CN116954565A - Smart city message platform publishing method - Google Patents
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Abstract
The invention provides a method for publishing a smart city message platform, and belongs to the field of smart cities. The smart city message platform release method comprises the following steps of; communicating with a city planning department, and knowing information of future development planning and targets, key fields, demands and problems of a city; the system is communicated with urban management departments, including traffic management, environmental protection departments and public safety, and the demands and problems of all departments are known; communicating with information technology professionals and software developers, knowing the technical feasibility and technical implementation scheme, and providing information about data sources and data processing; communicate with data analysts and data scientists to understand the feasibility and method of data acquisition and processing.
Description
Technical Field
The invention relates to the technical field of smart cities, in particular to a smart city message platform release method.
Background
Initially, the smart city concept originated from the rapid development and urbanization of information technology, which was originally developed using conventional internet and mobile communication technologies for information delivery and basic service provision. With the rise of the Internet of things and sensor technology, the smart city message platform gradually merges real-time data collection and processing, and intelligent management of city facilities and resources is achieved. Then, the application of artificial intelligence and big data analysis promotes the intelligent upgrading of the smart city message platform, and provides more accurate prediction and decision support. Meanwhile, security and privacy protection become new challenges, and as blockchain and encryption technologies are applied, smart city message platforms gradually enhance data security. In the future, 5G technology and edge computing are expected to further promote innovation of the smart city message platform, realize faster and efficient data transmission and intelligent application, and bring more convenient and intelligent living experience for city residents.
The traditional smart city message platform publishing method has some defects. Firstly, due to the limited technology and data sources, the traditional platform has relatively simple functions, and cannot meet the complex urban management requirements. Second, traditional platforms may face data islanding and information fragmentation problems, and lack of sharing and integration of data between different departments and systems results in limited decision efficiency and accuracy. In addition, the conventional platform is vulnerable to network attacks and data leakage because of the relative weakness of information security measures. In addition, for large-scale cities, the expansibility and performance of the conventional platform may be insufficient, resulting in slow response speed. In view of the foregoing, the conventional smart city message platform has some drawbacks in terms of functions, data integration, security and expansibility, and the like, and innovations and technical upgrades are needed to cope with increasingly complex city management challenges.
Disclosure of Invention
1. Technical problem to be solved
The invention aims to provide a smart city message platform release method to solve the problems in the prior art:
the traditional smart city message platform publishing method has some defects. Firstly, due to the limited technology and data sources, the traditional platform has relatively simple functions, and cannot meet the complex urban management requirements. Second, traditional platforms may face data islanding and information fragmentation problems, and lack of sharing and integration of data between different departments and systems results in limited decision efficiency and accuracy. In addition, the conventional platform is vulnerable to network attacks and data leakage because of the relative weakness of information security measures. In addition, for large-scale cities, the expansibility and performance of the conventional platform may be insufficient, resulting in slow response speed. In view of the foregoing, the conventional smart city message platform has some drawbacks in terms of functions, data integration, security and expansibility, and the like, and innovations and technical upgrades are needed to cope with increasingly complex city management challenges.
2. Technical proposal
The smart city message platform publishing method is characterized in that: the smart city message platform release method comprises the following steps of;
s1, communicating with an urban planning department, and knowing information of future development planning and targets, key fields, demands and problems of the city;
the system is communicated with urban management departments, including traffic management, environmental protection departments and public safety, and the demands and problems of all departments are known;
communicating with information technology professionals and software developers, knowing the technical feasibility and technical implementation scheme, and providing information about data sources and data processing;
communicating with data analysts and data scientists to know the feasibility and method of data acquisition and processing;
communicate with citizens and resident representatives to learn about their needs and desires;
communicating with government related departments, including scientific and technical departments and informatization departments, and knowing the requirements and policies of the government on data security and privacy protection;
according to the collected data, the whole target and range of the smart city message platform are defined, and the demand analysis is carried out, so that a demand analysis result is obtained;
s2, on the basis of the demand analysis result, carrying out system design and planning of a smart city message platform;
determining the overall architecture, the functional module, the data flow and the interface design of the smart city message platform, and formulating a detailed technical scheme and plan based on the overall architecture, the functional module, the data flow and the interface design of the smart city message platform and combining the expansibility and maintainability;
s3, developing a smart city message platform according to the system design and planning;
the method comprises the steps of development of a front-end interface, coding of a back-end logic, and design and construction of a database;
after development is completed, performing system test;
s4, after the system test is passed, deploying the smart city message platform into a production environment based on the construction of a server, the configuration of a database and network setting;
carrying out daily operation and maintenance work on the smart city message platform;
s5, after the deployment is completed, the functions and advantages of the smart city message platform are publicized to the target users and the public, including publishing messages on media and holding popularization activities.
