Smart city monitoring management method based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a smart city monitoring management method based on artificial intelligence, comprising the following steps: step A: data acquisition and preprocessing: acquiring data related to city operation from a plurality of heterogeneous data sources; step B, cross-domain data fusion: carrying out feature extraction and fusion on the preprocessed data; c, performing intelligent analysis and prediction, and applying a machine learning algorithm; and step D, decision support and response: transmitting an analysis result and prediction and early warning information to urban managers and related departments through the system. By introducing an artificial intelligence technology, a deep learning model, a transfer learning technology and a semantic web technology or ontology method, the problem of cross-domain data fusion is effectively solved. The methods can process and analyze heterogeneous data from different data sources and convert the data into a unified and comparable format, so that seamless joint and integration of cross-domain data are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, specifically a smart city monitoring and management method based on artificial intelligence. BACKGROUND

[0002] With the acceleration of urbanization, cities are facing many challenges such as environmental pollution, traffic congestion, energy consumption, public safety, etc. These problems not only affect the sustainable development of the city, but also reduce the quality of life of residents. Traditional city monitoring and management methods mainly rely on manual inspection and simple data analysis, which is inefficient and difficult to cover comprehensively.

[0003] After searching, the patent with patent number CN117994112A discloses a smart city monitoring and management method and system based on multi-domain data integration analysis, which belongs to the field of data analysis. The present application obtains administrative boundary data and city area data of the city; according to the administrative boundary data and city area data, the city is divided into multiple regions with equal area; based on power supply data, water supply data, and natural gas supply data, the supply index of all regions is calculated; based on road repair data and noise data, the environmental index of all regions is calculated; based on traffic data and garbage recycling data, the maintenance index of all regions is calculated. The present application accurately monitors various fields of city management and evaluates the level of city management.

[0004] After searching, the patent with patent number CN115860983A discloses a smart city gas safety monitoring and operation management method and system based on the Internet of Things, which belongs to the field of city gas safety monitoring and operation management technology. The method includes: specifying city area division, integrating and classifying gas users, obtaining gas user basic parameters, analyzing gas user basic parameters, screening gas meter replacement users, obtaining target replacement user parameters, integrating target replacement users, and transmitting gas meter replacement time period information. The present application can effectively improve the matching degree between the actual replacement time point of the gas meter and the demand replacement time point of the gas meter, thereby reducing the safety hazards of the gas meter. Not only is it conducive to the coordinated management of gas meter replacement work, but also provides reliable data support for the replacement of gas meters.

[0005] However, the existing monitoring management method has difficulty in cross-domain data fusion, the data from different sources has heterogeneity, how to effectively fuse these cross-domain data to provide more comprehensive and accurate environmental information is a problem to be solved, secondly, the existing monitoring management system has limitations in prediction and early warning, cannot accurately predict the trend and potential risk of environmental change, the monitoring system has hysteresis, it is difficult to provide timely and effective support for government decision-making, based on this, the present application designs a smart city monitoring management method based on artificial intelligence to solve the above problems. SUMMARY

[0006] The present application aims to provide a smart city monitoring management method based on artificial intelligence, which solves the problems of data fusion and inaccurate prediction in the background art.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] The smart city monitoring management method based on artificial intelligence comprises the following steps:

[0009] Step A: data acquisition and preprocessing, acquiring city operation related data from multiple heterogeneous data sources and preprocessing;

[0010] Step B: cross-domain data fusion, feature extraction and fusion of preprocessed data, construction of unified data model or data warehouse;

[0011] Step C: intelligent analysis and prediction, applying machine learning algorithm to deeply analyze city operation state data, establishing prediction model, and setting warning threshold according to prediction result, realizing early warning;

[0012] Step D: decision support and response, transmitting analysis result and prediction warning information to city managers and relevant departments through system, automatically generating or recommending emergency response scheme according to warning level and emergency degree.

