Urban management method and system based on big data

Abstract

The invention relates to the technical field of city management, in particular to a city management method and system based on big data. According to the method, urban management demand information is obtained and converted into overall management numerical value information, resource configuration parameters are scientifically formulated in combination with geographic space distribution information, optimal allocation of resources is achieved, and then corresponding management instructions are triggered based on the configuration parameters and management numerical values. Monitoring the regional state in real time according to the management instruction and evaluating the deviation with the target; finally, the management strategy is dynamically adjusted according to the deviation value, and continuous optimization of the management effect is ensured. According to the method, the change of the urban environment can be quickly responded, potential problems can be predicted, measures can be taken in advance, and the accuracy and efficiency of urban management are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of urban management, and in particular to an urban management method and system based on big data. Background Art

[0002] With the acceleration of urbanization and the expansion of population size, urban management faces increasingly complex challenges. Efficient and accurate resource allocation and service provision have become key issues in urban management. The rapid development of big data technology has provided new solutions for urban management, making data-driven refined management possible.

[0003] At present, big data technology has begun to be applied in the field of urban management. For example, sensor networks are used to collect urban environmental data, traffic flow is optimized through data analysis, and geographic information systems are used for urban planning. The application of these technologies has improved the efficiency of urban management, improved the quality of public services, and provided data support for urban decision-making.

[0004] However, existing urban management methods often analyze historical data statically and lack real-time dynamic adjustment mechanisms, making it difficult to respond to changes in urban needs in a timely manner. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem that existing urban management methods are difficult to respond to changes in urban needs in a timely manner, this application provides an urban management method and system based on big data, which adopts the following technical solutions: In a first aspect, the present application provides a city management method based on big data, comprising the following steps: Obtaining city management demand information, and obtaining overall management numerical information based on the demand information; Acquire geographic space distribution information, and acquire resource configuration parameters according to the geographic space distribution information and the overall management numerical information, wherein the geographic space distribution information includes the location and distribution of various public service facilities in the city, and the resource configuration parameters include the resource allocation amount and service frequency in each area; triggering a management instruction according to the resource configuration parameter and the overall management value information; Monitor the regional status and obtain regional status detection information, compare the regional status detection information with the corresponding target value in the overall management numerical information, and evaluate the deviation value between the actual status and the target status; The deviation value is compared with a preset change threshold value. If the deviation value exceeds the preset change threshold range, an adjustment parameter is formulated according to the deviation value, and a corresponding management adjustment instruction is executed according to the adjustment parameter.

[0006] By adopting the above technical solution, this application first obtains urban management demand information and converts it into overall management numerical information, combines it with geographic spatial distribution information, scientifically formulates resource allocation parameters, and realizes optimal allocation of resources. Then, based on the configuration parameters and management values, the corresponding management instructions are triggered, and the regional status is monitored in real time according to the management instructions and the deviation from the target is evaluated; finally, the management strategy is dynamically adjusted according to the deviation value to ensure continuous optimization of the management effect; it can not only respond quickly to changes in the urban environment, but also predict potential problems and take measures in advance, which significantly improves the accuracy and efficiency of urban management.

[0007] Optionally, the obtaining of city demand information specifically includes the following steps: Collect multi-source data through the city data integration hub; Analyze the city's operating status and identify management needs based on the multi-source data collected; Based on the analysis results, generate urban management demand information including management objectives, management time period and resource requirements; The city data integration hub includes: Data fusion processing platform, used to integrate data from different departments; The protocol parsing module includes a preset protocol parser, which is used to parse various data access protocols through the protocol parser, so that the data fusion processing platform can correctly read and process data from different sources.

[0008] By adopting the above technical solution, in urban traffic management, the data formats and transmission protocols of the transportation department, public security department and meteorological department may be completely different, resulting in data islands and hindering comprehensive and accurate decision-making; this application first collects multi-source data from different departments and systems through the urban data integration center, and integrates these data using the data fusion processing platform; then parses various data access protocols through a preset protocol parser to ensure the correct reading and processing of data, and then analyzes the city's operating status based on the integrated data to identify management needs; finally, generates urban management demand information including management goals, time periods and resource requirements; not only solves the technical difficulties of data integration, but also extracts specific management demand information through intelligent analysis.

[0009] Optionally, obtaining overall management value information according to the demand information specifically includes the following steps: Extracting the management target and the management time period from the demand information, and using the management target as a preset management target; According to the management time period, the management target and the resource demand, obtain resource configuration time, service frequency time and resource configuration intensity; The preset management target, resource configuration time, service frequency time and resource configuration intensity are associated to obtain overall management value information.

[0010] By adopting the above technical solution, since urban management demand information often contains a large amount of unstructured data and fuzzy expressions, traditional methods are difficult to convert it into actionable management indicators; this application first extracts management goals and time periods from the demand information, sets the management goals as preset goals, and calculates resource allocation time, service frequency time and resource allocation intensity based on management time periods, goals and resource requirements; finally, these parameters are associated and integrated to form overall management numerical information; not only does it achieve the transformation from qualitative to quantitative, but it also establishes a multi-dimensional and dynamic management indicator system.

