Integrated 5G communication module and smart device connection method

Abstract

This invention relates to the field of 5G device connectivity technology and discloses an integrated method for connecting a 5G communication module and a smart device. The method includes acquiring 5G communication module configuration data and smart device connectivity requirement data, determining initial connection parameters based on these two types of data; then constructing a connection controller model adapted to the initial connection parameters and scaling the parameters of this model; subsequently converting the connection controller model into an equivalent model and conducting connection stability analysis to obtain the parameter adjustment range; furthermore, performing a comprehensive robustness analysis on the parameters to obtain parameter adjustment criteria; finally, modifying the connection parameters according to the parameter adjustment criteria and applying them to the connection control process between the 5G communication module and the smart device. This method can achieve stable and efficient connection between the 5G communication module and the smart device, adapting to different scenarios and device requirements, fully leveraging the advantages of 5G technology, and is applicable to smart device connectivity scenarios in multiple fields.

Description

Technical Field

[0001] This invention relates to the field of 5G device connectivity technology, specifically to a method for connecting an integrated 5G communication module with a smart device. Background Technology

[0002] With the rapid development of 5G communication technology, its high bandwidth, low latency, and wide connectivity have gradually become crucial supports for efficient data interaction in smart devices. Integrated 5G communication modules, due to their small size, low power consumption, and strong compatibility, are widely used in smart devices in fields such as smart homes, industrial IoT, and intelligent transportation. In practical application scenarios, smart devices exhibit diverse characteristics, with significant differences in data transmission rate requirements, connection stability requirements, and power consumption control standards. For example, smart cameras in smart homes have high requirements for data transmission rates and real-time performance, while smart sensors prioritize low power consumption and long-term stable connections. Furthermore, the configuration parameters of integrated 5G communication modules are adjustable, including frequency band selection, transmit power, and modulation methods. The settings of these parameters directly affect the connection effect between the module and the smart device.

[0003] In the process of connecting integrated 5G communication modules with smart devices, the connection is usually achieved by pre-setting fixed parameters. This approach does not fully consider the compatibility between the configuration data of the 5G communication module and the connection requirements of the smart device, resulting in connection parameters that do not match the actual application scenario. In the parameter determination stage, there is a lack of systematic analysis of both data; initial connection parameters are often determined based on experience or simple testing, making it difficult to guarantee the rationality of the initial parameters and thus affecting the stability of the subsequent connection process. Furthermore, in terms of connection controller model construction, existing methods mostly use general models without adapting them to the determined initial connection parameters. Insufficient compatibility between the model and parameters easily leads to problems such as data transmission stuttering and connection interruptions.

[0004] Existing technologies lack a comprehensive optimization process for connection controller models. Some methods only perform simple parameter adjustments without scaling, resulting in poor model adaptability across different application scenarios. Other methods attempt model optimization but fail to convert the model into an equivalent model for connection stability analysis. This prevents accurate determination of the parameter adjustment range and comprehensive robustness analysis to establish adjustment criteria, leaving parameter modifications without a scientific basis. Consequently, it's difficult to guarantee that the adjusted parameters meet the stable connection requirements of 5G communication modules and smart devices under various complex environments. These issues lead to low connection efficiency between integrated 5G communication modules and smart devices, hindering the full utilization of 5G communication technology and limiting the further application and development of smart devices in various fields. Summary of the Invention

[0005] The purpose of this invention is to provide an integrated 5G communication module and a method for connecting smart devices, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for connecting an integrated 5G communication module to a smart device, the method comprising:

[0007] Obtain 5G communication module configuration data and smart device connection requirements data;

[0008] Based on the 5G communication module configuration data and smart device connection requirement data, determine the initial connection parameters;

[0009] Construct a connection controller model adapted to the initial connection parameters;

[0010] Parameter scaling is performed on the connection controller model;

[0011] The connection controller model is converted into an equivalent model, and connection stability analysis is performed to obtain the parameter adjustment range;

[0012] A comprehensive robustness analysis was performed on the parameters to obtain parameter adjustment criteria;

[0013] The connection parameters are modified according to the parameter adjustment criteria and applied to the connection control process between the 5G communication module and the smart device.

[0014] Preferably, the acquisition of 5G communication module configuration data and smart device connection requirement data includes:

[0015] Acquire smart device type data and 5G communication module operating scenario data. The smart device type data includes device category and usage protocol, and the 5G communication module operating scenario data includes signal coverage range and network congestion level.

[0016] Acquire smart device performance data, which includes target transmission rate and target latency requirements;

[0017] Based on the smart device type data, 5G communication module operating scenario data, and smart device performance data, smart device connection requirement data is generated.

[0018] Preferably, based on the 5G communication module configuration data and smart device connection requirement data, the initial connection parameters are determined, including:

[0019] Calculate the original connection parameters based on the target transmission rate and target latency requirements in the smart device connection demand data;

[0020] The original connection parameters are optimized according to a preset optimization threshold to generate initial connection parameters.

