Connection method for integrated 5G communication module and intelligent equipment

Abstract

The invention relates to the technical field of 5G equipment connection, and discloses an integrated 5G communication module and intelligent equipment connection method. The method comprises the following steps: acquiring 5G communication module configuration data and intelligent equipment connection demand data, and determining initial connection parameters based on the two types of data; constructing a connection controller model matched with the initial connection parameters, and performing parameter scaling on the model; converting the connection controller model into an equivalent model, and carrying out connection stability analysis to obtain a parameter adjustment range; performing comprehensive robustness analysis on the parameters to obtain a parameter adjustment criterion; and finally modifying the connection parameters according to the parameter adjustment criterion, and applying the connection parameters to the connection control process of the 5G communication module and the intelligent equipment. According to the method, stable and efficient connection between the 5G communication module and the intelligent equipment can be achieved, different scene and equipment requirements are met, the advantages of the 5G technology are fully played, and the method is suitable for multi-field intelligent equipment connection scenes.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of 5G device connection, and particularly relates to a connection method of an integrated 5G communication module and an intelligent device. BACKGROUND

[0002] With the rapid development of 5G communication technology, the characteristics of high bandwidth, low latency and wide connection gradually become an important support for intelligent devices to realize efficient data interaction. Integrated 5G communication modules are widely used in intelligent devices in the fields of smart home, industrial Internet of Things, intelligent transportation and the like due to the advantages of small size, low power consumption and strong compatibility. In actual application scenarios, the types of intelligent devices present diversification characteristics, and different types of intelligent devices have significant differences in data transmission rate requirements, connection stability requirements and power consumption control standards, for example, the intelligent camera in the smart home has higher requirements for data transmission rate and real-time performance, while the intelligent sensor pays more attention to low power consumption and long-time stable connection. Meanwhile, the configuration parameters of the integrated 5G communication module are adjustable, including frequency band selection, transmission power and modulation mode, and the setting of these parameters directly affects the connection effect between the module and the intelligent device.

[0003] In the connection process of the integrated 5G communication module and the intelligent device, the connection is usually realized by using the preset fixed parameters. This method does not fully consider the matching of the 5G communication module configuration data and the intelligent device connection requirement data, resulting in that the connection parameters do not fit the actual application scenarios. In the parameter determination link, the systematic analysis of the data of the two is lacking, and the initial connection parameters are often determined depending on experience or simple test, so it is difficult to guarantee the rationality of the initial parameters, and then the stability of the subsequent connection process is affected. In addition, in the connection controller model construction aspect, the existing method mostly uses a general model, and the adaptability design is not carried out for the determined initial connection parameters, the compatibility between the model and the parameters is insufficient, and problems such as data transmission lag and connection interruption are prone to occur.

[0004] The optimization of the connection controller model in the prior art lacks a perfect process, some methods only carry out simple parameter adjustment and do not carry out parameter scaling processing, so that the adaptability of the model in different application scenarios is poor; some methods try to optimize the model, but do not convert the model into an equivalent model for connection stability analysis, cannot accurately obtain the parameter adjustment range, and do not carry out comprehensive robustness analysis to determine the parameter adjustment criterion, so that the parameter modification lacks scientific basis and it is difficult to guarantee that the adjusted parameters can meet the stable connection requirements of the 5G communication module and the intelligent device in different complex environments. The existence of these problems leads to low connection efficiency of the integrated 5G communication module and the intelligent device, and the advantages of 5G communication technology cannot be fully played, which limits the further application and development of intelligent devices in various fields. SUMMARY

[0005] The present application aims to provide an integrated 5G communication module and smart device connection method to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides an integrated 5G communication module and smart device connection method, which comprises: Obtaining 5G communication module configuration data and smart device connection requirement 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 of the connection controller model; Convert the connection controller model to an equivalent model and perform connection stability analysis to obtain the parameter adjustment range; Comprehensive robustness analysis of the parameters to obtain the parameter adjustment criteria; According to the parameter adjustment criteria, modify the connection parameters and apply them to the connection control process of the 5G communication module and the smart device.

[0007] Preferably, obtaining 5G communication module configuration data and smart device connection requirement data comprises: Obtaining smart device type data and 5G communication module working scenario data, the smart device type data including device category and use protocol, and the 5G communication module working scenario data including signal coverage range and network congestion degree; Obtaining smart device performance data, the smart device performance data including target transmission rate and target delay requirement; Based on the smart device type data, 5G communication module working scenario data and smart device performance data, generate smart device connection requirement data.

