Intelligent active antenna Internet of Things networking system and method for smart cities

Abstract

The present invention discloses an intelligent active antenna Internet of Things networking system and method for smart cities, and specifically relates to the technical field of Internet of Things networking. The present invention first ensures that the communication quality between nodes meets the standards by installing and preliminarily debugging the intelligent active antenna nodes. Then, the network protocol stack is configured and the routing algorithm is set to optimize the data transmission path. Next, the adaptive tuning function is enabled, and the appropriate tuning algorithm is selected according to the traffic volume, communication quality requirements, frequency band density, and dynamic range of the area, and field tests are carried out. By analyzing the test results, the algorithm parameters are optimized, and the update coefficient is calculated using a logistic regression model to determine whether the algorithm or RF control firmware needs to be updated. Ultimately, real-time data acquisition and transmission are achieved, and a data management system is constructed to ensure the effectiveness of data storage, analysis, and visualization functions.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things networking, and more specifically, to an intelligent active antenna Internet of Things networking system and method for smart cities. Background Art

[0002] Smart cities are a new urban management model that leverages information technology and data analytics to improve operational efficiency and service quality, thereby achieving sustainable development. With the advancement of technologies such as the Internet, the Internet of Things (IoT), big data, and artificial intelligence, the construction of smart cities has entered a period of rapid development. Smart cities enhance the precision, efficiency, and sustainability of urban management through intelligent transformation of urban infrastructure, traffic management, public safety, and energy management.

[0003] As a core component of smart cities, the Internet of Things (IoT) primarily enables real-time monitoring, control, and optimization of urban resources and services through the interconnection of sensors, devices, and intelligent systems. IoT applications span a wide range of sectors, including intelligent transportation, smart homes, smart healthcare, and environmental monitoring.

[0004] Active antennas (ATAs) are a type of antenna technology that combines an antenna with a radio frequency (RF) amplifier. Unlike traditional passive antennas, ATAs incorporate integrated RF amplifiers, filters, modems, and other circuit components into the antenna system, improving signal transmission distance and quality while effectively reducing signal loss. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent active antenna Internet of Things networking system and method for smart cities to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for establishing an intelligent active antenna Internet of Things network for a smart city includes the following steps:

[0008] Configure the intelligent active antenna network protocol stack and set up routing algorithms to optimize data transmission paths;

[0009] Enable the adaptive tuning function to dynamically adjust the transmission power and operating frequency; according to the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range; the algorithm coefficient is obtained by weighted summation, the formula is as follows: F=αLL+βYQ+γMD+δFW; where F represents the algorithm coefficient; LL represents the traffic volume in the area, YQ represents the communication quality requirements, MD represents the frequency band density, FW represents the dynamic range, and α, β, γ, and δ are the weight coefficients of the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range respectively; based on the calculated algorithm coefficient, when the algorithm coefficient is higher than the system preset threshold, the spectrum scanning and frequency selection algorithm is selected; otherwise, the RSSI-based power adjustment algorithm is selected;

[0010] Optimize the RSSI-based power adjustment algorithm or spectrum scanning and frequency selection algorithm parameters based on the test results. Calculate the update coefficient using a logistic regression formula based on the signal strength fluctuation range and bit error rate. Use the update coefficient value to determine whether to update the RF control firmware.

[0011] In a preferred embodiment, before configuring the smart active antenna network protocol stack, it is necessary to configure the IP address, subnet mask and default gateway of the smart active antenna node, and use a signal strength test tool to measure the RSSI value between nodes; ensure that the RSSI value is within -70dBm to -30dBm.

[0012] In a preferred embodiment, the intelligent active antenna network protocol stack is configured, and TCP or UDP is selected according to the real-time requirement; and QoS parameters are configured for key services.

[0013] In a preferred embodiment, the RSSI-based power adjustment algorithm or spectrum scanning and frequency selection algorithm parameters are optimized according to the test results. Specifically, the communication quality is evaluated by measuring the signal-to-noise ratio and the bit error rate; the larger the values ​​of the signal-to-noise ratio and the bit error rate, the worse the communication quality; by evaluating the communication quality, the scanning interval is further optimized; the worse the communication quality, the shorter the scanning interval.

