Bluetooth low-power-consumption device remote wakeup method and system, storage medium and device

Abstract

The invention relates to the technical field of wireless communication, and discloses a Bluetooth low-power-consumption device remote wakeup method and system, a storage medium and a device, and the method comprises the steps: recognizing and configuring a Bluetooth low-power-consumption controller to maintain a minimum function unit set, and automatically optimizing the power consumption distribution of each unit; effective remote wake-up signals are detected and identified through a multi-layer signal verification mechanism; starting a grading module activation process sorted according to a preset priority, and preloading a function module corresponding to the high-frequency operation based on the user behavior prediction model; a special Bluetooth low-power-consumption communication protocol is configured, and connection parameters are dynamically adjusted according to communication requirements to manage the connection state; an independent power supply control strategy is adopted for different functional modules, the service life and endurance of the Bluetooth low-power-consumption equipment are prolonged and prolonged through an energy collection technology and storage optimization, and the problem that the wireless intelligent audio equipment cannot be remotely awakened in a low-power-consumption state is solved through a layered awakening architecture and an intelligent power consumption management strategy.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method, system, storage medium and device for remotely waking up a Bluetooth low-power device. Background Art

[0002] Bluetooth Low Energy (BLE) technology is a representative technology of the Internet of Things (IoT). It is widely used in low-cost, low-power computing devices and in short-range wireless communication scenarios with low data rates and low duty cycles. With the development of the IoT, Bluetooth Low Energy (BLE) added a coded physical layer and two coding schemes in Bluetooth 5.0 in 2016. This increases the transmission range of BLE signals by up to four times without increasing transmit power, significantly expanding the application areas and development prospects of BLE in the IoT. Furthermore, the current market for smart audio devices is rapidly developing, and users' demands for device portability, battery life, and intelligence are also increasing. However, existing technologies face significant technical bottlenecks in low-power management and remote control.

[0003] Traditional BLE devices, after entering deep sleep mode, often require a physical button or a specific hardware interrupt to wake up, making true remote control impossible. This is because traditional power management solutions typically rely on simple on / off controls, either completely shutting down the wireless communication module to save power or keeping it running at full power to maintain connectivity. This fails to strike the optimal balance between power consumption and functionality. Even some products that support Bluetooth wake-up often suffer from issues such as excessive power consumption, long wake-up latency, and unstable connections. In particular, in Suspend mode, most devices disable all non-essential modules, including the Bluetooth communication unit, to maximize power savings. This results in the device losing its ability to receive remote control commands and, consequently, unable to receive remote wake-up signals. Users must physically touch the device to reactivate it, significantly impacting usability. Furthermore, existing BLE wake-up technologies often rely on periodic scanning, which not only adds unnecessary power consumption but can also cause wake-up failures or excessive delays due to timing mismatches.

[0004] These technical defects restrict the further development of smart audio devices and the improvement of user experience, and there is an urgent need to solve the above industry pain points through system-level innovation. Summary of the Invention

[0005] The purpose of the present invention is to address the shortcomings of the above-mentioned prior art and to provide a method, system, storage medium and device for remote wake-up of Bluetooth low-power devices. By introducing a layered wake-up architecture and an intelligent power consumption management strategy, the present invention successfully solves the technical problem that wireless smart audio devices cannot be remotely woken up in a low-power state.

[0006] In one aspect, a method for remotely waking up a Bluetooth low energy device is provided, comprising the following steps: S1: Identify and configure the minimum set of functional units required for the Bluetooth low energy controller to maintain remote wake-up functionality, and automatically optimize the power consumption allocation of each unit by dynamically calculating the optimal power consumption threshold; S2: Detects and identifies valid remote wake-up signals through a multi-layer signal verification mechanism, including physical layer signal strength detection, data link layer packet header verification, and application layer command parsing; S3: After identifying a valid wake-up signal, the hierarchical module activation process is started according to the preset priority. At the same time, the functional modules corresponding to high-frequency operations are preloaded based on the user behavior prediction model; S4: Configure the Bluetooth low energy communication protocol dedicated to low-power scenarios, dynamically adjust the connection parameters according to communication requirements to manage the connection status; S5: Adopt independent power control strategies for different functional modules, and extend the life and endurance of Bluetooth low energy devices through energy harvesting technology and storage optimization.

[0007] Wherein, step S1 further includes: The minimum functional unit of the Bluetooth low energy controller includes: a radio frequency receiver, a minimum baseband processor core, and a wake-up signal detection unit. Based on the basic power consumption characteristics and configurable activation coefficients of each functional module, a power consumption analysis algorithm is used to identify the minimum functional unit set required to maintain the remote wake-up function. Based on battery power, historical usage patterns, and environmental conditions, the optimal power consumption threshold is dynamically calculated to automatically optimize energy consumption allocation in different usage scenarios.

