System and method for a magnetic charging case of an adaptive noise-canceling headphone

Abstract

The present invention relates to the field of audio devices, and discloses a system and method for a magnetic charging case of an adaptive noise-canceling headphone, including: when the noise-canceling headphone is in use, motion data and ambient noise are collected in real time, the motion state of the user is judged, the ambient noise energy entropy is calculated, the scene noise level of the user's location is judged, a noise-canceling strategy is selected for the noise-canceling headphone in combination with the user's motion state, the historical usage data of the noise-canceling headphone is analyzed, the expected usage period of the user is predicted, the remaining power required by the noise-canceling headphone is predicted according to the expected usage period, the actual remaining power is compared, and it is judged whether the power is sufficient. If it is not sufficient, a power risk signal is generated, the real-time collection of motion data and ambient noise is suspended, a model is established, the noise-canceling strategy of the noise-canceling headphone during the prediction period is selected through the model, and the ambient noise and motion state are re-detected at the inspection time point to update the prediction model and the preselected noise-canceling strategy, and the charging power is dynamically adjusted when the noise-canceling headphone is charging.

Description

Technical Field

[0001] The present invention relates to the technical field of audio devices, and particularly relates to a system and method for a magnetic charging case of an adaptive noise-canceling headphone. Background Art

[0002] With the rapid development of technology and the continuous improvement of people's requirements for the quality of life, noise-canceling headphones have become an indispensable electronic product in modern life. Especially in a noisy environment, noise-canceling headphones can effectively isolate external noise and provide a relatively quiet space for users. Whether it is commuting, studying or resting, it can greatly improve the user experience. However, in the actual use of noise-canceling headphones, users often encounter some problems, which not only affect the noise-canceling effect, but may also bring inconvenience to users.

[0003] Traditional noise-canceling headphones usually adopt a fixed noise-canceling strategy and cannot be dynamically adjusted according to the actual environmental noise situation where the user is located. For example, in a quiet library and a noisy street, users may need different noise-canceling intensities to achieve the best noise-canceling effect. However, the fixed noise-canceling strategy often cannot meet this demand, resulting in an unsatisfactory noise-canceling effect. At the same time, the power management of noise-canceling headphones is an important issue. Real-time detection of environmental noise and motion state requires continuous activation of sensors and processors, resulting in a sharp increase in device power consumption. In the prior art, when the noise-canceling headphones are in the real-time detection mode, the battery life is shortened to less than 3 hours, which cannot meet the user's usage requirements. Users may interrupt use due to insufficient power during the use process, affecting the use experience. When charging, the charging power cannot be dynamically adjusted according to the actual usage situation of the headphones and the user's usage habits, resulting in low charging efficiency.

[0004] In view of the above problems, the present invention proposes a system and method for a magnetic charging case of an adaptive noise-canceling headphone. Summary of the Invention

[0005] The purpose of the present invention is to provide a system and method for a magnetic charging case of an adaptive noise-canceling headphone to solve at least one of the above-mentioned prior art problems.

[0006] In a first aspect, the present invention provides a method for a magnetic charging case of an adaptive noise-canceling headphone, including the following steps:

[0007] When the noise-canceling headphone is in use, determine the user's motion state according to the collected motion data, determine the noise energy entropy of the environment according to the collected environmental noise, and judge the scene noise level of the location where the user is based on the noise energy entropy;

[0008] Comprehensively analyze the scene noise level and the motion state to select a noise-canceling strategy;

[0009] Determine whether the remaining power required by the noise-canceling headphones is sufficient within the prediction time;

[0010] If it is not sufficient, during the prediction period, suspend real-time collection, and select a preselected noise-canceling strategy for the prediction period of the noise-canceling headphones through the prediction model;

[0011] Based on the motion data and environmental noise before the inspection time point, calibrate the prediction model and obtain the calibrated preselected noise-canceling strategy;

[0012] Among them, the prediction time consists of several prediction periods, and the inspection time point is the end point of the prediction period.

[0013] In a second aspect, the present invention provides a system for a magnetic charging case of an adaptive noise-canceling headphone, including the following modules:

[0014] Data acquisition module: When the noise-canceling headphones are in use, determine the user's motion state according to the collected motion data, and determine the noise energy entropy of the environment according to the collected environmental noise;

[0015] Strategy selection module: Based on the noise energy entropy, judge the scene noise level of the user's location, and select a noise-canceling strategy in combination with the user's motion state;

[0016] Power analysis module: Determine whether the remaining power required by the noise-canceling headphones is sufficient within the prediction time. If it is not sufficient, generate a power risk signal;

[0017] Strategy optimization module: If a power risk signal is received, during the prediction period, suspend real-time collection, establish a preselected noise-canceling strategy for the prediction period of the noise-canceling headphones through the prediction model, calibrate the prediction model based on the motion data and environmental noise before the inspection time point, and obtain the calibrated preselected noise-canceling strategy;

[0018] Charging management module: When the noise-canceling headphones are charging, analyze the power of the magnetic charging case and the noise-canceling headphones, and dynamically adjust the charging power in combination with the expected usage period.

[0019] Advantages of the present invention:

[0020] 1. By collecting motion data and environmental noise in real time, the present invention provides an accurate data basis for the headphones, can intelligently judge the scene noise level according to the user's motion state and environmental noise energy entropy, and select the most suitable noise-canceling strategy accordingly, improving the noise-canceling effect, ensuring that users can enjoy the best auditory experience in different scenarios, and enhancing the adaptability and user satisfaction of the headphones.

