Sunken urban global governance comprehensive management and control method and platform

Abstract

The invention discloses a sinking type city global governance comprehensive management and control method and platform, and relates to the technical field of city management, and the method comprises the steps: deploying intelligent sensing equipment at a key position of a target city, and capturing multi-dimensional city operation data in real time; the interactive social media platform is used for acquiring Internet feedback data of target city residents; carrying out cross-domain data integration on the obtained data, carrying out spatio-temporal data analysis, obtaining a city operation change rule, carrying out simulation decision simulation, and generating a regional governance decision; in the execution process, treatment effect feedback is collected, and strategy adjustment and algorithm model optimization are carried out. The technical problems of lack of effective information sharing and collaboration among departments, lagging of dynamic change response of the city and low timeliness and accuracy in a traditional city governance mode in the prior art are solved, and the technical effects of precision and high efficiency of city governance are achieved through sunken global governance comprehensive management and control.

Description

Technical Field

[0001] The present invention relates to the field of urban management technology, and in particular to a sinking comprehensive management and control method and platform for city-wide governance. Background Art

[0002] With the acceleration of urbanization, cities are facing increasingly complex management challenges, including traffic congestion, environmental pollution, frequent public safety incidents, and uneven resource allocation. Traditional urban governance models often rely on the subjective judgment of professionals and static data analysis, which cannot timely reflect the dynamic changes in urban operations and the real needs of residents. In addition, the phenomenon of information islands is serious, resulting in a lack of effective information sharing and coordination among departments, which affects the scientificity and effectiveness of governance decisions. Summary of the invention

[0003] The present application provides a sinking comprehensive management and control method and platform for the whole-area urban governance, which is used to solve the technical problems in the traditional urban governance mode in the existing technology, such as the lack of effective information sharing and coordination among departments, the delayed response to the dynamic changes of the city, and the low timeliness and accuracy.

[0004] The first aspect of the present application provides a sinking comprehensive management and control method for city-wide governance, the method comprising: deploying intelligent sensing equipment at key locations in the target city, and capturing multi-dimensional city operation data in real time based on the intelligent sensing equipment; interacting with social media platforms to obtain Internet feedback data from residents of the target city; cross-domain data integration of the multi-dimensional city operation data and the Internet feedback data to generate a city operation view; based on the city operation view, performing spatiotemporal data analysis to obtain the law of urban operation changes; performing simulation decision-making simulation based on the law of urban operation changes to generate regionalized governance decisions; during the execution of the regionalized governance decisions, collecting feedback on governance effects through an intelligent feedback mechanism, and adjusting strategies and optimizing algorithm models based on the feedback data.

[0005] The second aspect of the present application provides a sinking comprehensive management and control platform for the whole-area governance of the city, the platform comprising: a multi-dimensional city operation data acquisition module, the multi-dimensional city operation data acquisition module is used to deploy intelligent sensing equipment at key locations of the target city, and capture multi-dimensional city operation data in real time based on the intelligent sensing equipment; an Internet feedback data acquisition module, the Internet feedback data acquisition module is used to interact with social media platforms to obtain Internet feedback data of residents of the target city; a cross-domain data integration module, the cross-domain data integration module is used to perform cross-domain data integration of the multi-dimensional city operation data and the Internet feedback data to generate a city operation view; a spatiotemporal data analysis module, the spatiotemporal data analysis module is used to perform spatiotemporal data analysis based on the city operation view to obtain the law of urban operation changes; a simulation decision simulation module, the simulation decision simulation module is used to perform simulation decision simulation according to the law of urban operation changes to generate regionalized governance decisions; an intelligent feedback optimization module, the intelligent feedback optimization module is used to collect governance effect feedback through an intelligent feedback mechanism during the execution of the regionalized governance decision, and adjust the strategy and optimize the algorithm model according to the feedback data.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides a sinking comprehensive control method and platform for city-wide governance, which relates to the field of urban management technology. By deploying intelligent sensing equipment at key locations in the target city, multi-dimensional urban operation data can be captured in real time, and residents' feedback can be obtained through social media. These data are integrated to generate a city operation view, and spatiotemporal data analysis is performed to identify operation rules. Simulation decision-making simulations are performed based on these rules, and regional governance decisions are made. In the process of decision execution, effect feedback is collected through an intelligent feedback mechanism, and strategies are continuously adjusted and algorithm models are optimized. The technical problems of the traditional urban governance mode in the prior art, such as the lack of effective information sharing and coordination among departments, the delayed response to urban dynamic changes, and the low timeliness and accuracy, are solved. Through cross-domain data integration and spatiotemporal data analysis, combined with the intelligent feedback mechanism, comprehensive control of city-wide governance is carried out to achieve the technical effect of precise and efficient urban governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1A schematic flow chart of a sinking comprehensive management and control method for city-wide governance provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of the structure of a sinking urban comprehensive management and control platform provided in an embodiment of the present application.

