill-suited to the inherent needs of dynamic urban development, necessitating intelligent management systems that can adapt to the complexity, dynamism, and interconnectedness of cities.

[0003] The existing management model is based on a compartmentalized architecture, resulting in fragmented resource allocation, lack of cross-domain collaboration, and difficulty in forming a cohesive service. Technically, data perception is based on point-based monitoring, lacking multi-dimensional integration, and regional division and service deployment updates are lagging behind, leading to a continuous mismatch between supply and demand. It ignores the correlation between elements and lacks systematic consideration. It tends to be reactive and has difficulty in quickly allocating resources, which restricts the improvement of service quality. Summary of the Invention

[0004] This invention provides a smart city service management method and system to address the deficiencies in existing technologies.

[0005] On one hand, the present invention provides a smart city service management method, comprising:

[0006] Collect multi-dimensional operational data of the target city.

[0007] Multi-dimensional operational data are preprocessed, and urban functional correlation features are extracted through cross-modal feature fusion. A dynamic urban functional evaluation index system is constructed, and the city is divided into functional areas based on an improved adaptive density clustering algorithm.

[0008] Based on the functional area division results, and combined with the population density, service demand intensity and core functional positioning of each area, an initial service management deployment model is constructed, and the basic service types and deployment density benchmark values ​​of each area are calculated.

[0009] Construct a regional association weight matrix to quantify the spatial location association and functional complementarity association between functional areas.

[0010] A multi-objective optimization algorithm is designed with service coverage balance, resource allocation efficiency, and regional collaborative response speed as optimization objectives. The algorithm dynamically adjusts the basic service types and deployment density benchmarks of each functional area by combining the regional association weight matrix, thereby generating the optimal service management deployment scheme.

[0011] The optimal service management deployment scheme is transformed into executable resource scheduling instructions and facility operation and maintenance strategies, realizing dynamic adaptation and closed-loop optimization of service management.

[0012] According to the smart city service management method provided by the present invention, the multi-dimensional operational data includes geospatial data, population flow data, traffic operation data, public facility usage data, economic activity data, and environmental monitoring data.

[0013] According to a smart city service management method provided by the present invention, the process of preprocessing multi-dimensional operational data includes:

[0014] Standardize and convert the formats of various types of data, transforming unstructured data into structured data.

[0015] An outlier detection algorithm is used to identify and correct outliers in the data and remove invalid data.

[0016] Missing values ​​in the data are filled using spatiotemporal interpolation. Linear interpolation is used for missing time series data, while inverse distance weighted interpolation is used for missing spatially distributed data.

[0017] The processed data is normalized to map indicators of different magnitudes to a unified range, resulting in a standardized multidimensional dataset.

[0018] According to the smart city service management method provided by the present invention, the process of extracting urban function-related features through cross-modal feature fusion and constructing a dynamic urban function evaluation index system includes:

[0019] We extract regional morphological features from geospatial data, activity features from population flow data, and accessibility features from transportation data. Regional morphological features include building density and road network density. Activity features include population concentration and flow intensity. Accessibility features include average travel time and road network connectivity.

[0020] We employ an attention mechanism to weightedly fuse cross-modal features, calculate the contribution weights of different features to urban functions, and generate a function-related feature vector.

[0021] Construct an evaluation indicator system that includes both static and dynamic indicators. Static indicators reflect the inherent attributes of the region, including land use intensity and infrastructure coverage rate. Dynamic indicators reflect the real-time operational status, including population tidal coefficient and traffic load index.

[0022] The weights of each indicator are determined by the entropy weight method to form a comprehensive evaluation value.

[0023] According to a smart city service management method provided by the present invention, the process of dividing a city into functional areas based on an improved adaptive density clustering algorithm includes:

[0024] The target city is divided into equal grid units, and the functional association feature vector of each grid is used as a clustering sample.

[0025] An improved adaptive density clustering algorithm is used to dynamically adjust the radius parameter, which is set based on the feature similarity of the grid cells to identify the core grid.

[0026] Grid cells that are less than a preset threshold in distance from the core grid and whose feature similarity meets the preset conditions are grouped into the same cluster.

[0027] The clustering results are optimized by combining the natural geographical boundaries of the city, merging adjacent clusters with similar functional attributes to achieve functional area division, which includes commercial core area, residential area, industrial park, transportation hub area and ecological protection area.

[0028] According to a smart city service management method provided by the present invention, the process of calculating the basic service types and deployment density benchmark values ​​for each area includes:

[0029] Based on the core functional positioning of functional areas, determine the dominant service type of each area.

[0030] Based on population density data for each region, establish a correlation between population size and the number of basic service facilities, and determine the lower limit of the number of service facilities to meet basic needs.