[0021] 2. By analyzing the historical usage data of the noise-canceling headphones, the present invention can accurately predict the expected usage period of the user and the remaining power required, effectively avoiding the situation of insufficient power. Once the power is insufficient, the system immediately generates a risk signal and suspends unnecessary detections, saving power. At the same time, a model is established to select the noise-canceling strategy of the noise-canceling headphones during the predicted period through the model and recalibrate at the inspection time point to ensure that the noise-canceling effect is always optimal, improving the user experience and the headphone usage efficiency.

[0022] 3. By dynamically adjusting the charging power and flexibly selecting the charging gear according to the power difference and demand, the present invention not only meets the urgent charging needs but also avoids battery aging, effectively balancing the power reserves of the headphones and the charging case and ensuring the overall battery life of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0024] Figure 1 is a flowchart of the steps of a method for a magnetic charging case of an adaptive noise-canceling headphone provided by an embodiment of the present invention;

[0025] Figure 2 is a flowchart of the steps for obtaining the expected usage period in a method for a magnetic charging case of an adaptive noise-canceling headphone provided in the second embodiment of the present invention;

[0026] Figure 3 is a flowchart of the steps for dynamically adjusting the charging power in a method for a magnetic charging case of an adaptive noise-canceling headphone provided in the third embodiment of the present invention;

[0027] Figure 4 is a block diagram of a system for a magnetic charging case of an adaptive noise-canceling headphone provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment 1

[0029] AsFigure 1 As shown in the figure, a method for a magnetic charging case of an adaptive noise-canceling headphone provided by an embodiment of the present invention specifically includes the following steps:

[0030] Step 1: When the noise-canceling headphone is in use, collect motion data and ambient noise in real time through built-in sensors and a microphone array, judge the user's motion state, and calculate the ambient noise energy entropy;

[0031] In some embodiments, when the noise-canceling headphone is in use, data is collected through motion sensors such as an accelerometer and a gyroscope built into the headphone at the current detection time point, and the acceleration changes of the headphone on the x, y, and z axes and the rotational angular velocity of the headphone around the three coordinate axes are detected. Motion features are extracted from the collected acceleration and angular velocity data, and the extracted motion features are analyzed. The user's current motion state is judged through a preset motion state classification algorithm. The motion states include stationary, walking, running, riding in a vehicle, etc.;

[0032] It should be noted that the detection time point refers to the time node for collecting motion data. The interval period between adjacent detection time points is marked as the detection mobile phone end, and the duration of adjacent detection periods is the same;

[0033] During the current detection period, an array composed of several microphones samples the ambient noise at a fixed sampling frequency fs, and the time point for sampling the ambient noise is marked as the sampling moment. The microphones include a feedforward microphone and a feedback microphone. The feedforward microphone is responsible for capturing the ambient noise entering the headphone from the outside, and the feedback microphone is responsible for monitoring the residual noise signal in the ear canal. The microphones are marked with serial numbers i, i = 1, 2,..., n, where n represents the number of microphones;

[0034] Exemplarily, a common sampling frequency fs = 48000Hz, which means that the sound signal is sampled 48000 times per second;

[0035] The sound signal value collected by the microphone with serial number i at the k-th sampling moment during the current detection period is denoted as xi(k), where k = 0, 1, 2,...;

[0036] The obtained noise signal is amplified and filtered to remove high-frequency noise and interference, and through the formula Perform a fast Fourier transform on the processed noise signal to obtain a frequency-domain signal , where j represents the imaginary unit, , L represents the number of frequency points into which the fast Fourier transform decomposes the signal, = 0, 1, 2,..., L - 1;

[0037] According to the obtained frequency-domain signal, through the formula Calculate the proportion of the energy of each frequency component after Laplace smoothing in the total energy to obtain the probability distribution of the frequency components , where is the smoothing coefficient, ;

[0038] It should be noted that the function of the smoothing coefficient is to avoid problems in the operation when

[0039] Based on the obtained , through the formula jointly analyze the probability distributions of the frequency components of all microphones, and calculate the environmental noise energy entropy H within the current detection period;

[0040] It should be noted that the environmental noise energy entropy H represents the complexity of the environmental noise;

[0041] Step 2: Determine the scene noise level of the user's location according to the obtained environmental noise energy entropy, and select a noise reduction strategy for the noise-canceling headphones in combination with the user's motion state;

[0042] In some embodiments, compare the calculated environmental noise energy entropy H with the low threshold Hd of the environmental noise energy entropy and the high threshold Hh of the environmental noise energy entropy;

[0043] If the environmental noise energy entropy within the current detection period , it is determined that the scene noise level of the user's current location is quiet;

[0044] If the environmental noise energy entropy within the current detection period , it is determined that the scene noise level of the user's current location is low noise;

[0045] If the environmental noise energy entropy within the current detection period , it is determined that the scene noise level of the user's current location is high noise;

[0046] Establish a noise reduction strategy database including scene noise level, user motion state, and corresponding noise reduction strategies. The noise reduction strategy database stores the optimal noise reduction strategies corresponding to different combinations of environmental noise energy entropy ranges and user motion states;

[0047] Based on the environmental noise energy entropy within the current detection period and the determined user motion state, query the noise reduction strategy database, and select a suitable noise reduction strategy for the noise-canceling headphones according to the query result;

[0048] Exemplarily, preset the low threshold Hd of the environmental noise energy entropy to 3. If the environmental noise energy entropy within the current detection period , it is determined that the scene noise level at the user's current location is quiet. If the user is in a stationary or riding state at this time, the deep frequency-domain filtering combined with phase compensation algorithm can be selected as the noise reduction strategy. If the user is in a walking or running state at this time, the band-segmented noise reduction combined with the IMU dynamic compensation algorithm can be selected as the noise reduction strategy;