S6, after the smart city message platform is released, user feedback is continuously collected, and improvement and optimization are carried out according to feedback opinion.
Preferably, the smart city message platform publishing method according to claim 1, wherein the determining of the S2 overall architecture employs a distributed edge computing architecture to move part of the computing and data processing tasks to edge nodes near the city facility;
s211, determining the position of the deployment edge node according to the geographic layout and facility distribution of the city;
s212, establishing a distributed edge computing architecture, and determining a data flow and an interaction mode between an edge node and a central cloud server;
s213, formulating a resource management strategy of the edge node, and ensuring that the edge node bears required calculation and data processing tasks;
s214, deploying the distributed edge computing architecture into the smart city message platform to perform system testing and performance evaluation.
Preferably, the S2 functional module is divided into a data acquisition and processing module, a real-time monitoring and early warning module and an intelligent decision and recommendation module;
the data acquisition and processing module introduces a blockchain technology to link the data acquisition node and the data storage record;
the real-time monitoring and early warning module combines an edge computing technology to lower part of real-time monitoring and early warning processing tasks to edge nodes;
the intelligent decision and recommendation module introduces a Q-learning algorithm, so that the intelligent decision and recommendation module can continuously learn and optimize decision and recommendation results according to continuously changing city conditions and user requirements.
Preferably, the calculation formula of the Q-learning algorithm is as follows:
Q(s,a)＝Q(s,a)+α*(r+γ*max(Q(s',a'))-Q(s,a))
wherein,,
q (s, a) represents the Q value of performing action a in state s;
alpha is a learning rate for controlling the update rate of the Q value;
r is the instant prize obtained after performing action a;
gamma is a discount factor representing the degree of importance for future rewards;
max (Q (s ', a ')) represents an action selected to have the maximum Q value among actions executable in the state s '.
The Q-learning algorithm continuously updates the Q value function through continuous interaction with the environment, and finally enables the Q value function to be converged to an optimal value, so that intelligent decision and recommendation are realized;
in the smart city message platform, the state s is expressed as various state characteristics of the current city, including traffic flow, air quality and weather conditions;
action a represents different services and policies performed for the smart city message platform;
after the action is executed, the smart city message platform obtains instant rewards r according to actual conditions;
through continuous interaction with cities, the smart city message platform continuously optimizes the Q value function according to the updating rule of the Q-learning algorithm, so that intelligent decision and recommendation are realized.
Preferably, the development of the S3 front-end interface introduces an augmented reality technology and a virtual reality technology, combines city information with an actual scene, and experiences city services and decision effects in the virtual scene by a VR technology.
Preferably, the implementation steps of the introducing augmented reality technology and the virtual reality technology are as follows:
s31, communicating with citizens and resident representatives and city management departments, knowing the expectations and demands of users on VR functions, and definitely applying the scenes and functions of AR/VR in a front-end interface;
s32, designing a VR interaction flow of a user in a front-end interface according to VR function requirements, and enabling the VR to be in seamless connection with a traditional front-end interface based on user experience and interaction design;
s33, selecting VR technical platforms including ARKit, ARCore, unity;
s34, developing A according to the designed interaction flow and the selected technical platform
The VR functional module comprises modeling and rendering of a virtual city scene, fusion with actual data and user interaction;
s35, in the front-end interface, data interaction is carried out with the back-end data module.
Preferably, the S4 deployment and operation and maintenance introduces an autonomous evolution automation operation and maintenance technology, and utilizes artificial intelligence and machine learning algorithms to perform automation monitoring, fault diagnosis and performance optimization on the smart city message platform.
Preferably, the implementation steps of the automated operation and maintenance technology are as follows:
s41, constructing an automatic operation and maintenance platform of the smart city message platform, and integrating monitoring, diagnosing and optimizing functions;
and the running state of the platform is comprehensively monitored by collecting real-time data, logs and indexes.
