Urban ecological management and control method and system based on smart city

Abstract

The invention discloses an urban ecological management and control method and system based on a smart city. The system comprises a data acquisition module; the acquisition module is responsible for acquiring urban environment parameters; the data transmission module is responsible for ensuring rapid data transmission by adopting a low-delay wireless communication technology; the output processing and storage module is responsible for processing the collected data, identifying abnormal conditions and storing the data; the data analysis and decision module is responsible for analyzing the data in real time, and the data processing and storage module can efficiently process and store the collected data and identify abnormal conditions at the same time. The system is helped to find and process potential environmental problems in time, so that the stability and reliability of the system are improved. And the data analysis and decision-making module adopts an advanced anomaly detection algorithm to deeply analyze the data and quickly discover trends and anomalies. Based on the analysis results, the system can automatically provide decision support so as to effectively deal with environmental problems under complex conditions.

Description

Technical Field

[0001] The present invention relates to the field of smart city management and control technology, and in particular to a smart city-based urban ecological management and control method and system. Background Art

[0002] The smart city management system is a comprehensive system that uses advanced information technology and intelligent means to integrate, analyze and manage various elements in urban operations. It integrates cutting-edge technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence, aiming to achieve optimal allocation of urban resources, efficient provision of public services, and refined management of urban operations.

[0003] After searching, the invention patent with Chinese patent number CN114723328A discloses a method and system for urban ecological control based on smart city, which includes the following steps: step S1, using GIS platform to extract urban ecological basic data, obtain urban ecological network construction data source and basic information database; step S2, constructing ecological network space according to the basic information database to extract important ecological patches, and establishing a smart city model based on ecological source targets; step S3, establishing an evaluation mechanism for the ecological network structure based on the smart city model, conducting qualitative and quantitative planning analysis and planning improvement on the physical form, optimizing the ecological network according to the actual control mechanism of the physical form, determining the urban ecological network space pattern by formulating the urban basic ecological control area planning system, conducting single factor evaluation on ecological influencing factors, and obtaining a complete comprehensive ecological security pattern to comprehensively control the data of each subsystem, realize comprehensive control of the intelligent subsystem, and improve the independence of the system.

[0004] However, in actual use of the above invention, it is difficult to enhance the real-time performance of data collection, that is, it is difficult to ensure that the system can respond quickly to environmental changes. At the same time, it is difficult to improve the system's ability to cope with complex situations, such as automatic response to sudden environmental problems. Therefore, a method and system for urban ecological management based on smart cities are proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the prior art that it is difficult to enhance the real-time performance of data collection, that is, it is difficult to ensure that the system can respond quickly to environmental changes, and it is difficult to improve the system's ability to cope with complex situations, such as automatic response to sudden environmental problems, and to propose an urban ecological management method and system based on smart cities.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An urban ecological management and control system based on a smart city, comprising:

[0008] Data collection module; responsible for collecting urban environmental parameters;

[0009] Data transmission module: responsible for using low-latency wireless communication technology to ensure fast data transmission;

[0010] Data processing and storage module: responsible for processing the collected data, identifying abnormal situations, and storing the data;

[0011] Data analysis and decision-making module: responsible for real-time analysis of data and providing automatic decision support based on the analysis results;

[0012] Early warning and response module: responsible for automatically initiating emergency response measures based on early warning information;

[0013] Feedback and optimization module: responsible for receiving feedback from each module to help optimize the overall performance of the system;

[0014] The data acquisition module is responsible for collecting urban environmental data in real time by deploying sensors and IoT devices in key areas of the city, including air quality, water quality, noise, etc., to provide detailed environmental information for city managers. The data acquisition module transmits the collected urban environmental data to the data transmission module, which is responsible for using low-latency wireless communication technologies (such as LoRa, NB-I oT, etc.) to achieve fast and stable data transmission, and at the same time ensure the confidentiality and integrity of the data through encryption technology, prevent data leakage and tampering, and ensure the data security of the smart city. The data transmission module transmits the encrypted data to the data processing and storage module, and the data processing and storage module is responsible for processing and storing the collected data. The data processing and storage module transmits the processed data to the data analysis and decision-making module, and the data analysis and decision-making module is responsible for using anomaly detection algorithms to conduct in-depth analysis of the data, quickly discover trends and anomalies, and provide a scientific basis for urban management. It provides automatic decision-making support based on the analysis results, which helps smart cities make fast and accurate decisions when facing complex environmental problems. The data analysis and decision-making module transmits the analysis and decision results to the early warning and response module, and the early warning and response module is responsible for automatically initiating emergency response measures according to the early warning information of the early warning system, such as dispatching emergency resources and initiating emergency plans.

