Electric quantity management method and system for Bluetooth equipment

Abstract

The invention relates to the technical field of power management of Bluetooth devices, in particular to a power management method and system of a Bluetooth device, and the method comprises the steps: collecting a plurality of operation parameters of a Bluetooth master device and a Bluetooth slave device in a connection state, and calculating a link quality index comprehensively reflecting signal loss and interference according to a parameter set; according to a comparison result of the index and a preset threshold value, dynamically adjusting a master device broadcast interval and a slave device scanning window; meanwhile, obtaining the battery residual capacity of the slave device and a task queue with a priority mark; and finally, in combination with the adjusted communication parameters, the electric quantity state and the task requirements, generating a time-phased personalized electric quantity distribution strategy for each slave device. According to the method, link parameters are dynamically optimized according to the communication environment, and refined energy scheduling is performed based on the battery state and the task priority, so that the connection stability and the equipment cruising ability are effectively considered in the complex wireless environment.

Description

Technical Field

[0001] This invention relates to the field of Bluetooth device power management technology, and in particular to a method and system for power management of Bluetooth devices. Background Technology

[0002] Traditional Bluetooth devices, especially in Bluetooth Low Energy (BLE) networks consisting of master and slave devices, typically employ relatively simple power management strategies. Most methods rely on a single parameter—received signal strength—to determine connection status and adjust the device's duty cycle based on fixed thresholds or empirical rules, such as extending broadcast intervals or entering deep sleep mode. Other approaches primarily focus on the device's own battery level, uniformly reducing communication frequency or functional performance to extend battery life when the battery falls below a certain threshold. These methods form the foundation of current power-saving design practices for Bluetooth devices.

[0003] The aforementioned existing technical solutions have shortcomings. Relying on a single signal strength parameter cannot fully reflect the dynamically changing communication environment, leading to potentially inaccurate adjustment strategies. This could either sacrifice necessary connection stability or result in unnecessary power waste. Furthermore, this crude management based solely on remaining power completely ignores the actual needs of the tasks yet to be executed. When high-priority tasks require execution, the device may be unable to respond promptly due to the power management strategy having entered a low-power state, or unnecessary power throttling may occur even when power is sufficient, impacting user experience and system efficiency.

[0004] A more refined and intelligent method is needed to manage the power consumption of Bluetooth devices. This method should comprehensively assess multiple environmental factors affecting connection stability and dynamically optimize communication behavior accordingly. It should also incorporate task scheduling logic into power-saving design, ensuring that power allocation matches the tasks the device needs to perform and their importance, thereby minimizing overall energy consumption while ensuring the reliable execution of critical tasks. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a power management method and system for Bluetooth devices.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a power management method for a Bluetooth device, comprising: Collect a set of operating parameters of a Bluetooth master device and at least one Bluetooth slave device in a connected state. The set of operating parameters includes the master device's signal transmission power, the slave device's signal reception strength, the data transmission rate between devices, and the bit error rate of the communication link. The link quality index of the current communication environment is calculated based on the set of operating parameters. The link quality index comprehensively reflects the impact of signal propagation loss and channel interference on the stability of Bluetooth connection. Based on the comparison result between the link quality index and the preset quality threshold, the broadcast interval period of the Bluetooth master device and the scanning window duration of the Bluetooth slave device are dynamically adjusted. Obtain the remaining battery capacity percentage of the Bluetooth slave device and a preset task execution queue, wherein the task execution queue contains Bluetooth operation instructions to be executed and priority flags of the instructions; By combining the adjusted broadcast interval period, the scanning window duration, the remaining battery capacity percentage, and the task execution queue, a time-segmented power allocation strategy is generated for each Bluetooth slave device.

[0007] As a further aspect of the present invention, calculating the link quality index of the current communication environment based on the set of operating parameters includes: Extract the master device signal transmission power and the slave device signal reception strength from the set of operating parameters, and calculate the difference between the master device signal transmission power and the slave device signal as a path loss estimate; The rate attenuation coefficient is obtained by analyzing the ratio of the data transmission rate to the nominal maximum rate. The frequency of interference events is generated by counting the number of times the bit error rate of the communication link exceeds a specified threshold within a preset time window. The path loss estimate, the rate attenuation coefficient, and the frequency of interference events are input into a pre-built weighted evaluation model to calculate the quantified link quality index.

[0008] As a further aspect of the present invention, based on the comparison result between the link quality index and a preset quality threshold, the broadcast interval period of the Bluetooth master device and the scanning window duration of the Bluetooth slave device are dynamically adjusted, including: Multiple link quality index ranges are set, and each range is associated with a set of preset broadcast interval period parameters and scan window duration parameters; The calculated link quality index is matched with the multiple link quality index intervals to determine the target interval to which the link quality index belongs; A new broadcast interval period is selected from the broadcast interval period parameters associated with the target interval; Select a new scan window duration from the scan window duration parameters associated with the target interval; Update the current broadcast interval of the Bluetooth master device to the new broadcast interval; Update the Bluetooth device's current scan window duration to the new scan window duration.

[0009] As a further aspect of the present invention, after obtaining the remaining battery capacity percentage of the Bluetooth slave device and the preset task execution queue, the method further includes preprocessing the task execution queue: Parse each Bluetooth operation command in the task execution queue, identify the command type, and extract the priority flag; Based on the type and priority flag of the Bluetooth operation command, the task execution queue is divided into a high-priority command subset and a regular-priority command subset; Estimate the energy consumption units required to execute the high-priority instruction subset and the energy consumption units required to execute the normal-priority instruction subset.