[0031] By integrating population flow data, real-time public facility usage data, and user feedback information, a service demand intensity assessment model is constructed to calculate the urgency of demand for different service types in various regions.

[0032] Based on the core functional positioning, service type weights are set, and combined with the basic quantity and service demand intensity assessment results corresponding to population density, an initial service management deployment model is constructed. The deployment density benchmark values ​​of different service types in each region are obtained through weighted calculation.

[0033] According to the smart city service management method provided by the present invention, the process of quantifying the spatial location correlation and functional complementarity correlation between various functional areas includes:

[0034] Collect road network topology data between functional areas. The road network topology data includes road connection relationships, road grades and real-time traffic conditions. Calculate the weighted road network distance between functional areas based on the road network topology data to obtain the spatial location correlation.

[0035] Based on population flow monitoring data, this study analyzes the direction, scale, and purpose of population flows among different functional areas, identifying pairs of areas with frequent population interactions. It also statistically analyzes the total service resources and total service demand in each functional area, calculates the supply-demand gap for service resources, determines pairs of areas with complementary resource relationships, and obtains the degree of functional complementarity.

[0036] The spatial location correlation and functional complementarity correlation are standardized separately, and then fused and calculated according to the preset weight ratio to obtain the comprehensive correlation value between each region.

[0037] A regional association weight matrix is ​​constructed based on the comprehensive association degree value. The magnitude of the element values ​​in the regional association weight matrix reflects the tightness of the association between regions.

[0038] According to the smart city service management method provided by the present invention, the process of generating an optimal service management deployment scheme includes:

[0039] An improved NSGA-III algorithm is adopted, with service coverage balance, resource allocation efficiency, and regional collaborative response speed as optimization objectives.

[0040] A regional correlation weight matrix is ​​introduced as a constraint to force service resource linkage adjustment for regions with high correlation.

[0041] The optimal service management deployment scheme is obtained by generating a Pareto optimal solution set through iterative calculation.

[0042] According to the smart city service management method provided by the present invention, the process of realizing dynamic adaptation and closed-loop optimization of service management includes:

[0043] The optimal service management deployment scheme is analyzed, the adjustment content and density change parameters of basic service types in each functional area are extracted, and transformed into resource scheduling instructions. The resource scheduling instructions include adding or removing service facilities and allocating service personnel.

[0044] Based on the type of service facility, the importance of its location, and the time distribution characteristics of service demand, a facility operation and maintenance strategy is formulated. The facility operation and maintenance strategy includes equipment maintenance cycle, inspection route planning, and service personnel scheduling plan.

[0045] Resource scheduling instructions and facility operation and maintenance strategies are standardized and encapsulated using a common communication protocol for urban IoT.

[0046] The encapsulated instructions and policies are transmitted to the relevant management system through the city's IoT bus, and corresponding operations are executed according to the resource scheduling instructions and facility operation and maintenance policies.

[0047] The system collects execution result data in real time, including the operational status of service facilities, service coverage, and response time, and compares and analyzes this data with the optimization objectives.

[0048] If the deviation between the execution result data and the optimization target exceeds the preset range, a second optimization will be performed, and the service management deployment plan, corresponding resource scheduling instructions, and facility operation and maintenance strategies will be readjusted to form a closed-loop management mechanism for continuous improvement.

[0049] On the other hand, the present invention also provides a smart city service management system, comprising:

[0050] The data acquisition module is used to collect multi-dimensional operational data of the target city.

[0051] The regional division module is used to preprocess multi-dimensional operational data, extract urban functional correlation features through cross-modal feature fusion, construct a dynamic urban functional evaluation index system, and divide the city into functional regions based on an improved adaptive density clustering algorithm.

[0052] The deployment calculation module is used to construct an initial service management deployment model based on the functional area division results, combined with the population density, service demand intensity and core function positioning of each area, and to calculate the basic service type and deployment density benchmark value of each area.

[0053] The correlation calculation module is used to construct a regional correlation weight matrix to quantify the spatial location correlation and functional complementarity correlation between various functional areas.

[0054] The deployment optimization module is used to design multi-objective optimization algorithms. With service coverage balance, resource allocation efficiency and regional collaborative response speed as optimization objectives, it dynamically adjusts the basic service types and deployment density benchmark values ​​of each functional area in combination with the regional association weight matrix to generate the optimal service management deployment scheme.

[0055] The optimization execution module is used to transform the optimal service management deployment plan into executable resource scheduling instructions and facility operation and maintenance strategies, so as to realize dynamic adaptation and closed-loop optimization of service management.