[0049] Exemplarily, the preset low threshold of environmental noise energy entropy Hd = 3, and the preset high threshold of environmental noise energy entropy Hd = 5. If the environmental noise energy entropy within the current detection period , it is determined that the scene noise level at the user's current location is low noise. If the user is in a stationary or walking state at this time, the CNN neural network to suppress medium and high frequency noise combined with the traditional spectral subtraction method can be selected as the noise reduction strategy. If the user is in a riding or running state at this time, the enhanced human voice frequency band combined with suppressing sudden noise can be selected as the noise reduction strategy;

[0050] Exemplarily, the preset high threshold of environmental noise energy entropy Hd = 5. If the environmental noise energy entropy within the current detection period , it is determined that the scene noise level at the user's current location is high noise. Since the environmental noise energy is too high at this time, no matter what motion state the user is in, the full-function active noise reduction system combined with the neural network enhancement core algorithm can be selected as the noise reduction strategy;

[0051] The technical solution of the embodiment of the present invention is as follows: When the noise-canceling headphones are in use, the motion data and environmental noise are collected in real time through the built-in sensors and microphone array, the user's motion state is judged, and the environmental noise energy entropy is calculated, providing a data basis for subsequently selecting a suitable noise reduction strategy according to the user's motion state and environmental noise level. The scene noise level at the user's location is judged according to the obtained environmental noise energy entropy, and a noise reduction strategy is selected for the noise-canceling headphones in combination with the user's motion state to meet the noise reduction requirements of the user in different scenarios.

[0052] Embodiment 2

[0053] As Figure 1 shown, a method for a magnetic charging box of an adaptive noise-canceling headphone provided by an embodiment of the present invention specifically includes the following steps:

[0054] Step 3: Analyze the historical usage data of the noise-canceling headphones, predict the user's expected usage period, predict the remaining power required by the noise-canceling headphones according to the expected usage period, and compare it with the actual remaining power to determine whether the power is sufficient. If it is not sufficient, generate a power risk signal;

[0055] In some embodiments, historical usage data of the noise-canceling headphones is obtained. The historical usage data includes the usage periods of the noise-canceling headphones in each day within the historical usage time and the power consumption of the noise-canceling headphones during the usage periods. The historical usage time represents the period starting from the time node when the noise-canceling headphones are first used by the user and ending at the current time node. The usage period of the noise-canceling headphones represents the period when the noise-canceling headphones are in the usage state within the historical usage time;

[0056] It should be noted that there are several usage periods in each day of the historical usage time;

[0057] As Figure 2 shown, the specific steps for obtaining the expected usage period are as follows;

[0058] Based on any day within the historical usage time, obtain the interval duration between two adjacent usage periods in a day. The interval duration represents the duration of the interval period between the end point of the usage period with a prior time sequence and the start point of the usage period with a subsequent time sequence, and compare the interval duration with the interval threshold;

[0059] If the interval duration between two adjacent usage periods in a day is less than the interval threshold, merge the two adjacent usage periods into one usage period. The merged usage period starts from the start point of the usage period with a prior time sequence among the two usage periods and ends at the end point of the usage period with a subsequent time sequence;

[0060] It should be noted that two adjacent usage periods with too short interval durations in a day can be regarded as one usage period. The purpose of this step is to merge adjacent usage periods with too short interval durations to reduce subsequent statistical errors;

[0061] Traverse each day within the historical usage time to obtain several merged or unmerged usage periods. Mark the start point of each usage period as the start time, and count all the start times within the historical usage time;

[0062] Take the absolute value of the difference between any two start times and obtain the start time difference between the two start times;

[0063] Exemplarily, if there is a start time of 9:35 in a certain day of the historical usage time and a start time of 9:37 in another day, the start time difference between the two start times is 2 minutes;

[0064] Compare the start time difference between the two start times with the interval threshold. If the start time difference between the two start times is less than the interval threshold, classify the two start times into one start time group;

[0065] Traverse all startup times within the historical usage time until all startup times with a startup time difference less than the interval threshold are included in the startup time group. If there is a startup time that has not been included in any startup time group at this time, then this startup time itself is a startup time group;

[0066] It should be noted that the purpose of the above steps is to combine startup times that are close in time within the historical usage time into one startup time group, which is convenient for subsequent statistical analysis of a large number of startup times;

[0067] Based on any startup time group, obtain the number of startup times in the startup time group, and perform a ratio process with the total number of all startup times within the historical usage time to obtain the startup frequency value of the startup time group;

[0068] Compare the obtained startup frequency value with the startup frequency threshold;

[0069] If the startup frequency value of the startup time group is less than or equal to the startup frequency threshold, it means that the number of times the user uses the noise-canceling headphones within the time range included in the startup time group is small;

[0070] If the startup frequency value of the startup time group is greater than the startup frequency threshold, it means that the number of times the user uses the noise-canceling headphones within the time range included in the startup time group is large, and mark the startup time group as a frequently used time group;

[0071] Based on the obtained frequently used time group, obtain all the startup times included in the frequently used time group, sum them up and take the average to obtain the expected startup time of the noise-canceling headphones. Obtain the duration of the usage periods corresponding to all the startup times included in the frequently used time group, and perform a sum and average process to obtain the expected usage duration corresponding to the expected startup time;