[0014] In a preferred embodiment, the signal strength fluctuation range and the bit error rate are determined to select whether to use the RF control firmware; the signal strength fluctuation range and the bit error rate are normalized; specifically, the update coefficient is calculated using the following formula: ; Where P represents the update coefficient; bd represents the signal strength fluctuation range, wm represents the bit error rate, a and b are the regression coefficients of the signal strength fluctuation range and the bit error rate, both greater than zero; e is the natural base.

[0015] In a preferred embodiment, an update coefficient is determined. When the update coefficient is greater than a system preset threshold, the RF control firmware is upgraded via OTA. When the update coefficient is less than the system preset threshold, the original RF control firmware is maintained.

[0016] In a preferred embodiment, the system includes the following modules: an intelligent active antenna node module, a network protocol stack configuration module, and an adaptive tuning function module;

[0017] The intelligent active antenna node module is used to configure the basic parameters of the node; test the basic functions of the node, use the ping command to test the connectivity and latency between nodes; and measure signal strength and signal-to-noise ratio;

[0018] The network protocol stack configuration module selects the appropriate protocol stack based on the application scenario; ensures that the protocol stack supports multi-hop routing and dynamic address allocation; selects the appropriate routing algorithm; adjusts routing parameters based on network topology and node density; and configures QoS parameters to optimize critical data transmission.

[0019] The adaptive tuning module is used to enable the adaptive tuning function and dynamically adjust the transmission power and operating frequency. It selects the adaptive tuning algorithm based on the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range. It optimizes the algorithm parameters based on test results. It also determines whether to update the RF control firmware based on the signal strength fluctuation range, spectrum analysis, and bit error rate.

[0020] The technical effects and advantages of the present invention are as follows:

[0021] The present invention first installs and preliminarily debugs the intelligent active antenna nodes to ensure that the communication quality between nodes meets the standards. Then, the network protocol stack is configured and the routing algorithm is set to optimize the data transmission path. Subsequently, the adaptive tuning function is enabled to select a suitable tuning algorithm based on factors such as the traffic volume, communication quality requirements, and frequency band density in the actual environment, such as an RSSI-based power adjustment algorithm or a spectrum scanning and frequency selection algorithm, so that the selected tuning algorithm is more in line with actual needs. The algorithm parameters are optimized by analyzing the test results, and the update coefficient is calculated using a logistic regression model. The need to update the algorithm and RF control firmware is determined based on the coefficient value, thereby avoiding the uncontrollable and wasteful use of manpower and material resources caused by fixed updates. Ultimately, real-time data acquisition and transmission functions are realized, and a data management system is constructed to achieve effective storage, in-depth analysis, and intuitive visualization of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0023] Figure 1 This is a flow chart of the method for establishing an intelligent active antenna Internet of Things for smart cities according to the present invention;

[0024] Figure 2 This is a structural diagram of the intelligent active antenna Internet of Things networking system for smart cities of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0026] The present invention first installs and preliminarily debugs the intelligent active antenna nodes to ensure that the communication quality between nodes meets the standards. Then, the network protocol stack is configured and the routing algorithm is set to optimize the data transmission path. Subsequently, the adaptive tuning function is enabled, and a suitable tuning algorithm is selected according to factors such as the traffic volume, communication quality requirements, and frequency band density in the actual environment, such as the RSSI-based power adjustment algorithm or the spectrum scanning and frequency selection algorithm, and its performance is tested in a real environment. The algorithm parameters are optimized by analyzing the test results, and the update coefficient is calculated using the logistic regression model. The coefficient value is used to determine whether the algorithm and RF control firmware need to be updated. Ultimately, real-time data acquisition and transmission functions are realized, and a data management system is constructed to achieve effective storage, in-depth analysis, and intuitive visualization of data.