S42, analyzing historical data by using a machine learning algorithm comprising linear regression, time sequence analysis, decision trees, random forests, a support vector machine, a K nearest neighbor algorithm and a network nerve, establishing a prediction model, and identifying a potential failure mode and performance bottlenecks;
s43, combining the real-time monitoring data and the machine learning model to automatically perform fault diagnosis;
once a problem is found, the automatic operation and maintenance platform can quickly locate a fault reason and send out an alarm to inform operation and maintenance personnel to process the fault reason;
s44, automatically performing performance optimization, adjusting resource allocation and increasing load balancing according to the diagnosis result and the historical data of the operation and maintenance platform;
s45, establishing a continuous monitoring and optimizing mechanism, feeding monitoring data and optimizing results back to the machine learning algorithm, and performing automatic operation and maintenance of autonomous evolution.
Preferably, the S5 propaganda is performed by adopting an AI artificial intelligent voice assistant, and a personalized AI voice assistant is developed for the smart city message platform;
formulating a functional range of the AI voice assistant, including answering questions, providing services, voice interaction modes, developing an AI voice assistant application by using a voice recognition technology and a natural language processing technology, and designing a virtual character with affinity and uniqueness for the AI voice assistant;
establishing a knowledge base and a data set for the AI voice assistant, wherein the knowledge base and the data set comprise relevant information of a smart city message platform and answers to common questions;
adding a voice interaction function in a front end interface of the smart city message platform, and allowing a user to conduct real-time voice communication with the AI voice assistant;
performing user experience test of the AI voice assistant, collecting user feedback, and performing optimization and improvement according to user opinion;
through the propaganda channel, promote function and the application method of AI voice assistant, attract more users with AI voice assistant interdynamic, have solved function and service of wisdom city message platform.
Preferably, the step S5 of popularizing and establishing an urban partnership program of the smart city message platform, and establishing partnership with enterprises, community organizations, schools and research institutions in the city to jointly promote the construction and propaganda of the smart city;
the objects of the enterprises, community organizations, schools and research institutions in the cooperative cities cover various fields and social levels;
making a detailed partner program, including a partner target, a partner content and a resource investment;
negotiating with potential partners, introducing functions and services of the smart city message platform, and discussing the possibility and the willingness of the partners together;
customizing personalized service and popularization strategies according to the requirements and characteristics of the partners;
and the method and the system hold propaganda activities together with the partners, and expand the coverage range and influence of propaganda by combining the resources and influence of the partners.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention integrates various data sources including sensor data, social media data, public service data and the like, and realizes comprehensive perception and collection of city information; compared with the traditional single data source, the method and the system can provide more comprehensive and diversified city information, so that the information acquired by the user is more comprehensive and accurate.
(2) According to the invention, the history data is analyzed and mined by using a reinforcement learning algorithm, the information pushing strategy is optimized, and personalized and accurate message pushing service is realized; the information pushing method is more intelligent and efficient, and redundancy of information and interference of junk information are avoided.
(3) According to the invention, through the augmented reality technology, interactive AR display points are arranged in the city, so that citizens can interact with the platform in real time through mobile phones, and the development and management conditions of the city are known; the interactivity enhances the participation degree and interest of citizens on the city information, and improves the effect of city information transmission.
(4) The invention establishes partnership with enterprises, community organizations, schools, research institutions and the like in the city to jointly promote the construction and propaganda of the smart city; the partnership expands the resources and influence of the platform and promotes the joint participation and support of urban construction.
Drawings
FIG. 1 is a schematic overall flow chart of the present invention.
