An Internet-based intelligent headphone operation status analysis and control system

Abstract

This invention discloses an internet-based intelligent headphone operation status analysis and control system, relating to the field of headphone operation status monitoring technology. It includes: a status acquisition module for collecting headphone battery level change data and temperature data from multiple heating zones; and, when the headphone is being worn, correcting the temperature data based on a dual baseline of ambient temperature and skin temperature to generate corrected temperature data for each zone; the status acquisition module synchronizes the corrected temperature data with the battery level change data, constructing temperature curves and battery decay curves, encapsulating them into operation curve data by timestamps, and transmitting them to a task association module. This invention achieves deep modeling of the operation status by deploying temperature acquisition units in multiple heating zones of the headphone and combining this with a battery monitoring chip to obtain state of charge change information, constructing temperature and battery hyperbola data under a unified time axis.

Description

Technical Field

[0001] This invention relates to the field of headphone operating status monitoring technology, specifically to an Internet-based intelligent headphone operating status analysis and control system. Background Technology

[0002] Against the backdrop of the rapid development of mobile smart terminals, smart headphones have gradually evolved from traditional audio playback devices into smart devices with multi-functional integration capabilities. Typical applications include voice calls, voice assistant interaction, noise reduction mode control, music playback, and game sound effect enhancement. With the increase in the types of functions and processing complexity, headphones exhibit a significant temperature rise during continuous operation, especially during prolonged wear or high-power tasks, which can easily cause user discomfort or fluctuations in device performance. Currently, some smart headphone products are equipped with basic monitoring components such as internal temperature sensors and power monitoring units to collect operating status data and improve device stability and user experience.

[0003] In existing technologies, monitoring temperature and battery level changes in headphones typically employs a separate data acquisition and control strategy. This involves recording temperature and battery data separately and triggering a protection mechanism based on preset thresholds. While this approach is practical in typical usage scenarios, providing initial identification and processing for abnormal overheating or rapid battery depletion, some solutions also incorporate power consumption models based on user behavior characteristics to achieve personalized control strategies. However, in situations with varying task types and complex environmental conditions, there is still room for improvement in analyzing the correlation between temperature and battery level and their temporal coupling.

[0004] With the enhancement of computing and communication capabilities, more and more smart headphones are equipped to interact with the cloud and coordinate with other terminal devices. Leveraging the data processing capabilities of the cloud and the potential for multi-device linkage, more refined status analysis and energy efficiency optimization can be achieved. Based on this, how to integrate and analyze the temperature data and power change process of multiple zones inside the headphones, identify subtle abnormal trends in the headphone's operating status, and achieve cross-device collaborative control through the Internet mechanism has become one of the forward-looking research directions in the current development of headphone intelligence. Therefore, this invention proposes an Internet-based smart headphone operating status analysis and control system. Summary of the Invention

[0005] The purpose of this invention is to provide an Internet-based intelligent headphone operation status analysis and control system to solve the problems mentioned in the background art.

[0006] The present invention can be achieved through the following technical solution: an Internet-based intelligent earphone operation status analysis and control system, including a status acquisition module, a task association module, a status judgment module and a collaborative control module;

[0007] The status acquisition module is used to collect temperature data of each of the multiple heating zones inside the earphone, and to obtain the power change data of the remaining power of the earphone in combination with the power monitoring chip.

[0008] When the headphones are in the wearing state, the status acquisition module corrects the temperature data of each heating zone based on the dual baselines of the external ambient temperature and the temperature of the headphones in contact with the skin, and obtains the corrected temperature data of each heating zone.

[0009] After the correction is completed, the corrected temperature data of each heating zone is synchronized with the power change data of the corresponding time period. Smooth temperature curves for each heating zone are constructed and breakpoints are retained. In the discharge state, the power decay curve is generated after static self-calibration and monotonicity constraint on the percentage of state of charge sample. The two types of curves are packaged into running curve data with a unified timestamp and the running curve data is transmitted to the task association module.

[0010] The task association module is used to receive running curve data and obtain the task type currently being executed by the headphones. The task association module binds the running curve data with the task type and transmits it to the cloud via the Internet.

[0011] The cloud-based system establishes a task mapping library based on historical operating curve data of multiple headphones under different task types; the task mapping library is used to characterize the temperature rise characteristics and power consumption characteristics under different task types.

[0012] When the headset enters a specific task type, the task mapping library generates a power allocation strategy for that task and sends it to the headset, enabling the status judgment module to make status judgments in conjunction with the power allocation strategy during task execution.

[0013] The status judgment module is used to receive the temperature curves of each heating zone and the power decay curve provided by the status acquisition module during the execution of the headphone task, and compare and analyze them with the power allocation strategy issued by the task association module.

[0014] The status judgment module is specifically configured to: perform low-pass filtering on the temperature curves of each heating zone and the power decay curve; calculate the cross-correlation function of the two curves within a fixed time window to obtain the maximum peak value and its corresponding lag time; calculate the moving average of the lag time within a continuous fixed time window; when the lag time continues to increase or decrease and exceeds its preset continuous threshold, and the temperature change rate of each heating zone temperature curve and the power decay rate of the power decay curve both exceed the corresponding preset threshold and continue to exceed the delay tolerance time, determine that the headphones are in an abnormal operating mode, and classify the abnormal operating mode into heat dissipation degradation type abnormality or load burst type abnormality according to the direction of change of the lag time, and transmit the abnormal information to the collaborative control module;

[0015] The collaborative control module receives abnormal operating mode information sent by the status judgment module, broadcasts the abnormal operating mode to other terminal devices bound to the headset via the Internet, and controls the other terminal devices to perform collaborative load reduction operations, including reducing playback volume, pausing high bitrate audio streams, or transferring some audio processing tasks to the other terminal devices, in order to reduce the heat load and power consumption of the headset.