[0011] Optionally, obtaining resource configuration time, service frequency time and resource configuration intensity according to the management time period, the management target and the resource demand specifically includes the following steps: Inputting the management time period, the management target and the resource demand into a preset spatiotemporal resource demand prediction model to obtain a predicted spatiotemporal resource demand distribution; Based on the predicted spatiotemporal resource demand distribution, a multi-objective optimization algorithm is used to calculate the resource allocation plan, where the optimization objectives include resource utilization efficiency, service coverage, and response time; Based on the resource configuration scheme, resource configuration time, service frequency time and resource configuration intensity are obtained.

[0012] By adopting the above technical solution, since urban resource demand is highly dynamic and unbalanced in time and space, the traditional static resource allocation method is difficult to meet the actual needs of complex urban environments; this application first inputs the management time period, management objectives and resource demand into the preset spatiotemporal resource demand prediction model to obtain the predicted spatiotemporal resource demand distribution, and then based on this predicted distribution, uses a multi-objective optimization algorithm to calculate the resource allocation plan, and the optimization objectives include resource utilization efficiency, service coverage and response time; finally, according to the optimized configuration plan, the specific resource allocation time, service frequency time and resource allocation intensity are determined; the accuracy and efficiency of resource allocation are improved, and it can also effectively respond to emergencies and long-term trend changes in urban management, significantly improving the scientificity and flexibility of urban management.

[0013] Optionally, the obtaining of geographic spatial distribution information specifically includes the following steps: Process geospatial data using the place name and address standardization and service sharing platform; Generating geospatial distribution information including the location and distribution of various public service facilities in the city based on the geospatial data; The place name and address standardization and service sharing platform includes: Place name and address standardization tools, used to automatically correct and unify place name information; Place name database, used to store standardized place name address information; The place name service API includes a place name address query module and a spatial analysis module. The place name address query module is used to query place name addresses, and the spatial analysis module is used to analyze and process geographic spatial data.

[0014] By adopting the above technical solution, in urban planning, the address expressions used by different departments may be different, resulting in inconsistent coordinates of the same location in different systems, affecting the accuracy of decision-making; this application first uses a place name and address standardization tool to automatically correct and unify place name information, and stores the standardized information in a place name database; then provides place name and address query and spatial analysis functions through a place name service API; finally, based on the processed geospatial data, generates geospatial distribution information including the location and distribution of various public service facilities in the city; can efficiently process a large amount of non-standard geographic information, and at the same time establishes a unified place name database, providing a reliable data foundation for various urban management applications; finally, the place name service API is used to achieve in-depth analysis and flexible application of geospatial data.

[0015] Optionally, generating geographic spatial distribution information including the location and distribution of various public service facilities in the city based on the geographic spatial data specifically includes the following steps: Classifying the geospatial data and dividing the public service facilities to obtain classified geospatial data; Based on the classified geospatial data, the spatial distribution patterns of various public service facilities are identified, and the clustering results of facility distribution are obtained; Obtain administrative division boundary data, calculate the density of various public service facilities in each administrative area, obtain the facility density distribution map, and generate the accessibility index heat map of various public service facilities; Based on the facility distribution clustering results, the facility density distribution map and the accessibility index heat map, geographic space distribution information is generated.

[0016] By adopting the above technical solution, this application first classifies the geographic spatial data, divides the public service facilities, identifies the spatial distribution patterns of various types of facilities based on the classified data, and obtains clustering results; then, combined with the administrative division boundary data, the facility density in each area is calculated, and a density distribution map and an accessibility index heat map are generated; finally, the clustering results, density distribution map and accessibility heat map are integrated to generate comprehensive geographic spatial distribution information; not only the location and number of facilities are taken into account, but also their distribution pattern and accessibility are analyzed. By generating a facility density distribution map and an accessibility index heat map, the spatial differences of public services are intuitively displayed, providing a more intuitive and comprehensive reference; finally, a geographic spatial distribution information system is constructed to provide data support for urban planning and optimal resource allocation.

[0017] In a second aspect, the present application provides a city management system based on big data, comprising: The overall management numerical information acquisition module is used to acquire the city management demand information, and acquire the overall management numerical information according to the demand information; A resource configuration parameter acquisition module is used to acquire geographic space distribution information, and acquire resource configuration parameters according to the geographic space distribution information and the overall management value information, wherein the geographic space distribution information includes the location and distribution of various public service facilities in the city, and the resource configuration parameters include the resource allocation amount and service frequency in each area; A management instruction triggering module, used to trigger a management instruction according to the resource configuration parameters and the overall management value information; A deviation value evaluation module, used to monitor the regional state and obtain regional state detection information, compare the regional state detection information with the corresponding target value in the overall management numerical information, and evaluate the deviation value between the actual state and the target state; The management adjustment instruction execution module is used to compare the deviation value with a preset change threshold. If the deviation value exceeds the preset change threshold range, an adjustment parameter is formulated according to the deviation value, and a corresponding management adjustment instruction is executed according to the adjustment parameter.

