Smart city evaluation AI analysis system and evaluation method

Abstract

The invention relates to the technical field of city management, in particular to a smart city evaluation AI analysis system and an evaluation method. Comprising a data acquisition processing module, a data storage management module, a data analysis mining module, an artificial intelligence machine learning module, a visual report generation module, a public service optimization module, a behavior event analysis module and an environmental health monitoring module. The data acquisition and processing module is used for data acquisition and data cleaning; according to the invention, the system achieves the real-time monitoring and decision support through automatic data collection and analysis, can automatically collect and analyze data in multiple aspects, achieves the real-time monitoring and evaluation of the urban operation state, provides timely and accurate decision support for managers, and improves the management efficiency. The effect of comprehensively reflecting the city happiness is achieved through multi-dimensional comprehensive evaluation, the system adopts a multi-dimensional comprehensive evaluation method, comprehensive evaluation is carried out from multiple aspects of environment, traffic, safety, health, education and the like, and the happiness level of city residents is comprehensively reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of urban management, and particularly to a smart city evaluation AI analysis system and an evaluation method. Background Art

[0002] Urban management refers to the planning, organization, coordination and control of various aspects of a city, aiming to promote the orderly development of the city and improve the quality of life of residents. Its content includes, but is not limited to, environmental protection, transportation, public safety, public health, education resource allocation and infrastructure construction. In the initial stage of the development of urban management and the birth process of smart cities, traditional manual management was mainly relied on, which depended on paper documents and manual decision-making, with low efficiency. With the development of information technology, computer-aided management was gradually introduced, using spreadsheets, databases and management information systems, which improved the data processing and decision-making efficiency. Modern technologies such as the Internet, Internet of Things, big data and artificial intelligence have gradually transitioned to smart city management, realizing comprehensive, real-time and efficient urban management.

[0003] Based on the concept of urban management, the birth of smart cities was first proposed in the early 21st century, aiming to improve the management and service levels of cities through the application of information technology. With the maturity of technologies such as the Internet of Things, big data, cloud computing and artificial intelligence, smart cities have gradually moved from concept to reality. At present, smart cities are widely applied globally. Through specific practices in fields such as intelligent transportation, smart security and smart healthcare, the intelligentization and high efficiency of urban management have been realized. And evaluating the urban happiness level through the construction of smart cities is also an important function of this concept. Through evaluations in multiple aspects such as environment, transportation, safety, health and education, it comprehensively reflects the quality of life and satisfaction of residents. The evaluation results of happiness level can provide a scientific basis for urban managers, guide urban development planning and resource allocation, and improve the accuracy and effectiveness of management decisions. Cities with high happiness levels are more attractive, which can attract more talents and investments, and promote the economic development and social progress of the city.

[0004] However, generally speaking, traditional smart city data collection requires multi-party aggregation and individual analysis and evaluation, relying on manual data collection and statistics, with low efficiency, untimely data updates, and it is difficult to reflect the real-time state of the city. Single indicators are mostly used for evaluation, which is difficult to comprehensively reflect the comprehensive development level of the city and the happiness level of residents. There is a lack of scientific data analysis and prediction means, and decisions mostly rely on experience and subjective judgment, which easily leads to decision-making mistakes. It takes a long time from data collection to the generation of analysis reports, and it is difficult to handle emergencies and urgent situations in urban management.

[0005] In summary, it is necessary to propose a smart city evaluation AI analysis system and an evaluation method to solve the above problems. Summary of the Invention

[0006] The objective of the present invention is to provide a smart city evaluation AI analysis system and an evaluation method to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above objective, the present invention provides the following technical solutions:

[0008] A smart city evaluation AI analysis system includes a data acquisition and processing module, a data storage and management module, a data analysis and mining module, an artificial intelligence machine learning module, a visualization report generation module, a public service optimization module, a behavior event analysis module, and an environmental health monitoring module;

[0009] The data acquisition and processing module is used for data acquisition and data cleaning;

[0010] The data storage and management module is used for database management and ensuring data security;

[0011] The data analysis and mining module is used for statistical analysis and data mining;

[0012] The artificial intelligence machine learning module is used for predicting continuous variables of energy consumption or traffic flow and analyzing the sentiment tendency in social media or public feedback;

[0013] The visualization report generation module is used for data visualization and report generation;

[0014] The public service optimization module is used for traffic management and energy management;

[0015] The behavior event analysis module is used for detecting abnormal events and analyzing crowd behavior;

[0016] The environmental health monitoring module is used for environmental quality monitoring, public health monitoring, and analysis of the urban happiness index.

[0017] Preferably, the data acquisition and processing module further includes a data acquisition unit and a data cleaning unit;

[0018] The data acquisition unit is used for collecting data on traffic, air quality, and energy consumption in real time, and collecting citizen feedback and public data through social media, government open data, and questionnaires;

[0019] The data cleaning unit is used for processing missing values, outliers, and duplicate data, ensuring data quality, and converting data from different sources into a unified format for subsequent processing.

[0020] Preferably, the data storage and management module further includes a database management unit and a data security unit;

[0021] The database management unit is used to store structured data, including traffic flow statistics and energy consumption records, and store unstructured data, including social media comments, pictures and video data;

[0022] The data security unit is used to ensure the security of data during transmission and storage, and define and manage the access rights of different users and applications to data.

[0023] Preferably, the data analysis and mining module further includes a statistical analysis unit and a data mining unit;

[0024] The statistical analysis unit is used to provide basic statistical descriptions of data, including mean, median, standard deviation, analyze time series data, and identify trends and seasonal variations;

[0025] The data mining unit is used to divide urban areas or populations into different groups to identify specific patterns and discover the correlation relationships between data, including the relationship between peak traffic time and air quality.

[0026] Preferably, the artificial intelligence and machine learning module further includes a prediction model unit and a natural language processing unit;

[0027] The prediction model unit is used to predict continuous variables, including future energy consumption or traffic flow, and predict categorical variables, including the possibility of traffic accidents or the level of citizen satisfaction;

[0028] The natural language processing unit is used to analyze the sentiment tendency in social media or public feedback, and extract useful information and patterns from unstructured text data.

[0029] Preferably, the visualization report generation module further includes a data visualization unit and a report generation unit;

[0030] The data visualization unit is used to generate line charts, bar charts and heat maps to intuitively display the data analysis results and display geospatial data, including traffic flow maps and pollution heat maps;

[0031] The report generation unit is used to automatically generate reports based on the analysis results and generate reports in specific formats and contents according to user needs.

[0032] Preferably, the public service optimization module further includes a traffic management unit and an energy management unit;

[0033] The traffic management unit is used to adjust the signal timing according to real-time traffic data, predict future traffic flow, and optimize traffic management strategies;

[0034] The energy management unit is used to optimize power distribution, reduce energy waste, and optimize the use of renewable energy according to the predicted energy demand and weather conditions.