[0010] As a further aspect of the present invention, the step of combining the adjusted broadcast interval period, the scanning window duration, the remaining battery capacity percentage, and the task execution queue to generate a time-segmented power allocation strategy for each Bluetooth slave device includes: Divide a complete future management cycle into multiple consecutive time slices; Based on the remaining battery capacity percentage and the preset total battery capacity, calculate the total available energy budget; Based on the estimated energy consumption units of the high-priority instruction subset and the regular-priority instruction subset, and the temporal dependencies of the instructions, the instructions are allocated to specific time slices for execution. For each time slice, the expected energy consumption of the time slice is calculated based on the type of instruction to be executed therein, and in combination with the broadcast interval period and the duration of the scan window that are effective within the time slice. The expected energy consumption of the time slice is matched with the total available energy budget. If the expected consumption exceeds the budget, the execution order of instructions in subsequent time slices is adjusted or the duration of the scanning window is modified, ultimately forming the time-segmented power allocation strategy that includes the energy budget and instruction execution plan for each time slice.

[0011] As a further aspect of the present invention, after matching the expected energy consumption of the time slice with the total available energy budget, the method further includes generating a supplementary energy acquisition instruction: When the total expected energy consumption of all time slices in the time-segmented power allocation strategy is less than a certain percentage of the total available energy budget, an idle time period is generated. During the idle time period, a low-power environment parameter acquisition command is sent to the Bluetooth slave device. The low-power environment parameter acquisition command is used to collect internal sensor data of the device other than communication data, and the energy consumption of executing the low-power environment parameter acquisition command is lower than a preset threshold. The low-power environment parameter acquisition command is inserted into the corresponding idle time period of the time-sharing power allocation strategy.

[0012] As a further aspect of the present invention, it also includes dynamic correction based on the actual energy consumption feedback during the execution of the time-sharing power allocation strategy: After each time slice of the time-segmented power allocation strategy is executed, the actual energy consumption and battery voltage change of the Bluetooth slave device are collected. The actual energy consumption value of the time slice is compared with the expected energy consumption of the time slice in the strategy, and the energy consumption deviation rate is calculated. If the energy consumption deviation rate continues to exceed the allowable deviation range, the energy consumption estimation unit under the same command or parameter configuration will be updated using the actual energy consumption value. Using the updated energy consumption estimation units, the time-segmented power allocation strategy for time slices that have not yet been executed is recalculated and adjusted.

[0013] As a further aspect of the present invention, updating the energy consumption estimation unit under the same type of instruction or parameter configuration using the actual energy consumption value includes: Record the type of instruction executed within the time slice that generates the energy consumption deviation rate, as well as the specific values ​​of the effective broadcast interval period and the duration of the scanning window, to form an energy consumption correction record; Collect multiple energy consumption correction records and classify and aggregate them according to instruction type and parameter configuration; For each combination of instructions and parameter configurations, calculate the average of the actual energy consumption values ​​in all records, and use the average value as the new energy consumption estimation unit. Replace the original data with the new energy consumption estimation unit for subsequent strategy generation and correction.

[0014] As a further aspect of the present invention, it also includes collaborative power-saving management on the Bluetooth host device side: Monitor the battery status reported by all connected Bluetooth devices and the time-sharing power allocation strategy being implemented; It is predicted that in the near future, multiple Bluetooth slave devices will simultaneously initiate high-energy communication requests at certain times. For the predicted time points when high-energy communication requests are initiated simultaneously, the Bluetooth master device actively coordinates and adjusts the communication timing with some slave devices to stagger the high-energy communication requests in time, so as to avoid multiple slave devices being in a high-power state in the same time slice. The Bluetooth master device actively coordinates the predicted timing of the simultaneous initiation of high-energy communication requests, including: The Bluetooth master device analyzes the time-sharing power allocation strategy of all slave devices to identify the time periods when large data transmissions or continuous signal scanning are planned. If two or more slave devices are found to have overlapping periods of high power consumption, the Bluetooth master device sends a timing adjustment request to the slave devices involved in the overlapping periods. The timing adjustment request includes a suggested new execution time window, which is calculated by the Bluetooth master device based on the task priority and battery status of each slave device. The Bluetooth slave device that receives the timing adjustment request decides whether to adopt the new execution time window based on its own situation, and feeds back the decision result to the Bluetooth master device. The Bluetooth master device updates its internal device coordination scheduling table based on the feedback received.

[0015] As a further aspect of the present invention, the present invention also includes a power management system for a Bluetooth device, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the power management method for a Bluetooth device as described above.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: By collecting data on the master device's signal transmission power, slave device's signal reception strength, data transmission rate, and link error rate, and then calculating a comprehensive link quality index, this method achieves a synchronous quantitative assessment of signal propagation loss and channel interference. Based on the real-time assessment results of this index, the broadcast interval and scanning window are dynamically adjusted to ensure that communication behavior accurately matches the current channel conditions. In a high-quality link environment, extending the broadcast interval and shortening the scanning window directly reduces the meaningless active time and idle listening power consumption of the radio frequency unit; in a low-quality link environment, increasing the broadcast frequency and expanding the listening window maintains link reliability, avoiding energy waste caused by data packet retransmission and repeated scanning due to connection interruption.