[0008] Preferably, based on the 5G communication module configuration data and smart device connection requirement data, determining the initial connection parameters comprises: According to the target transmission rate and target delay requirement in the smart device connection requirement data, calculate the original connection parameters; According to the preset optimization threshold, optimize the original connection parameters to generate the initial connection parameters.

[0009] Preferably, constructing a connection controller model adapted to the initial connection parameters comprises: Equivalent representation of the initial connection parameters as a state space model, the state space model including system state vector and control input; Based on the state space model and a preset controller framework, a connection controller model is constructed, the connection controller model comprising an observer module and a feedback control module.

[0010] Preferably, the connection controller model is subjected to parameter scaling, comprising: The observer module is subjected to observer parameter scaling, comprising adjustment of an observer bandwidth parameter. The feedback control module is subjected to feedback controller parameter scaling, comprising adjustment of a controller bandwidth parameter.

[0011] Preferably, the connection controller model is converted into an equivalent model, and connection stability analysis is performed to obtain a parameter adjustment range, comprising: The connection controller model is converted into a two-degree-of-freedom equivalent model. A closed-loop characteristic equation is constructed, and based on the closed-loop characteristic equation, stability of the connection control system is analyzed. According to the stability analysis result, a safe selection range of the connection parameters is determined.

[0012] Preferably, the parameters are subjected to comprehensive robustness analysis to obtain a parameter adjustment criterion, comprising: A test index is defined, the test index comprising a gain margin, a stability margin, and a relative time delay margin. The test index is evaluated based on the scaled parameter space to generate a robust adjustment criterion. The robust adjustment criterion comprises a trade-off parameter adjustment rule for the observer bandwidth parameter and a control response rule for the controller bandwidth parameter.

[0013] Preferably, the connection parameters are modified according to the parameter adjustment criterion, comprising: The observer bandwidth parameter is adjusted according to the trade-off parameter adjustment rule in the robust adjustment criterion. The controller bandwidth parameter is adjusted according to the control response rule in the robust adjustment criterion. The modified connection parameters are generated.

[0014] Preferably, the method is applied to a connection control process of a 5G communication module and a smart device, comprising: Based on the modified connection parameters, a connection control instruction is generated. The connection control instruction is executed to complete initial connection of the 5G communication module and the smart device. Connection state data is monitored for subsequent optimization iteration.

[0015] Preferably, the connection state data is monitored for subsequent optimization iteration, comprising: Connection quality data and environmental interference data are collected in real time. updating smart device connection requirement data based on the connection quality data and the environmental interference data; The updated smart device connection requirement data is fed back into the initial connection parameter determination process.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This integrated 5G communication module and smart device connection method, by acquiring 5G communication module configuration data and smart device connection requirement data, can fully grasp the key information affecting the connection between the two. This provides a comprehensive data foundation for determining subsequent connection parameters, avoiding the problem of traditional methods where parameter settings are out of sync with actual requirements due to incomplete data acquisition. Determining the 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 source and laying a good foundation for subsequent stable connections.

[0017] Constructing a connection controller model that adapts to the initial connection parameters ensures good compatibility between the model and the initial parameters, reducing connection issues caused by model-parameter mismatches. This allows the connection controller to function more efficiently in subsequent connection control processes, accurately responding to data interaction requirements between 5G communication modules and smart devices, and improving the smoothness of data transmission. Parameter scaling of the connection controller model adjusts the range and accuracy of model parameters based on different application scenarios and device characteristics, enhancing the model's adaptability in diverse scenarios. This allows the same model to be applied to different types of smart devices and 5G communication modules with different configurations, expanding the method's scope of application.

[0018] Converting the connection controller model to an equivalent model and performing a connection stability analysis allows for a more intuitive and accurate assessment of the model's connection performance under different parameter combinations. This allows for a clearer range of parameter adjustments, providing a clear direction for subsequent parameter modifications and avoiding blind adjustments. A comprehensive robustness analysis of the parameters yields parameter adjustment guidelines that fully account for various interference factors that may arise in actual applications, such as signal strength fluctuations, external electromagnetic interference, and device load changes. This ensures that the adjusted parameters maintain a good connection even in complex and changing environments, mitigating connection instability caused by environmental changes.

