Bluetooth earphone pairing connection method and system

Abstract

The invention is suitable for the field of Bluetooth connection control, and provides a Bluetooth headset pairing connection method and system, and the method comprises the steps: collecting multi-dimensional biological feature data and multi-dimensional environmental perception data of a user in a multi-modal manner, and constructing a target feature vector; based on the target feature vector, calculating an association matching degree between the target feature vector and the target feature vector; based on a three-level data verification mechanism, judging a relation between the association matching degree and a preset matching degree, and generating a verification instruction; according to the method and the system, through multi-dimensional data acquisition, feature vector construction and dynamic matching calculation, unauthorized use is effectively prevented, non-inductive interaction experience is realized based on a dynamic connection strategy of environment perception and a user state, and the user experience is improved. And meanwhile, self-adaptive template updating and intelligent priority adjustment further meet the differentiated use habits of the users.

Description

Technical Field

[0001] The present invention belongs to the field of Bluetooth connection control, and in particular relates to a Bluetooth headset pairing connection method and system. Background Art

[0002] With the popularization of wireless audio technology, Bluetooth headsets have become an important device for users' daily use. The pairing and connection process of existing Bluetooth headsets usually relies on manual operation by the user, such as entering the pairing mode through physical buttons, selecting the device name in the mobile phone's Bluetooth list, entering the verification password, etc. This traditional method has the problems of cumbersome operation and long time consumption in multi-device scenarios. Especially when users need to frequently switch between multiple devices such as mobile phones, tablets, and laptops, the repeated search and confirmation steps reduce the user experience. In addition, the existing technology lacks an active identification mechanism for the identity of the device user, and cannot automatically adapt the connection strategy according to the user's personalized needs, resulting in difficulty in balancing the security and convenience of the device.

[0003] Currently, the connection control of Bluetooth headsets is mainly based on device-level signal interaction, such as device address matching and key exchange through the Bluetooth protocol, but intelligent perception of user usage status has not yet been achieved. In actual applications, users may face inconvenient operation scenarios such as occupied hands and distracted vision. Traditional physical interaction methods are difficult to meet the convenient connection needs in these scenarios. At the same time, in situations where multiple people share the same headset or device, existing technologies cannot effectively distinguish the identities of different users, resulting in the inability to achieve personalized adaptation of connection configurations and usage preferences, which limits the intelligent development of devices. In order to solve the above technical problems, it is urgent to develop a more mature Bluetooth headset pairing connection method and system. Summary of the Invention

[0004] The purpose of the present invention is to provide a Bluetooth headset pairing and connection method and system, aiming to solve the problems raised in the above background technology.

[0005] The present invention is implemented as follows: on the one hand, a Bluetooth headset pairing and connection method, the method comprising:

[0006] Multimodally collect user multi-dimensional biometric data and multi-dimensional environmental perception data, and construct targeted feature vectors;

[0007] Based on the targeting feature vector, calculating the correlation matching degree between the targeting feature vector and the target feature vector;

[0008] Based on the three-level data verification mechanism, the relationship between the associated matching degree and the preset matching degree is determined;

[0009] Based on the judgment result of the relationship between the associated matching degree and the preset matching degree, the automatic pairing or disconnection operation of the Bluetooth headset and the target device is triggered.

[0010] As a further solution of the present invention, the multimodal collection of user multidimensional biometric data and multidimensional environmental perception data and the construction of a targeted feature vector specifically include:

[0011] Collect and calculate EEG signals Wave energy ratio ;

[0012] The EEG signal Wave energy ratio The calculation process is:

[0013] ;

[0014] Where, Frequency The power spectral density at ;

[0015] Collect and calculate EEG signals Wave energy ratio ;

[0016] The EEG signal Wave energy ratio The calculation process is:

[0017] ;

[0018] Collect the RMSSD value of heart rate variability ;

[0019] The RMSSD value of the heart rate variability The calculation process is:

[0020] Extract RR interval sequence by peak detection algorithm ;

[0021] ;

[0022] Where, Is the loop count variable, used to traverse adjacent Interval data, for Interphase sequence The total number of data points in ;

[0023] Collect the geometric feature vector values of the auricle contour ;

[0024] Collect fingerprint capacitive sensor pressure distribution matrix ;

[0025] Collect real-time values of ear canal temperature sensors ;

[0026] Collect the amplitude of mandibular movement electromyographic signals ;

[0027] Collect pupil diameter change rate ;

[0028] The pupil diameter change rate The calculation process is:

[0029] ;

[0030] Where, for pupil diameter at the moment;

[0031] Collecting speech bone conduction vibration frequency characteristics .

