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

Abstract

The invention discloses an intelligent active antenna Internet of Things networking system and method for a smart city, and particularly relates to the technical field of Internet of Things networking, and the method comprises the steps: firstly, enabling the communication quality between nodes to reach a standard through the installation and preliminary debugging of intelligent active antenna nodes; then, a network protocol stack is configured, a routing algorithm is set, and a data transmission path is optimized; and then, starting an adaptive tuning function, selecting a proper tuning algorithm according to the traffic flow, the communication quality requirement, the frequency band density and the dynamic range of the located section, and carrying out field testing. By analyzing a test result, algorithm parameters are optimized, an update coefficient is calculated by using a logistic regression model, and whether an algorithm or radio frequency control firmware needs to be updated is judged. Finally, real-time data acquisition and transmission are realized, a data management system is constructed, and validity of data storage, analysis and visual display functions is ensured.

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] A smart city is a new urban management model that uses information technology and data analysis means to improve the efficiency of urban operations and service quality and achieve sustainable development. With the development of technologies such as the Internet, Internet of Things (IoT), big data, and artificial intelligence, the construction of smart cities has entered a stage of rapid development. Smart cities improve the accuracy, efficiency, and sustainability of urban management through intelligent transformation in aspects such as urban infrastructure, traffic management, public safety, and energy management.

[0003] As a core component of smart cities, the Internet of Things mainly realizes real-time monitoring, control, and optimization of urban resources and services through the interconnection of sensors, devices, and intelligent systems. The applications of the Internet of Things cover multiple fields, such as intelligent transportation, smart home, intelligent healthcare, environmental monitoring, etc.

[0004] An intelligent active antenna is an antenna technology that combines an antenna and a radio frequency (RF) amplifier. Different from traditional passive antennas, an intelligent active antenna adds integrated RF amplifiers, filters, modems, and other circuit components to the antenna system, which can improve the transmission distance and quality of signals and effectively reduce signal loss. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, 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 background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: An intelligent active antenna Internet of Things networking method for smart cities, comprising the following steps: Configure the intelligent active antenna network protocol stack and set a routing algorithm to optimize the data transmission path; Enable the adaptive tuning function to dynamically adjust the transmission power and operating frequency; select a power adjustment algorithm based on RSSI or a spectrum scanning and frequency selection algorithm according to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; optimize the parameters of the power adjustment algorithm based on RSSI or the spectrum scanning and frequency selection algorithm according to the test results; calculate the update coefficient through a logistic regression formula based on the signal strength fluctuation range and bit error rate, and select whether to update the RF control firmware based on the value of the update coefficient.

[0007] In a preferred embodiment, the IP address, subnet mask, and default gateway of the intelligent active antenna node need to be configured before configuring the intelligent active antenna network protocol stack, and the RSSI value between nodes is measured using a signal strength test tool; ensure that the RSSI value is within -70dBm to -30dBm.

[0008] In a preferred embodiment, when configuring the intelligent active antenna network protocol stack, TCP or UDP is selected according to real-time requirements; QoS parameters are configured for critical services.

[0009] In a preferred embodiment, the adaptive tuning function is enabled to dynamically adjust the transmit power and operating frequency; according to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; the algorithm coefficient is obtained by weighted summation, and the formula is as follows: F = αLL + βYQ + γMD + δFW; where F represents the algorithm coefficient; LL represents the traffic flow of the location, 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 flow of the location, communication quality requirements, frequency band density, and dynamic range, respectively; 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.

[0010] In a preferred embodiment, the parameters of the RSSI-based power adjustment algorithm or the spectrum scanning and frequency selection algorithm are optimized according to the test results. Specifically, the communication quality is evaluated by measuring indicators such as signal-to-noise ratio and bit error rate; the larger the values of the signal-to-noise ratio and bit error rate, the worse the communication quality; by evaluating the communication quality, the scanning interval time is further optimized. The worse the communication quality, the shorter the scanning interval time.