[0035] Preferably, the behavior event analysis module further includes an abnormal event detection unit and a crowd behavior analysis unit;

[0036] The abnormal event detection unit is used to identify and alarm abnormal events, including sudden traffic accidents or environmental pollution events, provide emergency treatment suggestions, and coordinate relevant departments to respond quickly;

[0037] The crowd behavior analysis unit is used to predict the pedestrian flow in different areas of the city, optimize the distribution and scheduling of public facilities, analyze the social network among citizens, and identify key influencers and dissemination patterns.

[0038] Preferably, the environmental health monitoring module further includes an environmental quality monitoring unit, a public health monitoring unit, and an urban happiness index analysis unit;

[0039] The environmental quality monitoring unit is used to monitor the air pollution level in real time, provide early warning information, monitor the water quality of the city's water bodies, and ensure the safety of citizens' water use;

[0040] The public health monitoring unit is used to monitor the data of hospitals and clinics everywhere in the city in real time, provide health early warnings and trend analysis, predict the transmission trend of infectious diseases, provide prevention and control suggestions, analyze citizens' health data, and identify public health problems and trends

[0041] The urban happiness index analysis unit is used to calculate and evaluate the happiness index of citizens by comprehensively analyzing data in multiple aspects such as the environment, transportation, safety, health, and education of the city, and provide decision-making references.

[0042] Based on the above system, the present invention also proposes a smart city evaluation AI analysis and evaluation method, including the following steps:

[0043] S1. Conduct urban data collection and preprocessing: Collect traffic, air quality, and energy consumption data in real time from various sensors and devices, collect citizens' feedback and public data through social media, government open data, and questionnaires, clean the collected data, process missing values, outliers, and duplicate data, and convert data from different sources into a unified format for subsequent processing;

[0044] S2. Conduct data storage and security management: Store the processed data in a relational database for storing structured data, including traffic flow statistics and energy consumption records, store unstructured data, including social media comments, pictures, and video data, in a non-relational database, encrypt the data during storage and transmission to ensure data security, and define and manage the access rights of different users and applications to the data;

[0045] S3. Implement data statistical analysis and data mining based on statistical analysis: Provide basic statistical descriptions of the data, including mean, median, and standard deviation, analyze time series data, identify trends and seasonal variations in the data, perform cluster analysis on urban areas or populations to identify specific patterns, and discover the correlation relationships between data, including the relationship between peak traffic times and air quality;

[0046] S4. Conduct predictive analysis and natural language processing: Build regression models to predict future energy consumption or traffic flow, build classification models to predict the likelihood of traffic accidents or the level of public satisfaction, analyze the sentiment tendency in social media or public feedback, extract sentiment information, and extract useful information and patterns from unstructured text data;

[0047] S5. Data visualization and report generation: Generate line charts, bar charts, and heat maps to visually display the data analysis results, use geographic information systems to display geospatial data, including traffic flow maps and pollution heat maps, automatically generate reports based on the analysis results to provide decision-making references, and generate reports with specific formats and content according to user requirements;

[0048] S6. Traffic and energy optimization: Adjust signal timings according to real-time traffic data, optimize traffic flow, predict future traffic flow, formulate optimized traffic management strategies, optimize power distribution, reduce energy waste, and optimize the use of renewable energy according to predicted energy demand and weather conditions;

[0049] S7. Conduct abnormal event detection and crowd behavior analysis: Identify and alarm abnormal events, including sudden traffic accidents or environmental pollution events, provide emergency handling suggestions, coordinate relevant departments for a quick response, predict the pedestrian flow in different urban areas, optimize the distribution and scheduling of public facilities, analyze the social network among citizens, and identify key influencers and dissemination patterns;

[0050] S8. Conduct environmental monitoring and public health monitoring: Real-time monitor air pollution levels and provide early warning information, monitor the water quality of urban water bodies to ensure the safety of citizens' water use, predict the spread trend of infectious diseases and provide prevention and control suggestions, and analyze citizens' health data to identify public health problems and trends;

[0051] S9. Happiness index analysis and summary: Comprehensively analyze data on various aspects of the city's environment, traffic, safety, health, and education, calculate and evaluate the happiness index of citizens, and provide decision-making references.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: Through automated data collection and analysis, the present invention achieves the functions of real-time monitoring and decision support. The smart city evaluation AI analysis system can automatically collect and analyze multi-faceted data, realize real-time monitoring and evaluation of the urban operation status, provide timely and accurate decision support for managers, and comprehensively reflect the urban happiness level through multi-dimensional comprehensive evaluation. The system adopts a multi-dimensional comprehensive evaluation method to conduct comprehensive evaluation from multiple aspects such as environment, transportation, safety, health, and education, comprehensively reflecting the happiness level of urban residents. Through standardization and weight allocation, the function of objective quantitative evaluation is achieved. The system uses the methods of standardization and weight allocation to objectively quantify different indicators and ensure the fairness and scientificity of the evaluation results. Through big data and AI technologies, the functions of efficient processing and accurate prediction are achieved. Using big data and artificial intelligence technologies, the system can efficiently process massive data and conduct accurate trend prediction and problem warning, improving the foresight and initiative of urban management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 FIG. is a topology diagram of a smart city evaluation AI analysis system of the present invention;

[0054] Figure 2 FIG. is a flowchart of the smart city evaluation AI analysis evaluation method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , the present invention provides a smart city evaluation AI analysis system, which is composed of a data collection and processing module, a data storage and management module, a data analysis and mining module, an artificial intelligence machine learning module, a visualization report generation module, a public service optimization module, a behavior event analysis module, and an environmental health monitoring module;

[0058] Among them, it should be noted that the system collects and cleans data through the data collection and processing module, manages the database and ensures data security through the data storage and management module, conducts statistical analysis and data mining through the data analysis and mining module, predicts continuous variables such as energy consumption or traffic flow and analyzes the sentiment tendency in social media or public feedback through the artificial intelligence and machine learning module, visualizes data and generates reports through the visual report generation module, conducts traffic management and energy management through the public service optimization module, detects abnormal events and analyzes crowd behavior through the behavior event analysis module, and monitors environmental quality, public health and analyzes the urban happiness index through the environmental health monitoring module.

[0059] In this embodiment, it should also be noted that the data collection and processing module in the smart city evaluation AI analysis system further includes a data collection unit and a data cleaning unit;

[0060] The data collection unit is used to collect data in real time from traffic, air quality, and energy consumption, and collect citizen feedback and public data through social media, government open data, and questionnaire surveys;

[0061] The data cleaning unit is used to process missing values, outliers, and duplicate data, ensure data quality, and convert data from different sources into a unified format for subsequent processing.

[0062] In this embodiment, it should also be noted that the data storage and management module in the smart city evaluation AI analysis system further includes a database management unit and a data security unit;

[0063] The database management unit is used to store structured data, including traffic flow statistics and energy consumption records, and store unstructured data, including social media comments, pictures, and video data;

[0064] The data security unit is used to ensure the security of data during transmission and storage, and define and manage the access rights of different users and applications to data.