[0015] The above technical solution further includes:

[0016] Preferably, the data acquisition module includes an environmental sensor unit, a meteorological sensor unit, a soil sensor unit and a data integration and result output unit. The environmental sensor unit is responsible for monitoring urban air quality (PM2.5, PM10, etc.), urban water quality (pH value, dissolved oxygen, turbidity, etc.) and urban noise level. The meteorological sensor unit is responsible for real-time collection of temperature, humidity, wind speed and wind direction meteorological data. The soil sensor unit is responsible for monitoring soil moisture, temperature and nutrient content. The data integration and result output unit is responsible for integrating the data collected by each sensor unit and passing the integration results to the data transmission module.

[0017] Preferably, the data transmission module uses encryption technology to ensure data security in the following specific steps:

[0018] Data encryption: Before data is sent, symmetric encryption (such as AES) is used to encrypt the data to ensure the security of the data during transmission. Different encryption levels can be used according to the sensitivity of the data, and stronger encryption algorithms are used for important data.

[0019] Authentication and authorization: Ensure that all sensors and terminal devices are authenticated before transmitting data to prevent unauthorized devices from accessing.

[0020] Data integrity check: Generate a hash value before data transmission, and the receiver performs a hash check after receiving the data to ensure that the data has not been tampered with during transmission;

[0021] Secure communication protocol: During data transmission, HTTPS and TLS are used to ensure the security of data during transmission and prevent man-in-the-middle attacks;

[0022] Abnormal monitoring and response: Monitor abnormal behaviors during data transmission, such as traffic surges, abnormal access, etc. If an abnormality is found, an alarm is automatically triggered and predetermined security response measures are initiated, such as suspending data transmission and isolating affected devices.

[0023] Preferably, the data analysis and decision-making module includes a data analysis unit, a trend prediction unit, an ecosystem service value assessment unit, a planning strategy optimization unit, a decision effect simulation unit and a decision support system integration unit. The data analysis unit uses an anomaly detection algorithm to conduct in-depth analysis of the data. The trend prediction unit uses a machine learning algorithm to model historical data and predict the changing trend of environmental indicators in the future. The ecosystem service value assessment unit processes the collected environmental data by establishing an ecosystem service value assessment model to assess the value of various services provided by the urban ecosystem, such as air purification, water conservation, and carbon storage. The planning strategy optimization unit generates a variety of urban planning strategy solutions based on the ecosystem service value assessment results and trend prediction using multi-objective optimization algorithms, such as genetic algorithms and particle swarm algorithms. The decision effect simulation unit simulates the impact of different decisions, such as environmental protection decisions and urban planning decisions, on the urban ecosystem. The analysis results of each unit of the decision support system integration unit are integrated into a unified decision support system, and the decision results are transmitted to the early warning and response module.

[0024] Preferably, the specific steps of the data analysis unit using anomaly detection algorithm to perform in-depth analysis on the data include:

[0025] Data preprocessing: Clean the data, remove missing values, outliers, and noise, ensure data accuracy, standardize the data to the same range, and select important features related to environmental indicators, such as timestamp, sensor type, geographic location, etc.;

[0026] Anomaly detection model construction: The isolation forest algorithm is selected to construct multiple decision trees. Each tree is generated based on random features and samples. The higher the isolation degree of the sample point, the higher its anomaly score. The formula for calculating the anomaly score of each sample is:

[0027]

[0028] Among them, E h (x) represents the path length of sample x in the hth tree, and c(n) is the normalization factor of the feature number;

[0029] Anomaly detection: Use normal data to train the model so that it can identify normal conditions. Input the real-time collected data into the trained model, calculate the anomaly score, determine whether it is an anomaly, set the threshold based on historical data and industry standards, and determine the anomaly and normal conditions. For example, set the threshold to 0.5. When the anomaly score exceeds this value, it is marked as an anomaly.