[0010] Explanation of the accompanying drawings: multi-dimensional urban operation data acquisition module 11, Internet feedback data acquisition module 12, cross-domain data integration module 13, spatiotemporal data analysis module 14, simulation decision-making simulation module 15, intelligent feedback optimization module 16. DETAILED DESCRIPTION

[0011] The present application provides a sinking comprehensive management and control method and platform for the whole-area urban governance, which is used to solve the technical problems in the traditional urban governance mode in the existing technology, such as the lack of effective information sharing and coordination among departments, the delayed response to the dynamic changes of the city, and the low timeliness and accuracy.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a sinking comprehensive management and control method for the whole city governance, the method comprising: P10: Deploy smart sensing devices at key locations in target cities to capture multi-dimensional urban operation data in real time based on smart sensing devices.

[0015] Optionally, deploying smart sensing equipment at key locations in the target city, such as transportation hubs, environmental monitoring stations, and public facilities, is the first step to achieve city-wide governance. The smart sensing equipment, such as sensors, cameras, and monitoring equipment, can capture multi-dimensional urban operation data in real time, including traffic flow (referring to the number of vehicles and pedestrians passing through a specific road section), environmental quality (such as air quality index, noise level, and water quality), public safety incidents (such as fire, theft, or accidents), and public facility status (such as the working status of street lights, the filling level of trash cans, etc.).

[0016] When deploying smart sensor equipment, it is necessary to select locations with good coverage and reliability to ensure comprehensive data collection. For example, deploying traffic flow monitors near transportation hubs can obtain real-time information about the flow of vehicles and pedestrians, helping city managers analyze peak traffic hours and causes of congestion. At the same time, setting up sensors at environmental monitoring stations can regularly collect air and water quality data from the surrounding environment, thereby monitoring the environmental health of the city.

[0017] To achieve this process, wireless communication technology (such as IoT technology) can be used to ensure efficient and stable data transmission between sensors and data centers. IoT devices send collected data to the central database in real time through the network, forming a real-time updated urban data network. Through the data collection platform, managers can view these key indicators in real time and make analysis and decisions as needed.

[0018] The real-time capture of this multi-dimensional data not only provides a scientific basis for urban management, but also lays the foundation for subsequent urban operation analysis and governance decisions. By analyzing these data, potential problems in urban operations can be revealed, providing support for more intelligent urban management.

[0019] P20: Interact with social media platforms to obtain Internet feedback data from residents in target cities.

[0020] Specifically, in order to fully and deeply understand the needs, concerns and satisfaction of residents in the target city, it is possible to interact with major social media platforms to obtain rich Internet feedback data. The social media platforms may include mobile applications, social media websites, and official city websites. Through these platforms, city managers can establish effective communication channels with residents and collect residents' opinions, suggestions and complaints on urban governance in real time, thereby enhancing the transparency and participation of governance.

[0021] In order to achieve effective interaction with social media platforms, API (application programming interface) integration and crawler technology can be used. Through API integration, it is directly connected to the data system of social media platforms to obtain residents' discussions and feedback on various aspects of urban life in real time. At the same time, for some platforms with limited openness or requiring deep data mining, advanced crawler technology can be used to efficiently capture relevant data according to preset rules and algorithms. For example, crawler rules can be set by keywords such as "traffic congestion" or "public safety" to collect residents' comments on hot issues in the city.

[0022] After cleaning, classification and semantic analysis, the acquired data forms a structured resident feedback data set. For example, sentiment analysis, topic classification and semantic mining can be performed on text data. For example, through sentiment analysis technology, it is possible to automatically determine whether residents' comments are positive, negative or neutral, so as to quickly understand residents' satisfaction with urban governance measures. These data not only include residents' direct feelings about urban infrastructure, public services, environmental quality, etc., but also their expectations and suggestions for urban governance. After being sorted and analyzed, these Internet feedback data will be integrated with the multi-dimensional urban operation data collected by intelligent sensing devices to form a more comprehensive view of urban operation, which not only provides a more detailed perspective for urban management, but also makes governance decisions more targeted and effective. Through this interaction, city managers can better understand residents' needs and improve the satisfaction and effectiveness of governance measures.

[0023] P30: Perform cross-domain data integration on the multi-dimensional city operation data and the Internet feedback data to generate a city operation view.

[0024] Furthermore, step P30 of the embodiment of the present application further includes: P31: Divide the target city into multiple zones according to its regional scale and regional characteristics; P32: Obtain the central points of the multiple zones and deploy multiple edge computing nodes, which are communicated and connected with the intelligent sensor devices in each zone; P33: Based on the multiple edge computing nodes, perform cross-domain data integration on the multi-dimensional city operation data and the Internet feedback data to generate a city operation view.

[0025] It should be understood that in the process of implementing urban governance, cross-domain data integration of multi-dimensional urban operation data and Internet feedback data to generate a city operation view is a key step.

[0026] First, the target city is divided into multiple zones according to its regional size and characteristics. These zones can be divided based on factors such as geography, function or population density, with the aim of more effectively managing and analyzing the operation of different areas. For example, the governance strategies and concerns for busy commercial areas will be different from those for quiet residential areas. For busy commercial areas, the collection of traffic flow data can be increased, while in environmentally sensitive industrial areas, priority should be given to collecting pollutant emission data.