[0008] Furthermore, in step S2, the multi-layer signal verification mechanism includes: At the physical layer, the received signal strength is detected through a signal strength evaluation algorithm, and the signal detection parameters are adaptively adjusted to suit different usage environments; At the data link layer, the integrity of the data packet header and the compatibility of the protocol version are verified through the packet header matching algorithm; At the application layer, the instruction content is parsed through the instruction legitimacy algorithm and the comprehensive effectiveness score is calculated.

[0009] Preferably, the adaptively adjusting the signal detection parameters further includes: The signal feature model is established based on the real-time monitoring of the environmental noise level and the historical signal quality data, and the signal detection parameters are adaptively adjusted. If the false awakening rate exceeds a first preset threshold, the detection parameters are automatically adjusted to improve the detection sensitivity; If the missed detection rate exceeds a second preset threshold, the detection parameters are automatically adjusted to reduce the detection sensitivity.

[0010] Furthermore, in step S3, the grading module activation process includes: S31: After receiving a valid wake-up signal, the modules are sorted according to the urgency and response time requirements of the functional modules to obtain the preset priority order, and a module activation sequence is generated according to the order; S32: Using an exponential power consumption rising curve to control the startup time interval of each module, and completing the activation of all core functional modules within a predetermined time threshold.

[0011] Furthermore, in step S3, preloading the functional modules corresponding to high-frequency operations based on the user behavior prediction model includes: Collecting device usage time, geographic location, and historical operation records to construct a multidimensional feature vector, and constructing the user behavior prediction model based on the multidimensional feature vector; The execution probability distribution of each operation instruction is calculated through machine learning algorithm. When the probability of a specific operation exceeds the preset threshold value, the preloading operation of the corresponding functional module is triggered.

[0012] Furthermore, in step S4, configuring a Bluetooth low energy communication protocol dedicated to low power consumption scenarios includes: A lightweight communication protocol including a simplified frame structure is configured as a Bluetooth low energy communication protocol to reduce data transmission volume; For complex instructions including device status, bit compression encoding technology is used to reduce the payload.

[0013] Furthermore, in step S4, the management of the connection state includes: Based on communication needs, the connection interval parameters are dynamically adjusted according to the real-time connection quality evaluation results. The connection quality evaluation calculates a comprehensive score by weighted calculation of the normalized values ​​of signal reception strength, data transmission delay and connection success rate. When the score falls below a predetermined threshold, it automatically attempts to reconnect or performs a communication channel switching operation.

[0014] Furthermore, in step S5, the independent power supply control strategy includes: The power distribution algorithm divides the functional modules into three independent power domains: high, medium, and low. At the same time, dynamic voltage and frequency adjustment based on load prediction is implemented for the main processor. Calculates the minimum operating voltage and frequency combination that meets performance requirements based on real-time workloads.

[0015] Furthermore, in step S5, the energy harvesting technology and storage optimization include: By building an energy harvesting model, integrated photovoltaic energy is collected to compensate for standby power loss; The health index is calculated based on the battery capacity attenuation characteristics and the number of charge and discharge cycles. When the health index falls below a predetermined threshold, the power consumption limit mode is activated.

[0016] Preferably, the method further includes establishing a performance monitoring closed loop, specifically comprising: Build a comprehensive performance evaluation model to collect key performance indicators in real time, including wake-up response time, false wake-up rate, standby power consumption, and connection success rate. If the monitoring data deviates from the preset range, the self-optimization mechanism will be triggered. Locate the root cause of performance deviations through anomaly detection algorithms.

[0017] More preferably, the self-optimization mechanism includes: Construct a multi-objective optimization function including power consumption, response time, and false wake-up rate indicators; Gradient descent algorithm is used to solve the optimal parameter configuration; A long-term performance evolution model is established based on the reinforcement learning framework and the decision-making strategy is continuously updated.

[0018] On the other hand, a remote wake-up system for a Bluetooth low energy device is provided, comprising: Initialization and power configuration module, which is used to identify and configure the minimum set of functional units required by the Bluetooth low energy controller to maintain remote wake-up function, and automatically optimize the power consumption allocation of each unit by dynamically calculating the optimal power consumption threshold; Wake-up signal detection and identification module, which is used to detect and identify valid remote wake-up signals through a multi-layer signal verification mechanism including physical layer signal strength detection, data link layer packet header verification and application layer command parsing; A hierarchical wake-up control module is used to start the hierarchical module activation process sorted by preset priority after identifying a valid wake-up signal. At the same time, it preloads the functional modules corresponding to high-frequency operations based on the user behavior prediction model; The communication optimization and connection management module is used to configure the Bluetooth low-power communication protocol specifically for low-power scenarios, dynamically adjust connection parameters according to communication requirements, and manage the connection status; The energy management and energy-saving optimization module is used to adopt independent power control strategies for different functional modules and extend the life and endurance of Bluetooth low-power devices through energy harvesting technology and storage optimization.