[0018] Optionally, the overall management value information acquisition module includes: The city data integration hub is used to collect data from multiple sources; Demand analysis module, used to analyze the city operation status and identify management needs based on the collected multi-source data; A demand information generation module is used to generate urban management demand information including management objectives, management time periods and resource requirements based on the analysis results; The city data integration hub includes: Data fusion processing platform, used to integrate data from different departments; The protocol parsing module includes a preset protocol parser, which is used to parse various data access protocols through the protocol parser, so that the data fusion processing platform can correctly read and process data from different sources.

[0019] Optionally, the overall management value information acquisition module further includes: A target extraction unit, configured to extract the management target and the management time period from the demand information, and use the management target as a preset management target; A resource configuration calculation unit, configured to obtain resource configuration time, service frequency time and resource configuration intensity according to the management time period, the management target and the resource demand; The numerical information associating unit is used to associate the preset management target, resource configuration time, service frequency time and resource configuration intensity to obtain overall management numerical information.

[0020] Optionally, the resource configuration calculation unit includes: A spatiotemporal resource demand prediction model, for inputting the management time period, the management target and the resource demand to obtain a predicted spatiotemporal resource demand distribution; A multi-objective optimization algorithm module is used to calculate resource allocation solutions based on the predicted spatiotemporal resource demand distribution, where the optimization objectives include resource utilization efficiency, service coverage, and response time; The resource configuration parameter generation module is used to obtain resource configuration time, service frequency time and resource configuration intensity based on the resource configuration plan.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application first obtains urban management demand information and converts it into overall management numerical information, combines it with geographic spatial distribution information, scientifically formulates resource allocation parameters, and achieves optimal resource allocation. Then, based on the configuration parameters and management numerical values, corresponding management instructions are triggered, and the regional status is monitored in real time according to the management instructions, and the deviation from the target is evaluated; finally, the management strategy is dynamically adjusted according to the deviation value to ensure the continuous optimization of the management effect; it can not only quickly respond to changes in the urban environment, but also predict potential problems and take measures in advance, which significantly improves the accuracy and efficiency of urban management; 2. This application first collects multi-source data from different departments and systems through the city data integration center, and integrates these data using the data fusion processing platform; then parses various data access protocols through a preset protocol parser to ensure the correct reading and processing of data, and then analyzes the city operation status based on the integrated data to identify management needs; finally, generates city management demand information including management goals, time periods and resource requirements; not only solves the technical difficulties of data integration, but also extracts specific management demand information through intelligent analysis; 3. This application first inputs the management time period, management objectives and resource requirements into the preset spatiotemporal resource demand prediction model to obtain the predicted spatiotemporal resource demand distribution, and then uses a multi-objective optimization algorithm to calculate the resource allocation plan based on this predicted distribution. The optimization objectives include resource utilization efficiency, service coverage and response time; finally, based on the optimized configuration plan, the specific resource allocation time, service frequency time and resource allocation intensity are determined; the accuracy and efficiency of resource allocation are improved, and it can also effectively respond to emergencies and long-term trend changes in urban management, significantly improving the scientificity and flexibility of urban management. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of a city management method based on big data in an embodiment of the present application; Figure 2 It is a flowchart of step S100 in a city management method based on big data in an embodiment of the present application; Figure 3 This is another flowchart of step S100 in a city management method based on big data in an embodiment of the present application; Figure 4 This is a flow chart of step S150 in a city management method based on big data in an embodiment of the present application; Figure 5 It is a flowchart of step S200 in a city management method based on big data in an embodiment of the present application; Figure 6 This is a flow chart of step S220 in a city management method based on big data in an embodiment of the present application; Figure 7 This is a module diagram of a city management system based on big data in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0024] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0025] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0026] In the first aspect, the present application provides a city management method based on big data, referring to Figure 1 , including the following steps: S100. Obtain city management demand information, and obtain overall management numerical information based on the demand information.

[0027] Among them, obtaining urban management demand information can be achieved in a variety of ways, such as collecting multi-source data through the urban data integration center, or obtaining it through questionnaires, public feedback, etc. The overall management numerical information is obtained through analysis and calculation based on demand information, including key indicators such as management goals, resource allocation time, service frequency time, and resource allocation intensity.

[0028] Specifically, the management target and management time period are first extracted from the demand information, and the management target is used as the preset management target. Then, according to the management time period, management target and resource demand, the resource allocation time, service frequency time and resource allocation intensity are calculated through a certain algorithm or model. Finally, this information is associated to form a complete overall management numerical information. For example, taking urban greening management as an example, the overall management numerical information includes key indicators such as greening coverage target, tree planting plan time, maintenance frequency and resource input intensity. First, the greening management target and implementation time period are extracted from the demand information, such as "increase the urban greening coverage rate by 2% in the next year". Then, according to this time period, management target and available resources, the tree planting plan schedule, tree maintenance frequency and required human and material input are calculated through the urban greening model. Finally, this information is associated to form a complete overall management numerical information.