[0027] Next, obtain the central point of each partition and deploy multiple edge computing nodes. These edge computing nodes are the core nodes connecting the intelligent sensor devices in each area, responsible for receiving and processing data from sensors in real time. The advantage of edge computing is that it can be processed near the data source, reducing latency, and improving the efficiency and response speed of data processing. At the same time, edge computing nodes can reduce dependence on central servers and optimize bandwidth usage. The deployment of the edge computing nodes can be based on the density of urban hot spots, and the number and distribution of nodes can be dynamically adjusted through a coverage optimization algorithm. For example, the K-means clustering algorithm is used to group the data points of the sensor equipment, determine the center position of each partition, and then adjust the number and distribution of nodes according to traffic flow and equipment coverage to ensure the comprehensiveness of data collection in each area.

[0028] Finally, based on the multiple edge computing nodes established, cross-domain data integration is performed on multi-dimensional urban operation data and Internet feedback data. Through edge computing nodes, data from different sources are summarized and analyzed in real time to generate a city operation view. This process includes multiple links such as data collection, cleaning, conversion and fusion. Specifically, edge computing nodes receive data such as traffic flow, environmental quality, and public facility status from smart sensor devices in real time, and compare and associate them with resident feedback data obtained from social media platforms. Exemplarily, data cleaning is first performed using a time series-based anomaly detection algorithm (such as the LOF algorithm) to remove mutated sensor data or irrelevant Internet feedback. Then, the cleaned multi-source data is matched and associated through key fields such as timestamps and geographic locations. For example, the dynamic time warping algorithm is used to align sensor data and feedback data of different time scales to form a consistent spatiotemporal distribution. Finally, weights are assigned to data based on the credibility of the data source, the frequency of data collection, etc., to achieve a unified expression of data. For example, traffic flow data can be given a higher weight, while data with strong subjectivity in resident feedback can be given a relatively lower weight.

[0029] Combined with algorithms and models, these data are integrated into a unified and coherent view of urban operations. This view includes key indicators of the city, such as traffic flow, air quality, public safety incidents and resident feedback, providing a comprehensive and dynamic view of the city's operating status. For example, data integration tools and API interfaces can be used to achieve seamless docking and integration of data, ensuring the compatibility and availability of data between different sources.

[0030] Through the above steps, city managers can obtain in-depth information on urban operations, provide data support for subsequent decision-making, and promote intelligent and refined urban governance.

[0031] Furthermore, step P33 of the embodiment of the present application also includes: P33-1: Based on the multiple edge computing nodes, the multi-dimensional city operation data and the Internet feedback data are cleaned in different regions; P33-2: Based on the data key fields, the cleaned multi-dimensional city operation data and the Internet feedback data are integrated across domains to obtain data association results, wherein the data key fields include but are not limited to timestamp and geographic location; P33-3: Based on the data association results, a city operation view is generated, and the city operation view includes city key indicators and time data series.

[0032] Optionally, to ensure the effective integration of multi-dimensional urban operation data and Internet feedback data, first, based on multiple edge computing nodes, the collected multi-dimensional urban operation data and Internet feedback data are cleaned by region. The purpose of this process is to remove noise and redundant information in the data and improve the quality and accuracy of the data. The data cleaning process may include removing duplicate records, processing missing values, correcting data formats, and standardizing data types. Using data cleaning tools and algorithms (such as the Pandas library in Python), this process can be automated to improve processing efficiency and reduce human errors. For example, for traffic flow data collected by sensor equipment, time series anomaly detection algorithms (such as the local outlier factor algorithm) can be used to identify and filter mutation values; for social media feedback data, natural language processing technology can be used for keyword extraction and sentiment classification to remove irrelevant or emotional information.

[0033] Next, the cleaned data is integrated across domains based on key fields to obtain data association results. The key data fields include timestamps and geographic locations, where timestamps can record the specific time when data is generated, and geographic locations refer to the location where data is collected. These fields are crucial for identifying and associating different data sources. By using the data integration platform and API, the cleaned urban operation data can be effectively connected with the Internet feedback data to generate data association results. Exemplarily, during the integration process, a dynamic time warping algorithm can be used to solve the alignment problem caused by different sampling frequencies in the time series, and the sampling points can be adjusted to align the time axis so that data from different sources can be consistent in the time dimension. At the same time, for the matching of geographic locations, a range search based on longitude and latitude can be introduced to quickly locate the specific areas mentioned in the residents' feedback, associate with the sensor data, and obtain data association results, including the key indicators of all partitions and their spatiotemporal distribution. This cross-domain integration not only improves the availability of data, but also lays the foundation for subsequent analysis.