[0030] Abnormal records: record the abnormal events identified, including time, location, abnormal score and other information;

[0031] Feedback and optimization: The effect of the anomaly detection model is evaluated through confusion matrix, accuracy, and recall rate indicators. The model parameters are continuously adjusted according to the feedback to improve feature selection and enhance detection accuracy.

[0032] Preferably, the early warning and response module includes an early warning unit, a notification and alarm unit, a response and processing unit, and a data integration and sharing unit. The early warning unit is responsible for quickly identifying potential problems, and the notification and alarm unit is responsible for sending early warning information to relevant personnel, including through various channels such as mobile phone text messages, APP push, and emails. The response and processing unit is responsible for formulating detailed emergency response plans, including specific operating procedures and resource scheduling plans. The data integration and sharing unit is responsible for establishing a data sharing platform for relevant departments to achieve information exchange and collaboration.

[0033] Preferably, the feedback mode of the feedback and optimization module includes:

[0034] Citizen participation and feedback: Establish an online platform or mobile application to allow citizens to report environmental changes, make comments and suggestions, and conduct citizen questionnaires regularly to evaluate the effectiveness of the system and user satisfaction;

[0035] Data-driven feedback: By monitoring data integrity and accuracy, sensor failures or data anomalies can be identified and monitoring plans can be adjusted in a timely manner;

[0036] Multi-party collaborative feedback: Establish information sharing and feedback mechanisms with various departments to achieve cross-departmental data and experience integration;

[0037] Social benefit assessment: Assessing changes in urban ecosystem services.

[0038] Preferably, an urban ecological control method based on a smart city corresponding to an urban ecological control system based on a smart city comprises the following steps:

[0039] S1: Deploy sensors and IoT devices in key areas of the city to monitor environmental parameters in real time;

[0040] S2: Uses low-latency wireless communication technology to ensure fast and reliable data transmission. During data transmission, encryption technology is used to ensure data security.

[0041] S3: Quickly clean and analyze the collected environmental data to identify abnormal situations;

[0042] S4: Use anomaly detection algorithms to analyze environmental data in real time. Based on the analysis results, the system provides automatic decision support:

[0043] S5: Set the warning threshold of environmental indicators. When the monitoring data exceeds the threshold, the warning system will be triggered immediately, and emergency response measures will be automatically initiated based on the warning information.

[0044] S6: Establish a system feedback mechanism to collect feedback from users and the system.

[0045] The present invention has the following beneficial effects:

[0046] 1. In the present invention, the data transmission module adopts low-latency wireless communication technology to achieve fast and stable data transmission. This not only ensures the real-time nature of the data, but also enables the system to respond quickly to environmental changes, effectively improving the response speed of the system.

[0047] 2. In the present invention, the data processing and storage module can efficiently process and store the collected data and identify abnormal situations. It helps the system to promptly discover and deal with potential environmental problems, thereby improving the stability and reliability of the system. The data analysis and decision-making module uses advanced anomaly detection algorithms to conduct in-depth analysis of the data and quickly discover trends and anomalies. Based on these analysis results, the system can automatically provide decision support, thereby effectively dealing with environmental problems in complex situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a system architecture diagram of an urban ecological management and control system based on a smart city proposed by the present invention;

[0049] Figure 2 This is a flow chart of an urban ecological management and control method based on smart city proposed by the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] like Figure 1 As shown, an urban ecological management and control system based on a smart city includes:

[0052] Data collection module; responsible for collecting urban environmental parameters;

[0053] Data transmission module: responsible for using low-latency wireless communication technology to ensure fast data transmission;

[0054] Data processing and storage module: responsible for processing the collected data, identifying abnormal situations, and storing the data;

[0055] Data analysis and decision-making module: responsible for real-time analysis of data and providing automatic decision support based on the analysis results;

[0056] Early warning and response module: responsible for automatically initiating emergency response measures based on early warning information;

[0057] Feedback and optimization module: responsible for receiving feedback from each module to help optimize the overall performance of the system;