[0017] The system acquires the remaining battery capacity of the devices and their priority-marked task execution queues, combining this information with dynamically optimized communication parameters to generate a time-segmented power allocation strategy. This mechanism extends power management from physical layer communication control to application layer task scheduling. Based on the actual battery degradation curve and real-time task priority requirements, the system plans the device's active cycle and sleep depth on a timeline. Communication resources and computing time slots are pre-allocated for high-priority tasks, ensuring deterministic service response; strict energy-saving strategies are implemented during low-priority or idle periods, reducing basic standby power consumption. This strategy achieves optimal allocation of battery energy across time and task dimensions, systematically reducing overall energy consumption while maintaining critical business continuity. Attached Figure Description

[0018] Figure 1 This is a flowchart of the power management method for Bluetooth devices according to the present invention; Figure 2 A flowchart for dynamically adjusting the broadcast interval and scanning window; Figure 3 A graph showing the expected power consumption of a Bluetooth device over a 24-hour time slice. Figure 4 A diagram illustrating the effect of collaborative energy-saving management for Bluetooth devices; Figure 5 This is a distribution diagram showing the overlapping of high-energy-consuming tasks from multiple devices. Detailed Implementation

[0019] 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.

[0020] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] See Figure 1This solution provides a power management method for Bluetooth devices. The overall implementation is as follows: A Bluetooth master device establishes a connection with at least one Bluetooth slave device and enters a connected state. In this state, the system begins collecting a set of operating parameters, including the master device's signal transmission power, the slave device's signal reception strength, the data transmission rate between devices, and the bit error rate of the communication link. The collected operating parameters are used to calculate the link quality index of the current communication environment. This index is a comprehensive quantitative value that reflects the combined impact of signal propagation loss and channel interference on Bluetooth connection stability. The system compares the calculated link quality index with a preset quality threshold and dynamically adjusts the broadcast interval of the Bluetooth master device based on the comparison result, while simultaneously adjusting the scanning window duration of the Bluetooth slave devices. Simultaneously, the system obtains the remaining battery capacity percentage of each Bluetooth slave device and its preset task execution queue. The task execution queue contains Bluetooth operation commands to be executed and the priority marker for each command. The system comprehensively considers the adjusted broadcast interval period, scanning window duration, remaining battery capacity percentage, and task execution queue information to generate a time-segmented power allocation strategy for each Bluetooth slave device. This strategy plans the energy usage schedule for different time periods.

[0022] In one embodiment of the present invention, during the specific implementation, when the Bluetooth master device and Bluetooth slave device are connected, the system collects a set of operating parameters. This set of operating parameters includes the master device's signal transmission power, the slave device's signal reception strength, the data transmission rate between devices, and the bit error rate of the communication link. Based on the set of operating parameters, the link quality index of the current communication environment is calculated. The link quality index comprehensively reflects the impact of signal propagation loss and channel interference on the stability of the Bluetooth connection. In some embodiments, the master device's signal transmission power and the slave device's signal reception strength are extracted from the set of operating parameters. The difference between the master device's signal transmission power and the slave device's signal reception strength is calculated as an estimated path loss value. For example, when the master device's signal transmission power is 10dBm and the slave device's signal reception strength is -50dBm, the estimated path loss value is 60dB. The rate attenuation coefficient is obtained by analyzing the ratio of the data transmission rate to the nominal maximum rate. For example, when the data transmission rate is 2Mbps and the nominal maximum rate is 3Mbps, the rate attenuation coefficient is the ratio of 2 divided by 3.

[0023] In practical implementation, the frequency of interference events is generated by counting the number of times the bit error rate (BER) of the communication link exceeds a specified threshold within a preset time window. If the preset time window is set to 10 seconds and the specified BER threshold is 0.0001, and an event with a BER exceeding 0.0001 occurs 3 times within 10 seconds, the interference event frequency is recorded as 3. It can be understood that the path loss estimate, rate attenuation coefficient, and interference event frequency are input into a pre-built weighted evaluation model to calculate a quantified link quality index. The path loss estimate and interference event frequency need to be normalized before input to convert them into dimensionless score values. The path loss estimate is normalized by subtracting the minimum reference path loss estimate and dividing by the reference range of the path loss estimate. When the minimum reference path loss estimate is set to 30dB and the reference range is set to 70dB, for a path loss estimate of 60dB, its normalized score is (60-30) / 70. The frequency of interference events is normalized by dividing by the maximum number of interference events allowed within a preset time window. When the maximum number of interference events allowed within the preset time window is set to 10, the normalized score for an interference event frequency of 3 is 3 / 10. The rate attenuation coefficient, which is a ratio between 0 and 1, can be directly used as the normalized score in the calculation. The weighted evaluation model uses the following formula to calculate the link quality index: in: This represents the link quality index. This represents the normalized path loss score. This represents the normalized rate decay score (i.e., the rate decay coefficient). This represents the normalized interference frequency score. , , It is a preset weighting coefficient and satisfies In some embodiments, the weighting coefficient , , Configure according to the needs of the communication environment, such as in scenarios where signal propagation loss is the main factor. Set to 0.5. Set to 0.3, Set to 0.2. Optionally, the link quality index output by the weighted evaluation model is used for subsequent comparison and adjustment. A higher link quality index value indicates a better communication environment, while a lower link quality index value indicates a worse communication environment.