[0056] This invention provides a smart city service management method and system. Through multi-dimensional data fusion, dynamic regional division, and intelligent optimization algorithms, it achieves precise and collaborative service management. By integrating multi-dimensional data such as geospatial and population flow data, it breaks down departmental data barriers and forms a comprehensive perception network. The preprocessing stage transforms unstructured data into structured data and extracts urban functional association features using cross-modal feature fusion technology. Based on an improved adaptive density clustering algorithm, combined with real-time population and traffic data, it dynamically adjusts regional boundaries, making functional area division more aligned with the actual urban operation. The initial service management deployment model combines population density, demand intensity, and core functions to achieve a rational allocation of basic resources. A regional association weight matrix is ​​introduced to quantify the spatial and functional associations between regions. A multi-objective optimization algorithm generates a globally optimal solution, balancing coverage balance, resource efficiency, and collaborative response. It possesses strong generalization capabilities and can adapt to different city sizes and development stages. By dynamically adjusting service deployment, it addresses demand fluctuations caused by population tides and emergencies, enhancing urban service resilience. Simultaneously, optimized resource allocation reduces ineffective investment, lowers urban management costs, and provides strong support for sustainable development. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0058] Figure 1 This is a flowchart illustrating a smart city service management method provided in an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the structure of a smart city service management system provided in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0061] The following is combined with Figures 1-2 This invention describes a smart city service management method and system.

[0062] Figure 1This is a flowchart illustrating a smart city service management method provided in an embodiment of the present invention.

[0063] like Figure 1 As shown in the embodiment of the present invention, a smart city service management method and system are provided. The executing entity can be a smart city service management method, which includes:

[0064] Collect multi-dimensional operational data of the target city.

[0065] Multi-dimensional operational data includes geospatial data, population flow data, traffic operation data, public facility usage data, economic activity data, and environmental monitoring data. Geospatial data includes urban road network topology, building distribution, and land use types. Population flow data includes real-time population density, tidal flow patterns, and dwell time. Traffic operation data covers road speed, public transportation passenger volume, and parking lot occupancy rates. Public facility usage data includes average daily visitor numbers and peak-hour distribution for schools, hospitals, and supermarkets. Economic activity data includes enterprise distribution, commercial turnover, and industrial tax revenue data. Environmental monitoring data covers air quality index, noise levels (decibels), and water quality parameters.

[0066] In this embodiment, a comprehensive perception network is constructed to collect multi-dimensional operational data of the target city. Diverse data collection methods are used to achieve a comprehensive capture of the city's dynamic and static characteristics. The specific process is as follows:

[0067] Geospatial data collection was conducted jointly by relevant departments and surveying and mapping institutions, employing a combination of UAV aerial surveying and ground-based laser scanning. UAVs periodically photographed the entire city along pre-set routes (once a month), generating high-precision digital elevation models and orthophotos, and extracting three-dimensional parameters such as the location, height, and outline of buildings. Ground-based laser radar scanned along the city's main roads, acquiring detailed data on the road network topology, including road width, intersection types, number of lanes, and median distribution. Simultaneously, combined with land use planning archives, land use types such as commercial, residential, and industrial were marked, forming a geospatial database covering the entire city.

[0068] Population flow data is collected through intelligent sensing devices deployed at key nodes. Facial recognition cameras and infrared counting sensors are installed in densely populated areas such as bus stops, subway stations, and commercial complexes to count the flow of people entering and exiting in real time, and the real-time population density is analyzed in conjunction with mobile phone signaling data.

[0069] Economic activity data is acquired through a multi-departmental data sharing mechanism. Relevant departments provide information such as enterprise registration addresses and business scopes to form enterprise distribution maps; tax data from various industries are shared, and combined with the turnover reports of merchants in commercial complexes, the economic activity of different regions is analyzed; regular industry surveys are conducted to collect data such as output value and employment in industrial parks, supplementing in-depth information on economic activities and ensuring that data covers multiple levels from micro-businesses to macro-industries.

[0070] Environmental monitoring data is collected in real time through a distributed monitoring network. Air quality monitoring stations are deployed in grids in urban built-up areas to detect the concentration of pollutants such as PM2.5, PM10, and sulfur dioxide every hour, generating an air quality index. Noise sensors are installed along main roads and around residential areas to record environmental noise decibel values ​​24 hours a day, distinguishing between traffic noise and residential noise. Environmental protection departments regularly sample and test rivers, lakes, and groundwater intake points to obtain water resource quality parameters such as pH value, dissolved oxygen, and pollutant content. Combined with real-time data from automatic water quality monitoring stations, a complete environmental quality file is formed.

[0071] All collected data is uploaded to the city's data platform in real time via an encrypted transmission protocol. It is then categorized and stored in a directory based on data type, while also recording metadata such as collection time, device number, and data accuracy.

[0072] Multi-dimensional operational data are preprocessed, and urban functional correlation features are extracted through cross-modal feature fusion. A dynamic urban functional evaluation index system is constructed, and the city is divided into functional areas based on an improved adaptive density clustering algorithm.