[0021] Preferably, constructing a connection controller model adapted to the initial connection parameters includes:

[0022] The initial connection parameters are equivalently represented as a state-space model, which includes the system state vector and control input.

[0023] Based on the state-space model and the preset controller framework, a connection controller model is constructed, which includes an observer module and a feedback control module.

[0024] Preferably, parameter scaling of the connection controller model includes:

[0025] The observer module is scaled, including the observer bandwidth parameter is adjusted.

[0026] The feedback control module is subjected to feedback controller parameter scaling, including adjustment of the controller bandwidth parameter.

[0027] Preferably, the connection controller model is converted into an equivalent model, and connection stability analysis is performed to obtain the parameter adjustment range, including:

[0028] The connection controller model is converted into a two-degree-of-freedom equivalent model.

[0029] Construct a closed-loop characteristic equation, and analyze the stability of the connected control system based on the closed-loop characteristic equation;

[0030] Based on the stability analysis results, the safe selection range of connection parameters is determined.

[0031] Preferably, a comprehensive robustness analysis is performed on the parameters to obtain parameter adjustment criteria, including:

[0032] Define the test metrics, which include gain margin, stability margin, and relative delay margin;

[0033] Based on the scaling parameter space, the test index is evaluated, and a robust adjustment criterion is generated.

[0034] Among them, the robust tuning criteria include the trade-off tuning rules for the observer bandwidth parameter and the control response rules for the control bandwidth parameter.

[0035] Preferably, modifying the connection parameters according to the parameter adjustment criteria includes:

[0036] Adjust the observer bandwidth parameter according to the compromise parameter tuning rules in the robust tuning criteria;

[0037] Adjust the control bandwidth parameters according to the control response rules in the robust adjustment criteria;

[0038] Generate the modified connection parameters.

[0039] Preferably, the connection control process between the 5G communication module and the smart device includes:

[0040] Based on the modified connection parameters, a connection control command is generated;

[0041] Execute the connection control command to complete the initial connection between the 5G communication module and the smart device;

[0042] Monitor connection status data for subsequent optimization and iteration.

[0043] Preferably, monitoring connection status data is used for subsequent optimization iterations, including:

[0044] Real-time acquisition of connection quality data and environmental interference data;

[0045] Based on the connection quality data and environmental interference data, update the smart device connection requirement data;

[0046] The updated smart device connectivity requirements data are fed back into the initial connectivity parameter determination process.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This integrated 5G communication module and smart device connection method, by acquiring 5G communication module configuration data and smart device connection requirement data, can comprehensively grasp the key information affecting the connection between the two, providing a comprehensive data foundation for determining subsequent connection parameters. This avoids the problem of parameter settings being out of touch with actual needs due to incomplete data acquisition in traditional methods. Determining initial connection parameters based on these two types of data ensures that the initial parameters fully match the hardware characteristics of the 5G communication module and the actual connection requirements of the smart device, improving the rationality of the connection parameters from the outset and laying a solid foundation for stable subsequent connections.

[0049] Constructing a connection controller model adapted to the initial connection parameters ensures good compatibility between the model and the initial parameters, reducing connection problems caused by model-parameter mismatch. This allows the connection controller to function more efficiently in subsequent connection control processes, accurately responding to the data interaction needs between the 5G communication module and smart devices, and improving the smoothness of data transmission. Parameter scaling of the connection controller model allows adjustment of the range and accuracy of model parameters according to different application scenarios and device characteristics, enhancing the model's adaptability to diverse scenarios. This enables the same model to be applicable to different types of smart devices and 5G communication modules with different configurations, expanding the applicability of the method.

[0050] Converting the connection controller model into an equivalent model and performing connection stability analysis allows for a more intuitive and accurate evaluation of the model's connection performance under different parameter combinations. This provides a clear direction for subsequent parameter modifications, avoiding blind adjustments. A comprehensive robustness analysis of the parameters yields parameter adjustment criteria that fully consider various interference factors that may arise in practical applications, such as signal strength fluctuations, external electromagnetic interference, and changes in equipment load. This ensures that the adjusted parameters maintain a good connection state even in complex and changing environments, reducing connection instability caused by environmental changes.

[0051] Modifying connection parameters according to parameter adjustment guidelines and applying them to the connection control process ensures that parameter modifications are scientifically based, enabling precise optimization of connection performance and effectively solving the problem of inconsistent connection results caused by the lack of standardized parameter adjustments in traditional methods. Overall, this method, through systematic process design, achieves the rational determination of connection parameters between 5G communication modules and smart devices, the adaptation, construction, and optimization of models, and the scientific adjustment of parameters. It enables stable and efficient connections between the two in different application scenarios and complex environments, fully leveraging the advantages of 5G communication technology to meet the needs of various smart devices in data transmission, interactive response, and other aspects, promoting the wider application of smart devices in smart homes, industrial IoT, intelligent transportation, and other fields. Attached Figure Description

[0052] Figure 1 This is a schematic diagram illustrating the working principle of the integrated 5G communication module and smart device connection method described in this invention.