Detailed Description
Examples: referring to fig. 1, the smart city message platform publishing method includes the steps of;
1. the smart city message platform publishing method is characterized in that: the smart city message platform publishing method comprises the following steps;
s1, communicating with an urban planning department, and knowing information of future development planning and targets, key fields, demands and problems of the city;
the system is communicated with urban management departments, including traffic management, environmental protection departments and public safety, and the demands and problems of all departments are known;
communicating with information technology professionals and software developers, knowing the technical feasibility and technical implementation scheme, and providing information about data sources and data processing;
communicating with data analysts and data scientists to know the feasibility and method of data acquisition and processing;
communicate with citizens and resident representatives to learn about their needs and desires;
communicating with government related departments, including scientific and technical departments and informatization departments, and knowing the requirements and policies of the government on data security and privacy protection;
according to the collected data, the whole target and range of the smart city message platform are defined, and the demand analysis is carried out, so that a demand analysis result is obtained;
s2, on the basis of a demand analysis result, carrying out system design and planning of a smart city message platform;
determining the overall architecture, the functional modules, the data flow and the interface design of the smart city message platform, and formulating detailed technical schemes and plans based on the overall architecture, the functional modules, the data flow and the interface design of the smart city message platform and combining the expansibility and the maintainability;
specifically, the overall architecture is as follows:
the intelligent city message platform adopts a distributed architecture and comprises edge nodes, a cloud server and a mobile terminal, wherein the edge nodes are used for processing real-time data and events, the cloud server is responsible for data storage and offline processing, and the mobile terminal provides message pushing and service access for users;
specifically, the functional modules are as follows:
and the data acquisition and processing module is used for: the system is responsible for collecting data from various sensors, devices and databases, and preprocessing and cleaning the data;
real-time monitoring and early warning module: monitoring and analyzing the real-time data, finding out abnormal conditions in time and triggering early warning;
and the data storage and management module is used for: the cloud server is responsible for storing the acquired data in the cloud server and managing and maintaining the acquired data;
intelligent decision and recommendation module: through data analysis and machine learning algorithm, intelligent decision and recommendation service is provided to assist city management and resident life;
message pushing and service access module: pushing the real-time information and the service to the mobile terminal and providing a service access interface;
specifically, the data flow is as follows:
and (3) data acquisition: various sensors and devices collect real-time data and transmit the real-time data to an edge node;
and (3) real-time processing: the edge node processes and analyzes the real-time data, and triggers early warning or pushes the real-time data to the cloud server;
and (3) data storage: the processed data are stored in a database of the cloud server;
data analysis: the cloud server performs data analysis and intelligent decision on the stored data;
message pushing: pushing the real-time information and the service to the mobile terminal according to the analysis result;
specifically, the interface is designed as follows:
data acquisition interface: data communication is carried out with the sensor and the equipment, and real-time data is received;
real-time monitoring and early warning interface: the edge node pushes real-time monitoring data and early warning information to the cloud server;
data storage and management interface: the cloud server provides an interface for data storage and management;
intelligent decision and recommendation interface: the cloud server provides intelligent decision and recommendation services for the mobile terminal;
mobile terminal interface: an interface for providing the mobile terminal with a function of an access platform and receiving message pushing;
s3, developing a smart city message platform according to system design and planning;
the method comprises the steps of development of a front-end interface, coding of a back-end logic, and design and construction of a database;
after development is completed, performing system test;
s4, after the system test is passed, deploying the smart city message platform into a production environment based on the construction of a server, the configuration of a database and network setting;
carrying out daily operation and maintenance work on the smart city message platform;
s5, after deployment is completed, the functions and advantages of the smart city message platform are publicized to target users and the public, including publishing messages on media and holding popularization activities;
s6, after the smart city message platform is released, user feedback is continuously collected, and improvement and optimization are carried out according to feedback opinion.
S2, determining an overall architecture by adopting a distributed edge computing architecture, and moving part of computing and data processing tasks to edge nodes nearby urban facilities;
s211, determining the position of the deployment edge node according to the geographic layout and facility distribution of the city;
s212, establishing a distributed edge computing architecture, and determining a data flow and an interaction mode between an edge node and a central cloud server;
s213, formulating a resource management strategy of the edge node, and ensuring that the edge node bears required calculation and data processing tasks;
s214, deploying the distributed edge computing architecture into a smart city message platform for system testing and performance evaluation.
S2, the functional module is divided into a data acquisition and processing module, a real-time monitoring and early warning module and an intelligent decision and recommendation module;
the data acquisition and processing module introduces a blockchain technology and links the data acquisition node and the data storage record;
the real-time monitoring and early warning module combines an edge computing technology, and part of real-time monitoring and early warning processing tasks are lowered to edge nodes;
the intelligent decision and recommendation module introduces a Q-learning algorithm, so that the intelligent decision and recommendation module can continuously learn and optimize decision and recommendation results according to continuously changing city conditions and user requirements.
