Intelligent tablet and peripheral device pairing-free ad hoc network communication method and system

Abstract

The application relates to the technical field of wireless communication, and discloses a smart panel and peripheral device non-pairing ad hoc network communication method and system, which is applied to a peripheral device end. The method comprises the following steps: in response to to-be-transmitted data, a first feature set of peripheral motion state and signal environment change is acquired to generate a dynamic urgency coefficient, and a second feature set representing the time effectiveness demand of the data itself is acquired to generate a data time effectiveness coefficient; a coupling weight factor and a demand mapping factor are acquired according to the two coefficients; a demand-channel coupling coefficient is generated by combining the current BLE broadcast channel quality; a constraint coefficient is generated based on the short board effect of peripheral hardware resources; finally, the above factors are comprehensively adjusted and modified in a multi-stage manner within the minimum and maximum broadcast interval range allowed by the protocol, a target broadcast interval is determined, and transmission is controlled. The application realizes the dynamic balance of real-time response, power consumption saving and channel efficiency.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and in particular relates to a method and system for unpaired self-organizing network communication between a smart tablet and peripheral devices. Background Technology

[0002] Bluetooth Low Energy (BLE) technology, with its advantages of low power consumption, low cost, and ease of deployment, plays a crucial role in wireless interaction between smart tablets and various peripherals (such as sensors, electronic pens, and medical monitoring terminals). In scenarios involving rapid self-organizing networks without human intervention, systems generally rely on BLE broadcast channels to achieve device discovery and unidirectional or quasi-bidirectional data transmission, avoiding the cumbersome operations of traditional pairing and binding processes. However, existing unpaired broadcast communication solutions have long employed a fixed broadcast interval strategy, leading to significant drawbacks. When the broadcast interval is set too short, the peripheral RF module remains in a high-frequency transmission state, not only causing severe idle power consumption waste and significantly shortening the working life of battery-powered devices, but also exacerbating channel congestion and co-channel collisions in the 2.4GHz band, resulting in a decrease in overall communication efficiency. Conversely, if the broadcast interval is set too long to reduce power consumption, in dynamic scenarios such as rapid peripheral movement, sudden changes in sensor data, or impending connection interruption, the smart tablet cannot promptly perceive and respond to critical business changes, leading to data transmission delays or even loss, severely impacting applications with high real-time requirements. Some existing studies attempt to coarsely adjust the broadcast interval in a stepwise manner based on a single parameter (such as remaining power or motion status). However, such methods lack comprehensive consideration of environmental electromagnetic interference, data timeliness urgency, and the combined effects of multiple factors. The adjustment mechanism is too simplistic and cannot adapt to the complex and ever-changing wireless environment and service requirements. It is difficult to achieve a fine dynamic balance between power consumption control and real-time response. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for unpaired self-organizing network communication between a smart tablet and peripheral devices, in order to solve the above-mentioned problems.

[0004] This invention is implemented as follows: a method for unpaired self-organizing network communication between a smart tablet and peripheral devices, applied to the peripheral device end, includes the following steps: in response to the service data to be transmitted, a first feature set characterizing the peripheral device's motion state and signal environment changes is obtained, and a dynamic urgency coefficient is generated; a second feature set characterizing the timeliness requirement of the service data to be transmitted itself is obtained, and a data timeliness coefficient is generated; based on the dynamic urgency coefficient and the data timeliness coefficient, a coupling weight factor and a demand mapping factor are obtained; a third feature set characterizing the current BLE broadcast channel quality is obtained, and a weighted coupling operation is performed on the third feature set using the coupling weight factor to generate a demand-channel coupling coefficient; a fourth feature set characterizing the peripheral device's hardware resource margin is obtained, and the fourth feature set is minimized based on the shortest board effect principle to generate a constraint coefficient; based on the demand mapping factor, the demand-channel coupling coefficient, and the constraint coefficient, the dynamic range between the minimum and maximum broadcast intervals allowed by the protocol is adjusted and corrected in multiple levels to determine the target BLE broadcast interval, and the BLE radio frequency unit of the peripheral device end is controlled to transmit broadcast packets according to the target BLE broadcast interval.

[0005] A further technical solution involves calculating the dynamic urgency coefficient as follows: A first feature set is obtained, comprising the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within a time window; the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within the time window are then normalized to obtain values ​​ranging from [value missing]. The acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistical index within the time window are obtained, and the acceleration amplitude change rate, relative RSSI change rate, and interaction frequency within the time window are positively correlated with the acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistical index within the time window, respectively. The acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistical index within the time window are weighted and fused, and a saturated nonlinear mapping is applied to the weighted fusion result to obtain the dynamic urgency coefficient. The saturated nonlinear mapping is configured such that when the weighted fusion result approaches zero, the dynamic urgency coefficient approaches zero; when the weighted fusion result increases, the dynamic urgency coefficient monotonically increases and asymptotically converges to 1.

[0006] A further technical solution involves normalizing the acceleration amplitude change rate, relative RSSI change rate, and interaction frequency within the time window as follows: The current acceleration amplitude change rate and interaction frequency within the time window are compared with the upper limit of acceleration amplitude change rate saturation and the upper limit of interaction frequency saturation, respectively. After truncating the upper limit of the ratio to 1, the acceleration amplitude change rate index and the interaction frequency statistical index within the time window are obtained. The absolute value of the current relative RSSI change rate is compared with the upper limit of RSSI change rate saturation. After truncating the upper limit of the ratio to 1, the relative RSSI change rate index is obtained.

[0007] A further technical solution involves calculating the data timeliness coefficient as follows: A second feature set is obtained, comprising the sensor value change rate, information age (AoI), and time since the last successful interaction; the sensor value change rate, information age (AoI), and time since the last successful interaction are then normalized to obtain a value range of [value missing]. The sensor numerical change rate index, information age (AoI) index, and time since last successful interaction index are used to calculate the data timeliness coefficient. These indices are directly proportional to the sensor numerical change rate index, information age (AoI) index, and time since last successful interaction index, respectively. A weighted fusion of these indices is performed, and a saturated nonlinear mapping is applied to the weighted fusion result to obtain the data timeliness coefficient. The saturated nonlinear mapping is configured such that: when the weighted fusion result approaches zero, the data timeliness coefficient approaches zero; and when the weighted fusion result increases, the data timeliness coefficient monotonically increases and asymptotically converges to 1.

[0008] A further technical solution is to normalize the sensor value change rate, information age (AoI), and time since the last successful interaction by: dividing the current sensor value change rate, information age (AoI), and time since the last successful interaction by the saturation upper limit of the sensor value change rate, the maximum information age allowed by the service, and the saturation threshold of time since the last successful interaction, respectively, and truncating the upper limit of the ratio to 1 to obtain the sensor value change rate index, information age (AoI) index, and time since the last successful interaction index.

[0009] A further technical solution involves calculating the coupling weight factor and the demand mapping factor as follows: The dynamic urgency coefficient and the data timeliness coefficient are subjected to a central trend fusion operation to obtain the coupling weight factor. When both the dynamic urgency coefficient and the data timeliness coefficient are at their maximum values, the coupling weight factor takes the maximum value. When either the dynamic urgency coefficient or the data timeliness coefficient decreases, the coupling weight factor is monotonically non-increasing. The dynamic urgency coefficient and the data timeliness coefficient are then subjected to a geometric average to obtain the demand mapping factor.

[0010] A further technical solution involves calculating the demand-channel coupling coefficient as follows: obtaining a third feature set, which includes channel utilization and broadcast packet loss rate; summing the channel utilization and the broadcast packet loss rate to obtain a channel congestion metric; and performing a weighted attenuation operation on the channel congestion metric using a coupling weight factor to obtain the demand-channel coupling coefficient. The weighted attenuation operation is configured such that the demand-channel coupling coefficient monotonically decreases as the channel congestion metric increases, and the larger the coupling weight factor, the slower the rate at which the demand-channel coupling coefficient attenuates as the channel congestion metric increases.

[0011] A further technical solution involves calculating the constraint coefficient as follows: Obtain a fourth feature set, which includes the remaining battery charge percentage, chip junction temperature, and remaining stack space of the RTOS task; then, ratio the difference between the current chip junction temperature and the lower limit of the normal operating temperature with the difference between the upper limit of the temperature triggering thermal throttling and the lower limit of the normal operating temperature, and truncate the ratio to the output range. Then, the chip junction temperature index is obtained; the ratio of the remaining stack space of the current RTOS task to the minimum safe stack depth threshold is processed, and the ratio is truncated to an upper limit of 1, and the complement of the ratio is taken to obtain the stack exhaustion risk index; based on the remaining battery power percentage, the thermal safety margin corresponding to the chip junction temperature index, and the stack safety margin corresponding to the stack exhaustion risk index, a minimization optimization operation is performed to obtain the constraint coefficient, such that the constraint coefficient is equal to the minimum value among the remaining battery power percentage, the thermal safety margin, and the stack safety margin.

[0012] A further technical solution involves calculating the target BLE broadcast interval as follows: Based on a demand mapping factor, a theoretical target broadcast interval is determined between the minimum and maximum broadcast intervals allowed by the protocol, wherein the theoretical target broadcast interval decreases as the demand mapping factor increases; based on a demand-channel coupling coefficient, the theoretical target broadcast interval is adaptively adjusted to obtain a coupling-corrected broadcast interval, wherein the coupling-corrected broadcast interval increases as the demand-channel coupling coefficient decreases; based on a constraint coefficient, a constraint lower limit broadcast interval is determined, wherein the constraint lower limit broadcast interval increases as the constraint coefficient decreases and is not less than the minimum broadcast interval allowed by the protocol; the larger of the coupling-corrected broadcast interval and the constraint lower limit broadcast interval is selected and compared with the maximum broadcast interval allowed by the protocol, and the smaller one is determined as the target BLE broadcast interval to control the BLE radio frequency unit of the peripheral terminal to transmit broadcast packets.

[0013] A smart tablet and peripheral device unpaired self-organizing network communication system includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned smart tablet and peripheral device unpaired self-organizing network communication method. Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves fine-grained dynamic adjustment of the BLE broadcast interval by integrating multi-dimensional parameters such as peripheral motion, signal environment, data timeliness, channel quality, and hardware resources in real time. While ensuring the real-time transmission of critical business data, it effectively reduces the power consumption of peripherals during idle, significantly extends battery life, and alleviates channel congestion.

