Urban life entity full-period intelligent management digital system

Abstract

The invention relates to the technical field of smart cities, and discloses an urban life entity full-cycle smart management digital system, which comprises a multi-source data acquisition module, an edge computing node module, a data transmission and integration module, an intelligent analysis and prediction module, an intelligent decision support and feedback module and a system integration and user interface module. The multi-source data acquisition module is used for acquiring environment data, traffic data and infrastructure data of urban operation states through different types of sensors, cameras, mobile devices and unmanned aerial vehicles; and the edge computing node module is used for carrying out data localization processing and pre-analysis at a position close to a data source. Through the standardization and semantic integration technology of multi-source data, the common problems of data islands and heterogeneous data in city management are effectively solved, and the system realizes cross-department and cross-platform data sharing and fusion by utilizing the ontology and a data mapping tool, so that the interoperability among different systems is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart city technology, and specifically to a digital system for full-cycle smart governance of urban life forms. Background Art

[0002] With the accelerated development of global urbanization, the challenges faced by cities are becoming increasingly complex and diverse, including traffic congestion, environmental pollution, energy management, public safety and aging infrastructure. In the traditional urban management model, data lacks real-time performance, data islands exist between systems, and management methods rely on manual decision-making, resulting in inefficient urban governance, slow response speed and low resource utilization. These problems urgently need to be solved with new technologies and management models.

[0003] In recent years, the concept of smart city has developed rapidly, aiming to improve the efficiency of urban management and the quality of life of citizens through information and communication technology. Smart city technology involves the integrated application of multiple cutting-edge technologies such as the Internet of Things, big data analysis, cloud computing, and artificial intelligence. However, the existing smart city system still faces many technical bottlenecks in terms of massive data processing, real-time analysis, and intelligent decision support.

[0004] The data collection of existing smart city systems mostly relies on decentralized sensor networks. The amount of data generated by sensors is large, which leads to delays and bandwidth bottlenecks in the data transmission process. In addition, there is a lack of unified data collection standards and multi-source data integration technology, which results in low data transmission efficiency and inability to achieve multi-dimensional data fusion.

[0005] Urban management involves multiple departments, and each department uses different data collection systems and management platforms, resulting in inconsistent data standards and formats, forming a "data island" phenomenon, limiting the ability of cross-departmental information integration and comprehensive data analysis, and affecting the efficiency of overall urban governance;

[0006] Existing analysis methods usually rely on static models and have low response capabilities to complex events in dynamic urban environments. Traditional machine learning and prediction algorithms have problems with low model accuracy and weak generalization capabilities when facing large-scale, multi-dimensional data, and are unable to accurately predict emergencies and trend changes in urban operations.

[0007] Existing urban governance systems mostly rely on predefined rules and manual intervention and lack flexible decision-making support tools. When faced with complex and unpredictable urban problems, such as sudden traffic accidents and natural disasters, traditional systems are unable to respond and adjust resource allocation in a timely manner. In addition, the system has limitations in optimizing urban resource utilization, reducing energy consumption and reducing operating costs.

[0008] Therefore, technicians in this field provide a digital system for full-cycle intelligent governance of urban life to solve the problems raised in the above background technology. Summary of the invention

[0009] In view of the shortcomings of the existing technology, the present invention provides a digital system for intelligent governance of the entire life cycle of an urban organism, which solves the problems of inefficiency, response delay and data silos in the existing urban management system in multi-source data integration, real-time analysis, intelligent decision support and rapid response to emergencies.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a digital system for the full-cycle intelligent governance of urban life, including a multi-source data acquisition module, an edge computing node module, a data transmission and integration module, an intelligent analysis and prediction module, an intelligent decision support and feedback module, and a system integration and user interface module;

[0011] The multi-source data acquisition module is used to obtain environmental data, traffic data and infrastructure data of the city's operating status through different types of sensors, cameras, mobile devices and drones;

[0012] The edge computing node module is used to perform local processing and pre-analysis of data at a location close to the data source;

[0013] The data transmission and integration module is used for efficient transmission and cross-departmental integration of multi-source data, and for standardization and semantic integration of data;

[0014] The intelligent analysis and prediction module performs real-time analysis and dynamic prediction of data based on big data analysis, machine learning and time series models;

[0015] The intelligent decision support and feedback module automatically generates control strategies based on the analysis results, realizes adaptive optimization of urban resources, and forms a closed-loop feedback;

[0016] The system integration and user interface module is used to provide an intelligent data visualization and interaction platform for city managers and citizens.