[0058] The data acquisition module is responsible for collecting urban environmental data in real time by deploying sensors and Internet of Things devices in key areas of the city. The data acquisition module transmits the collected urban environmental data to the data transmission module. The data transmission module is responsible for using low-latency wireless communication technology to achieve fast and stable data transmission, and at the same time ensures the confidentiality and integrity of the data through encryption technology. The data transmission module transmits the encrypted data to the data processing and storage module. The data processing and storage module is responsible for processing and storing the collected data. The data processing and storage module transmits the processed data to the data analysis and decision-making module. The data analysis and decision-making module is responsible for using anomaly detection algorithms to conduct in-depth analysis of the data, quickly discover trends and anomalies, and provide automatic decision-making support based on the analysis results. The data analysis and decision-making module transmits the analysis and decision results to the early warning and response module. The early warning and response module is responsible for automatically initiating emergency response measures based on the early warning information of the early warning system.

[0059] In an embodiment of the present invention, for example, when a city encounters water deterioration, water quality monitoring sensors and Internet of Things devices are deployed in key waters of the city (such as rivers, lakes, reservoirs, etc.) to collect water quality parameters in real time, such as dissolved oxygen, pH value, turbidity, heavy metal content, etc., and the collected water quality data is transmitted to the data transmission module. The data transmission module adopts LoRa wireless communication technology with a transmission rate of 50kbps and a transmission distance of up to 10km, ensuring fast and stable data transmission. The data transmission module transmits the water quality data information to the data processing and storage module, which decrypts and preprocesses the received data, identifies abnormal values ​​or missing values ​​in the data, and makes necessary corrections or fills, and processes the data. The water quality data after the calculation is stored in the database for subsequent analysis and query. The data analysis and decision-making module uses anomaly detection algorithms to conduct in-depth analysis of water quality data and quickly discover trends and anomalies in water quality deterioration. Based on the analysis results, the data analysis and decision-making module provides automatic decisions, such as issuing water quality deterioration warnings and recommending emergency measures. The warning and response module receives water quality deterioration warning information from the data analysis and decision-making module, and automatically initiates emergency response measures according to the settings of the warning system, such as shutting down upstream pollution sources, starting water purification equipment, and issuing warning information to relevant departments and the public. During the implementation of emergency response measures, each module of the system will collect feedback information, such as water quality improvement and the effectiveness of emergency measures.

[0060] Abnormal data record:

[0061] Time: XX hour XX minute on XX day of December 2023

[0062] Location: An industrial area in the city

[0063] Abnormal indicators: air quality (PM2.5 concentration)

[0064] Abnormal value: 250 μg / m 3 (far exceeding the normal standard of 10μg / m 3 Up to 150 μg / m 3 )

[0065] Note: The system has automatically marked it as abnormal and triggered an early warning message.

[0066] In one embodiment, the data acquisition module includes an environmental sensor unit, a meteorological sensor unit, a soil sensor unit, and a data integration and result output unit. The environmental sensor unit is responsible for monitoring urban air quality, urban water quality, and urban noise level. The meteorological sensor unit is responsible for real-time collection of meteorological data such as temperature, humidity, wind speed, and wind direction. The soil sensor unit is responsible for monitoring soil moisture, temperature, and nutrient content. The data integration and result output unit is responsible for integrating the data collected by each sensor unit and transmitting the integration result to the data transmission module. Among them, the environmental sensor (monitors PM2.5, PM10, air quality index, etc., with an accuracy of ±5%), the meteorological sensor (monitors temperature, humidity, wind speed, and wind direction, with an accuracy of ±0.5°C, ±3%RH, ±0.1m / s, and ±5°, respectively), and the soil sensor (monitors soil moisture, temperature, and nutrient content, with a humidity accuracy of ±2% and a temperature accuracy of ±0.5°C).

[0067] In one embodiment, the specific steps of the data transmission module to ensure data security through encryption technology are as follows:

[0068] Data encryption: Before data is sent, symmetric encryption is used to encrypt the data to ensure the security of the data during transmission;

[0069] Authentication and authorization: Ensure that all sensors and terminal devices are authenticated before transmitting data to prevent unauthorized devices from accessing.

[0070] Data integrity check: Generate a hash value before data transmission, and the receiver performs a hash check after receiving the data to ensure that the data has not been tampered with during transmission;

[0071] Secure communication protocol: During data transmission, HTTPS and TLS are used to ensure the security of data during transmission and prevent man-in-the-middle attacks;

[0072] Abnormal monitoring and response: Monitor abnormal behavior during data transmission. If an abnormality is found, an alarm will be automatically triggered and the predetermined security response measures will be initiated.