[0016] A further technical improvement of the present invention is that: during the execution of the coordinated load reduction operation, the coordinated control module feeds back the coordinated load reduction execution result to the status acquisition module, so that the status acquisition module records the temperature rise and power decay changes before and after the load reduction when generating subsequent running curve data, for use by the task mapping library for updating.

[0017] A further technical improvement of the present invention is that the method for correcting the temperature data of each heating zone by the status acquisition module includes the following steps:

[0018] S1. When the headphones are being worn, collect the real-time temperature data of each heating zone in the headphones at a preset sampling period, and add a timestamp to each real-time temperature data.

[0019] S2. An external ambient temperature acquisition unit is set in the area of ​​the earphone shell that is in direct contact with the outside air but not with the skin to continuously collect external ambient temperature data and add a timestamp within the same sampling period.

[0020] S3. A skin temperature acquisition unit is set at the position where the earphone directly contacts the ear skin. When the earphone is in the wearing state, the temperature data of the earphone in contact with the skin is collected as the skin temperature baseline. A timestamp is added within the same sampling period to keep the time synchronized with the external ambient temperature baseline and the temperature data of each heat-generating zone.

[0021] S4. Perform moving average filtering on the external ambient temperature baseline and the earphone contact skin temperature baseline respectively;

[0022] If the baseline change exceeds a preset threshold within a single sampling period, the baseline data for that period is deemed invalid, and the baseline data from the previous valid period is used instead.

[0023] S5. For any heat-generating zone, calculate the temperature difference between the zone's temperature data and the external ambient temperature baseline to obtain the ambient temperature difference, and the temperature difference between the zone and the skin temperature baseline in contact with the earphone to obtain the skin temperature difference.

[0024] S6. Preset the environmental temperature difference weight and skin temperature difference weight for different heating zones;

[0025] The correction amount is obtained by multiplying the ambient temperature difference by the ambient temperature difference weight and adding the skin temperature difference multiplied by the skin temperature difference weight.

[0026] S7. For each heat-generating zone, subtract the corresponding correction amount from its original temperature data to obtain the corrected temperature data for that zone; when the correction temperature change between two consecutive sampling periods exceeds the preset limit, the excess part is truncated to the limit.

[0027] A further technical improvement of the present invention is that the system further introduces a skin contact quality grading mechanism to improve the accuracy of temperature correction for the headphone's heating zones and reduce temperature baseline misjudgment caused by poor contact between the headphone and the wearer's skin, including:

[0028] Z1. Pressure sensors and capacitance sensors are installed at multiple preset locations on the earphone shell to detect whether the location is in direct contact with the user's skin.

[0029] In wear detection mode, the system sequentially reads the output signals of each sensor and compares the signal values ​​with preset contact thresholds;

[0030] When the sensor output value at a certain location exceeds the contact threshold, that location is determined to be a skin contact point;

[0031] Z2. For locations identified as skin contact points, the contact quality is further graded according to a preset grading system by combining the pressure value of the pressure sensor and the capacitance change of the capacitance sensor.

[0032] The system adjusts the weight of the skin temperature baseline based on the contact quality grading results;

[0033] Z3. Based on the internal structure layout data of the earphone, the system obtains the spatial coordinate difference between the temperature acquisition unit of each heating zone and the determined skin contact point, and calculates the shortest path distance between the two.

[0034] Z4. The system sets a distance correction coefficient based on the distance between each heating zone and the skin contact point;

[0035] Z5. Among the identified contact locations, calculate the total contact area between the headphones and the skin;

[0036] The system sets an air heat dissipation correction factor based on the proportion of the air contact area to the total area of ​​the headphone shell, and combines it with the distance correction coefficient to form a comprehensive correction coefficient;

[0037] Z6. For each heat-generating zone, the system performs a weighted calculation on its original temperature data, skin temperature baseline, distance correction coefficient, and air heat dissipation correction factor to obtain corrected temperature data.

[0038] A further technical improvement of the present invention is that: while collecting temperature data of each heating zone, the status acquisition module determines in real time whether the headphones are being worn;

[0039] When the device is determined to be in an unworn state, the temperature data of each heating zone collected in this state is marked as the ambient temperature baseline data. This data is excluded from the direct participation of the temperature curves of each heating zone in the generation process, and is only used as an external ambient temperature reference in the correction calculation of the temperature data in the worn state.

[0040] A further technical improvement of the present invention is that: the task mapping library makes personalized adjustments to the thermal sensitivity threshold corresponding to each task type based on the historical running curve data of each user. The thermal sensitivity threshold is used as a set of threshold parameters for determining risk under the task type, including at least the temperature change rate threshold, the power decay rate threshold, and the delay tolerance time associated with the two for use by the status judgment module.

[0041] A further technical improvement of the present invention is that, during the process of building the task mapping library in the cloud, based on historical running curve data, the temperature change rate of the heating zone and the power decay rate of multiple headphone devices under different task types are statistically analyzed in terms of their change range, trend, extreme value position and stable range throughout the entire task.

[0042] Furthermore, for each type of task, the cloud statistically analyzes the standard behavioral range of the temperature curve and power decay curve of each heat-generating zone in normal operation mode, and establishes a set of operating curve behavior boundary parameters bound to the task type.

[0043] The cloud platform combines the task type with the boundary parameter set of the running curve behavior to generate corresponding temperature rise rate thresholds and power decay rate thresholds, and sets a hysteresis change sensitivity factor and a task duration correction coefficient for these thresholds.

[0044] The threshold parameter set of temperature rise rate threshold and power decay rate threshold specific to this task type is dynamically sent to the earphone end and used as the comparison standard for the status judgment module to perform status judgment.