[0014] 2. This invention introduces a hardware constraint mechanism based on the bottleneck effect. When any resource such as battery, temperature or memory is under pressure, broadcast behavior can be forcibly restricted, fundamentally avoiding performance degradation or system crashes caused by device overload, and greatly improving the robustness and reliability of the communication system. Attached Figure Description

[0015] Figure 1 The flowchart illustrates a method for unpaired self-organizing network communication between a smart tablet and peripheral devices, as provided by this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In traditional, unpaired self-organizing network communication between smart tablets and peripherals, the fixed broadcast interval strategy has several drawbacks. When the broadcast interval is too short, the peripheral RF module remains in a high-frequency transmission state for extended periods, leading to increased power consumption during idle and channel congestion in the 2.4GHz band, resulting in a higher probability of co-channel collisions. Conversely, when the broadcast interval is too long, changes in peripheral movement or sudden changes in service data cannot be detected in a timely manner, causing delays or loss of critical service data transmission. The core issue lies in the lack of a real-time fusion mechanism for multi-dimensional influencing parameters. This prevents the coordinated implementation of dynamic urgency assessment and channel adaptive adjustment, ultimately making it difficult to maintain a dynamic balance between power consumption and real-time performance.

[0018] For example, in medical monitoring scenarios, peripheral devices are wearable vital sign sensors, and smart tablets serve as monitoring terminals. When a patient experiences rapid displacement or sudden changes in physiological parameters, the sensors need to transmit data in real time. However, fixed broadcast intervals cannot adapt to changes in movement. At the same time, there are numerous wireless devices in the hospital environment, exacerbating electromagnetic interference in the 2.4GHz band. The fixed interval strategy causes the sensors to continuously transmit broadcast packets under channel congestion, resulting in critical alarm information failing to reach the monitoring terminal in a timely manner due to transmission delays. Furthermore, the peripheral device's battery is depleted more rapidly due to ineffective broadcasts.

[0019] If the above problems are not resolved, the reliability of system communication will be reduced, the integrity of business data cannot be guaranteed, security risks may be triggered in critical application scenarios, and peripheral hardware resources will reach the performance bottleneck prematurely due to continuous high-frequency transmission, resulting in a continuous deterioration of overall network efficiency.

[0020] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a method for unpaired self-organizing network communication between a smart tablet and a peripheral device, applied to the peripheral device end, including the following steps: In response to the service data to be transmitted, a first feature set characterizing the motion state and signal environment changes of the peripheral device is acquired, and a dynamic urgency coefficient is generated. This step aims to assess the dynamism of the peripheral device's current environment. For example, when the peripheral device is stationary, its motion state changes little, and the signal environment may be relatively stable; however, when the peripheral device is in a rapidly moving or frequently interfered environment, its motion state and signal environment may change drastically. The dynamic urgency coefficient can be generated in various ways. For example, it can be classified based on preset motion patterns (such as stationary, slow movement, and fast movement), and a urgency value can be assigned to each pattern. Alternatively, it can be based on the rate of change of the signal strength index (RSSI), where a high dynamic urgency is considered to exist when the rate of change of RSSI exceeds a certain threshold.

[0022] The process involves obtaining a second feature set characterizing the timeliness requirements of the business data to be transmitted and generating a data timeliness coefficient. This step aims to assess the real-time requirements of the business data. For example, some non-critical log data may have lower timeliness requirements, while real-time monitoring of vital signs data or control commands may have very high timeliness requirements. The generation of the data timeliness coefficient can be preset according to the type of business data. For example, business data can be divided into "high timeliness," "medium timeliness," and "low timeliness" levels, and a corresponding timeliness value can be assigned to each level. Alternatively, the timeliness requirement can be considered high when the difference between the timestamp generated by the data and the current time exceeds a certain threshold.

[0023] Based on the dynamic urgency coefficient and the data timeliness coefficient, coupling weight factors and demand mapping factors are obtained. This step aims to comprehensively consider the dynamics of the peripheral environment and the timeliness of business data to generate comprehensive parameters for subsequent adjustments. For example, the coupling weight factor can be obtained by taking a simple arithmetic mean or weighted average of the dynamic urgency coefficient and the data timeliness coefficient. The demand mapping factor can be obtained by multiplying the dynamic urgency coefficient and the data timeliness coefficient or by taking the maximum value. These factors will reflect the current comprehensive broadcast demand intensity.

[0024] A third feature set characterizing the current BLE broadcast channel quality is obtained, and a weighted coupling operation is performed on this third feature set using a coupling weight factor to generate a demand-channel coupling coefficient. This step aims to assess the current wireless channel environment's capacity to support broadcast demands. For example, the third feature set may include parameters such as channel occupancy rate and channel interference level. Channel quality assessment can be based on historical data statistics or real-time monitoring. The weighted coupling operation can employ a simple linear weighting method, multiplying parameters such as channel utilization and broadcast packet loss rate with the coupling weight factor and summing the results to reflect the adaptability of the channel environment under current demands.

[0025] A fourth feature set representing the hardware resource adequacy of peripheral devices is obtained, and this fourth feature set is minimized and synthesized based on the principle of the weakest link effect to generate constraint coefficients. This step aims to consider the hardware limitations of the peripheral device itself and avoid device performance degradation or failure due to over-broadcasting. For example, the fourth feature set may include remaining battery power, chip temperature, memory usage, etc. Minimization synthesis based on the principle of the weakest link effect can be achieved by simply normalizing the various indicators in the fourth feature set and taking the minimum value as the constraint coefficient. For example, when the battery power is below a certain threshold, the broadcast intensity should be reduced even if other resources are sufficient.

[0026] Based on the demand mapping factor, demand-channel coupling coefficient, and constraint coefficients, the dynamic range between the minimum and maximum broadcast intervals allowed by the protocol is adjusted and corrected in multiple stages to determine the target BLE broadcast interval. This process then controls the BLE radio frequency unit at the peripheral end to transmit broadcast packets according to the target BLE broadcast interval. This step is the core decision-making step of the entire method, integrating all factors such as environmental dynamism, data timeliness, channel conditions, and hardware limitations to determine an optimal broadcast interval. For example, the broadcast interval can first be initially adjusted between the minimum and maximum intervals based on the demand mapping factor. Subsequently, the initially adjusted interval is corrected based on the demand-channel coupling coefficient to adapt to the current channel conditions. Finally, combined with the constraint coefficients, the corrected interval is subjected to a final restrictive adjustment to ensure that the broadcast behavior does not exceed the hardware's capacity.

[0027] The aforementioned technical solution achieves fine-grained dynamic adjustment of the BLE broadcast interval by integrating multi-dimensional influencing parameters such as peripheral motion status, signal environment changes, service data timeliness requirements, BLE broadcast channel quality, and peripheral hardware resource margins in real time. Compared to existing technologies that use fixed broadcast intervals or make coarse adjustments based on only a single parameter, the method of this application can more accurately reflect the complexity of the current communication scenario. For example, in the above example, when user A is stationary and data timeliness requirements are not high, this method can intelligently extend the broadcast interval, thereby effectively reducing the idling power consumption of peripherals, extending the working life of battery-powered equipment, and alleviating channel congestion in the 2.4GHz band. When user A is moving violently or enters an interference area, this method can quickly shorten the broadcast interval to ensure the real-time transmission of critical service data and avoid data delays or loss. When peripheral hardware resources (such as battery power) are limited, this method can prioritize resource protection, appropriately extend the broadcast interval, and avoid equipment overload or premature shutdown. Therefore, the method of this application achieves a dynamic balance between power consumption, real-time performance and channel efficiency, significantly improving the adaptability and robustness of unpaired self-organizing network communication between smart tablets and peripherals.

[0028] This application further proposes a method for calculating the dynamic urgency coefficient as follows: The first feature set is obtained, which includes the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interaction within a time window. The rate of change of acceleration amplitude refers to the instantaneous rate of change of the peripheral's acceleration or its average amplitude over a period of time, reflecting the intensity of the peripheral's motion. For example, it can be obtained by calculating the absolute value of the amplitude difference of the accelerometer within a continuous sampling period, or by taking the rate of change of the envelope after high-pass filtering the acceleration amplitude. The rate of change of relative RSSI refers to the rate of change or amplitude of the signal strength indication (RSSI) received by the peripheral over a period of time, reflecting the stability or trend of the signal environment between the peripheral and the smart tablet. For example, it can be obtained by calculating the difference between the RSSI values ​​of continuously received broadcast packets, or by calculating the rate of change after performing a moving average of the RSSI values. The frequency of interaction within a time window refers to the number of times data interaction is successfully performed between the peripheral and the smart tablet within a preset time window, reflecting the activity level of communication between the peripheral and the smart tablet. For example, it can count the number of broadcast packets successfully sent or received by peripheral devices in the last 5 or 10 seconds, or count the number of times a connection is established and data is transmitted between peripheral devices and smart tablets within a specific time period.

[0029] Normalize the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within the time window to obtain values ​​ranging from [value range missing]. The acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistical index within the time window are calculated, and the acceleration amplitude change rate, relative RSSI change rate, and interaction frequency within the time window are positively correlated with the acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistical index within the time window, respectively; the normalization method for the acceleration amplitude change rate, relative RSSI change rate, and interaction frequency within the time window is as follows: The current rate of change of acceleration amplitude and the frequency of interactions within the time window are compared with the upper limit of the saturation of the rate of change of acceleration amplitude and the saturation threshold of the frequency of interactions within the time window, respectively. After truncating the upper limit of the ratio to 1 using a min function, the rate of change of acceleration amplitude and the frequency of interactions within the time window are obtained. Similarly, the absolute value of the current relative RSSI rate of change is compared with the upper limit of the saturation of the rate of change of RSSI. After truncating the upper limit of the ratio to 1 using a min function, the relative RSSI rate of change index is obtained.