[0053] Figure 2 Flowchart for determining initial connection parameters;

[0054] Figure 3 A flowchart for constructing the connection controller model;

[0055] Figure 4 A flowchart for scaling the parameters of the connected controller model. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Please see Figure 1 This invention provides a method for connecting an integrated 5G communication module to a smart device, the method comprising:

[0058] The system acquires configuration data from the 5G communication module and connection requirement data from the smart device. The configuration data covers the module's inherent characteristics and operating status, while the connection requirement data reflects the smart device's specific communication performance requirements. Based on these two types of data, the system calculates initial connection control parameters. Subsequently, a connection controller model matching these initial parameters is constructed. To improve the model's adaptability and performance, the parameters are scaled. Next, the scaled controller model is converted into an equivalent model, and a stability analysis of the connection control system is performed based on this equivalent model to determine the safe adjustment range of key parameters. Then, a comprehensive robustness analysis is conducted on these parameters to evaluate their performance under various uncertainty conditions, and specific parameter adjustment criteria are generated accordingly. Finally, the connection parameters are modified according to these criteria, and the optimized parameters are applied to the actual connection control process between the 5G communication module and the smart device to establish and maintain a stable and reliable communication connection.

[0059] Example 1: See Figure 2 Acquiring smart device type data first requires identifying the device category and its communication protocol. For example, in an Industrial Internet of Things (IIoT) scenario, a smart device might be a high-precision sensor deployed on a production line to monitor parameters such as temperature, vibration, or pressure in real time. These devices typically employ low-power, low-complexity communication protocols, such as MQTT or CoAP, to reduce energy consumption and adapt to limited computing resources. In the consumer electronics field, a smart device might be a mobile terminal supporting high-definition video streaming, with its communication protocol based on HTTP / 2 or WebSocket to meet the requirements of high throughput and low latency. Furthermore, some specialized devices (such as remote medical diagnostic instruments or in-vehicle terminals for autonomous vehicles) may employ customized proprietary protocols to ensure data security and real-time performance. Therefore, acquiring device type data requires not only distinguishing the device's functional category but also clarifying the characteristics of its communication protocol, including the protocol stack hierarchy, data encapsulation methods, and potential Quality of Service (QoS) mechanisms.

[0060] Signal coverage is typically determined by measuring the Reference Signal Received Power (RSRP) or Signal-to-Interference-Noise Ratio (SINR). For example, in indoor environments, signal strength may be unevenly distributed due to the influence of walls and obstacles, with weaker coverage in some areas and stronger signals in areas closer to the base station. Assessing network congestion involves monitoring the number of users in the current cell, resource block occupancy, or average queuing delay. For example, network congestion may be higher during peak hours or in densely populated urban areas, leading to reduced available bandwidth or increased transmission latency; while in suburban areas or during off-peak hours, network resources may be more abundant. Furthermore, operational scenario data must also consider the dynamic characteristics of 5G networks, such as mobility management (frequency handover), beamforming adjustments, or the allocation of network slicing resources. These factors collectively determine the actual available resources and stability of 5G communication modules in the current environment.

[0061] The target transmission rate is typically determined based on the device's data generation characteristics or application scenario. For example, industrial sensors may only require a transmission rate of a few kilobits per second to meet the needs of periodic status reporting, while 4K video surveillance equipment may require a continuous throughput of tens of megabits per second to ensure smooth video playback. The target latency requirement is related to the device's real-time requirements. For instance, emergency braking command transmission in an autonomous driving system must meet millisecond-level end-to-end latency, while ordinary file synchronization applications may tolerate second-level latency. Furthermore, some applications may be sensitive to jitter (latency variations), such as real-time voice communication or online gaming, while others (such as background data backup) have a higher tolerance for jitter. Therefore, obtaining performance data requires a comprehensive consideration of rate, latency, jitter, and potential reliability metrics (such as the upper limit of packet loss rate).

[0062] After collecting the aforementioned data, the system needs to integrate it into structured smart device connectivity requirement data. This process involves normalizing or quantizing the raw data so that subsequent algorithms can parse it uniformly. For example, protocol information in device type data may be mapped to standardized communication mode identifiers, while signal strength and congestion levels in operational scenario data may be quantified into discrete levels (e.g., high / medium / low). Target rates and latency in performance data are usually retained directly in numerical form, but weighting coefficients may be added based on device type or scenario characteristics to reflect the priority differences between different requirements. The final connectivity requirement data will serve as input for subsequent parameter calculations.