[0024] In constructing a weighted evaluation model to calculate the link quality index, the weighting coefficients... , , The determination of the link quality index is not arbitrary but relies on a preliminary model training and calibration phase. This phase establishes a quantitative relationship between parameters and connection stability by analyzing a large number of historical communication data samples. Before deployment or during initial operation, the system enters a data collection phase. During this phase, the Bluetooth master and slave devices transmit data under various known communication environments, which are pre-labeled as "excellent," "good," "medium," and "poor." The system simultaneously collects path loss estimates, rate attenuation coefficients, and interference event frequencies under these environments as input features, and records whether the communication link remains stable over a subsequent period as training labels. After collecting a sufficient number of samples, the system uses classification algorithms such as logistic regression or support vector machines to train the sample set. The goal of the algorithm is to find a set of weight coefficients that allows the model's output link quality index to most accurately distinguish between communication environment categories with different stability levels. The training process iteratively optimizes the results to ultimately determine the set of weights that achieves the highest classification accuracy. , , The system calculates and embeds these weighting coefficients into the device's firmware for subsequent real-time calculations. This process ensures that the weighting coefficients have clear physical meaning and statistical basis, and can objectively reflect the actual impact of different parameters on connection stability.

[0025] In the specific implementation of normalizing path loss estimates and interference event frequencies, the key step lies in determining the values ​​of normalization parameters such as the "minimum reference path loss estimate," the "reference range," and the "maximum number of interference events allowed within a preset time window." These parameters are not fixed but are dynamically acquired and maintained through an adaptive learning process in actual deployment scenarios. In the initial stage, the system uses a set of conservative preset default values, such as setting the minimum reference path loss estimate to 30dB, the reference range to 70dB, and the maximum allowed number of interference events to 10. During continuous operation, the system establishes a historical data buffer, continuously recording the observed path loss estimates and actual interference event frequencies. The minimum reference path loss estimate is updated based on the minimum value in the historical records. However, to avoid the influence of accidental extreme values, the update logic typically employs a sliding window or percentile statistical method, for example, taking the 5th percentile value from the most recent 1000 records as the minimum reference value.

[0026] The reference range for path loss estimation is obtained by statistically analyzing the difference between historical maximum and minimum values, followed by smoothing filtering. For interference event frequency, the maximum allowed number of occurrences is also based on historical statistics. The system analyzes the distribution of interference event frequency during stable communication periods and sets, for example, the 95th percentile as the maximum allowed number of occurrences. This dynamic and adaptive parameter acquisition mechanism ensures that the normalization process fits the specific radio environment of the equipment, effectively mapping raw observations of different dimensions and ranges to a unified and comparable dimensionless scoring interval.

[0027] In one embodiment of the present invention, see [reference] Figure 2 In practical implementation, the system presets multiple link quality index intervals. Each link quality index interval is associated with a set of preset broadcast interval period parameters and scan window duration parameters. For example, the link quality index interval "Excellent" corresponds to the broadcast interval period parameters [100ms, 200ms] and scan window duration parameters [20ms, 30ms], while the link quality index interval "Good" corresponds to the broadcast interval period parameters [300ms, 400ms] and scan window duration parameters [15ms, 20ms]. The calculated link quality index is matched with multiple link quality index intervals to determine the target interval to which the link quality index belongs. For example, a link quality index of 85 falls into the target interval "Excellent". A new broadcast interval period is selected from the broadcast interval period parameters associated with the target interval, for example, 150ms is selected from [100ms, 200ms]. A new scan window duration is selected from the scan window duration parameters associated with the target interval, for example, 25ms is selected from [20ms, 30ms]. Update the current broadcast interval of the Bluetooth master device to a new broadcast interval, and update the current scan window duration of the Bluetooth slave device to a new scan window duration.

[0028] In some embodiments, the selection of the broadcast interval period and the scanning window duration follows a preset calculation relationship. The broadcast interval period parameter set and the scanning window duration parameter set are associated through a lookup table, which stores the mapping relationship between the link quality index range and parameter pairs. After obtaining the remaining battery capacity percentage of the Bluetooth slave device and the preset task execution queue, the task execution queue is preprocessed. Each Bluetooth operation command in the task execution queue is parsed, the command type is identified, and the priority flag is extracted. For example, if the command type is identified as "data transmission" and the priority flag is "high", the task execution queue is split into a high-priority command subset and a regular-priority command subset according to the type and priority flag of the Bluetooth operation command. For example, commands marked "high" are assigned to the high-priority command subset, and commands marked "medium" or "low" are assigned to the regular-priority command subset. The energy consumption units required to execute the high-priority command subset and the energy consumption units required to execute the regular-priority command subset are estimated.

[0029] Optionally, instruction type identification is based on the instruction's opcode field, and priority tag extraction is based on the instruction's associated tag field. It can be understood that historical energy consumption records are referenced when estimating energy consumption units; these records store the average energy consumption values ​​for different instruction types under typical parameter configurations. In some embodiments, the preprocessing of the task execution queue also includes sorting a subset of instructions based on the instruction's timestamp field or dependency field. It can be understood that separating the high-priority instruction subset from the regular-priority instruction subset provides structured input for subsequently generating a time-segmented power allocation strategy.