[0019] Modifying the connection parameters according to the parameter adjustment criteria and applying them to the connection control process provides a scientific basis for parameter modification, enabling precise optimization of connection performance and effectively resolving the issue of uneven connection results caused by the lack of standardization in parameter adjustment 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 adaptive construction and optimization of models, and the scientific adjustment of parameters. This method can achieve stable and efficient connections between the two in different application scenarios and complex environments, fully leveraging the advantages of 5G communication technology, meeting the needs of various smart devices in data transmission, interactive response, and other aspects, and promoting the wider application of smart devices in smart homes, industrial Internet of Things, smart transportation, and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a working principle diagram of the method for connecting an integrated 5G communication module and a smart device according to the present invention; Figure 2 Flowchart for initial connection parameter determination; Figure 3 Flowchart constructed for the connection controller model; Figure 4 Flowchart for model parameter scaling of a connected controller. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1 The present invention provides a method for connecting an integrated 5G communication module to a smart device, the method comprising: The system obtains the configuration data of the 5G communication module and the connection requirements data of the smart device. The configuration data covers the module's inherent characteristics and operating status, while the connection requirements data reflects the smart device's specific requirements for communication performance. Based on these two types of data, the system calculates initial connection control parameters. Subsequently, a connection controller model is constructed to match these initial parameters. To improve the model's adaptability and performance, the model's 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. A comprehensive robustness analysis of these parameters is then performed to evaluate their performance under various uncertainties. Based on this robustness, specific parameter adjustment criteria are generated. Finally, the connection parameters are modified based on these criteria and 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.

[0023] Example 1: See Figure 2 Acquiring smart device type data first requires identifying the device type and its communication protocol. For example, in the Industrial Internet of Things (IIoT) scenario, a smart device might be a high-precision sensor deployed on a production line, monitoring parameters such as temperature, vibration, or pressure in real time. Such devices typically use 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 sector, a smart device might be a mobile terminal supporting high-definition video streaming, using a communication protocol based on HTTP / 2 or WebSocket to meet high throughput and low latency requirements. Furthermore, certain specialized devices, such as remote medical diagnostic instruments or onboard terminals for autonomous vehicles, may use customized proprietary protocols to ensure data security and real-time performance. Therefore, acquiring device type data requires not only distinguishing the functional category of the device but also clarifying the characteristics of its communication protocol, including the protocol stack hierarchy, data encapsulation methods, and possible Quality of Service (QoS) mechanisms.

[0024] Signal coverage is typically determined by measuring the Reference Signal Received Power (RSRP) or the Signal-to-Interference-and-Noise Ratio (SINR). For example, in indoor environments, signal strength can be unevenly distributed due to the influence of walls and obstacles, with weak coverage in some areas and stronger signals closer to the base station. Assessing network congestion involves monitoring the number of users, resource block occupancy, or average queuing delay within the current cell. For example, during peak hours or in dense urban areas, network congestion may be high, resulting in reduced available bandwidth or increased transmission delay; whereas in suburban areas or during off-peak hours, network resources may be more abundant. Furthermore, scenario data must also consider the dynamic characteristics of 5G networks, such as mobility management (switching frequency), beamforming adjustments, and the allocation of network slice resources. These factors together determine the actual available resources and stability of the 5G communication module in the current environment.

[0025] The target transmission rate is typically determined based on the device's data generation characteristics or application scenario. For example, an industrial sensor may only require a transmission rate of a few thousand bits per second to meet periodic status reporting requirements, while a 4K video surveillance device may require a sustained throughput of tens of megabits to ensure smooth video. The target latency requirement is related to the device's real-time requirements. For example, emergency braking command transmission in an autonomous driving system must meet millisecond-level end-to-end latency, while common file synchronization applications may tolerate seconds-level latency. In addition, some applications, such as real-time voice communications or online gaming, may be sensitive to jitter (latency variations), while others, such as background data backup, have a higher tolerance for jitter. Therefore, the acquisition of performance data requires a comprehensive consideration of rate, latency, jitter, and possible reliability indicators (such as an upper limit on packet loss rate).

[0026] After completing the collection of the above data, the system needs to integrate it into structured smart device connection demand data. This process involves normalizing or quantizing the raw data so that subsequent algorithms can uniformly analyze it. For example, the protocol information in the device type data may be mapped to a standardized communication mode identifier, while the signal strength and congestion level in the work scenario data may be quantified into discrete levels (such as high / medium / low). The target rate and latency in the performance data are usually retained directly in numerical form, but weight coefficients may be added based on the device type or scenario characteristics to reflect the priority differences between different requirements. The final generated connection demand data will serve as the input basis for subsequent parameter calculations.