[0013] Preferably, in step A, the following steps are included: step a, acquiring city operation related data from multiple heterogeneous data sources, including but not limited to environmental monitoring data, traffic data, energy consumption data, public safety data; step b, preprocessing the acquired data, including data cleaning, format conversion, missing value filling, outlier detection and processing, to ensure data quality.

[0014] Preferably, in step B, the following steps are included: step a, using artificial intelligence technology, neural network model in deep learning or transfer learning technology, feature extraction and fusion of preprocessed data, solving data heterogeneity problem; step b, building a unified data model or data warehouse, realizing seamless connection and integration of cross-domain data, forming a comprehensive city operation status data set.

[0015] Preferably, in step C, the following steps are included: step a, applying machine learning algorithm, through time series analysis, regression analysis, clustering analysis, association rule mining, in-depth analysis of city operation status data, identifying potential problems and trends; step b, establishing prediction model, long short-term memory network (LSTM), support vector machine (SVM) or random forest, predicting future trends of key indicators, including environmental pollution level, traffic congestion condition, energy consumption trend; step c, according to the prediction result, setting early warning threshold, realizing early warning of potential risks such as environmental change and public safety event.

[0016] Preferably, in step D, the following steps are included: step a, delivering analysis results and prediction warning information to city managers and relevant departments through visual interface or automatic notification system, providing scientific decision basis; step b, according to warning level and emergency degree, automatically generating or recommending emergency response scheme, including resource allocation, traffic diversion, pollution control and other measures; step c, introducing feedback mechanism, adjusting prediction model and warning strategy according to actual response effect, continuously optimizing monitoring and management process.

[0017] Preferably, the cross-domain data fusion step in step B further includes: using semantic web technology or ontology method to analyze and map the semantics of data, enhancing the interoperability between data; through data dimension reduction technology, principal component analysis (PCA) or t-SNE, reducing data dimension, improving data fusion efficiency.

[0018] Preferably, the intelligent analysis and prediction step in step C further includes: using ensemble learning method, combining prediction results of multiple machine learning models, improving prediction accuracy; using online learning technology, making prediction model real-time update, adapting to dynamic changes of city operation status.

[0019] Preferably, the decision support and response step in step D further includes: introducing artificial intelligence technology assisted decision support system (AI-DSS), automatically generating optimal or suboptimal management strategy according to historical data and current situation; establishing cross-department collaboration platform, promoting information sharing and collaborative action, improving overall efficiency of city management; regularly evaluating and optimizing performance of monitoring and management system, including data processing speed, prediction accuracy, warning response time, ensuring efficient operation and continuous improvement of system.

[0020] Preferably, the data acquisition in step A also includes the following processes: a, using Internet of Things (IoT) technology, real-time collection of environmental parameters, traffic flow, and key data of energy consumption through sensor networks deployed throughout the city; b, integrating information from social media, news reports, and other unstructured data sources, extracting valuable information related to city operation through natural language processing (NLP) technology; c, using blockchain technology to ensure data integrity, transparency, and tamper resistance, especially in the management of public safety data, to enhance data credibility.

[0021] Preferably, the cross-domain data fusion in step B also includes the following processes: a, using Federated Learning framework to achieve cross-institutional and cross-domain data sharing and model training while protecting data privacy, improving the breadth and depth of data fusion; b, applying Graph Neural Networks (GNNs) to process data with complex association relationships, such as traffic networks and social networks, to better capture the implicit relationships between data.

[0022] Preferably, the intelligent analysis and prediction in step C also includes the following processes: a, introducing Autoencoders or Variational Autoencoders (VAEs) for data dimensionality reduction and feature learning to extract more representative features for prediction; b, using Bayesian Networks or Markov Chain Monte Carlo (MCMC) for uncertainty analysis to quantify the uncertainty of prediction results and provide more comprehensive information for decision-making; c, combining Geographic Information Systems (GIS) and spatio-temporal data analysis techniques for comprehensive analysis of city operation data in space and time, revealing spatial distribution patterns and spatio-temporal evolution modes.