[0030] In one embodiment of the present invention, a complete future management cycle is divided into multiple consecutive time slices. For example, the management cycle is 24 hours, and each time slice is 1 hour long, resulting in 24 consecutive time slices for the system. Based on the remaining battery capacity percentage of the Bluetooth slave device and the preset total battery capacity, the total available energy budget is calculated. For example, if the remaining battery capacity percentage is 60% and the preset total battery capacity is 100mAh, the total available energy budget is 60mAh. According to the energy consumption estimation units of the high-priority instruction subset and the regular-priority instruction subset, and the time dependencies of the instructions, the instructions are allocated to specific time slices for execution. For example, a high-priority data transmission instruction that needs to be executed immediately is allocated to the first time slice, while a regular-priority device status query instruction that can be postponed is allocated to the third time slice.

[0031] For each time slice, the expected energy consumption is calculated based on the type of instruction to be executed, combined with the broadcast interval and scan window duration within the time slice. For example, if a high-priority data transmission needs to be executed within a time slice, and the broadcast interval and scan window duration within that time slice are 200ms and 25ms respectively, the system determines the expected energy consumption of executing this instruction under given communication parameters by querying the energy consumption model table. The expected energy consumption of the time slice is matched with the total available energy budget. If the expected consumption exceeds the budget, the execution order of instructions in subsequent time slices is adjusted or the scan window duration is modified. For example, if the calculation shows that the total expected energy consumption of the first ten time slices exceeds the 60mAh budget, the system will postpone a non-urgent instruction in the eleventh time slice to a later time slice, or shorten the scan window duration of the twelfth time slice from 30ms to 20ms to reduce energy consumption. This ultimately forms a time-segmented power allocation strategy that includes the energy budget and instruction execution plan for each time slice.

[0032] In some embodiments, the expected energy consumption of a time slice It can be calculated using the following formula: in: This represents the expected energy consumption for a time slice. This indicates the number of Bluetooth operation commands planned within that time slice. Indicates the first The unit of energy consumption estimation for each instruction. Indicates the first The base number of times an instruction is executed. This indicates the total duration of the scan window within that time slice. This represents the scanning power consumption per unit time. This indicates the number of broadcast events expected to be received within that time slice. This represents the power consumption for processing a single broadcast event. It's understandable that the parameters required for calculation come from the device's pre-stored power consumption characteristic table and the plan in the time-based power allocation strategy.

[0033] In practice, the expected energy consumption of each time slice is matched with the total available energy budget. When the total expected energy consumption of all time slices in the time-segmented power allocation strategy is less than a specific percentage of the total available energy budget, an idle time period is generated. For example, if the total expected energy consumption of all time slices is 45mAh and the total available energy budget is 60mAh, and the specific percentage is set to 80%, then consumption below 48mAh is considered to have idle capacity, and the system will identify the time slice with lower expected consumption as an idle time period. During the idle time period, a low-power environmental parameter acquisition command is sent to the Bluetooth slave device. This command is used to collect data from internal sensors of the device other than communication data, such as data from a temperature sensor or accelerometer. The execution power consumption of the low-power environmental parameter acquisition command is lower than a preset threshold, for example, 0.1mAh per acquisition. The low-power environmental parameter acquisition command is then inserted into the corresponding idle time period of the time-segmented power allocation strategy. For example, a low-power temperature acquisition command is inserted into the time slice of the early morning period when communication activity is expected to be low.

[0034] Optionally, a specific ratio is dynamically set by the system based on historical energy consumption data. It can be understood that the insertion of low-power environment parameter acquisition commands enriches the data acquisition capabilities of Bluetooth slave devices during low-activity periods. In some embodiments, the identification of idle time periods is achieved by comparing the expected energy consumption of each time slice with the average energy consumption level.

[0035] See Figure 3 This is a graph showing the expected power consumption of a Bluetooth device over a 24-hour time slice, illustrating the expected power consumption composition for each time slice within a 24-hour management cycle. High-priority commands are the core driver of power consumption, and their fluctuations directly affect the total power consumption for the entire day. Regular commands and scan / broadcast power consumption are relatively stable, forming the baseline power consumption. High-power consumption periods are concentrated in the early morning (1h), morning (10h), and evening (21h), potentially corresponding to peak business periods or critical data transmission windows. Low-power consumption periods can serve as ideal windows for inserting low-power environment acquisition commands to enhance the device's data acquisition capabilities. The fluctuations in high-priority command power consumption reflect the business load of Bluetooth connections at different times. This provides input for dynamically adjusting the broadcast interval and scan window duration, for example, appropriately shortening the broadcast interval during high-load periods to improve connection stability while controlling scan power consumption.

[0036] In one embodiment of the present invention, dynamic correction is performed based on the actual energy consumption feedback during the execution of the time-sharing power allocation strategy. After each time slice of the time-sharing power allocation strategy is executed, the actual energy consumption value and battery voltage change of the Bluetooth slave device are collected. For example, after a time slice, the system reads the fuel gauge chip and obtains an actual energy consumption value of 2.5mAh, while simultaneously monitoring a drop in battery voltage from 3.7V to 3.68V. The actual energy consumption value of the time slice is compared with the expected energy consumption of the time slice in the strategy to calculate the energy consumption deviation rate. For example, if the expected energy consumption of the time slice is 2.0mAh and the actual energy consumption value is 2.5mAh, the calculated energy consumption deviation rate is 25%. If the energy consumption deviation rate continues to exceed the allowable deviation range, the energy consumption estimation unit under the same command or parameter configuration is updated using the actual energy consumption value. For example, the allowable deviation range is ±10%. When the energy consumption deviation rate of three consecutive time slices exceeds 10%, the system triggers the update process. Using the updated energy consumption estimation unit, the time-sharing power allocation strategy for time slices that have not yet been executed is recalculated and adjusted. For example, the energy budget for the remaining 8 hours of time slices is recalculated and the instruction plan is adjusted using the new energy consumption estimation unit.