[0019] In addition, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for remotely waking up a Bluetooth low energy device as described above is implemented.

[0020] At the same time, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the remote wake-up method for a Bluetooth low energy device as described in any one of the above items.

[0021] Compared with the prior art, the present invention has the following beneficial effects: This invention uses a minimum functional unit identification algorithm to significantly reduce standby power consumption from the traditional 15-20mA to 1.84mA, significantly reducing power consumption. At the same time, through a dynamic power consumption threshold adjustment mechanism, energy consumption allocation is optimized in real time according to battery status and usage mode, extending the device's battery life by 3-5 times; The present invention combines a three-layer signal screening mechanism with an adaptive detection algorithm to achieve nearly 100% wake-up accuracy and an extremely low false wake-up rate, ensuring the reliability of remote control. This invention uses a progressive module activation strategy to control the full wake-up time to less than 100 milliseconds, a significant improvement over the 800-millisecond response time of traditional solutions. The intelligent prediction algorithm further significantly shortens the response time for commonly used functions by machine learning user behavior patterns. This invention significantly improves data transmission efficiency through lightweight communication protocol design, achieves significant overall power consumption reduction through multi-level power management strategy, and enables the system to adapt to different user habits through online learning algorithm, improving overall performance by 25-40%. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a method for remotely waking up a Bluetooth low energy device according to the present invention; Figure 2 This is a structural block diagram of a remote wake-up system for a Bluetooth low energy device according to the present invention; Figure 3 The figure is a schematic diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] This invention provides a hierarchical wake-up architecture that implements intelligent power management using BLE low-energy Bluetooth technology. Its core innovation lies in its "minimum power monitoring + rapid response" mechanism. When the device enters Suspend mode, only the minimal functional units of the BLE controller remain operational, while all other modules are completely shut down. An optimized signal detection algorithm and dynamic power adjustment strategy enable reliable remote wake-up in extremely low-power states. Furthermore, an intelligent learning algorithm automatically adjusts wake-up sensitivity and response strategies based on user habits, ensuring rapid response while maximizing energy savings and providing a seamless user experience.

[0025] The specific implementation of the present invention is described below with reference to the accompanying drawings and embodiments.

[0026] Example 1 like Figure 1 As shown, a technical solution for a remote wake-up method for a Bluetooth low energy device provided in this embodiment includes the following steps: S1: Identify and configure the minimum set of functional units required for the Bluetooth low energy controller to maintain remote wake-up functionality, and automatically optimize the power consumption allocation of each unit by dynamically calculating the optimal power consumption threshold; S2: Detects and identifies valid remote wake-up signals through a multi-layer signal verification mechanism, including physical layer signal strength detection, data link layer packet header verification, and application layer command parsing; S3: After identifying a valid wake-up signal, the hierarchical module activation process is started according to the preset priority. At the same time, the functional modules corresponding to high-frequency operations are preloaded based on the user behavior prediction model; S4: Configure the Bluetooth low energy communication protocol dedicated to low-power scenarios, dynamically adjust the connection parameters according to communication requirements to manage the connection status; S5: Adopt independent power control strategies for different functional modules, and extend the life and endurance of Bluetooth low energy devices through energy harvesting technology and storage optimization.

[0027] First, perform initialization in step S1. During the device startup phase, first analyze and configure the functional modules of the BLE controller. The minimum functional unit of the Bluetooth low energy controller includes: a radio frequency receiver, a minimum baseband processor core, and a wake-up signal detection unit. Based on the basic power consumption characteristics and configurable activation coefficients of each functional module, a power consumption analysis algorithm is used to identify the minimum functional unit set required to maintain the remote wake-up function. The algorithm formula is as follows: in, is the total power consumption of the minimum power consumption unit, For the The basic power consumption of each functional module is For the The activation coefficient of each functional module, = is the total number of functional modules in the BLE controller. This formula reduces standby power consumption by over 90% compared to the 15-20mA operating current of traditional solutions, providing a foundation for long standby periods.

[0028] In this embodiment, for the classic BLE 5.0 controller, in the minimum functional unit, the RF receiver consumes 2.5mA, the baseband processor consumes 1.8mA, and the wake-up signal detection unit consumes 0.3mA. The values ​​are set to 0.4, 0.3, and 1.0 respectively, and the calculation is .