[0034] Finally, based on the data association results, a city operation view is generated. This view integrates the city's key indicators (such as real-time traffic flow, air quality, public safety incidents, etc.) and time data series to show the dynamic operation status of the city. Through data visualization tools (such as Tableau or Power BI), these key indicators are presented in the form of charts, heat maps or time series charts. The view generation process includes indicator calculation, dynamic update and abnormal annotation. For example, when the traffic flow in a certain area exceeds the set threshold, the area can be highlighted in the view, and prompts and recommended governance measures are provided. Make the data more intuitive and easy to understand. In addition, the city operation view also supports multi-dimensional data analysis and mining, helping decision makers to have a deeper understanding of the city's operation status and potential problems, so as to formulate more accurate and effective governance strategies.

[0035] P40: Based on the city operation view, conduct spatiotemporal data analysis to obtain the changing rules of city operation.

[0036] Furthermore, step P4 of the embodiment of the present application also includes: P41: Based on the city operation view, multi-dimensional data extraction is performed according to the city key indicators to obtain multiple key indicator data sequences, and the key indicator data sequences include timestamp and geographic location information; P42: A spatiotemporal analysis model is constructed, and the multiple key indicator data sequences are input into the spatiotemporal analysis model for analysis to obtain the changing rules of the city operation.

[0037] In a possible embodiment of the present application, performing spatiotemporal data analysis based on the city operation view to obtain the laws of change in urban operation is an important part of urban governance.

[0038] To achieve this goal, firstly, multi-dimensional data extraction is performed according to the key indicators of the city based on the city operation view. This process aims to extract multiple key indicator data series from the integrated data set. These series contain timestamp and geographic location information, which are crucial information for analyzing the laws of changes in urban operation. By using data analysis tools (such as the Pandas library in Python or R language), time series data of each key indicator can be easily extracted and sorted. These key indicators may include traffic flow, air quality index, resident satisfaction, etc., providing multi-angle insights into the operation status of the city. Exemplarily, in the extraction process, the time and space resolution of the indicator is first determined according to its nature. For example, traffic flow indicators may need to be recorded at time intervals of minutes, while air quality indexes are more reasonably recorded at hourly levels. The resolution of geographic location depends on regional divisions, such as by roads, blocks or administrative districts.

[0039] To ensure the integrity and accuracy of the extracted data, the time window sliding method can be used to smooth the traffic flow data and remove abnormal peaks; for resident feedback data, the sentiment analysis algorithm can be used to extract keywords reflecting satisfaction and map their geographic locations to specific areas. The key indicator data sequence finally generated, that is, a multidimensional data set containing time, space and attribute values, lays the foundation for spatiotemporal analysis.

[0040] Furthermore, a spatiotemporal analysis model is constructed, and the extracted data series of multiple key indicators are input into the model for analysis. The spatiotemporal analysis model can process the time and space dimensions of data to reveal the changing laws of urban operations. By using machine learning algorithms (such as time series analysis, regression analysis, cluster analysis, etc.), potential trends, cyclical changes and anomalies in the data can be identified.

[0041] For example, using time series prediction algorithms (such as ARIMA models or LSTM neural networks), we can predict the changing trends of traffic flow and provide a basis for decision-making in urban traffic management. In addition, spatial analysis technologies (such as geographic information systems GIS) can help analyze the differences between different regions and identify the abnormal performance of specific regions in certain key indicators, thereby providing support for targeted governance measures.

[0042] For example, input the traffic flow time series and use LSTM to predict the traffic flow changes in each area within the next hour. For environmental quality data, use ST-GCN to capture the correlation between pollutant concentrations in different regions and analyze the pollution propagation path. Use fast Fourier transform (FFT) to perform frequency domain analysis on time series data to identify traffic peaks and periodic characteristics of environmental quality changes. Extract seasonal changes in resident feedback data, such as the impact of holidays on traffic flow and public service satisfaction. Based on GIS (geographic information system), analyze the spatial dependencies between regions, such as using heat maps to show the spatial diffusion pattern of traffic congestion. Use the GWR model to identify the unique impact of certain areas, such as the significant contribution of industrial areas to surrounding air quality.

[0043] Furthermore, according to the analysis results, the dynamic change rules, spatial distribution rules and resident feedback rules are extracted. Among them, the dynamic change rules include the peak trend of traffic flow in different time periods, such as the surge in traffic during the morning and evening peak hours; the changes in environmental quality in different seasons, such as the higher concentration of pollutants in winter. The spatial distribution rules include the distribution characteristics of pollutant concentrations in various areas of the city; the propagation path and impact range of traffic congestion between different areas. The resident feedback rules include the distribution of residents' satisfaction with the governance measures at a specific time and place; the correlation between feedback opinions and actual operation data, such as the consistency of traffic congestion complaints and flow data.

[0044] Furthermore, data visualization tools (such as Tableau and Power BI) are used to present the analysis results as dynamic charts, including time series charts to show the dynamic trends of traffic flow and air quality. Heat maps are used to show the spatial distribution of resident satisfaction and environmental quality. Interactive dashboards are used to support city managers to view the operating status of specific areas and time periods in real time.