[0017] Preferably, the multi-source data acquisition module includes an environmental monitoring sensor network, intelligent traffic monitoring equipment, and an unmanned aerial vehicle system;

[0018] The environmental monitoring sensor network is used to collect environmental data on air quality, noise level, temperature and humidity;

[0019] The intelligent traffic monitoring equipment includes a video camera, a vehicle-mounted sensor and a signal acquisition device for collecting information on traffic flow, speed and road status;

[0020] The drone system is used to dynamically collect data from remote and hard-to-cover areas, including real-time monitoring of construction, natural disaster conditions, and municipal facility maintenance.

[0021] Preferably, the multi-source data acquisition module uses a multi-sensor fusion algorithm to fuse the collected data, specifically a weighted Kalman filter algorithm, wherein the weight factor is dynamically adjusted according to the reliability of the sensor and the real-time data status, and the algorithm satisfies the following formula:

[0022]

[0023] in, is the fused state estimate, K k is the dynamically calculated Kalman gain, which is adjusted based on the sensor’s signal-to-noise ratio, z k is the observation value, and H is the observation matrix.

[0024] Preferably, the edge computing node module realizes local processing of data by deploying low-latency computing devices in high data generation areas, and uses micro AI models to pre-analyze data, reducing data transmission volume by more than 50%.

[0025] Preferably, the data transmission and integration module adopts a multi-layer data security protocol for transmission, specifically including:

[0026] Use MQTT protocol and TLS / SSL encryption technology to transmit data to ensure data transmission security;

[0027] AES-256 is used to encrypt sensitive data in transmission, and public key infrastructure mechanisms are used to implement identity authentication and data integrity verification.

[0028] Preferably, the data integration process adopts an automated data mapping technology based on ontology and machine learning, and data normalization is performed by the following standardized formula:

[0029]

[0030] Among them, D norm is the standardized data value, D is the original data, min(D) and max(D) represent the minimum and maximum values ​​in the data set.

[0031] Preferably, the intelligent analysis and prediction module constructs a long short-term memory network model for predictive analysis of traffic flow, energy demand and environmental changes, and uses a recursive neural network to model long-term trends.

[0032] Preferably, the cell state update and hidden state calculation of the LSTM model are implemented by the following formula: calculation of the input gate, forget gate and output gate:

[0033] i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0034] f t =σ(W f ·[h t-1 ,x t ]+b f ),

[0035] o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0036] Cell state update and hidden layer state:

[0037] C t =f t ·C t-1 +i t tanh(W c ·[h t-1 ,x t ]+b c ),

[0038] h t =o t tanh(C t ),

[0039] Among them, i t represents the activation value of the input gate at time step t;

[0040] f t Represents the activation value of the forget gate at time step t;

[0041] o t represents the activation value of the output gate at time step t;

[0042] C t represents the cell state at time step t;

[0043] h t represents the hidden state at time step t;

[0044] x t represents the input data at time step t;

[0045] ht-1 represents the hidden state of the previous time step t-1;

[0046] W i , W f , W o , W c Represents the weight parameters of the LSTM network;

[0047] b i 、b f 、b o 、b c Represents the bias term of the LSTM network;

[0048] σ represents the logistic sigmoid activation function; tanh represents the hyperbolic tangent activation function.

[0049] Preferably, the intelligent decision support and feedback module combines the Bayesian optimization algorithm to perform multi-objective optimization scheduling, and realizes real-time emergency response in emergencies through fuzzy logic control.