[0030] The saturation limits for acceleration amplitude change rate, interaction frequency within a time window, and RSSI change rate are used to define the boundary values ​​for normalization processing, ensuring that the original data does not exceed the preset effective range when mapped to the exponent. These limits and thresholds can be determined based on the physical characteristics of the peripheral device, the sensor range, empirical data from actual application scenarios, or system design specifications. For example, the saturation limit for acceleration amplitude change rate can be set to the maximum acceleration change rate that the peripheral device may reach during violent motion, or it can be set according to the physical limits of the sensor output; the interaction frequency within a time window can be set according to the service's requirements for interaction density or the system's processing capacity; and the saturation limit for RSSI change rate can be determined based on the typical range of wireless signal propagation characteristics and environmental interference. The setting of these parameters aims to provide a stable reference benchmark for subsequent ratio processing, avoiding distortion of the normalization result due to the original data being too large or too small. The purpose of ratio processing is to uniformly map the original data with different dimensions and numerical ranges to a relative scale, enabling effective comparison and fusion. By comparing the current value with the corresponding saturation limit or threshold, a preliminary normalization result can be obtained. Subsequently, the upper limit of the ratio is truncated to 1 using the min function to ensure that the normalized exponent value does not exceed 1. This not only prevents numerical overflow but also ensures that these exponents stably represent their relative strength between 0 and 1, i.e., the percentage or proportion of the current state relative to the saturation state. For example, when the rate of change of acceleration amplitude reaches or exceeds its saturation upper limit, its exponent value will be 1, indicating that the factor has reached its maximum urgency. Regarding the relative RSSI rate of change, since its rate of change can be positive (signal enhancement) or negative (signal weakening), its absolute value better reflects the drastic change in the signal environment. Dramatic changes usually indicate instability in the communication environment or rapid movement of peripherals, thus increasing the urgency of the broadcast. Therefore, normalizing its absolute value ensures that regardless of whether the signal is enhancing or weakening, a large change in amplitude results in a high exponent value. Similarly, by comparing it to the saturation upper limit of the RSSI rate of change and truncating it using the min function, the exponent of the relative RSSI rate of change is ensured to remain stable within the range of 0 to 1, accurately reflecting the urgency of changes in the signal environment.

[0031] The acceleration amplitude change rate index, the relative RSSI change rate index, and the interaction frequency statistical index within the time window are weighted and fused, and a saturated nonlinear mapping is applied to the weighted fusion result to obtain the dynamic urgency coefficient. The saturated nonlinear mapping is configured such that: when the weighted fusion result approaches zero, the dynamic urgency coefficient approaches zero; when the weighted fusion result increases, the dynamic urgency coefficient monotonically increases and asymptotically converges to 1. The specific formula for calculating the dynamic urgency coefficient is as follows: in, This is a dynamic urgency coefficient. The rate of change of acceleration amplitude is the exponent. The relative RSSI rate of change index This is a statistical index of interaction frequency within a time window. , and For the range of values The dynamic urgency weight, and Dynamic urgency weight , and These parameters, used to adjust the relative importance of each parameter in the first feature set in the dynamic urgency assessment, are configurable and allow the system to flexibly adjust the emphasis on the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within a time window based on different application scenarios, peripheral types, or user preferences. For example, in motion-sensitive applications, the weight of the rate of change of acceleration amplitude can be increased; in scenarios with high requirements for connection stability, the weight of the rate of change of relative RSSI can be increased.

[0032] The characteristic of this formula is that when the internal weighted sum is small, The value increases slowly; as the weighted sum increases, The value will increase rapidly, but will always approach 1 and will not exceed 1. This non-linear fusion method can better simulate the non-linear characteristics of urgency perception in real-world scenarios. For example, when multiple urgency indicators reach high levels simultaneously, the overall urgency will be significantly enhanced. Dynamic Urgency Coefficient It is an indicator that comprehensively quantifies the broadcast urgency caused by changes in the peripheral device's motion state and signal environment. Its value range is... The larger the value, the greater the urgency of the broadcast. This coefficient serves as an important input for subsequent broadcast interval adjustment, guiding the system to make more intelligent decisions under different dynamic environments.

[0033] This scheme first identifies acceleration amplitude change rate, relative RSSI change rate, and interaction frequency within a time window as key indicators characterizing the dynamic urgency of peripherals. These indicators capture the dynamic changes of peripherals from three dimensions: the intensity of peripheral movement, the stability of the signal environment, and the activity of communication. To enable effective comparison and fusion of these heterogeneous data, the system performs a normalization mapping, converting their original values ​​into a unified exponential form with a value range of [0,1], namely the acceleration amplitude change rate exponent, the relative RSSI change rate exponent, and the interaction frequency statistical exponent within the time window. This normalization process eliminates dimensional differences and ensures the fairness of subsequent calculations. Subsequently, to reflect the relative importance of different indicators in specific application scenarios, a dynamic urgency weight is introduced. , and These weights allow the system to be flexibly configured according to actual needs. For example, in scenarios requiring rapid response to changes in motion, a higher weight can be assigned to the rate of change of acceleration amplitude. Finally, these normalized exponents and their corresponding weights are substituted into the nonlinear fusion formula. This formula cleverly combines multiple factors to generate a dynamic urgency coefficient between [0,1). The exponential form of this formula ensures that when any one or more input metrics are high, It will increase rapidly, thus sensitively reflecting the urgency of broadcasting. A higher value indicates more drastic changes in the peripheral's motion state or signal environment, and a higher requirement for the real-time performance of the broadcast. Through the above calculations, this scheme provides a crucial dynamic urgency coefficient for determining the target BLE broadcast interval. This coefficient, as an important input for evaluating the dynamic needs of the peripheral, together with the data timeliness coefficient, influences the generation of the coupling weight factor and the demand mapping factor, thus participating in the fine-tuning of the broadcast interval. This multi-dimensional, weighted, and nonlinear fusion calculation method enables the dynamic urgency coefficient to more accurately and sensitively reflect the actual dynamic urgency of the peripheral, thereby providing a solid foundation for subsequent adaptive adjustment of the broadcast interval and solving the problem of traditional methods lacking precise quantification and comprehensive consideration when evaluating dynamic urgency.

[0034] In one specific implementation, the peripheral device can periodically (e.g., every 100 milliseconds) collect accelerometer data and calculate its rate of change of acceleration amplitude over the most recent second. Simultaneously, the peripheral device continuously monitors the RSSI value of received smart tablet broadcast packets and calculates its relative RSSI rate of change over the most recent 5 seconds. Furthermore, the peripheral device maintains a counter to record the number of successful interactions with the smart tablet (e.g., sending or receiving acknowledgment packets) within the most recent 10 seconds, serving as the interaction frequency within a time window. Assuming that at a certain moment, the peripheral device detects an acceleration amplitude change rate of 0.5 g / s and a relative RSSI change rate of -10 dBm / s (indicating a rapid decrease in signal strength), the interaction frequency within the time window is 8 times. For normalization, a saturation upper limit of 1 g / s for the acceleration amplitude change rate, a saturation upper limit of 20 dBm / s for the relative RSSI change rate, and a saturation threshold of 10 interactions within the time window can be preset. This can be achieved through a normalization mapping, such as using a linear mapping with a truncation upper limit of 1: the acceleration amplitude change rate exponent. =min(0.5 / 1,1)=0.5; Relative RSSI rate of change index =min(abs(-10) / 20,1)=0.5; Interaction frequency statistics index within the time window =min(8 / 10,1)=0.8. Assume the preset dynamic urgency weights are respectively... =0.4, =0.3, =0.3 (satisfying the sum of 1). Substitute these exponents and weights into the formula: The calculated dynamic urgency coefficient Approximately 0.446. This value will serve as input for calculating the coupling weight factor and demand mapping factor in subsequent steps, thus affecting the determination of the final target BLE broadcast interval. For example, when the peripheral is stationary and the signal is stable, the various indices and... A value close to 0 indicates low broadcast urgency; when peripherals are in violent motion or the signal deteriorates sharply, all indices and The rapid increase indicates a high degree of urgency in broadcasting, necessitating shorter broadcast intervals.

[0035] Through the above technical solution, this application provides a method for accurately quantifying the dynamic urgency of peripherals. By comprehensively considering multi-dimensional characteristics such as the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within a time window, and introducing a configurable dynamic urgency weight, the system can flexibly and accurately assess the urgency of broadcasts based on the actual motion state of the peripherals and changes in the signal environment. In particular, by normalizing and mapping these characteristics, the influence of data with different dimensions is eliminated, ensuring the fairness of the assessment; furthermore, through a nonlinear fusion formula, the dynamic urgency coefficient is... It can sensitively reflect changes in urgency under the combined effect of multiple factors. This precise and adaptive dynamic urgency coefficient calculation method provides a reliable and refined input basis for determining the dynamic adjustment of the target BLE broadcast interval. It enables the system to accurately identify high demands for broadcast real-time performance when peripherals are in turbulent motion, the signal environment is deteriorating, or interactions are frequent, thereby guiding the broadcast interval to be adjusted towards shorter intervals, ensuring the timely transmission of critical business data and avoiding data delays or loss. Meanwhile, when peripherals are in a stable state... A lower value helps the system determine whether the broadcast interval can be appropriately extended, thereby effectively reducing the power consumption of peripherals, extending battery life, reducing channel occupancy in the 2.4GHz band, and optimizing overall communication efficiency.

[0036] This application further proposes a method for calculating the data timeliness coefficient as follows: The second feature set is obtained, which includes the sensor value change rate, information age (AoI), and time since the last successful interaction. The sensor value change rate reflects the dynamic nature of the data itself, such as the degree of fluctuation of physical quantities like temperature, humidity, and acceleration over a short period of time. The information age (AoI) measures the time elapsed from data generation to reception, reflecting the "freshness" of the data. The time since the last successful interaction reflects the activity level of inter-device communication and potential connection stability requirements. These features can be obtained in real time from sensor data, communication module status, or system logs on the peripheral device.