[0072] Combine the expected startup time and the corresponding expected usage duration to obtain the expected usage period of the noise-canceling headphones within a day;

[0073] It should be noted that there may not be only one expected usage period for the noise-canceling headphones. The user may use the noise-canceling headphones multiple times in a day;

[0074] Obtain the power consumption of the noise-canceling headphones within the usage periods corresponding to all the startup times in the frequently used time group and sum them up to obtain the total power consumption. Sum up the durations of the usage periods corresponding to all the startup times in the frequently used time group to obtain the total usage duration. Perform a ratio process on the obtained total power consumption and the total usage duration to obtain the expected power consumption rate for the corresponding expected usage period;

[0075] Obtain the durations of all usage periods within the historical usage time, sum them up and take the average to get the average usage duration of the noise-canceling headphones within the historical usage time. Sum up the power consumption of the noise-canceling headphones during all usage periods and take the average to get the average power consumption of the noise-canceling headphones within the historical usage time. Process the ratio of the obtained average power consumption to the average usage duration to get the average power consumption rate of the noise-canceling headphones;

[0076] If the noise-canceling headphones are in the usage state at the current time node, determine whether the current time node is within any expected usage period;

[0077] If the current time node is within any expected usage period, obtain the interval duration between the current time node and the end point of the expected usage period where the current time node is located, and perform a multiplication process with the expected power consumption rate of the expected usage period to get the remaining power demand of the noise-canceling headphones;

[0078] If the current time node is not within any expected usage period, obtain the duration of the current usage period of the noise-canceling headphones at the current time node, perform a difference process between the average usage duration of the noise-canceling headphones and the duration of the current usage period, and perform a multiplication process with the obtained difference and the average power consumption rate of the noise-canceling headphones to get the remaining power demand of the noise-canceling headphones;

[0079] It should be noted that the current usage period refers to the period between the time point when the noise-canceling headphones are started to be used this time and the current time point;

[0080] Obtain the actual remaining power of the noise-canceling headphones at the current time node, perform a difference process with the remaining power demand at this time to get the remaining power difference of the noise-canceling headphones, and compare it with the remaining power difference threshold;

[0081] If the remaining power difference of the noise-canceling headphones is greater than or equal to the remaining power difference threshold, it indicates that according to the user's usage habits, the remaining power of the noise-canceling headphones is sufficient;

[0082] If the remaining power difference of the noise-canceling headphones is less than the remaining power difference threshold, it indicates that according to the user's usage habits, the remaining power of the noise-canceling headphones is insufficient, and a power risk signal is generated;

[0083] Step Four: If the generated power risk signal is received, suspend the real-time acquisition of motion data and environmental noise, establish a prediction model to select a preselected noise reduction strategy for the prediction period, and re-acquire the environmental noise and motion state at the inspection time point to update the prediction model and the preselected noise reduction strategy;

[0084] In some embodiments, if the generated power risk signal is received, suspend the real-time detection of environmental noise and motion state during the prediction time;

[0085] Among them, the prediction time consists of several prediction periods. The end point of the prediction period is marked as the inspection time point, which is also the starting point of the next prediction period. The duration of each prediction period is the same;

[0086] Obtain the motion state of the noise-canceling headphones during each detection period within the current used period and the environmental noise energy entropy , t = 1, 2, 3, ……, m; where t represents the serial number of the detection period sorted by time within the current used period, and m represents the number of detection periods within the current used period;

[0087] The motion state is represented in a discrete form. When the motion state is stationary, = 0. When the motion state is walking, = 1. When the motion state is running, = 2. When the motion state is in a vehicle, = 3;

[0088] Calculate and compare the probabilities of the noise-canceling headphones being in each motion state within the current used period, and output the motion state with the highest probability as the motion state of the noise-canceling headphones during the prediction period;

[0089] Construct a prediction model to predict the environmental noise energy entropy of the c-th detection period in the future , and the specific formula is: , where represents the change rate, represents the intercept. Combine the environmental noise energy entropy and fit by the least squares method: , ;

[0090] According to the prediction model obtained by fitting calculation, predict and calculate the environmental noise energy entropy of the c-th detection period during the prediction period, obtain the predicted environmental noise energy entropy range of each detection period during the prediction period, and conduct an overlap analysis with the judgment environmental noise energy entropy range of any scene noise level;

[0091] Specifically, based on the judgment environmental noise energy entropy range of any scene noise level, obtain the length of the overlapping range between the judgment environmental noise energy entropy range and the predicted environmental noise energy entropy range, and perform a ratio process with the length of the predicted environmental noise energy entropy range to obtain the noise probability of the noise-canceling headphones under the scene noise level during the prediction period;

[0092] Compare the noise probabilities of the noise-canceling headphones under each scene noise level, and output the scene noise level with the highest noise probability as the predicted scene noise level of the noise-canceling headphones during the prediction period;

[0093] The obtained predicted motion state and predicted scene noise level are substituted into the noise reduction strategy database for query to obtain the preselected noise reduction strategy during the predicted period, and the noise reduction strategy of the noise reduction headset is adjusted to the preselected noise reduction strategy;

[0094] At the inspection time point during the predicted period, the environmental noise and motion state are collected through sensors, the environmental noise entropy is calculated, and the prediction model and motion state probability are updated according to the collection and calculation results;

[0095] The calculated environmental noise entropy and motion state are substituted into the noise reduction strategy database for query to obtain the actual noise reduction strategy. If the obtained actual noise reduction strategy is the same as the currently applied preselected noise reduction strategy, the noise reduction strategy remains unchanged. If the obtained actual noise reduction strategy is different from the currently applied preselected noise reduction strategy, the noise reduction strategy for the next predicted period is predicted based on the updated prediction model and motion state probability, and a new preselected noise reduction strategy is obtained and applied;