[0027] Example 1

[0028] The present invention provides a smart city-oriented intelligent active antenna Internet of Things networking method, such as Figure 1 As shown, the specific steps include:

[0029] Step S1: Install the intelligent active antenna node and perform preliminary debugging; test the communication quality between nodes;

[0030] Step S2: configuring the smart active antenna network protocol stack and setting the routing algorithm to optimize the data transmission path;

[0031] Step S3: Enable the adaptive tuning function to dynamically adjust the transmit power and operating frequency; select an adaptive tuning algorithm (RSSI-based power adjustment algorithm or spectrum scanning and frequency selection algorithm) based on the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range; test the performance of the adaptive tuning function in a real environment; optimize the algorithm parameters based on the test results; calculate the update coefficient using a logistic regression formula based on the signal strength fluctuation range and bit error rate, and use the value of the update coefficient to decide whether to update the algorithm and RF control firmware;

[0032] Step S4: Realize real-time data collection and transmission; establish a data management system to support data storage, analysis and visualization.

[0033] In step S1, secure the smart active antenna node to the intended location (such as a wall or tower) and use expansion bolts or fixtures for installation. Connect the antenna to the node's RF interface, ensuring it faces the target coverage area. Connect the power adapter to the node and ensure the power voltage meets the device's requirements. For outdoor installations, install a lightning protection device and, if necessary, an uninterruptible power supply or solar power system.

[0034] Use a computer or mobile phone to connect to the Smart Active Antenna Node's management interface via Wi-Fi or a wired connection. Enter the default username and password. Configure the node's IP address, subnet mask, and default gateway, and enable DHCP. Set the operating frequency band (such as 2.4 GHz or 5 GHz), channel number, and transmit power. Ensure the node is successfully connected to the main network and test network connectivity.

[0035] Use a signal strength test tool to measure the RSSI values ​​between nodes. Ensure that the RSSI values ​​are within a reasonable range (for example, -60 dBm to -80 dBm). Send a certain number of data packets (for example, 1000) to test the bit error rate (BER) at the receiving end. If the BER is too high, check the signal strength or adjust the antenna direction.

[0036] Check the node's log files to confirm that there are no errors or warnings. Check the device temperature to ensure that the node's operating temperature is within the normal range (e.g., ≤50°C).

[0037] In step S2, configuring the smart active antenna network protocol stack mainly includes the following steps:

[0038] Step A1: Determine the network architecture. Select the appropriate protocol stack based on networking requirements. IPv6 is recommended for large-scale IoT networks, supporting a larger address space and more efficient routing mechanisms. IPv4 is suitable for small networks or scenarios where existing devices support IPv4. TCP / IP is suitable for scenarios requiring high reliability. UDP is suitable for scenarios requiring high real-time performance. Determine the network's hierarchical structure and assign appropriate functional modules to each layer.

[0039] Step A2: Configure network parameters; IP address allocation, assign a unique IP address to each smart active antenna node; subnet mask and gateway settings, configure the subnet mask and default gateway to ensure that the nodes can communicate correctly; MTU optimization, adjust the MTU value according to the network environment to avoid transmission problems caused by overly large data packets.

[0040] Step A3: Transport layer optimization; select the transport protocol, choose TCP or UDP based on real-time requirements; configure QoS parameters for critical services to ensure that higher-priority packets are transmitted first.

[0041] Step A4: Application layer function development; data encapsulation and parsing. Encapsulation mechanism: Packaging raw data into data packets that conform to the protocol format; for example, using JSON or XML for data encapsulation. Parsing mechanism: Decoding the data packets at the receiving end to extract useful information; ensuring data format compatibility between the upper and lower computers.

[0042] Exception handling mechanism, error detection: implement checksum, cyclic redundancy check and other mechanisms to detect data packet errors; retransmission mechanism: trigger retransmission requests for lost or damaged data packets; support automatic retransmission or manual triggering.

[0043] Encrypt data transmission and use SSL / TLS protocol to encrypt sensitive data; ensure that data is not stolen or tampered with during transmission; implement device authentication mechanism to prevent unauthorized access.

[0044] In the intelligent active antenna IoT networking system, setting up routing algorithms is a key step in optimizing data transmission paths and improving network performance. The following are the detailed steps and methods:

[0045] Step B1: Select a suitable routing algorithm;

[0046] AODV features: On-demand routing: The routing discovery process is initiated only when needed, reducing network overhead; Strong dynamic adaptability: Suitable for scenarios with frequent node movement and fast network topology changes.