[0037] In some embodiments, energy consumption deviation rate Calculated using the following formula: in: Indicates the energy consumption deviation rate. This represents the actual energy consumption value collected. This indicates the expected energy consumption for that time slice in the time-segmented power allocation strategy. It's understood that the allowable deviation range is preset as a fixed percentage or a dynamically changing threshold based on device type and application scenario. The actual energy consumption value is used to update the energy consumption estimate unit for the same type of instruction or parameter configuration. The specific values ​​of the instruction type executed within the time slice that generates the energy consumption deviation rate, as well as the effective broadcast interval and scan window duration, are recorded to form an energy consumption correction record. For example, a record might show an instruction type of "data transmission," a broadcast interval of 150ms, a scan window duration of 20ms, and an actual energy consumption value of 2.5mAh. Multiple energy consumption correction records are collected and categorized and aggregated according to instruction type and parameter configuration (see Table 1). The aggregated record forms an energy consumption correction record table similar to the following.

[0038] Table 1: Energy Consumption Correction Record Table In practice, for each combination of instructions and parameter configurations, the average value of the actual energy consumption across all records is calculated, and this average value is used as the new energy consumption estimation unit. For example, in the table above, the "Data Transmission" instruction with a broadcast interval of 150ms and a scan window duration of 20ms has its new energy consumption estimation unit updated to 2.48mAh. This new energy consumption estimation unit replaces the original data and is used for subsequent strategy generation and correction. For instance, when generating the time-segmented power allocation strategy for the next management cycle or correcting the remaining strategy for the current cycle, the updated 2.48mAh is directly used as the energy consumption estimation unit for that instruction under the corresponding parameter configuration.

[0039] Optionally, the collection and updating of energy consumption correction records are continuously performed in the background, forming a self-correcting database of energy consumption estimation units. It is understood that dynamic correction of energy consumption estimation units makes the formulation of time-sharing power allocation strategies more accurate. In some embodiments, battery voltage changes are also used as auxiliary verification parameters in determining the energy consumption deviation rate. Optionally, when the actual energy consumption value is consistently lower than the expected energy consumption and exceeds the deviation range, an update process is also triggered to optimize the utilization rate of the energy budget. It is understood that recalculating and adjusting the strategy for time slices that have not yet been executed ensures that the power management scheme can adapt to changes in the equipment's operating environment or its own state.

[0040] See Figure 4 This is a diagram illustrating the effect of collaborative energy-saving management for Bluetooth devices. It visually demonstrates the comparison of total energy consumption before and after the Bluetooth master device staggered high-energy communication requests from multiple slave devices, highlighting the core value of collaborative energy-saving management. Without coordination, high-energy communication requests from multiple slave devices concentrated in the 5th time slice, resulting in a peak total energy consumption of 8.6mAh, far exceeding the device's instantaneous power supply capacity and posing a risk of system crash. After coordination, the master device actively adjusted the communication timing of some slave devices, staggering the execution of high-energy tasks, reducing the peak to 3.2mAh, ensuring the system operated within a safe load range. In the uncoordinated state, energy consumption was extremely unevenly distributed, with high-energy consumption periods concentrated, leading to rapid battery discharge and shortened device battery life. After coordination, energy consumption was evenly distributed across multiple time slices, avoiding localized high power consumption and effectively extending the overall battery life of Bluetooth slave devices.