[0011] In a preferred embodiment, the signal strength fluctuation range and bit error rate are determined to select whether to use the radio frequency control firmware; the signal strength fluctuation range and bit error rate are normalized; specifically, the update coefficient is calculated by 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 bit error rate, both of which are greater than zero; e is the natural logarithm base.

[0012] In a preferred embodiment, when the update coefficient is determined, when the update coefficient is greater than the system preset threshold, the radio frequency control firmware is upgraded through OTA. When the update coefficient is less than the system preset threshold, the original radio frequency control firmware is maintained.

[0013] In a preferred embodiment, it includes the following modules: intelligent active antenna node module, network protocol stack configuration module, 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, and use the ping command to test the connectivity and latency between nodes; measure the signal strength and signal-to-noise ratio; The network protocol stack configuration module selects a suitable protocol stack according to the application scenario; ensures that the protocol stack supports multi-hop routing and dynamic address allocation; selects a suitable routing algorithm; adjusts the routing parameters according to the network topology and node density; configures QoS parameters to optimize the transmission of critical data; The adaptive tuning function module is used to enable the adaptive tuning function and dynamically adjust the transmit power and operating frequency; select the adaptive tuning algorithm according to the traffic flow, communication quality requirements, frequency band density, and dynamic range in the location; optimize the algorithm parameters according to the test results; select whether to update the RF control firmware according to the signal strength fluctuation range, spectrum analysis, and bit error rate.

[0014] The technical effects and advantages of the present invention: First, the present invention installs and preliminarily debugs the intelligent active antenna nodes to ensure that the communication quality between nodes meets the standard. Then, it configures the network protocol stack and sets the routing algorithm to optimize the data transmission path. Subsequently, it enables the adaptive tuning function and selects a suitable tuning algorithm according to factors such as traffic flow, communication quality requirements, and frequency band density in the actual environment, such as the power adjustment algorithm based on RSSI or the spectrum scanning and frequency selection algorithm, making the selected tuning algorithm more in line with the actual needs. It optimizes the algorithm parameters by analyzing the test results, calculates the update coefficient using the logistic regression model, and determines whether to update the algorithm and RF control firmware based on the coefficient value; avoiding the uncontrollability and waste of human and material resources caused by fixed updates. Finally, it realizes the real-time data acquisition and transmission function and constructs a data management system to achieve the effective storage, in-depth analysis, and intuitive visual display of data. Brief Description of the Drawings

[0015] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings; Figure 1 is a schematic flow chart of the intelligent active antenna Internet of Things networking method for smart cities according to the present invention; Figure 2 is a schematic structural diagram of the intelligent active antenna Internet of Things networking system for smart cities according to the present invention. Detailed Embodiments

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with 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.

[0017] The present invention first installs and preliminarily debugs intelligent active antenna nodes to ensure that the communication quality between nodes meets the standards. Then, it configures the network protocol stack and sets the routing algorithm to optimize the data transmission path. Subsequently, it enables the adaptive tuning function, selects a suitable tuning algorithm according to factors such as vehicle flow, communication quality requirements, and frequency band density in the actual environment, such as the power adjustment algorithm based on RSSI or the spectrum scanning and frequency selection algorithm, and tests its performance in the real environment. By analyzing the test results, it optimizes the algorithm parameters, calculates the update coefficient using the logistic regression model, and determines whether to update the algorithm and radio frequency control firmware based on the coefficient value. Finally, it realizes the real-time data collection and transmission function, and constructs a data management system to achieve effective storage, in-depth analysis, and intuitive visual display of data.