Specifically, the data acquisition and processing module implements the steps of:
determining a data acquisition node: selecting proper sensors and equipment, and adding blockchain identification marks for the sensors and the equipment;
data chaining: recording the acquired data on a blockchain, wherein the acquired data comprises information such as sources, acquisition time and the like of the data;
and (3) data processing verification: during the data transmission and processing process, the data is verified, and the integrity and the authenticity of the data are ensured;
specifically, the real-time monitoring and early warning module implements the steps of:
determining an edge node: selecting a proper edge node, and deploying a real-time monitoring and early warning algorithm;
shunting real-time data: transmitting part of real-time data to an edge node for processing, so that the burden of a cloud server is reduced;
real-time collaboration: the cloud server and the edge node cooperate in real time to jointly complete real-time monitoring and early warning tasks;
specifically, the intelligent decision and recommendation module implements the steps of:
data preparation: preparing historical data and decision results as training data for reinforcement learning;
and (3) constructing a reinforcement learning model: constructing a reinforcement learning model comprising a state space, an action space, a reward function and the like;
model training: training the reinforcement learning model by using historical data, and continuously optimizing the decision-making capability of the model;
real-time decision: applying the trained model to real-time data to make intelligent decision and recommendation;
the Q-learning algorithm has the following calculation formula:
Q(s,a)＝Q(s,a)+α*(r+γ*max(Q(s',a'))-Q(s,a))
wherein,,
q (s, a) represents the Q value of performing action a in state s;
alpha is a learning rate for controlling the update rate of the Q value;
r is the instant prize obtained after performing action a;
gamma is a discount factor representing the degree of importance for future rewards;
max (Q (s ', a ')) represents an action having the largest Q value among actions executable in the state s ';
the Q-learning algorithm continuously updates the Q value function through continuous interaction with the environment, and finally enables the Q value function to be converged to an optimal value, so that intelligent decision and recommendation are realized;
in the smart city message platform, the state s is expressed as various state characteristics of the current city, including traffic flow, air quality and weather conditions;
action a represents different services and policies performed for the smart city message platform;
after the action is executed, the smart city message platform obtains the instant rewards r according to the actual situation;
through continuous interaction with cities, the smart city message platform continuously optimizes the Q value function according to the updating rule of the Q-learning algorithm, so that intelligent decision and recommendation are realized.
And S3, the development of a front-end interface introduces an augmented reality technology and a virtual reality technology, combines city information with an actual scene, and experiences city service and decision effect in the virtual scene by the user through AR and VR technologies.
The implementation steps of introducing the augmented reality technology and the virtual reality technology are as follows:
s31, communicating with citizens and resident representatives and city management departments, knowing the expectations and demands of users on AR/VR functions, and definitely applying the scenes and the functions of the AR/VR in a front-end interface;
s32, according to AR/VR function requirements, designing an AR/VR interaction flow of a user in a front-end interface, and enabling the AR/VR to be in seamless connection with a traditional front-end interface based on user experience and interaction design;
s33, selecting an AR/VR technical platform comprising ARKit, ARCore, unity;
s34, developing an AR/VR functional module according to the designed interaction flow and the selected technical platform, wherein the AR/VR functional module comprises modeling and rendering of a virtual city scene, fusion with actual data and user interaction;
s35, in the front-end interface, data interaction is carried out with the back-end data module.
S4, deployment and operation and maintenance are conducted to an automatic operation and maintenance technology which is automatically evolved, and automatic monitoring, fault diagnosis and performance optimization are conducted on the smart city message platform by utilizing an artificial intelligence and machine learning algorithm.
The implementation steps of the automatic operation and maintenance technology are as follows:
s41, constructing an automatic operation and maintenance platform of the smart city message platform, and integrating monitoring, diagnosing and optimizing functions;
and the running state of the platform is comprehensively monitored by collecting real-time data, logs and indexes.