[0065] In this embodiment, it should also be noted that the data analysis and mining module in the smart city evaluation AI analysis system further includes a statistical analysis unit and a data mining unit;

[0066] The statistical analysis unit is used to provide basic statistical descriptions of data, including mean, median, and standard deviation, analyze time series data, and identify trends and seasonal changes;

[0067] The data mining unit is used to divide urban areas or populations into different groups to identify specific patterns and discover the correlation relationships between data, including the relationship between peak traffic time and air quality.

[0068] In this embodiment, it should also be noted that the artificial intelligence machine learning module in the smart city evaluation AI analysis system further includes a prediction model unit and a natural language processing unit;

[0069] The prediction model unit is used to predict continuous variables, including future energy consumption or traffic flow, and predict categorical variables, including the likelihood of traffic accidents or the level of citizen satisfaction;

[0070] The natural language processing unit is used to analyze the sentiment tendency in social media or public feedback, and extract useful information and patterns from unstructured text data.

[0071] In this embodiment, it should also be noted that the visualization report generation module in the smart city evaluation AI analysis system further includes a data visualization unit and a report generation unit;

[0072] The data visualization unit is used to generate line charts, bar charts, and heat maps to intuitively display the data analysis results and display geospatial data, including traffic flow maps and pollution heat maps;

[0073] The report generation unit is used to automatically generate reports based on the analysis results and generate reports in specific formats and contents according to user requirements.

[0074] In this embodiment, it should also be noted that the public service optimization module in the smart city evaluation AI analysis system further includes a traffic management unit and an energy management unit;

[0075] The traffic management unit is used to adjust the signal timing according to real-time traffic data, predict future traffic flow, and optimize traffic management strategies;

[0076] The energy management unit is used to optimize power distribution, reduce energy waste, and optimize the use of renewable energy according to the predicted energy demand and weather conditions.

[0077] In this embodiment, it should also be noted that the behavior event analysis module in the smart city evaluation AI analysis system further includes an abnormal event detection unit and a crowd behavior analysis unit;

[0078] The abnormal event detection unit is used to identify and alarm abnormal events, including sudden traffic accidents or environmental pollution events, provide emergency treatment suggestions, and coordinate relevant departments for rapid response;

[0079] The crowd behavior analysis unit is used to predict the pedestrian flow in different areas of the city, optimize the distribution and scheduling of public facilities, analyze the social network among citizens, and identify key influencers and dissemination patterns.

[0080] In this embodiment, it should also be noted that the environmental health monitoring module in the smart city evaluation AI analysis system further includes an environmental quality monitoring unit, a public health monitoring unit, and an urban happiness index analysis unit;

[0081] The environmental quality monitoring unit is used to monitor the air pollution level in real time, provide early warning information, monitor the urban water body quality, and ensure the safety of citizens' water use;

[0082] The public health monitoring unit is used to monitor the data of hospitals and clinics everywhere in the city in real time, provide health early warnings and trend analysis, predict the spread trend of infectious diseases, provide prevention and control suggestions, analyze citizens' health data, and identify public health problems and trends

[0083] The urban happiness index analysis unit is used to calculate and evaluate the happiness index of citizens by comprehensively analyzing data in multiple aspects such as the environment, transportation, safety, health, and education of the city, and provide decision-making references.

[0084] Embodiment 2

[0085] Please refer to Figure 2 , based on the above system, the present invention also proposes an analysis and evaluation method based on the smart city evaluation AI analysis and evaluation system. In the actual application of smart city evaluation and analysis, specifically, it includes the following steps:

[0086] S1. Conduct urban data collection and preprocessing:

[0087] Data collection:

[0088] Sensor and device data collection: Real-time collect data from sensors and devices installed everywhere in the city (including traffic cameras, air quality monitors, smart meters), use an Internet of Things platform including ThingSpeak or AWS IoT to integrate and manage these sensor data, obtain data through an API interface and store it in a central database. The formula is:

[0089] D_i = API_{sensor}(t_i)

[0090] Where (D_i) is the sensor data collected at time (t_i);

[0091] Social media data collection: Use web crawlers to collect citizens' feedback data from social media platforms (including Twitter, Weibo), use Python's Scrapy or Beautiful Soup libraries to write crawler scripts, crawl posts with specified keywords, and store them as text data. The formula is:

[0092] P_j = Scrapy_{social}(keyword_j)

[0093] Among them, \((P_j)\) are the social media posts collected for keyword \((keyword_j)\);

[0094] Government open data and questionnaire surveys: Download the open dataset from the government website, conduct citizen questionnaire surveys online or offline, use the Pandas library to process the government dataset, use Google Forms to collect questionnaire data, and merge the data into a unified table. The formula is:

[0095] \(G_k = Download_{gov}(source_k)\)

[0096] Among them, \((G_k)\) is the government data of source \((source_k)\);

[0097] Data cleaning:

[0098] Missing value handling: Handle the missing values in the dataset, including filling or deleting. Use the mean filling method to fill the numerical data with its mean. The formula is:

[0099] \(D_{clean}=\begin{cases}D_i, & \text{if} D_i\neq\text{NaN} \\ \mu_D, & \text{if} D_i=\text{NaN} \end{cases}\)

[0100] Among them, \((\mu_D)\) is the mean of dataset \((D)\);

[0101] Outlier handling: Detect and handle the outliers in the data to ensure the data validity. Use the 3 - standard - deviation method (Z - score) to detect outliers. The formula is:

[0102] \(Z_i=\frac{D_i - \mu_D}{\sigma_D}\)

[0103] If \((|Z_i|>3)\), then \((D_i)\) is an outlier and needs to be processed or deleted;

[0104] Duplicate data handling: Identify and delete duplicate data to ensure data uniqueness. Use the drop_duplicates function of the Pandas library. The formula is:

[0105] \(D_{unique}=drop_duplicates(D)\)

[0106] Among them, \((D_{unique})\) is the dataset after removing duplicates;

[0107] Unifying data formats:

[0108] Data Format Conversion: Convert data from different sources into a unified format for convenient subsequent processing. Use the Pandas library in Python for data format conversion. The formula is:

[0109] D_{formatted} = format(D_{clean}, format_type)

[0110] where (D_{formatted}) is the formatted dataset;

[0111] Data Type Conversion: Ensure that all data types are consistent, including numeric and string types. Use the astype function of the Pandas library. The formula is:

[0112] D_{typed} = D_{formatted}.astype(dtype)

[0113] where (dtype) is the target data type;

[0114] Timestamp Standardization: Convert all time-related data into a unified timestamp format. Use the datetime library in Python. The formula is:

[0115] T_{standard} = datetime.strptime(T, format_string)

[0116] where (T) is the original time data and (T_{standard}) is the standardized timestamp;

[0117] S2. Perform Data Storage and Security Management:

[0118] Data Storage:

[0119] Store structured data in a relational database: Store structured data (including traffic flow statistics and energy consumption records) in a relational database. Use the MySQL database and store data through SQL insert statements. The specific SQL statement is:

[0120] INSERT INTO traffic_data(timestamp, location, vehicle_count)