[0047] Advantages: Low overhead, routing requests are sent only when necessary, reducing the transmission of control messages. Suitable for mobile nodes, and can quickly adapt to changes in node location.

[0048] DSDV features: A distance vector-based routing algorithm that maintains routing tables by periodically broadcasting routing information; requires periodic updates: performs better in environments where network topology changes slowly.

[0049] Advantages: Simple and reliable, relatively simple to implement, suitable for static or low-dynamic networks; suitable for scenarios where node locations are relatively fixed.

[0050] Step B2: Implement the routing algorithm and select an appropriate programming language based on project requirements. Write the corresponding routing module based on the selected routing algorithm. If there is existing open source code, it can be ported and optimized. Ensure that the routing module can seamlessly integrate with the network protocol stack. Verify that the data packet format, interface functions, etc. match.

[0051] Manage and dynamically update routing tables: Monitor network topology changes in real time; update routing tables based on these changes to ensure data transmission paths are up-to-date. Regularly refresh routing information: Set a reasonable refresh cycle to prevent outdated routing information; shorten the refresh cycle in dynamic networks and extend it appropriately in static networks. Optimization goals: Improve routing table accuracy and timeliness; reduce routing loops and invalid paths.

[0052] Step B3: Neighbor discovery and path maintenance;

[0053] The neighbor discovery mechanism is implemented as follows:

[0054] Heartbeat packet mechanism: heartbeat packets are sent regularly to adjacent nodes to detect neighbor status; the heartbeat packet contains information such as node ID and current status.

[0055] Neighbor list maintenance: Update the neighbor list based on heartbeat packet feedback; if a neighbor node does not respond for a long time, it is marked as offline.

[0056] Optimization goals: Ensure the timeliness and accuracy of neighbor discovery; reduce network overhead during neighbor discovery.

[0057] The steps to implement path maintenance are as follows:

[0058] Path quality monitoring: Monitors performance indicators such as latency and packet loss rate of existing paths; if path quality deteriorates, initiates the rerouting process.

[0059] Broken link repair: When a link interruption is detected, the route rediscovery mechanism is immediately activated; the optimal path is reselected based on the current network status.

[0060] Optimization goals: Improve path stability and reliability; reduce the impact of path interruptions on data transmission.

[0061] Step B4: Simulate network topology changes to verify the adaptability of the routing algorithm; test routing convergence time.

[0062] Use a network simulator to perform simulation tests; deploy test nodes in a real environment to observe algorithm performance.

[0063] The above steps complete the setup and implementation of the routing algorithm in the smart active antenna IoT networking system. Selecting an appropriate routing algorithm based on the network environment and combining it with dynamic tuning can significantly improve network performance and communication quality. In actual applications, algorithm parameters and implementation details must be continuously optimized based on test results to achieve optimal networking results.

[0064] In step S3, the adaptive tuning function is enabled to dynamically adjust the transmission power and operating frequency; the traffic volume, communication quality requirements, frequency band density, and dynamic range of the area are determined; the algorithm coefficient is obtained according to the weighted summation formula, which is as follows: F=αLL+βYQ+γMD+δFW; wherein F represents the algorithm coefficient; LL represents the traffic volume of the area, the greater the traffic volume of the area, the greater the algorithm coefficient, and vice versa; YQ represents the communication quality requirements, the greater the communication quality requirements, the greater the algorithm coefficient, and vice versa; MD represents the frequency band density, the greater the frequency band density, the greater the algorithm coefficient, and vice versa; FW represents the dynamic range, the greater the dynamic range, the greater the algorithm coefficient, and vice versa; α, β, γ, and δ are the weight coefficients of the traffic volume, communication quality requirements, frequency band density, and dynamic range of the area, respectively.

[0065] Furthermore, the traffic flow in the area can be captured by cameras. In high traffic flow scenarios, frequent vehicle movement will cause channel conditions to change rapidly and signal strength to fluctuate greatly. Spectrum scanning and frequency selection algorithms are suitable because they can quickly scan frequency bands and select the optimal frequency band to adapt to dynamically changing channel conditions.