S42, analyzing historical data by using a machine learning algorithm comprising linear regression, time sequence analysis, decision trees, random forests, a support vector machine, a K nearest neighbor algorithm and a network nerve, establishing a prediction model, and identifying a potential failure mode and performance bottlenecks;
specifically, linear regression is used for carrying out trend analysis on historical data, finding out the linear relation between the data and predicting;
for analyzing linear relationships in historical data, such as analyzing urban traffic flow versus time, predicting future traffic flow;
specifically, the decision tree is used for classification and regression tasks, and key features and decision rules can be mined from historical data;
rules and features used in mining historical data can be used for classification and regression tasks, for example, judging the degree of traffic jam through a decision tree;
specifically, a Support Vector Machine (SVM) is used for classification and regression tasks, and is suitable for complex nonlinear data distribution;
for classification and regression tasks, applicable to nonlinear data distribution, for example for identifying traffic accidents in cities;
specifically, the random forest is an integrated learning method, and the prediction accuracy and stability are improved through the combination of a plurality of decision trees;
the method is an integrated learning algorithm, improves prediction accuracy and stability through the combination of a plurality of decision trees, and can be used for classification and regression tasks;
specifically, the K-nearest neighbor algorithm is used for classifying or regressing based on the neighboring samples in the historical data;
the system is used for classifying or regressing according to similar samples in the historical data, for example, recommending a traffic trip scheme according to trip data of similar users;
specifically, the neural network is used for processing complex nonlinear problems, and characteristic extraction and learning are performed through the multi-layer neural network;
the method is suitable for processing complex nonlinear problems, such as urban image recognition or voice recognition by using a deep neural network, and complex feature extraction and learning of urban data;
in particular, time series analysis is used to predict and trend time-dependent data, such as ARIMA, LSTM;
the method is applicable to data with time series relation, and is used for analyzing trend, seasonal and periodical changes of the data, such as seasonal changes of urban environmental pollution indexes;
s43, combining the real-time monitoring data and the machine learning model to automatically perform fault diagnosis;
once a problem is found, the automatic operation and maintenance platform can quickly locate a fault reason and send out an alarm to inform operation and maintenance personnel to process the fault reason;
s44, automatically performing performance optimization, adjusting resource allocation and increasing load balancing according to the diagnosis result and the historical data of the operation and maintenance platform;
s45, establishing a continuous monitoring and optimizing mechanism, feeding monitoring data and optimizing results back to a machine learning algorithm, and performing automatic operation and maintenance of autonomous evolution.
S5, propaganda is carried out by adopting an AI artificial intelligent voice assistant, and a personalized AI voice assistant is developed for the intelligent city message platform;
the method comprises the steps of formulating a functional range of an AI voice assistant, including answering questions, providing services and voice interaction modes, developing an AI voice assistant application by using a voice recognition technology and a natural language processing technology, and designing a virtual character with affinity and uniqueness for the AI voice assistant;
establishing a knowledge base and a data set for an AI voice assistant, wherein the knowledge base and the data set comprise related information of a smart city message platform and answers to common questions;
adding a voice interaction function in a front-end interface of the smart city message platform, and allowing a user to conduct real-time voice communication with an AI voice assistant;
performing user experience test of the AI voice assistant, collecting user feedback, and performing optimization and improvement according to user opinion;
through propaganda channel, promote AI voice assistant's function and application method, attract more users to interact with AI voice assistant, have solved function and service of the intelligent city message platform.