[0096] It should be noted that the purpose of the above steps is to reduce the energy consumption of the noise reduction headset caused by real-time detection of environmental noise and motion state and frequent switching of noise reduction strategies, so that the noise reduction headset can last for the predicted demand usage time according to the user's usage habits to the maximum extent;

[0097] The technical solution of the embodiment of the present invention is as follows: Analyze the historical usage data of the noise reduction headset, predict the expected usage period of the user, and predict the remaining required power of the noise reduction headset according to the expected usage period. Compare the remaining required power with the actual remaining power to determine whether the power is sufficient. If it is not sufficient, generate a power risk signal. If the generated power risk signal is received, suspend the real-time detection of environmental noise and motion state, establish a model to predict the noise reduction strategy during the predicted period, and re-detect the environmental noise and motion state at the inspection time point to update the prediction model and preselected noise reduction strategy.

[0098] Embodiment III

[0099] As Figure 1 shown, a method for a magnetic charging case of an adaptive noise reduction headset provided by an embodiment of the present invention specifically further includes the following steps:

[0100] Step Five: When the noise reduction headset is charging, analyze the power of the magnetic charging case and the noise reduction headset, and dynamically adjust the charging power in combination with the expected usage period;

[0101] In some embodiments, if the noise reduction headset is in a charging state, dynamically adjust the charging power of the magnetic charging case in a gradient manner;

[0102] As Figure 3 shown, the specific steps for dynamically adjusting the charging power are as follows;

[0103] Specifically, obtain the difference between the remaining power of the magnetic charging case and the used power of the noise-canceling headphones to obtain the charging power difference, and compare it with the power difference threshold. If the charging power difference is greater than the power difference threshold, generate a sufficient power signal; otherwise, generate an insufficient power signal.

[0104] Obtain the interval duration between the current time point and the starting point of the next expected usage period of the noise-canceling headphones at the current time point, mark it as the required charging time, obtain the used power of the noise-canceling headphones, and perform a ratio process on the used power and the required charging time to calculate the required charging rate of the noise-canceling headphones.

[0105] Compare the required charging rate with the charging rate threshold. If the required charging rate is greater than the charging rate threshold, it indicates that the charging demand is urgent at this time, and generate an urgent signal; otherwise, generate a non-urgent signal.

[0106] Select a suitable charging power for the magnetic charging case according to the generated signal.

[0107] If the generated sufficient power signal and urgent signal are received, use a high-grade charging power to quickly charge the noise-canceling headphones.

[0108] If the generated sufficient power signal and non-urgent signal are received, use a medium-grade charging power to normally charge the noise-canceling headphones.

[0109] If the generated insufficient power signal is received, use a low-grade charging power to slowly charge the noise-canceling headphones. At this time, it is necessary to give priority to ensuring the power reserve of the charging case itself and only supplement the basic power for the noise-canceling headphones.

[0110] It should be noted that the purpose of the above steps is to avoid battery aging caused by long-term fast charging, give priority to meeting emergency charging needs, and at the same time balance the power reserves of the headphones and the charging case to ensure the overall battery life of the system.

[0111] The technical solution of the embodiment of the present invention is: when charging the noise-canceling headphones, dynamically adjust the charging power according to the power difference between the charging case and the headphones, the required charging time, and the required charging rate. When the power is sufficient and the charging demand is urgent, use high-grade fast charging; when the power is sufficient but the charging demand is not urgent, use medium-grade normal charging; when the power is insufficient, use low-grade slow charging to balance the power reserves of the headphones and the charging case, avoid battery aging, and ensure the overall battery life of the system.

[0112] Embodiment 4

[0113] As Figure 4 shown, a system of a magnetic charging case for an adaptive noise-canceling headphone provided by an embodiment of the present invention specifically includes the following modules:

[0114] Data acquisition module: When the noise-canceling headphones are in use, determine the user's motion state based on the collected motion data, and determine the noise energy entropy of the environment based on the collected ambient noise;

[0115] In this embodiment, when the noise-canceling headphones are in use, data is collected through motion sensors such as the built-in accelerometer and gyroscope of the headphones at the current detection time point. The acceleration changes of the headphones on the x, y, and z axes and the rotational angular velocities of the headphones around the three coordinate axes are detected. Motion features are extracted from the collected acceleration and angular velocity data, and the extracted motion features are analyzed. The user's current motion state is judged through a preset motion state classification algorithm;

[0116] During the current detection period, an array composed of several microphones samples the ambient noise at a fixed sampling frequency fs;

[0117] The obtained noise signal is amplified and filtered to remove high-frequency noise and interference, and the processed noise signal is subjected to a fast Fourier transform to obtain a frequency-domain signal. The proportion of the energy of each frequency component after Laplacian smoothing processing of the frequency-domain signal to the total energy is calculated to obtain the probability distribution of the frequency components;

[0118] The probability distributions of the frequency components of all microphones are jointly analyzed, and the ambient noise energy entropy H during the current detection period is calculated;

[0119] Strategy selection module: Based on the noise energy entropy, judge the scene noise level of the user's location, and select a noise-canceling strategy in combination with the user's motion state;

[0120] In this embodiment, the calculated ambient noise energy entropy H is compared with the low threshold Hd and the high threshold Hh of the ambient noise energy entropy;

[0121] If the ambient noise energy entropy during the current detection period , it is judged that the scene noise level of the user's current location is quiet;