[0063] The determination of initial connection parameters is divided into two stages: initial parameter calculation and optimization adjustment. In the initial parameter calculation stage, the system derives preliminary parameter combinations based on the target rate and latency requirements in the connection demand data, combined with the physical layer and link layer characteristics of the 5G communication module. For example, if the target rate is high and latency requirements are strict, a higher-order modulation scheme (such as 256-QAM) and a shorter transmission time interval (TTI) may be selected to improve spectral efficiency and reduce the waiting time per transmission. Simultaneously, the system also needs to consider the selection of a channel coding scheme (such as LDPC or Polar codes) to balance error correction capability and coding overhead. Furthermore, configuration parameters of multi-antenna technologies (such as MIMO) (such as the number of layers and precoding matrix) may also be incorporated into the calculation to fully utilize spatial diversity or multiplexing gain.

[0064] During the optimization and adjustment phase, the system filters or modifies the original parameters based on preset constraints. These constraints may include power consumption limitations (such as the energy budget of battery-powered devices), computational complexity limitations (such as the processing power of embedded devices), or spectrum regulatory requirements (such as the transmit power limit for a specific frequency band). For example, if the transmit power calculated from the original parameters is too high, exceeding the device's heat dissipation capacity or battery life requirements, the system may reduce the power and adjust other parameters (such as increasing the number of retransmissions or reducing the modulation order) to compensate for performance loss. The optimization process may employ iterative search or heuristic strategies to approximate the optimal parameter combination while satisfying the constraints. The final generated initial connection parameters will serve as the basis for subsequent controller model construction.

[0065] Example 2: See Figure 3 The first step in constructing the connection controller model is to represent the initial connection parameters equivalently as a state-space model. This transformation process needs to consider the dynamic characteristics of the connection system between the 5G communication module and the smart device. Taking device connection in the Industrial Internet of Things as an example, the system state vector may contain variables such as the current channel quality index, data buffer state, and transmission queue length. These state variables can comprehensively reflect the real-time status of the connection. Control inputs may include power adjustment commands, modulation and coding scheme selection commands, and resource block allocation commands, which directly affect the actual operating parameters of the communication module. In the process of constructing the state-space model, it is necessary to clarify the mathematical relationship between the state variables and the control inputs, which is usually determined by the physical characteristics of the communication system.

[0066] The establishment of a state-space model needs to consider the specificities of real-world application scenarios. Taking the vehicle-to-everything (V2X) scenario as an example, the connection state between the vehicle and the base station changes rapidly as the vehicle moves. In this case, the system state vector needs to include variables such as location information, speed, and historical channel quality to accurately describe this time-varying characteristic. Control inputs need to consider dynamic adjustments to beamforming parameters and the selection of switching timing. Establishing such a state-space model provides an accurate system description foundation for subsequent controller design.

[0067] The controller model employs a pre-defined framework, typically comprising two core components: an observer module and a feedback control module. The design of the observer module must consider the potential for incomplete observability in real-world systems. In 5G communication scenarios, certain key state variables may not be directly measurable, such as real-time channel interference levels and the processing capabilities of remote devices. The observer module estimates these unobservable state variables using measurable system outputs, such as received signal strength, bit error rate, and actual throughput. The observer design must balance estimation accuracy with computational complexity to accommodate the processing limitations of different smart devices.

[0068] The feedback control module calculates the optimal control input based on the state estimates provided by the observer and pre-defined performance targets, such as target transmission rate and maximum allowable latency. In live video applications, the feedback control module may need to adjust video encoding parameters and transmission priorities in real time to maintain a smooth viewing experience despite bandwidth fluctuations. The selection of the control algorithm needs to consider the dynamic characteristics of the system. For scenarios with high response speed requirements, predictive control strategies may be necessary; for scenarios with limited computing resources, simplified control algorithms may be required.

[0069] The observer module continuously updates its state estimates, providing the feedback control module with the latest system state information. The feedback control module then generates control commands based on this information, which are applied to the 5G communication module. This collaborative mechanism is particularly important in telemedicine applications. The observer module needs to accurately estimate network latency and jitter, while the feedback control module adjusts its data transmission strategy based on these estimates to ensure the real-time and reliable transmission of critical medical data. The parameters of both modules require careful calibration to guarantee the stability and response speed of the entire control system.

[0070] For intelligent devices with strong computing power, more complex control algorithms, such as model predictive control, can be used; for IoT devices with limited resources, simplified control strategies are required. In smart home scenarios, high-end smart gateways may employ full-state feedback control, while simple sensor nodes may use rule-based simplified control strategies. This differentiated implementation ensures the applicability of the controller model across different devices.

[0071] During deployment, the system pre-sets a set of initial parameters based on the typical operating scenarios of the devices. These parameters are dynamically adjusted based on connection quality data during actual operation. For example, in factory automation scenarios, the controller model automatically adjusts the observer bandwidth and control parameters according to the wireless environment characteristics of the area where the device is located to adapt to the signal propagation characteristics of different areas. This adaptive mechanism improves the robustness of the controller in different environments.

[0072] With device firmware upgrades or network configuration changes, the structure or parameters of the controller model may need to be adjusted. The system periodically evaluates the controller's performance metrics and triggers a model update process when necessary. In smart city applications, with adjustments to base station deployments or the launch of new services, the connection controller model may need to be retrained or its parameters tuned to adapt to the changed network environment. This continuous optimization mechanism ensures that the controller maintains good control performance over the long term.