[0032] As a further solution of the present invention, the multimodal collection of user multidimensional biometric data and multidimensional environmental perception data and the construction of a targeted feature vector specifically include:

[0033] Collect the target connection device receiving signal strength indication ;

[0034] Based on the target connected device receiving signal strength indication , calculate the device distance value ;

[0035] The device distance vector The calculation process is:

[0036] ;

[0037] Where, is the reference signal strength at 1 meter, is the path loss index;

[0038] Collect the average x, y, and z-axis acceleration of the headset's built-in three-axis accelerometer ;

[0039] Collect the decibel value of the external environmental noise of the headset .

[0040] As a further solution of the present invention, the calculation of the correlation matching degree between the targeting feature vector and the target feature vector based on the targeting feature vector specifically includes:

[0041] Get the target feature vector value ;

[0042] Calculate the target eigenvector value With the targeted feature vector The correlation matching degree ;

[0043] The correlation matching degree The calculation process is:

[0044] ;

[0045] ;

[0046] In the formula, Biometric sequence The dynamic time warping distance, Environmental characteristics Weighted Euclidean distance, For each environmental characteristic weight, Biometric sequence Weight.

[0047] As a further solution of the present invention, the three-level data verification mechanism specifically includes:

[0048] A 50 Hz Butterworth low-pass filter was used to remove power frequency interference from the heart rate variability signal and EEG signal;

[0049] When a single dimension feature of an organism is missing, enable the historical data interpolation algorithm:

[0050] ;

[0051] Where, After completion Dimensional biometric value, The number of historical data involved in the calculation, that is, the number of recent valid collections, For the recent The time interval between effective collections, For Moment Historical values of biometrics;

[0052] use In principle, mutation data points exceeding the mean ± 3 times the standard deviation were eliminated.

[0053] As a further solution of the present invention, the target feature vector generation includes a target calibration process, and the target calibration process specifically includes:

[0054] The first time the user uses the device, a 3-minute dynamic calibration is required to collect the following data to construct the target feature vector:

[0055] Resting state: EEG signal Wave baseline value, basic heart rate, ear canal temperature;

[0056] Movement status: 3D point cloud data of auricle contour and mandibular electromyography signal;

[0057] Speech calibration: bone conduction vibration frequency feature library;

[0058] The template is automatically updated every 50 uses, and a sliding window algorithm is used to retain the average of the last 20 valid data.

[0059] As a further solution of the present invention, the triggering of the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the relationship between the associated matching degree and the preset matching degree specifically includes:

[0060] When the correlation matching Greater than or equal to the association threshold, and the device distance value When the distance is less than or equal to the threshold, it will automatically connect to the device with the highest priority in the historical pairing list;

[0061] When the correlation matching Greater than or equal to the correlation threshold, and the EEG signal Wave energy ratio When the attention threshold is greater than the threshold, the pairing process with the specified target device is triggered.

[0062] As a further solution of the present invention, the triggering of the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the relationship between the associated matching degree and the preset matching degree specifically further includes:

[0063] When the correlation matching Greater than or equal to the association threshold, and the device distance value When the distance is greater than the threshold, the disconnection command is triggered;

[0064] When the correlation matching Greater than or equal to the correlation threshold, and the EEG signal Wave energy ratio When the heart rate variability threshold is exceeded, the disconnection command is triggered;

[0065] When the correlation matching Greater than or equal to the correlation threshold, and the external environmental noise decibel value When the noise level is greater than the ambient noise threshold, the exercise instruction is triggered.

[0066] As a further solution of the present invention, the Bluetooth headset pairing and connection method further includes setting priority identification rules for a scenario where multiple people wear the headset at the same time. The priority identification rules specifically include:

[0067] When multiple user biometric signals are detected, secondary verification is triggered:

[0068] Prioritize connecting to users with the highest historical usage frequency;

[0069] If the frequencies are the same, compare the EEG signals Wave energy ratio, higher value is preferred;

[0070] If the device still cannot be distinguished, the headset will announce "Multiple users detected, please touch the right ear handle to confirm your identity" and the final confirmation will be completed in combination with the fingerprint pressure distribution matrix.