[0037] Normalize the sensor numerical change rate, information age (AoI), and time since the last successful interaction by performing normalized mappings to obtain values ​​ranging from [value range to be filled in]. The sensor value change rate index, information age AoI index, and time since last successful interaction index are defined, and these three indices are directly proportional to the sensor value change rate index, information age AoI index, and time since last successful interaction index, respectively. The normalization methods for the sensor value change rate, information age AoI, and time since last successful interaction are as follows: The current sensor value change rate, information age (AoI), and time since the last successful interaction are compared with the upper limit of sensor value change rate saturation, the maximum information age allowed by the business, and the saturation threshold of time since the last successful interaction, respectively. A min function is then used to truncate the upper limit of the ratio to 1, yielding the sensor value change rate index, information age (AoI) index, and time since the last successful interaction index. The upper limit of sensor value change rate saturation defines the effective measurement range of sensor value change rate or the highest change rate of concern to the business. When the sensor value change rate reaches or exceeds this upper limit, its impact on data timeliness is considered to be at its maximum. This upper limit can be preset according to specific business scenarios and sensor types. For example, for temperature sensors, the saturation upper limit can be set to 5 degrees Celsius per second; for pressure sensors, it can be set to 10 Pascals per second. Another implementation method is to dynamically adjust this upper limit through historical data analysis or expert experience to adapt to sensor characteristics under different working modes or environmental conditions. The maximum information age allowed by the business represents the business's tolerance for data freshness, i.e., the maximum time interval allowed from data generation to processing or use. Data exceeding this time interval will have significantly reduced timeliness value. For example, in a real-time control system, the maximum information age allowed by the business might be set to 100 milliseconds; while in a non-real-time monitoring system, this value might be set to several seconds or even longer. This maximum information age can be hard-coded by the system designer according to application requirements, or allowed to be adjusted by users or administrators through a configuration interface. The saturation threshold of the time since the last successful interaction is used to measure the degree of communication interruption or inactivity between the peripheral device and the smart tablet. When the time since the last successful interaction reaches or exceeds this threshold, it indicates that there may be a problem with the communication link or that the peripheral device has not sent valid data for a long time, at which point the urgency of data transmission will increase significantly. This threshold can be determined according to the timeout mechanism of the communication protocol or the business's requirements for connection stability; for example, it can be set to 30 seconds. Another way to determine it is to dynamically calculate the threshold using an adaptive algorithm based on the network topology and device mobility. Ratio processing aims to convert physical quantities such as the original sensor value change rate, information age (AoI), and time since the last successful interaction into dimensionless relative values. By comparing the current measurement value with the corresponding upper saturation limit or threshold, the numerical ranges of different types of features can be standardized, making them comparable in subsequent calculations. For example, the current rate of change of the sensor value can be divided by the upper saturation limit of the sensor value's rate of change to obtain a ratio between 0 and a positive number. Another way to handle ratios is to transform the original value using a logarithmic or exponential function before performing the ratio processing to accommodate certain nonlinear characteristics. The min function's operation of truncating the upper limit of the ratio to 1 ensures that the result after ratio processing will not exceed 1.When the original measured value exceeds its corresponding saturation upper limit or threshold, the ratio may be greater than 1. In this case, the min function truncates it to 1, indicating that the influence of this feature has reached its maximum saturation state. This effectively prevents extreme outliers from having an excessive impact on subsequent calculations, maintaining the stability of the normalization results. Besides the min function, other saturation functions, such as the sigmoid or tanh functions, can be used to map the ratio to the [0,1] interval, but the min function provides a more direct hard truncation method. The sensor numerical change rate index is a quantitative representation of the sensor numerical change rate after normalization, with a value range of [0,1]. This index reflects the drastic nature of sensor numerical changes; a larger value indicates a more drastic change and a more urgent need for data updates. The Information Age (AoI) index is a quantitative representation of the Information Age (AoI) after normalization, with a value range of [0,1]. This index reflects the freshness of the data; a larger value indicates older data and a more urgent need for data updates. The Time Since Last Successful Interaction Index is a normalized quantification of the time since the last successful interaction, with a value ranging from [0,1]. This index reflects the activity level of the communication link or potential connection risks; a higher value indicates a longer period of inactivity and a more urgent need for data updates.

[0038] The sensor numerical change rate index, Information Age (AoI) index, and Time Since Last Successful Interaction index are weighted and fused, and a saturated nonlinear mapping is applied to the weighted fusion result to obtain the data timeliness coefficient. The saturated nonlinear mapping is configured such that: when the weighted fusion result approaches zero, the data timeliness coefficient approaches zero; when the weighted fusion result increases, the data timeliness coefficient monotonically increases and asymptotically converges to 1. The specific formula for calculating the data timeliness coefficient is as follows: in, This is the data timeliness coefficient. This is the exponent of the rate of change of sensor values. Information Age (AoI) Index This is the index of time since the last successful interaction. , and For the range of values The weight of data timeliness, and Data timeliness weight , and These are parameters used to quantify the importance of different timeliness characteristics. These weights can be preset according to specific application scenarios and business needs. For example, for control data with high real-time requirements, higher weights can be assigned to the sensor value change rate and the Age of Information (AoI); for scenarios requiring stable connections, the weight of the time since the last successful interaction can be appropriately increased. These weights can also be dynamically adjusted using machine learning algorithms based on historical data and user behavior to adapt to the ever-changing business environment.

[0039] The characteristic of this formula is that when the weighted sum... When smaller, A value close to 0 indicates that the timeliness of the data is not urgent; as the weighted sum increases, The value increases rapidly and approaches 1, indicating a very urgent need for data timeliness. This non-linear mapping amplifies the urgency signal, making the system more sensitive to changes in data timeliness. Data Timeliness Coefficient It is between The values ​​between these two ranges comprehensively reflect the urgency of current business data regarding refresh frequency. A higher value indicates a more urgent need for data refresh, and the system should tend to use shorter broadcast intervals to ensure timely data transmission. Conversely, a lower value indicates a more urgent need for data refresh. The smaller the value, the less urgent the data refresh requirement, and the system can appropriately extend the broadcast interval to save power consumption.

[0040] In this embodiment of the invention, the solution acquires and integrates a multi-dimensional second feature set, including sensor numerical change rate, Information Age (AoI), and time since the last successful interaction, to comprehensively capture the dynamic changes in data timeliness. These features undergo normalization mapping to eliminate the influence of different units, ensuring the fairness of the evaluation. Subsequently, by introducing data timeliness weights, the importance of each feature can be adjusted according to actual business needs. Finally, these weighted exponents are substituted into an exponential decay function to generate a data timeliness coefficient. This coefficient can accurately quantify the urgency of data refresh requirements, and its non-linear characteristics make the system more sensitive to changes in urgency. This comprehensive evaluation method avoids the one-sidedness of single parameters or simple calculation methods in traditional approaches, enabling the data timeliness coefficient to more accurately and comprehensively reflect actual needs, providing a reliable basis for subsequent broadcast interval adjustments. When combined with steps such as responding to the service data to be transmitted and obtaining the first feature set in the basic scheme, this scheme can more finely evaluate the overall transmission requirements of service data, thereby providing a more optimized broadcast strategy for unpaired self-organizing network communication between smart tablets and peripherals.

[0041] As a specific implementation, the peripheral device can be a smart pen, which integrates an inertial measurement unit (IMU) such as an accelerometer and gyroscope, as well as a pressure sensor for recording the writing trajectory. When the user writes with the smart pen, a large amount of motion and pressure data is generated. At this time, the peripheral device can monitor this sensor data in real time. For example, the upper limit of the sensor value change rate saturation can be set to 10 units / second, the maximum information age allowed by the business can be 5 seconds, and the saturation threshold of the time since the last successful interaction can be 30 seconds. For example, suppose the current sensor value change rate is 8 units / second, the information age AoI is 3 seconds, and the time since the last successful interaction is 20 seconds. First, the ratios are processed: the ratio of the sensor value change rate is 8 / 10 = 0.8; the ratio of the information age AoI is 3 / 5 = 0.6; and the ratio of the time since the last successful interaction is 20 / 30 ≈ 0.67. Then, the upper limit of the ratio is truncated to 1 using the min function: since all ratios are less than 1, the result remains unchanged after truncation. Ultimately, the sensor value change rate index is 0.8, the information age (AoI) index is 0.6, and the time since the last successful interaction index is 0.67. For another example, suppose the current sensor value change rate is 12 units / second, the information age (AoI) is 6 seconds, and the time since the last successful interaction is 40 seconds. Ratio processing is performed: the ratio of sensor value change rate is 12 / 10 = 1.2; the ratio of information age (AoI) is 6 / 5 = 1.2; and the ratio of time since the last successful interaction is 40 / 30 ≈ 1.33. Using the min function to truncate the ratios to an upper limit of 1: the sensor value change rate index is min(1.2,1) = 1; the information age (AoI) index is min(1.2,1) = 1; and the time since the last successful interaction index is min(1.33,1) = 1. Using the above method, regardless of how the original data changes, its normalized exponent always remains within the range of [0,1], providing a stable and standardized input for the subsequent calculation of the data timeliness coefficient, thus obtaining the sensor numerical change rate exponent. Information Age AoI Index and the time since the last successful interaction index Finally, these indices, along with preset data timeliness weights (e.g., ...), =0.5, =0.3, =0.2) Substitute into the formula Calculate the data timeliness coefficient When users write quickly, data changes drastically, and there is no interaction with the tablet for an extended period of time, , and The value will be higher, thus making A value close to 1 indicates that the need for data refresh is very urgent.

[0042] Through the above technical solution, this application can accurately quantify the timeliness requirements of business data, solving the problems of incomplete and inaccurate evaluation in traditional methods. By comprehensively considering multiple dimensions such as sensor value change rate, information age (AoI), and time since the last successful interaction, and performing weighted normalization processing, the data timeliness coefficient can more comprehensively and accurately reflect the urgency of data refresh requirements. This accurate evaluation provides a reliable basis for the subsequent dynamic adjustment of BLE broadcast intervals, thereby ensuring the real-time performance of critical business data while effectively avoiding unnecessary power consumption waste and channel congestion, achieving a refined dynamic balance between power consumption and real-time performance.

[0043] This application further proposes the following calculation methods for the coupling weight factor and the demand mapping factor: A central tendency fusion operation is performed on the dynamic urgency coefficient and the data timeliness coefficient to obtain a coupling weight factor. The coupling weight factor takes its maximum value when both the dynamic urgency coefficient and the data timeliness coefficient are at their maximum values; the coupling weight factor is monotonically non-increasing when either the dynamic urgency coefficient or the data timeliness coefficient decreases. The central tendency fusion operation is as follows: in, For coupling weighting factors, This is a dynamic urgency coefficient. This is the data timeliness coefficient.

[0044] The dynamic urgency coefficient and data timeliness coefficient are geometrically averaged to obtain the demand mapping factor; the geometric average processing is as follows: in, As a demand mapping factor, This is a dynamic urgency coefficient. This is the data timeliness coefficient.

[0045] In the above scheme, the dynamic urgency coefficient This coefficient characterizes the urgency of changes in the peripheral device's motion state and signal environment. The concept lies in quantifying the dynamics of the physical and communication environments in which the peripheral device operates, such as its speed, signal strength fluctuations, and interaction frequency with the smart tablet. When the peripheral device is in a state of rapid movement, unstable signal, or frequent interaction, this coefficient will be higher, indicating a more urgent broadcasting need. This coefficient can be obtained based on real-time monitoring and analysis of various sensor data, such as acceleration, RSSI (Received Signal Strength Indication), and interaction frequency. In addition to the above methods, this dynamic urgency coefficient can also be predicted and generated using machine learning models, combining historical data and current environmental characteristics. (Data Timeliness Coefficient) This coefficient characterizes the timeliness requirements of the data to be transmitted. The concept quantifies the effective time window from data generation to reception, as well as the rate of change of the data content itself. For example, this coefficient will be higher for control commands or sensor mutation data with extremely high real-time requirements, while it may be lower for periodically reported log data. This coefficient can be obtained based on a comprehensive evaluation of parameters such as the rate of change of sensor values, Information Age (AoI), and time since the last successful interaction. Besides the above methods, this data timeliness coefficient can also be determined through preset service types, user configuration, or dynamic adjustment based on the importance of the data content.