[0018] Embodiment A method for networking an intelligent active antenna Internet of Things for a smart city according to the present invention, as Figure 1 shown, specifically includes the following steps: Step S1: Install intelligent active antenna nodes and conduct preliminary debugging; test the communication quality between nodes; Step S2: Configure the intelligent active antenna network protocol stack and set the routing algorithm to optimize the data transmission path; Step S3: Enable the adaptive tuning function to dynamically adjust the transmit power and operating frequency; select an adaptive tuning algorithm (the power adjustment algorithm based on RSSI or the spectrum scanning and frequency selection algorithm) according to the vehicle flow, communication quality requirements, frequency band density, and dynamic range of the location; test the performance of the adaptive tuning function in the actual environment; optimize the algorithm parameters according to the test results; calculate the update coefficient through the logistic regression formula based on the signal strength fluctuation range and bit error rate, and select whether to update the algorithm and radio frequency control firmware based on the value of the update coefficient; Step S4: Realize real-time data collection and transmission; establish a data management system to support data storage, analysis, and visualization.

[0019] In step S1, fix the intelligent active antenna nodes at predetermined positions (such as on walls or poles) to ensure stability; use expansion bolts or fixing fixtures for installation; correctly connect the antenna to the radio frequency interface of the node to ensure that the antenna direction faces the target coverage area. Connect the power adapter to the node and ensure that the power supply voltage meets the device requirements; for outdoor installation, configure a lightning protection device; if necessary, configure an uninterruptible power supply or a solar power supply system.

[0020] Connect to the management interface of the smart active antenna node using a computer or mobile phone via Wi-Fi or wired connection; enter the default username and password. Configure the IP address, subnet mask, and default gateway of the node, and enable the DHCP function. Set the working frequency band (such as 2.4 GHz or 5 GHz), configure the channel number, and set the transmit power. Ensure that the node can successfully connect to the main network; test the network connectivity.

[0021] Use a signal strength test tool to measure the RSSI value between nodes; ensure that the RSSI value is within a reasonable range (such as -60dBm to -80 dBm); send a certain number of data packets (such as 1000), and test the bit error rate of the receiving end; if the bit error rate is too high, check the signal strength or adjust the antenna direction.

[0022] View the log file of the node to confirm that there are no error or warning messages; check the device temperature to ensure that the operating temperature of the node is within the normal range (such as ≤50°C).

[0023] In step S2, configuring the smart active antenna network protocol stack mainly includes the following steps: Step A1: Determine the network architecture; select a suitable protocol stack according to the networking requirements; IPv6: Recommended for large-scale IoT networking, supporting a larger address space and a more efficient routing mechanism; IPv4: Suitable for small networks or scenarios where existing devices support IPv4; TCP / IP: Suitable for scenarios with high reliability requirements; UDP: Suitable for scenarios with high real-time requirements. Determine the hierarchical structure of the network and allocate corresponding functional modules to each layer.

[0024] Step A2: Configure network parameters; IP address assignment, 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.

[0025] Step A3: Transport layer optimization; select a transport protocol, choose TCP or UDP according to real-time requirements; configure QoS parameters for critical services to ensure that data packets with higher priorities are transmitted first.

[0026] Step A4: Application layer function development; data encapsulation and parsing, encapsulation mechanism: package the original data into data packets that conform to the protocol format; for example, use JSON or XML format for data encapsulation. Parsing mechanism: decode the data packets at the receiving end and extract useful information; ensure data format compatibility between the upper and lower computers.

[0027] Exception handling mechanism, error detection: Implement mechanisms such as checksum and cyclic redundancy check to detect packet errors; Retransmission mechanism: For lost or damaged packets, trigger a retransmission request; Support automatic retransmission or manual trigger methods.

[0028] Encrypt data during transmission, use the SSL / TLS protocol to encrypt sensitive data; Ensure that data is not stolen or tampered with during transmission; Implement a device authentication mechanism to prevent unauthorized access.