[0045] The status judgment module automatically loads the threshold parameter set for the corresponding task type each time the task type is switched, and performs the determination of temperature rise rate and power decay rate based on this.

[0046] A further technical improvement of the present invention is that: the system constructs a set of multiple temperature threshold points for identifying temperature rise stages based on the thermal behavior characteristics of the headphones during operation, and the temperature threshold points are arranged in order of temperature increase;

[0047] The system collects corrected temperature data from each heating zone and determines the current temperature rise stage based on the constructed temperature threshold point set.

[0048] When the corrected temperature data reaches or exceeds a certain temperature threshold, the system calls a preset scaling factor according to the heating stage corresponding to that threshold, and performs further proportional correction processing on the corrected temperature data within that stage.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] This invention deploys temperature acquisition units in multiple heating zones of the earphone and combines them with a power monitoring chip to obtain information on changes in the state of charge. It constructs hyperbolic data of temperature and power on a unified time axis, realizing in-depth modeling of the operating status. In particular, in terms of temperature correction, it introduces a dual baseline mechanism of ambient temperature and skin temperature, and combines a multi-factor correction model with factors such as wearing quality, spatial distance and air contact area, making the temperature rise data more realistic and relevant to user experience, providing a high-quality data foundation for subsequent status judgment.

[0051] Furthermore, this invention achieves mapping modeling between headphone task types and operating curve characteristics through the collaborative design of the task association module and the cloud task mapping library. Based on multi-window scrolling observation and historical behavior statistics, it realizes personalized updates of thermal sensitivity thresholds under task types. The status judgment module adopts the method of cross-correlation hysteresis analysis between curves to accurately distinguish between load burst anomalies and heat dissipation degradation anomalies. Compared with the traditional single-point threshold judgment mechanism, this method can dynamically adapt to task switching and power consumption changes, enhancing the accuracy and practicality of judgment.

[0052] Furthermore, this invention achieves coordinated load reduction response among multiple devices through a collaborative control module. When the headphones detect an abnormal operating state, they can broadcast status information to the bound devices via the Internet, thereby controlling other terminal devices to participate in some audio processing tasks or reduce transmission intensity, realizing active unloading of heat load at the headphone end, effectively improving the overall energy efficiency and operational stability of the smart headphone system, enhancing user wearing comfort, and possessing adaptability to high-load tasks and complex wearing environments. Attached Figure Description

[0053] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0054] Figure 1 This is a schematic diagram of the external structure of the present invention. Detailed Implementation

[0055] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0056] Example 1

[0057] Please see Figure 1 As shown, the present invention provides an Internet-based intelligent headphone operation status analysis and control system, including a status acquisition module, a task association module, a status judgment module and a collaborative control module;

[0058] The status acquisition module is used to collect temperature data of each of the multiple heating zones inside the earphone, and combine it with the power monitoring chip to obtain the power change data of the remaining battery power of the earphone.

[0059] While collecting temperature data from each heating zone, the status acquisition module determines in real time whether the headphones are being worn. Specifically, the determination of whether the headphones are being worn is achieved using a wearing status detection component pre-installed in the headphones.

[0060] When the device is determined to be in an unworn state, the temperature data of each heating zone collected in this state is marked as the ambient temperature baseline data. This data is then excluded from direct participation in the generation process of the temperature curves of each heating zone. Instead, it is used only as an external ambient temperature reference in the correction calculation of the temperature data in the worn state, so as to eliminate the influence of the external environment on the analysis results of the operating curve data.

[0061] When the headphones are in the wearing state, the status acquisition module corrects the temperature data of each heating zone based on the dual baselines of the external ambient temperature and the temperature of the headphones in contact with the skin, and obtains the corrected temperature data of each heating zone.

[0062] Furthermore, based on the thermal behavior characteristics of the headphones during operation, the system constructs multiple temperature threshold point sets for identifying the temperature rise stage. The temperature threshold points are arranged in order of temperature increase, serving as a reference benchmark for identifying and correcting the trend of temperature data changes.

[0063] The system collects corrected temperature data from each heating zone and determines the current temperature rise stage based on the constructed temperature threshold point set.

[0064] When the corrected temperature data reaches or exceeds a certain temperature threshold, the system calls a preset scaling factor according to the heating stage corresponding to that threshold to further correct the corrected temperature data in that stage proportionally. The scaling factor is used to enhance the sensitivity of the temperature value, so that the temperature curve shows a more significant numerical jump in the high temperature stage.

[0065] The system uses the proportionally corrected temperature data as the basis for the sampling trigger mechanism. Because the amplitude of temperature data change is increased after correction, the sampling mechanism can identify the temperature rise signal earlier under the same threshold trigger conditions, thereby indirectly increasing the sampling frequency without directly modifying the sampling period setting.

[0066] The method for correcting the temperature data of each heating zone by the status acquisition module includes the following steps:

[0067] S1. When the headphones are in the wearing state, the real-time temperature data of each heating zone is collected in the multiple heating zones of the headphones at a preset sampling period, and a timestamp is added to each real-time temperature data. For example, in this embodiment, the data is collected in the sound zone, processor zone and battery zone of the headphones.

[0068] S2. An external ambient temperature acquisition unit is set in the area of ​​the earphone shell that is in direct contact with the outside air but not with the skin to continuously collect external ambient temperature data and add a timestamp within the same sampling period to ensure that the external ambient temperature baseline is consistent with the temperature data of each heat-generating zone in time.