[0046] The central tendency fusion calculation aims to use an arithmetic mean to represent the dynamic urgency coefficient. and data timeliness coefficient To perform fusion and obtain coupling weight factors This calculation method reflects the overall average level of the two input coefficients, ensuring that when both are at a high level, the fusion result is also high, reflecting the overall urgency. Simultaneously, when either input coefficient decreases, the fusion result becomes monotonically non-increasing, avoiding excessive influence from a single factor and making the fusion result more robust. Besides the arithmetic mean, central tendency fusion can also use weighted average, median, or mode, but this application explicitly uses the arithmetic mean to provide a simple and effective fusion strategy. Coupling weight factors It is a dynamic urgency coefficient and data timeliness coefficient The result of the centralized trend fusion calculation is used to comprehensively reflect the dynamic environmental changes of current peripherals and the timeliness requirements of service data. This factor plays an important role in subsequent channel coupling calculations, and its value directly affects the degree to which the broadcast interval responds to changes in the channel environment. This factor can be understood as a comprehensive assessment of the overall urgency of the current broadcast demand by the system; the larger the value, the more urgent the overall demand.

[0047] The geometric mean processing aims to use a geometric averaging method to reduce the dynamic urgency coefficient. and data timeliness coefficient To perform fusion and obtain demand mapping factors Geometric mean is particularly well-suited for assessing the synergistic effect of multiple factors. It is more sensitive to lower input values ​​and better captures the limiting effect on the overall outcome when any single factor is low. This approach more precisely reflects the multiplicative relationship between dynamic urgency and data timeliness; that is, the demand mapping factor only becomes significant when both are high. This will significantly increase the actual broadcast demand intensity, thus more accurately mapping the strength of the demand. Besides the geometric mean, harmonic mean or more complex nonlinear functions can also be considered, but the geometric mean provides an effective method to emphasize synergy while avoiding the influence of extreme values. Demand Mapping Factor It is a dynamic urgency coefficient and data timeliness coefficient The result after geometric averaging is used to map the aggregated demand intensity to the dynamic adjustment range of the broadcast interval. This factor directly participates in the calculation of the theoretical target broadcast interval, and its value determines the specific position of the broadcast interval between the minimum and maximum allowable values. This factor can be understood as a refined quantification of the current broadcast demand intensity by the system; the larger the value, the stronger the broadcast demand, and the shorter the broadcast interval may be required.

[0048] The solution in this application uses a dynamic urgency coefficient. and data timeliness coefficient Perform refined fusion processing to generate coupling weight factors. and demand mapping factor This provides accurate input for subsequent adaptive adjustment of the BLE broadcast interval. Specifically, the peripheral device first obtains a dynamic urgency coefficient based on its motion state, changes in the signal environment, and the timeliness requirements of the data to be transmitted. and data timeliness coefficient To comprehensively assess the urgency of these two different dimensions, this solution employs two complementary fusion strategies. On the one hand, it uses a central tendency fusion operation, i.e., an arithmetic mean, to... and Combine to generate coupling weight factors This arithmetic averaging method ensures that data is available even in highly dynamic peripheral environments with strict timeliness requirements. The value is also correspondingly high, reflecting the overall level of urgency. Meanwhile, its monotonically non-increasing characteristic ensures the robustness of the fusion results, avoiding the excessive influence of drastic fluctuations in a single factor on the overall assessment. On the other hand, through geometric averaging, the... and Combined with the generation of demand mapping factors The geometric mean's properties make this factor more sensitive to the synergistic effect of the two input coefficients; it is only effective when both dynamic urgency and data timeliness are high. This will significantly increase the accuracy of capturing the actual intensity of broadcast demand. This dual-factor, dual-fusion strategy allows the system to comprehensively evaluate broadcast demand from different perspectives, where the coupling weighting factor... Primarily used for subsequent coupling with channel quality, while requiring a mapping factor. This is then directly used for the preliminary determination of the theoretical broadcast interval. In this way, the scheme can more effectively integrate dynamic urgency and data timeliness, ensuring the accuracy and balance of broadcast interval adjustment, thereby achieving an optimized balance between power consumption and real-time performance in a dynamic environment.

[0049] The following is a concrete example to illustrate this. Suppose that at the peripheral device end, the dynamic urgency coefficient is obtained by calculating the first set of features, including the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within a time window. A value of 0.8 indicates that the peripheral device is in a state of rapid movement and frequent interaction. Simultaneously, the data timeliness coefficient is obtained by calculating a second set of features, including the sensor value change rate, Information Age (AoI), and time since the last successful interaction. A value of 0.9 (indicating extremely high timeliness requirements for current business data, such as real-time control commands). In this case, to calculate the coupling weight factor... ,Will and Substitute into the central tendency fusion calculation formula: =0.85. The resulting coupling weight factor. The value is 0.85, indicating that the dynamic environment and data timeliness requirements of current peripherals are generally very urgent. Next, in order to calculate the demand mapping factor... ,Will and Substitute into the geometric mean formula: =0.8485. The obtained demand mapping factor. It is approximately 0.8485. This is relatively high. The value will directly affect the calculation of the theoretical target broadcast interval, making it more inclined to a shorter broadcast interval to meet the current high urgency and timeliness requirements. In this way, this solution can dynamically generate factors that reflect the needs of peripherals based on their actual operating status and the characteristics of service data, providing a precise basis for subsequent broadcast interval adjustments.

[0050] Through the above technical solution, this application effectively solves the problem of insufficient precision in broadcast interval decision-making due to inaccurate factor calculation in traditional schemes. By combining central tendency fusion calculation and geometric mean processing, this scheme achieves a dynamic urgency coefficient. and data timeliness coefficient A comprehensive and refined integration. Coupling weighting factors. The arithmetic mean property ensures a robust assessment of the overall urgency level, avoiding the excessive influence of a single factor, thus enabling a more reasonable balance between demand and channel conditions in subsequent channel coupling. Demand mapping factor The geometric mean characteristic emphasizes dynamic urgency. and data timeliness The synergistic effect between these factors ensures that the demand intensity only significantly increases when both factors are high, thus providing a more accurate mapping relationship for determining the theoretical target broadcast interval. This dual-factor, dual-fusion strategy enables the system to more accurately capture the actual needs of peripherals, thereby better balancing power consumption and real-time performance in subsequent broadcast interval adjustments. This avoids power waste or data delay loss caused by improper broadcast interval settings, improving the adaptability and efficiency of unpaired ad hoc network communication.

[0051] This application further proposes a method for calculating the demand-channel coupling coefficient as follows: Obtain the third feature set, which includes channel utilization and broadcast packet loss rate; The channel congestion metric is obtained by summing the channel utilization rate and the broadcast packet loss rate. Channel utilization reflects the degree of channel occupancy or busyness. For example, it can be obtained by monitoring the proportion of time a specific BLE channel is occupied by other devices for data transmission over a period of time, or by evaluating the channel status information provided by the BLE controller. The broadcast packet loss rate reflects the proportion of broadcast packets lost during transmission. For example, it can be calculated by statistically analyzing the difference between the number of broadcast packets sent by the peripheral device and the number of broadcast packets successfully received by the smart tablet, or by estimating it based on the receiver's feedback on the failure to receive expected broadcast packets. The dynamic urgency coefficient is an indicator characterizing the peripheral device's movement state and changes in the signal environment; a higher value indicates a stronger broadcast urgency. The data timeliness coefficient is an indicator characterizing the timeliness requirements of the data to be transmitted; a higher value indicates a more urgent data refresh requirement.

[0052] The demand-channel coupling coefficient is obtained by performing a weighted attenuation operation on the channel congestion metric using a coupling weight factor. The weighted attenuation operation is configured such that the demand-channel coupling coefficient monotonically decreases as the channel congestion metric increases; and the larger the coupling weight factor, the slower the rate at which the demand-channel coupling coefficient attenuates with increasing channel congestion metric. The specific formula for calculating the demand-channel coupling coefficient is as follows: in, For demand-channel coupling coefficient, , A larger value indicates a stronger ability of the current channel environment to adapt to broadcast demands. For coupling weighting factors, For channel utilization, This refers to the broadcast packet loss rate.

[0053] The solution in this application achieves a specific exponential formula by substituting channel utilization, broadcast packet loss rate, dynamic urgency coefficient, and data timeliness coefficient into the demand-channel coupling coefficient. The method first obtains the channel utilization rate, which reflects the actual current channel conditions, for precise calculation. and broadcast packet loss rate At the same time, it incorporates the dynamic urgency coefficient. and data timeliness coefficient Coupled weighting factor formed by fusion In the formula middle, As the coefficient of the exponential term, the channel quality parameter The degree of impact can be dynamically adjusted based on the urgency of current business needs. The more urgent the business needs (i.e.,...) When the value is larger, even a small change in channel quality will have a significant impact. The value has a greater impact, making the system more sensitive to the channel environment. The use of the exponential function ensures... Value at Within a certain range, it provides a non-linear mapping relationship, which can smoothly reflect the transition of channel quality from good to deterioration. When channel utilization or broadcast packet loss rate increases, An increase in the value of leads to an increase in the absolute value of the negative value of the exponent term, thereby making A decrease in the value accurately reflects a decline in the channel environment's adaptability to broadcast demands. Conversely, when the channel environment is favorable, The value approaches 1. This calculation method, which comprehensively considers channel quality and service requirements, makes the demand-channel coupling coefficient... It can accurately quantify the adaptability of the current channel environment to broadcast requirements, providing a refined basis for the dynamic adjustment of the target BLE broadcast interval, thereby optimizing the overall performance of the unpaired self-organizing network communication method between smart tablets and peripherals.

[0054] As a specific implementation method, the peripheral device can periodically monitor the channel utilization of the BLE channel it is in. and broadcast packet loss rate For example, the peripheral's BLE module can be configured to perform channel scanning during non-broadcast periods, counting the energy or number of data packets of other BLE signals detected within a specific time window to estimate channel utilization. Broadcast packet loss rate can be calculated by having the peripheral send a broadcast packet; if no response or acknowledgment is received from the smart tablet within a preset time, the broadcast packet is considered likely lost and accumulated. Simultaneously, the peripheral calculates a dynamic urgency coefficient based on its own motion state, changes in the signal environment, and the timeliness requirements of the data to be transmitted. and data timeliness coefficient These coefficients are further processed through a central tendency fusion operation to obtain the coupling weight factor. Subsequently, the data will be acquired in real time. , and the calculated Substitute into the formula The current demand-channel coupling coefficient can then be calculated. For example, when channel utilization is high and broadcast packet loss rate is also high, even if the service demand is not urgent, The value will also be large, leading to A significant decrease in the value indicates a poor channel environment, making it difficult to meet broadcast requirements. Conversely, if the channel is idle and the packet loss rate is low, then... A larger value indicates a good channel environment.