[0023] Preferably, the decision support and response in step D also includes the following processes: a, developing interactive interfaces based on Augmented Reality (AR) or Virtual Reality (VR) to provide immersive decision support experience for city managers, enhancing the intuitiveness and accuracy of decision-making; b, using Crowd Wisdom or Citizen Science projects to encourage public participation in monitoring and solving city problems, forming a virtuous cycle of government-public co-governance.

[0024] Preferably, it also includes the following processes: a, constructing an adaptive learning system, dynamically adjusting the data processing, analysis and prediction strategies and parameters according to the changes of city operation state and the evolution of data characteristics; b, implementing data governance framework, including data quality monitoring, data life cycle management, data security and privacy protection strategy, to ensure the compliance and availability of data; c, promoting cross-city, cross-border smart city data sharing and cooperation, promoting knowledge and experience exchange through global data network, and improving the intelligent level of global city governance.

[0025] Compared with the prior art, the beneficial effects achieved by the present application are:

[0026] 1、The present application effectively solves the problem of cross-domain data fusion by introducing artificial intelligence technology, deep learning model, transfer learning technology, and semantic web technology or ontology method. These methods can process and analyze data from different data sources with heterogeneous data, and convert them into unified and comparable formats, thereby realizing seamless connection and integration of cross-domain data.

[0027] 2、The present application conducts in-depth analysis of city operation state data through various machine learning algorithms and intelligent analysis techniques, such as time series analysis, regression analysis, clustering analysis, and association rule mining. By constructing prediction models such as long short-term memory network (LSTM), support vector machine (SVM), and random forest, the present application can accurately predict key indicators such as environmental pollution level, traffic congestion condition, and energy consumption trend.

[0028] 3、The present application can automatically generate optimal or suboptimal management strategies according to historical data and current situation through the introduction of artificial intelligence technology assisted decision support system (AI-DSS), providing scientific and efficient decision-making basis for city managers. In addition, by establishing a cross-department collaboration platform, information sharing and collaborative action are promoted, and the overall efficiency of city management is improved. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The method flowchart of the present application;

[0030] Figure 2 The method flowchart of the present application;

[0031] Figure 3 The content diagram of the smart city monitoring and management method of the present application;

[0032] Figure 4 The process diagram of the smart city monitoring and management method of the present application. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0034] Embodiment 1;

[0035] Please refer to Figures 1-4 In the embodiments of the present application, the smart city monitoring and management method based on artificial intelligence includes the following steps: step A: data acquisition and preprocessing, acquiring city operation related data from multiple heterogeneous data sources and preprocessing; step B: cross-domain data fusion, feature extraction and fusion of the preprocessed data, construction of a unified data model or data warehouse; step C: intelligent analysis and prediction, application of machine learning algorithms for in-depth analysis of city operation state data, establishment of a prediction model, and setting of warning thresholds according to prediction results to achieve early warning; step D: decision support and response, transmission of analysis results and prediction warning information to city managers and relevant departments through the system, automatic generation or recommendation of emergency response plans according to warning levels and emergency degrees.

[0036] In step A, the following steps are included: step a, acquiring city operation related data from multiple heterogeneous data sources, including but not limited to environmental monitoring data, traffic data, energy consumption data, public safety data; step b, preprocessing the acquired data, including data cleaning, format conversion, missing value filling, and outlier detection and processing to ensure data quality.

[0037] In step B, the following steps are included: step a, using artificial intelligence technology, neural network model or transfer learning technology in deep learning to extract and fuse features of the preprocessed data, solving the problem of data heterogeneity; step b, constructing a unified data model or data warehouse to realize seamless connection and integration of cross-domain data, forming a comprehensive city operation state data set.

[0038] In step C, the following steps are included: step a, applying machine learning algorithms to analyze city operation state data in depth through time series analysis, regression analysis, clustering analysis, and association rule mining to identify potential problems and trends; step b, establishing a prediction model, long short-term memory network (LSTM), support vector machine (SVM), or random forest, to predict future trends of key indicators, including environmental pollution level, traffic congestion condition, and energy consumption trend; step c, setting warning thresholds according to prediction results to achieve early warning of potential risks such as environmental changes and public safety incidents.