[0122] If the ambient noise energy entropy during the current detection period , it is judged that the scene noise level of the user's current location is low noise;

[0123] If the ambient noise energy entropy during the current detection period , it is judged that the scene noise level of the user's current location is high noise;

[0124] A noise-canceling strategy database including scene noise level, user motion state, and corresponding noise-canceling strategies is established. The best noise-canceling strategies corresponding to different combinations of ambient noise energy entropy ranges and user motion states are stored in the noise-canceling strategy database;

[0125] Based on the environmental noise energy entropy and the determined user motion state within the current detection period, query the noise reduction strategy database, and according to the query result, select a suitable noise reduction strategy for the noise reduction headphones;

[0126] Power analysis module: Determine whether the remaining required power of the noise reduction headphones is sufficient within the predicted time. If it is not sufficient, generate a power risk signal;

[0127] In this embodiment, historical usage data of the noise reduction headphones is obtained. The historical usage data includes the usage period of the noise reduction headphones and the power consumption of the noise reduction headphones within the usage period for each day during the historical usage time. The historical usage time represents the period starting from the time node when the noise reduction headphones are first used by the user and ending at the current time node. The usage period of the noise reduction headphones represents the period when the noise reduction headphones are in the usage state during the historical usage time;

[0128] Based on any day within the historical usage time, obtain the interval duration between two adjacent usage periods within a day. The interval duration represents the duration of the interval period between the end point of the usage period with a prior time sequence and the start point of the usage period with a subsequent time sequence, and compare the interval duration with the interval threshold;

[0129] If the interval duration between two adjacent usage periods within a day is less than the interval threshold, merge the two adjacent usage periods into one usage period. The merged usage period starts from the start point of the usage period with a prior time sequence and ends at the end point of the usage period with a subsequent time sequence among the two usage periods;

[0130] Traverse each day within the historical usage time to obtain several merged or non - merge - required usage periods. Mark the start point of each usage period as the start time, and count all the start times within the historical usage time;

[0131] Take the absolute value of the difference between any two start times and obtain the start time difference between the two start times;

[0132] Compare the start time difference between the two start times with the interval threshold. If the start time difference between the two start times is less than the interval threshold, then classify the two start times into one start time group;

[0133] Traverse all the start times within the historical usage time until all the start times with a start time difference less than the interval threshold are classified into start time groups. If there is a start time that has not been classified into any start time group at this time, then this start time itself is a start time group;

[0134] Based on any start time group, obtain the number of start times in the start time group, and perform a ratio process with the total number of all start times within the historical usage time to obtain the start frequency value of the start time group;

[0135] Compare the obtained startup frequency usage value with the startup frequency usage threshold;

[0136] If the startup frequency usage value of the startup time group is less than or equal to the startup frequency usage threshold, it indicates that the user uses the noise-canceling headphones less frequently within the time range included in the startup time group;

[0137] If the startup frequency usage value of the startup time group is greater than the startup frequency usage threshold, it indicates that the user uses the noise-canceling headphones more frequently within the time range included in the startup time group. Mark the startup time group as the frequently used time group;

[0138] Based on the obtained frequently used time group, obtain all the startup times included in the frequently used time group, sum them up and take the average to get the expected startup time of the noise-canceling headphones. Obtain the duration of the usage period corresponding to all the startup times included in the frequently used time group, sum them up and take the average to get the expected usage duration corresponding to the expected startup time;

[0139] Combine the expected startup time and the corresponding expected usage duration to obtain the expected usage period of the noise-canceling headphones within a day;

[0140] Obtain the power consumption of the noise-canceling headphones within the usage period corresponding to all the startup times in the frequently used time group and sum them up to get the total power consumption. Sum up the durations of the usage periods corresponding to all the startup times in the frequently used time group to get the total usage duration. Divide the obtained total power consumption by the total usage duration to get the expected power consumption rate for the corresponding expected usage period;

[0141] Obtain the durations of all usage periods within the historical usage time, sum them up and take the average to get the average usage duration of the noise-canceling headphones within the historical usage time. Sum up the power consumption of the noise-canceling headphones within all usage periods and take the average to get the average power consumption of the noise-canceling headphones within the historical usage time. Divide the obtained average power consumption by the average usage duration to get the average power consumption rate of the noise-canceling headphones;

[0142] If the noise-canceling headphones are in use at the current time node, determine whether the current time node is within any of the expected usage periods;

[0143] If the current time node is within any of the expected usage periods, obtain the interval duration between the current time node and the end point of the expected usage period where the current time node is located, and multiply it by the expected power consumption rate of the expected usage period to get the remaining required power of the noise-canceling headphones;

[0144] If the current time node is not within any expected usage period, obtain the duration of the current usage period of the noise-canceling headphones at the current time node, calculate the difference between the average usage duration per time of the noise-canceling headphones and the duration of the current usage period, and multiply the obtained difference by the average power consumption rate of the noise-canceling headphones to obtain the remaining required power of the noise-canceling headphones;

[0145] Obtain the actual remaining power of the noise-canceling headphones at the current time node, calculate the difference between it and the remaining required power at this time to obtain the remaining power difference of the noise-canceling headphones, and compare it with the remaining power difference threshold;

[0146] If the remaining power difference of the noise-canceling headphones is greater than or equal to the remaining power difference threshold, it indicates that according to the user's usage habits, the remaining power of the noise-canceling headphones is sufficient;

[0147] If the remaining power difference of the noise-canceling headphones is less than the remaining power difference threshold, it indicates that according to the user's usage habits, the remaining power of the noise-canceling headphones is insufficient, and a power risk signal is generated;