[0027] The determination of initial connection parameters is divided into two stages: original parameter calculation and optimization and adjustment. During the original parameter calculation stage, the system derives a preliminary parameter combination 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 the latency requirements are strict, a high-order modulation method (such as 256-QAM) and a shorter transmission time interval (TTI) may be selected to improve spectral efficiency and reduce the waiting time for a single transmission. At the same time, the system also needs to consider the choice of channel coding scheme (such as LDPC or Polar code) to balance error correction capability and coding overhead. In addition, the configuration parameters of multi-antenna technologies (such as MIMO) (such as the number of layers and precoding matrix) may also be included in the calculation to fully utilize spatial diversity or multiplexing gain.

[0028] During the optimization and adjustment phase, the system filters or modifies the original parameters based on preset constraints. These constraints may include power consumption limits (such as the energy budget of battery-powered devices), computational complexity limits (such as the processing capabilities of embedded devices), or spectrum regulatory requirements (such as the upper limit of transmit power in 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 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 use 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 the subsequent controller model construction.

[0029] Example 2: See Figure 3 The first step in building a connection controller model is to represent the initial connection parameters equivalently as a state-space model. This conversion process needs to take into account the dynamic characteristics of the connection system between the 5G communication module and the smart device. Taking the device connection in the Industrial Internet of Things as an example, the system state vector may contain variables such as the current channel quality indicator, data buffer status, and transmission queue length. These state variables can fully reflect the real-time status of the connection. Control inputs may include power adjustment instructions, modulation and coding scheme selection instructions, resource block allocation instructions, etc. These inputs directly affect the actual operating parameters of the communication module. In the process of building the state-space model, it is necessary to clarify the mathematical relationship between the state variables and the control inputs. This relationship is usually determined by the physical characteristics of the communication system.

[0030] The development of a state-space model must consider the specificities of actual application scenarios. For example, in the connected vehicle (IoV) scenario, the connection status between a vehicle and a base station changes rapidly as the vehicle moves. In this case, the system state vector must include variables such as location information, speed, and historical channel quality to accurately describe this time-varying behavior. Control inputs must consider factors such as the dynamic adjustment of beamforming parameters and the selection of switching timing. Establishing this state-space model provides an accurate system description foundation for subsequent controller design.

[0031] The controller model adopts a pre-defined framework structure, which generally consists of 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 systems. In 5G communication scenarios, certain key state variables, such as the real-time channel interference level and the processing power of remote devices, may not be directly measurable. The observer module estimates these non-observable state variables using measurable system outputs such as received signal strength, bit error rate, and actual throughput. The observer design must balance estimation accuracy and computational complexity to accommodate the processing power limitations of different smart devices.

[0032] The feedback control module calculates the optimal control input based on the state estimate provided by the observer and preset performance goals, such as target transmission rate and maximum allowable delay. In live video applications, the feedback control module may need to adjust video encoding parameters and transmission priority in real time to maintain a smooth viewing experience despite bandwidth fluctuations. The choice of control algorithm needs to consider the dynamic characteristics of the system. For scenarios with high response speed requirements, a predictive control strategy may be required; for scenarios with limited computing resources, a simplified control algorithm may be required.

[0033] The observer module continuously updates state estimates, providing the feedback control module with the latest system status information. Based on this information, the feedback control module generates control instructions for the 5G communication module. This collaborative mechanism is particularly important in telemedicine applications. The observer module must accurately estimate network latency and jitter, and the feedback control module adjusts the 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 tuning to ensure the stability and responsiveness of the entire control system.

[0034] For smart devices with strong computing power, more complex control algorithms, such as model predictive control, can be used. For resource-constrained IoT devices, simplified control strategies are required. In smart home scenarios, high-end smart gateways may employ full state feedback control, while simple sensor nodes may employ simplified rule-based control strategies. This differentiated implementation ensures the applicability of controller models across different devices.

[0035] During the deployment phase, the system presets a set of initial parameters based on typical device operating scenarios. These parameters are dynamically adjusted during actual operation based on connection quality data. For example, in a factory automation scenario, the controller model automatically adjusts the observer bandwidth and control parameters based on the wireless environment characteristics of the device, adapting to the signal propagation characteristics of each area. This adaptive mechanism improves the controller's robustness in diverse environments.