[0029] Then we proceed to the dynamic power consumption threshold setting in step S1. The system dynamically calculates the optimal power consumption threshold based on battery power, historical usage patterns, and environmental conditions, automatically optimizing energy consumption allocation under different usage scenarios. The specific algorithm formula is as follows: in, is the dynamic power consumption threshold, is the baseline power consumption value, is the time attenuation coefficient, is the current standby time, is a standardized time unit, is the battery charge correction factor, and B is the current battery charge percentage. By dynamically adjusting the power consumption threshold using this algorithm, the system can automatically optimize energy consumption in different usage scenarios, extending device battery life by 20-30%.

[0030] In this embodiment, when the battery power is 80% and the standby time is 2 hours, , , hours, then , calculated .

[0031] Then, in step S2, intelligent wake-up signal detection and identification is performed. First, the multi-layer signal verification mechanism is executed. In order to accurately identify valid wake-up signals in a low-power state, the system adopts a three-layer screening architecture: physical layer signal strength detection, data link layer header verification, and application layer instruction parsing. The multi-layer signal verification mechanism includes: At the physical layer, the received signal strength is detected through a signal strength evaluation algorithm, and the signal detection parameters are adaptively adjusted to suit different usage environments; At the data link layer, the integrity of the data packet header and the compatibility of the protocol version are verified through the packet header matching algorithm; At the application layer, the instruction content is parsed through the instruction legitimacy algorithm and the comprehensive effectiveness score is calculated.

[0032] The specific algorithm formula is as follows: in, Indicates the signal effectiveness score (0-1), represents the signal strength evaluation function, represents the packet header matching function, Indicates the instruction validity function. The specific calculation process of each function is as follows: , , , in, For the The weight of the instruction feature, is the matching degree of the corresponding feature, represents cyclic redundancy check, The three-layer filtering mechanism described by this algorithm effectively prevents false wakeups, reducing the false wakeup rate to below 0.1% while ensuring 100% recognition of true wakeup signals.

[0033] In this embodiment, if the signal is received , the packet header CRC check passes and the version matches, and the instruction is a standard wake-up command: , , , , because , it is determined to be a valid wake-up signal.

[0034] 14. In the physical layer, the adaptively adjusting the signal detection parameters further includes: The signal feature model is established based on the real-time monitoring of the environmental noise level and the historical signal quality data, and the signal detection parameters are adaptively adjusted. If the false awakening rate exceeds a first preset threshold, the detection parameters are automatically adjusted to improve the detection sensitivity; If the missed detection rate exceeds a second preset threshold, the detection parameters are automatically adjusted to reduce the detection sensitivity.

[0035] Specifically, the system automatically adjusts the signal detection parameters based on the ambient noise level and historical detection data. The algorithm formula is as follows: in, is the learning rate coefficient (0.01-0.1), is the average strength of the effective signal, is the average intensity of ambient noise, The adaptive adjustment achieved through the above algorithm formula enables the system to adapt to different usage environments, improves detection accuracy by 15-25%, and reduces unnecessary power consumption.

[0036] In this example, we set the scene to an office environment and the initial detection threshold to -70dBm. After 24 hours of learning, the system detects an average signal strength of -58dBm and a noise strength of -85dBm. The learning rate α is 0.05, so the new threshold is: -70 + 0.05×(-58-(-85)) = -70 + 1.35 = -68.65dBm.

[0037] Then, after receiving a valid wake-up signal, the system proceeds to step S3, where it uses a hierarchical activation strategy to gradually start each functional module according to priority, ensuring a fast response while avoiding current shock. Specifically, the hierarchical module activation process includes: S31: After receiving a valid wake-up signal, the modules are sorted according to the urgency and response time requirements of the functional modules to obtain the preset priority order, and a module activation sequence is generated according to the order; S32: Using an exponential power consumption rising curve to control the startup time interval of each module, and completing the activation of all core functional modules within a predetermined time threshold.

[0038] The power consumption smooth startup formula is as follows: in, for System power consumption at the moment, For minimum standby power consumption, is the full load working power consumption, The above algorithm formula is used for hierarchical activation, which avoids the impact of instantaneous high current on the battery, prolongs the battery life, and controls the full wake-up time to less than 100ms.

[0039] The wake-up sequence of this embodiment is: (1) Time 0ms: Activate the main CPU core (additional power consumption 5mA) (2) Time 20ms: Start audio DSP (additional power consumption 8mA) (3) Time 50ms: Activate the Wi-Fi module (additional power consumption 12mA) (4) Time 80ms: Start the user interface (additional power consumption 3mA) Assuming τ = 30ms, the system power consumption at t = 60ms is: P(60) = 1.84 + (28-1.84) × (1-e^(-60 / 30)) = 1.84 + 26.16 × 0.865 =24.47mA.