[0055] Through the above technical solution, this application can accurately quantify the adaptability of the current channel environment to broadcast requirements, solving the problems of coarse adjustment granularity and inability to comprehensively integrate the synergistic effects of channel interference and service requirements in existing methods. This method, by introducing a coupling weight factor, allows the assessment of channel quality to be closely integrated with the dynamic urgency and data timeliness requirements of peripherals, achieving a refined perception of channel environment adaptability. This enables the unpaired self-organizing network communication method between smart tablets and peripherals to more accurately reflect the actual communication environment and service requirements when determining the target BLE broadcast interval. This ensures the real-time nature of critical service data while effectively avoiding power waste and channel congestion caused by improper broadcast interval settings, thus improving communication efficiency and system stability.

[0056] This application further proposes a method for calculating the constraint coefficient as follows: The fourth feature set is obtained, which includes the remaining battery percentage, chip junction temperature, and remaining stack space of RTOS tasks. This data can be directly collected by peripheral sensors, such as battery management unit (BMU) providing battery level data, on-chip temperature sensors providing chip junction temperature, and the real-time operating system (RTOS) providing task stack usage. Alternatively, this data can be obtained through firmware or driver interfaces. The remaining battery percentage directly reflects the device's battery life and is typically measured and calculated in real-time by the battery management chip using voltage and current integration. The chip junction temperature reflects the chip's heat dissipation and potential overheating risk, and can be measured using an integrated temperature sensor within the chip. The remaining stack space of RTOS tasks reflects the system's memory resource constraints, preventing stack overflows that could lead to system crashes. The RTOS kernel typically provides an API to query the remaining stack space of tasks.

[0057] The ratio of the difference between the current chip junction temperature and the lower limit of the normal operating temperature is calculated with the ratio of the upper limit of the thermal throttling temperature to the lower limit of the normal operating temperature. The ratio is then truncated to the output range using the min and max functions. Then, the chip junction temperature index is obtained; its function is to standardize the original temperature value to an index between 0 and 1, so as to facilitate comprehensive calculation with other indicators. It can be implemented in the firmware of the microcontroller (MCU).

[0058] The ratio of the remaining stack space of the current RTOS task to the minimum safe stack depth threshold is processed, and the ratio is truncated to an upper limit of 1 using the min function. The complement of the ratio (i.e., one minus the ratio) is then taken to obtain the stack exhaustion risk index. Its function is to convert the stack space into a risk index. The larger the value, the higher the risk. It is also standardized to between 0 and 1 and can be implemented in the RTOS task scheduler or memory management module.

[0059] The normal operating temperature limit, the upper temperature limit for triggering thermal throttling, and the minimum safe stack depth threshold are preset reference values ​​or critical values ​​used to evaluate the hardware status, serving as a benchmark for quantifying hardware resource adequacy. These thresholds can be pre-stored in the peripheral's non-volatile memory (such as EEPROM or Flash) and loaded during system initialization, or dynamically configured via OTA (Over-The-Air) updates.

[0060] Based on the remaining battery charge percentage, the thermal safety margin corresponding to the chip junction temperature index, and the stack safety margin corresponding to the stack depletion risk index, a minimization optimization operation is performed to obtain a constraint coefficient, such that the constraint coefficient is equal to the minimum value among the remaining battery charge percentage, the thermal safety margin, and the stack safety margin; the specific formula for calculating the constraint coefficient is as follows: in, For constraint coefficients, , A smaller value indicates stronger hardware constraints and a lower maximum allowed broadcast strength. This represents the remaining battery charge percentage. This refers to the chip junction temperature index. The stack exhaustion risk index.

[0061] This implementation aims to generate a comprehensive constraint coefficient by quantifying the hardware resource sufficiency of peripheral devices to guide the adaptive adjustment of BLE broadcast intervals. Its core lies in first identifying and acquiring key indicators affecting hardware performance and stability, namely the fourth feature set, including the remaining battery percentage, chip junction temperature, and remaining stack space of RTOS tasks. To enable unified evaluation and comparison of these heterogeneous physical quantities, this solution standardizes the chip junction temperature and remaining stack space of RTOS tasks. Specifically, the chip junction temperature is calculated and truncated by comparing it with a preset normal operating temperature limit and a temperature limit triggering thermal throttling, resulting in a chip junction temperature index between 0 and 1. This index directly reflects the risk of chip overheating. Similarly, the remaining stack space of RTOS tasks is calculated and truncated by comparing it with a safe minimum stack depth threshold, and then taking its complement, resulting in a stack exhaustion risk index between 0 and 1. The higher the index, the more strained the memory resources. The remaining battery percentage, being a percentage, can be directly used as an evaluation indicator. Subsequently, based on the principle of the weakest link effect, this scheme performs a comprehensive calculation to minimize the three standardized indicators (remaining battery capacity percentage, the complement of the chip junction temperature index, and the complement of the stack depletion risk index), that is, takes the minimum value of the three as the final constraint coefficient. The logic behind this minimization approach is that the overall hardware resource margin of a peripheral device is limited by its weakest link. For example, even if the battery is fully charged, an overheated chip or running out of memory should be considered a significant hardware constraint. Therefore, A smaller value indicates stronger hardware constraints, requiring the system to adopt a more conservative broadcast strategy, such as increasing the broadcast interval to reduce power consumption and resource usage. In this way, this solution can comprehensively and accurately assess the hardware health of peripherals and transform it into a quantifiable constraint factor. This provides a reliable basis for the dynamic adjustment of the target BLE broadcast interval, avoiding communication interruptions or device failures due to insufficient hardware resources, and ensuring the stability and reliability of BLE communication in complex and ever-changing application scenarios.

[0062] In one specific implementation, the peripheral device can employ a low-power microcontroller (MCU), such as an ARM Cortex-M series processor, which integrates a temperature sensor to monitor the chip junction temperature. The battery management unit (BMU) is responsible for real-time monitoring of the remaining battery power and transmitting the battery percentage data to the MCU via the I2C bus. The real-time operating system (RTOS) running on the MCU, such as FreeRTOS or RT-Thread, provides an API interface for querying the current remaining stack space of each task through its task scheduler. When calculating constraint coefficients, the MCU first reads the current chip junction temperature from its internal temperature sensor and obtains the remaining battery percentage from the BMU. Simultaneously, it obtains the minimum remaining stack space of all critical tasks through the RTOS API and selects the smallest one as the current RTOS task's remaining stack space. Assuming a preset normal operating temperature limit of 25°C, a thermal throttling temperature limit of 85°C, and a safe minimum stack depth threshold of 128 bytes, when the MCU reads the current chip junction temperature as 65°C, the difference between this and the normal operating temperature limit is 40°C, and the difference between the thermal throttling temperature limit and the normal operating temperature limit is 60°C. The chip junction temperature index is calculated as min(max((65-25) / (85-25),0),1)=min(max(40 / 60,0),1)=min(0.667,1)=0.667. When the MCU finds that the remaining stack space of the current RTOS task is 256 bytes, its ratio to the minimum safe stack depth threshold of 128 bytes is 256 / 128=2. After truncating the upper limit of the ratio to 1 using the min function, we get 1. Taking its complement, i.e., 1-1=0, we get a stack exhaustion risk index of 0. Assume that the remaining battery percentage is 75% (i.e., 0.75). Substituting these values ​​into the formula... The final constraint coefficients The value is 0.333. This value will be used to adjust the target BLE broadcast interval, guiding the system to select a suitable broadcast interval under the current hardware resource conditions to balance performance and resource consumption.

[0063] Through the above technical solution, this application can accurately quantify the hardware resource sufficiency of peripheral devices, avoiding the problem of improper broadcast interval adjustment caused by inaccurate hardware constraint assessment in traditional solutions. Specifically, by comprehensively considering multiple key hardware indicators such as the remaining battery percentage, chip junction temperature, and remaining stack space of RTOS tasks, and by using standardized processing and the principle of minimizing the bottleneck effect, it ensures that the constraint coefficient can truly reflect the most stringent resource limitations. This allows the system to fully consider the actual hardware carrying capacity of peripherals when adjusting the BLE broadcast interval, effectively avoiding communication interruptions or device failures caused by hardware problems such as battery depletion, chip overheating, or memory overflow. For example, when the battery is low, the chip temperature is too high, or memory is scarce, the constraint coefficient will decrease significantly, prompting the system to choose a larger broadcast interval, reducing the workload and resource consumption of the RF module, extending the device's battery life, and ensuring system stability. Conversely, when hardware resources are sufficient, the constraint coefficient is higher, allowing the system to use a smaller broadcast interval to improve real-time performance based on business needs. This refined hardware constraint evaluation mechanism, combined with the consideration of service requirements and channel environment in the basic scheme, constitutes a more comprehensive and robust broadcast interval adaptive adjustment strategy, which significantly improves the reliability, stability and resource utilization efficiency of unpaired self-organizing network communication between smart tablets and peripherals.

[0064] This application further proposes a method for calculating the target BLE broadcast interval as follows: Based on the demand mapping factor, a theoretical target broadcast interval is determined between the minimum and maximum broadcast intervals allowed by the protocol, wherein the theoretical target broadcast interval decreases as the demand mapping factor increases. The minimum and maximum broadcast intervals allowed by the protocol or hardware define the physical and protocol limitations of the BLE broadcast interval. The minimum broadcast interval is the shortest broadcast period supported by the BLE protocol or a specific hardware platform, ensuring that the frequency of broadcast packet transmission is not too high, leading to system instability or power consumption runaway. The maximum broadcast interval is the longest broadcast period supported by the BLE protocol or hardware, used to prevent excessively long broadcast intervals from causing communication interruptions or failing to meet data timeliness requirements. These are the fundamental boundaries for all subsequent dynamic adjustments. These preset values ​​can be obtained by reading the configuration parameters of the BLE protocol stack or querying hardware registers, or they can be hardcoded or configured during system initialization according to predefined BLE specifications and peripheral hardware capabilities.

[0065] The specific formula for calculating the target broadcast interval is as follows: in, For the theoretical target broadcast interval, As a demand mapping factor, The minimum broadcast interval allowed by the protocol or hardware. Maximum broadcast interval allowed by the protocol or hardware; demand mapping factor This reflects the overall urgency of current business data transmission. The purpose of this step is to preliminarily determine a theoretical broadcast interval based on this urgency. (When the demand mapping factor...) The larger the value (indicating a more urgent need), the more urgent the need. The smaller the value, the better the theoretical target broadcast interval. The closer to the minimum broadcast interval This prioritizes real-time performance. Conversely, when the demand mapping factor... The smaller the value (indicating less urgent demand), the better. The larger the value, the greater the theoretical target broadcast interval. The closer to the maximum broadcast interval To save power. The processor at the peripheral end receives the demand mapping factor. Then, you can directly substitute this value into the above formula to perform floating-point operations and calculate the result. Alternatively, a lookup table can be pre-stored, based on different... Value range, mapped to the corresponding Values ​​to simplify real-time calculations.