[0073] The process of preprocessing multi-dimensional operational data includes:

[0074] Data of all types undergoes format standardization and conversion, transforming unstructured data into structured data. Specifically, for video surveillance footage, frame extraction and object detection algorithms are used to extract structured parameters such as pedestrian and vehicle traffic flow; for text reports (such as environmental monitoring logs and citizen complaint records), natural language processing technology is used for entity recognition and keyword extraction, converting them into quantitative indicators (such as complaint type and frequency). Simultaneously, a unified data timestamp format and spatial coordinate system (using Gauss-Kruger projection) are implemented to ensure consistency in the spatiotemporal reference of data from different sources.

[0075] Outlier detection algorithms are employed to identify and correct outliers in the data, removing invalid data. First, statistical distribution analysis is performed on the structured data. For numerical data, the Z-score method is used; data values ​​deviating from the mean by more than three times the standard deviation are marked as outliers. For categorical data, the Isolation Forest algorithm is used to identify samples deviating from the normal distribution. Marked outliers are then corrected by combining historical data from the same period and data from adjacent regions. If correction is not possible, the data is deemed invalid and removed. Missing values ​​are filled using spatiotemporal interpolation methods. Linear interpolation is used for missing time-series data, and inverse distance weighted interpolation is used for spatially distributed missing data.

[0076] The processed data is normalized to map indicators of different magnitudes to a unified interval, resulting in a standardized multidimensional dataset. The min-max normalization method is used to linearly transform all indicator values ​​to the [0,1] interval. For inverse indicators (such as average travel time and noise decibels), forward normalization is performed first, followed by normalization, to ensure consistency in indicator direction.

[0077] The process of extracting urban function-related features through cross-modal feature fusion and constructing a dynamic urban function assessment index system includes:

[0078] This study extracts regional morphological features from geospatial data, activity features from population flow data, and accessibility features from traffic data. Regional morphological features include building density and road network density. Activity features include population concentration and flow intensity. Accessibility features include average travel time and road network connectivity. Building density is calculated as the ratio of building area per unit grid to the total grid area; road network density is the ratio of total road length per grid to grid area. Population concentration is represented by the ratio of peak population per grid to grid area; flow intensity is calculated by the difference in pedestrian traffic at grid entrances and exits per unit time. Average travel time is derived from traffic data statistics during morning and evening peak hours; road network connectivity is the ratio of the number of actually connected road intersections per grid to the theoretical maximum possible number of connected intersections. An attention mechanism is used to weightedly fuse cross-modal features, calculate the contribution weights of different features to urban functions, and generate a functional association feature vector.

[0079] A multi-head self-attention model is constructed, taking regional morphology, activity, and accessibility features as input sequences, and converting them into vectors of the same dimension through feature embedding. The model automatically learns the importance weights of different features in describing urban functions by calculating the similarity matrix between features (such as the Pearson correlation coefficient between building density and population agglomeration). The weighted feature vectors are concatenated to form a 256-dimensional functional association feature vector, fully preserving the cross-modal feature association information.

[0080] An evaluation indicator system comprising static and dynamic indicators is constructed. Static indicators reflect the inherent attributes of the region, including land use intensity and infrastructure coverage rate. Dynamic indicators reflect real-time operational status, including population tidal coefficient and traffic load index. Land use intensity is measured by the proportion of construction land area within the grid; the infrastructure coverage rate is the ratio of the actual number of public facilities within the grid to the planned standard number. The population tidal coefficient is the ratio of population density during morning and evening peak hours, reflecting the tidal effect of population flow; the traffic load index is the ratio of actual traffic volume to the road's designed capacity, reflecting the degree of traffic congestion.

[0081] The weights of each indicator are determined using the entropy weighting method to form a comprehensive evaluation value. First, the information entropy of each indicator is calculated, and then the weight is calculated based on the entropy value. Finally, the standardized values ​​of each indicator are weighted and summed with their corresponding weights to obtain the comprehensive evaluation value for each grid, which quantitatively reflects the functional attributes of the area.

[0082] The process of dividing cities into functional zones based on an improved adaptive density clustering algorithm includes:

[0083] The target city is divided into equal grid cells, and the functional correlation feature vector of each grid cell is used as a clustering sample. The grid cell size can be set to 100 meters × 100 meters, which ensures spatial accuracy while controlling the amount of computation.

[0084] For irregular areas on the city's edge, the grid is trimmed according to the actual geographical boundaries to ensure that each grid corresponds to a clearly defined spatial extent. A unique identifier is assigned to each grid, and its functional feature vector is associated with its spatial coordinate information to form a clustering input dataset.