[0073] By transforming the initial connection parameters into a state-space model and constructing a connection controller model that includes an observer module and a feedback control module, a dynamic control foundation is provided for the connection between the 5G communication module and smart devices. This model can adapt to the needs of different device types and application scenarios, achieving intelligent adjustment of connection parameters through the synergy of state estimation and feedback control. The implementation of this stage provides the necessary control framework for subsequent parameter scaling and stability analysis.

[0074] Example 3: See Figure 4 After the controller model is constructed, the implementation process enters the parameter scaling and stability analysis phase. This phase first scales the controller model's parameters, then converts it into an equivalent model for stability assessment, and finally determines the safe adjustment range of the parameters.

[0075] The observer bandwidth parameter directly affects the dynamic response characteristics of state estimation. In specific applications, such as in remotely operated engineering machinery control scenarios, when the motion state of the robotic arm needs to be updated frequently, the observer bandwidth may need to be increased to quickly track changes in state variables such as joint angles and end-effector positions. However, in static data acquisition scenarios for environmental monitoring sensors, excessively high observer bandwidth may amplify measurement noise, requiring an appropriate reduction of this parameter. The scaling process is usually normalized based on the system's time scale or performance requirements to ensure comparability of parameter adjustments. For feedback control modules, the core lies in adjusting their controller bandwidth parameter. This parameter determines the closed-loop system's response speed to changes in setpoints and external disturbances. In vehicle-to-everything (V2X) platooning control, maintaining close following distances requires high controller bandwidth for rapid acceleration adjustment; however, in background data synchronization applications, excessively high bandwidth may lead to unnecessary control oscillations, necessitating a reduction in this parameter to smooth the transmission rate. Controller bandwidth scaling is also standardized based on system characteristics to ensure consistency with observer bandwidth adjustments.

[0076] After parameter scaling, the connected controller model is transformed into a mathematically equivalent two-degree-of-freedom (DOF) control model. This transformation aims to decouple the system's reference input tracking performance from its interference suppression performance, simplifying subsequent stability analysis. The two-DOF model clearly separates the propagation paths of the setpoint response and the interference response. For example, in augmented reality applications carried by 5G networks, the two-DOF model can independently optimize the response speed of viewpoint switching during user movement (setpoint tracking) and the ability to suppress network jitter interference in image rendering.

[0077] Based on the transformed two-degree-of-freedom equivalent model, a characteristic equation describing the entire closed-loop connected control system is constructed. This equation reveals the inherent dynamic characteristics of the system, and the distribution of its roots directly determines the system's stability. The form of the characteristic equation depends on the specific controller structure and the model of the controlled object. Assuming a simplified second-order system model, its closed-loop characteristic equation can be expressed as:

[0078]

[0079] in: Represents a complex frequency variable (Laplace operator), used to analyze the dynamic response of a system; The damping ratio represents the damping ratio of the system and reflects the degree of attenuation of the system's oscillation response. This represents the undamped natural frequency of the system and is related to the system's response speed. It is important to emphasize that... and The values ​​are not fixed; they are determined by the controller parameters (especially the scaled observer bandwidth). and controller bandwidth The functional relationship is determined by both the controlled 5G connection system itself and the dynamic characteristics of the system. For example, Possibly with Proportional, and May be affected and The effect of the ratio.

[0080] The core of stability analysis lies in solving the roots (i.e., closed-loop poles) of the characteristic equation and determining their positions in the complex plane. If the real parts of all roots are negative, the system is stable; if there are roots with zero or positive real parts, the system may experience sustained oscillations or divergence. In specific analysis, the sign of the real parts of the roots can be directly determined using algebraic criteria (such as the Routh criterion), without needing to explicitly solve for the values ​​of the roots. For example, when analyzing the stability of industrial wireless control loops, a Routh table can quickly determine the stability of the system under given conditions. and Under different parameter combinations, is the system stable? A more intuitive method is to calculate the eigenvalues ​​and observe their positions. The distance from the imaginary axis to the left half of the complex plane reflects the system's stability margin. During the analysis, special attention needs to be paid to the impact of parameter changes on the eigenvalue locus. For example, gradually increasing the controller bandwidth... Observe whether the dominant root approaches the imaginary axis or crosses the imaginary axis into the right half-plane.

[0081] Based on the results of the stability analysis, the key control parameters (mainly the observer bandwidth) were determined. and controller bandwidth The safe selection range for parameters. This range defines the boundaries for parameter adjustment, within which parameter changes can maintain system stability. Methods for determining the range include: parameter scanning method: in and Grid sampling is performed within the possible value range of the parameter, and stability is assessed for each parameter combination point (e.g., calculating whether the maximum value of the real part of the eigenvalue is less than zero). All stable parameter points are connected to form the boundary of a stable parameter region. For example, in a UAV high-definition image transmission scenario, scanning is performed at different flight altitudes (affecting channel latency). and Combine these parameters to identify the range that ensures stable transmission of the video stream.