[0066] Based on the demand-channel coupling coefficient, the theoretical target broadcast interval is adaptively adjusted to obtain the coupling-corrected broadcast interval, wherein the coupling-corrected broadcast interval increases as the demand-channel coupling coefficient decreases; the specific calculation formula for the target broadcast interval is as follows: in, To adjust the broadcast interval for coupling, For demand-channel coupling coefficient, It is a very small positive number (used to prevent the denominator from being 0). For the theoretical target broadcast interval, The minimum broadcast interval allowed by the protocol or hardware; demand-channel coupling coefficient. This characterizes the ability to adapt to the broadcast requirements of peripherals in the current channel environment. This step aims to further refine the theoretical target broadcast interval based on channel quality. When the channel environment is good (i.e., If the value is large, then the denominator The larger the value, the greater the correction term. Smaller, thus enabling coupling correction broadcast interval Closer to the minimum broadcast interval This is to make full use of favorable channel conditions. Conversely, when the channel environment is poor (i.e., If the value is small, then the correction term is large, making... Closer It may even increase to avoid wasting resources due to frequent broadcasting under poor channel conditions. Minimal positive number. The introduction of this is to prevent when A division-by-zero error occurs when the result is 0, ensuring computational stability. The peripheral processor obtains the demand-channel coupling coefficient. and theoretical target broadcast interval Then, you can perform floating-point operations on the above formula to obtain... Alternatively, the calculation of the formula can be performed through a hardware accelerator or DSP module to improve computational efficiency and real-time performance.

[0067] Based on the constraint coefficient, a lower constraint broadcast interval is determined. This lower constraint broadcast interval increases as the constraint coefficient decreases, and is not less than the minimum broadcast interval allowed by the protocol. The specific calculation formula for the lower constraint broadcast interval is as follows: To constrain the lower limit of the broadcast interval, where, For constraint coefficients, The minimum broadcast interval allowed by the protocol or hardware. The maximum broadcast interval allowed by the protocol or hardware; constraint factor. This reflects the margin of peripheral hardware resources, such as battery level, chip temperature, and memory usage. The purpose of this step is to determine a minimum broadcast interval allowed under current hardware conditions, based on hardware resource constraints. When the constraint coefficient... The smaller the value (indicating stronger hardware constraints, such as low battery or high temperature), the better. The larger the value, the greater the constraint on the lower limit of the broadcast interval. The larger the value, the more it limits the broadcast strength, thus avoiding hardware overload or resource exhaustion. Conversely, when... The larger the number (with sufficient hardware resources), the better. The closer This allows for more frequent broadcasts. The peripheral processor obtains the constraint coefficients. Then, you can substitute the values ​​into the above formula to calculate and obtain the result. Or, during system runtime, based on preset hardware resource thresholds and The mapping relationship is dynamically adjusted. .

[0068] The larger of the coupling-corrected broadcast interval and the constraint lower limit broadcast interval is selected and compared with the maximum broadcast interval allowed by the protocol. The smaller one is determined as the target BLE broadcast interval to control the BLE radio frequency unit of the peripheral terminal to transmit broadcast packets. The specific calculation formula for the target BLE broadcast interval is as follows: in, For the target BLE broadcast interval, To constrain the lower limit of broadcast intervals, To adjust the broadcast interval for coupling, The maximum broadcast interval allowed by the protocol or hardware; this step is to finally determine the target BLE broadcast interval. The key is that it comprehensively considers channel environment correction and hardware resource constraints. The max function ensures the final broadcast interval. It will not be lower than the minimum broadcast interval allowed by hardware resources. This means broadcasting is performed while meeting hardware constraints. Simultaneously, the min function ensures the final broadcast interval. It will not exceed the maximum broadcast interval allowed by the protocol or hardware. This limits the broadcast interval to within the legal range. This multi-level correction and truncation mechanism achieves a fine balance between meeting hardware constraints, adapting to the channel environment, and responding to service requirements. The peripheral processor will... , and As input, the min and max functions are executed to obtain the final target BLE broadcast interval. Alternatively, it can be done using a state machine or decision tree, based on... and The relative size, and with The comparison is used to determine the final result. .

[0069] This application's solution first obtains the minimum and maximum broadcast intervals allowed by the protocol or hardware, setting clear upper and lower limits for the dynamic adjustment of the broadcast interval, ensuring the legality and security of the adjustment. Based on this, a demand mapping factor is used to initially adjust the broadcast interval, enabling it to directly respond to the timeliness requirements of service data. When demand is urgent, the broadcast interval tends towards the minimum value to ensure real-time performance; conversely, it tends towards the maximum value to save power. Subsequently, a demand-channel coupling coefficient is used to perform channel environment adaptive correction on the initially adjusted broadcast interval, allowing the broadcast interval to be dynamically adjusted according to the current channel quality. When channel conditions are good, the broadcast interval is further shortened to improve communication efficiency; when channel conditions are poor, the broadcast interval is appropriately extended to avoid resource waste. Simultaneously, a constraint coefficient is introduced to set a minimum lower limit for the broadcast interval allowed by hardware resources, ensuring that broadcasting behavior will not overload or damage the equipment under conditions of low battery power, high chip temperature, or limited memory. Finally, the min and max functions are used to comprehensively judge and truncate all the correction results, ensuring that the final target BLE broadcast interval not only meets the service requirements and adapts to the channel environment, but also strictly adheres to hardware resource constraints and protocol specifications. This multi-level, progressive calculation and correction mechanism makes the adjustment of the broadcast interval no longer a coarse single parameter adjustment, but a fine-grained balance between real-time performance, power consumption, and system stability. This effectively solves the problems of imprecise adjustment and inability to adapt to changing environments and hardware constraints in traditional solutions.

[0070] The following is a concrete example to illustrate this. Suppose that the minimum broadcast interval allowed by the BLE protocol of a certain peripheral device is... The maximum broadcast interval is 20 milliseconds. The timeout is 1000 milliseconds. This is the demand mapping factor calculated by the system when the peripheral device is in a high-speed motion state and transmitting high-priority, time-sensitive data. Higher, for example, 0.9. At this point, according to the formula... Theoretical target broadcast interval It will approach For example, the calculated value is 118 milliseconds. If the channel environment is good at this time, the demand-channel coupling coefficient... If it is relatively high, for example, 0.8, then according to the formula... Coupling Correction Broadcast Interval Will further towards For example, a calculated time of 142.5 milliseconds. Meanwhile, if the peripheral battery has sufficient power, the chip temperature is normal, and memory usage is low, then the constraint coefficient... Higher, for example, 0.9, according to the formula constrain the lower limit of broadcast interval It will also approach For example, the calculated value is 118 milliseconds. Finally, these values ​​are substituted into... The target BLE broadcast interval was obtained. The interval is 142.5 milliseconds. This indicates that under conditions of urgent demand, good channel conditions, and sufficient hardware resources, the system will choose a shorter broadcast interval to ensure real-time performance. Conversely, if the peripheral device is stationary, transmitting low-priority data, and the battery power is low, or the channel environment is congested, then... , and They may all be lower, for example It is 0.1. It is 0.1. It is 0.2. At this time, It could be 902 milliseconds. It may increase further due to poor channel conditions (but will be) limit), It could be 804 milliseconds. Ultimately, the target BLE broadcast interval... It will be set to 1000 milliseconds to prioritize power saving and avoid frequent broadcasting under poor channel conditions.

[0071] Through the above technical solution, this application can achieve fine-grained dynamic adjustment of the BLE broadcast interval, effectively balancing power consumption and real-time performance. This method can adaptively respond to complex changes in peripheral motion states, signal environment, service data timeliness, and peripheral hardware resource margins, avoiding problems such as power waste, data latency, or decreased communication efficiency caused by traditional fixed broadcast intervals or coarse adjustment schemes. Furthermore, by introducing extremely small positive numbers to prevent division by zero, and by using the min and max functions to ensure that the broadcast interval is always within a legal and safe range, the robustness and stability of the system are improved.

[0072] In some of the solutions mentioned above in this application, a method for unpaired self-organizing network communication between smart tablets and peripherals is proposed to dynamically adjust the broadcast interval and solve the power consumption waste and latency problems caused by fixed broadcast strategies. However, in actual device integration, this method requires a specific hardware system implementation to ensure efficient execution and real-time response of the method logic and avoid resource consumption or low execution efficiency that may be caused by software implementation.

[0073] In response, this application proposes a smart tablet and peripheral device unpaired self-organizing network communication system, including a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the smart tablet and peripheral device unpaired self-organizing network communication method described above.

[0074] The processor in this system is the core component responsible for executing instructions, performing arithmetic and logical operations, and controlling system operations. It can be a microcontroller (MCU), microprocessor (MPU), digital signal processor (DSP), field-programmable gate array (FPGA), or application-specific integrated circuit (ASIC), providing the necessary computing and control capabilities for the unpaired self-organizing network communication method between the smart tablet and peripherals. The memory is a hardware device used to store data and instructions, and can include random access memory (RAM, such as SRAM and DRAM), read-only memory (ROM, such as Flash Memory and EEPROM), solid-state drive (SSD), or hard disk drive (HDD), used to store the computer programs and runtime data required for the unpaired self-organizing network communication method between the smart tablet and peripherals. The memory stores the computer program, which is a set of instructions used to guide the processor to perform specific tasks, ensuring that the logic of the unpaired self-organizing network communication method between the smart tablet and peripherals can be persistently saved and invoked by the processor when needed. The steps of implementing a method for unpaired self-organizing network communication between a smart tablet and peripheral devices when the processor executes a computer program mean that the processor reads and executes computer program instructions in memory to complete all operations and calculations defined by the method, transforming the abstract method logic into actual physical operations, and realizing dynamic adaptive adjustment of the BLE broadcast interval.

