Earphone, wearing comfort early warning method thereof and control device

Abstract

This application discloses an earphone and its wearing comfort warning method and control device, relating to the field of smart wearable device technology. The earphone wearing comfort warning includes: dynamically acquiring multi-dimensional wearing data of a target user wearing the earphone, the multi-dimensional wearing data including at least wearing pressure, wearing temperature, wearing humidity, ear skin conductivity data, wearing scenario information, and earphone micro-motion indicators; inputting the multi-dimensional wearing data into a pre-trained comfort prediction model to generate a wearing comfort score for the target user at the current moment; if the wearing comfort score is less than a first preset score threshold, generating a first warning strategy based on the wearing comfort score, wherein the warning intensity of the first warning strategy is negatively correlated with the wearing comfort score; and outputting warning prompt information based on the first warning strategy. This application can improve the accuracy and rationality of earphone wearing warnings, thereby enhancing the user experience.

Description

Technical Field

[0001] This application relates to the field of smart wearable device technology, and in particular to an earphone and its wearing comfort warning method and control device. Background Technology

[0002] As headphones are worn daily, the issue of comfort during prolonged use is becoming increasingly prominent.

[0003] Currently, headphones typically rely on a fixed threshold based on a single type of wearing data to trigger wearing warnings. However, single-dimensional data is insufficient to accurately represent the true wearing comfort level, easily leading to false or frequent triggering of wearing warnings, thus impacting the user experience. Summary of the Invention

[0004] The main purpose of this application is to provide an earphone and a method and control device for warning of wearing comfort, which aims to improve the accuracy and rationality of earphone wearing warnings and enhance the user experience.

[0005] This application provides a headphone wearing comfort warning method, the method comprising: Dynamically acquire multi-dimensional wearing data of the target user when wearing headphones. The multi-dimensional wearing data includes at least wearing pressure, wearing temperature, wearing humidity, ear skin electrical conductivity data, wearing scenario information, and headphone micro-motion indicators. The multidimensional wearing data is input into a pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment. If the wearing comfort score is less than a first preset score threshold, a first warning strategy is generated based on the wearing comfort score, wherein the warning intensity of the first warning strategy is negatively correlated with the wearing comfort score; The warning message is output based on the first warning strategy.

[0006] In one embodiment, the step of generating a first warning strategy based on the wearing comfort score includes: Determine the first warning level corresponding to the wearing comfort score; Based on the preset mapping relationship between warning levels and warning strategies, the warning strategy corresponding to the first warning level is obtained and used as the first warning strategy.

[0007] In one embodiment, the step of determining the first warning level corresponding to the wearing comfort score includes: If the wearing comfort score is less than the first preset score threshold and greater than or equal to the second preset score threshold, then the first warning level is determined to be a level one warning level. If the wearing comfort score is less than the second preset score threshold and greater than or equal to the third preset score threshold, then the first warning level is determined to be a level two warning level. If the wearing comfort score is less than the third preset score threshold, then the first warning level is determined to be a level three warning level.

[0008] In one embodiment, when the first warning level is the third-level warning level, after the step of outputting the warning prompt information based on the first warning strategy, the method further includes: After a preset duration of outputting the warning message, the system checks whether the earphone and the target user's device are in a connected state. If so, disconnect the connection between the headset and the user equipment.

[0009] In one embodiment, before the step of inputting the multidimensional wearing data into a pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment, the method further includes: Determine whether the value of each wear data in the multidimensional wear data is less than or equal to the preset safety threshold corresponding to each wear data; If the values ​​of each of the wearing data are less than or equal to the preset safety threshold corresponding to each of the wearing data, then the step of inputting the multidimensional wearing data into the pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment is executed. If any of the wearing data exceeds a preset safety threshold, the wearing data exceeding the preset safety threshold is considered abnormal data, and a second warning strategy is generated based on the abnormal data, wherein the warning intensity of the second warning strategy is positively correlated with the value of the abnormal data. The warning message is output based on the second warning strategy.

[0010] In one embodiment, the step of generating a second early warning strategy based on the abnormal data includes: Determine the second early warning level corresponding to the abnormal data; Based on the preset mapping relationship between warning levels and warning strategies, the warning strategy corresponding to the second warning level is obtained and used as the second warning strategy.