[0039] In Step D, the following steps are included: Step a, delivering the analysis results and predictive warning information to city managers and relevant departments through a visualization interface or an automatic notification system, providing scientific decision-making basis; Step b, automatically generating or recommending emergency response schemes according to the warning level and emergency degree, including resource allocation, traffic diversion, pollution control and other measures; Step c, introducing a feedback mechanism to adjust the prediction model and warning strategy according to the actual response effect, continuously optimizing the monitoring and management process.

[0040] The working principle of the embodiment of the present application is as follows: first, in Step A, the system collects a large amount of data related to city operation from multiple heterogeneous data sources, which covers environmental monitoring, traffic conditions, energy consumption, public safety and other aspects. In order to ensure the accuracy and availability of the data, the system then preprocesses the data, including data cleaning to remove useless or incorrect information, format conversion to unify the data format, missing value filling to fill in the blanks in the data, and outlier detection and processing to identify and correct abnormal data points.

[0041] In Step B, the system uses artificial intelligence technology, especially neural network models or transfer learning techniques in deep learning, to extract and fuse features from the preprocessed data. This process aims to solve the problem of data heterogeneity, converting data from different sources and formats into a unified and comparable format. Then, the system builds a unified data model or data warehouse, realizing seamless connection and integration of cross-domain data, forming a comprehensive city operation status data set.

[0042] In Step C, the system applies machine learning algorithms to conduct in-depth analysis of city operation status data. Through time series analysis, regression analysis, clustering analysis and association rule mining, the system can identify potential problems and trends in city operation. On this basis, the system establishes prediction models such as long short-term memory network (LSTM), support vector machine (SVM) or random forest, to predict future trends of key indicators such as environmental pollution level, traffic congestion condition and energy consumption trend. According to the prediction results, the system sets warning thresholds to realize early warning of potential risks such as environmental changes and public safety incidents.

[0043] In Step D, the system delivers the analysis results and predictive warning information to city managers and relevant departments through a visualization interface or an automatic notification system. These information provides scientific decision-making basis for city managers, helping them to develop targeted management strategies. According to the warning level and emergency degree, the system also automatically generates or recommends emergency response schemes, such as resource allocation, traffic diversion and pollution control measures. In addition, the system also introduces a feedback mechanism to adjust the prediction model and warning strategy according to the actual response effect, continuously optimizing the monitoring and management process.

[0044] Embodiment 2;

[0045] See Figures 1-4 In the embodiment of the present application, the cross-domain data fusion step in step B further includes: using semantic web technology or ontology method to analyze and map the semantics of data, enhancing the interoperability between data; reducing the dimensionality of data through data dimensionality reduction techniques, principal component analysis (PCA) or t-SNE, improving the efficiency of data fusion.

[0046] The intelligent analysis and prediction step in step C further includes: using ensemble learning method to combine the prediction results of multiple machine learning models to improve the prediction accuracy; using online learning technology to make the prediction model real-time update to adapt to the dynamic changes of city operation status.

[0047] The decision support and response step in step D further includes: introducing artificial intelligence technology assisted decision support system (AI-DSS) to automatically generate optimal or suboptimal management strategies according to historical data and current situation; establishing a cross-department collaboration platform to promote information sharing and collaborative action, improving the overall efficiency of city management; regularly evaluating and optimizing the performance of the monitoring and management system, including data processing speed, prediction accuracy, early warning response time, to ensure the efficient operation and continuous improvement of the system.

[0048] The working principle of the embodiment of the present application is: first, in the cross-domain data fusion step, the system not only uses artificial intelligence technology such as deep learning model for feature extraction and fusion, but also introduces semantic web technology or ontology method. At the same time, in order to improve the efficiency of data fusion, the system also uses data dimensionality reduction techniques such as principal component analysis (PCA) or t-SNE, so that the data is more concise and easy to process while keeping the key features. In the intelligent analysis and prediction step, the system not only applies multiple machine learning algorithms for in-depth analysis, but also uses ensemble learning method. This method combines the prediction results of multiple machine learning models to make full use of the advantages of each model, improving the accuracy and stability of prediction.