[0040] On this basis, in step S3, the system uses machine learning algorithms to analyze user behavior patterns, predict possible operational needs, and load relevant functional modules in advance. Specifically, it includes: Collecting device usage time, geographic location, and historical operation records to construct a multidimensional feature vector, and constructing the user behavior prediction model based on the multidimensional feature vector; The execution probability distribution of each operation instruction is calculated through machine learning algorithm. When the probability of a specific operation exceeds the preset threshold value, the preloading operation of the corresponding functional module is triggered.

[0041] Among them, the user behavior prediction model is expressed as follows: , in, is the probability of performing an action in a specific context, is the weight matrix, is the context feature vector (time, location, historical operations, etc.), The above algorithm and formula implement an intelligent prediction mechanism, reducing the response time of commonly used functions by users by over 70%, significantly improving user experience satisfaction.

[0042] In one embodiment, the system detects that there is an 85% probability that a user will play music between 8:00 and 8:30 every morning, so it preloads the audio decoding module and the network connection module at 7:55. When the prediction accuracy reaches 82%, the user operation response time is reduced from an average of 800ms to 150ms.

[0043] Next, for low-power scenarios, we configured step S4 for communication protocol optimization and connection management. First, we designed a dedicated BLE communication protocol to minimize data transmission overhead. Specifically, we: A lightweight communication protocol including a simplified frame structure is configured as a Bluetooth low energy communication protocol to reduce data transmission volume; For complex instructions including device status, bit compression encoding technology is used to reduce the payload.

[0044] In this embodiment, the protocol frame structure optimization is expressed as follows: Frame = [Preamble(1B) | DeviceID(2B) | CMD(1B) | Data(0-8B) | CRC(1B)], The compression algorithm formula is as follows: , , in, Indicates the size of the compressed data. Indicates the original data size, Indicates the compression ratio, Indicates redundant items.

[0045] By implementing protocol optimization through the aforementioned algorithm, communication overhead can be reduced by over 80%, which in turn reduces the operating time of the RF module and further saves power consumption.

[0046] In this embodiment, while the standard Bluetooth data packet is a minimum of 27 bytes, the optimized wake-up command requires only 5 bytes, improving data transmission efficiency by 81%. For complex commands that include device status, bit compression technology compresses 12 bytes of data to 6 bytes, reducing transmission time from 2.4ms to 1.2ms.

[0047] Secondly, the connection management in step S4 includes: Based on communication needs, the connection interval parameters are dynamically adjusted according to the real-time connection quality evaluation results. The connection quality evaluation calculates a comprehensive score by weighted calculation of the normalized values ​​of signal reception strength, data transmission delay and connection success rate. When the score falls below a predetermined threshold, it automatically attempts to reconnect or performs a communication channel switching operation.

[0048] The connection interval adaptive algorithm formula is as follows: , in, represents the basic connection interval, represents the calculated implementation connection interval, is the adjustment coefficient, is the activity factor.

[0049] The comprehensive score The calculation formula is as follows: , in, Indicates the signal reception strength. Indicates data transmission delay, Indicates the connection success rate. 、 and Represent the weights of the above three indicators respectively. In this embodiment, the weights of each normalized parameter are , , .

[0050] The intelligent connection management achieved through the above algorithm formula can reduce communication-related power consumption by 40-60% while ensuring communication quality.

[0051] In this embodiment, the connection interval is dynamically adjusted from the default 100ms to 30ms during active use to ensure low-latency response; during standby, it is automatically extended to 500ms to reduce unnecessary communication overhead. Based on quality assessment, if the connection quality falls below 0.7, the system automatically attempts to reconnect or switch communication channels.

[0052] Then, step S5 is performed for power management and energy-saving optimization. In this case, refined power management uses independent power control strategies for different functional modules. The independent power control strategies further include: The power distribution algorithm divides the functional modules into three independent power domains: high, medium, and low. At the same time, dynamic voltage and frequency adjustment based on load prediction is implemented for the main processor. Calculates the minimum operating voltage and frequency combination that meets performance requirements based on real-time workloads.

[0053] The dynamic voltage frequency scaling (DVFS) algorithm formula is as follows: in, Indicates the optimal operating voltage for dynamic adjustment, The lowest safe voltage supported by the chip. is the actual frequency required by the current workload, The minimum operating frequency allowed by the chip; Indicates the dynamic power consumption of the chip, is the capacitive load factor, is the actual working voltage, The above algorithm implements multi-level power management, reducing overall system power consumption by over 95% while maintaining the normal operation of essential functions.