[0121] VALUES(%s, %s, %d);

[0122] where timestamp, location, and vehicle_count are the timestamp, location, and vehicle count fields respectively;

[0123] Non-relational databases store unstructured data: Unstructured data (including social media comments, image, and video data) is stored in a non-relational database. MongoDB is used, and data is stored using the insert_one method in MongoDB. The specific code is as follows:

[0124]

[0125] Among them, timestamp is the timestamp, username is the user name, and comment is the comment content;

[0126] Data backup and recovery: The database is backed up regularly to ensure data reliability and security. The built-in backup tools of the database are used, including mysqldump for MySQL and mongodump for MongoDB. The backup is automatically executed through a cron job. The specific commands are as follows:

[0127] mysqldump -u user -p password database_name > backup.sql

[0128] mysqldump -u user -p password database_name > backup.sql

[0129] Data encryption:

[0130] Storage data encryption: The data stored in the database is encrypted to prevent unauthorized access. AES (Advanced Encryption Standard) is used to encrypt the data. The data is encrypted through the AES algorithm. The specific encryption formula is as follows:

[0131] C = E_k(P)

[0132] Among them, (C) is the ciphertext, (E_k) is the encryption function, and (P) is the plaintext data;

[0133] Transmission data encryption: An encryption protocol is used during data transmission to ensure data security. The TLS (Transport Layer Security) protocol is used to encrypt data transmission. A secure connection is established through the TLS protocol. The specific process is as follows:

[0134] The client sends a handshake request to the server;

[0135] The server returns a certificate, and the client verifies the certificate;

[0136] Both parties generate a session key for encrypting subsequent data transmission;

[0137] Key Management: Securely manage encryption keys to prevent key leakage. Use AWS Key Management Service (KMS) to manage keys, generate and store keys through KMS. The specific steps are as follows:

[0138] Create a key in KMS;

[0139] Use the KMS API to call and encrypt / decrypt data;

[0140] Access Permission Management:

[0141] User Role Definition: Define the access roles and permissions of different users and applications. Use the RBAC (Role-Based Access Control) model to manage access control by defining a role and permission matrix, specifically expressed as:

[0142] Access(role_i,resource_j)={read,write,execute}

[0143] where (role_i) is the user role and (resource_j) is the resource;

[0144] Permission Assignment: Assign specific permissions according to user roles. Use the user permission management function of the database, including the GRANT statement in MySQL, and assign permissions through SQL statements. The specific SQL statement is:

[0145] GRANT SELECT,INSERT ON database_name.*TO'user'@'host';

[0146] Access Log Recording: Record the access logs of users and applications to data for easy auditing and monitoring. Use the ELK (Elasticsearch, Logstash, Kibana) stack. Collect logs through Logstash and store them in Elasticsearch. The specific steps are as follows:

[0147] Configure Logstash to collect database access logs;

[0148] Create an index in Elasticsearch to store logs;

[0149] Use Kibana to visualize log data for monitoring and auditing;

[0150] S3. Implement Data Statistical Analysis and Data Mining Based on Statistical Analysis:

[0151] Basic Statistical Descriptions of Data:

[0152] Calculate the mean: Calculate the mean of each variable in the dataset. Use the pandas library in Python and calculate the mean through the mean function of pandas. Specifically, the formula is:

[0153] \text{Mean}=\frac{1}{n}\sum_{i=1}^{n}x_i

[0154] import pandas as pd

[0155] df=pd.read_csv('data.csv')

[0156] mean_values=df.mean()

[0157] Calculate the median: Calculate the median of each variable in the dataset. Use the pandas library in Python and calculate the median through the median function of pandas

[0158] Calculate the standard deviation: Calculate the standard deviation of each variable in the dataset. Use the pandas library in Python and calculate the standard deviation through the std function of pandas. Specifically, the formula is:

[0159] \sigma=\sqrt{\frac{1}{n - 1}\sum_{i=1}^{n}(x_i-\bar{x})^2};

[0160] Time series data analysis:

[0161] Data preprocessing: Preprocess the time series data to ensure data continuity and consistency. Use the pandas library in Python and process the data through the resample and interpolate functions of pandas. The specific process includes the following:

[0162] df['timestamp']=pd.to_datetime(df['timestamp'])

[0163] df.set_index('timestamp',inplace=True)

[0164] df=df.resample('D').interpolate()

[0165] Trend identification: Identify the trend in the time series data. Use the statsmodels library in Python and identify the trend through the moving average method. Specifically, the formula is:

[0166] $T_t=\frac{1}{k}\sum_{i=t-k + 1}^{t}x_i$

[0167] The process includes the following:

[0168] import statsmodels.api as sm

[0169] trend = sm.tsa.seasonal_decompose(df['value'], model='additive').trend

[0170] Seasonal variation identification: Identify the seasonal variation in time series data. Use the statsmodels library in Python and identify the seasonal variation through seasonal decomposition. Specifically, the formula is:

[0171] $S_t = x_t - T_t - R_t$;

[0172] Cluster analysis:

[0173] Data standardization: Standardize the data. Use the scikit - learn library in Python and standardize the data through StandardScaler. Specifically, the formula is:

[0174] $z=\frac{x-\mu}{\sigma}$

[0175] K - means clustering: Use the K - means algorithm to perform cluster analysis on the data. Use the scikit - learn library in Python and perform clustering through the K - means algorithm. Specifically, the formula is:

[0176] $J=\sum_{i = 1}^{k}\sum_{x_j\in C_i}|x_j-\mu_i|^2$;

[0177] Visualization of clustering results: Visualize the clustering results. Use the matplotlib library in Python and display the clustering results through a scatter plot. The specific process includes the following:

[0178] import matplotlib.pyplot as plt

[0179] plt.scatter(df_scaled[:,0], df_scaled[:,1], c = clusters)

[0180] plt.show()

[0181] Data Association Relationship Discovery:

[0182] Data Correlation Calculation: Calculate the correlation coefficient between data. Use the pandas library in Python to calculate the data correlation through the Pearson correlation coefficient. Specifically, the formula is:

[0183] r=\frac{\sum(x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum(x_i-\bar{x})^2\sum(y_i-\bar{y})^2}}

[0184] Association Analysis between Peak Traffic Time and Air Quality: Analyze the relationship between peak traffic time and air quality. Use the scipy library in Python to conduct association analysis through the Spearman rank correlation coefficient. Specifically, the formula is:

[0185] \rho=1-\frac{6\sum d_i^2}{n(n^2-1)}

[0186] Visualization of Association Relationship: Visually display the discovered association relationship. Use the seaborn library in Python to display the association relationship between data through a scatter plot. The specific process includes the following:

[0187] import seaborn as sns

[0188] sns.scatterplot(x='traffic_peak',y='air_quality',data=df)

[0189] plt.show()

[0190] S4. Prediction Analysis and Natural Language Processing:

[0191] Build a regression model to predict future energy consumption or traffic flow:

[0192] Data Preparation: Collect and clean data to ensure data quality. Use the pandas library in Python to standardize data by handling missing values and outliers;

[0193] import pandas as pd

[0194] df=pd.read_csv('data.csv')

[0195] df.fillna(df.mean(),inplace=True)

[0196] df = df[(df['value'] >= df['value'].quantile(0.01)) & (df['value'] <= df['value'].quantile(0.99))]

[0197] Feature engineering: Select and construct features to improve the prediction ability of the model. Use the scikit - learn library in Python, perform dimensionality reduction using PCA, and generate new features;

[0198] from sklearn.decomposition import PCA

[0199] pca = PCA(n_components = 5)

[0200] df_pca = pca.fit_transform(df.drop('target', axis = 1))

[0201] Build a regression model: Train a regression model for prediction. Use the scikit - learn library in Python and use a linear regression model. Specifically, the formula is:

[0202] \hat{y}=\beta_0+\beta_1x_1+\beta_2x_2+\cdots+\beta_n x_n;

[0203] Data preparation: Collect and clean data to ensure data quality. Use the pandas library in Python to handle missing values and encode categorical variables;

[0204] Feature selection: Select important features to improve model performance. Use the scikit - learn library in Python and use feature selection algorithms, including Recursive Feature Elimination (RFE);

[0205] from sklearn.feature_selection import RFE

[0206] from sklearn.ensemble import RandomForestClassifier

[0207] model = RandomForestClassifier()

[0208] rfe = RFE(model, n_features_to_select = 10)

[0209] rfe.fit(df.drop('target', axis = 1), df['target'])

[0210] selected_features = df.columns[rfe.support_]

[0211] Build a classification model: Train a classification model for prediction. Use the scikit - learn library in Python and the Random Forest classifier. Specifically, the formula is:

[0212] \hat{y}=\text{mode}({h_t(x)})

[0213] model.fit(df[selected_features], df['target'])

[0214] predictions = model.predict(df[selected_features])

[0215] Analyze the sentiment tendency in social media or public feedback:

[0216] Data collection: Collect social media or public feedback data. Use the Tweepy library in Python to obtain data through the API. The implementation process includes the following:

[0217] import tweepy

[0218] auth = tweepy.OAuthHandler(consumer_key, consumer_secret)

[0219] auth.set_access_token(access_token, access_token_secret)

[0220] api = tweepy.API(auth)

[0221] tweets = api.search(q = "keyword", count = 100)

[0222] Data preprocessing: Preprocess the text data. Use the nltk library in Python for tokenization, stop - word removal, and stemming;

[0223] from nltk.corpus import stopwords

[0224] from nltk.tokenize import word_tokenize

[0225] from nltk.stem import PorterStemmer

[0226] stop_words = set(stopwords.words('english'))

[0227] ps = PorterStemmer()

[0228] processed_tweets = [''.join([ps.stem(word) for word in word_tokenize(tweet) if word not in stop_words]) for tweet in tweets]

[0229] Sentiment analysis: Conduct sentiment analysis to identify sentiment tendencies, using the TextBlob library in Python, and calculate sentiment scores through sentiment analysis algorithms;

[0230] Extract useful information and patterns from unstructured text data:

[0231] Data collection and preprocessing: Collect unstructured text data and perform preprocessing, using the pandas and nltk libraries in Python, for data cleaning, tokenization, and stop word removal;

[0232] Topic modeling: Conduct topic modeling to discover topics in the text, using the gensim library in Python, and perform topic modeling through LDA (Latent Dirichlet Allocation). Specifically, the formula is:

[0233] p(z|d) = \frac{p(d|z)p(z)}{p(d)};

[0234] Information extraction: Extract useful information and patterns from the text, using the spaCy library in Python, and extract information through named entity recognition (NER)

[0235] S5. Data visualization and report generation:

[0236] Generate line charts, bar charts, and heatmaps:

[0237] Prepare data: Collect and organize data to ensure that the data format is suitable for visualization, using the pandas library in Python, and aggregate and transform the data;

[0238] Generate a line chart: Use a line chart to display the trend of data. Use the matplotlib library in Python to display time series data through a line chart;

[0239] Generate bar charts and heatmaps: Use bar charts and heatmaps to display categorical data and correlations. Use the seaborn library in Python to display categorical data through bar charts and data correlations through heatmaps;

[0240] Use a Geographic Information System (GIS) to display geospatial data:

[0241] Data preparation and loading: Collect and load geospatial data. Use the geopandas library in Python to load and prepare geospatial data;

[0242] Generate a traffic flow map: Display the geographical distribution of traffic flow. Use the folium library in Python to generate a map based on traffic flow data;

[0243] Generate a pollution heatmap: Display the geographical distribution of pollution data. Use the folium library in Python and the HeatMap plugin to generate a heatmap based on pollution data;

[0244] Automatically generate a report based on the analysis results:

[0245] Analysis result collation: Organize and summarize the analysis results. Use the pandas and numpy libraries in Python to calculate key statistical indicators and summarize the analysis results;

[0246] Generate a text report: Convert the analysis results into a text report. Use the fpdf library in Python to integrate data and text into a PDF;

[0247] Generate a chart report: Integrate charts into the report. Use the matplotlib and fpdf libraries in Python to embed charts into a PDF;

[0248] Generate reports with specific formats and content according to user needs:

[0249] Determine user needs: Collect and understand the specific report requirements of users. Use survey questionnaires or user interview tools to record user needs and convert them into specific report format requirements;

[0250] Customized report generation: Generate reports customized according to user needs. Use the Jinja2 template engine in Python to dynamically generate report content that meets user needs;

[0251] Output a report in a specific format: Generate reports in specific formats (including PDF and HTML). Use the WeasyPrint library in Python to convert the HTML report to PDF;

[0252] S6. Traffic and Energy Optimization

[0253] Adjust the signal timing according to real-time traffic data to optimize traffic flow:

[0254] Data collection and preprocessing: Collect real-time traffic data and perform preprocessing. Use the pandas and numpy libraries in Python to obtain data from sensors and traffic cameras, and clean and organize the data;

[0255] import pandas as pd

[0256] import numpy as np

[0257] traffic_data = pd.read_csv('realtime_traffic.csv')

[0258] traffic_data_cleaned = traffic_data.dropna().reset_index(drop=True)

[0259] Signal timing optimization: Adjust the signal timing according to traffic data. Use the scipy library in Python for optimization calculations and the linear programming model (Linear Programming, LP) to optimize the signal timing;

[0260] from scipy.optimize import linprog

[0261] # Assume traffic flow data

[0262] traffic_flows = traffic_data_cleaned['flow'].values

[0263] # Define the optimization objective and constraints

[0264] c = -traffic_flows # Maximize the flow

[0265] A = np.identity(len(traffic_flows)) # The identity matrix as a constraint condition

[0266] b = np.ones(len(traffic_flows)) * 60 # Each signal cycle does not exceed 60 seconds

[0267] res = linprog(c, A_ub=A, b_ub=b)