[0066] High requirements for signal strength and spectrum utilization, typically requiring a low bit error rate and high throughput, are suitable for spectrum scanning and frequency selection algorithms, as they can proactively avoid interfering frequency bands and improve communication quality. For less demanding communication quality, focusing primarily on power consumption and cost, RSSI-based power adjustment algorithms can be selected to balance power consumption and communication quality by dynamically adjusting transmit power. The specific communication quality requirements are set by the operator.

[0067] In urban high-rise buildings, many different devices may be present, resulting in limited frequency resources. Multiple devices sharing the same frequency band can easily cause interference. Spectrum scanning and frequency selection algorithms are suitable for improving spectrum utilization by scanning idle bands or those with less interference. For bands with more abundant frequency resources and less interference between devices, RSSI-based power adjustment algorithms can be used to dynamically adjust transmit power to optimize power consumption and communication quality. Field measurements can be performed using a professional spectrum analyzer, which scans signal strength within a specified frequency range to help identify busy and idle bands.

[0068] The signal strength varies widely, requiring rapid response and adjustment. Spectrum scanning and frequency selection algorithms are suitable because they can quickly switch to the optimal frequency band when the signal strength varies greatly. Signal strength varies less, and channel conditions are relatively stable. An RSSI-based power adjustment algorithm can be selected to optimize power consumption and communication quality by dynamically adjusting the transmit power.

[0069] According to the calculated algorithm coefficient, when the algorithm coefficient is higher than the system preset threshold, the spectrum scanning and frequency selection algorithm is selected; otherwise, the RSSI-based power adjustment algorithm is selected.

[0070] After selecting an algorithm, it is necessary to test and optimize it in a real-world environment. Communication quality should be evaluated by measuring indicators such as the signal-to-noise ratio and bit error rate. Larger values ​​for the signal-to-noise ratio and bit error rate indicate poorer communication quality. By evaluating communication quality, the scanning interval can be further optimized. The worse the communication quality, the shorter the interval.

[0071] Determine the signal strength fluctuation range and bit error rate to choose whether to update the update algorithm and RF control firmware; normalize the signal strength fluctuation range and bit error rate; specifically, calculate the update coefficient using the following formula: ; Where P represents the update coefficient; bd represents the signal strength fluctuation range. The larger the signal strength fluctuation range, the larger the update coefficient, and vice versa; wm represents the bit error rate. The larger the bit error rate, the larger the update coefficient, and vice versa; a and b are the regression coefficients of the signal strength fluctuation range and the bit error rate, both greater than zero; e is the natural base.

[0072] The signal strength fluctuation range is obtained using the Wireshark tool. A bit error meter is used to test the bit error rate of the communication link. The bit error rate is calculated by sending a known data stream and comparing the received results.

[0073] An update coefficient is determined. When the update coefficient is greater than a preset threshold of the system, the RF control firmware is upgraded via OTA.

[0074] In step S4, real-time data collection and transmission are realized; a data management system is established, and the collected data information is visualized using the Power BI tool to facilitate staff operations; some data is processed at the edge node, and key results are transmitted to the cloud for further analysis, and a real-time database is used to store and manage real-time data.

[0075] Example 2

[0076] The present invention provides an intelligent active antenna Internet of Things networking system for smart cities, such as Figure 2 As shown, it specifically includes the following modules: intelligent active antenna node module, network protocol stack configuration module, and adaptive tuning function module;

[0077] The intelligent active antenna node module is used to configure the basic parameters of the node; test the basic functions of the node, use the ping command to test the connectivity and latency between nodes; and measure signal strength and signal-to-noise ratio;

[0078] The network protocol stack configuration module selects the appropriate protocol stack based on the application scenario; ensures that the protocol stack supports multi-hop routing and dynamic address allocation; selects the appropriate routing algorithm; adjusts routing parameters based on network topology and node density; and configures QoS parameters to optimize critical data transmission.

[0079] The adaptive tuning module is used to enable the adaptive tuning function and dynamically adjust the transmission power and operating frequency. It selects the adaptive tuning algorithm based on the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range. It optimizes the algorithm parameters based on test results. It also determines whether to update the RF control firmware based on the signal strength fluctuation range, spectrum analysis, and bit error rate.