[0054] In this embodiment, when the main processor is in standby listening mode, the operating frequency is reduced from 100 MHz to 8 MHz, the voltage is reduced from 3.3 V to 1.8 V, and the dynamic power consumption is reduced from: P1 = C × 3.32 × 100MHz = 1089C Down to: P2 = C × 1.8 2 × 8MHz = 25.92C The power consumption reduction ratio is: (1089-25.92) / 1089 = 97.6%.

[0055] The energy harvesting technology and storage optimization in step S5 further include: By building an energy harvesting model, integrated photovoltaic energy is collected to compensate for standby power loss; The health index is calculated based on the battery capacity attenuation characteristics and the number of charge and discharge cycles. When the health index falls below a predetermined threshold, the power consumption limit mode is activated.

[0056] Specifically, the integrated environmental energy harvesting technology can further extend the device life. It is expressed as follows: , in, is the energy conversion efficiency, is the ambient energy density, For collection time, is the utilization factor.

[0057] In addition, the health index The calculation formula is as follows: in, is the current actual capacity, The factory nominal capacity. is the time aging attenuation coefficient, is the cyclic aging attenuation coefficient.

[0058] The above energy harvesting technology can extend the device's battery life by 15-30%, and the battery life management strategy can extend the battery life by more than 50%.

[0059] In this embodiment, under indoor lighting conditions (200-500 lux), the integrated photovoltaic cell can collect 0.5-1.2mWh of energy per hour. While this doesn't fully support device operation, it can offset 30-50% of standby power consumption. Combined with battery health management, when the battery health falls below 80%, the system automatically lowers the power consumption threshold to extend the remaining battery life.

[0060] Finally, the method also includes establishing a performance monitoring closed loop, specifically including: Build a comprehensive performance evaluation model to collect key performance indicators in real time, including wake-up response time, false wake-up rate, standby power consumption, and connection success rate. If the monitoring data deviates from the preset range, the self-optimization mechanism will be triggered. Locate the root cause of performance deviations through anomaly detection algorithms.

[0061] The self-optimization mechanism includes: Construct a multi-objective optimization function including power consumption, response time, and false wake-up rate indicators; Gradient descent algorithm is used to solve the optimal parameter configuration; A long-term performance evolution model is established based on the reinforcement learning framework and the decision-making strategy is continuously updated.

[0062] Specifically, we have established a comprehensive performance monitoring system to track the key indicators of the system in real time. It is expressed as follows: , in, For the The weight of each performance indicator, For the The actual value of the performance indicator, is the total number of monitoring indicators, Represents the normalization function.

[0063] In this embodiment, key performance indicators (KPIs) include: wake-up response time, target <100ms; false wake-up rate, target <0.1%; standby power consumption, target <2mA; connection success rate, target >99%.

[0064] The above-mentioned real-time monitoring system ensures the stability of equipment performance, and the automatic optimization mechanism controls the performance fluctuation range within 5%.

[0065] In this embodiment, when the system detects three consecutive wake-up response times exceeding 150ms, it automatically analyzes the cause and finds that the CPU frequency is too low. The system automatically increases the minimum CPU frequency from 4MHz to 8MHz, and the response time returns to less than 80ms.

[0066] Secondly, an online learning algorithm is used to continuously optimize system parameters based on actual usage data. The parameter optimization objective function It is expressed as follows: , Then, through gradient descent optimization, the updated parameter vector It is expressed as follows: , Finally, through Q-learning reinforcement learning application, it is expressed as follows: , in, is the real-time power consumption of the system, is the wake-up response time, is the false wake-up rate, 、 and Indicates the weight coefficient of each indicator; represents the current parameter vector, Represents the learning rate, which is used to control the convergence speed. represents the gradient of the cost function; Indicates the current system status. Indicates the action to be performed (parameter adjustment), represents the state-action value function, represents an immediate reward (performance improvement), represents the discount factor, represents the maximum expected reward for the next state.

[0067] Through the above-mentioned machine learning optimization, the system can adapt to the usage habits of different users, the overall performance is improved by 25-40%, and user satisfaction is significantly improved.

[0068] In this example, after 30 days of online learning, the system automatically discovered that users rarely use their devices between midnight and 6:00 AM. Therefore, the system lowered the detection sensitivity during this period, further reducing power consumption. It also discovered that users are more likely to use their devices between 6:00 PM and 7:00 PM on weekdays. Therefore, the system prepared the relevant modules in advance, reducing response time.

[0069] In summary, through the implementation of the six-step technical solution, this invention successfully achieves reliable remote wake-up functionality with extremely low power consumption, providing a complete technical solution for BLE wireless smart audio devices. This solution not only addresses the power consumption and functionality challenges of traditional technologies but also enables system self-optimization through intelligent algorithms, offering broad application prospects and significant technical value.