S5, promoting and establishing an urban partnership project of a smart city message platform, establishing partnership with enterprises, community organizations, schools and research institutions in the city, and jointly promoting the construction and propaganda of the smart city;
the objects of the enterprises, community organizations, schools and research institutions in the cooperative cities cover various fields and social levels;
making a detailed partner program, including a partner target, a partner content and a resource investment;
negotiating with potential partners, introducing functions and services of the smart city message platform, and discussing the possibility and the willingness of the partners together;
customizing personalized service and popularization strategies according to the requirements and characteristics of the partners;
and the method and the system hold propaganda activities together with the partners, and expand the coverage range and influence of propaganda by combining the resources and influence of the partners.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The smart city message platform publishing method is characterized in that: the smart city message platform release method comprises the following steps of;
s1, acquiring information of future urban development planning and targets, key fields, demands and problems of an urban planning department;
obtaining requirements and problems of urban management departments, including traffic management, environmental protection departments and public safety communication;
acquiring technical feasibility and technical implementation schemes of information technology professionals and software developers and information about data sources and data processing;
the feasibility and the method for acquiring and processing the data of a data analyst and a data scientist are obtained;
acquiring the demands and expected feedback of citizens and residents;
acquiring requirements and policies in terms of data security and privacy protection of government-related departments including a scientific and technological department and an informatization department;
according to the collected data, the whole target and range of the smart city message platform are defined, and the demand analysis is carried out, so that a demand analysis result is obtained;
s2, on the basis of the demand analysis result, carrying out system design and planning of a smart city message platform;
determining the overall architecture, the functional module, the data flow and the interface design of the smart city message platform, and formulating a detailed technical scheme and plan based on the overall architecture, the functional module, the data flow and the interface design of the smart city message platform and combining the expansibility and maintainability;
s3, developing a smart city message platform according to the system design and planning;
the method comprises the steps of development of a front-end interface, coding of a back-end logic, and design and construction of a database;
after development is completed, performing system test;
s4, after the system test is passed, deploying the smart city message platform into a production environment based on the construction of a server, the configuration of a database and network setting;
carrying out daily operation and maintenance work on the smart city message platform;
s5, after the deployment is completed, the functions and advantages of the smart city message platform are publicized to the target users and the public, including publishing messages on media and holding popularization activities;
s6, after the smart city message platform is released, user feedback is continuously collected, and improvement and optimization are carried out according to feedback opinion.
2. The smart city message platform publishing method of claim 1, wherein the determining of the S2 overall architecture employs a distributed edge computing architecture to move part of the computing and data processing tasks to edge nodes near the city facility;
s211, determining the position of the deployment edge node according to the geographic layout and facility distribution of the city;
s212, establishing a distributed edge computing architecture, and determining a data flow and an interaction mode between an edge node and a central cloud server;
s213, formulating a resource management strategy of the edge node, and ensuring that the edge node bears required calculation and data processing tasks;
s214, deploying the distributed edge computing architecture into the smart city message platform to perform system testing and performance evaluation.
3. The smart city message platform publishing method of claim 1, wherein the S2 functional module is divided into a data acquisition and processing module, a real-time monitoring and early warning module, and an intelligent decision and recommendation module;
the data acquisition and processing module introduces a blockchain technology to link the data acquisition node and the data storage record;
the real-time monitoring and early warning module combines an edge computing technology to lower part of real-time monitoring and early warning processing tasks to edge nodes;
the intelligent decision and recommendation module introduces a Q-learning algorithm, so that the intelligent decision and recommendation module can continuously learn and optimize decision and recommendation results according to continuously changing city conditions and user requirements.
4. The smart city message platform publishing method of claim 3, wherein the Q-learning algorithm has the following formula:
q (s, a) =q (s, a) +α (r+γ) max (Q (s ', a')) -Q (s, a)) wherein,
q (s, a) represents the Q value of performing action a in state s;
alpha is a learning rate for controlling the update rate of the Q value;
r is the instant prize obtained after performing action a;
gamma is a discount factor representing the degree of importance for future rewards;
max (Q (s ', a ')) represents an action having the largest Q value among actions executable in the state s ';
the Q-learning algorithm continuously updates the Q value function through continuous interaction with the environment, and finally enables the Q value function to be converged to an optimal value, so that intelligent decision and recommendation are realized;
in the smart city message platform, the state s is expressed as various state characteristics of the current city, including traffic flow, air quality and weather conditions;
action a represents different services and policies performed for the smart city message platform;
after the action is executed, the smart city message platform obtains instant rewards r according to actual conditions;
through continuous interaction with cities, the smart city message platform continuously optimizes the Q value function according to the updating rule of the Q-learning algorithm, so that intelligent decision and recommendation are realized.
5. The smart city message platform publishing method of claim 1, wherein the development of the S3 front end interface introduces an augmented reality technique and a virtual reality technique, combines city information with an actual scene, and a user experiences city services and decision effects in the virtual scene through VR technology.
6. The smart city message platform publishing method of claim 5, wherein the steps of introducing the augmented reality technology and the virtual reality technology are as follows:
s31, communicating with citizens and resident representatives and city management departments, knowing the expectations and demands of users on VR functions, and definitely applying the scene and functions of VR in a front-end interface;
s32, designing a VR interaction flow of a user in a front-end interface according to VR function requirements, and enabling the VR to be in seamless connection with a traditional front-end interface based on user experience and interaction design;
s33, selecting VR technical platforms including ARKit, ARCore, unity;
s34, developing VR functional modules according to the designed interaction flow and the selected technical platform, wherein the VR functional modules comprise modeling and rendering of virtual city scenes, fusion with actual data and user interaction;
s35, in the front-end interface, data interaction is carried out with the back-end data module.