[0069] S3. A skin temperature acquisition unit is set at the position where the earphone directly contacts the ear skin. When the earphone is in the wearing state, the temperature data of the earphone in contact with the skin is collected as the skin temperature baseline. A timestamp is added within the same sampling period to keep the time synchronized with the external ambient temperature baseline and the temperature data of each heat-generating zone.

[0070] S4. Perform moving average filtering on the external ambient temperature baseline and the earphone contact skin temperature baseline respectively (window length preferably 1 second) to reduce temperature fluctuations in a short period of time; when the baseline change amplitude exceeds the preset threshold (preferably 2℃) in a single sampling period, the baseline data of that period is determined to be invalid, and the baseline data of the previous valid period is used.

[0071] S5. For any heat-generating zone, calculate the temperature difference between the zone's temperature data and the external ambient temperature baseline to obtain the ambient temperature difference, and the temperature difference between the zone and the skin temperature baseline in contact with the earphone to obtain the skin temperature difference.

[0072] S6. For different heating zones, preset the environmental temperature difference weight and the skin temperature difference weight, and the sum of the two is 1;

[0073] The correction amount is obtained by multiplying the ambient temperature difference by the ambient temperature difference weight and adding the skin temperature difference multiplied by the skin temperature difference weight.

[0074] In this embodiment, the environmental temperature difference weight and the skin temperature difference weight are determined based on the thermal conductivity characteristics of the headphone structure during the headphone factory calibration stage. For example, the environmental temperature difference weight of the sound-emitting zone can be set to 0.3 and the skin temperature difference weight to 0.7; the environmental temperature difference weight of the processor zone can be set to 0.5 and the skin temperature difference weight to 0.5; and the environmental temperature difference weight of the battery zone can be set to 0.6 and the skin temperature difference weight to 0.4.

[0075] S7. For each heating zone, subtract the corresponding correction amount from its original temperature data to obtain the corrected temperature data for that zone. When the change in corrected temperature between two consecutive sampling periods exceeds the preset limit, the excess part is truncated to the limit to avoid sudden changes in the corrected temperature.

[0076] S8. Output the corrected temperature data obtained by each heating zone in each sampling period in chronological order to form an independent temperature data sequence, and record the original temperature data, the external ambient temperature baseline, the earphone contact skin temperature baseline, the correction amount and the corrected temperature for subsequent temperature curve generation, heating status analysis and control strategy calling.

[0077] After the correction is completed, the corrected temperature data of each heating zone is synchronized with the power change data of the corresponding time period. Smooth temperature curves for each heating zone are constructed and breakpoints are retained. In the discharge state, the power decay curve is generated after static self-calibration and monotonicity constraint on the percentage of state of charge sample. The two types of curves are packaged into running curve data with a unified timestamp and the running curve data is transmitted to the task association module.

[0078] Specifically, including:

[0079] Time axis alignment and sample admission: Using the sampling window timestamp as the sole time reference, the corrected temperature data of each heating zone is paired with the power change data (state of charge percentage sample and battery current sample) of the same window according to the timestamp; if any data is missing at a certain timestamp and there are fewer than 2 consecutive missing sampling windows, the channel is linearly interpolated; if the threshold is exceeded, it is marked as an "invalid segment" and the segment will not participate in the subsequent curve generation.

[0080] Generation of temperature curves for each heating zone: For each heating zone, the corrected temperature data of the zone are concatenated into the original temperature sequence according to the timestamp order;

[0081] Applying a zero-phase moving average or an equivalent FIR low-pass filter (with a cutoff frequency that is proportional to the sampling rate) to the original temperature sequence yields a smoothed temperature sequence.

[0082] For samples with single-point mutations (adjacent differences exceeding a set threshold), neighborhood linear reconstruction is used for replacement.

[0083] Breakpoints are retained at both ends of the "invalid segment", and no interpolation is performed between the breakpoints;

[0084] The smoothed sequence, with breakpoints preserved, is defined as the temperature curve for each heating zone of the partition, in the form of (timestamp, temperature value) pairs. Three curves are obtained for each of the three partitions.

[0085] Power decay curve generation based on power change data: The state is determined according to the battery current sample of the same window; current ≥ threshold is defined as discharge, <− threshold is defined as charging, and the rest is static.

[0086] Only during the discharge state, extract the percentage of state of charge samples from the same window and concatenate them according to the timestamp to form the original discharge sequence;

[0087] In a static and temperature-stable range, the zero-drift state of charge is corrected with the minimum step size (without changing the monotonicity).

[0088] The discharge sequence is subjected to "non-incremental constraint smoothing" to eliminate the rebound point and ensure that the sequence is monotonically non-incremental;

[0089] Zero-phase smoothing is performed on the constrained discharge sequence to obtain a stable capacity decrease trajectory over time.

[0090] A smooth and monotonous discharge sequence is defined as a charge decay curve in the form of (timestamp, state of charge percentage) pairs; the charging segment is stored separately as a charge recovery track and is not included in the decay curve.

[0091] Synchronization and encapsulation into running curve data: Confirm that under the same timestamp, all three temperature curves and power decay curves for each heating zone have valid points;

[0092] For each valid timestamp, generate a running curve recording unit, with fields including at least: timestamp, task identifier, sound zone temperature value, processor zone temperature value, battery zone temperature value, state of charge percentage, battery current, sampling window identifier, and comfort risk identifier;

[0093] The time series formed by the continuous operation curve recording unit is transmitted to the task association module and the status judgment module as "operation curve data containing temperature curves and power decay curves of each heating zone".

[0094] The task association module is used to receive the running curve data and obtain the task type currently being executed by the headset. The task association module binds the running curve data with the task type and transmits it to the cloud via the Internet.

[0095] The cloud-based system establishes a task mapping library based on historical operating curve data of multiple headphones under different task types; the task mapping library is used to characterize the temperature rise characteristics and power consumption characteristics under different task types.