[0029] S200: Obtain geographic space distribution information, and obtain resource configuration parameters according to the geographic space distribution information and overall management numerical information.

[0030] Among them, the geographic spatial distribution information includes the location and distribution of various public service facilities in the city, and the resource allocation parameters include the resource allocation and service frequency in each area. The acquisition of geographic spatial distribution information can be achieved through a geographic information system or other spatial data processing platforms. This information includes the location coordinates, distribution density and other data of various public service facilities in the city. The resource allocation parameters are the result of specific allocation of resources to each region after comprehensively considering the geographic spatial distribution and overall management needs.

[0031] Specifically, the geospatial data is first classified and processed to obtain the distribution map of different types of public service facilities. Then, combined with the goals and requirements in the overall management numerical information, the amount of resources and service frequency that should be allocated to each area are calculated. For example, the geospatial distribution information includes the location and distribution of existing green facilities such as green spaces, parks, and roadside trees in the city. Resource allocation parameters include the area of ​​green space that needs to be added in each area, the number of trees, and the corresponding maintenance resource allocation. The existing green space data is first analyzed using a geographic information system to obtain a green distribution map of each area. Then, combined with the green coverage rate target in the overall management numerical information, the area of ​​green space and the number of trees that need to be added in each area are calculated. At the same time, according to the characteristics of each area (such as population density, air quality, etc.), the priority of tree planting and maintenance and the resource allocation ratio are determined.

[0032] S300: triggering a management instruction according to resource configuration parameters and overall management value information.

[0033] The triggering of management instructions is to convert the resource configuration parameters and overall management numerical information obtained in the previous steps into a specific executable action plan.

[0034] Specifically, by designing a decision support system, resource allocation parameters and overall management numerical information are input into it. The system will generate a series of specific management instructions based on preset rules and processes. These instructions may include personnel transfer, equipment procurement, service adjustment, etc. For example, the trigger of the management instruction is to convert the greening resource allocation parameters and overall management numerical information obtained in the previous steps into a specific executable greening action plan, and input the resource allocation parameters and overall management numerical information into it through the urban greening management decision support system. The system will generate a series of specific greening management instructions based on preset greening rules and processes. These instructions include tree planting plans, green space construction plans, maintenance personnel scheduling plans, etc. for each area. For example, the system may generate a specific instruction of "building a new 2-hectare park in area A and planting 500 local tree species."

[0035] S400, monitor the regional status and obtain regional status detection information, compare the regional status detection information with the corresponding target value in the overall management numerical information, and evaluate the deviation value between the actual status and the target status.

[0036] Among them, the monitoring of regional status can be achieved by collecting data in real time through various sensors, cameras and other equipment, or through regular manual inspections and reports.

[0037] Specifically, a comprehensive monitoring platform is established to collect information from various sensors and data sources. The platform automatically processes this data and generates standardized regional status reports. The system then compares the key indicators in these reports with the target values ​​in the overall management numerical information and calculates the deviation value. For example, a greening monitoring platform is established to collect remote sensing data, on-site detection data, and inspection reports. The platform automatically processes this data and generates standardized greening status reports. The system then compares the key indicators in these reports (such as green coverage, tree survival rate, vegetation health index, etc.) with the target values ​​in the overall management numerical information and calculates the deviation value.

[0038] S500, comparing the deviation value with a preset change threshold value. If the deviation value exceeds the preset change threshold range, formulating adjustment parameters according to the deviation value, and executing corresponding management adjustment instructions according to the adjustment parameters.

[0039] The change threshold is the acceptable deviation range pre-set based on management experience and scientific analysis. When the deviation value exceeds this range, it means that the current management measures need to be adjusted. The formulation of adjustment parameters is based on the size and direction of the deviation value, combined with management experience and intelligent algorithms. Management adjustment instructions are new action plans generated based on the adjustment parameters.

[0040] Specifically, an adaptive adjustment system is designed to continuously monitor the change of deviation value. When the system detects that the deviation value exceeds the threshold, the adjustment process is automatically triggered. The system analyzes the nature and degree of the deviation, combines historical data and preset rules, and generates corresponding adjustment parameters. These parameters may include resource reallocation, service frequency adjustment, etc. Based on these parameters, the system generates new management adjustment instructions. For example, the change threshold is the acceptable deviation range preset based on greening management experience and ecological analysis. When the deviation value exceeds this range, it means that the current greening measures need to be adjusted. By designing an adaptive adjustment system for greening management, the deviation value changes of greening conditions are continuously monitored. When the system detects that the deviation value exceeds the threshold, the adjustment process is automatically triggered. The system analyzes the nature and degree of the deviation, combines historical data and preset rules, and generates corresponding adjustment parameters. These parameters may include increasing the number of tree plantings, adjusting the frequency of watering and fertilizing, and replacing more suitable vegetation species. Based on these parameters, the system generates new greening management adjustment instructions. For example, if the survival rate of trees in a certain area is lower than expected, the system may generate adjustment instructions such as increasing maintenance personnel, adjusting watering plans, or replacing more stress-resistant tree species.