[0070] On the other hand, this embodiment also provides a remote wake-up system for Bluetooth low energy devices, such as Figure 2 Shown, including: an initialization and power consumption configuration module 10 for identifying and configuring a minimum set of functional units required by a Bluetooth low energy controller to maintain a remote wake-up function, and automatically optimizing the power consumption allocation of each unit by dynamically calculating an optimal power consumption threshold; The wake-up signal detection and identification module 20 is used to detect and identify valid remote wake-up signals through a multi-layer signal verification mechanism including physical layer signal strength detection, data link layer packet header verification, and application layer instruction parsing; The hierarchical wake-up control module 30 is used to start the hierarchical module activation process sorted by preset priority after identifying a valid wake-up signal, and at the same time, preload the functional modules corresponding to high-frequency operations based on the user behavior prediction model; The communication optimization and connection management module 40 is used to configure the Bluetooth low energy communication protocol dedicated to low power consumption scenarios, dynamically adjust the connection parameters according to the communication requirements, and manage the connection status; The energy management and energy-saving optimization module 50 is used to adopt independent power control strategies for different functional modules and extend the life and endurance of Bluetooth low-power devices through energy harvesting technology and storage optimization.

[0071] It should be noted that the steps in the Bluetooth low-power device remote wake-up method provided in this embodiment can be implemented based on the corresponding modules in the Bluetooth low-power device remote wake-up system. Those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, that is, the embodiments in the system can be understood as preferred examples for implementing the method, which will not be elaborated here.

[0072] This embodiment also provides an electronic device, such as Figure 3 As shown, the electronic device includes a processor 14 and a memory 13 . The memory 13 stores machine-executable instructions that can be executed by the processor 14 . The processor 14 executes the machine-executable instructions to implement the above-mentioned audio control method.

[0073] Further, Figure 3 The electronic device shown further includes a bus 12 and a communication interface 11 , and the processor 14 , the communication interface 11 and the memory 13 are connected via the bus 12 .

[0074] The memory 13 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 11 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 12 may be an ISA bus, a PCI bus, or an EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0075] The processor 14 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method may be completed by hardware integrated logic circuits or software instructions in the processor 14. The processor 14 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the various methods, steps, and logic block diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment may be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 1001 , and the processor 1000 reads the information in the memory 1001 and completes the steps of the audio control method in combination with its hardware.

[0076] The present disclosure also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, which enables the computer to execute the steps of the audio control method when the computer program runs on the computer.

[0077] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that do not depart from the principles of the present invention, which are apparent to those skilled in the art, should also be considered within the scope of protection of the present invention.

[0078] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for remotely waking up a Bluetooth low energy device, characterized in that: The steps include: S1: Identify and configure the minimum set of functional units required for the Bluetooth low energy controller to maintain remote wake-up functionality, and automatically optimize the power consumption allocation of each unit by dynamically calculating the optimal power consumption threshold; S2: Detects and identifies valid remote wake-up signals through a multi-layer signal verification mechanism, including physical layer signal strength detection, data link layer packet header verification, and application layer command parsing; S3: After identifying a valid wake-up signal, the hierarchical module activation process is started according to the preset priority. At the same time, the functional modules corresponding to high-frequency operations are preloaded based on the user behavior prediction model; S4: Configure the Bluetooth low energy communication protocol dedicated to low-power scenarios, dynamically adjust the connection parameters according to communication requirements to manage the connection status; S5: Adopt independent power control strategies for different functional modules, and extend the life and endurance of Bluetooth low energy devices through energy harvesting technology and storage optimization.

2. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: Step S1 further comprises: The minimum functional unit of the Bluetooth low energy controller includes: a radio frequency receiver, a minimum baseband processor core, and a wake-up signal detection unit. Based on the basic power consumption characteristics and configurable activation coefficients of each functional module, a power consumption analysis algorithm is used to identify the minimum functional unit set required to maintain the remote wake-up function. Based on battery power, historical usage patterns, and environmental conditions, the optimal power consumption threshold is dynamically calculated to automatically optimize energy consumption allocation in different usage scenarios.

3. The remote wakeup method for a Bluetooth low energy device according to claim 1, wherein: In step S2, the multi-layer signal verification mechanism includes: At the physical layer, the received signal strength is detected through a signal strength evaluation algorithm, and the signal detection parameters are adaptively adjusted to suit different usage environments; At the data link layer, the integrity of the data packet header and the compatibility of the protocol version are verified through the packet header matching algorithm; At the application layer, the instruction content is parsed through the instruction legitimacy algorithm and the comprehensive effectiveness score is calculated.