[0096] When the headset enters a specific task type, the task mapping library generates the power allocation strategy for that task and sends it to the headset, enabling the status judgment module to make status judgments in conjunction with the power allocation strategy during task execution.

[0097] The task mapping library personalizes the thermal sensitivity thresholds corresponding to each task type based on each user's historical running curve data. The thermal sensitivity thresholds are the set of threshold parameters for determining risk under the task type, including at least the temperature change rate threshold, the power decay rate threshold, and the delay tolerance time associated with both, which are used by the status judgment module.

[0098] Specifically, it includes:

[0099] A1. The task mapping library receives the running curve data encapsulated and uploaded by the task association module according to a unified timestamp, and establishes a scrolling observation window in the cloud based on the user identifier and task type.

[0100] The scrolling observation window is a time window for statistical analysis of the running curve data of the same user and the same task type in the cloud. It is continuously scrolled to form a multi-window sequence, which is used to identify long-term usage habits.

[0101] A2. Within each scrolling observation window, calculate the temperature and power indicators corresponding to the task type based on the running curve data.

[0102] Temperature-related indicators should include at least the window average and window maximum of the temperature change rate of each heat-generating zone's temperature curve;

[0103] The power consumption indicators should include at least the average and maximum values ​​of the power consumption rate of the power consumption decay curve within a certain window, and should also include the percentage of cumulative time exceeding the threshold.

[0104] A3. When it is detected that for the same task type, within a preset number of scrolling observation windows, the cumulative duration exceeding the threshold (i.e., within a certain scrolling observation window, the ratio of the cumulative duration satisfying "temperature change rate ≥ current temperature change rate threshold" or "battery decay rate ≥ current battery decay rate threshold" to the total duration of the window) is continuously not lower than the first proportion threshold, the thermal sensitivity threshold of the task type is lowered according to the preset adjustment step size, and the amplitude limit boundary and hysteresis condition are set to avoid frequent jitter.

[0105] When the cumulative duration exceeding the threshold is lower than the second proportional threshold for several consecutive windows, the thermal sensitivity threshold for this task type is slowly increased according to the preset adjustment step size, and does not exceed the limit boundary.

[0106] A4. Write the adjusted thermal sensitivity threshold along with the update timestamp and trigger reason into the task mapping library, and before the earphone is about to enter this task type, the task association module calls the task mapping library to generate and send the power allocation strategy to the earphone.

[0107] During task execution, the status judgment module makes a judgment based on the updated thermal sensitivity threshold. When the abnormal triggering conditions are met, it outputs abnormal operation mode information to the collaborative control module to perform collaborative load reduction operation.

[0108] During the process of building the task mapping library in the cloud, based on historical operation curve data, the temperature change rate of the heating zone and the power decay rate of multiple headphone devices under different task types are statistically analyzed throughout the task, including the change range, trend, extreme value position and stable range of the temperature change rate of the heating zone and the power decay rate.

[0109] Furthermore, for each task type (such as voice calls, music playback, game running, voice assistant interaction, etc.), the cloud statistically analyzes the standard behavior range of the temperature curve and power decay curve of each heat-generating zone in normal operation mode, and establishes a set of operation curve behavior boundary parameters bound to the task type as a reference for subsequent threshold setting.

[0110] The cloud combines task type and the boundary parameter set of running curve behavior to generate corresponding temperature rise rate threshold and power decay rate threshold respectively, and sets hysteresis change sensitivity factor and task duration correction coefficient for the threshold to enhance its adaptability under long tasks and high power tasks.

[0111] The threshold parameter set of temperature rise rate threshold and power decay rate threshold specific to this task type is dynamically sent to the earphone end and used as the comparison standard for the status judgment module to perform status judgment.

[0112] The status judgment module automatically loads the threshold parameter set for the corresponding task type each time the task type is switched, and performs the determination of temperature rise rate and power decay rate based on this. When the status change during task execution causes the running curve to approach or exceed the threshold, the system records the relevant events and feeds them back to the task mapping library, and further updates and optimizes the threshold parameters under the task type.

[0113] The status judgment module is used to receive the temperature curves and power decay curves of each heating zone provided by the status acquisition module during the execution of the headphone task, and compare and analyze them with the power allocation strategy issued by the task association module.

[0114] The status judgment module is specifically configured to: perform low-pass filtering on the temperature curves and power decay curves of each heating zone; calculate the cross-correlation function of the two curves within a fixed time window to obtain the maximum peak value and its corresponding lag time; calculate the moving average of the lag time within a continuous fixed time window; when the lag time continues to increase or decrease and exceeds its preset continuous threshold, and the temperature change rate of each heating zone temperature curve and the power decay rate of the power decay curve both exceed the corresponding preset threshold and continue to exceed the delay tolerance time, the headphone is determined to be in an abnormal operating mode, and the abnormal operating mode is classified into heat dissipation degradation type abnormality or load burst type abnormality according to the direction of change of lag time, and the abnormal information is transmitted to the collaborative control module.

[0115] Specifically, heat dissipation degradation anomalies (continuously increasing lag time) manifest as follows: after the power consumption of the headphones increases, the response to temperature rise becomes slower and slower; that is: when the power consumption has changed (the rate of power decay increases), the temperature curve response lag time continues to increase.

[0116] The reason is that the equipment's heat dissipation capacity has decreased, resulting in heat accumulation, slow heating, and a significant lag.

[0117] Sudden load anomalies (continuously decreasing lag time) manifest as: the headphone temperature suddenly rises at a certain moment, and the battery level also drops rapidly; that is: the temperature and battery level changes are almost synchronized or the lag time shortens rapidly; the cause is: a sudden increase in instantaneous load, such as the start of a high-power computing task or a sudden increase in audio processing.