[0029] In the intelligent active antenna Internet of Things networking system, setting the routing algorithm is a key link to optimize the data transmission path and improve network performance. The following are the detailed steps and methods: Step B1: Select a suitable routing algorithm; Features of AODV: Route establishment on demand: Only start the route discovery process when needed, reducing network overhead; Strong dynamic adaptability: Suitable for scenarios where nodes move frequently and the network topology changes rapidly.

[0030] Advantages: Low overhead, only send route requests when necessary, reducing the transmission of control messages. Suitable for mobile nodes and can quickly adapt to changes in node positions.

[0031] Features of DSDV: A distance-vector based routing algorithm that maintains the routing table by periodically broadcasting routing information; Requires periodic updates: Performs better in an environment where the network topology changes slowly.

[0032] Advantages: Simple and reliable, relatively easy to implement, suitable for static or low-dynamic networks; Suitable for scenarios where node positions are relatively fixed.

[0033] Step B2: Implement the routing algorithm, select a suitable programming language according to project requirements; Write the corresponding routing module according to the selected routing algorithm; If there is existing open-source code, it can be transplanted and optimized; Ensure that the routing module can be seamlessly docked with the network protocol stack; Verify whether the packet format, interface functions, etc. match.

[0034] Manage the routing table and update the routing table dynamically: Implement real-time monitoring of network topology changes; Update the routing table according to the changes to ensure the latestness of the data transmission path. Refresh routing information regularly: Set a reasonable refresh period to avoid outdated routing information; Shorten the refresh period in a dynamic network and extend it appropriately in a static network. Optimization goals: Improve the accuracy and timeliness of the routing table; Reduce routing loops and invalid paths.

[0035] Step B3: Neighbor discovery and path maintenance; The implementation steps of the neighbor discovery mechanism are as follows: Heartbeat packet mechanism: Regularly send heartbeat packets to adjacent nodes to detect the status of neighbors; The heartbeat packet contains information such as node ID and current status.

[0036] Neighbor list maintenance: Update the neighbor list according to the feedback of heartbeat packets; If a neighbor node does not respond for a long time, mark it as offline.

[0037] Optimization goals: Ensure the timeliness and accuracy of neighbor discovery; Reduce the network overhead during neighbor discovery.

[0038] The implementation steps of path maintenance are as follows: Path quality monitoring: Monitor performance indicators such as latency and packet loss rate of existing paths; If the path quality is found to decline, start the re-routing process. Broken link repair: When a link break is detected, immediately start the routing re-discovery mechanism; Re-select the optimal path according to the current network status.

[0039] Optimization goals: Improve the stability and reliability of the path; Reduce the impact of path interruption on data transmission.

[0040] Step B4: Simulate network topology changes to verify the adaptability of the routing algorithm; Test the routing convergence time.

[0041] Use a network simulator for simulation testing; Deploy test nodes in the actual environment and observe the performance of the algorithm.

[0042] Through the above steps, the setting and implementation of the routing algorithm in the intelligent active antenna IoT networking system can be completed. According to the characteristics of the network environment, select a suitable routing algorithm and combine it with the dynamic tuning function, which can significantly improve the network performance and communication quality. In actual applications, it is necessary to continuously optimize the algorithm parameters and implementation details according to the test results to achieve the best networking effect.

[0043] In step S3, enable the adaptive tuning function to dynamically adjust the transmit power and operating frequency; Determine according to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; Obtain the algorithm coefficient according to the weighted summation formula, and the formula is as follows: F = αLL + βYQ + γMD + δFW; where F represents the algorithm coefficient; LL represents the traffic flow of the location, the greater the traffic flow of the location, 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 flow of the location, communication quality requirements, frequency band density, and dynamic range respectively.

[0044] Furthermore, the vehicle flow of the area can be captured by a camera. In a high vehicle flow scenario, frequent vehicle movement will cause rapid changes in channel conditions and large fluctuations in signal strength. It is suitable to choose the spectrum scanning and frequency selection algorithm because it can quickly scan the frequency band and select the optimal frequency band to adapt to the dynamically changing channel conditions.