[0073] In the embodiment of the present invention, the symmetric encryption uses the AES algorithm, and the encryption process can be expressed as follows:

[0074] C=E(K,D)

[0075] Among them, C represents ciphertext, E represents encryption function, K represents key, and D represents data. In abnormal monitoring and response, machine learning algorithms can be used for anomaly detection, and the formula is:

[0076]

[0077] Where P(A|D) represents the probability of anomaly A occurring after observing data D.

[0078] In one embodiment, the data analysis and decision-making module includes a data analysis unit, a trend prediction unit, an ecosystem service value assessment unit, a planning strategy optimization unit, a decision effect simulation unit and a decision support system integration unit. The data analysis unit uses an anomaly detection algorithm to conduct in-depth analysis of the data. The trend prediction unit uses a machine learning algorithm to model historical data and predict the changing trend of environmental indicators in the future. The ecosystem service value assessment unit processes the collected environmental data by establishing an ecosystem service value assessment model to assess the value of various services provided by the urban ecosystem. The planning strategy optimization unit generates a variety of urban planning strategy plans based on the ecosystem service value assessment results and trend prediction using a multi-objective optimization algorithm. The decision effect simulation unit simulates the impact of different decisions on the urban ecosystem. The decision support system integration unit integrates the analysis results of each unit into a unified decision support system, and transmits the decision results to the early warning and response module.

[0079] In the embodiment of the present invention, the formula for evaluating the carbon storage value in the ecosystem service value evaluation model is:

[0080] V=C×A×P

[0081] Among them, V represents the ecological service value, C represents the unit value, A represents the area, and P represents the carbon storage.

[0082] The specific formula of decision fusion can be expressed as:

[0083] D final =f(D 1 ,D 2 ...D n )

[0084] Among them, D i It is represented as the decision result of each unit, and f is a fusion function used to comprehensively process multiple decision results D1, D2, ...Dn to generate a unified, more accurate or reliable decision result D final This function can be a weighted average, voting mechanism, maximum / minimum selection, weighted sum, and many other methods.

[0085] In one embodiment, the specific steps of the data analysis unit using anomaly detection algorithm to perform in-depth analysis on the data include:

[0086] Data preprocessing: clean the data, standardize the data to the same range, and select important features related to environmental indicators;

[0087] Anomaly detection model construction: The isolation forest algorithm is selected to construct multiple decision trees. Each tree is generated based on random features and samples. The higher the isolation degree of the sample point, the higher its anomaly score. The formula for calculating the anomaly score of each sample is:

[0088]

[0089] Among them, E h (x) represents the path length of sample x in the hth tree, and c(n) is the normalization factor of the feature number;

[0090] Anomaly detection: Use normal data to train the model so that it can identify normal conditions. Input the real-time collected data into the trained model, calculate the anomaly score, determine whether it is an anomaly, set thresholds based on historical data and industry standards, and determine abnormal and normal conditions.

[0091] Abnormal records: record the abnormal events identified;

[0092] Feedback and optimization: The effect of the anomaly detection model is evaluated through confusion matrix, accuracy, and recall rate indicators. The model parameters are continuously adjusted according to the feedback to improve feature selection and enhance detection accuracy.

[0093] In one embodiment, the early warning and response module includes an early warning unit, a notification and alarm unit, a response and processing unit, and a data integration and sharing unit. The early warning unit is responsible for quickly identifying potential problems, the notification and alarm unit is responsible for sending early warning information to relevant personnel, the response and processing unit is responsible for formulating detailed emergency response plans, and the data integration and sharing unit is responsible for establishing a data sharing platform for relevant departments to achieve information exchange and collaboration.

[0094] In the embodiment of the present invention, the risk assessment formula of the early warning unit can be expressed as: R = P × I

[0095] Among them, R represents risk, P represents the probability of an event, and I represents the impact of the event. The alarm triggering conditions in the notification and alarm unit are:

[0096]

[0097] Wherein, T represents the alarm threshold.