[0011] In one embodiment, the step of determining the second warning level corresponding to the abnormal data includes: Determine the percentage by which the value of the abnormal data is greater than the preset security threshold corresponding to the abnormal data; If the percentage is less than the first preset percentage threshold, then the second warning level is determined to be a Level 1 warning level; If the percentage is greater than or equal to the first preset percentage threshold and less than the second preset percentage threshold, then the second warning level is determined to be a level two warning level. If the percentage is greater than or equal to the second preset percentage threshold, then the second warning level is determined to be a Level III warning level.

[0012] In one embodiment, the method further includes: Based on the multi-dimensional wearing data, wearing adjustment suggestions are generated for the target user; The user device of the target user is controlled to display the wearing adjustment suggestions.

[0013] In addition, to achieve the above objectives, this application also provides a control device, the control device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the headphone wearing comfort warning method as described above.

[0014] In addition, to achieve the above objectives, this application also provides an earphone, which includes an earphone body, a plurality of pressure sensors, bioelectric sensors, motion sensors, temperature and humidity sensors, environmental sensors, and control devices as described above. Each of the pressure sensors, the bioelectric sensors, the motion sensors, the temperature and humidity sensors, the environmental sensors, and the control devices is disposed in the earphone body, and each of the pressure sensors is arranged in a distributed array in the ear contact area of ​​the earphone body.

[0015] This application provides a headphone wearing comfort early warning method, comprising: dynamically acquiring multi-dimensional wearing data of a target user while wearing headphones, the multi-dimensional wearing data including at least wearing pressure, wearing temperature, wearing humidity, ear skin conductivity data, wearing scenario information, and headphone micro-motion indicators; inputting the multi-dimensional wearing data into a pre-trained comfort prediction model to generate a wearing comfort score for the target user at the current moment; if the wearing comfort score is less than a first preset score threshold, generating a first early warning strategy based on the wearing comfort score, wherein the early warning intensity of the first early warning strategy is negatively correlated with the wearing comfort score; and outputting early warning information based on the first early warning strategy.

[0016] Therefore, the technical solution provided in this application integrates multi-dimensional wearing data for comprehensive judgment, avoiding the problem that single-type data cannot accurately reflect the actual wearing comfort state, making the basis for warning triggering more reliable. At the same time, a quantitative wearing comfort score is output through a comfort prediction model, and the warning strength of the warning strategy is negatively correlated with this wearing comfort score, so that the strength of the warning prompt can match the user's actual wearing discomfort, rather than using a fixed prompt method, thereby effectively improving the accuracy and rationality of headphone wearing warnings and enhancing the user experience. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the headphone wearing comfort warning method provided in the first embodiment of this application; Figure 2 A flowchart illustrating the headphone wearing comfort warning method provided in the second embodiment of this application; Figure 3 A flowchart illustrating the headphone wearing comfort warning method provided in the third embodiment of this application; Figure 4 A flowchart illustrating the headphone wearing comfort warning method provided in the fourth embodiment of this application; Figure 5 This is a schematic diagram of the hardware operating environment involved in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the earphone provided in an embodiment of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] As headphones are worn daily, the issue of comfort during prolonged use is becoming increasingly prominent.

[0024] Currently, headphones typically rely on a fixed threshold based on a single type of wearing data to trigger wearing warnings. However, single-dimensional data is insufficient to accurately represent the true wearing comfort level, easily leading to false or frequent triggering of wearing warnings, thus impacting the user experience.

[0025] Based on this, this application provides a headphone wearing comfort early warning method, comprising: dynamically acquiring multi-dimensional wearing data of a target user while wearing headphones, the multi-dimensional wearing data including at least wearing pressure, wearing temperature, wearing humidity, ear skin conductivity data, wearing scenario information, and headphone micro-motion indicators; inputting the multi-dimensional wearing data into a pre-trained comfort prediction model to generate a wearing comfort score for the target user at the current moment; if the wearing comfort score is less than a first preset score threshold, generating a first early warning strategy based on the wearing comfort score, wherein the early warning intensity of the first early warning strategy is negatively correlated with the wearing comfort score; and outputting early warning information based on the first early warning strategy.

[0026] Therefore, the technical solution provided in this application integrates multi-dimensional wearing data for comprehensive judgment, avoiding the problem that single-type data cannot accurately reflect the actual wearing comfort state, making the basis for warning triggering more reliable. At the same time, a quantitative wearing comfort score is output through a comfort prediction model, and the warning strength of the warning strategy is negatively correlated with this wearing comfort score, so that the strength of the warning prompt can match the user's actual wearing discomfort, rather than using a fixed prompt method, thereby effectively improving the accuracy and rationality of headphone wearing warnings and enhancing the user experience.