[0045] There are high requirements for signal strength and spectrum utilization rate, usually requiring low bit error rate and high throughput; it is suitable to choose the spectrum scanning and frequency selection algorithm because it can actively avoid interference frequency bands and improve communication quality. If the requirement for communication quality is not high and power consumption and cost are mainly concerned; the RSSI-based power adjustment algorithm can be selected to balance power consumption and communication quality by dynamically adjusting the transmission power. Specifically, the level of the communication quality requirement is input by the staff themselves.

[0046] In urban high-rise buildings, there may be many different devices, and the frequency band resources are tight. Multiple devices share the same frequency band, which is likely to cause interference. It is suitable to choose the spectrum scanning and frequency selection algorithm to improve the spectrum utilization rate by scanning free frequency bands or frequency bands with less interference; for relatively loose frequency band resources and less interference between devices; the RSSI-based power adjustment algorithm can be selected to optimize power consumption and communication quality by dynamically adjusting the transmission power. A professional spectrum analyzer can be used for on-site measurement. The spectrum analyzer can scan the signal strength within a specified frequency range to help identify busy frequency bands and free frequency bands.

[0047] The signal strength change range is large, and rapid response and adjustment are required; it is suitable to choose the spectrum scanning and frequency selection algorithm because it can quickly switch to the optimal frequency band when the signal strength changes greatly; the signal strength change is small and the channel conditions are relatively stable; the RSSI-based power adjustment algorithm can be selected to optimize power consumption and communication quality by dynamically adjusting the transmission power.

[0048] 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.

[0049] After selecting the algorithm, it is necessary to test and optimize in the actual environment, and evaluate the communication quality by measuring indicators such as signal-to-noise ratio and bit error rate; the larger the values of the signal-to-noise ratio and bit error rate, the worse the communication quality; by evaluating the communication quality, further optimize the scanning interval time. The worse the communication quality, the shorter the interval time.

[0050] Determine the signal strength fluctuation range and bit error rate to select whether to update the algorithm and RF control firmware; normalize the signal strength fluctuation range and bit error rate; specifically, calculate the update coefficient through the following formula: ; where P represents the update coefficient; bd represents the signal strength fluctuation range, and the larger the signal strength fluctuation range, the larger the update coefficient, and vice versa; wm represents the bit error rate, and 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 number.

[0051] The signal strength fluctuation range is obtained using the Wireshark tool; the bit error rate of the communication link is tested using a bit error rate tester, and the bit error rate is calculated by sending a known data stream and comparing the received results.

[0052] Determine the update coefficient. When the update coefficient is greater than the system preset threshold, the radio frequency control firmware is upgraded through OTA.

[0053] In step S4, real-time data collection and transmission are implemented; a data management system is established, and the collected data information is visualized using the Power BI tool to facilitate the operation of the staff; part of the data is processed at the edge node, and the key results are transmitted to the cloud for further analysis, and a real-time database is used to store and manage real-time data. Embodiment

[0054] An intelligent active antenna Internet of Things networking system for a smart city according to the present invention, as Figure 2 shown, specifically includes 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, and use the ping command to test the connectivity and latency between nodes; measure the signal strength and signal-to-noise ratio; The network protocol stack configuration module selects a suitable protocol stack according to the application scenario; ensures that the protocol stack supports multi-hop routing and dynamic address allocation; selects a suitable routing algorithm; adjusts the routing parameters according to the network topology and node density; configures QoS parameters to optimize the transmission of key data; The adaptive tuning function module is used to enable the adaptive tuning function and dynamically adjust the transmit power and operating frequency; select an adaptive tuning algorithm according to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; optimize the algorithm parameters according to the test results; select whether to update the radio frequency control firmware according to the signal strength fluctuation range, spectrum analysis, and bit error rate.

[0055] Those of ordinary skill in the art will realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0056] In 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be electrical, mechanical, or other forms.