[0050] Preferably, the intelligent decision support and feedback module implements closed-loop feedback based on a proportional-integral-differential (PID) control algorithm, and the specific formula of the PID control is:

[0051]

[0052] Where u(t) is the control input, e(t) is the error signal, and K p , K i , K d It is a control parameter used to adjust the stability and accuracy of system response.

[0053] The present invention provides a digital system for intelligent management of the entire life cycle of urban organisms. It has the following beneficial effects:

[0054] 1. The present invention effectively solves the common data island and heterogeneous data problems in urban management through the standardization and semantic integration technology of multi-source data, and uses ontology and data mapping tools to systematically realize cross-departmental and cross-platform data sharing and integration, improve the interoperability between different systems, and support more comprehensive and accurate urban management decisions.

[0055] 2. The present invention integrates AI-driven analysis and prediction modules, based on big data and machine learning models, can dynamically predict problems in urban operations, and make intelligent optimization in resource allocation, realize adaptive allocation of urban resources, effectively reduce resource waste, and improve the accuracy and efficiency of urban management.

[0056] 3. The present invention reduces the data processing load of the central cloud platform by deploying edge computing devices near the source of data generation. Edge computing can pre-filter and compress data and transmit valuable information to the cloud for further analysis. This method improves the processing efficiency of the system and significantly reduces the occupancy of cloud resources and data transmission costs.

[0057] 4. By integrating intelligent algorithms and multi-sensor data analysis modules, the system can detect abnormal conditions in urban operations in real time and automatically trigger an alarm mechanism to notify relevant departments for processing. Its automated monitoring and early warning functions significantly improve the city's response speed to emergencies and emergency management capabilities.

[0058] 5. By adopting advanced machine learning algorithms, the present invention achieves high-precision results in urban data prediction and analysis. The LSTM model can effectively capture the long-term and short-term trends of various data in urban operations, improve the prediction accuracy of key indicators such as traffic flow, energy demand, and environmental changes, and support more scientific urban management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0060] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0061] The present invention is described in detail below in conjunction with the accompanying drawings:

[0062] Example:

[0063] Please see attached Figure 1 , the embodiment of the present invention provides a digital system for the full-cycle intelligent governance of urban life, including a multi-source data acquisition module, an edge computing node module, a data transmission and integration module, an intelligent analysis and prediction module, an intelligent decision support and feedback module, and a system integration and user interface module;

[0064] A multi-source data acquisition module, which is used to obtain environmental data, traffic data, and infrastructure data on the city's operating status through different types of sensors, cameras, mobile devices, and drones;

[0065] Edge computing node module, used for local processing and pre-analysis of data close to the data source;

[0066] Data transmission and integration module, used for efficient transmission of multi-source data and cross-departmental integration, and for standardization and semantic integration of data;

[0067] Intelligent analysis and prediction module, which performs real-time analysis and dynamic prediction of data based on big data analysis, machine learning and time series models;

[0068] Intelligent decision support and feedback module, which automatically generates control strategies based on analysis results, realizes adaptive optimization of urban resources, and forms a closed-loop feedback loop;

[0069] System integration and user interface modules are used to provide an intelligent data visualization and interaction platform for city managers and citizens.

[0070] The benefit of the multi-source data collection module is that it can fully capture the multi-dimensional data of the city's operating status, improve the richness of the data, and help improve the accuracy of data collection and reduce the deviation caused by a single data source;

[0071] The benefits of edge computing node modules are to reduce the burden on central cloud computing resources, improve the overall processing efficiency of the system, and ensure the continuity and stability of data collection and processing;

[0072] The benefit of the data transmission and integration module is to enhance the collaborative working ability between systems, effectively prevent data leakage and unauthorized access, and provide a solid data foundation for subsequent intelligent analysis;

[0073] The benefit of the intelligent analysis and prediction module is that it uses big data technology to extract useful information from massive data, identify hidden trends and patterns, and provide a scientific basis for urban planning and management. The system can gradually improve the accuracy of analysis and prediction during use.