[0148] Strategy optimization module: If a power risk signal is received, during the prediction period, suspend real-time acquisition, establish a preselected noise reduction strategy for the prediction period of the noise-canceling headphones through a prediction model, calibrate the prediction model based on the motion data and environmental noise before the inspection time point, and obtain the calibrated preselected noise reduction strategy;

[0149] In this embodiment, if the generated power risk signal is received, suspend the real-time detection of environmental noise and motion state;

[0150] Obtain the motion state of the noise-canceling headphones at each detection period during the current usage period and the environmental noise energy entropy , t = 1, 2, 3, ……, m; where t represents the serial number of the detection period sorted by time within the current usage period, and m represents the number of detection periods within the current usage period;

[0151] The motion state is represented in a discrete form. When the motion state is stationary, = 0, when the motion state is walking, = 1, when the motion state is running, = 2, when the motion state is in a vehicle, = 3;

[0152] Calculate and compare the probabilities of the noise-canceling headphones being in each motion state during the current usage period, and output the motion state with the highest probability as the motion state of the noise-canceling headphones during the prediction period;

[0153] The prediction period represents a period for predicting the noise reduction strategy based on the environmental noise entropy and the motion state in the current used period at the current time point. The end of the prediction period is marked as the test time point, and the test time point is also the starting point of the next prediction period. The duration of each prediction period is the same;

[0154] Construct a prediction model to predict the environmental noise energy entropy in the future c-th detection period , the specific formula is: ,in represents the rate of change, represents the intercept, through the environmental noise energy entropy The least squares fitting calculation was performed;

[0155] According to the prediction model obtained by fitting calculation, the environmental noise energy entropy of the cth detection period within the prediction period is predicted and calculated to obtain the predicted environmental noise energy entropy range of each detection period within the prediction period, and the overlap with the judgment environmental noise energy entropy range of any scene noisiness is analyzed;

[0156] Specifically, based on the determined environmental noise energy entropy range of any scene noisiness, the length of the overlapped range between the determined environmental noise energy entropy range and the predicted environmental noise energy entropy range is obtained, and the length of the predicted environmental noise energy entropy range is processed by ratio, so as to obtain the probability of the noise reduction headphones being noisy under the scene noisiness within the predicted period;

[0157] Compare the noisy probabilities of the noise cancelling headphones under different scene noisiness levels, and output the scene noisiness level with the highest noisy probability as the predicted scene noisy level of the noise cancelling headphones within the predicted period;

[0158] Substituting the obtained predicted motion state and predicted scene noisiness into the noise reduction strategy database for query, obtaining the pre-selected noise reduction strategy within the predicted period, and adjusting the noise reduction strategy of the noise reduction headset to the pre-selected noise reduction strategy;

[0159] At the test time point of the prediction period, the environmental noise and motion state are collected by sensors, the environmental noise entropy is calculated, and the prediction model and motion state probability are updated according to the collected calculation results;

[0160] Substitute the calculated environmental noise entropy and motion state into the noise reduction strategy database for query to obtain the actual noise reduction strategy. If the actual noise reduction strategy obtained is the same as the currently applied pre-selected noise reduction strategy, the noise reduction strategy is not changed. If the actual noise reduction strategy obtained is different from the currently applied pre-selected noise reduction strategy, the noise reduction strategy in the next prediction period is predicted according to the updated prediction model and motion state probability, and a new pre-selected noise reduction strategy is obtained and applied.

[0161] Charging management module: When the noise-canceling headphones are charging, analyze the battery levels of the magnetic charging case and the noise-canceling headphones, and dynamically adjust the charging power in combination with the expected usage period.

[0162] In this embodiment, if the noise-canceling headphones are in the charging state, dynamically adjust the charging power of the magnetic charging case in a gradient manner.

[0163] The specific steps for dynamically adjusting the charging power are as follows;

[0164] Specifically, obtain the difference between the remaining battery level of the magnetic charging case and the used battery level of the noise-canceling headphones to obtain the charging power difference, and compare it with the power difference threshold. If the charging power difference is greater than the power difference threshold, generate a sufficient power signal; otherwise, generate an insufficient power signal.

[0165] Obtain the interval duration between the current time point and the start of the next expected usage period of the noise-canceling headphones at the current time point, marked as the required charging time. Obtain the used battery level of the noise-canceling headphones, and perform a ratio process on the used battery level and the required charging time to calculate the required charging rate of the noise-canceling headphones.

[0166] Compare the required charging rate with the charging rate threshold. If the required charging rate is greater than the charging rate threshold, it means that the charging demand is urgent at this time, and generate an urgent signal; otherwise, generate a non-urgent signal.

[0167] Select a suitable charging power for the magnetic charging case according to the generated signal.

[0168] If the generated sufficient power signal and urgent signal are received, use the high gear charging power to quickly charge the noise-canceling headphones.

[0169] If the generated sufficient power signal and non-urgent signal are received, use the medium gear charging power to normally charge the noise-canceling headphones.

[0170] If the generated insufficient power signal is received, use the low gear charging power to slowly charge the noise-canceling headphones. At this time, it is necessary to prioritize the battery reserve of the charging case itself and only supplement the basic power for the noise-canceling headphones.