[0071] As a further solution of the present invention, in another aspect, a Bluetooth headset pairing and connection system comprises:

[0072] Multimodal acquisition module, used for multimodal acquisition of user multi-dimensional biometric data and multi-dimensional environmental perception data;

[0073] Targeted feature vector module, used to construct targeted feature vectors;

[0074] A calculation module, configured to calculate the correlation matching degree between the targeting feature vector and the target feature vector based on the targeting feature vector;

[0075] A discrimination module, used to discriminate the relationship between the associated matching degree and the preset matching degree based on a three-level data verification mechanism;

[0076] The trigger module is used to trigger the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the judgment result of the association matching degree and the preset matching degree relationship.

[0077] The present invention provides a Bluetooth headset pairing and connection method and system. This method and system effectively prevent unauthorized use through multi-dimensional data collection, feature vector construction and dynamic matching calculation. Based on the dynamic connection strategy of environmental perception and user status, a seamless interactive experience is achieved. At the same time, adaptive template updates and intelligent priority adjustments are carried out to further meet the differentiated usage habits of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 The main flow chart of a Bluetooth headset pairing and connection method.

[0079] Figure 2 It is a main structure diagram of a Bluetooth headset pairing and connection system. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0082] The present invention provides a Bluetooth headset pairing and connection method and system, which solve the technical problems in the background technology.

[0083] like Figure 1 FIG. 1 is a main flow chart of a Bluetooth headset pairing and connection method provided by an embodiment of the present invention, wherein the Bluetooth headset pairing and connection method includes:

[0084] Step S100: multimodally collecting multi-dimensional biometric data and multi-dimensional environmental perception data of the user, and constructing a targeted feature vector;

[0085] Step S200: Based on the targeting feature vector, calculating the correlation matching degree between the targeting feature vector and the target feature vector;

[0086] Step S300: Based on the three-level data verification mechanism, determine the relationship between the correlation matching degree and the preset matching degree;

[0087] Step S400: triggering an automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the association matching degree and the preset matching degree relationship;

[0088] In this embodiment, multi-dimensional data collection is first performed through biometric sensors and an environmental perception module. Biometric features encompass nine dimensions, including EEG signals, heart rate variability, auricle geometry, fingerprint pressure distribution, and bone conduction vibration frequency, accurately capturing the user's physiological and behavioral characteristics. The environmental perception module then acquires three-dimensional environmental information, such as device distance, motion acceleration, and ambient noise, in real time, to construct a targeted feature vector encompassing the user's state and usage scenario. At the data processing level, a dynamic time warping (DTW) algorithm is used to process the biometric time series data. Differences in environmental features are quantified using weighted Euclidean distance, and an adaptive weight allocation strategy is employed to fuse the two features. This allows for high-precision correlation and matching calculations between the targeted and target feature vectors, effectively addressing the low accuracy of single-dimensional recognition in traditional pairing. Furthermore, a three-level data verification mechanism, consisting of noise filtering, feature completion, and outlier detection, is implemented to ensure data reliability and enhance system robustness in complex environments. Based on the verification results and pre-set rules, intelligent connection management between the Bluetooth headset and the target device is implemented. When the matching degree reaches the threshold and the proximity condition is met, the device with the highest priority is automatically connected. If abnormal conditions such as a nervous user or excessive ambient noise are detected, emergency disconnect protection is triggered. For special scenarios such as multiple users sharing devices or devices with similar characteristics, multi-dimensional priority identification and multi-level verification strategies are designed to avoid device control conflicts and misconnections.

[0089] As a preferred embodiment of the present invention, the multimodal collection of user multidimensional biometric data and multidimensional environmental perception data and the construction of a targeted feature vector specifically include:

[0090] Collect and calculate EEG signals Wave energy ratio ;

[0091] The EEG signal Wave energy ratio The calculation process is:

[0092] ;

[0093] Where, Frequency The power spectral density at ;

[0094] Collect and calculate EEG signals Wave energy ratio ;

[0095] The EEG signal Wave energy ratio The calculation process is:

[0096] ;

[0097] Collect the RMSSD value of heart rate variability ;

[0098] The RMSSD value of the heart rate variability The calculation process is:

[0099] Extract RR interval sequence by peak detection algorithm ;

[0100] ;

[0101] Where, Is the loop count variable, used to traverse adjacent Interval data, for Interphase sequence The total number of data points in ;

[0102] Collect the geometric feature vector values of the auricle contour ;

[0103] Collect fingerprint capacitance sensor pressure distribution matrix ;

[0104] Collect real-time values of ear canal temperature sensors ;

[0105] Collect the amplitude of mandibular movement electromyographic signals ;

[0106] Collect pupil diameter change rate ;

[0107] The pupil diameter change rate The calculation process is:

[0108] ;

[0109] Where, for pupil diameter at the moment;

[0110] Collecting speech bone conduction vibration frequency characteristics .