[0074] The benefit of the intelligent decision support and feedback module is that the system can automatically adjust resource allocation according to actual conditions, achieve efficient use of urban resources, and improve the system's responsiveness and management effectiveness;

[0075] The benefit of the system integration and user interface module is that it provides citizens with a way to participate in urban management, allowing them to provide feedback and suggestions through the platform, thereby improving public participation and satisfaction, promoting transparency and democratization of urban governance, and simplifying the operating interface so that managers and citizens can easily use the platform for real-time monitoring, information query and interactive operations, thereby improving the user experience of the smart city system.

[0076] The multi-source data acquisition module includes environmental monitoring sensor networks, intelligent traffic monitoring equipment, and drone systems;

[0077] Environmental monitoring sensor networks to collect environmental data on air quality, noise levels, temperature and humidity;

[0078] Intelligent traffic monitoring equipment, including video cameras, vehicle-mounted sensors and signal collection devices, for collecting information on traffic flow, speed and road conditions;

[0079] Unmanned aerial vehicle systems for dynamic data collection in remote and hard-to-cover areas, including real-time monitoring of construction, natural disasters, and municipal facility maintenance;

[0080] The multi-source data acquisition module uses a multi-sensor fusion algorithm to fuse the collected data. Specifically, it adopts a weighted Kalman filter algorithm, in which the weight factor is dynamically adjusted according to the reliability of the sensor and the real-time data status. The algorithm satisfies the following formula:

[0081]

[0082] in, is the fused state estimate, K k is the dynamically calculated Kalman gain, which is adjusted based on the sensor’s signal-to-noise ratio, z k is the observation value, and H is the observation matrix.

[0083] The role of the environmental monitoring sensor network is to collect environmental data such as air quality, noise level, temperature and humidity in the city. Its data provides key information support for environmental status assessment, pollution source monitoring and weather change prediction, which helps to better understand the dynamic changes of the urban ecological environment;

[0084] Intelligent traffic monitoring equipment collects information such as traffic flow, speed and road status in real time. Through the equipment, it can monitor traffic conditions, detect congestion problems, detect vehicle speeds and road usage, and provide data support for urban traffic management and optimization;

[0085] The role of drone systems is to collect data from remote and hard-to-cover areas, especially in construction monitoring, natural disaster emergency response, and maintenance and inspection of municipal facilities. Drone systems can provide high-altitude perspectives and flexible data collection capabilities, covering areas that are difficult to reach with traditional monitoring methods.

[0086] The role of the multi-sensor fusion algorithm is to fuse data from different sensors to eliminate the deviation of a single data source, improve data accuracy and consistency, and use the weighted Kalman filter algorithm to dynamically adjust the weight factors of different sensors according to real-time status and reliability to ensure the best data fusion effect and support more accurate urban status assessment and prediction.

[0087] The edge computing node module realizes local data processing by deploying low-latency computing devices in high data generation areas, and uses micro AI models to pre-analyze data, reducing data transmission by more than 50%.

[0088] The role of deploying low-latency computing devices in high-data generation areas: The edge computing node module deploys low-latency computing devices in high-data generation areas in the city, so that data can be processed locally near the data source. Its localized processing method reduces the time delay for data transmission to remote servers and improves the system's real-time requirements for data;

[0089] The role of using micro AI models to pre-analyze data: By running lightweight micro AI models on edge devices, the collected data can be pre-analyzed and preliminarily screened. The micro AI model can quickly identify important information and abnormal situations in the data locally. Its pre-analysis processing enables the system to make preliminary judgments on the data quickly and intelligently.

[0090] The effect of reducing data transmission volume by more than 50%: By performing local data processing and pre-analysis at the edge node, the amount of data uploaded to the central data processing platform is significantly reduced, the pressure on network bandwidth is reduced, the burden on cloud computing resources is reduced, and the system is made more efficient and stable.