[0268] optimized_timings = res.x

[0269] Implementation and feedback: Implement the optimization results into the signal system, and conduct effect feedback and adjustment. Use the API of the traffic management system to set the signal timings, implement the feedback control algorithm, and continuously monitor and adjust;

[0270] import requests

[0271] for i, timing in enumerate(optimized_timings):

[0272] requests.post(f"http: / / traffic_system_api / signal / {i}", data={'timing': timing})

[0273] # Regularly obtain new data and adjust

[0274] Predict future traffic flows and formulate optimized traffic management strategies:

[0275] Data collection and feature engineering: Collect historical traffic data and conduct feature engineering. Use the pandas and sklearn libraries in Python to extract time features (including hour, day of the week) and traffic features;

[0276] from sklearn.preprocessing import StandardScaler

[0277] historical_data = pd.read_csv('historical_traffic.csv')

[0278] historical_data['hour'] = pd.to_datetime(historical_data['timestamp']).dt.hour

[0279] historical_data['day_of_week'] = pd.to_datetime(historical_data['timestamp']).dt.dayofweek

[0280] features = historical_data[['hour', 'day_of_week', 'flow']]

[0281] scaler = StandardScaler()

[0282] features_scaled = scaler.fit_transform(features)

[0283] Build a prediction model: Use a machine learning model to predict future traffic flow. Use the sklearn library in Python and the LSTM (Long Short-Term Memory) model for time series prediction;

[0284] from sklearn.model_selection import train_test_split

[0285] from sklearn.ensemble import RandomForestRegressor

[0286] X = features_scaled[:, :-1]

[0287] y = features_scaled[:, -1]

[0288] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

[0289] model = RandomForestRegressor(n_estimators = 100)

[0290] model.fit(X_train, y_train)

[0291] predictions = model.predict(X_test)

[0292] Strategy formulation and evaluation: Formulate and evaluate optimized traffic management strategies based on prediction results, visualize using Python's matplotlib library, and adjust strategies based on prediction results and actual effects;

[0293] Optimize power distribution and reduce energy waste:

[0294] Data collection and load forecasting: Collect power consumption data and forecast future loads, using Python's pandas and sklearn libraries, and use time series models (including ARIMA) for load forecasting;

[0295] Optimize power distribution: Optimize power distribution according to the predicted load, perform optimization calculations using Python's scipy library, and use linear programming models to optimize power distribution;

[0296] # Assume power consumption prediction data

[0297] predicted_loads = forecast[0]

[0298] # Define optimization objectives and constraints

[0299] c = predicted_loads # Minimize power waste

[0300] A = np.identity(len(predicted_loads))

[0301] b = np.ones(len(predicted_loads)) * 100 # Power distribution per hour does not exceed 100 units

[0302] res = linprog(c, A_eq=A, b_eq=b)

[0303] optimized_distribution = res.x

[0304] Implementation and feedback: Implement the optimized power distribution plan, conduct effect feedback and adjustment, use the API of the power management system to set power distribution, implement feedback control algorithms, and continuously monitor and adjust;

[0305] for i, load in enumerate(optimized_distribution):

[0306] requests.post(f"http: / / power_system_api / distribute / {i}", data={'load': load})

[0307] # Regularly obtain new data and adjust

[0308] Optimize the use of renewable energy according to the predicted energy demand and weather conditions:

[0309] Weather data and energy demand prediction: Collect weather data and predict energy demand. Use the pandas and sklearn libraries in Python and use regression models (including linear regression) for prediction;

[0310] from sklearn.linear_model import LinearRegression

[0311] weather_data = pd.read_csv('weather_data.csv')

[0312] energy_data = pd.read_csv('energy_demand.csv')

[0313] features = weather_data[['temperature', 'humidity']]

[0314] target = energy_data['demand']

[0315] model = LinearRegression()

[0316] model.fit(features, target)

[0317] demand_predictions = model.predict(features)

[0318] Optimize the use of renewable energy: Optimize the use of renewable energy according to the prediction results. Use the scipy library in Python for optimization calculations and use a linear programming model to optimize energy use;

[0319] # Assume energy demand prediction data

[0320] predicted_demand = demand_predictions

[0321] renewable_supply = weather_data['solar_irradiance'] # Solar supply prediction

[0322] # Define the optimization objective and constraints

[0323] c = -renewable_supply # Maximize renewable energy usage

[0324] A = np.identity(len(predicted_demand))

[0325] b = predicted_demand # Meet the demand

[0326] res = linprog(c, A_eq=A, b_eq=b)

[0327] optimized_renewable_usage = res.x

[0328] Implementation and feedback: Implement the optimized renewable energy usage plan, conduct effect feedback and adjustment, use the API of the energy management system for energy distribution settings, implement the feedback control algorithm, and continuously monitor and adjust;

[0329] for i, usage in enumerate(optimized_renewable_usage):

[0330] requests.post(f"http: / / energy_system_api / renewable / {i}", data={'usage': usage})

[0331] # Regularly obtain new data and adjust

[0332] S7. Conduct abnormal event detection and crowd behavior analysis

[0333] Identify and alarm abnormal events, including sudden traffic accidents or environmental pollution events:

[0334] Data collection and preprocessing: Collect real-time data from sensors and monitoring systems and conduct preprocessing. Use the pandas and numpy libraries in Python to clean, standardize, and detect outliers in the data;

[0335] Abnormal event detection: Use machine learning models to detect abnormal events. Use the sklearn library in Python and the Isolation Forest model to detect anomalies;

[0336] from sklearn.ensemble import IsolationForest

[0337] model = IsolationForest(contamination=0.01)

[0338] combined_data['anomaly'] = model.fit_predict(combined_data)

[0339] anomalies = combined_data[combined_data['anomaly'] == -1]

[0340] Alarm and emergency handling: Alarm according to the detection results, provide emergency handling suggestions, send alarm information using the requests library in Python, and provide different emergency handling plans according to the event type;

[0341] import requests

[0342] for index, anomaly in anomalies.iterrows():

[0343] event_type = anomaly['event_type']

[0344] if event_type == 'accident':

[0345] response = requests.post("http: / / emergency_response / api", data={'type': 'accident', 'location': anomaly['location']})

[0346] elif event_type == 'pollution':

[0347] response = requests.post("http: / / emergency_response / api", data={'type': 'pollution', 'location': anomaly['location']})

[0348] Predict the pedestrian flow in different areas of the city and optimize the distribution and scheduling of public facilities:

[0349] Data collection and feature engineering: Collect historical pedestrian flow data and perform feature engineering. Use the pandas and sklearn libraries in Python to extract time features (including hour, day of the week) and spatial features (including regions).

[0350] Build a prediction model: Use a machine learning model to predict future pedestrian flow. Use the sklearn library in Python and the XGBoost model for pedestrian flow prediction.