[0041] In one embodiment, referring to Figure 2 In step S100, obtaining city demand information specifically includes the following steps: S110. Collect multi-source data through the city data integration center.

[0042] Among them, the urban data integration center is a comprehensive data processing system, which includes a data fusion processing platform and a protocol analysis module. The data fusion processing platform is mainly responsible for integrating data from different departments, such as environmental monitoring departments, traffic management departments, urban planning departments, etc. The protocol analysis module contains a variety of preset protocol analyzers, which can handle different data access protocols to ensure that the data fusion processing platform can correctly read and process data from various sources.

[0043] Specifically, the city data integration center can collect data through multiple channels. For example, it can directly connect to the databases of various city departments to obtain various monitoring data in real time; it can also collect real-time environmental data through IoT devices, such as sensors distributed throughout the city; it can also obtain citizens' opinions and suggestions on city management through social media platforms and public feedback systems. After protocol parsing and preliminary processing, these multi-source data are integrated into the data fusion processing platform to provide a basis for subsequent analysis and decision-making.

[0044] S120. Analyze the city operation status and identify management needs based on the collected multi-source data.

[0045] In this embodiment, analyzing the city operation status and identifying management needs are completed through an intelligent analysis system.

[0046] Specifically, the intelligent analysis system first cleans and preprocesses the data to remove outliers and redundant information. Then, it evaluates the city's operating conditions from different perspectives, such as environmental quality, traffic flow, and the utilization rate of public facilities. Then, by comparing historical data with preset urban development goals, it identifies current problems and potential management needs. For example, if the system finds that the air quality data in a certain area is continuously below the standard, or the utilization rate of public transportation has dropped significantly, these situations will be marked as management needs that need attention.

[0047] S130. Based on the analysis results, generate city management demand information including management objectives, management time period and resource requirements.

[0048] Among them, the urban management needs information is a comprehensive report that not only includes the identified management needs, but also further proposes specific management goals, implementation time periods and estimates of required resources.

[0049] Specifically, the system automatically generates preliminary management target recommendations based on the identified management needs, combined with urban development plans and available resources. For example, for air quality issues, the system may recommend "reducing PM2.5 concentrations by 15% in the next 6 months" as a management goal. The time planning module will consider the urgency and difficulty of the problem and set a reasonable time period for each management goal. The resource assessment module will estimate the human, material and financial resources required to achieve these goals based on historical data and experience from similar projects. Ultimately, the system will integrate this information into a detailed urban management needs information report to provide a reference for decision makers.

[0050] In one embodiment, referring to Figure 3 In step S100, according to the demand information, the overall management value information is obtained, which specifically includes the following steps: S140: extracting a management target and a management time period from the demand information, and taking the management target as a preset management target.

[0051] Among them, demand information is a comprehensive document containing a variety of urban management related data.

[0052] Specifically, the system uses methods such as keyword matching and semantic analysis to process demand information. For example, for urban greening management, the system may identify a management goal such as "increase the urban greening coverage rate by 5% in the next two years." In this example, "increase the greening coverage rate by 5%" is extracted as the management goal, and "the next two years" is identified as the management time period. The system will store this information in a structured database. At the same time, the system will also verify the extracted information to ensure that it meets the predefined management goal scope and time format specifications.

[0053] S150. Obtain resource configuration time, service frequency time and resource configuration intensity according to the management time period, management objectives and resource requirements.

[0054] In this embodiment, the length of the management time period, the difficulty of the management target and the limitation of available resources are comprehensively considered to calculate the optimal resource allocation plan.

[0055] Specifically, the system first divides the management time period into several small time units, and then allocates corresponding tasks and resources to each time unit according to the nature and difficulty of the management target.

[0056] S160: Associating the preset management target, resource configuration time, service frequency time and resource configuration intensity to obtain overall management numerical information.

[0057] Specifically, the system builds a four-dimensional data cube that includes time dimension, target dimension, resource dimension, and intensity dimension. In this model, the preset management goal may be broken down into multiple sub-goals, each of which is associated with a specific time point, resource allocation, and service frequency. The system assigns specific values ​​to each data point, such as the increase in green area at a certain point in time, the number of seedlings required, the number of maintenance personnel, etc.

[0058] In one embodiment, referring to Figure 4 In step S150, according to the management time period, management objectives and resource requirements, resource configuration time, service frequency time and resource configuration intensity are obtained, which specifically includes the following steps: S151. Input the management time period, management target and resource demand into a preset spatiotemporal resource demand prediction model to obtain a predicted spatiotemporal resource demand distribution.