[0085] An improved adaptive density clustering algorithm dynamically adjusts the radius parameter, which is set based on the feature similarity of grid cells to identify the core grid. The algorithm initially sets a minimum radius (covering 3-5 adjacent grids). By calculating the cosine similarity of feature vectors between grids, the radius is automatically expanded for grids with a similarity higher than 0.8 until all high-similarity grids are included; for low-similarity regions, the radius remains small. The core grid is defined as a sample containing at least 10 other grids within its current radius, ensuring sufficient representativeness of the core region.

[0086] Grid cells that are less than a preset threshold in distance from the core grid and whose feature similarity meets preset conditions are grouped into the same cluster. The distance is calculated using Euclidean distance, and the preset threshold is dynamically adjusted based on the average road network density of the city.

[0087] The feature similarity condition is set to cosine similarity ≥ 0.7 to ensure that the functional attributes of grids within the same cluster are highly consistent. For non-core grids, if both the distance and similarity with a core grid meet the condition, they are assigned to the cluster to which that core grid belongs.

[0088] The clustering results are optimized by combining the natural geographical boundaries of the city, merging adjacent clusters with similar functional attributes to achieve functional area division, which includes commercial core area, residential area, industrial park, transportation hub area and ecological protection area.

[0089] Natural geographical boundaries include physical barriers such as rivers, mountains, and railways. If the clustering results cross such boundaries, the clusters are split according to the boundary location. The similarity of the comprehensive evaluation value of adjacent clusters is calculated, and clusters with a similarity ≥ 0.6 and spatial contiguousness are merged to avoid excessive fragmentation of functional areas. Finally, the functional areas are labeled according to the dominant features of the grid within each cluster, forming a complete urban functional zoning map.

[0090] Based on the functional zoning results, and combined with the population density, service demand intensity, and core functional positioning of each region, an initial service management deployment model is constructed. The basic service types and deployment density baseline values ​​for each region are calculated. The process includes:

[0091] Based on the core functional positioning of each functional area, the dominant service types for each area are determined. The core commercial area focuses on public safety and traffic management, supplemented by government services and environmental governance; the residential area emphasizes medical care, education, and community government services; the industrial park focuses on production-supporting government services and traffic management; the transportation hub area strengthens traffic management and public safety; and the ecological protection area focuses on environmental governance, supplemented by basic government services.

[0092] Based on population density data for each region, a correlation between population size and the number of basic service facilities is established to determine the lower limit for the number of service facilities to meet basic needs. The service coverage base is calculated based on the sum of the region's permanent residents and the average daily floating population. For example, one community government service point and two public security posts are allocated per 10,000 people. Based on this, and combined with adjustments for the region's area, it is ensured that the service radius of the facilities does not exceed a reasonable range.

[0093] By integrating population flow data, real-time public facility usage data, and user feedback, a service demand intensity assessment model is constructed to calculate the urgency of demand for different service types in various regions. Model inputs include parameters such as the percentage of population flowing in during the morning rush hour, real-time facility utilization rates (e.g., hospital registration queue times), and the proportion of service shortage issues in user complaints. A demand intensity index is calculated using the analytic hierarchy process (AHP) and categorized into high, medium, and low levels to reflect the actual supply and demand gap for different service types.

[0094] Based on the core functional positioning, service type weights are set. Combined with the assessment results of basic quantity and service demand intensity corresponding to population density, an initial service management deployment model is constructed. Weighted calculations are then used to derive the deployment density benchmark values ​​for different service types in each area. The weight for dominant service types is set at 0.6-0.7, and for auxiliary service types at 0.3-0.4; for high demand intensity, a coefficient of 1.2-1.5 is applied, and for low demand intensity, a coefficient of 0.5-0.8 is applied. The density benchmark value is calculated using the formula: basic quantity × weight × demand coefficient. For example, the benchmark value for traffic management facilities in the core commercial area = basic population × 0.7 × 1.5. The final result is rounded to the nearest integer to ensure feasibility.

[0095] Constructing a regional association weight matrix to quantify the spatial location association and functional complementarity association among functional regions, the process includes:

[0096] Road network topology data between functional areas is collected, including road connectivity, road class, and real-time traffic conditions. Weighted road network distances between functional areas are calculated based on this data to obtain spatial location correlation. Road classes are assigned weights of 4, 3, 2, and 1 for expressways, arterial roads, secondary arterial roads, and local roads, respectively. Real-time traffic conditions are corrected using a traffic congestion index; higher congestion indices result in greater weight reductions. The weighted road network distance is the sum of the products of the physical distance of each road segment and its corresponding weight coefficient. Shorter distances indicate higher spatial location correlation. The distance is converted to a correlation value between 0 and 1 using 1 / (1 + weighted distance). Based on population flow monitoring data, the direction, scale, and purpose of population flow between functional areas are analyzed to identify pairs of areas with frequent population interaction. The total service resources and total service demand of each functional area are statistically analyzed to calculate the supply-demand gap and determine pairs of areas with complementary resource relationships, thus obtaining functional complementarity correlation. The scale of population flow is measured by the proportion of the average daily two-way flow of people to the total population of the region. If it exceeds 20%, it is marked as frequent interaction. The supply and demand gap of service resources is calculated by (total resources - total demand) / total demand. A positive value indicates a resource surplus, and a negative value indicates a resource shortage. When the surplus type of region A matches the shortage type of region B, it is determined to be resource complementarity. The stronger the complementarity, the higher the correlation value.