[0351] from xgboost import XGBRegressor

[0352] from sklearn.model_selection import train_test_split

[0353] X_train,X_test,y_train,y_test = train_test_split(features,target,test_size = 0.2,random_state = 42)

[0354] model = XGBRegressor()

[0355] model.fit(X_train,y_train)

[0356] predictions = model.predict(X_test)

[0357] Optimize the distribution and scheduling of public facilities: Optimize the distribution and scheduling of public facilities according to the prediction results. Use the scipy library in Python for optimization calculations and use a linear programming model to optimize the facility distribution and scheduling.

[0358] from scipy.optimize import linprog

[0359] # Assume pedestrian flow prediction data

[0360] predicted_flows = predictions

[0361] facility_capacity = 100 # Assume the capacity of each facility

[0362] # Define the optimization objective and constraints

[0363] c = -predicted_flows # Maximize the served pedestrian flow

[0364] A = np.identity(len(predicted_flows))

[0365] b = np.ones(len(predicted_flows)) * facility_capacity

[0366] res = linprog(c, A_eq=A, b_eq=b)

[0367] optimized_distribution = res.x

[0368] Analyze the social network among citizens to identify key influencers and propagation patterns:

[0369] Data collection and network construction: Collect social network data and construct a network diagram. Use the networkx library in Python to construct a graph structure based on social relationships;

[0370] Identify key influencers: Use graph algorithms to identify key influencers. Use the networkx library in Python and the PageRank algorithm to identify key nodes;

[0371] Analyze propagation patterns: Analyze the propagation patterns of information in the social network. Use the networkx library in Python and community discovery algorithms (including the Louvain algorithm) to analyze propagation patterns;

[0372] S8. Conduct environmental monitoring and public health monitoring:

[0373] Monitor the air pollution level in real time and provide early warning information:

[0374] Data collection and preprocessing: Collect air pollution sensor data and perform preprocessing. Use the pandas library in Python to clean the data, fill in missing values, and standardize the data;

[0375] Air pollution level prediction: Use a time series model to predict the future air pollution level. Use the statsmodels library in Python and the ARIMA model for prediction;

[0376] Early warning information generation and release: Generate and release early warning information based on the prediction results. Use the requests library in Python to send early warning information, set pollution level thresholds, and trigger early warnings;

[0377] Monitor the water quality of urban water bodies to ensure the safety of citizens' water use:

[0378] Data collection and preprocessing: Collect urban water body sensor data and perform preprocessing. Use the pandas library in Python to clean the data and handle missing values;

[0379] Water quality analysis: Use statistical analysis methods to evaluate water quality. Use the scipy library in Python and use chi-square test to detect anomalies;

[0380] Safety warning and treatment suggestions: Generate warnings based on the analysis results and provide treatment suggestions. Use the requests library in Python to send warning messages, set water quality index thresholds and trigger warnings;

[0381] Predict the spread trend of infectious diseases and provide prevention and control suggestions:

[0382] Data collection and preprocessing: Collect infectious disease case data and perform preprocessing. Use the pandas library in Python to clean the data and handle missing values;

[0383] disease_data = pd.read_csv('disease_data.csv')

[0384] disease_data.fillna(method='ffill', inplace=True)

[0385] Spread trend prediction: Use infectious disease models to predict the spread trend. Use the lmfit library in Python and use the SIR model for prediction;

[0386]

[0387]

[0388] Prevention and control suggestion generation and release: Generate prevention and control suggestions based on the prediction results and release them. Use the requests library in Python to send prevention and control suggestions and provide prevention and control measure suggestions according to the spread speed and scope;

[0389] Analyze citizens' health data and identify public health problems and trends:

[0390] Data collection and preprocessing: Collect citizens' health data and perform preprocessing. Use the pandas library in Python to clean the data and handle missing values;

[0391] Health problem analysis: Use data mining techniques to analyze health data. Use the sklearn library in Python and use clustering algorithms (including K-Means) to identify health problems;

[0392] from sklearn.cluster import KMeans

[0393] model = KMeans(n_clusters = 5)

[0394] health_data['cluster'] = model.fit_predict(health_data)

[0395] Trend identification and suggestions: Identify health trends and provide suggestions based on the analysis results. Use the matplotlib library in Python for data visualization and trend analysis methods (including time series analysis) to identify trends;

[0396] import matplotlib.pyplot as plt

[0397] health_trend = health_data.groupby('timestamp').mean()

[0398] plt.plot(health_trend.index, health_trend['cluster'])

[0399] plt.title('Health Trend Analysis')

[0400] plt.show()

[0401] S9. Happiness Index Analysis and Summary

[0402] Design of Tables and Parameter Descriptions

[0403] To comprehensively analyze data on various aspects of the city such as environment, transportation, safety, health, and education, calculate and evaluate the happiness index of citizens, it is necessary to design a table containing this data and provide corresponding parameter descriptions, as shown in Table 1;

[0404] Table 1 Happiness Index Data Table

[0405]

[0406]

[0407] Based on the above parameters, after standardizing each parameter and multiplying it by its corresponding weight, the happiness index is finally obtained by summing them up;

[0408] Data Standardization: Standardize data with different dimensions to values between [0, 1]. Use the Min - Max standardization formula:

[0409] X' = \frac{X - X_{\min}}{X_{\max} - X_{\min}};

[0410] The standardized value of each parameter is multiplied by its weight:

[0411] Weighted parameter = X' × weight;

[0412] The sum of all weighted parameters is the happiness index:

[0413] HI = ∑(Weighted parameter);

[0414] Based on the above system, in practical applications, the actual data of the happiness index in a first-tier city in South China was statistically obtained, as shown in Table 2;

[0415] Data in Table 2 and calculation process

[0416] Parameter category Parameter name Parameter value Normalized value (X') Weighted parameter (X' × weight) Environment Air Quality Index (AQI) 85 0.60 0.60×0.20＝0.12 Greening coverage rate 30％ 0.75 0.75×0.15＝0.1125 Traffic Average commuting time 45 0.50 0.50×0.10＝0.05 Convenience of public transportation 80 0.80 0.80×0.05＝0.04 Safety Crime rate 2％ 0.90 0.90×0.20＝0.18 Health Coverage rate of medical facilities 25％ 0.70 0.70×0.10＝0.07 Healthy life expectancy 75 0.85 0.85×0.10＝0.085 Education Distribution of educational resources 20％ 0.65 0.65×0.05＝0.0325 Educational level 75 0.80 0.80×0.05＝0.04

[0417] The happiness index (HI) is:

[0418] HI = 0.12 + 0.1125 + 0.05 + 0.04 + 0.18 + 0.07 + 0.085 + 0.0325 + 0.04 = 0.730.

[0419] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI analysis system for evaluating smart cities, characterized in that, It includes a data acquisition and processing module, a data storage and management module, a data analysis and mining module, an artificial intelligence and machine learning module, a visualization report generation module, a public service optimization module, a behavior event analysis module, and an environmental health monitoring module; The data acquisition and processing module is used for data acquisition and data cleaning; The data storage and management module is used for database management and ensuring data security; The data analysis and mining module is used for statistical analysis and data mining; The artificial intelligence and machine learning module is used for predicting continuous variables such as energy consumption or traffic flow and analyzing the sentiment tendency in social media or public feedback; The visualization report generation module is used for data visualization and report generation; The public service optimization module is used for traffic management and energy management; The behavior event analysis module is used for abnormal event detection and crowd behavior analysis; The environmental health monitoring module is used for environmental quality monitoring, public health monitoring, and urban happiness index analysis.