[0041] In one embodiment of the present invention, the Bluetooth master device performs collaborative power-saving management. The Bluetooth master device monitors the battery status reported by all connected Bluetooth slave devices and the currently executing time-sharing power allocation strategy. For example, the Bluetooth master device maintains a device status table, which records that slave device A's remaining battery capacity is 45% and that slave device A's time-sharing power allocation strategy plans to perform large-volume data transmission between 14:00 and 14:30. The system predicts the time points in the future when multiple Bluetooth slave devices will simultaneously initiate high-power communication requests. For example, by analyzing the device status table, the Bluetooth master device predicts that slave device A plans to transmit at 14:00, and slave device B plans to transmit at 14:15. The two overlap in the time period from 14:15 to 14:30, and this overlapping time point is marked as a potential high-power concurrency point. For predicted times when high-power communication requests are initiated simultaneously, the Bluetooth master device actively coordinates by adjusting the communication timing with some slave devices to stagger the timing of these requests, preventing multiple slave devices from being in a high-power state within the same time slice. For example, the Bluetooth master device might attempt to reschedule a high-power communication request from slave device B to start at 14:45. In some embodiments, the Bluetooth master device analyzes the time-sharing power allocation strategies of all slave devices to identify time periods planned for large data transfers or continuous signal scanning. For example, the analysis might find that slave device C has a 10-minute active scanning task at 16:00, and slave device D has a file transfer task at 16:05, with a 5-minute overlap in high-power time periods. If two or more slave devices are found to have overlapping high-power time periods, the Bluetooth master device sends timing adjustment requests to some of the slave devices involved in the overlapping time periods. For example, it might send timing adjustment requests containing suggestions for new execution time windows to both slave devices C and D. The timing adjustment request includes a suggested new execution time window, which is calculated by the Bluetooth master device based on the task priority and battery status of each slave device. For example, a scheduling priority score can be calculated using the following formula. Used for decision-making: in: This represents the scheduling priority score of a Bluetooth slave device. This indicates the normalized priority value of the high-power task that the Bluetooth slave device is about to perform. This indicates the percentage of remaining battery capacity of the Bluetooth device. and It is a preset weighting coefficient and . Bluetooth slave devices with lower scores are more likely to be advised to adjust their original time windows. Upon receiving a timing adjustment request, the Bluetooth slave device decides whether to adopt the new execution time window based on its own situation and feeds back its decision to the Bluetooth master device. For example, slave device C might refuse to adjust due to an urgent task, while slave device D might agree to postpone the task to 16:30 due to low battery power. Understandably, the Bluetooth master device updates its internal device coordination scheduling table based on the received feedback. This table records the execution time of high-energy-consuming tasks for each Bluetooth slave device after final approval or negotiation. The Bluetooth master device prioritizes processing relevant communication requests within the corresponding time period based on this table. In practice, collaborative energy-saving management on the Bluetooth master device side is an ongoing process. Whenever a new time-sharing power allocation strategy is generated or updated, the Bluetooth master device re-executes the prediction and coordination process.

[0042] Optionally, when calculating the new execution time window, the Bluetooth master device may refer to historical scheduling success rates and the response habits of Bluetooth slave devices. In some embodiments, the timing adjustment request may also include small incentive parameters provided by the Bluetooth master device, such as a commitment to provide higher communication bandwidth in the adjusted time window. It is understood that this proactive coordination initiated by the Bluetooth master device helps to balance the power consumption distribution among multiple Bluetooth slave devices at the system level.

[0043] See Figure 5 This is a graph showing the overlapping distribution of high-energy-consuming tasks across multiple slave devices. It illustrates the energy consumption distribution of Bluetooth slave devices A, B, and C during the period from 14:00 to 14:55 before coordinated scheduling, and the resulting peak total energy consumption. Between 14:20 and 14:35, the high-energy-consuming tasks (such as large data transmission and continuous scanning) of the three slave devices highly overlapped, resulting in a peak total energy consumption of 9.1 mAh. This directly verifies the scenario described in the patent where "multiple slave devices simultaneously initiate high-energy-consuming communication requests," providing a clear target for subsequent coordinated scheduling. The energy consumption distribution in the uncoordinated state is extremely uneven, with concentrated periods of high energy consumption and extreme peak values, seriously threatening device battery life and system stability. This graph provides strong quantitative evidence for the necessity of the "Bluetooth master device-side coordinated energy-saving management" method, proving that off-peak scheduling is a key means to solve the problem of high power consumption during multi-device concurrency.

[0044] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A power management method for a Bluetooth device, characterized in that, The method includes: Collect a set of operating parameters of a Bluetooth master device and at least one Bluetooth slave device in a connected state. The set of operating parameters includes the master device's signal transmission power, the slave device's signal reception strength, the data transmission rate between devices, and the bit error rate of the communication link. The link quality index of the current communication environment is calculated based on the set of operating parameters. The link quality index comprehensively reflects the impact of signal propagation loss and channel interference on the stability of Bluetooth connection. Based on the comparison result between the link quality index and the preset quality threshold, the broadcast interval period of the Bluetooth master device and the scanning window duration of the Bluetooth slave device are dynamically adjusted. Obtain the remaining battery capacity percentage of the Bluetooth slave device and a preset task execution queue, wherein the task execution queue contains Bluetooth operation instructions to be executed and priority flags of the instructions; By combining the adjusted broadcast interval period, the scanning window duration, the remaining battery capacity percentage, and the task execution queue, a time-segmented power allocation strategy is generated for each Bluetooth slave device.

2. The power management method for a Bluetooth device according to claim 1, characterized in that, The link quality index of the current communication environment is calculated based on the set of operating parameters, including: Extract the master device signal transmission power and the slave device signal reception strength from the set of operating parameters, and calculate the difference between the master device signal transmission power and the slave device signal as a path loss estimate; The rate attenuation coefficient is obtained by analyzing the ratio of the data transmission rate to the nominal maximum rate. The frequency of interference events is generated by counting the number of times the bit error rate of the communication link exceeds a specified threshold within a preset time window. The path loss estimate, the rate attenuation coefficient, and the frequency of interference events are input into a pre-built weighted evaluation model to calculate the quantified link quality index.

3. The power management method for a Bluetooth device according to claim 2, characterized in that, Based on the comparison result between the link quality index and the preset quality threshold, the broadcast interval period of the Bluetooth master device and the scanning window duration of the Bluetooth slave device are dynamically adjusted, including: Multiple link quality index ranges are set, and each range is associated with a set of preset broadcast interval period parameters and scan window duration parameters; The calculated link quality index is matched with the multiple link quality index intervals to determine the target interval to which the link quality index belongs; A new broadcast interval period is selected from the broadcast interval period parameters associated with the target interval; Select a new scan window duration from the scan window duration parameters associated with the target interval; Update the current broadcast interval of the Bluetooth master device to the new broadcast interval; Update the Bluetooth device's current scan window duration to the new scan window duration.