[0045] Through this series of analyses, city managers can gain in-depth insights into the changing patterns of urban operations, including traffic patterns during peak hours, seasonal fluctuations in environmental quality, and the temporal characteristics of residents’ feedback. This not only provides a scientific basis for urban governance, but also lays the foundation for formulating dynamic adjustment strategies and optimizing resource allocation.

[0046] Furthermore, to construct a spatiotemporal analysis model, step P41 of the embodiment of the present application further includes: P41-1: According to the key indicator type, match the spatiotemporal analysis method to perform deep model training to obtain the spatiotemporal analysis model; P41-2: Wherein, the spatiotemporal analysis model includes an operation data analysis unit, a public feedback analysis unit and a fusion analysis unit, and the fusion analysis unit fuses the results according to the timestamp and geographic location information.

[0047] Specifically, the specific process of building a spatiotemporal analysis model can be, first, according to the type of key indicators, such as traffic flow, matching the appropriate spatiotemporal analysis method for deep model training. This process involves selecting appropriate algorithms (such as deep learning models, time series analysis methods, etc.) to process different types of key indicator data. For example, for traffic flow prediction, recurrent neural networks (RNN) or long short-term memory networks (LSTM) can be used. These algorithms are good at processing time series data and can capture long-term dependencies and nonlinear characteristics in the data. In this process, the model is optimized through the training data set to ensure that the model can accurately identify and predict the changing laws of urban operations.

[0048] Among them, the spatiotemporal analysis model includes three main units: an operation data analysis unit, a public feedback analysis unit, and a fusion analysis unit. The operation data analysis unit is responsible for processing multi-dimensional urban operation data, analyzing the changing trends of indicators such as traffic flow and environmental quality through statistical and machine learning methods, identifying key operation modes and abnormal events, and using LSTM to predict future trends, such as changes in traffic flow during peak hours in the morning and evening, and building a heat map based on CNN to intuitively display the regional distribution of environmental pollution. The public feedback analysis unit focuses on analyzing the opinions and suggestions provided by residents through social media and feedback platforms, using natural language processing technology to perform sentiment analysis on feedback, and evaluating the public's satisfaction and concerns about urban governance measures. For example, the bag-of-words model is used for topic classification to identify residents' specific concerns about traffic congestion and air quality, and to judge residents' satisfaction with existing governance measures through sentiment analysis, and the results are output in the form of scores.

[0049] The fusion analysis unit is responsible for combining the operation data and the public feedback data, and fusing the results based on the timestamp and geographic location information. The key to this unit is to identify the correlation between different data sources, so as to form a comprehensive view. Exemplarily, the analysis results are weighted and fused according to the reliability of the data source. For example, sensor data is given a higher weight, while the subjective opinion in the public feedback is relatively low. After weighted fusion, the data of different time series are aligned through dynamic time warping (DTW) to ensure the consistency of the results in the time dimension. Finally, the data of the time dimension is spatially aligned to match the geographic location of the data from different sources, and a comprehensive spatiotemporal analysis result is generated, that is, the law of urban operation change. The law of urban operation change not only includes the dynamic trend of key indicators in different urban areas, but also includes the response and satisfaction of the public in the region to governance measures, which can provide a comprehensive reference for urban managers.

[0050] Through the above steps, the constructed spatiotemporal analysis model can achieve in-depth analysis of the changing laws of urban operation. This analysis not only provides a scientific basis for urban managers, helps to formulate more accurate governance strategies, but also enhances the adaptability and response speed of governance measures.

[0051] P50: Based on the changing laws of urban operation, conduct simulation decision-making to generate regional governance decisions.

[0052] Furthermore, step P50 of the embodiment of the present application also includes: P51: Clarify the decision-making objectives of the simulation decision-making, which include but are not limited to improving traffic flow, improving environmental quality, and improving public safety; P52: Based on the decision-making objectives, define key variables and build a simulation model; P53: Based on the law of change in urban operation, conduct an operation effectiveness evaluation, and generate multiple temporary governance decisions based on the evaluation results; P54: Based on the simulation model, simulate the multiple temporary governance decisions, and based on the simulation results, prioritize the various governance plans to obtain the optimal decision plan as the regional governance decision.

[0053] It should be understood that simulation decision-making simulations should be conducted according to the laws of change in urban operation to generate regionalized governance decisions to promote the sustainable development of cities.

[0054] First, the decision goals of the simulation need to be clear. These goals may include improving traffic flow (by reducing congestion and improving traffic efficiency), improving environmental quality (such as reducing air pollution and improving greening rate), and improving public safety (by reducing accident rates and improving emergency response speed). Clear decision goals can provide a clear direction for subsequent model construction and decision simulation.

[0055] Furthermore, according to the decision-making objectives, key variables are defined and simulation models are constructed. Key variables may include the rate of change of traffic flow, air quality index, and the use of public facilities. By using modeling methods such as system dynamics or discrete event simulation, a simulation model that can reflect the characteristics of urban operation and the impact of governance measures is constructed. These models usually require parameter adjustment based on historical data to ensure their accuracy and reliability. Technically, simulation software such as MATLAB, Simul8, or AnyLogic can be used to build and run the model.