[0097] Spatial location correlation and functional complementarity correlation are standardized separately, and then fused according to preset weight ratios to obtain the comprehensive correlation value between each region. The standardization process uses the min-max method to map both types of correlation to the 0-1 interval. The preset weights for spatial location correlation are 0.6 and functional complementarity correlation are 0.4. The comprehensive value is calculated by multiplying spatial correlation by 0.6 and functional correlation by 0.4. The closer the comprehensive value is to 1, the stronger the regional correlation.

[0098] A regional association weight matrix is ​​constructed based on the comprehensive association degree value. The magnitude of the element value in the regional association weight matrix reflects the tightness of the association between regions. The matrix is ​​an N×N symmetric matrix (N is the total number of functional regions), with rows and columns corresponding to each functional region. The value of the matrix element (i,j) is the comprehensive association degree between region i and region j. The diagonal elements (region self-association) are set to 1, thus forming a complete regional association weight matrix.

[0099] A multi-objective optimization algorithm is designed, with service coverage balance, resource allocation efficiency, and regional collaborative response speed as optimization objectives. It dynamically adjusts the basic service types and deployment density baselines for each functional area using a regional correlation weight matrix to generate the optimal service management deployment scheme. The process includes:

[0100] An improved NSGA-III algorithm is adopted, with service coverage balance, resource allocation efficiency, and regional collaborative response speed as optimization objectives. Service coverage balance is measured by the Gini coefficient of spatial coverage of service facilities; resource allocation efficiency is calculated as the ratio of service personnel to operating costs per unit of service facility, with the objective of maximizing per capita service output; regional collaborative response speed is evaluated by the average time spent scheduling service resources between related regions, with the objective of minimizing cross-regional response latency. The algorithm introduces an adaptive crossover operator based on the traditional NSGA-III, dynamically adjusting the crossover probability according to the iteration stage to improve solution diversity. A regional correlation weight matrix is ​​introduced as a constraint, forcing coordinated adjustment of service resources in highly correlated regions. A correlation threshold is set; when the adjustment of service type or density in a region exceeds 10%, its related regions must simultaneously make coordinated adjustments within ±5%. For example, if a transportation hub and a commercial core area have a high correlation, and the former increases traffic management facilities, the latter needs to correspondingly increase the density of public safety facilities to ensure that resource allocation matches the needs of regional interaction.

[0101] The optimal service management deployment scheme is obtained by generating a Pareto optimal solution set through iterative calculation. The initial population size of the algorithm is set to 100, with each individual representing a set of service deployment parameter combinations. The algorithm converges after 50 generations of iterations. Pareto optimal solutions are selected by sorting by congestion distance. The final scheme is selected from the solution set based on urban development priorities. The scheme must clearly define the list of service types to be added or removed in each area and the magnitude of density adjustment, and include comparative data on the target optimization before and after the adjustment.

[0102] The optimal service management deployment scheme is transformed into executable resource scheduling instructions and facility operation and maintenance strategies to achieve dynamic adaptation and closed-loop optimization of service management. The process includes:

[0103] The optimal service management deployment scheme is analyzed, the adjustment content and density change parameters of basic service types in each functional area are extracted, and transformed into resource scheduling instructions. The resource scheduling instructions include adding or removing service facilities and allocating service personnel.

[0104] Based on the type of service facility, the importance of its location, and the time distribution characteristics of service demand, a facility operation and maintenance strategy is formulated. The facility operation and maintenance strategy includes equipment maintenance cycle, inspection route planning, and service personnel scheduling plan.

[0105] Resource scheduling instructions and facility operation and maintenance strategies are standardized and encapsulated using a common communication protocol for urban IoT.

[0106] The encapsulated instructions and policies are transmitted to the relevant management system through the city's IoT bus, and corresponding operations are executed according to the resource scheduling instructions and facility operation and maintenance policies.

[0107] The system collects execution result data in real time, including the operational status of service facilities, service coverage, and response time, and compares and analyzes this data with the optimization objectives.

[0108] If the deviation between the execution result data and the optimization target exceeds the preset range, a second optimization will be performed, and the service management deployment plan, corresponding resource scheduling instructions, and facility operation and maintenance strategies will be readjusted to form a closed-loop management mechanism for continuous improvement.