[0118] The collaborative control module receives abnormal operating mode information sent by the status judgment module and broadcasts the abnormal operating mode to other terminal devices bound to the headset via the Internet; and controls other terminal devices to perform collaborative load reduction operations, including reducing playback volume, pausing high bitrate audio streams, or transferring some audio processing tasks to other terminal devices, in order to reduce the heat load and power consumption of the headset.

[0119] During the coordinated load reduction operation, the coordinated control module feeds back the results of the coordinated load reduction to the status acquisition module, so that the status acquisition module records the temperature rise and power decay changes before and after the load reduction when generating subsequent operation curve data, for use by the task mapping library for updating.

[0120] In this embodiment, the coordinated load reduction operation includes:

[0121] Playback parameter adjustments:

[0122] Automatically reduce the playback volume to a preset safe level.

[0123] Without affecting the core voice interaction, reduce the output power of the high-frequency or low-frequency bands to reduce the power consumption of the headphone speaker driver.

[0124] Data transmission optimization:

[0125] Pause or switch high-bitrate audio streams to low-bitrate streams to reduce the decoding computation load;

[0126] In a multi-device environment, tasks such as audio decoding and equalization processing are transferred to terminals with stronger computing power, and only the final audio data is transmitted to the headphones.

[0127] Task load balancing:

[0128] Some real-time audio processing tasks (such as noise reduction calculation, audio mixing, and 3D sound effect rendering) are transferred to the local processing unit of the bound terminal for execution;

[0129] When the collaborating device has better network bandwidth, the streaming media request and caching tasks are transferred to the collaborating device, and the result data is pushed to the headset through the local area link;

[0130] Operation execution feedback:

[0131] During the coordinated load reduction operation, the coordinated control module will feed back the load reduction execution results (including execution start and end times, adjustment range, and task transfer details) to the status acquisition module in real time;

[0132] When generating subsequent operation curve data, the status acquisition module records the temperature rise, power attenuation, and audio quality changes before and after load reduction, and synchronizes these records to the task mapping library to update the power allocation strategy and thermal sensitivity threshold for the corresponding task type.

[0133] Example 2

[0134] An internet-based intelligent headphone operation status analysis and control system includes a status acquisition module, a task association module, a status judgment module, and a collaborative control module.

[0135] The status acquisition module is used to collect temperature data of each of the multiple heating zones inside the earphone, and combine it with the power monitoring chip to obtain the power change data of the remaining battery power of the earphone.

[0136] When the headphones are in the wearing state, the status acquisition module corrects the temperature data of each heating zone based on the dual baselines of the external ambient temperature and the temperature of the headphones in contact with the skin, and obtains the corrected temperature data of each heating zone.

[0137] The method for correcting the temperature data of each heating zone by the status acquisition module includes the following steps:

[0138] S1. When the headphones are in the wearing state, the real-time temperature data of each heating zone is collected in the multiple heating zones of the headphones at a preset sampling period, and a timestamp is added to each real-time temperature data. For example, in this embodiment, the data is collected in the sound zone, processor zone and battery zone of the headphones.

[0139] S2. An external ambient temperature acquisition unit is set in the area of ​​the earphone shell that is in direct contact with the outside air but not with the skin to continuously collect external ambient temperature data and add a timestamp within the same sampling period to ensure that the external ambient temperature baseline is consistent with the temperature data of each heat-generating zone in time.

[0140] S3. A skin temperature acquisition unit is set at the position where the earphone directly contacts the ear skin. When the earphone is in the wearing state, the temperature data of the earphone in contact with the skin is collected as the skin temperature baseline. A timestamp is added within the same sampling period to keep the time synchronized with the external ambient temperature baseline and the temperature data of each heat-generating zone.

[0141] S4. Perform moving average filtering on the external ambient temperature baseline and the earphone contact skin temperature baseline respectively (window length preferably 1 second) to reduce temperature fluctuations in a short period of time; when the baseline change amplitude exceeds the preset threshold (preferably 2℃) in a single sampling period, the baseline data of that period is determined to be invalid, and the baseline data of the previous valid period is used.

[0142] S5. For any heat-generating zone, calculate the temperature difference between the zone's temperature data and the external ambient temperature baseline to obtain the ambient temperature difference, and the temperature difference between the zone and the skin temperature baseline in contact with the earphone to obtain the skin temperature difference.

[0143] Compared to Comparative Document 1, the system further introduces a skin contact quality grading mechanism to improve the accuracy of headphone heating zone temperature correction and reduce temperature baseline misjudgment caused by poor contact between the headphones and the wearer's skin, including:

[0144] Z1. Determination of the contact point between the earphone and the skin: Pressure sensors and capacitive sensors are placed at multiple preset locations on the earphone shell to detect whether the location is in direct contact with the user's skin. In wear detection mode, the system sequentially reads the output signals of each sensor to obtain the pressure value. With capacitance change ;

[0145] Pressure value With preset contact pressure threshold Compare and simultaneously measure the change in capacitance. Compared with the preset capacitance change threshold Compare;

[0146] like ≥ and ≥ If the location is determined to be a skin contact point, its three-dimensional coordinates in the headphone structural coordinate system are recorded. , , );

[0147] Z2. Determination of skin contact quality grading:

[0148] For locations identified as skin contact points, the pressure value is considered. With capacitance change Classify;

[0149] High-quality contact: ∈[ , ],and ≥ ;in, The minimum pressure threshold for high-quality contact; The highest pressure threshold for high-quality contact; The minimum capacitance change threshold for high-quality contact;