[0091] The data transmission and integration module uses a multi-layer data security protocol for transmission, including:

[0092] Use MQTT protocol and TLS / SSL encryption technology to transmit data to ensure data transmission security;

[0093] Combine AES-256 to encrypt sensitive data in transit, and use public key infrastructure mechanisms to implement identity authentication and data integrity verification;

[0094] The data integration process uses automated data mapping technology based on ontology and machine learning, and normalizes the data using the following standardized formula:

[0095]

[0096] Among them, D norm is the standardized data value, D is the original data, min(D) and max(D) represent the minimum and maximum values ​​in the data set.

[0097] The role of using MQTT protocol and TLS / SSL encryption technology to transmit data: By using lightweight message queue telemetry transmission protocol and TLS / SSL encryption technology, the security of data transmission is guaranteed. MQTT protocol can efficiently transmit data packets, while TLS / SSL encryption technology provides encryption protection to prevent data from being stolen or tampered with during transmission;

[0098] Combine AES-256 encryption with the role of public key infrastructure mechanism: In the process of data transmission, the advanced encryption standard is used to encrypt sensitive data to ensure that even if the data transmitted on the network is intercepted, it cannot be easily decoded. The public key infrastructure mechanism is used to achieve identity authentication and data integrity verification, effectively preventing unauthorized users from accessing data;

[0099] The role of automated data mapping technology based on ontology and machine learning: In the process of data integration, ontology-based semantic technology and machine learning algorithms can be used to achieve automated data mapping and standardized processing, helping data from different sources to form a consistent semantic understanding and ensuring seamless integration of data across departments and platforms.

[0100] The role of data normalization: Through the data normalization formula, data from different sources and dimensions are standardized to the same scale range, eliminating the impact of data magnitude, making the data more consistent and comparable in subsequent analysis and modeling processes.

[0101] The intelligent analysis and prediction module builds a long short-term memory network model for predictive analysis of traffic flow, energy demand and environmental changes, and uses a recursive neural network to model long-term trends;

[0102] The cell state update and hidden state calculation of the LSTM model are implemented through the following formula: Calculation of input gate, forget gate and output gate:

[0103] i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0104] f t =σ(W f ·[h t-1 ,x t ]+b f ),

[0105] o t =σ(W o ·[h t-1 ,x t ]+b o ),

[0106] Cell state update and hidden layer state:

[0107] C t =f t ·C t-1 +i t tanh(W c ·[h t-1 ,x t ]+b c ),

[0108] h t =o t tanh(C t ),

[0109] Among them, i t represents the activation value of the input gate at time step t;

[0110] f t Represents the activation value of the forget gate at time step t;

[0111] o t represents the activation value of the output gate at time step t;

[0112] C t represents the cell state at time step t;

[0113] h t represents the hidden state at time step t;

[0114] x t represents the input data at time step t;

[0115] h t-1 represents the hidden state of the previous time step t-1;

[0116] W i , W f , W o , W c Represents the weight parameters of the LSTM network;

[0117] b i 、b f 、b o 、b c Represents the bias term of the LSTM network;

[0118] σ represents the logistic sigmoid activation function; tanh represents the hyperbolic tangent activation function.

[0119] The role of long short-term memory network models in predictive analysis: The LSTM model is used to predict key dynamics in cities, such as traffic flow, energy demand, and environmental changes. It can effectively capture the details of short-term changes and the correlations in long-term trends, and provide more accurate and reliable prediction results. Its function is crucial in urban management, helping to optimize resource allocation, alleviate traffic pressure, and improve the accuracy of environmental management;

[0120] The role of recursive neural networks in long-term trend modeling: Recursive neural networks have good capabilities in processing time series data and are used to capture long-term trends in data. By using recursive neural networks to model long-term trends, the system can better understand the laws of long-term evolution of urban data and provide data support for long-term urban planning and strategic decision-making;

[0121] The role of the LSTM model in updating the cell state and calculating the hidden state: The LSTM model controls and manages information through the input gate, forget gate, and output gate to ensure that the cell state and hidden state can be accurately updated at each time step. The gating mechanism in its formula determines the determination, forgetting, and output of information. By controlling the flow of information, LSTM can effectively solve the gradient vanishing problem of traditional recursive neural networks in long sequence training, making the prediction model more stable and efficient.