[0082] Boundary condition method: This method directly solves for critical parameter values ​​using stability boundary conditions (such as the characteristic equation having roots on the imaginary axis). For example, let the characteristic equation... ( (where is the real frequency), substitute it into the equation and separate the real and imaginary parts to obtain two values ​​related to . and parameters ( , The equations are given. Solving these equations yields the critical parameter curve, which represents the boundary of the stable region. In wide-area measurement systems for smart grids, it is necessary to accurately solve for the maximum allowable value that ensures the synchronous stability of the phase measurement unit data. Value. Consider engineering margin: The theoretical stability boundary is often derived under an ideal model. Real-world systems contain unmodeled dynamics, parameter perturbations, and time-varying disturbances, thus requiring the introduction of engineering margin. For example, within the theoretical stability boundary, a certain stability margin is reserved (e.g., requiring the real part of the eigenvalue to be less than a certain negative value). This allows for the determination of the safety parameter range in practical engineering applications. In high-speed railway mobile communication scenarios, the uncertainties caused by Doppler frequency shift and frequent switching necessitate a more conservative parameter range than the theoretical values.

[0083] The final determined safety range of parameters (e.g., , This provides a fundamental constraint for subsequent robustness analysis and final parameter tuning. This range ensures that the basic stability of the system can be maintained regardless of parameter adjustments during subsequent optimization.

[0084] Example 4: The primary task of robustness analysis is to define a set of performance metrics that comprehensively reflect system performance. In 5G communication and smart device connectivity control scenarios, these metrics should include at least three key parameters: gain margin reflects the stability boundary of the system when the open-loop gain changes; phase margin characterizes the system's tolerance to phase lag; and relative delay margin quantifies the extent to which the system can withstand additional delay without becoming unstable. Taking wireless control in a smart factory as an example, the gain margin metric can assess the system's stability reserve when the wireless channel suddenly deteriorates (e.g., signal attenuation due to equipment movement); the phase margin metric can be used to analyze the system's robustness under phase fluctuations caused by multipath effects; and the relative delay margin is particularly suitable for evaluating the system's adaptability to changes in processing delay caused by load fluctuations at edge computing nodes.

[0085] Evaluating these metrics requires a systematic analysis within a scaled parameter space. The parameter space typically uses the observer bandwidth parameter and the control bandwidth parameter as two main dimensions. In practice, a gridded sampling method can be used, selecting several typical values ​​in each dimension to form a parameter combination matrix.

[0086] Table 1: Robustness evaluation metrics for the combination of observer bandwidth and control bandwidth.

[0087]

[0088] Referring to Table 1, we can summarize the impact of parameter changes on system robustness. In vehicular network applications, increasing the observer bandwidth usually improves the speed of state estimation, but may simultaneously reduce the system's phase margin; increasing the control bandwidth can accelerate the system response speed, but will reduce the gain margin and delay margin. These patterns constitute the foundation of parameter tuning criteria.

[0089] The robust tuning criteria generated based on the evaluation results need to define two core rules: the trade-off tuning rule and the control response rule. The trade-off tuning rule specifies the balance between observer bandwidth and control bandwidth. In remote surgical robot control scenarios, this rule might require maximizing observer bandwidth to achieve fast state estimation while ensuring minimum phase margin; in smart home device networking scenarios, it might emphasize appropriately reducing control bandwidth to reduce energy consumption while meeting basic latency margin. The control response rule specifically specifies the selection strategy for control bandwidth in different application scenarios. For real-time video surveillance systems, it might be necessary to prioritize higher control bandwidth to achieve rapid bitrate adjustment; for environmental monitoring sensor networks, lower control bandwidth might be allowed to extend battery life.

[0090] In industrial automation scenarios, modifying observer bandwidth parameters requires balancing the frequency of changes in equipment motion with the level of measurement noise. For example, a relatively high observer bandwidth is needed for a high-speed robotic arm to track rapidly changing position and velocity states; while for static or low-speed moving equipment, the observer bandwidth can be reduced to minimize noise interference. Modifying control bandwidth parameters requires balancing response speed with stability. In smart grid frequency control applications, a higher control bandwidth enables rapid frequency adjustment, but it's crucial to ensure it doesn't trigger control oscillations; in logistics and warehousing AGV scheduling systems, a moderate control bandwidth ensures trajectory tracking accuracy while avoiding excessive consumption of communication resources.

[0091] The specific implementation of parameter modification can adopt a gradual approximation method. Taking UAV swarm formation control as an example, the initial parameters may be set to medium observer bandwidth and medium control bandwidth. By monitoring the formation maintenance accuracy and communication load in real time, the two bandwidth parameters are gradually fine-tuned: if a large position synchronization error is found, the observer bandwidth is appropriately increased; if significant oscillations in control commands are found, the control bandwidth is appropriately decreased. This gradual adjustment can find the optimal parameter combination in the actual operating environment.