[0036] With device firmware upgrades or network configuration changes, the controller model structure or parameters may need to be adjusted. The system regularly evaluates controller performance metrics and triggers model updates when necessary. In smart city applications, as base station deployments adjust or new services go online, the connection controller model may need to be retrained or re-adjusted to adapt to the changed network environment. This continuous optimization mechanism ensures that the controller maintains excellent control performance over the long term.

[0037] By transforming initial connection parameters into a state-space model and constructing a connection controller model consisting of an observer module and a feedback control module, a dynamic control foundation is provided for connecting 5G communication modules to smart devices. This model can adapt to the needs of different device types and application scenarios, and through the synergy of state estimation and feedback control, it enables intelligent adjustment of connection parameters. This phase of implementation provides the necessary control framework for subsequent parameter scaling and stability analysis.

[0038] Example 3: See Figure 4 After the connection controller model is built, the implementation process enters the parameter scaling and stability analysis phase. This phase first scales the controller model parameters, then converts it into an equivalent model for stability assessment, and ultimately determines the safe adjustment range of the parameters.

[0039] The observer bandwidth parameter directly affects the dynamic response characteristics of state estimation. In specific applications, such as remotely operated construction machinery control scenarios, when the robot's motion state requires high-frequency updates, the observer bandwidth may need to be increased to quickly track changes in state variables such as joint angles and end position. In static data acquisition scenarios such as environmental monitoring sensors, excessively high observer bandwidth can amplify measurement noise, necessitating appropriate reduction. The scaling process is typically normalized based on the system's timescale or performance requirements to ensure comparable parameter adjustments. For feedback control modules, the key lies in adjusting the controller bandwidth parameter. This parameter determines the closed-loop system's response speed to setpoint changes and external disturbances. In connected vehicle platooning control, maintaining close vehicle spacing requires a high controller bandwidth for fast acceleration adjustments. In background data synchronization applications, excessive bandwidth can lead to unwanted control oscillations, necessitating reduction to smooth the transmission rate. Controller bandwidth scaling is also standardized based on system characteristics to ensure consistency with observer bandwidth adjustment.

[0040] After parameter scaling, the connected controller model is converted to a mathematically equivalent two-degree-of-freedom control model. This conversion aims to decouple the system's reference input tracking performance from its disturbance rejection performance, simplifying subsequent stability analysis. The two-degree-of-freedom model clearly separates the transmission paths of the setpoint response and disturbance response. For example, in augmented reality applications carried by 5G networks, the two-degree-of-freedom model can independently optimize the responsiveness of viewpoint switching during user movement (setpoint tracking) and the ability to suppress network jitter interference on image rendering.

[0041] Based on the converted two-degree-of-freedom equivalent model, the 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 controlled object model. Assuming a simplified second-order system model, its closed-loop characteristic equation can be expressed as:

[0042] in: Represents a complex frequency variable (Laplacian operator) and is used to analyze the dynamic response of the system; Represents the damping ratio of the system, reflecting the attenuation degree of the system response oscillation; Represents the undamped natural frequency of the system and is related to the response speed of the system. It should be emphasized that and The values ​​of are not fixed; they are determined by the controller parameters (especially the scaled observer bandwidth and controller bandwidth ) and the dynamic characteristics of the controlled 5G connection system itself. For example, may be proportional to , while may be affected by and .

[0043] The core of stability analysis is to solve the roots of the characteristic equation (i.e., closed-loop poles) and determine their positions on 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 oscillation or divergence. In specific analysis, the real part signs of the roots can be directly determined by algebraic criteria (such as Routh criterion) without explicitly solving the root values. For example, when analyzing the stability of an industrial wireless control loop, the Routh table can quickly determine whether the system is stable under a given and parameter combination. A more intuitive method is to calculate the characteristic roots and observe their positions. The distance from the left half of the complex plane to the imaginary axis reflects the stability margin of the system. During the analysis process, special attention should be paid to the influence of parameter changes on the trajectory of the characteristic roots. For example, gradually increasing the controller bandwidth , observe whether the dominant root approaches or crosses the imaginary axis into the right half plane.

[0044] According to the results of stability analysis, determine the safe selection range of key control parameters (mainly the observer bandwidth and the controller bandwidth ). This range defines the boundary of parameter adjustment, within which the stability of the system can be maintained. The methods for determining the range include: parameter scanning method: grid sampling within the possible value range of and , and performing stability judgment (such as calculating the maximum real part of the characteristic root) for each parameter combination point. Connect all stable parameter points to form the boundary of the stable parameter region. For example, in the unmanned aerial vehicle high-definition image transmission scenario, scan the combination of and at different flight altitudes (which affect the channel delay) to find the parameter range that ensures stable video stream transmission.