[0111] When this embodiment is used, the EEG signal is an electroencephalogram signal. After wearing the Bluetooth headsets on both sides, the EEG signal can be collected. Wave energy ratio When collecting and calculating the EEG signal, the earphone integrates a dry electrode EEG sensor in the temporal part, and collects the original EEG signal with the reference electrode (mastoid process behind the ear) as the reference; after 50Hz notch filtering and 0.5-30Hz bandpass filtering, the signal is calculated by short-time Fourier transform (STFT). The ratio of the energy of the wave frequency band (8-12Hz) to the total effective frequency band (1-30Hz) is also measured while performing EEG signal analysis. Wave energy ratio When collecting and calculating Shared EEG sensors, calculated through short-time Fourier transform The ratio of the energy of the wave frequency band (13-30Hz) to the total effective frequency band; the root mean square value of the difference between adjacent RR intervals in collecting heart rate variability When the earphone concha cavity is integrated with a photoplethysmography (PPG) sensor (525nm green light wavelength), the pulse wave signal at the fingertips / ear edges is collected at a sampling rate of 256Hz. The RR interval sequence is extracted through the peak detection algorithm, and the RMSSD (root mean square difference between adjacent RR intervals) is calculated to reflect the parasympathetic nerve activity. The smaller the value, the weaker the autonomic nerve regulation ability. When collecting the geometric feature vector value of the auricle contour, the RR interval sequence is extracted and the RMSSD (root mean square difference between adjacent RR intervals) is calculated. When wearing, the earphones are equipped with a micro 3D structured light sensor on the outside, which emits 1500nm infrared structured light and receives the light reflected from the ear surface through the CMOS camera. The CMOS camera can be integrated into the earphone storage compartment, generates point cloud data through phase calculation, and uses the ICP algorithm to align it to the standard coordinate system, extracts feature parameters, and forms a 128-dimensional feature vector. When collecting the pressure distribution matrix of the fingerprint capacitance sensor, The earphone handle is integrated with an 8×8 capacitive fingerprint sensor, and the surface is covered with a pressure-sensitive layer. When the user touches it, the sensor simultaneously collects the capacitance value (reflecting the fingerprint ridges) and the pressure value (reflecting the pressing intensity), forming an 8×8 pressure-capacitance matrix. When collecting the real-time value of the ear canal temperature sensor, When the earphone is in the ear, it integrates a micro NTC thermistor, which collects temperature data at a sampling rate of 1Hz after contacting the ear canal skin and converts it into Celsius temperature. When the earphones are placed at the concha position, surface electromyography electrodes are integrated to collect masseter muscle electromyography signals at a sampling rate of 1000 Hz. After 20-500 Hz band-pass filtering and full-wave rectification, the root mean square (RMS) amplitude within a 100 ms sliding window is calculated. When collecting the pupil diameter change rate, the root mean square (RMS) amplitude within a 100 ms sliding window is calculated. The micro camera is integrated on the outside of the earphone compartment to emit 850nm infrared fill light; the pupil area is extracted through image threshold segmentation and the diameter change rate is calculated; when collecting the bone conduction vibration frequency characteristics of speech When the sound is emitted, the MEMS accelerometer is integrated inside the earphone to collect the skull vibration signal during the sound production; after the FFT transformation, the main frequency and sub-frequency in the 100-1000Hz frequency band are extracted to form a feature vector.

[0112] As a preferred embodiment of the present invention, the multimodal collection of user multidimensional biometric data and multidimensional environmental perception data and the construction of a targeted feature vector specifically include:

[0113] Collect the target connection device receiving signal strength indication ;

[0114] Based on the target connected device receiving signal strength indication , calculate the device distance value ;

[0115] The device distance vector The calculation process is:

[0116] ;

[0117] Where, is the reference signal strength at 1 meter, is the path loss index;

[0118] Collect the average x, y, and z-axis acceleration of the headset's built-in three-axis accelerometer ;

[0119] Collect the decibel value of the external environmental noise of the headset ;

[0120] In this embodiment, the Bluetooth Low Energy (BLE) module is used to collect the target device's (Received Signal Strength Indicator), device distance value based on logarithmic distance path loss model ; Collect the average acceleration of the x, y, and z axes of the headset's built-in three-axis accelerometer The external microphone of the headset collects the ambient sound pressure signal, and after A-weighted filtering, the decibel value of the external ambient noise of the headset is calculated by the root mean square (RMS). .