[0171] The above has described an embodiment of the present invention in detail, but the described content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for an adaptive noise reduction headset magnetic charging box, characterized in that: The following steps are involved: When the noise-cancelling headphones are used, the user's motion state is determined based on the collected motion data, the noise energy entropy of the environment is determined based on the collected ambient noise, and the noisiness of the scene at the user's location is judged based on the noise energy entropy; Comprehensively analyze the scene noise and motion status to select a noise reduction strategy; Determine whether the remaining power of the noise-cancelling headphones is sufficient within the predicted time; The method for obtaining the required remaining power is: Get the time difference between two startup times in the historical usage time. If the time difference is less than the interval threshold, the two startup times are grouped into one startup time group. Performing data processing on the number of startup times in the startup time group to obtain a startup frequency value, and if the startup frequency value is greater than a startup frequency threshold, marking the startup time group as a frequent time group; Get the expected power consumption rate for all usage periods in the frequent usage time group; get the average power consumption rate of the noise-cancelling headset during the historical usage time; If the current time node is within the expected usage period, the data is processed in combination with the expected power consumption rate during the expected usage period; if it is not within the expected usage period, the data is processed in combination with the average power consumption rate of the noise cancelling headphones to obtain the required remaining power of the noise cancelling headphones; The method for obtaining the expected usage period is as follows: Performing data processing on all the start-up times and the duration of the corresponding usage periods contained in the frequency usage time group to obtain the expected usage period of the noise reduction headset in a single day; If it is insufficient, real-time collection is suspended during the forecast period, and a pre-selected noise reduction strategy is selected for the forecast period of the noise reduction headphones through the forecast model; Based on the motion data and environmental noise before the inspection time point, the prediction model is calibrated, and a calibrated pre-selected noise reduction strategy is obtained; The prediction time consists of several prediction periods, and the test time point is the end point of the prediction period.

2. The method of the magnetic charging box of the adaptive noise reduction headset according to claim 1 is characterized in that: The method for determining the scene noisiness at the user's location is: The environmental noise energy entropy H is compared with the environmental noise energy entropy low threshold Hd and the environmental noise energy entropy high threshold Hh respectively; like , judging that the scene noise level at the user's current location is quiet; like , judging that the scene noise level at the user's current location is low noise; like , and judge that the scene noise level of the user's current location is highly noisy.

3. The method of the magnetic charging box for adaptive noise reduction headphones according to claim 1, characterized in that: The analysis process of whether the remaining power demand is sufficient is as follows: The actual remaining power of the noise cancelling headphones is compared with the required remaining power. If the actual remaining power is less than the required remaining power, the required remaining power is insufficient, and a power risk signal is generated.

4. The method for a magnetic charging box for an adaptive noise reduction headset according to claim 1, characterized in that: The method for obtaining the pre-selected noise reduction strategy is as follows: Calculate the probability of the noise cancelling headset being in each motion state during the current usage period, and select the motion state with the highest probability as the predicted motion state of the noise cancelling headset; Combined with the historical environmental noise energy entropy, a prediction model is constructed by fitting with the least square method. The environmental noise energy entropy range within the prediction period is predicted and data analyzed. The probability of noise reduction headphones being noisy under the scene noisiness is calculated, and the scene noisiness with the highest probability of noise is used as the predicted scene noisiness of the noise reduction headphones. The pre-selected noise reduction strategy is obtained based on the predicted motion state and the predicted scene noisiness query.

5. The method for a magnetic charging box for an adaptive noise reduction headset according to claim 1, characterized in that: The calibrated pre-selected noise reduction strategy is obtained as follows: At the inspection time point of the prediction period, the environmental noise and motion status are collected, the environmental noise entropy is calculated, and the prediction model and motion state probability are updated. The actual noise reduction strategy is obtained based on the calculation results. If the actual noise reduction strategy is different from the currently applied pre-selected noise reduction strategy, the pre-selected noise reduction strategy is updated and applied based on the updated prediction model and motion state probability.

6. The method of the magnetic charging box of the adaptive noise reduction headset according to claim 1, characterized in that: The following steps are also included: When the noise cancelling headphones are charging, the power level of the magnetic charging box and the noise cancelling headphones is analyzed, and the charging power is dynamically adjusted based on the expected usage period; The method of dynamically adjusting the charging power is as follows: Get the remaining power of the magnetic charging box and the power used by the noise cancelling headphones, and determine whether the charging power is sufficient; If sufficient, analyze and obtain the required charging rate between the current time and the starting point of the next expected usage period. If the required charging rate is greater than the charging rate threshold, use the high-speed charging power for charging. Otherwise, use the medium-speed charging power for charging. If it is insufficient, use low-level charging power to charge.

7. A system for a magnetic charging box of an adaptive noise reduction headset, the system being used to implement the method as claimed in any one of claims 1 to 6, characterized in that: include: Data collection module: when the noise reduction headphones are in use, the user's motion state is determined based on the collected motion data, and the noise energy entropy of the environment is determined based on the collected ambient noise; Strategy selection module: Based on the noise energy entropy, the noise level of the scene where the user is located is determined, and the noise reduction strategy is selected based on the user's motion status; Power analysis module: determines whether the remaining power of the noise-cancelling headphones is sufficient within the predicted time. If not, generates a power risk signal; Strategy optimization module: If a power risk signal is received, real-time collection is suspended during the prediction period, and a pre-selected noise reduction strategy is selected for the prediction period of the noise reduction headphones through the prediction model. The prediction model is calibrated based on the motion data and environmental noise before the test time point, and the calibrated pre-selected noise reduction strategy is obtained; Charging management module: When the noise-canceling headphones are charging, the power level of the magnetic charging box and the noise-canceling headphones is analyzed, and the charging power is dynamically adjusted based on the expected usage period.

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