[0092] The verification process typically includes two phases: simulation testing and real-world testing. In the deployment of smart city traffic signal control systems, the control effect of new parameters under various traffic flow scenarios is first verified on a simulation platform, including the response speed and stability performance to different sudden traffic flows. Subsequently, small-scale field tests are conducted at real intersections to observe the control performance of the parameters in a real wireless environment. Only after passing these two phases of verification will the modified parameters be officially applied to the production environment.

[0093] The final modified connection parameters will be permanently embedded in the device configuration. In practice, different embedding methods can be used depending on the device type and application scenario. For high-performance industrial control equipment, a dynamic parameter table might be used, allowing for automatic adjustment within a certain range based on real-time network conditions. For resource-constrained IoT terminals, a fixed parameter group might be used, with parameter settings optimized through device firmware updates. Regardless of the method used, parameter configuration must consider maintainability to facilitate subsequent parameter optimization based on changes in the network environment or business needs.

[0094] Example 5: Based on the optimized connection parameter set, the system generates a specific sequence of connection control instructions. These instructions need to be translated into low-level operations executable by the 5G communication module. At the physical layer, instructions may include configuring carrier aggregation schemes, setting modulation and coding strategy indexes, and determining transmission power levels. Taking a high-definition mobile live streaming scenario as an example, for the optimized parameters, the system will generate carrier aggregation binding instructions to merge multiple 5G carriers to expand transmission bandwidth; at the same time, it will specify the use of a combination of high-order modulation schemes and medium-level error correction codes to balance transmission rate and anti-interference capability. At the media access control layer, instructions may involve configuring scheduling request cycles, setting the number of hybrid automatic repeat request processes, and mapping logical channel priorities. In a remote surgical system, critical instruction data streams will be mapped to high-priority logical channels, while configuring shorter retransmission intervals. At the transport layer, instructions may include adjusting the TCP initial window size and selecting congestion control algorithms. For large-scale IoT device firmware upgrade tasks, instructions may specify the use of time-tolerant transport protocols and set larger initial congestion windows.

[0095] Connection control commands are sent to the execution unit of the 5G communication module through a standard module interface. The loading and activation of commands employ a layered verification mechanism: the physical layer configuration is activated after verification by the baseband processing unit; higher-level protocol configurations are loaded layer by layer during protocol stack initialization. In the smart port container tracking system, when the device wakes up from sleep mode, optimized parameter commands are loaded into the communication module in batches. After each layer of the protocol stack completes self-checks, it sends a ready signal to the application layer. Subsequently, the communication module initiates a connection establishment process with the target smart device according to the command parameters. This process follows the standard 5G access protocol, but uses optimized values ​​for key parameters. In the connection establishment process of the industrial AGV control system, the communication module uses an optimized power ramp-up step size during the random access phase; the signaling configuration parameters used in the RRC connection establishment phase are all derived from previous robust optimization results.

[0096] After establishing a connection, the system enters the status monitoring phase. Monitoring focuses on three types of data collection: connection quality indicators include real-time throughput statistics, end-to-end latency measurements, packet loss rate, and wireless link failure event counts. In remote digital twin factory applications, the terminal collects the actual transmission rate every 500 milliseconds and calculates the end-to-end latency of the service flow using timestamped probe messages. Environmental interference data includes reference signal reception quality measurements, interference noise intensity index, and a list of reference signal strengths from neighboring cells. Vehicle-to-everything (V2X) OBU devices continuously scan and record signal quality fluctuations and neighboring cell interference changes in the serving cell while in motion. Network load indicators include resource block utilization statistics and protocol data unit queue length monitoring. The smart city street light control system periodically reports MAC layer cache status reports, indicating the degree of network congestion.

[0097] The monitoring data is stored using a circular buffer structure, forming a complete monitoring snapshot every 15 minutes. The analysis process detects two types of key events: performance drift events are triggered when key indicators deviate from the set range for three consecutive cycles; environmental mutation events are triggered when network measurements change drastically in a short period of time. In the distributed energy monitoring system, an event marker is generated when the voltage data transmission delay continuously exceeds a 50-millisecond threshold, or when the wireless channel quality index suddenly drops by 10dB.

[0098] Smart device connectivity requirements data are transformed into a dynamic structure containing three levels of fields: basic requirements retain the original target rate and latency; adaptive requirements add an environmental compensation coefficient; and flexible requirements expand tolerance boundaries. In smart agricultural irrigation systems, when the system detects an increase in latency due to base station switching, it recalculates the reasonable range for the target latency and simultaneously corrects the network coverage level in the environmental assessment report.