[0080] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0082] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0084] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The method for establishing an intelligent active antenna Internet of Things network for smart cities is characterized by: The following steps are involved: Configure the intelligent active antenna network protocol stack and set up routing algorithms to optimize data transmission paths; Enable the adaptive tuning function to dynamically adjust the transmission power and operating frequency; according to the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range; the algorithm coefficient is obtained by weighted summation, the formula is as follows: F=αLL+βYQ+γMD+δFW; where F represents the algorithm coefficient; LL represents the traffic volume in the area, YQ represents the communication quality requirements, MD represents the frequency band density, FW represents the dynamic range, and α, β, γ, and δ are the weight coefficients of the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range respectively; based on the calculated algorithm coefficient, when the algorithm coefficient is higher than the system preset threshold, the spectrum scanning and frequency selection algorithm is selected; otherwise, the RSSI-based power adjustment algorithm is selected; Optimize the RSSI-based power adjustment algorithm or spectrum scanning and frequency selection algorithm parameters based on the test results. Calculate the update coefficient using a logistic regression formula based on the signal strength fluctuation range and bit error rate. Use the update coefficient value to determine whether to update the RF control firmware.

2. The method for establishing an intelligent active antenna Internet of Things for smart cities according to claim 1, characterized in that: Before configuring the smart active antenna network protocol stack, you need to configure the IP address, subnet mask, and default gateway of the smart active antenna node. Use a signal strength test tool to measure the RSSI value between nodes and ensure that the RSSI value is within -70dBm to -30dBm.

3. The method for establishing an intelligent active antenna Internet of Things for smart cities according to claim 1, characterized in that: Configure the intelligent active antenna network protocol stack, selecting TCP or UDP based on real-time requirements; and configure QoS parameters for critical services.

4. The method for establishing an intelligent active antenna Internet of Things for smart cities according to claim 1, characterized in that: Based on the test results, optimize the RSSI-based power adjustment algorithm or spectrum scanning and frequency selection algorithm parameters. Specifically, evaluate communication quality by measuring the signal-to-noise ratio and bit error rate. Larger values ​​of the signal-to-noise ratio and bit error rate indicate poorer communication quality. By evaluating communication quality, further optimize the scanning interval. The worse the communication quality, the shorter the scanning interval.

5. The method for establishing an intelligent active antenna Internet of Things for smart cities according to claim 1, characterized in that: Determine the signal strength fluctuation range and bit error rate to select whether to use RF control firmware; normalize the signal strength fluctuation range and bit error rate; specifically, calculate the update coefficient using the following formula: ; Where P represents the update coefficient; bd represents the signal strength fluctuation range, wm represents the bit error rate, a and b are the regression coefficients of the signal strength fluctuation range and the bit error rate, respectively, both greater than zero; e is the natural base.

6. The method for establishing an intelligent active antenna Internet of Things for smart cities according to claim 5, characterized in that: An update coefficient is determined. When the update coefficient is greater than a system preset threshold, the RF control firmware is upgraded via OTA. When the update coefficient is less than the system preset threshold, the original RF control firmware is retained.

7. The intelligent active antenna Internet of Things networking system for smart cities is characterized by: The networking system is based on the method according to any one of claims 1 to 6, and comprises the following modules: an intelligent active antenna node module, a network protocol stack configuration module, and an adaptive tuning function module; The intelligent active antenna node module is used to configure the basic parameters of the node; test the basic functions of the node, use the ping command to test the connectivity and latency between nodes; and measure signal strength and signal-to-noise ratio; The network protocol stack configuration module selects the appropriate protocol stack based on the application scenario; ensures that the protocol stack supports multi-hop routing and dynamic address allocation; selects the appropriate routing algorithm; adjusts routing parameters based on network topology and node density; and configures QoS parameters to optimize critical data transmission. The adaptive tuning module is used to enable the adaptive tuning function and dynamically adjust the transmission power and operating frequency. It selects the adaptive tuning algorithm based on the traffic volume in the area, communication quality requirements, frequency band density, and dynamic range. It optimizes the algorithm parameters based on test results. It also determines whether to update the RF control firmware based on the signal strength fluctuation range, spectrum analysis, and bit error rate.

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