[0121] As a preferred embodiment of the present invention, the calculation of the correlation matching degree between the targeting feature vector and the target feature vector based on the targeting feature vector specifically includes:

[0122] Get the target feature vector value ;

[0123] Calculate the target eigenvector value With the targeted feature vector The correlation matching degree ;

[0124] The correlation matching degree The calculation process is:

[0125] ;

[0126] ;

[0127] In the formula, Biometric sequence The dynamic time warping distance, Environmental characteristics Weighted Euclidean distance, For each environmental characteristic weight, Biometric sequence Weight.

[0128] It should be understood that this technical solution first obtains the target feature vector value , the target feature vector value is a preset value, which is determined by the biometric part and environmental characteristics Composition, while targeting feature vector Also includes biometrics and environmental characteristics . Calculate the target feature vector With the targeted feature vector The correlation matching degree When considering biological and environmental characteristics, the difference between biological feature sequences is measured by dynamic time warping distance, and environmental features are calculated using weighted Euclidean distance. Finally, the correlation matching degree is obtained by fusion calculation of the two with the weight of biological features. .

[0129] As a preferred embodiment of the present invention, the three-level data verification mechanism specifically includes:

[0130] A 50 Hz Butterworth low-pass filter was used to remove power frequency interference from the heart rate variability signal and EEG signal;

[0131] When a single biological dimension feature is missing, the historical data interpolation algorithm is enabled:

[0132] ;

[0133] Where, After completion Dimensional biometric value, The number of historical data involved in the calculation, that is, the number of recent valid collections, For the recent The time interval between effective collections, For Moment Historical values of biometrics;

[0134] use In principle, mutation data points exceeding the mean ± 3 times the standard deviation were eliminated.

[0135] When this embodiment is applied, at the first level, a 50Hz Butterworth low-pass filter is used for the heart rate variability signal and EEG signal. Its core function is to effectively filter out power frequency interference (such as the 50Hz interference caused by the power grid), ensure the purity of the signal, and avoid interference misleading the biometric analysis. At the second level, when a single biological dimension feature is missing (such as the sensor is not started or the data transmission is interrupted), the historical data interpolation algorithm is enabled to calculate the missing value through the most recently collected historical data to maintain the integrity of the data and prevent the analysis result deviation or system misjudgment due to missing data. At the third level, use In principle, mutation data points that exceed ±3 times the standard deviation of the mean are eliminated, which can identify and remove accidental outliers (such as data mutations caused by sudden noise interference), making the data distribution more consistent with normal rules and improving the stability and credibility of the data.

[0136] As another preferred embodiment of the present invention, the target feature vector generation includes a target calibration process, and the target calibration process specifically includes:

[0137] The first time the user uses the device, a 3-minute dynamic calibration is required to collect the following data to construct the target feature vector:

[0138] Resting state: EEG signal Wave baseline value, basic heart rate, ear canal temperature;

[0139] Movement status: 3D point cloud data of auricle contour and mandibular electromyography signal;

[0140] Speech calibration: bone conduction vibration frequency feature library;

[0141] The template is automatically updated every 50 uses, and a sliding window algorithm is used to retain the average of the last 20 valid data.

[0142] In this embodiment, when the user first uses the technology, a 3-minute dynamic calibration is arranged to construct the target feature vector. In the resting state (sitting for 30 seconds), the EEG signal is collected. The waveform baseline, basal heart rate, and ear canal temperature reflect the user's physiological characteristics when they are at rest. During movement (five head rotations), 3D point cloud data of the auricle outline and mandibular electromyography signals are collected to record changes in the user's biometric characteristics during activity. Voice calibration (reading "connect device") collects a bone conduction vibration frequency feature library to supplement language-related biometric information. Furthermore, to ensure the accuracy and adaptability of the template, it is automatically updated every 50 uses, using a sliding window algorithm to retain the average of the most recent 20 valid data points. This allows the template to adapt to changes in the user's characteristics, continuously improving recognition accuracy and reliability.