[0150] Medium quality contact: ∈[ , ]or ∈[ , ];in, This represents the minimum pressure threshold for medium-mass contact. The highest pressure threshold for medium-mass contact; The lower limit threshold for capacitance change in medium-mass contact; The upper limit threshold for capacitance change in medium-mass contact;

[0151] Low-quality contact: < and < ;in, The upper limit pressure threshold for low-quality contact; The upper limit capacitance change threshold for low-quality contacts;

[0152] The system adjusts the baseline weight of skin temperature based on the contact quality grading results. and will The three-dimensional coordinates in Z1 are passed to subsequent computation links;

[0153] Z3. Calculation of the distance between the heating zone and the skin contact area:

[0154] Based on the internal structure layout data of the headphones, the system obtains the coordinates of the temperature acquisition units of each heat-generating zone. , , ) relative to the coordinates of the already determined skin contact point ( , , Calculate the shortest path distance between the two by taking the spatial coordinate difference between them. :

[0155] ;

[0156] Z4. Setting the distance-to-temperature correction factor:

[0157] The result obtained from Z3 Normalize to the [0,1] interval to obtain ;

[0158] in , Determined by the extreme values ​​of the headphone structure;

[0159] The maximum path distance between all heating zones and skin contact points is used as a reference upper limit for the normalized denominator;

[0160] The minimum path distance between all heating zones and skin contact points is used as a reference lower limit for the normalized denominator;

[0161] Calculate the distance correction factor based on the normalization result. ;

[0162] ;

[0163] in, This is the minimum value of the distance correction factor (i.e., the correction factor at the farthest distance), obtained through experimental calibration, and reflects the lowest value of the effect of skin thermal conductivity.

[0164] This is the maximum value of the distance correction factor (i.e., the correction factor when the distance is closest), obtained through experimental calibration, and reflects the maximum value of the effect of skin thermal conductivity;

[0165] Let be the normalized distance coefficient of the j-th heat-generating zone, with a range of [0,1]; when When =1, it indicates that the distance between the heating zone and the skin contact point is at its minimum. ;when When =0, it indicates that the distance is at its maximum value. ;

[0166] This distance correction factor Used to adjust the intensity of the effect of the skin temperature baseline on different heat zones;

[0167] Z5. Air contact area and heat dissipation correction:

[0168] Among the identified contact locations, the total contact area between the headphones and the skin is calculated. It is obtained by summing the known distribution of contact points and the sum of the points on the surface:

[0169] ;in, The area of ​​a single contact point;

[0170] Let the total surface area of ​​the headphone shell be... The air contact area is ;

[0171] Calculate the air heat dissipation correction factor Where α is the experimentally calibrated air heat dissipation coefficient, reflecting the trend of "large contact area → small air contact area → reduced heat dissipation capacity";

[0172] Z6. Comprehensive correction of temperature data:

[0173] The system obtains the raw temperature data for each heat-generating zone j. Dynamic corrections are implemented to address temperature measurement errors caused by poor sensor contact, thermal runaway, and environmental interference. This includes two parts of correction:

[0174] z61. Skin Temperature Difference Compensation: When the system detects insufficient contact between the temperature measuring point and the skin, skin temperature difference compensation is used. Combined with weights Distance correction factor Air heat dissipation correction factor Perform positive correction and compensation;

[0175] z62. Ambient thermal interference cancellation: When the system detects that the temperature is too high and may be due to ambient thermal interference, it uses the original temperature and the ambient reference temperature. The difference - and combined with environmental weights A downward adjustment will be made.

[0176] The final corrected temperature data is as follows:

[0177] ;

[0178] In the formula, The environmental temperature difference correction weights are preferably 0.3–0.5, and are generated by system configuration or training model.

[0179] Correcting temperature data This information is used for subsequent status judgment module analysis to achieve accurate correction of the temperature of the heating zone.

[0180] S8. Output the corrected temperature data obtained by each heating zone in each sampling period in chronological order to form an independent temperature data sequence, and record the original temperature data, the external ambient temperature baseline, the earphone contact skin temperature baseline, the correction amount and the corrected temperature for subsequent temperature curve generation, heating status analysis and control strategy calling.

[0181] After the correction is completed, the corrected temperature data of each heating zone is synchronized with the power change data of the corresponding time period. Smooth temperature curves for each heating zone are constructed and breakpoints are retained. In the discharge state, the power decay curve is generated after static self-calibration and monotonicity constraint on the percentage of state of charge sample. The two types of curves are packaged into running curve data with a unified timestamp and the running curve data is transmitted to the task association module.

[0182] The task association module is used to receive the running curve data and obtain the task type currently being executed by the headset. The task association module binds the running curve data with the task type and transmits it to the cloud via the Internet.

[0183] The cloud-based system establishes a task mapping library based on historical operating curve data of multiple headphones under different task types; the task mapping library is used to characterize the temperature rise characteristics and power consumption characteristics under different task types.

[0184] When the headset enters a specific task type, the task mapping library generates the power allocation strategy for that task and sends it to the headset, enabling the status judgment module to make status judgments in conjunction with the power allocation strategy during task execution.

[0185] The status judgment module is used to receive the temperature curves and power decay curves of each heating zone provided by the status acquisition module during the execution of the headphone task, and compare and analyze them with the power allocation strategy issued by the task association module.

[0186] The status judgment module is specifically configured to: perform low-pass filtering on the temperature curves and power decay curves of each heating zone; calculate the cross-correlation function of the two curves within a fixed time window to obtain the maximum peak value and its corresponding lag time; calculate the moving average of the lag time within a continuous fixed time window; when the lag time continues to increase or decrease and exceeds its preset continuous threshold, and the temperature change rate of each heating zone temperature curve and the power decay rate of the power decay curve both exceed the corresponding preset threshold and continue to exceed the delay tolerance time, the headphone is determined to be in an abnormal operating mode, and the abnormal operating mode is classified into heat dissipation degradation type abnormality or load burst type abnormality according to the direction of change of lag time, and the abnormal information is transmitted to the collaborative control module.