[0059] In this embodiment, the spatiotemporal resource demand prediction model is a complex system based on a machine learning algorithm that can predict resource demand at different time points and different geographical locations in the future based on historical data and current inputs.

[0060] Specifically, the spatiotemporal resource demand forecasting model is a forecasting model pre-set for various urban management projects, including sub-models for various projects. For example, for garbage collection projects, the model will consider factors such as seasonal changes, holidays, and population density; for road maintenance projects, the model will consider factors such as traffic flow, weather conditions, and the aging rate of pavement materials. The input data of the model includes historical resource demand data, weather forecasts, population flow data, and large-scale event arrangements. The model will first subdivide the input management time period, such as subdividing a one-month time period into different time periods every day. Then, the model will predict the distribution of resource demand based on the subdivided time periods and geographical locations.

[0061] S152. Based on the predicted spatiotemporal resource demand distribution, a multi-objective optimization algorithm is used to calculate a resource allocation plan, wherein the optimization objectives include resource utilization efficiency, service coverage, and response time.

[0062] Among them, the multi-objective optimization algorithm is an advanced algorithm that can simultaneously consider multiple potentially conflicting objectives. In this step, the best balance point is found among the three objectives of resource utilization efficiency, service coverage, and response time.

[0063] Specifically, the algorithm first generates an initial population, where each individual represents a possible resource allocation solution. Then, the algorithm iteratively performs the following steps: evaluates the performance of each solution on the three objectives, performs non-dominated sorting, calculates the congestion, selects excellent individuals, and performs crossover and mutation operations to generate new solutions. This process is repeated many times until the preset number of iterations is reached or the convergence condition is met.

[0064] S153. Based on the resource allocation plan, obtain resource allocation time, service frequency time and resource allocation intensity.

[0065] In one embodiment, referring to Figure 5 In step S200, obtaining geographic spatial distribution information specifically includes the following steps: S210. Use the place name and address standardization and service sharing platform to process geospatial data.

[0066] Among them, the place name and address standardization and service sharing platform is a comprehensive geographic information processing system, which includes place name and address standardization tools, place name databases and place name service APIs. The place name and address standardization tools can automatically correct and unify place name information from different sources, solving the problem of data inconsistency. The place name database serves as a central repository to store all standardized place name and address information. The place name service API provides flexible data access and analysis capabilities, among which the place name and address query module supports precise and fuzzy queries, while the spatial analysis module can perform complex geospatial data processing.

[0067] Specifically, when the system receives new geospatial data, it will first be processed by the place name address standardization tool. The standardization tool will check the format of the address to ensure that it conforms to the predefined standard format. At the same time, the tool will also check whether the address exists in the current place name database. If not, it will try to match the closest address or mark it as requiring further verification. The processed data will be stored in the place name database. Afterwards, other systems or users can access this data through the place name service API.

[0068] S220. Generate geographic spatial distribution information including the location and distribution of various public service facilities in the city based on the geographic spatial data.

[0069] In one embodiment, referring to Figure 6 In step S220, based on the geographic spatial data, the geographic spatial distribution information including the location and distribution of various public service facilities in the city is generated, which specifically includes the following steps: S221. Classify the geographic spatial data, divide the public service facilities, and obtain the classified geographic spatial data.

[0070] In this embodiment, this step involves the systematic classification and organization of the original geospatial data. The system will classify various public service facilities into different categories based on predefined classification criteria. When classifying, the system will analyze the attribute data of each facility, such as name, address, size, etc., and then automatically classify according to preset rules. For some facilities that are difficult to classify automatically, the system may mark them for manual review. After the classification is completed, each public service facility will be assigned one or more category labels, which together with the geographic coordinates of the facility constitute the classified geospatial data.

[0071] S222. Based on the classified geographic spatial data, identify the spatial distribution patterns of various types of public service facilities and obtain facility distribution clustering results.

[0072] S223. Obtain administrative division boundary data, calculate the density of various public service facilities in each administrative area, obtain a facility density distribution map, and generate a heat map of accessibility indicators for various public service facilities.

[0073] In this embodiment, the system first needs to integrate the administrative division boundary data and define the boundaries of each administrative district in the city. Then, the system will perform spatial overlay analysis on the classified public service facility data obtained in the previous step and these administrative boundaries, calculate the density of various facilities in each administrative district, and generate a heat map of accessibility indicators for various public service facilities.

[0074] S224. Generate geographic spatial distribution information based on the facility distribution clustering results, the facility density distribution map, and the accessibility index heat map.

[0075] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0076] On the second aspect, the present application provides a city management system based on big data. The city management system based on big data of the present application is described below in combination with the above-mentioned city management method based on big data.