[0099] The updated requirement data is fed back to the parameter initialization process via a dedicated interface. The system automatically initiates a new round of control parameter calculations without interrupting the current connection. In the manufacturing execution system, a parameter recalculation process is initiated in the background, preserving existing connection parameters while preparing the new parameter set. Once the calculation is complete and verified, a seamless parameter switch is executed. The UAV swarm control system adopts a dual configuration area design: the active area runs the current parameters, while the standby area loads the new parameter set. Seamless updates are achieved by switching configuration areas via command words. Each iteration records the parameter revision history and performance change trajectory, providing analytical basis for long-term optimization.

[0100] The implementation process establishes a complete monitoring and feedback loop. The monitoring cycle is reset after each parameter update, creating a continuous optimization iterative rhythm. In the commercial complex's customer flow analysis system, a full parameter evaluation process is automatically triggered during daily off-peak hours, updating the connection strategy based on 24-hour monitoring data to ensure optimal configuration at the start of the next day's operations. This iterative mechanism enables the system to continuously adapt to changes in the network environment and business requirements.

[0101] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0102] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for connecting an integrated 5G communication module to a smart device, characterized in that, include: Obtain 5G communication module configuration data and smart device connection requirements data; Based on the 5G communication module configuration data and smart device connection requirement data, determine the initial connection parameters; Construct a connection controller model adapted to the initial connection parameters; Parameter scaling is performed on the connection controller model; The connection controller model is converted into an equivalent model, and connection stability analysis is performed to obtain the parameter adjustment range; A comprehensive robustness analysis was performed on the parameters to obtain parameter adjustment criteria; The connection parameters are modified according to the parameter adjustment criteria and applied to the connection control process between the 5G communication module and the smart device.

2. The method for connecting an integrated 5G communication module to a smart device according to claim 1, characterized in that, Obtain 5G communication module configuration data and smart device connection requirement data, including: Acquire smart device type data and 5G communication module operating scenario data. The smart device type data includes device category and usage protocol, and the 5G communication module operating scenario data includes signal coverage range and network congestion level. Acquire smart device performance data, which includes target transmission rate and target latency requirements; Based on the smart device type data, 5G communication module operating scenario data, and smart device performance data, smart device connection requirement data is generated.

3. The method for connecting an integrated 5G communication module with a smart device according to claim 1, characterized in that, Based on the 5G communication module configuration data and smart device connection requirement data, the initial connection parameters are determined, including: Calculate the original connection parameters based on the target transmission rate and target latency requirements in the smart device connection demand data; The original connection parameters are optimized according to a preset optimization threshold to generate initial connection parameters.

4. The method for connecting an integrated 5G communication module to a smart device according to claim 1, characterized in that, Constructing a connection controller model adapted to the initial connection parameters includes: The initial connection parameters are equivalently represented as a state-space model, which includes the system state vector and control input. Based on the state-space model and the preset controller framework, a connection controller model is constructed, which includes an observer module and a feedback control module.

5. The method for connecting an integrated 5G communication module to a smart device according to claim 4, characterized in that, Parameter scaling of the connection controller model includes: The observer module is scaled, including the observer bandwidth parameter is adjusted. The feedback control module is subjected to feedback controller parameter scaling, including adjustment of the controller bandwidth parameter.

6. The method for connecting an integrated 5G communication module to a smart device according to claim 1, characterized in that, The connection controller model is converted into an equivalent model, and connection stability analysis is performed to obtain the parameter adjustment range, including: The connection controller model is converted into a two-degree-of-freedom equivalent model. Construct a closed-loop characteristic equation, and analyze the stability of the connected control system based on the closed-loop characteristic equation; Based on the stability analysis results, the safe selection range of connection parameters is determined.

7. The method for connecting an integrated 5G communication module to a smart device according to claim 1, characterized in that, A comprehensive robustness analysis was performed on the parameters to obtain parameter adjustment criteria, including: Define the test metrics, which include gain margin, stability margin, and relative delay margin; Based on the scaling parameter space, the test index is evaluated, and a robust adjustment criterion is generated. Among them, the robust tuning criteria include the trade-off tuning rules for the observer bandwidth parameter and the control response rules for the control bandwidth parameter.

8. The method for connecting an integrated 5G communication module to a smart device according to claim 7, characterized in that, Modify the connection parameters according to the parameter adjustment criteria, including: Adjust the observer bandwidth parameter according to the compromise parameter tuning rules in the robust tuning criteria; Adjust the control bandwidth parameters according to the control response rules in the robust adjustment criteria; Generate the modified connection parameters.

9. The method for connecting an integrated 5G communication module to a smart device according to claim 8, characterized in that, The connection control process applied to 5G communication modules and smart devices includes: Based on the modified connection parameters, a connection control command is generated; Execute the connection control command to complete the initial connection between the 5G communication module and the smart device; Monitor connection status data for subsequent optimization and iteration.

10. The method for connecting an integrated 5G communication module to a smart device according to claim 9, characterized in that, Monitor connection status data for subsequent optimization and iteration, including: Real-time acquisition of connection quality data and environmental interference data; Based on the connection quality data and environmental interference data, update the smart device connection requirement data; The updated smart device connectivity requirements data are fed back into the initial connectivity parameter determination process.

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