[0056] Next, the operation effectiveness is evaluated based on the law of urban operation changes. By analyzing the operation effect of existing governance measures, their impact on traffic flow, environmental quality and public safety is evaluated, including the use of quantitative indicators (such as average vehicle waiting time, pollutant concentration reduction) to evaluate the governance measures and obtain their effectiveness. Based on the evaluation results, multiple temporary governance decisions are generated, such as adjusting the traffic light cycle, adding environmental protection facilities, and strengthening public safety monitoring. These temporary decisions will serve as an important basis for the input of the simulation model.

[0057] Finally, based on the constructed simulation model, multiple temporary governance decisions are simulated. This process involves testing different governance schemes to observe their performance and effects under specific conditions. For example, simulate the traffic flow distribution under different signal light cycles; test the improvement effect of different greening rate increases on PM2.5 concentration; simulate the reduction of accident response time due to increased monitoring coverage. By analyzing the simulation results, the various governance schemes can be prioritized, the measures with the greatest impact can be identified, and the optimal decision scheme can be finally obtained as a regional governance decision. Technically, sensitivity analysis and multi-objective optimization methods can be used to ensure the optimal balance of the selected scheme in terms of cost, effect and implementation difficulty. For example, using the TOPSIS algorithm, a decision matrix is ​​generated based on the evaluation indicators of multiple governance schemes (such as congestion index reduction, PM2.5 concentration reduction, and implementation cost), and the performance of each scheme on multiple evaluation indicators is dimensionless and standardized. Then, weights are assigned according to the importance of the indicators, and the Euclidean distance between each scheme and the positive ideal solution (best value combination) and the negative ideal solution (worst value combination) is calculated. Through the relative closeness formula, the degree to which each plan is close to the positive ideal solution is evaluated, and the plans are sorted by closeness, and the plan with the best closeness is selected as the regional governance decision.

[0058] Through this series of steps, urban managers can make more scientific and reasonable regional governance decisions, thereby improving the effectiveness and sustainability of urban governance.

[0059] P60: During the implementation of the regionalized governance decision, feedback on governance effectiveness is collected through an intelligent feedback mechanism, and strategy adjustments and algorithm model optimization are made based on the feedback data.

[0060] Furthermore, step P60 of the embodiment of the present application also includes: P61: Establish a smart feedback mechanism by setting up multiple feedback channels and collecting feedback on governance effects, which includes feedback data on multiple key governance indicators; P62: Perform quantitative and qualitative analysis based on the feedback data on the multiple key governance indicators to obtain governance evaluation results; P63: According to the governance evaluation results, optimize the governance strategy, and perform adaptive learning optimization on the spatiotemporal analysis model based on the optimization results.

[0061] Optionally, collecting feedback on governance effectiveness through an intelligent feedback mechanism and making strategy adjustments and algorithm model optimization accordingly is a key link in ensuring continuous improvement in urban governance.

[0062] First, a smart feedback mechanism will be established to collect feedback on governance effects by setting up multiple feedback channels. These feedback channels can include mobile applications, social media platforms, questionnaires, telephone hotlines, etc., to ensure that residents and relevant stakeholders can easily express their views and suggestions on governance measures. The governance effect feedback collected will include feedback data on multiple key governance indicators, such as changes in traffic flow, environmental quality monitoring results, and resident satisfaction survey data. This diversified feedback mechanism can increase resident participation and ensure more representative decision-making.

[0063] Furthermore, quantitative and qualitative analysis is conducted based on the collected feedback data of multiple key governance indicators. Quantitative analysis can evaluate the actual impact of governance measures on key indicators through statistical methods (such as regression analysis, variance analysis, etc.), while qualitative analysis can identify the issues that residents are generally concerned about and the aspects that they are satisfied with by classifying the themes of residents' feedback. This analysis process can be carried out using data analysis tools (such as R language, NumPy and Pandas libraries in Python), so as to systematically evaluate the governance effect and obtain governance evaluation results.

[0064] Finally, the governance strategy is tuned based on the governance evaluation results. Specifically, governance measures that perform poorly on certain key indicators can be identified and modified or replaced. At the same time, based on the tuning results, the spatiotemporal analysis model is adaptively learned and optimized. This process involves updating the model parameters and structure to reflect the latest governance effects and feedback information. Using machine learning techniques (such as reinforcement learning or transfer learning), the model can gradually improve the accuracy of predictions and the understanding of the laws of urban operation, thereby enhancing the pertinence and effectiveness of governance measures.

[0065] Through this series of feedback and optimization steps, a dynamically adjusted governance system will be formed to continuously improve the scientific nature and adaptability of urban governance, ensuring that governance measures can meet the needs of residents and achieve sustainable development.