[0109] In summary, this embodiment provides a smart city service management method. Through multi-dimensional data fusion, dynamic regional division, and intelligent optimization algorithms, it achieves precise and collaborative service management. By integrating multi-dimensional data such as geospatial and population flow data, it breaks down departmental data barriers and forms a comprehensive perception network. The preprocessing stage transforms unstructured data into structured data and extracts urban functional association features using cross-modal feature fusion technology. Based on an improved adaptive density clustering algorithm, combined with real-time population and traffic data, it dynamically adjusts regional boundaries, making functional area division more aligned with the actual urban operation. The initial service management deployment model combines population density, demand intensity, and core functions to achieve a rational allocation of basic resources. A regional association weight matrix is ​​introduced to quantify the spatial and functional associations between regions. A multi-objective optimization algorithm generates a globally optimal solution, balancing coverage balance, resource efficiency, and collaborative response. It possesses strong generalization capabilities and can adapt to different city sizes and development stages. By dynamically adjusting service deployment, it addresses demand fluctuations caused by population tides and emergencies, enhancing the resilience of urban services. At the same time, optimized resource allocation reduces ineffective investment, lowers urban management costs, and provides strong support for sustainable development.

[0110] Based on the same general inventive concept, this invention also protects a smart city service management system. The following describes a smart city service management system provided by this invention. The smart city service management system described below and the smart city service management method described above can be referred to and correspond to each other.

[0111] Figure 2 This is a schematic diagram of the structure of a smart city service management system provided in an embodiment of the present invention.

[0112] like Figure 2 As shown, a smart city service management system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor includes a data acquisition module, a region division module, a layout calculation module, a correlation calculation module, a layout optimization module, and an optimization execution module.

[0113] The data acquisition module is used to collect multi-dimensional operational data of the target city.

[0114] The region division module is used to preprocess multi-dimensional operational data, extract urban functional association features through cross-modal feature fusion, construct a dynamic urban functional evaluation index system, and divide the city into functional regions based on an improved adaptive density clustering algorithm.

[0115] The deployment calculation module is used to construct an initial service management deployment model based on the functional area division results, combined with the population density, service demand intensity and core function positioning of each area, and to calculate the basic service type and deployment density benchmark value of each area.

[0116] The correlation calculation module is used to construct a regional correlation weight matrix to quantify the spatial correlation and functional complementarity between functional regions.

[0117] The deployment optimization module is used to design multi-objective optimization algorithms. With service coverage balance, resource allocation efficiency and regional collaborative response speed as optimization objectives, it dynamically adjusts the basic service types and deployment density benchmarks of each functional area in combination with the regional association weight matrix to generate the optimal service management deployment scheme.

[0118] The optimization execution module is used to transform the optimal service management deployment plan into executable resource scheduling instructions and facility operation and maintenance strategies, so as to realize dynamic adaptation and closed-loop optimization of service management.