[0027] The subject of the headphone wearing comfort warning method of this application can be a control device with data processing, network communication and program operation functions, or it can be a headphone including a control device. This embodiment does not specifically limit it in this regard.

[0028] The following description uses a control device as the execution subject to illustrate the various embodiments.

[0029] This application presents a headphone wearing comfort warning method according to a first embodiment. Please refer to [link / reference]. Figure 1 The headphone wearing comfort warning method may include steps S10~S40: Step S10: Dynamically acquire multi-dimensional wearing data of the target user when wearing headphones. The multi-dimensional wearing data includes at least wearing pressure, wearing temperature, wearing humidity, ear skin electrical conductivity data, wearing scenario information and headphone micro-motion indicators. It's important to note that the target user is the user currently wearing the headphones. Multidimensional wearing data refers to a dataset collected simultaneously from multiple sensory dimensions, reflecting the user's headphone wearing status. Wearing pressure refers to the pressure value generated by the contact area between the headphones and the user's ear, reflecting the tightness of the fit and localized pressure. Wearing temperature refers to the temperature inside the ear canal when the user wears the headphones. Wearing humidity refers to the humidity inside the ear canal when the user wears the headphones. Ear skin conductivity data refers to the conductivity value of the user's ear skin, reflecting sweat secretion. Wearing scenario information refers to the category identifier of the user's current usage scenario, used to distinguish different usage environments (e.g., exercise, sitting). Headphone micro-motion index refers to the minute amount of movement of the headphones relative to the ear canal during wear, reflecting the stability of the headphone fit.

[0030] When dynamically acquiring multidimensional wearing data of a target user while wearing headphones, the data can be acquired in real time or periodically. This embodiment does not impose any specific limitations on this.

[0031] In one feasible implementation, multiple pressure sensors can be installed on the earphone body, and each pressure sensor can be arranged in a distributed array in the ear contact area of ​​the earphone body. Thus, multiple pressure sensors can be used to collect pressure data at multiple points on the ear contact area, and the maximum pressure data can be used as the wearing pressure when the target user is wearing the earphone at the current moment. This accurately reflects the maximum pressure on the ear during earphone wearing, avoiding the risk of wearing discomfort caused by only collecting single-point pressure or average pressure and masking local high pressure points.

[0032] In one feasible implementation, a temperature and humidity sensor can be installed on the earphone itself, thereby allowing the sensor to directly collect the wearing temperature and humidity of the target user at the current moment when wearing the earphone.

[0033] In one feasible implementation, a bioelectric sensor can be installed on the earphone body, thereby enabling the direct acquisition of ear skin conductance data of the target user at the current moment when wearing the earphone.

[0034] In one feasible implementation, an environmental sensor (such as a sound sensor, motion sensor, etc.) can be installed on the earphone itself, thereby enabling the use of the environmental sensor to directly collect information about the wearing scenario when the target user is currently wearing the earphone.

[0035] In one feasible implementation, a motion sensor, such as an IMU (Inertial Measurement Unit), can be installed on the earphone itself. This allows the motion sensor to directly collect the earphone's micro-motion indicators when the target user is wearing the earphone at the current moment.

[0036] In one feasible implementation, to improve the accuracy of the subsequently determined wearing comfort score, after obtaining the multidimensional wearing data of the target user wearing the headphones, the multidimensional wearing data can be cleaned to filter out abnormal data; and the wearing data in the cleaned multidimensional wearing data can be normalized to unify the wearing data to the same dimension, so as to obtain the processed multidimensional wearing data.

[0037] Step S20: Input the multidimensional wearing data into the pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment; It should be noted that the comfort prediction model can be obtained through iterative training using historical multidimensional wearing data as input and the corresponding wearing comfort scores as output. The wearing comfort score is used to characterize the level of comfort a user experiences when wearing the headphones.

[0038] Step S30: If the wearing comfort score is less than the first preset score threshold, generate a first warning strategy based on the wearing comfort score, wherein the warning intensity of the first warning strategy is negatively correlated with the wearing comfort score. It should be noted that the first preset scoring threshold refers to a pre-set minimum comfort score limit value used to trigger a wearing comfort warning. This can be a default value, such as 85, or it can be flexibly set by the user according to actual conditions. This embodiment does not specifically limit this. The first warning strategy is the warning execution plan generated based on the wearing comfort score. Warning intensity refers to the strength of the warning prompt, which can be specifically reflected in the frequency of prompts, the mandatory nature of the prompt method, or the prominence of the prompt signal. The warning intensity of the first warning strategy is negatively correlated with the wearing comfort score; that is, the higher the wearing comfort score, the lower the warning intensity of the first warning strategy, and vice versa.