[0075] This application's solution integrates the unpaired self-organizing network communication method between a smart tablet and peripherals into a specific hardware system, achieving the effective operation of this method. The processor in this system acts as the core computing unit, responsible for acquiring various parameters in real time, including the peripheral's motion state, signal environment, service data timeliness requirements, BLE broadcast channel quality, and peripheral hardware resource margin. The memory provides storage space for these parameters, intermediate calculation results, and the final computer program. By executing the computer program stored in memory, the processor can efficiently perform complex calculations, such as generating dynamic urgency coefficients, data timeliness coefficients, coupling weight factors, demand mapping factors, demand-channel coupling coefficients, and constraint coefficients. The calculation and comprehensive application of these coefficients enable the system to perform multi-level adjustments and corrections to the dynamic range between the minimum and maximum broadcast intervals allowed by the protocol based on multi-dimensional real-time information, thereby determining and controlling the BLE radio frequency unit to transmit broadcast packets according to the target BLE broadcast interval. This tight integration of hardware and software ensures that the method logic can be executed in a highly efficient and low-latency manner, overcoming the inherent defects of traditional fixed broadcast interval strategies that struggle to balance power consumption and real-time performance. By systematically implementing the above methods, the system can intelligently adjust its broadcast behavior according to the constantly changing external environment and internal needs, achieving a fine dynamic balance between power consumption and real-time performance.

[0076] In one specific implementation, the processor in this system can be an ARM Cortex-M series microcontroller, such as the STM32F4 series, which has sufficient processing power and real-time performance to execute complex broadcast interval adjustment algorithms. The memory may include on-chip flash memory for storing the computer program (firmware), and on-chip or external SRAM / DRAM for runtime data storage, for example, configuring 512KB of Flash and 128KB of SRAM. The computer program is compiled binary code containing all the logic and algorithms for implementing the unpaired self-organizing network communication method between the smart tablet and peripherals. This code is loaded from flash memory into SRAM at system startup and executed by the processor. In actual operation, the processor can periodically (e.g., every 100 milliseconds) acquire raw data from connected sensors (such as accelerometers, RSSI receivers) and system status modules (such as battery management units, temperature sensors, RTOS task schedulers). This data is input into a computer program, which calculates key parameters such as the dynamic urgency coefficient and data timeliness coefficient based on preset algorithms (e.g., by calculating the acceleration amplitude change rate index, information age AoI index, etc., and substituting them into the corresponding formulas), and finally determines the target BLE broadcast interval. The processor then sends instructions to the BLE radio frequency unit through a specific hardware interface (such as SPI, UART) to update its broadcast interval parameters, thereby realizing the dynamic adjustment of broadcast behavior.

[0077] Through the above technical solution, this application provides a hardware implementation scheme for a method of unpaired ad hoc network communication between a smart tablet and peripherals. This system, through the collaborative work of the processor and memory, provides a solid physical foundation for the real-time and efficient execution of complex algorithms, effectively avoiding resource bottlenecks and low execution efficiency that may arise from purely software solutions. This tight integration of hardware and software enables the smart tablet and peripherals to dynamically and accurately adjust the BLE broadcast interval based on various real-time factors such as peripheral motion status, signal environment, service data timeliness requirements, channel quality, and their own hardware resource margins. This not only significantly reduces the idle power consumption of peripherals and extends the working life of battery-powered devices, but also ensures the timely transmission of critical service data, avoiding delays and data loss caused by excessively long broadcast intervals. Thus, a better dynamic balance is achieved between power consumption and real-time performance, greatly improving the reliability and user experience of unpaired ad hoc network communication.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for unpaired self-organizing network communication between a smart tablet and peripheral devices, applied to the peripheral device end, characterized in that: Includes the following steps: In response to the service data to be transmitted, a first feature set characterizing the motion state of peripheral devices and changes in the signal environment is obtained, and a dynamic urgency coefficient is generated; Obtain the second feature set that characterizes the timeliness requirements of the service data to be transmitted, and generate the data timeliness coefficient; Based on the dynamic urgency coefficient and the data timeliness coefficient, obtain the coupling weight factor and the demand mapping factor; Obtain the third feature set characterizing the current BLE broadcast channel quality, and use the coupling weight factor to perform a weighted coupling operation on the third feature set to generate the demand-channel coupling coefficient; Obtain the fourth feature set representing the hardware resource margin of peripheral devices, and minimize the fourth feature set based on the shortest board effect principle to generate constraint coefficients; Based on the demand mapping factor, demand-channel coupling coefficient, and constraint coefficient, the dynamic range between the minimum and maximum broadcast intervals allowed by the protocol is adjusted and corrected in multiple levels to determine the target BLE broadcast interval, and the BLE radio frequency unit at the peripheral end is controlled to transmit broadcast packets according to the target BLE broadcast interval.

2. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 1, characterized in that, The dynamic urgency coefficient is calculated as follows: Obtain the first feature set, which includes the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interaction within the time window; Normalize the rate of change of acceleration amplitude, the rate of change of relative RSSI, and the frequency of interactions within the time window to obtain values ​​ranging from [value range missing]. The acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistics index within the time window are positively correlated with the acceleration amplitude change rate index, relative RSSI change rate index, and interaction frequency statistics index within the time window, respectively. The acceleration amplitude change rate index, the relative RSSI change rate index, and the interaction frequency statistical index within the time window are weighted and fused, and a saturated nonlinear mapping is applied to the weighted fusion result to obtain the dynamic urgency coefficient. The saturated nonlinear mapping is configured such that when the weighted fusion result approaches zero, the dynamic urgency coefficient approaches zero; and when the weighted fusion result increases, the dynamic urgency coefficient monotonically increases and asymptotically converges to 1.

3. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 2, characterized in that, The normalization methods for the acceleration amplitude change rate, relative RSSI change rate, and interaction frequency within the time window are as follows: The current rate of change of acceleration amplitude and the frequency of interaction within the time window are compared with the upper limit of the saturation of the rate of change of acceleration amplitude and the saturation threshold of the frequency of interaction within the time window, respectively. After truncating the upper limit of the ratio to 1, the rate of change of acceleration amplitude and the statistical index of the frequency of interaction within the time window are obtained. The relative RSSI rate of change is obtained by comparing the absolute value of the current relative RSSI rate of change with the upper limit of the RSSI rate of change saturation, and then truncating the upper limit of the ratio to 1.

4. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 1, characterized in that, The data timeliness coefficient is calculated as follows: Obtain the second feature set, which includes the sensor value change rate, information age (AoI), and time since the last successful interaction; Normalize the sensor numerical change rate, information age (AoI), and time since the last successful interaction by performing normalized mappings to obtain values ​​ranging from [value range to be filled in]. The sensor value change rate index, information age AoI index, and time since last successful interaction index are all positively correlated with the sensor value change rate index, information age AoI index, and time since last successful interaction index, respectively. The sensor numerical change rate index, information age (AoI) index, and time since last successful interaction index are weighted and fused, and a saturated nonlinear mapping is applied to the weighted fusion result to obtain the data timeliness coefficient. The saturated nonlinear mapping is configured such that when the weighted fusion result approaches zero, the data timeliness coefficient approaches zero; and when the weighted fusion result increases, the data timeliness coefficient monotonically increases and gradually converges to 1.

5. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 4, characterized in that, The normalization methods for the sensor numerical change rate, Information Age (AoI), and time since the last successful interaction are as follows: The current sensor value change rate, information age (AoI), and time since the last successful interaction are compared with the upper limit of sensor value change rate saturation, the maximum information age allowed by the business, and the saturation threshold of time since the last successful interaction, respectively. After truncating the upper limit of the ratio to 1, the sensor value change rate index, information age (AoI) index, and time since the last successful interaction index are obtained.

6. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 1, characterized in that, The coupling weight factor and demand mapping factor are calculated as follows: The dynamic urgency coefficient and the data timeliness coefficient are subjected to a central trend fusion calculation to obtain a coupling weight factor. The coupling weight factor takes the maximum value when both the dynamic urgency coefficient and the data timeliness coefficient are at their maximum values. When either the dynamic urgency coefficient or the data timeliness coefficient decreases, the coupling weight factor is monotonically non-increasing. The dynamic urgency coefficient and data timeliness coefficient are processed by geometric mean to obtain the demand mapping factor.

7. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 1, characterized in that, The demand-channel coupling coefficient is calculated as follows: Obtain the third feature set, which includes channel utilization and broadcast packet loss rate; The channel utilization rate is summed with the broadcast packet loss rate to obtain the channel congestion metric. The demand-channel coupling coefficient is obtained by performing a weighted attenuation operation on the channel congestion metric using a coupling weight factor. The weighted attenuation operation is configured such that: when the channel congestion metric increases, the demand-channel coupling coefficient decreases monotonically; and the larger the coupling weight factor, the slower the rate at which the demand-channel coupling coefficient attenuates as the channel congestion metric increases.

8. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 1, characterized in that, The constraint coefficient is calculated as follows: Obtain the fourth feature set, which includes the remaining battery power percentage, chip junction temperature, and remaining stack space of the RTOS task. The ratio of the difference between the current chip junction temperature and the lower limit of the normal operating temperature to the difference between the upper limit of the thermal throttling temperature and the lower limit of the normal operating temperature is processed and truncated to the output range. Then, the chip junction temperature index was obtained; The stack exhaustion risk index is obtained by taking the ratio of the remaining stack space of the current RTOS task to the minimum safe stack depth threshold, truncating the ratio to a maximum of 1, and then taking the complement of the ratio. Based on the remaining battery charge percentage, the thermal safety margin corresponding to the chip junction temperature index, and the stack safety margin corresponding to the stack depletion risk index, a minimization optimization operation is performed to obtain a constraint coefficient, such that the constraint coefficient is equal to the minimum value among the remaining battery charge percentage, the thermal safety margin, and the stack safety margin.

9. The method for unpaired self-organizing network communication between a smart tablet and peripheral devices according to claim 1, characterized in that, The target BLE broadcast interval is calculated as follows: Based on the demand mapping factor, a theoretical target broadcast interval is determined between the minimum and maximum broadcast intervals allowed by the protocol, wherein the theoretical target broadcast interval decreases as the demand mapping factor increases; Based on the demand-channel coupling coefficient, the theoretical target broadcast interval is adaptively adjusted to obtain the coupling-corrected broadcast interval, wherein the coupling-corrected broadcast interval increases as the demand-channel coupling coefficient decreases; Based on the constraint coefficient, a lower limit broadcast interval is determined. The lower limit broadcast interval increases as the constraint coefficient decreases, and is not less than the minimum broadcast interval allowed by the protocol. The larger of the coupling-corrected broadcast interval and the constraint lower limit broadcast interval is selected and compared with the maximum broadcast interval allowed by the protocol. The smaller one is determined as the target BLE broadcast interval to control the BLE radio frequency unit of the peripheral terminal to transmit broadcast packets.

10. A smart tablet and peripheral device unpaired self-organizing network communication system, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the steps of the smart tablet and peripheral unpaired self-organizing network communication method as described in any one of claims 1 to 9.

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