[0119] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1.A smart city service management method, characterized by, include: Collect multi-dimensional operational data of the target city; The multi-dimensional operational data is preprocessed, and urban functional correlation features are extracted through cross-modal feature fusion. A dynamic urban functional evaluation index system is constructed, and the city is divided into functional areas based on an improved adaptive density clustering algorithm. Based on the functional area division results, combined with the population density, service demand intensity and core functional positioning of each area, an initial service management deployment model is constructed, and the basic service types and deployment density benchmark values ​​of each area are calculated. Constructing a regional association weight matrix to quantify the spatial location association and functional complementarity association among functional regions, the process includes: Collect road network topology data between functional areas. The road network topology data includes road connection relationships, road grades and real-time traffic conditions. Calculate the weighted road network distance between functional areas based on the road network topology data to obtain the spatial location correlation. Based on population flow monitoring data, we analyze the direction, scale and purpose of population flow between functional areas to identify pairs of areas with frequent population interaction; we also statistically analyze the total amount of service resources and total service demand in each functional area, calculate the supply and demand gap of service resources, determine pairs of areas with complementary resource relationships, and obtain the degree of functional complementarity. The spatial location correlation degree and the functional complementarity correlation degree are standardized respectively, and then fused and calculated according to a preset weight ratio to obtain the comprehensive correlation degree value between each region. A regional association weight matrix is ​​constructed based on the comprehensive association degree value, and the magnitude of the element value in the regional association weight matrix reflects the degree of association between regions; A multi-objective optimization algorithm is designed, with service coverage balance, resource allocation efficiency, and regional collaborative response speed as optimization objectives. The algorithm dynamically adjusts the basic service types and deployment density baselines for each functional region based on the aforementioned regional association weight matrix, generating an optimal service management deployment scheme. The process includes: An improved NSGA-III algorithm is adopted, with service coverage balance, resource allocation efficiency, and regional collaborative response speed as optimization objectives; The aforementioned regional correlation weight matrix is ​​introduced as a constraint condition to force service resource linkage adjustment for regions with high correlation. The optimal service management deployment scheme is obtained by generating a Pareto optimal solution set through iterative calculation; The optimal service management deployment scheme is transformed into executable resource scheduling instructions and facility operation and maintenance strategies to achieve dynamic adaptation and closed-loop optimization of service management. The process includes: The optimal service management deployment scheme is analyzed, the adjustment content and density change parameters of the basic service types in each functional area are extracted, and converted into resource scheduling instructions. The resource scheduling instructions include adding or removing service facilities and allocating service personnel. Based on the type of service facility, the importance of the area where it is located, and the time distribution characteristics of service demand, a facility operation and maintenance strategy is formulated. The facility operation and maintenance strategy includes equipment maintenance cycle, inspection route planning, and service personnel scheduling plan. The resource scheduling instructions and facility operation and maintenance strategies are standardized and encapsulated using a common communication protocol for urban Internet of Things (IoT). The encapsulated instructions and policies are transmitted to the relevant management system via the city's Internet of Things bus, and corresponding operations are performed according to the resource scheduling instructions and facility operation and maintenance policies. Real-time collection of execution result data, including the operational status of service facilities, service coverage, and response time, and comparison and analysis with optimization targets; If the deviation between the execution result data and the optimization target exceeds a preset range, a second optimization will be performed, and the service management deployment scheme, corresponding resource scheduling instructions, and facility operation and maintenance strategies will be readjusted to form a closed-loop management mechanism for continuous improvement. 2.The smart city service management method of claim 1, wherein, The multi-dimensional operational data includes geospatial data, population flow data, traffic operation data, public facility usage data, economic activity data, and environmental monitoring data. 3.The method of claim 1, wherein, The preprocessing process for the multi-dimensional operational data includes: Perform format standardization and conversion on various types of data to transform unstructured data into structured data; An outlier detection algorithm is used to identify and correct outliers in the data and remove invalid data. Missing values ​​in data are filled using spatiotemporal interpolation. Linear interpolation is used for missing time series data, and inverse distance weighted interpolation is used for missing spatially distributed data. The processed data is normalized to map indicators of different magnitudes to a unified range, resulting in a standardized multidimensional dataset. 4.The smart city service management method of claim 1, wherein, The process of extracting urban function-related features through cross-modal feature fusion and constructing a dynamic urban function assessment index system includes: Regional morphological features are extracted from geospatial data, activity features are extracted from population flow data, and accessibility features are extracted from transportation data. The regional morphological features include building density and road network density; the activity features include population concentration and flow intensity; and the accessibility features include average travel time and road network connectivity. An attention mechanism is used to weight and fuse cross-modal features, calculate the contribution weights of different features to urban functions, and generate a function-related feature vector. An evaluation index system is constructed, which includes static and dynamic indicators. The static indicators reflect the inherent attributes of the region, including land use intensity and infrastructure coverage rate. The dynamic indicators reflect the real-time operating status, including population tidal coefficient and traffic load index. The weights of each indicator are determined by the entropy weight method to form a comprehensive evaluation value. 5.The smart city service management method of claim 1, wherein, The process of dividing cities into functional zones based on an improved adaptive density clustering algorithm includes: The target city is divided into equal grid units, and the functional association feature vector of each grid is used as a clustering sample. An improved adaptive density clustering algorithm is used to dynamically adjust the radius parameter, which is set based on the feature similarity of the grid cells to identify the core grid. Grid cells that are less than a preset threshold in distance from the core grid and whose feature similarity meets a preset condition are grouped into the same cluster; The clustering results are optimized by combining the natural geographical boundaries of the city, merging adjacent clusters with similar functional attributes to achieve functional area division, which includes commercial core area, residential area, industrial park, transportation hub area and ecological protection area. 6.The smart city service management method of claim 1, wherein, The process of calculating the baseline values ​​for basic service types and deployment density in each region includes: Based on the core functional positioning of functional areas, determine the dominant service type of each area; Based on population density data for each region, establish a correlation between population size and the number of basic service facilities, and determine the lower limit of the number of service facilities to meet basic needs. By integrating population flow data, real-time usage data of public facilities, and user feedback information, a service demand intensity assessment model is constructed to calculate the urgency of demand for different types of services in each region. Based on the core functional positioning, service type weights are set, and combined with the basic quantity and service demand intensity assessment results corresponding to population density, an initial service management deployment model is constructed. The deployment density benchmark values ​​of different service types in each region are obtained through weighted calculation. 7.A smart city service management system, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the program, it implements a smart city service management method as described in any one of claims 1 to 6.

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