[0057] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0059] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for networking an Internet of Things with intelligent active antennas for a smart city, characterized in that, It includes the following steps: Configure the intelligent active antenna network protocol stack and set the routing algorithm to optimize the data transmission path; Enable the adaptive tuning function to dynamically adjust the transmit power and operating frequency; Select the RSSI-based power adjustment algorithm or the spectrum scanning and frequency selection algorithm according to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; Optimize the parameters of the RSSI-based power adjustment algorithm or the spectrum scanning and frequency selection algorithm according to the test results; Calculate the update coefficient through the logical regression formula based on the signal strength fluctuation range and bit error rate, and select whether to update the radio frequency control firmware based on the value of the update coefficient.

2. The intelligent active antenna Internet of Things networking method for smart cities according to claim 1, characterized in that: Before configuring the intelligent active antenna network protocol stack, it is necessary to configure the IP address, subnet mask, and default gateway of the intelligent 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.

3. The intelligent active antenna Internet of Things networking method for a smart city according to claim 1, characterized in that: When configuring the intelligent active antenna network protocol stack, select TCP or UDP according to the real-time requirement; Configure QoS parameters for critical services.

4. The intelligent active antenna Internet of Things networking method for a smart city according to claim 1, wherein: Enable the adaptive tuning function to dynamically adjust the transmit power and operating frequency; According to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; Obtain the algorithm coefficient through weighted summation, and the formula is as follows: F = αLL + βYQ + γMD + δFW; where F represents the algorithm coefficient; LL represents the traffic flow of the location, 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 flow, communication quality requirements, frequency band density, and dynamic range of the location respectively; According to the calculated algorithm coefficient, when the algorithm coefficient is higher than the system preset threshold, select the spectrum scanning and frequency selection algorithm; Otherwise, select the RSSI-based power adjustment algorithm.

5. The intelligent active antenna Internet of Things networking method for smart cities according to claim 1, wherein: When optimizing the parameters of the RSSI-based power adjustment algorithm or the spectrum scanning and frequency selection algorithm according to the test results, specifically, evaluate the communication quality by measuring indicators such as signal-to-noise ratio and bit error rate; The larger the values of the signal-to-noise ratio and bit error rate, the worse the communication quality; By evaluating the communication quality, further optimize the scanning interval time. The worse the communication quality, the shorter the scanning interval time.

6. The intelligent active antenna Internet of Things networking method 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 through the following formula: ; Where P represents the update coefficient; bd represents the signal strength fluctuation range, wm represents the bit error rate, and a and b are the regression coefficients of the signal strength fluctuation range and bit error rate, both of which are greater than zero; e is the natural logarithm base.

7. The method for networking the intelligent active antenna Internet of Things for a smart city according to claim 6, characterized in that: Determine the update coefficient. When the update coefficient is greater than the system preset threshold, upgrade the radio frequency control firmware through OTA. When the update coefficient is less than the system preset threshold, keep the original radio frequency control firmware.

8. The intelligent active antenna Internet of Things networking system for smart cities is characterized in that: It includes the following modules: intelligent active antenna node module, network protocol stack configuration module, 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, and use the ping command to test the connectivity and latency between nodes; Measure the signal strength and signal-to-noise ratio; The network protocol stack configuration module selects an appropriate protocol stack according to the application scenario; ensures that the protocol stack supports multi-hop routing and dynamic address allocation; selects a suitable routing algorithm; adjusts routing parameters according to the network topology and node density; configures QoS parameters to optimize the transmission of critical data; The adaptive tuning function module is used to enable the adaptive tuning function, dynamically adjust the transmit power and operating frequency; select an adaptive tuning algorithm according to the traffic flow, communication quality requirements, frequency band density, and dynamic range of the location; optimize the algorithm parameters according to the test results; select whether to update the RF control firmware according to the signal strength fluctuation range, spectrum analysis, and bit error rate.

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