[0066] In summary, the embodiments of the present application have at least the following technical effects: This application deploys intelligent sensing equipment at key locations in the city to capture multi-dimensional urban operation data in real time, and uses interactive social media platforms to obtain feedback from residents, integrate these data, generate a city operation view, and then conduct spatiotemporal data analysis to reveal the laws of urban operation, conduct simulation decision-making simulations based on the analysis results, and make regional governance decisions. During the implementation process, feedback on governance effects is collected through an intelligent feedback mechanism, and strategy adjustments and algorithm optimization are made based on feedback data, thereby achieving dynamic and scientific urban management.

[0067] The technical effect of achieving precise and efficient urban governance has been achieved through cross-domain data integration and spatiotemporal data analysis, combined with an intelligent feedback mechanism, to conduct comprehensive management and control of the entire region.

[0068] Embodiment 2 is based on the same inventive concept as the above-mentioned embodiment of a sinking city-wide comprehensive management and control method, such as Figure 2 As shown, the present application provides a sinking city-wide comprehensive management and control platform, and the platform and method embodiments in the embodiments of the present application are based on the same inventive concept. Among them, the platform includes: The multi-dimensional city operation data acquisition module 11 is used to deploy intelligent sensing devices at key locations in the target city and capture multi-dimensional city operation data in real time based on the intelligent sensing devices.

[0069] The Internet feedback data acquisition module 12 is used to interact with the social media platform to acquire Internet feedback data of residents in the target city.

[0070] The cross-domain data integration module 13 is used to perform cross-domain data integration on the multi-dimensional city operation data and the Internet feedback data to generate a city operation view.

[0071] The spatiotemporal data analysis module 14 is used to perform spatiotemporal data analysis based on the city operation view to obtain the law of changes in city operation.

[0072] The simulation decision module 15 is used to perform simulation decision simulation according to the changing rules of urban operation and generate regionalized governance decisions.

[0073] The intelligent feedback optimization module 16 is used to collect feedback on governance effects through an intelligent feedback mechanism during the execution of the regionalized governance decision, and to adjust strategies and optimize algorithm models based on the feedback data.

[0074] Furthermore, the domain data integration module 13 is also used to perform the following steps: The target city is divided into multiple zones according to its regional scale and regional characteristics; the central points of the multiple zones are obtained, and multiple edge computing nodes are deployed, and the edge computing nodes are communicatively connected with the intelligent sensor devices in each zone; based on the multiple edge computing nodes, the multi-dimensional city operation data and the Internet feedback data are integrated across domains to generate a city operation view.

[0075] Furthermore, the domain data integration module 13 is also used to perform the following steps: Based on the multiple edge computing nodes, the multi-dimensional city operation data and the Internet feedback data are cleaned in different regions; based on the data key fields, the cleaned multi-dimensional city operation data and the Internet feedback data are integrated across domains to obtain data association results, wherein the data key fields include but are not limited to timestamps and geographic locations; based on the data association results, a city operation view is generated, and the city operation view includes city key indicators and time data series.

[0076] Furthermore, the spatiotemporal data analysis module 14 is also used to perform the following steps: Based on the city operation view, multi-dimensional data extraction is performed according to the city key indicators to obtain multiple key indicator data sequences, and the key indicator data sequences include timestamp and geographic location information; a spatiotemporal analysis model is constructed, and the multiple key indicator data sequences are input into the spatiotemporal analysis model for analysis to obtain the changing rules of the city operation.

[0077] Furthermore, the spatiotemporal data analysis module 14 is also used to perform the following steps: According to the key indicator type, the spatiotemporal analysis method is matched to perform deep model training to obtain the spatiotemporal analysis model; wherein the spatiotemporal analysis model includes an operation data analysis unit, a public feedback analysis unit and a fusion analysis unit, and the fusion analysis unit fuses the results according to the timestamp and geographic location information.

[0078] Furthermore, the simulation decision module 15 is also used to perform the following steps: Clarify the decision objectives of the simulation decision-making simulation, which include but are not limited to improving traffic flow, improving environmental quality, and improving public safety; define key variables and build a simulation model based on the decision objectives; conduct an operation effectiveness evaluation based on the law of change of urban operation, and generate multiple temporary governance decisions based on the evaluation results; simulate the multiple temporary governance decisions based on the simulation model, and prioritize the various governance plans based on the simulation results to obtain the optimal decision plan as the regional governance decision.

[0079] Furthermore, the intelligent feedback optimization module 16 is further configured to perform the following steps: By setting up multiple feedback channels, an intelligent feedback mechanism is established, and feedback on governance effects is collected, wherein the feedback on governance effects includes feedback data on multiple key governance indicators; quantitative and qualitative analysis is performed based on the feedback data on the multiple key governance indicators to obtain governance evaluation results; based on the governance evaluation results, the governance strategy is tuned, and the spatiotemporal analysis model is adaptively learned and optimized based on the tuning results.