[0098] In one embodiment, the feedback mode of the feedback and optimization module includes:

[0099] Citizen participation and feedback: Establish an online platform or mobile application to allow citizens to report environmental changes, make comments and suggestions, and conduct citizen questionnaires regularly to evaluate the effectiveness of the system and user satisfaction;

[0100] Data-driven feedback: By monitoring data integrity and accuracy, sensor failures or data anomalies can be identified and monitoring plans can be adjusted in a timely manner;

[0101] Multi-party collaborative feedback: Establish information sharing and feedback mechanisms with various departments to achieve cross-departmental data and experience integration;

[0102] Social benefit assessment: Assessing changes in urban ecosystem services.

[0103] like Figure 2 As shown, a smart city-based urban ecological management method includes the following steps:

[0104] S1: Deploy sensors and IoT devices in key areas of the city to monitor environmental parameters in real time;

[0105] S2: Uses low-latency wireless communication technology to ensure fast and reliable data transmission. During data transmission, encryption technology is used to ensure data security.

[0106] S3: Quickly clean and analyze the collected environmental data to identify abnormal situations;

[0107] S4: Use anomaly detection algorithms to analyze environmental data in real time. Based on the analysis results, the system provides automatic decision support:

[0108] S5: Set the warning threshold of environmental indicators. When the monitoring data exceeds the threshold, the warning system will be triggered immediately, and emergency response measures will be automatically initiated based on the warning information.

[0109] S6: Establish a system feedback mechanism to collect feedback from users and the system.

[0110] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An urban ecological management and control system based on smart city, characterized in that: include: Data acquisition module; Responsible for collecting urban environmental parameters; Data transmission module: responsible for using low-latency wireless communication technology to ensure fast data transmission; Data processing and storage module: responsible for processing the collected data, identifying abnormal situations, and storing the data; Data analysis and decision-making module: responsible for real-time analysis of data and providing automatic decision support based on the analysis results; Early warning and response module: responsible for automatically initiating emergency response measures based on early warning information; Feedback and optimization module: responsible for receiving feedback from each module to help optimize the overall performance of the system; The data acquisition module is responsible for collecting urban environmental data in real time by deploying sensors and Internet of Things devices in key areas of the city. The data acquisition module transmits the collected urban environmental data to the data transmission module. The data transmission module is responsible for using low-latency wireless communication technology to achieve fast and stable data transmission, and at the same time ensures the confidentiality and integrity of the data through encryption technology. The data transmission module transmits the encrypted data to the data processing and storage module. The data processing and storage module is responsible for processing and storing the collected data. The data processing and storage module transmits the processed data to the data analysis and decision-making module. The data analysis and decision-making module is responsible for using anomaly detection algorithms to conduct in-depth analysis of the data, quickly discover trends and anomalies, and provide automatic decision-making support based on the analysis results. The data analysis and decision-making module transmits the analysis and decision results to the early warning and response module. The early warning and response module is responsible for automatically initiating emergency response measures based on the early warning information of the early warning system.

2. According to the urban ecological management and control system based on smart city in claim 1, it is characterized in that: The data acquisition module includes an environmental sensor unit, a meteorological sensor unit, a soil sensor unit and a data integration and result output unit. The environmental sensor unit is responsible for monitoring urban air quality, urban water quality and urban noise level. The meteorological sensor unit is responsible for real-time collection of temperature, humidity, wind speed and wind direction meteorological data. The soil sensor unit is responsible for monitoring soil moisture, temperature and nutrient content. The data integration and result output unit is responsible for integrating the data collected by each sensor unit and passing the integration results to the data transmission module.

3. According to the urban ecological management and control system based on smart city in claim 1, it is characterized in that: The specific steps of the data transmission module to ensure data security through encryption technology are as follows: Data encryption: Before data is sent, symmetric encryption is used to encrypt the data to ensure the security of the data during transmission; Authentication and authorization: Ensure that all sensors and terminal devices are authenticated before transmitting data to prevent unauthorized devices from accessing. Data integrity check: Generate a hash value before data transmission, and the receiver performs a hash check after receiving the data to ensure that the data has not been tampered with during transmission; Secure communication protocol: During data transmission, HTTPS and TLS are used to ensure the security of data during transmission and prevent man-in-the-middle attacks; Abnormal monitoring and response: Monitor abnormal behavior during data transmission. If an abnormality is found, an alarm will be automatically triggered and the predetermined security response measures will be initiated.