[0049] In the decision support and response step, the system not only transmits analysis results and early warning information through visual interface and automatic notification system, but also introduces artificial intelligence technology assisted decision support system (AI-DSS). This system can automatically generate optimal or suboptimal management strategies according to historical data and current situation, providing more scientific and efficient decision basis for city managers. At the same time, in order to promote information sharing and collaborative action between departments, the system also establishes a cross-department collaboration platform. This platform breaks down the information barriers between departments, improving the overall efficiency of city management.

[0050] Embodiment 3;

[0051] Please refer to Figures 1-4 In the embodiments of the present application, the data acquisition in step A also includes the following processes: a, using Internet of Things (IoT) technology, real-time collection of environmental parameters, traffic flow, and key data of energy consumption through sensor networks deployed in various parts of the city; b, integration of information from social media, news reports, and unstructured data sources, extraction of valuable information related to city operation through natural language processing (NLP) technology; c, using blockchain technology to ensure data integrity, transparency, and non-tamperability, especially in the management of public safety data, to enhance the credibility of data.

[0052] The cross-domain data fusion in step B also includes the following processes: a, using Federated Learning framework to achieve cross-institutional and cross-domain data sharing and model training under the premise of protecting data privacy, improving the breadth and depth of data fusion; b, applying Graph Neural Networks (GNNs) to process data with complex association relationships, such as traffic networks and social networks, to better capture the implicit relationships between data.

[0053] The intelligent analysis and prediction in step C also includes the following processes: a, introducing Autoencoders or Variational Autoencoders (VAEs) for data dimensionality reduction and feature learning to extract more representative features for prediction; b, using Bayesian Networks or Markov Chain Monte Carlo (MCMC) methods for uncertainty analysis to quantify the uncertainty of prediction results and provide more comprehensive information for decision-making; c, combining Geographic Information Systems (GIS) and spatio-temporal data analysis techniques for comprehensive analysis of city operation data in space and time, revealing spatial distribution patterns and spatio-temporal evolution modes.

[0054] The decision support and response in step D also includes the following processes: a, developing interactive interfaces based on Augmented Reality (AR) or Virtual Reality (VR) to provide immersive decision support experiences for city managers, enhancing the intuitiveness and accuracy of decision-making; b, using Crowd Wisdom or Citizen Science projects to encourage public participation in monitoring and solving city problems, forming a virtuous cycle of government-public co-governance.

[0055] It also includes the following processes: a, building an adaptive learning system, dynamically adjusting the data processing, analysis and prediction strategy and parameters according to the changes of city operation state and the evolution of data characteristics; b, implement data governance framework, including data quality monitoring, data life cycle management, data security and privacy protection policy, to ensure data compliance and availability; c, promote cross-city, cross-border smart city data sharing and cooperation, promote knowledge and experience exchange through global data network, and improve the intelligent level of global urban governance.

[0056] The working principle of the embodiment of the application is: first, in the data acquisition stage, the application makes full use of the Internet of Things (IoT) technology. Through the deployment of sensor networks in various parts of the city, the system can collect key data such as environmental parameters, traffic flow, energy consumption in real time, providing a rich data source for monitoring the operation state of the city. At the same time, the system also integrates information from unstructured data sources such as social media, news reports, etc., using natural language processing (NLP) technology to extract valuable information, further enriching the data content. In the cross-domain data fusion stage, the application adopts the Federated Learning (Federated Learning) framework, and applies Graph Neural Networks (GNNs) to process data with complex correlation relationships, such as traffic networks, social networks, etc., better capturing the implicit relationships between data, providing a more accurate and comprehensive data basis for data analysis and prediction.