[0122] The intelligent decision support and feedback module combines the Bayesian optimization algorithm to perform multi-objective optimization scheduling, and uses fuzzy logic control to achieve real-time emergency response in emergencies;

[0123] The intelligent decision support and feedback module implements closed-loop feedback based on the proportional-integral-differential (PID) control algorithm. The specific formula of PID control is:

[0124]

[0125] Where u(t) is the control input, e(t) is the error signal, and K p , K i , K d It is a control parameter used to adjust the stability and accuracy of system response.

[0126] Predicting the city's operating status: The intelligent analysis and prediction module uses long-short-term memory network models and recursive neural networks to analyze and predict key urban dynamics such as traffic flow, energy demand, and environmental changes. The memory mechanism of the long-short-term memory network helps capture the laws of short-term and long-term data changes, allowing the system to predict potential urban problems in advance and take corresponding preventive measures;

[0127] The role of data processing and trend modeling: Using long short-term memory networks and recursive neural network models to conduct in-depth data analysis and modeling can effectively process time series data and identify long-term trends and short-term fluctuations in urban systems. Its trend modeling capabilities help to formulate more forward-looking urban management strategies, thereby improving the city's planning and response capabilities;

[0128] Information filtering and decision support: The long short-term memory network model of the intelligent analysis and prediction module filters, stores and processes information through the input gate, forget gate and output gate mechanism to ensure that information valuable to the current prediction is retained. In this way, the system can provide efficient decision support for city managers.

[0129] The memory and information processing capabilities of the LSTM network model can handle short-term and long-term time dependencies, improve the prediction accuracy of traffic flow, energy demand and environmental changes, and its high-precision prediction capability helps cities plan resource allocation in advance and optimize management strategies.

[0130] The intelligent analysis and prediction module performs well in real-time data processing and dynamic adjustment of prediction models, and can quickly respond to changing situations in the city. Its rapid response capability enables the city management system to take timely actions to deal with emergencies such as traffic jams and peak energy demand, thereby reducing the impact on city operations.

[0131] By accurately predicting the city's resource needs, the system can effectively reduce resource waste and excessive energy use. City managers can optimize traffic light scheduling, energy allocation, and environmental governance measures based on intelligent analysis results, thereby achieving more efficient and sustainable urban management;

[0132] The long short-term memory network model can handle long-term dependencies and solve the gradient vanishing problem in traditional neural networks. The system has significant advantages when facing complex time series data, making it robust and reliable in long-term trend forecasting and big data analysis.

[0133] The intelligent analysis and prediction module transforms data analysis into a prediction model through deep learning technology, providing scientific basis and data support for city managers to help make more accurate and effective management decisions. Data-driven urban governance can improve the rationality of decision-making and execution efficiency.

[0134] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital system for intelligent governance of the entire life cycle of a city, characterized by: It includes multi-source data acquisition module, edge computing node module, data transmission and integration module, intelligent analysis and prediction module, intelligent decision support and feedback module, system integration and user interface module; The multi-source data acquisition module is used to obtain environmental data, traffic data and infrastructure data of the city's operating status through different types of sensors, cameras, mobile devices and drones; The edge computing node module is used to perform local processing and pre-analysis of data at a location close to the data source; The data transmission and integration module is used for efficient transmission and cross-departmental integration of multi-source data, and for standardization and semantic integration of data; The intelligent analysis and prediction module performs real-time analysis and dynamic prediction of data based on big data analysis, machine learning and time series models; The intelligent decision support and feedback module automatically generates control strategies based on the analysis results, realizes adaptive optimization of urban resources, and forms a closed-loop feedback; The system integration and user interface module is used to provide an intelligent data visualization and interaction platform for city managers and citizens.