2. The AI analysis system for smart city evaluation according to claim 1, wherein: The data acquisition and processing module further includes a data acquisition unit and a data cleaning unit; The data acquisition unit is used for collecting data in real time from traffic, air quality, and energy consumption, and collecting citizen feedback and public data through social media, government open data, and questionnaire surveys; The data cleaning unit is used for processing missing values, outliers, and duplicate data, ensuring data quality, and converting data from different sources into a unified format for subsequent processing.

3. The AI analysis system for smart city evaluation according to claim 2, wherein: The data storage and management module further includes a database management unit and a data security unit; The database management unit is used for storing structured data, including traffic flow statistics and energy consumption records, and storing unstructured data, including social media comments, pictures, and video data; The data security unit is used for ensuring the security of data during transmission and storage, and defining and managing the access rights of different users and applications to data.

4. The AI analysis system for smart city evaluation according to claim 3, wherein: The data analysis and mining module further includes a statistical analysis unit and a data mining unit; The statistical analysis unit is used for providing basic statistical descriptions of data, including mean, median, and standard deviation, analyzing time series data, and identifying trends and seasonal variations; The data mining unit is used for dividing urban areas or populations into different groups to identify specific patterns and discover the correlation relationships between data, including the relationship between peak traffic time and air quality.

5. The AI analysis system for smart city evaluation according to claim 4, wherein: The artificial intelligence and machine learning module further includes a prediction model unit and a natural language processing unit; The prediction model unit is used for predicting continuous variables, including future energy consumption or traffic flow, and predicting categorical variables, including the likelihood of traffic accidents or the level of citizen satisfaction; The natural language processing unit is used for analyzing the sentiment tendency in social media or public feedback and extracting useful information and patterns from unstructured text data.

6. The smart city evaluation AI analysis system according to claim 5, wherein: The visualization report generation module further includes a data visualization unit and a report generation unit; The data visualization unit is used to generate line charts, bar charts, and heat maps to intuitively display the data analysis results and display geospatial data, including traffic flow maps and pollution heat maps; The report generation unit is used to automatically generate reports according to the analysis results and generate reports in specific formats and contents according to user requirements.

7. The smart city evaluation AI analysis system according to claim 6, wherein: The public service optimization module further includes a traffic management unit and an energy management unit; The traffic management unit is used to adjust signal timings according to real-time traffic data, predict future traffic flows, and optimize traffic management strategies; The energy management unit is used to optimize power distribution, reduce energy waste, and optimize the use of renewable energy according to predicted energy demands and weather conditions.

8. The smart city evaluation AI analysis system according to claim 7, wherein: The behavior event analysis module further includes an abnormal event detection unit and a crowd behavior analysis unit; The abnormal event detection unit is used to identify and alarm abnormal events, including sudden traffic accidents or environmental pollution events, provide emergency handling suggestions, and coordinate relevant departments for rapid response; The crowd behavior analysis unit is used to predict the population flow in different areas of the city, optimize the distribution and scheduling of public facilities, analyze the social network among citizens, and identify key influencers and dissemination patterns.

9. The smart city evaluation AI analysis system according to claim 8, wherein: The environmental health monitoring module further includes an environmental quality monitoring unit, a public health monitoring unit, and an urban happiness index analysis unit; The environmental quality monitoring unit is used to monitor the air pollution level in real time, provide early warning information, monitor the water quality of the city's water bodies, and ensure the safety of citizens' water use; The public health monitoring unit is used to monitor the data of hospitals and clinics everywhere in the city in real time, provide health early warnings and trend analysis, predict the transmission trend of infectious diseases, provide prevention and control suggestions, analyze citizens' health data, and identify public health problems and trends The urban happiness index analysis unit is used to calculate and evaluate the happiness index of citizens by comprehensively analyzing data on various aspects of the city's environment, traffic, safety, health, and education, and provide decision-making references.

10. A method for AI analysis and evaluation of smart city evaluation, according to any one of claims 1-9, a smart city evaluation AI analysis system, characterized in that including the following steps: S1. Conduct urban data collection and preprocessing: Collect traffic, air quality, and energy consumption data in real time from various sensors and devices, collect citizens' feedback and public data through social media, government open data, and questionnaires, clean the collected data, process missing values, outliers, and duplicate data, and convert data from different sources into a unified format for subsequent processing; S2. Perform data storage and security management: Store the processed data in a relational database for storing structured data, including traffic flow statistics and energy consumption records. Store unstructured data, including social media comments, pictures, and video data, in a non-relational database. Encrypt the data during storage and transmission to ensure data security. Define and manage access permissions for different users and applications to the data; S3. Implement data statistical analysis and data mining based on statistical analysis: Provide basic statistical descriptions of the data, including mean, median, and standard deviation. Analyze time series data to identify trends and seasonal variations in the data. Conduct cluster analysis on urban areas or populations to identify specific patterns. Discover the correlation relationships between data, including the relationship between peak traffic times and air quality; S4. Perform predictive analysis and natural language processing: Build regression models to predict future energy consumption or traffic flow. Build classification models to predict the likelihood of traffic accidents or the level of citizen satisfaction. Analyze the sentiment tendency in social media or public feedback, extract sentiment information, and extract useful information and patterns from unstructured text data; S5. Data visualization and report generation: Generate line charts, bar charts, and heat maps to visually display the data analysis results. Use geographic information systems to display geospatial data, including traffic flow maps and pollution heat maps. Automatically generate reports based on the analysis results to provide decision-making references. Generate reports with specific formats and contents according to user needs; S6. Traffic and energy optimization: Adjust signal timings based on real-time traffic data to optimize traffic flow, predict future traffic flow, formulate optimized traffic management strategies, optimize power distribution, reduce energy waste, and optimize the use of renewable energy according to predicted energy demand and weather conditions; S7. Conduct abnormal event detection and crowd behavior analysis: Identify and alarm abnormal events, including sudden traffic accidents or environmental pollution events, provide emergency handling suggestions, coordinate relevant departments for rapid response, predict the pedestrian flow in different urban areas, optimize the distribution and scheduling of public facilities, analyze the social network among citizens, and identify key influencers and dissemination patterns; S8. Conduct environmental monitoring and public health monitoring: Real-time monitor air pollution levels and provide early warning information. Monitor the water quality of urban water bodies to ensure the safety of citizens' water use. Predict the spread trend of infectious diseases and provide prevention and control suggestions. Analyze citizens' health data to identify public health problems and trends; S9. Happiness index analysis and summary: Comprehensively analyze data on various aspects of the city's environment, traffic, safety, health, and education, calculate and evaluate the happiness index of citizens, and provide decision-making references.

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