[0187] The collaborative control module receives abnormal operating mode information sent by the status judgment module and broadcasts the abnormal operating mode to other terminal devices bound to the headset via the Internet; and controls other terminal devices to perform collaborative load reduction operations, including reducing playback volume, pausing high bitrate audio streams, or transferring some audio processing tasks to other terminal devices, in order to reduce the heat load and power consumption of the headset.

[0188] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0189] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations 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 scope of the present invention.

Claims

1. A smart headphone operation status analysis and control system based on the Internet, characterized in that, include: The status acquisition module is used to collect data on the changes in the battery level of the headphones and the temperature data of multiple heating zones. When the headphones are being worn, the temperature data is corrected based on the dual baselines of the external ambient temperature and skin temperature to generate corrected temperature data for each zone. The status acquisition module will synchronize the corrected temperature data and power change data, construct temperature curves and power decay curves, encapsulate them into running curve data according to timestamps, and transmit them to the task association module. The task association module is used to obtain the current task type of the headphones and upload the running curve data to the cloud, so that the cloud can build a task mapping library based on the historical running curve data, generate the corresponding power allocation strategy when the headphones enter the corresponding task type, and send the power allocation strategy to the headphones. The status judgment module performs filtering and cross-correlation analysis on the temperature curve and the power decay curve to determine the change in lag time. Combining the temperature rise rate and the power decay rate, it identifies anomalies of heat dissipation degradation or load burst. The collaborative control module receives abnormal information, broadcasts it to the bound terminal, and performs collaborative load reduction operations to reduce thermal load and energy consumption.

2. The Internet-based intelligent earphone operation status analysis and control system according to claim 1, characterized in that, The working method of the status determination module includes: Calculate the cross-correlation function of the two curves within a fixed time window to obtain the maximum peak value and its corresponding lag time; The lag time within a continuous fixed time window is calculated by moving average. When the lag time continues to increase or decrease and exceeds its preset continuous threshold, and the temperature change rate of the temperature curve of each heating zone and the power decay rate of the power decay curve both exceed the corresponding preset threshold and continue to exceed the delay tolerance time, the headphones are determined to be in an abnormal operating mode, and the abnormal operating mode is distinguished according to the direction of change of the lag time.

3. The Internet-based intelligent earphone operation status analysis and control system according to claim 2, characterized in that, During the coordinated load reduction operation, the coordinated control module feeds back the results of the coordinated load reduction to the status acquisition module, so that the status acquisition module records the temperature rise and power decay changes before and after the load reduction when generating subsequent operation curve data, for use by the task mapping library for updating.

4. The Internet-based intelligent earphone operation status analysis and control system according to claim 1, characterized in that, While collecting temperature data from each heating zone, the status acquisition module determines in real time whether the headphones are being worn. When the device is determined to be in an unworn state, the temperature data of each heating zone collected in this state is marked as the ambient temperature baseline data. This data is excluded from the direct participation of the temperature curves of each heating zone in the generation process, and is only used as an external ambient temperature reference in the correction calculation of the temperature data in the worn state.

5. The Internet-based intelligent earphone operation status analysis and control system according to claim 1, characterized in that, The task mapping library adjusts the thermal sensitivity thresholds of each task type individually based on the user's historical running curve data. The thermal sensitivity thresholds include a temperature change rate threshold, a power decay rate threshold, and a delay tolerance time. The task mapping library builds a scrolling observation window in the cloud according to users and task types, counts temperature and power indicators, and calculates the percentage of cumulative time exceeding the threshold. When the percentage is continuously higher or lower than the preset threshold, the thermal sensitivity threshold is adjusted down or up respectively, and the updated results are used to generate a power allocation strategy and send it to the headset.

6. The Internet-based intelligent earphone operation status analysis and control system according to claim 1, characterized in that, During the process of building the task mapping library in the cloud, based on historical operation curve data, the temperature change rate of the heating zone and the power decay rate of multiple headphone devices under different task types are statistically analyzed throughout the task, including the change range, trend, extreme value position and stable range of the temperature change rate of the heating zone and the power decay rate. Furthermore, for each type of task, the cloud statistically analyzes the standard behavioral range of the temperature curve and power decay curve of each heat-generating zone in normal operation mode, and establishes a set of operating curve behavior boundary parameters bound to the task type. The cloud platform combines the task type with the boundary parameter set of the running curve behavior to generate corresponding temperature rise rate thresholds and power decay rate thresholds, and sets a hysteresis change sensitivity factor and a task duration correction coefficient for these thresholds. The threshold parameter set of temperature rise rate threshold and power decay rate threshold specific to this task type is dynamically sent to the earphone end and used as the comparison standard for the status judgment module to perform status judgment. The status judgment module automatically loads the threshold parameter set for the corresponding task type each time the task type is switched, and performs the determination of temperature rise rate and power decay rate based on this.

7. The Internet-based intelligent earphone operation status analysis and control system according to claim 1, characterized in that, Based on the thermal behavior characteristics of the headphones during operation, the system constructs multiple temperature threshold point sets for identifying temperature rise stages, with the temperature threshold points arranged in order of temperature increase. The system collects corrected temperature data from each heating zone and determines the current temperature rise stage based on the constructed temperature threshold point set. When the corrected temperature data reaches or exceeds a certain temperature threshold, the system calls a preset scaling factor according to the heating stage corresponding to that threshold, and performs further proportional correction processing on the corrected temperature data within that stage.

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