2. According to claim 1, a digital system for intelligent management of urban life forms throughout the entire cycle is characterized in that: The multi-source data acquisition module includes an environmental monitoring sensor network, intelligent traffic monitoring equipment, and an unmanned aerial vehicle system; The environmental monitoring sensor network is used to collect environmental data on air quality, noise level, temperature and humidity; The intelligent traffic monitoring equipment includes a video camera, a vehicle-mounted sensor and a signal acquisition device for collecting information on traffic flow, speed and road status; The drone system is used to dynamically collect data from remote and hard-to-cover areas, including real-time monitoring of construction, natural disaster conditions, and municipal facility maintenance.

3. According to claim 2, a digital system for intelligent management of urban life forms throughout the entire cycle is characterized in that: The multi-source data acquisition module uses a multi-sensor fusion algorithm to fuse the collected data, specifically a weighted Kalman filter algorithm, wherein the weight factor is dynamically adjusted according to the reliability of the sensor and the real-time data status, and the algorithm satisfies the following formula: in, is the fused state estimate, K k is the dynamically calculated Kalman gain, which is adjusted based on the sensor’s signal-to-noise ratio, z k is the observation value, and H is the observation matrix.

4. According to claim 1, a digital system for intelligent management of urban life cycle, characterized in that: The edge computing node module realizes local data processing by deploying low-latency computing devices in high data generation areas, and uses micro AI models to pre-analyze data, reducing data transmission by more than 50%.

5. According to claim 1, a digital system for intelligent management of urban life forms throughout the entire cycle is characterized in that: The data transmission and integration module adopts a multi-layer data security protocol for transmission, specifically including: Use MQTT protocol and TLS / SSL encryption technology to transmit data to ensure data transmission security; AES-256 is used to encrypt sensitive data in transmission, and public key infrastructure mechanisms are used to implement identity authentication and data integrity verification.

6. According to claim 5, a digital system for intelligent management of urban life forms throughout the entire cycle is characterized in that: The data integration process uses automated data mapping technology based on ontology and machine learning, and normalizes the data using the following standardized formula: Among them, D norm is the standardized data value, D is the original data, min(D) and max(D) represent the minimum and maximum values ​​in the data set.

7. According to claim 1, a digital system for intelligent management of urban life cycle, characterized in that: The intelligent analysis and prediction module constructs a long short-term memory network model for predictive analysis of traffic flow, energy demand and environmental changes, and uses a recursive neural network to model long-term trends.

8. According to claim 7, a digital system for intelligent management of urban life forms throughout the entire cycle is characterized in that: The cell state update and hidden state calculation of the LSTM model are implemented by the following formula: Calculation of input gate, forget gate and output gate: i t =σ(W i ·[h t-1 ,x t ]+b i ), f t =σ(W f ·[h t-1 ,x t ]+b f ), the t =σ(W o ·[h t-1 ,x t ]+b o ), Cell state update and hidden layer state: C t =f t ·C t-1 +i t ·tanh(W c ·[h t-1 ,x t ]+b c ), h t =o t ·tanh(C t ), Among them, i t represents the activation value of the input gate at time step t; f t Represents the activation value of the forget gate at time step t; o t represents the activation value of the output gate at time step t; C t represents the cell state at time step t; h t represents the hidden state at time step t; x t represents the input data at time step t; h t-1 represents the hidden state of the previous time step t-1; W i , W f , W o , W c Represents the weight parameters of the LSTM network; b i 、b f 、b o 、b c Represents the bias term of the LSTM network; σ represents the logistic sigmoid activation function; tanh represents the hyperbolic tangent activation function.

9. According to claim 1, a digital system for intelligent management of urban life cycle, characterized in that: The intelligent decision support and feedback module combines the Bayesian optimization algorithm to perform multi-objective optimization scheduling, and realizes real-time emergency response in emergencies through fuzzy logic control.

10. According to claim 9, a digital system for intelligent management of urban life forms throughout the entire cycle is characterized in that: The intelligent decision support and feedback module implements closed-loop feedback based on a proportional-integral-derivative (PID) control algorithm. The specific formula of the PID control is: Where u(t) is the control input, e(t) is the error signal, and K p , K i , K d It is a control parameter used to adjust the stability and accuracy of system response.

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