[0143] As another preferred embodiment of the present invention, the triggering of the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the relationship between the associated matching degree and the preset matching degree specifically includes:

[0144] When the correlation matching Greater than or equal to the association threshold, and the device distance value When the distance is less than or equal to the threshold, it will automatically connect to the device with the highest priority in the historical pairing list;

[0145] When the correlation matching Greater than or equal to the correlation threshold, and the EEG signal Wave energy ratio When the attention threshold is greater than the threshold, the pairing process with the specified target device is triggered.

[0146] In this embodiment, when the correlation matching degree is applied, When the association threshold is reached or exceeded and the device distance is within the distance threshold, the system automatically connects to the device with the highest priority in the historical pairing list, allowing users to quickly use commonly used devices. When the wave energy ratio is higher than the attention threshold, it indicates that the user is in a focused state, which triggers the pairing process with the specified target device to achieve on-demand connection, improving the intelligence of the connection and user experience.

[0147] As another preferred embodiment of the present invention, the triggering of the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the relationship between the associated matching degree and the preset matching degree specifically further includes:

[0148] When the correlation matching Greater than or equal to the association threshold, and the device distance value When the distance is greater than the threshold, the disconnection command is triggered;

[0149] When the correlation matching Greater than or equal to the correlation threshold, and the EEG signal Wave energy ratio When the heart rate variability threshold is exceeded, the disconnection command is triggered;

[0150] When the correlation matching Greater than or equal to the correlation threshold, and the external environmental noise decibel value When the noise level is greater than the ambient noise threshold, the exercise instruction is triggered.

[0151] In this embodiment, when the correlation matching degree is applied, When the preset correlation threshold is reached or exceeded, and the distance between the devices exceeds the distance threshold, it indicates that the distance between the devices is too far and the signal transmission stability is reduced. At this time, the disconnection instruction is triggered to avoid invalid connection. If the correlation matching degree is too high, the device will be disconnected. Meet the requirements, while EEG signal If the wave energy ratio is higher than the heart rate variability threshold, it indicates that the user may be in an abnormal physiological state such as tension or excitement. The system will trigger a disconnection command to reduce external interference to protect the user's state. If the standard is met and the decibel value of the external environmental noise exceeds the environmental noise threshold, it means that the environmental noise may affect the user experience or hearing health, and it will also trigger the disconnection command to protect the user's rights and interests in a timely manner. This mechanism realizes intelligent, safe and humanized connection management by dynamically sensing the environment and user physiological characteristics.

[0152] As another preferred embodiment of the present invention, the Bluetooth headset pairing and connection method further includes setting priority identification rules for a scenario where multiple people wear the headset at the same time. The priority identification rules specifically include:

[0153] When multiple user biometric signals are detected, secondary verification is triggered:

[0154] Prioritize connecting to users with the highest historical usage frequency;

[0155] If the frequencies are the same, compare the EEG signals Wave energy ratio, higher value is preferred;

[0156] If the device still cannot be distinguished, the headset will announce "Multiple users detected, please touch the right ear handle to confirm your identity" and the final confirmation will be completed in combination with the fingerprint pressure distribution matrix.

[0157] When this embodiment is used, when the headset detects multiple users' biometric signals, the system immediately triggers the secondary verification process. First, it makes a judgment based on the historical usage frequency, giving priority to connecting to the user with the highest usage frequency, fully respecting the user's usage habits; if there is a situation where the usage frequency is the same, the system compares the EEG signals to verify the authenticity of the user. The system uses the proportion of wave energy to give priority to users who are more focused and more likely to be the target users, and further accurately distinguishes them from the dimension of physiological characteristics; if it is still uncertain, the user will be prompted through the earphone voice, and the final identity confirmation will be carried out in combination with the fingerprint pressure distribution matrix. The system pre-stores the fingerprint-pressure composite template of the preset user (collected and encrypted during the first calibration). When the user touches to trigger the verification, the feature vector collected in real time will be matched with the preset template for dynamic time warping (DTW) verification.

[0158] As another preferred embodiment of the present invention, on the other hand, a Bluetooth headset pairing and connection system includes:

[0159] The multimodal acquisition module 100 is used for multimodally acquiring multi-dimensional biometric data of the user and multi-dimensional environmental perception data;

[0160] A targeting feature vector module 200 is used to construct a targeting feature vector;

[0161] The calculation module 300 is used to calculate the correlation matching degree between the targeting feature vector and the target feature vector based on the targeting feature vector;

[0162] A determination module 400 is configured to determine the relationship between the associated matching degree and the preset matching degree based on a three-level data verification mechanism;

[0163] The trigger module 500 is used to trigger the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the judgment result of the relationship between the associated matching degree and the preset matching degree.