[0077] Reference Figure 7 , a city management system based on big data, including: The overall management numerical information acquisition module is used to obtain the city management demand information, and obtain the overall management numerical information according to the demand information; The resource configuration parameter acquisition module is used to obtain geographic space distribution information, and obtain resource configuration parameters according to the geographic space distribution information and the overall management numerical information, wherein the geographic space distribution information includes the location and distribution of various public service facilities in the city, and the resource configuration parameters include the resource allocation and service frequency in each area; A management instruction triggering module is used to trigger management instructions according to resource configuration parameters and overall management value information; The deviation value evaluation module is used to monitor the regional status and obtain the regional status detection information, compare the regional status detection information with the corresponding target value in the overall management numerical information, and evaluate the deviation value between the actual status and the target status; The management adjustment instruction execution module is used to compare the deviation value with the preset change threshold. If the deviation value exceeds the preset change threshold range, an adjustment parameter is formulated according to the deviation value, and the corresponding management adjustment instruction is executed according to the adjustment parameter.

[0078] In one embodiment, the overall management value information acquisition module includes: The city data integration hub is used to collect data from multiple sources; Demand analysis module, used to analyze the city operation status and identify management needs based on the collected multi-source data; A demand information generation module is used to generate urban management demand information including management objectives, management time periods and resource requirements based on the analysis results; The city data integration hub includes: Data fusion processing platform, used to integrate data from different departments; The protocol parsing module includes a preset protocol parser, which is used to parse various data access protocols through the protocol parser, so that the data fusion processing platform can correctly read and process data from different sources.

[0079] In one embodiment, the overall management value information acquisition module further includes: A target extraction unit, used to extract a management target and a management time period from the demand information, and use the management target as a preset management target; A resource configuration calculation unit, used to obtain resource configuration time, service frequency time and resource configuration intensity according to the management time period, management objectives and resource requirements; The numerical information association unit is used to associate the preset management target, resource configuration time, service frequency time and resource configuration intensity to obtain the overall management numerical information.

[0080] In one embodiment, the resource configuration calculation unit includes: The spatiotemporal resource demand prediction model is used to input the management time period, management objectives and resource demand to obtain the predicted spatiotemporal resource demand distribution; A multi-objective optimization algorithm module is used to calculate resource allocation solutions based on the predicted spatiotemporal resource demand distribution, where the optimization objectives include resource utilization efficiency, service coverage, and response time; The resource configuration parameter generation module is used to obtain resource configuration time, service frequency time and resource configuration intensity based on the resource configuration plan.

[0081] In one embodiment, the resource configuration parameter acquisition module includes: Place name and address standardization and service sharing platform for processing geospatial data; A geographic space distribution information generation unit, used to generate geographic space distribution information including the location and distribution of various public service facilities in the city based on the geographic space data; Among them, the place name and address standardization and service sharing platform includes: Place name and address standardization tools, used to automatically correct and unify place name information; Place name database, used to store standardized place name address information; The place name service API includes a place name and address query module and a spatial analysis module. The place name and address query module is used to query place names and addresses, and the spatial analysis module is used to analyze and process geographic spatial data.

[0082] In one embodiment, the geographic space distribution information generating unit includes: A data classification module is used to classify geographic spatial data, divide public service facilities, and obtain classified geographic spatial data; The spatial distribution pattern recognition module is used to identify the spatial distribution patterns of various public service facilities based on the classified geographic spatial data and obtain the facility distribution clustering results; The density distribution calculation module is used to obtain administrative division boundary data, calculate the density of various public service facilities in each administrative area, obtain the facility density distribution map, and generate the accessibility index heat map of various public service facilities; The geographic space distribution information generation module is used to generate geographic space distribution information based on the facility distribution clustering results, facility density distribution map and accessibility index heat map.

[0083] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A city management method based on big data, characterized in that: The steps include: Obtaining city management demand information, and obtaining overall management numerical information based on the demand information; Acquire geographic space distribution information, and acquire resource configuration parameters according to the geographic space distribution information and the overall management numerical information, wherein the geographic space distribution information includes the location and distribution of various public service facilities in the city, and the resource configuration parameters include the resource allocation amount and service frequency in each area; triggering a management instruction according to the resource configuration parameter and the overall management value information; Monitor the regional status and obtain regional status detection information, compare the regional status detection information with the corresponding target value in the overall management numerical information, and evaluate the deviation value between the actual status and the target status; The deviation value is compared with a preset change threshold value. If the deviation value exceeds the preset change threshold range, an adjustment parameter is formulated according to the deviation value, and a corresponding management adjustment instruction is executed according to the adjustment parameter.

2. The city management method based on big data according to claim 1 is characterized in that: The obtaining of city demand information specifically includes the following steps: Collect multi-source data through the city data integration hub; Analyze the city's operating status and identify management needs based on the multi-source data collected; Based on the analysis results, generate urban management demand information including management objectives, management time period and resource requirements; The city data integration hub includes: Data fusion processing platform, used to integrate data from different departments; The protocol parsing module includes a preset protocol parser, which is used to parse various data access protocols through the protocol parser, so that the data fusion processing platform can correctly read and process data from different sources.