4. The urban ecological management and control system based on smart city according to claim 1 is characterized in that: The data analysis and decision-making module includes a data analysis unit, a trend prediction unit, an ecosystem service value assessment unit, a planning strategy optimization unit, a decision effect simulation unit and a decision support system integration unit. The data analysis unit uses an anomaly detection algorithm to conduct in-depth analysis of the data. The trend prediction unit uses a machine learning algorithm to model historical data and predict the changing trend of environmental indicators in the future. The ecosystem service value assessment unit processes the collected environmental data by establishing an ecosystem service value assessment model to assess the value of various services provided by the urban ecosystem. The planning strategy optimization unit generates a variety of urban planning strategy plans based on the ecosystem service value assessment results and trend prediction using a multi-objective optimization algorithm. The decision effect simulation unit simulates the impact of different decisions on the urban ecosystem. The analysis results of each unit of the decision support system integration unit are integrated into a unified decision support system, and the decision results are transmitted to the early warning and response module.

5. The urban ecological management and control system based on smart city according to claim 1 is characterized in that: The specific steps of the data analysis unit using anomaly detection algorithm to perform in-depth analysis on the data include: Data preprocessing: clean the data, standardize the data to the same range, and select important features related to environmental indicators; Anomaly detection model construction: The isolation forest algorithm is selected to construct multiple decision trees. Each tree is generated based on random features and samples. The higher the isolation degree of the sample point, the higher its anomaly score. The formula for calculating the anomaly score of each sample is: Among them, E h( x ) represents the path length of sample x in the hth tree, c ( n ) is the normalization factor of the feature number; Anomaly detection: Use normal data to train the model so that it can identify normal conditions. Input the real-time collected data into the trained model, calculate the anomaly score, determine whether it is an anomaly, set thresholds based on historical data and industry standards, and determine abnormal and normal conditions. Abnormal records: record the abnormal events identified; Feedback and optimization: The effect of the anomaly detection model is evaluated through confusion matrix, accuracy, and recall rate indicators. The model parameters are continuously adjusted according to the feedback to improve feature selection and enhance detection accuracy.

6. The urban ecological management and control system based on smart city according to claim 1 is characterized in that: The early warning and response module includes an early warning unit, a notification and alarm unit, a response and processing unit, and a data integration and sharing unit. The early warning unit is responsible for quickly identifying potential problems, the notification and alarm unit is responsible for sending early warning information to relevant personnel, the response and processing unit is responsible for formulating detailed emergency response plans, and the data integration and sharing unit is responsible for establishing a data sharing platform for relevant departments to achieve information exchange and collaboration.

7. The urban ecological management and control system based on smart city according to claim 1 is characterized in that: The feedback mode of the feedback and optimization module includes: Citizen participation and feedback: Establish an online platform or mobile application to allow citizens to report environmental changes, make comments and suggestions, and conduct citizen questionnaires regularly to evaluate the effectiveness of the system and user satisfaction; Data-driven feedback: By monitoring data integrity and accuracy, sensor failures or data anomalies can be identified and monitoring plans can be adjusted in a timely manner; Multi-party collaborative feedback: Establish information sharing and feedback mechanisms with various departments to achieve cross-departmental data and experience integration; Social benefit assessment: Assessing changes in urban ecosystem services.

8. A smart city-based urban ecological management and control method, used in a smart city-based urban ecological management and control system as claimed in claim 1, characterized in that: The following steps are involved: S1: Deploy sensors and IoT devices in key areas of the city to monitor environmental parameters in real time; S2: Uses low-latency wireless communication technology to ensure fast and reliable data transmission. During data transmission, encryption technology is used to ensure data security. S3: Quickly clean and analyze the collected environmental data to identify abnormal situations; S4: Use anomaly detection algorithms to analyze environmental data in real time. Based on the analysis results, the system provides automatic decision support: S5: Set the warning threshold of environmental indicators. When the monitoring data exceeds the threshold, the warning system will be triggered immediately, and emergency response measures will be automatically initiated based on the warning information. S6: Establish a system feedback mechanism to collect feedback from users and the system.

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