[0045] Boundary solving method: directly solve the critical parameter values using stability boundary conditions (such as the characteristic equation having a root on the imaginary axis). For example, let ( be a real frequency), substitute it into the equation and separate the real and imaginary parts to obtain two equations about and parameters , ). Solving these equations can yield the critical parameter curve, which is the boundary of the stable region. In the application of smart grid wide-area measurement systems, it is necessary to accurately solve the maximum allowable value to ensure the synchronization and stability of the phase measurement unit data. Consider engineering margin: The theoretical stability boundary is often derived under an ideal model. In actual systems, there are unmodeled dynamics, parameter perturbations, and time-varying interferences, so engineering margins need to be introduced. For example, within the theoretical stability boundary, a certain stability margin can be reserved (such as requiring the real part of the characteristic root to be less than a certain negative value - ), thereby determining the safe parameter range for actual engineering applications. In high-speed railway mobile communication scenarios, due to the uncertainty caused by Doppler frequency shift and frequent handovers, a more conservative parameter range than the theoretical value is required.

[0046] The final parameter safety range (e.g. , ) provides a basic constraint for subsequent robustness analysis and final parameter adjustment. This range ensures that the basic stability of the system can be maintained regardless of how the parameters are adjusted during the subsequent optimization process.

[0047] Example 4: The first task of robustness analysis is to define a set of test indicators that can fully reflect the performance of the system. In the scenario of 5G communication and smart device connection control, these indicators need to include at least three types of key parameters: the gain margin reflects the stability boundary of the system when the open-loop gain changes, the phase margin characterizes the system's tolerance to phase lag, and the relative delay margin quantifies the extent to which the system can withstand additional delay without losing stability. Taking wireless control in smart factories as an example, the gain margin indicator can evaluate the stability reserve of the system when the wireless channel suddenly deteriorates (such as signal attenuation caused by equipment movement); the phase margin indicator can be used to analyze the robustness of the system under phase fluctuations caused by multipath effects; and the relative delay margin is particularly suitable for evaluating the system's adaptability under changes in processing delay caused by load fluctuations in edge computing nodes.

[0048] Evaluating these metrics requires a systematic analysis within a scaled parameter space. This parameter space typically has two primary dimensions: the observer bandwidth parameter and the control bandwidth parameter. In practice, a grid-based sampling approach can be employed, selecting several representative values ​​for each dimension to form a parameter combination matrix.

[0049] Table 1: Robustness evaluation metrics for the combination of observer bandwidth and control bandwidth.

[0050]

[0051] Table 1 summarizes the impact of parameter changes on system robustness. In connected vehicle applications, increasing the observer bandwidth typically improves state estimation speed but can reduce the system's phase margin. Increasing the control bandwidth can accelerate system response but reduce gain and delay margins. These patterns form the basis of parameter adjustment guidelines.

[0052] The robust adjustment criteria generated based on the evaluation results need to clarify two core rules: the compromise parameter adjustment rule and the control response rule. The compromise parameter adjustment rule stipulates the balance between the observer bandwidth and the control bandwidth. In the remote surgical robot control scenario, this rule may require that the observer bandwidth be increased as much as possible to achieve fast state estimation while ensuring the minimum phase margin; in the smart home device networking scenario, it may emphasize appropriately reducing the control bandwidth to reduce energy consumption while meeting the basic delay margin. The control response rule specifies the control bandwidth selection strategy for different application scenarios. For real-time video surveillance systems, it may be necessary to prioritize a higher control bandwidth to achieve fast bit rate adjustment; for environmental monitoring sensor networks, a lower control bandwidth may be allowed to extend battery life.

[0053] In industrial automation scenarios, modifying the observer bandwidth parameters requires considering the balance between the frequency of changes in the device's motion state and the measurement noise level. For example, for a high-speed robotic arm, a relatively high observer bandwidth is required to track rapidly changing position and velocity states; for static or low-speed devices, the observer bandwidth can be reduced to reduce noise interference. When modifying the control bandwidth parameters, a trade-off between response speed and stability is required. In smart grid frequency control applications, a higher control bandwidth can achieve rapid frequency regulation, but it is necessary to ensure that control oscillations are not triggered. In logistics and warehousing AGV scheduling systems, a moderate control bandwidth can ensure trajectory tracking accuracy while avoiding excessive consumption of communication resources.