7. The smart city message platform publishing method of claim 1, wherein the S4 deployment and operation introduces an autonomous evolution automated operation and maintenance technique, and wherein the smart city message platform is automatically monitored, fault diagnosed, and performance optimized using artificial intelligence and machine learning algorithms.
8. The smart city message platform publishing method of claim 7, wherein the automated operation and maintenance technique is implemented as follows:
s41, constructing an automatic operation and maintenance platform of the smart city message platform, and integrating monitoring, diagnosing and optimizing functions;
and the running state of the platform is comprehensively monitored by collecting real-time data, logs and indexes.
S42, analyzing historical data by using a machine learning algorithm comprising linear regression, time sequence analysis, decision trees, random forests, a support vector machine, a K nearest neighbor algorithm and a network nerve, establishing a prediction model, and identifying a potential failure mode and performance bottlenecks;
s43, combining the real-time monitoring data and the machine learning model to automatically perform fault diagnosis;
once a problem is found, the automatic operation and maintenance platform can quickly locate a fault reason and send out an alarm to inform operation and maintenance personnel to process the fault reason;
s44, automatically performing performance optimization, adjusting resource allocation and increasing load balancing according to the diagnosis result and the historical data of the operation and maintenance platform;
s45, establishing a continuous monitoring and optimizing mechanism, feeding monitoring data and optimizing results back to the machine learning algorithm, and performing automatic operation and maintenance of autonomous evolution.
9. The smart city message platform publication method of claim 1, wherein said S5 promotion is performed using an AI artificial intelligence voice assistant to develop a personalized AI voice assistant for the smart city message platform;
formulating a functional range of the AI voice assistant, including answering questions, providing services, voice interaction modes, developing an AI voice assistant application by using a voice recognition technology and a natural language processing technology, and designing a virtual character with affinity and uniqueness for the AI voice assistant;
establishing a knowledge base and a data set for the AI voice assistant, wherein the knowledge base and the data set comprise relevant information of a smart city message platform and answers to common questions;
adding a voice interaction function in a front end interface of the smart city message platform, and allowing a user to conduct real-time voice communication with the AI voice assistant;
performing user experience test of the AI voice assistant, collecting user feedback, and performing optimization and improvement according to user opinion;
through the propaganda channel, promote function and the application method of AI voice assistant, attract more users with AI voice assistant interdynamic, have solved function and service of wisdom city message platform.
10. The smart city message platform publishing method of claim 1, wherein S5 popularizes and establishes a city partnership project of the smart city message platform, establishes partnership with enterprises, community organizations, schools, research institutions in the city, and objects in the cooperating cities including enterprises, community organizations, schools, research institutions cover various fields and social levels;
making a detailed partner program, including a partner target, a partner content and a resource investment;
propaganda and promoting functions and services of the smart city message platform, and discovering potential partners;
customizing personalized service and popularization strategies according to the requirements and characteristics of the partners;
and the method and the system hold propaganda activities together with the partners, and expand the coverage range and influence of propaganda by combining the resources and influence of the partners.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117216689A (en) * | 2023-11-08 | 2023-12-12 | 山东辰智电子科技有限公司 | Underground pipeline emission early warning system based on urban water conservancy data |
| CN117609812A (en) * | 2023-12-07 | 2024-02-27 | 中科报业智慧研究中心(深圳)有限公司 | Smart city information interaction method and system based on frequency division control |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117216689A (en) * | 2023-11-08 | 2023-12-12 | 山东辰智电子科技有限公司 | Underground pipeline emission early warning system based on urban water conservancy data |
| CN117216689B (en) * | 2023-11-08 | 2024-02-27 | 山东辰智电子科技有限公司 | Underground pipeline emission early warning system based on urban water conservancy data |
| CN117609812A (en) * | 2023-12-07 | 2024-02-27 | 中科报业智慧研究中心(深圳)有限公司 | Smart city information interaction method and system based on frequency division control |
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