[0080] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0082] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A sinking comprehensive management and control method for the whole city, characterized by: The method comprises: Deploy smart sensor devices at key locations in target cities to capture multi-dimensional city operation data in real time based on smart sensor devices; Interactive social media platforms to obtain Internet feedback data from residents in target cities; Performing cross-domain data integration on the multi-dimensional city operation data and the Internet feedback data to generate a city operation view; Based on the city operation view, perform spatiotemporal data analysis to obtain the changing rules of city operation; According to the changing laws of urban operation, simulate decision-making and generate regional governance decisions; During the execution of the regionalized governance decisions, feedback on governance effectiveness is collected through an intelligent feedback mechanism, and strategy adjustments and algorithm model optimization are performed based on the feedback data.

2. A sinking comprehensive management and control method for the whole city as claimed in claim 1, characterized in that: The multi-dimensional city operation data and Internet feedback data are integrated across domains to generate a city operation view, including: Divide the target city into multiple sub-regions according to its regional scale and regional characteristics; Obtaining the central points of the multiple partitions, deploying multiple edge computing nodes, and the edge computing nodes are communicatively connected with the intelligent sensor devices in each area; Based on the multiple edge computing nodes, the multi-dimensional city operation data and the Internet feedback data are integrated across domains to generate a city operation view.

3. A sinking comprehensive management and control method for the whole city as claimed in claim 2, characterized in that: Based on the multiple edge computing nodes, cross-domain data integration is performed on the multi-dimensional city operation data and the Internet feedback data, including: Based on the multiple edge computing nodes, performing regional data cleaning on the multi-dimensional city operation data and the Internet feedback data; Based on key data fields, cross-domain data integration is performed on the cleaned multi-dimensional city operation data and the Internet feedback data to obtain data association results, wherein the key data fields include but are not limited to timestamps and geographic locations; According to the data association result, a city operation view is generated, and the city operation view includes key city indicators and time data series.

4. A sinking comprehensive management and control method for the whole city as claimed in claim 3, characterized in that: Based on the city operation view, spatiotemporal data analysis is performed to obtain the changing rules of city operation, including: Based on the city operation view, multi-dimensional data extraction is performed according to the city key indicators to obtain multiple key indicator data sequences, wherein the key indicator data sequences include timestamp and geographic location information; A spatiotemporal analysis model is constructed, and the plurality of key indicator data sequences are input into the spatiotemporal analysis model for analysis to obtain the changing rules of the city operation.

5. A sinking comprehensive management and control method for the whole city as claimed in claim 4, characterized in that: Constructing spatiotemporal analysis models, including: According to the key indicator type, matching the spatiotemporal analysis method to perform deep model training to obtain the spatiotemporal analysis model; The spatiotemporal analysis model includes an operation data analysis unit, a public feedback analysis unit and a fusion analysis unit, and the fusion analysis unit fuses the results according to the timestamp and geographic location information.

6. A sinking comprehensive management and control method for the whole city as claimed in claim 1, characterized in that: According to the changing laws of urban operation, simulation decision-making is carried out to generate regional governance decisions, including: Clarify the decision-making objectives of the simulation, which include but are not limited to improving traffic flow, improving environmental quality, and improving public safety; Based on the decision-making objectives, key variables are defined and a simulation model is constructed; Based on the changing laws of urban operation, conduct an evaluation of the operation effectiveness and generate multiple temporary governance decisions based on the evaluation results; Based on the simulation model, the multiple temporary governance decisions are simulated, and based on the simulation results, the various governance plans are prioritized to obtain the optimal decision plan as the regionalized governance decision.

7. A sinking comprehensive management and control method for the whole city as claimed in claim 5, characterized in that: During the execution of the regionalized governance decision, feedback on governance effects is collected through an intelligent feedback mechanism, and strategy adjustments and algorithm model optimization are performed based on the feedback data, including: By setting up multiple feedback channels, establishing an intelligent feedback mechanism, and collecting feedback on governance effects, the governance effect feedback includes feedback data on multiple key governance indicators; Performing quantitative and qualitative analysis based on the feedback data of the multiple key governance indicators to obtain governance evaluation results; According to the governance assessment results, the governance strategy is tuned, and the spatiotemporal analysis model is adaptively learned and optimized according to the tuning results.

8. A sinking comprehensive management and control platform for the whole city, characterized by: The platform includes: A multi-dimensional city operation data acquisition module, which is used to deploy intelligent sensing devices at key locations in the target city and capture multi-dimensional city operation data in real time based on the intelligent sensing devices; An Internet feedback data acquisition module, which is used to interact with social media platforms to acquire Internet feedback data of residents in the target city; A cross-domain data integration module, the cross-domain data integration module is used to perform cross-domain data integration on the multi-dimensional city operation data and the Internet feedback data to generate a city operation view; A spatiotemporal data analysis module, which is used to perform spatiotemporal data analysis based on the city operation view to obtain the law of changes in city operation; A simulation decision-making module, which is used to perform simulation decision-making simulation according to the law of changes in the operation of the city and generate regional governance decisions; An intelligent feedback optimization module is used to collect feedback on governance effects through an intelligent feedback mechanism during the execution of the regionalized governance decision, and to adjust strategies and optimize algorithm models based on the feedback data.

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