[0057] In the intelligent analysis and prediction stage, the application introduces Autoencoders or Variational Autoencoders (VAEs) for data dimensionality reduction and feature learning, extracting more representative features for prediction. At the same time, Bayesian Networks or Markov Chain Monte Carlo methods (MCMC) are used for uncertainty analysis, quantifying the uncertainty of the prediction results, providing more comprehensive information for decision-making. In the decision support and response stage, the application develops an interactive interface based on Augmented Reality (AR) or Virtual Reality (VR) to provide an immersive decision support experience for city managers, enhancing the intuitiveness and accuracy of decision-making. At the same time, using Crowd Wisdom or Citizen Science projects, the public is encouraged to participate in the monitoring and solving of urban problems, forming a virtuous cycle of government-public co-governance.

[0058] Working principle: First, collect city operation data from multiple heterogeneous data sources, covering key areas such as environmental monitoring, transportation, energy consumption, and public safety. After preprocessing to ensure data quality, use artificial intelligence technology for cross-domain data fusion to build a unified data model. Then, use machine learning algorithms to analyze city operation status, establish prediction models and set warning thresholds. Finally, deliver analysis results and warning information to city managers, automatically generate emergency response plans, and achieve scientific decision-making and efficient response.

[0059] In the data fusion stage, the invention not only uses artificial intelligence technology, but also introduces semantic web technology to analyze data semantics, enhance interoperability, and use data dimension reduction technology to improve fusion efficiency. In intelligent analysis and prediction, integrated learning methods are used to improve prediction accuracy, and online learning technology is used to update the model in real time. In decision support and response, AI-DSS is introduced to automatically generate management strategies, establish a cross-department collaboration platform, and regularly evaluate system performance to ensure continuous improvement.

[0060] Using Internet of Things technology to collect key data in real time, integrating unstructured data source information, and using blockchain technology to ensure data credibility. In cross-domain data fusion, the federated learning framework is used to protect privacy, and graph neural networks are used to capture data implicit relationships. In intelligent analysis and prediction, self-encoder is introduced for feature learning, Bayesian network is used for uncertainty analysis, and GIS and spatio-temporal data analysis technology are combined. In decision support and response, develop AR / VR interactive interface to provide immersive decision-making experience, encourage public participation in city governance, and build adaptive learning system and data governance framework to promote cross-city data sharing and cooperation, and improve the intelligent level of global city governance.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart city monitoring and management method based on artificial intelligence, characterized in that: The following steps are involved: Step A: Data acquisition and preprocessing: acquiring city operation-related data from multiple heterogeneous data sources and preprocessing them; Step B: Cross-domain data fusion: extract and fuse features from pre-processed data to build a unified data model or data warehouse; Step C: Intelligent analysis and prediction: Apply machine learning algorithms to conduct in-depth analysis of urban operation status data, establish a prediction model, and set warning thresholds based on the prediction results to achieve early warning; Step D: Decision support and response, which transmits the analysis results and forecast warning information to city managers and relevant departments through the system, and automatically generates or recommends emergency response plans based on the warning level and urgency.

2. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The step A comprises the following steps: Step a: Acquire city operation-related data from multiple heterogeneous data sources, including but not limited to environmental monitoring data, traffic data, energy consumption data, and public safety data; Step b: preprocess the acquired data, including data cleaning, format conversion, missing value filling, outlier detection and processing to ensure data quality.

3. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The step B comprises the following steps: Step a: Using artificial intelligence technology, neural network models in deep learning, or transfer learning technology, perform feature extraction and fusion on the preprocessed data to solve the problem of data heterogeneity; Step b: Build a unified data model or data warehouse to achieve seamless connection and integration of cross-domain data and form a comprehensive urban operation status data set.

4. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The step C comprises the following steps: Step a: Apply machine learning algorithms to conduct in-depth analysis of urban operation status data through time series analysis, regression analysis, cluster analysis, and association rule mining to identify potential problems and trends; Step b: Build a prediction model, such as a long short-term memory network (LSTM), a support vector machine (SVM), or a random forest, to predict future trends of key indicators, including environmental pollution levels, traffic congestion, and energy consumption trends. Step c: Based on the prediction results, set the warning threshold to achieve early warning of potential risks such as environmental changes and public safety incidents.

5. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The step D comprises the following steps: Step a: Transmit analysis results and forecast warning information to city managers and relevant departments through a visual interface or automatic notification system to provide a basis for scientific decision-making; Step b: Automatically generate or recommend emergency response plans based on the warning level and urgency, including resource allocation, traffic diversion, pollution control and other measures; Step c: Introduce a feedback mechanism to adjust the prediction model and early warning strategy based on the actual response effect, and continuously optimize the monitoring management process.

6. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The cross-domain data fusion step in step B also includes: using semantic web technology or ontology methods to parse and map the semantics of the data to enhance the interoperability between data; using data dimensionality reduction technology, principal component analysis (PCA) or t-SNE to reduce data dimensions and improve data fusion efficiency.

7. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The intelligent analysis and prediction steps in step C also include: using an integrated learning method to combine the prediction results of multiple machine learning models to improve prediction accuracy; using online learning technology to enable the prediction model to be updated in real time to adapt to dynamic changes in the city's operating status.

8. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The decision support and response steps in step D also include: introducing an artificial intelligence technology-assisted decision support system (AI-DSS) to automatically generate optimal or suboptimal management strategies based on historical data and current situations; establishing a cross-departmental collaboration platform to promote information sharing and collaborative actions to improve the overall effectiveness of urban management; regularly evaluating and optimizing the performance of the monitoring and management system, including data processing speed, prediction accuracy, and early warning response time, to ensure efficient operation and continuous improvement of the system.

9. The smart city monitoring and management method based on artificial intelligence according to any one of claims 1 to 8, characterized in that: The data acquisition in step A also includes the following process: a. Leveraging Internet of Things (IoT) technology, a sensor network deployed throughout the city collects key data on environmental parameters, traffic flow, and energy consumption in real time; b. Integrate information from unstructured data sources such as social media and news reports, and extract valuable information related to urban operations through natural language processing (NLP) technology; c. Use blockchain technology to ensure data integrity, transparency, and immutability, especially in the management of public safety data, to enhance data credibility; The cross-domain data fusion in step B also The following processes are included: a. Adopting a federated learning framework to achieve cross-institutional and cross-domain data sharing and model training while protecting data privacy, thereby enhancing the breadth and depth of data integration; b. Applying Graph Neural Networks (GNNs) to process data with complex relationships, such as traffic networks and social networks, to better capture the implicit relationships between data; The intelligent analysis and prediction in step C also The following processes are included: a. Introducing autoencoders or variational autoencoders (VAEs) for data dimensionality reduction and feature learning to extract more representative features for prediction; b. Use Bayesian Networks or Markov Chain Monte Carlo (MCMC) methods to perform uncertainty analysis, quantify the uncertainty of the prediction results, and provide more comprehensive information for decision-making; c. Combining geographic information systems (GIS) and spatiotemporal data analysis technology, conduct a comprehensive spatial and temporal analysis of urban operation data to reveal spatial distribution patterns and spatiotemporal evolution patterns; The decision support and response in step D also The following processes are included: a. Developing interactive interfaces based on augmented reality (AR) or virtual reality (VR) to provide city managers with an immersive decision-making support experience, enhancing the intuitiveness and accuracy of decision-making; b. Utilize crowd intelligence or citizen science projects to encourage public participation in monitoring and resolving urban issues, thus forming a virtuous cycle of government-public co-governance.

10. The smart city monitoring and management method based on artificial intelligence according to claim 1 is characterized in that: The following procedures are also included: a. Build an adaptive learning system to dynamically adjust strategies and parameters for data processing, analysis, and prediction based on changes in urban operating conditions and the evolution of data characteristics; b. Implement a data governance framework, including data quality monitoring, data lifecycle management, data security, and privacy protection strategies, to ensure data compliance and availability; c. Promote cross-city and cross-border smart city data sharing and cooperation, promote the exchange of knowledge and experience through global data networks, and enhance the level of intelligent global urban governance.

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