[0054] Parameter modification can be implemented using a stepwise approach. For example, for UAV swarm formation control, the initial parameters might be set to medium observer bandwidth and medium control bandwidth. By monitoring the formation's accuracy and communication load in real time, the two bandwidth parameters are gradually fine-tuned. If large position synchronization errors are detected, the observer bandwidth is appropriately increased; if significant control command oscillation is detected, the control bandwidth is appropriately reduced. This gradual adjustment approach can identify the optimal parameter combination in the actual operating environment.

[0055] The verification process typically involves two phases: simulation testing and real-world testing. In the deployment of smart city traffic signal control systems, the effectiveness of new parameters in various traffic flow scenarios is first verified on a simulation platform, including response speed and stability to different unexpected traffic flows. This is followed by small-scale field testing at real intersections to observe the control performance of the parameters in a real wireless environment. Only after these two phases of verification are the modified parameters officially adopted in the production environment.

[0056] The resulting modified connection parameters are then fixed into the device configuration. Implementation methods vary depending on the device type and application scenario. For high-performance industrial control equipment, dynamic parameter tables might be used, allowing for automatic adjustments within a certain range based on real-time network conditions. For resource-constrained IoT terminals, fixed parameter sets might be used, allowing parameter settings to be optimized through device firmware updates. Regardless of the method used, parameter configuration must be maintainable to facilitate subsequent optimization based on changes in the network environment or business needs.

[0057] Example 5: Based on the optimized connection parameter set, the system generates a specific sequence of connection control instructions. These instructions need to be converted into low-level operations executable by the 5G communication module. At the physical layer, instructions may include configuring the carrier aggregation scheme, setting the modulation and coding strategy index, and determining the transmission power level. Taking the HD mobile live broadcast scenario as an example, based on the optimized parameters, the system will generate carrier aggregation binding instructions to combine multiple 5G carriers to expand the transmission bandwidth. It also specifies 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 the scheduling request period, setting the number of hybrid automatic repeat request processes, and logical channel priority mapping. In a telesurgery system, critical command data streams are mapped to high-priority logical channels and configured with a shorter retransmission interval. At the transport layer, instructions may include adjusting the TCP initial window size and selecting a congestion control algorithm. For large-scale IoT device firmware upgrades, instructions may specify the use of a time-tolerant transport protocol and a large initial congestion window.

[0058] Connection control commands are sent to the execution unit of the 5G communication module via a standard module interface. Command loading and activation utilizes a layered verification mechanism: physical layer configuration is activated after verification by the baseband processing unit; higher-level protocol configuration is loaded layer by layer during protocol stack initialization. In the smart port container tracking system, when a device awakens from sleep, optimized parameter commands are loaded into the communication module in batches. After each layer of the protocol stack completes self-tests, it sends a ready signal to the application layer. The communication module then initiates a connection establishment process with the target smart device based on the command parameters. This process adheres to the standard 5G access protocol, but uses optimized values ​​for key parameters. During the connection establishment process in the industrial AGV control system, the communication module uses an optimized power ramp step size during the random access phase; the signaling configuration parameters used during the RRC connection establishment phase are derived from previously developed robust optimization results.

[0059] 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 delay measurement, 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 delay of the service flow through a timestamped probe message. Environmental interference data includes reference signal reception quality measurement values, interference noise intensity index, and a list of adjacent cell reference signal strengths. The Internet of Vehicles OBU device continuously scans and records signal quality fluctuations of the serving cell and changes in interference in adjacent cells while moving. Network load indicators include resource block utilization statistics and protocol data unit queue length monitoring. The smart city street light control system periodically reports the MAC layer cache status report to indicate the degree of network congestion.

[0060] Monitoring data is stored in a circular buffer structure, generating a complete monitoring snapshot every 15 minutes. The analysis process detects two key events: performance drift events, triggered when key indicators deviate from the set range for three consecutive cycles; and environmental mutation events, triggered by sudden and drastic changes in network measurements. In the distributed energy monitoring system, event markers are generated when voltage data transmission delays continuously exceed the 50 millisecond threshold, or when the wireless channel quality index suddenly drops by 10 dB.

[0061] Smart device connection demand data is converted into a dynamic structure consisting of three levels of fields: basic requirements retain the original target rate and latency; adaptive requirements add an environmental compensation factor; and elastic requirements extend the tolerance boundary. In the smart agricultural irrigation system, when the system detects increased latency due to base station switching, it recalculates the target latency range and simultaneously adjusts the network coverage level in the environmental assessment report.