4. The method for remotely waking up a Bluetooth low energy device according to claim 3, wherein: The adaptively adjusting the signal detection parameters further comprises: The signal feature model is established based on the real-time monitoring of the environmental noise level and the historical signal quality data, and the signal detection parameters are adaptively adjusted. If the false awakening rate exceeds a first preset threshold, the detection parameters are automatically adjusted to improve the detection sensitivity; If the missed detection rate exceeds a second preset threshold, the detection parameters are automatically adjusted to reduce the detection sensitivity.

5. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: In step S3, the grading module activation process includes: S31: After receiving a valid wake-up signal, the modules are sorted according to the urgency and response time requirements of the functional modules to obtain the preset priority order, and a module activation sequence is generated according to the order; S32: Using an exponential power consumption rising curve to control the startup time interval of each module, and completing the activation of all core functional modules within a predetermined time threshold.

6. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: In step S3, preloading the function modules corresponding to high-frequency operations based on the user behavior prediction model further includes: Collecting device usage time, geographic location, and historical operation records to construct a multidimensional feature vector, and constructing the user behavior prediction model based on the multidimensional feature vector; The execution probability distribution of each operation instruction is calculated through machine learning algorithm. When the probability of a specific operation exceeds the preset threshold value, the preloading operation of the corresponding functional module is triggered.

7. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: In step S4, configuring a Bluetooth low energy communication protocol dedicated to low power scenarios includes: A lightweight communication protocol including a simplified frame structure is configured as a Bluetooth low energy communication protocol to reduce data transmission volume; For complex instructions including device status, bit compression encoding technology is used to reduce the payload.

8. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: In step S4, the management of the connection state further includes: Based on communication needs, the connection interval parameters are dynamically adjusted according to the real-time connection quality evaluation results. The connection quality evaluation calculates a comprehensive score by weighted calculation of the normalized values ​​of signal reception strength, data transmission delay and connection success rate. When the score falls below a predetermined threshold, it automatically attempts to reconnect or performs a communication channel switching operation.

9. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: In step S5, the independent power control strategy further includes: The power distribution algorithm divides the functional modules into three independent power domains: high, medium, and low. At the same time, dynamic voltage and frequency adjustment based on load prediction is implemented for the main processor. Calculates the minimum operating voltage and frequency combination that meets performance requirements based on real-time workloads.

10. The remote wakeup method for a Bluetooth low energy device according to claim 9, wherein: In step S5, the energy harvesting technology and storage optimization further includes: By building an energy harvesting model, integrated photovoltaic energy is collected to compensate for standby power loss; The health index is calculated based on the battery capacity attenuation characteristics and the number of charge and discharge cycles. When the health index falls below a predetermined threshold, the power consumption limit mode is activated.

11. The method for remotely waking up a Bluetooth low energy device according to claim 1, wherein: The method further includes establishing a performance monitoring closed loop, specifically comprising: Build a comprehensive performance evaluation model to collect key performance indicators in real time, including wake-up response time, false wake-up rate, standby power consumption, and connection success rate. If the monitoring data deviates from the preset range, the self-optimization mechanism will be triggered. Locate the root cause of performance deviations through anomaly detection algorithms.

12. The method for remotely waking up a Bluetooth low energy device according to claim 11, wherein: The self-optimization mechanism includes: Construct a multi-objective optimization function including power consumption, response time, and false wake-up rate indicators; Gradient descent algorithm is used to solve the optimal parameter configuration; A long-term performance evolution model is established based on the reinforcement learning framework and the decision-making strategy is continuously updated.

13. A remote wake-up system for a Bluetooth low energy device, characterized in that: include: Initialization and power configuration module, which is used to identify and configure the minimum set of functional units required by the Bluetooth low energy controller to maintain remote wake-up function, and automatically optimize the power consumption allocation of each unit by dynamically calculating the optimal power consumption threshold; Wake-up signal detection and identification module, which is used to detect and identify valid remote wake-up signals through a multi-layer signal verification mechanism including physical layer signal strength detection, data link layer packet header verification and application layer command parsing; A hierarchical wake-up control module is used to start the hierarchical module activation process sorted by preset priority after identifying a valid wake-up signal. At the same time, it preloads the functional modules corresponding to high-frequency operations based on the user behavior prediction model; The communication optimization and connection management module is used to configure the Bluetooth low-power communication protocol specifically for low-power scenarios, dynamically adjust connection parameters according to communication requirements, and manage the connection status; The energy management and energy-saving optimization module is used to adopt independent power control strategies for different functional modules and extend the life and endurance of Bluetooth low-power devices through energy harvesting technology and storage optimization.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the remote wake-up method for a Bluetooth low energy device according to any one of claims 1 to 12 is implemented.

15. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the remote wake-up method for a Bluetooth low energy device according to any one of claims 1 to 12.

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