[0164] The multimodal acquisition module 100 multimodally acquires the user's multi-dimensional biometric data and multi-dimensional environmental perception data. The targeting feature vector module 200 constructs a targeting feature vector. Based on the targeting feature vector, the calculation module 300 calculates the correlation matching degree between the targeting feature vector and the target feature vector. Based on the three-level data verification mechanism, the judgment module 400 judges the relationship between the correlation matching degree and the preset matching degree. Based on the judgment result of the relationship between the correlation matching degree and the preset matching degree, the trigger module 500 triggers the automatic pairing or disconnection operation between the Bluetooth headset and the target device.

[0165] The above-described embodiments of the present invention provide a Bluetooth headset pairing and connection method and a Bluetooth headset pairing and connection system. First, a biometric sensor and an environmental perception module collect multidimensional data. The biometric features include nine dimensions, including EEG signals, heart rate variability, auricle geometry, fingerprint pressure distribution, and bone conduction vibration frequency, accurately capturing the user's physiological and behavioral characteristics. The environmental perception module then acquires three-dimensional environmental information, such as device distance, motion acceleration, and ambient noise, in real time to construct a targeted feature vector that reflects the user's state and usage scenario. At the data processing level, a dynamic time warping (DTW) algorithm is used to process the biometric time series data. Differences in environmental features are quantified using weighted Euclidean distance. An adaptive weight allocation strategy is then employed to fuse the two features, achieving high-precision correlation matching calculations between the targeted and target feature vectors. This effectively addresses the low accuracy of single-dimensional recognition in traditional pairing. Furthermore, a three-level data verification mechanism, consisting of noise filtering, feature completion, and outlier detection, is implemented to ensure data reliability and enhance the system's robustness in complex environments. Based on the verification results and pre-set rules, intelligent connection management between the Bluetooth headset and the target device is implemented. When the matching degree reaches the threshold and the near-field conditions are met, the device with historical priority is automatically connected. When abnormal conditions such as the user being nervous or excessive ambient noise are detected, emergency disconnection protection is triggered. For special scenarios such as multiple people sharing and similar features, multi-dimensional priority identification and multi-level verification strategies are designed to avoid device control conflicts and misconnections. This method and system effectively prevent unauthorized use through multi-dimensional data collection, feature vector construction and dynamic matching calculation. The dynamic connection strategy based on environmental perception and user status achieves a seamless interactive experience. At the same time, adaptive template updates and intelligent priority adjustments further meet the differentiated usage habits of users.

[0166] In order to enable the above-mentioned method and system to be loaded and run smoothly, in addition to the various modules mentioned above, the system may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0167] The processor may be a central processing unit, other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the system, connecting various components using various interfaces and lines.

[0168] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0169] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A Bluetooth headset pairing and connection method, characterized in that: The method comprises: Multimodally collect user multi-dimensional biometric data and multi-dimensional environmental perception data, and construct targeted feature vectors; Based on the targeting feature vector, calculating the correlation matching degree between the targeting feature vector and the target feature vector; Based on the three-level data verification mechanism, the relationship between the associated matching degree and the preset matching degree is determined; Based on the judgment result of the relationship between the associated matching degree and the preset matching degree, the automatic pairing or disconnection operation of the Bluetooth headset and the target device is triggered.

2. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The multimodal collection of user multidimensional biometric data and multidimensional environmental perception data and the construction of a targeted feature vector specifically include: Collect and calculate EEG signals Wave energy ratio ; The EEG signal Wave energy ratio The calculation process is: ； Where, Frequency The power spectral density at ; Collect and calculate EEG signals Wave energy ratio ; The EEG signal Wave energy ratio The calculation process is: ； Collect the RMSSD value of heart rate variability ; The RMSSD value of the heart rate variability The calculation process is: Extract RR interval sequence by peak detection algorithm ; ； Where, Is the loop count variable, used to traverse adjacent Interval data, for Interphase sequence The total number of data points in ; Collect the geometric feature vector values of the auricle contour ; Collect fingerprint capacitance sensor pressure distribution matrix ; Collect real-time values of ear canal temperature sensors ; Collect the amplitude of mandibular movement electromyographic signals ; Collect pupil diameter change rate ; The pupil diameter change rate The calculation process is: ； Where, for pupil diameter at the moment; Collecting speech bone conduction vibration frequency characteristics .

3. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The multimodal collection of user multidimensional biometric data and multidimensional environmental perception data and the construction of a targeted feature vector specifically include: Collect the target connection device receiving signal strength indication ; Based on the target connected device receiving signal strength indication , calculate the device distance value ; The device distance vector The calculation process is: ； Where, is the reference signal strength at 1 meter, is the path loss index; Collect the average x, y, and z-axis acceleration of the headset's built-in three-axis accelerometer ; Collect the decibel value of the external environmental noise of the headset .

4. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The step of calculating the correlation matching degree between the targeting feature vector and the target feature vector based on the targeting feature vector specifically includes: Get the target feature vector value ; Calculate the target eigenvector value With the targeted feature vector The correlation matching degree ; The correlation matching degree The calculation process is: ； ； In the formula, Biometric sequence The dynamic time warping distance, Environmental characteristics Weighted Euclidean distance, For each environmental characteristic weight, Biometric sequence Weight.

5. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The three-level data verification mechanism specifically includes: A 50 Hz Butterworth low-pass filter was used to remove power frequency interference from the heart rate variability signal and EEG signal; When a single biological dimension feature is missing, the historical data interpolation algorithm is enabled: ； Where, After completion Dimensional biometric value, The number of historical data involved in the calculation, that is, the number of recent valid collections, For the recent The time interval between effective collections, For Moment Historical values of biometrics; use In principle, mutation data points exceeding the mean ± 3 times the standard deviation were eliminated.

6. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The target feature vector generation includes a target calibration process, which specifically includes: The first time the user uses the device, a 3-minute dynamic calibration is required to collect the following data to construct the target feature vector: Resting state: EEG signal Wave baseline value, basic heart rate, ear canal temperature; Movement status: 3D point cloud data of auricle contour and mandibular electromyography signal; Speech calibration: bone conduction vibration frequency feature library; The template is automatically updated every 50 uses, and a sliding window algorithm is used to retain the average of the last 20 valid data.

7. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The triggering of the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the relationship between the associated matching degree and the preset matching degree specifically includes: When the correlation matching Greater than or equal to the association threshold, and the device distance value When the distance is less than or equal to the threshold, it will automatically connect to the device with the highest priority in the historical pairing list; When the correlation matching Greater than or equal to the correlation threshold, and the EEG signal Wave energy ratio When the attention threshold is greater than the threshold, the pairing process with the specified target device is triggered.

8. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The triggering of the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the determination result of the relationship between the associated matching degree and the preset matching degree specifically includes: When the correlation matching Greater than or equal to the association threshold, and the device distance value When the distance is greater than the threshold, the disconnection command is triggered; When the correlation matching Greater than or equal to the correlation threshold, and the EEG signal Wave energy ratio When the heart rate variability threshold is exceeded, the disconnection command is triggered; When the correlation matching Greater than or equal to the correlation threshold, and the external environmental noise decibel value When the noise level is greater than the ambient noise threshold, the exercise instruction is triggered.

9. The Bluetooth headset pairing and connection method according to claim 1, characterized in that: The Bluetooth headset pairing and connection method further includes setting priority identification rules for scenarios where multiple people wear the headset at the same time. The priority identification rules specifically include: When multiple user biometric signals are detected, secondary verification is triggered: Prioritize connecting to users with the highest historical usage frequency; If the frequencies are the same, compare the EEG signals Wave energy ratio, higher value is preferred; If the device still cannot be distinguished, the headset will announce "Multiple users detected, please touch the right ear handle to confirm your identity" and the final confirmation will be completed in combination with the fingerprint pressure distribution matrix.

10. A Bluetooth headset pairing and connection system, characterized in that: Applying the Bluetooth headset pairing and connection method according to claim 1, the system includes: Multimodal acquisition module, used for multimodal acquisition of user multi-dimensional biometric data and multi-dimensional environmental perception data; Targeted feature vector module, used to construct targeted feature vectors; A calculation module, configured to calculate the correlation matching degree between the targeting feature vector and the target feature vector based on the targeting feature vector; A discrimination module, used to discriminate the relationship between the associated matching degree and the preset matching degree based on a three-level data verification mechanism; The trigger module is used to trigger the automatic pairing or disconnection operation between the Bluetooth headset and the target device based on the judgment result of the association matching degree and the preset matching degree relationship.

Images