3. The city management method based on big data according to claim 2 is characterized in that: According to the demand information, the overall management value information is obtained, which specifically includes the following steps: Extracting the management target and the management time period from the demand information, and using the management target as a preset management target; According to the management time period, the management target and the resource demand, obtain resource configuration time, service frequency time and resource configuration intensity; The preset management target, resource configuration time, service frequency time and resource configuration intensity are associated to obtain overall management value information.

4. The city management method based on big data according to claim 3 is characterized in that: According to the management time period, the management target and the resource demand, obtaining the resource configuration time, the service frequency time and the resource configuration intensity specifically includes the following steps: Inputting the management time period, the management target and the resource demand into a preset spatiotemporal resource demand prediction model to obtain a predicted spatiotemporal resource demand distribution; Based on the predicted spatiotemporal resource demand distribution, a multi-objective optimization algorithm is used to calculate the resource allocation plan, where the optimization objectives include resource utilization efficiency, service coverage, and response time; Based on the resource configuration scheme, resource configuration time, service frequency time and resource configuration intensity are obtained.

5. The city management method based on big data according to claim 1 is characterized in that: The obtaining of geographic spatial distribution information specifically includes the following steps: Process geospatial data using the place name and address standardization and service sharing platform; Generating geospatial distribution information including the location and distribution of various public service facilities in the city based on the geospatial data; The place name and address standardization and service sharing platform includes: Place name and address standardization tools, used to automatically correct and unify place name information; Place name database, used to store standardized place name address information; The place name service API includes a place name address query module and a spatial analysis module. The place name address query module is used to query place name addresses, and the spatial analysis module is used to analyze and process geographic spatial data.

6. The city management method based on big data according to claim 5 is characterized in that: According to the geographic spatial data, the geographic spatial distribution information including the location and distribution of various public service facilities in the city is generated, which specifically includes the following steps: Classifying the geospatial data and dividing the public service facilities to obtain classified geospatial data; Based on the classified geospatial data, the spatial distribution patterns of various public service facilities are identified, and the clustering results of facility distribution are obtained; Obtain administrative division boundary data, calculate the density of various public service facilities in each administrative area, obtain the facility density distribution map, and generate the accessibility index heat map of various public service facilities; Based on the facility distribution clustering results, the facility density distribution map and the accessibility index heat map, geographic space distribution information is generated.

7. A city management system based on big data, characterized in that: include: The overall management numerical information acquisition module is used to acquire the city management demand information, and acquire the overall management numerical information according to the demand information; A resource configuration parameter acquisition module is used to acquire geographic space distribution information, and acquire resource configuration parameters according to the geographic space distribution information and the overall management value information, wherein the geographic space distribution information includes the location and distribution of various public service facilities in the city, and the resource configuration parameters include the resource allocation amount and service frequency in each area; A management instruction triggering module, used to trigger a management instruction according to the resource configuration parameters and the overall management value information; A deviation value evaluation module, used to monitor the regional state and obtain regional state detection information, compare the regional state detection information with the corresponding target value in the overall management numerical information, and evaluate the deviation value between the actual state and the target state; The management adjustment instruction execution module is used to compare the deviation value with a preset change threshold. If the deviation value exceeds the preset change threshold range, an adjustment parameter is formulated according to the deviation value, and a corresponding management adjustment instruction is executed according to the adjustment parameter.

8. The city management system based on big data according to claim 7 is characterized in that: The overall management value information acquisition module includes: The city data integration hub is used to collect data from multiple sources; Demand analysis module, used to analyze the city operation status and identify management needs based on the collected multi-source data; A demand information generation module is used to generate urban management demand information including management objectives, management time periods and resource requirements based on the analysis results; The city data integration hub includes: Data fusion processing platform, used to integrate data from different departments; The protocol parsing module includes a preset protocol parser, which is used to parse various data access protocols through the protocol parser, so that the data fusion processing platform can correctly read and process data from different sources.

9. The city management system based on big data according to claim 8 is characterized in that: The overall management value information acquisition module also includes: A target extraction unit, configured to extract the management target and the management time period from the demand information, and use the management target as a preset management target; A resource configuration calculation unit, configured to obtain resource configuration time, service frequency time and resource configuration intensity according to the management time period, the management target and the resource demand; The numerical information associating unit is used to associate the preset management target, resource configuration time, service frequency time and resource configuration intensity to obtain overall management numerical information.

10. The city management system based on big data according to claim 9 is characterized in that: The resource configuration calculation unit includes: A spatiotemporal resource demand prediction model, for inputting the management time period, the management target and the resource demand to obtain a predicted spatiotemporal resource demand distribution; A multi-objective optimization algorithm module is used to calculate resource allocation solutions based on the predicted spatiotemporal resource demand distribution, where the optimization objectives include resource utilization efficiency, service coverage, and response time; The resource configuration parameter generation module is used to obtain resource configuration time, service frequency time and resource configuration intensity based on the resource configuration plan.

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