[0062] Updated demand 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. Within the manufacturing execution system, a parameter recalculation process is initiated in the background, preserving existing connection parameters while preparing a new parameter set. Once the calculations are complete and verified, a seamless parameter switch is executed. The drone swarm control system utilizes a dual configuration zone design: the active zone operates with the current parameters, while the standby zone loads the new parameter set. Command words are used to switch configuration zones for seamless updates. Each iteration records the parameter revision history and performance change trajectory, providing an analytical basis for long-term optimization.

[0063] The implementation process establishes a complete monitoring feedback loop. The monitoring cycle is reset after each parameter update, creating an iterative rhythm of continuous optimization. In the commercial complex passenger flow analysis system, a full parameter evaluation process is automatically triggered during daily low-peak hours. Connection strategies are updated 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 evolving network environments and changing business requirements.

[0064] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention 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 requirement data; Determining initial connection parameters based on the 5G communication module configuration data and the smart device connection requirement data; Constructing a connection controller model adapted to the initial connection parameters; performing parameter scaling on the connection controller model; Converting the connection controller model into an equivalent model, performing connection stability analysis, and obtaining a parameter adjustment range; Conduct a comprehensive robustness analysis 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, wherein: Obtain 5G communication module configuration data and smart device connection requirement data, including: Obtain smart device type data and 5G communication module working scenario data, wherein the smart device type data includes device category and usage protocol, and the 5G communication module working scenario data includes signal coverage and network congestion level; Acquire smart device performance data, wherein the smart device performance data includes target transmission rate and target delay requirements; Based on the smart device type data, 5G communication module working scenario data and smart device performance data, smart device connection requirement data is generated.

3. The method for connecting an integrated 5G communication module to a smart device according to claim 1, wherein: Determining initial connection parameters based on the 5G communication module configuration data and the smart device connection requirement data includes: Calculating original connection parameters according to the target transmission rate and target delay 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, wherein: Constructing a connection controller model adapted to the initial connection parameters, including: Equivalently representing the initial connection parameters as a state space model, wherein the state space model includes a system state vector and a control input; Based on the state space model and the preset controller framework, a connection controller model is constructed, and the connection controller model 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, wherein: Parameter scaling of the connection controller model includes: performing observer parameter scaling on the observer module, including adjusting an observer bandwidth parameter; Feedback controller parameter scaling is performed on the feedback control module, including adjustment of controller bandwidth parameters.

6. The method for connecting an integrated 5G communication module to a smart device according to claim 1, wherein: Convert the connection controller model to an equivalent model and perform connection stability analysis to obtain parameter adjustment ranges, including: Converting the connection controller model into a two-degree-of-freedom equivalent model; constructing a closed-loop characteristic equation, and analyzing the stability of the connection control system based on the closed-loop characteristic equation; Based on the stability analysis results, determine the safe selection range of connection parameters.

7. The method for connecting an integrated 5G communication module to a smart device according to claim 1, wherein: A comprehensive robustness analysis of the parameters is performed to obtain parameter adjustment criteria, including: Defining test indicators, wherein the test indicators include gain margin, stability margin and relative delay margin; evaluating the test metric based on the scaled parameter space to generate a robust adjustment criterion; Among them, the robust adjustment criteria include the compromise adjustment rules of the observer bandwidth parameters and the control response rules of the control bandwidth parameters.

8. The method for connecting an integrated 5G communication module to a smart device according to claim 7, wherein: Modifying connection parameters according to the parameter adjustment criteria includes: Adjusting the observer bandwidth parameter according to the compromise parameter adjustment rule in the robust adjustment criterion; adjusting a control bandwidth parameter according to a control response rule in the robust adjustment criterion; Generates the modified connection parameters.

9. The method for connecting an integrated 5G communication module to a smart device according to claim 8, wherein: The connection control process applied to 5G communication modules and smart devices includes: generating a connection control instruction based on the modified connection parameters; Execute the connection control instruction to complete the initial connection between the 5G communication module and the smart device; Monitor connection status data for subsequent optimization iterations.

10. The method for connecting an integrated 5G communication module to a smart device according to claim 9, wherein: Monitor connection status data for subsequent optimization iterations, including: Collect connection quality data and environmental interference data in real time; updating smart device connection requirement data based on the connection quality data and the environmental interference data; The updated smart device connection requirement data is fed back into the initial connection parameter determination process.

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