4. The power management method for a Bluetooth device according to claim 3, characterized in that, After obtaining the remaining battery capacity percentage of the Bluetooth slave device and the preset task execution queue, the process further includes preprocessing the task execution queue: Parse each Bluetooth operation command in the task execution queue, identify the command type, and extract the priority flag; Based on the type and priority flag of the Bluetooth operation command, the task execution queue is divided into a high-priority command subset and a regular-priority command subset; Estimate the energy consumption units required to execute the high-priority instruction subset and the energy consumption units required to execute the normal-priority instruction subset.

5. The power management method for a Bluetooth device according to claim 4, characterized in that, The process of combining the adjusted broadcast interval period, the scanning window duration, the remaining battery capacity percentage, and the task execution queue to generate a time-based power allocation strategy for each Bluetooth slave device includes: Divide a complete future management cycle into multiple consecutive time slices; Based on the remaining battery capacity percentage and the preset total battery capacity, calculate the total available energy budget; Based on the estimated energy consumption units of the high-priority instruction subset and the regular-priority instruction subset, and the temporal dependencies of the instructions, the instructions are allocated to specific time slices for execution. For each time slice, the expected energy consumption of the time slice is calculated based on the type of instruction to be executed therein, and in combination with the broadcast interval period and the duration of the scan window that are effective within the time slice. The expected energy consumption of the time slice is matched with the total available energy budget. If the expected consumption exceeds the budget, the execution order of instructions in subsequent time slices is adjusted or the duration of the scanning window is modified, ultimately forming the time-segmented power allocation strategy that includes the energy budget and instruction execution plan for each time slice.

6. The power management method for a Bluetooth device according to claim 5, characterized in that, After matching the expected energy consumption of the time slice with the total available energy budget, the process also includes generating a supplementary energy acquisition command: When the total expected energy consumption of all time slices in the time-segmented power allocation strategy is less than a certain percentage of the total available energy budget, an idle time period is generated. During the idle time period, a low-power environment parameter acquisition command is sent to the Bluetooth slave device. The low-power environment parameter acquisition command is used to collect internal sensor data of the device other than communication data, and the energy consumption of executing the low-power environment parameter acquisition command is lower than a preset threshold. The low-power environment parameter acquisition command is inserted into the corresponding idle time period of the time-sharing power allocation strategy.

7. The power management method for a Bluetooth device according to claim 6, characterized in that, It also includes dynamic correction based on actual energy consumption feedback during the execution of the time-sharing power allocation strategy: After each time slice of the time-segmented power allocation strategy is executed, the actual energy consumption and battery voltage change of the Bluetooth slave device are collected. The actual energy consumption value of the time slice is compared with the expected energy consumption of the time slice in the strategy, and the energy consumption deviation rate is calculated. If the energy consumption deviation rate continues to exceed the allowable deviation range, the energy consumption estimation unit under the same command or parameter configuration will be updated using the actual energy consumption value. Using the updated energy consumption estimation units, the time-segmented power allocation strategy for time slices that have not yet been executed is recalculated and adjusted.

8. The power management method for a Bluetooth device according to claim 7, characterized in that, Update the energy consumption estimation unit under the same command or parameter configuration using the actual energy consumption value, including: Record the type of instruction executed within the time slice that generates the energy consumption deviation rate, as well as the specific values ​​of the effective broadcast interval period and the duration of the scanning window, to form an energy consumption correction record; Collect multiple energy consumption correction records and classify and aggregate them according to instruction type and parameter configuration; For each combination of instructions and parameter configurations, calculate the average of the actual energy consumption values ​​in all records, and use the average value as the new energy consumption estimation unit. Replace the original data with the new energy consumption estimation unit for subsequent strategy generation and correction.

9. The power management method for a Bluetooth device according to claim 8, characterized in that, It also includes collaborative power-saving management on the Bluetooth host device side: Monitor the battery status reported by all connected Bluetooth devices and the time-sharing power allocation strategy being implemented; It is predicted that in the near future, multiple Bluetooth slave devices will simultaneously initiate high-energy communication requests at certain times. For the predicted time points when high-energy communication requests are initiated simultaneously, the Bluetooth master device actively coordinates and adjusts the communication timing with some slave devices to stagger the high-energy communication requests in time, so as to avoid multiple slave devices being in a high-power state in the same time slice. The Bluetooth master device actively coordinates the predicted timing of the simultaneous initiation of high-energy communication requests, including: The Bluetooth master device analyzes the time-sharing power allocation strategy of all slave devices to identify the time periods when large data transmissions or continuous signal scanning are planned. If two or more slave devices are found to have overlapping periods of high power consumption, the Bluetooth master device sends a timing adjustment request to the slave devices involved in the overlapping periods. The timing adjustment request includes a suggested new execution time window, which is calculated by the Bluetooth master device based on the task priority and battery status of each slave device. The Bluetooth slave device that receives the timing adjustment request decides whether to adopt the new execution time window based on its own situation, and feeds back the decision result to the Bluetooth master device. The Bluetooth master device updates its internal device coordination scheduling table based on the feedback received.

10. A power management system for a Bluetooth device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power management method for a Bluetooth device according to any one of claims 1 to 9.

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