[0039] In one feasible implementation, step S30 may include steps S31-S32: Step S31: Determine the first warning level corresponding to the wearing comfort score; It should be noted that the first warning level refers to the warning level indicator determined by the numerical value of the wearing comfort score. It is used to distinguish different degrees of discomfort, and different warning levels correspond to different warning intensities.

[0040] In one feasible implementation, step S31 may include steps S311 to S313: Step S311: If the wearing comfort score is less than the first preset score threshold and greater than or equal to the second preset score threshold, then the first warning level is determined to be the first warning level. Step S312: If the wearing comfort score is less than the second preset score threshold and greater than or equal to the third preset score threshold, then the first warning level is determined to be the second warning level. Step S313: If the wearing comfort score is less than the third preset score threshold, then the first warning level is determined to be the third warning level.

[0041] It should be noted that the second preset scoring threshold, which is a pre-set comfort scoring boundary value used to divide the first-level warning level and the second-level warning level, can be a default value, such as 75, or can be flexibly set by the user according to the actual situation. This embodiment does not impose specific limitations on this. The third preset scoring threshold, which is a pre-set comfort scoring boundary value used to divide the second-level warning level and the third-level warning level, can be a default value, such as 60, or can be flexibly set by the user according to the actual situation. This embodiment does not impose specific limitations on this. The first-level warning level is used to indicate a relatively mild degree of discomfort, the second-level warning level is used to indicate a relatively severe degree of discomfort, and the third-level warning level is used to indicate a severe degree of discomfort.

[0042] For example, the warning strategy corresponding to the first-level warning level can be a light prompt such as a low-frequency vibration alert, an SMS notification, or a pop-up notification; this embodiment does not specifically limit this. The warning strategy corresponding to the second-level warning level can include a combination of voice prompts and visual prompts; this embodiment does not specifically limit this. The third-level warning level can include a strong prompt such as a combination of high-frequency vibration prompts, voice prompts, and visual prompts; it can also include a prompt suggesting that the user take a rest; this embodiment does not specifically limit this.

[0043] This implementation method divides the continuous values ​​of the wearing comfort score into three warning level ranges by setting three preset scoring thresholds, so that the high and low scores can be clearly correlated with different warning levels. Therefore, this implementation method establishes a clear and unambiguous correspondence between scores and levels, providing an objective basis for subsequent differentiated warning strategies based on different warning levels, effectively improving the accuracy and rationality of wearing comfort warnings.

[0044] This embodiment does not specifically limit the implementation of step S31. For example, in other feasible implementations, a relational table can be used to record the warning levels corresponding to different wearing comfort scores in advance. Thus, the first warning level corresponding to the wearing comfort score can be quickly determined by looking up the table.

[0045] Step S32: Based on the preset mapping relationship between warning levels and warning strategies, obtain the warning strategy corresponding to the first warning level and use it as the first warning strategy.

[0046] It should be noted that the mapping relationship between warning levels and warning strategies can be recorded using relational tables, key-value pairs, or other methods, and this embodiment does not impose any specific limitations on this.

[0047] In this embodiment, by first mapping the wearing comfort score to a warning level, and then obtaining the corresponding warning strategy based on the warning level, decoupling between the score and the strategy is achieved. This allows the logic for classifying warning levels and the specific configuration of the warning strategy to be adjusted independently. For example, when optimizing the prompting method, only the strategy parameters corresponding to each level in the mapping relationship need to be modified, without altering the correspondence rules between the score and the level, thereby improving the flexibility and maintainability of the warning mechanism.

[0048] This embodiment does not specifically limit the implementation of step S30. For example, in other feasible implementations, the negative correlation between the warning intensity of the first warning strategy and the wearing comfort score can also be utilized. Specifically, the wearing comfort score can be directly mapped to the warning intensity, and then the warning strategy associated with the warning intensity can be used as the first warning strategy.

[0049] Step S40: Output early warning information based on the first early warning strategy.

[0050] As can be seen from the above, the technical solution provided in this embodiment integrates multi-dimensional wearing data for comprehensive judgment, avoiding the problem that single-type data cannot accurately reflect the actual wearing comfort state, making the basis for warning triggering more reliable. At the same time, by outputting a quantitative wearing comfort score through a comfort prediction model, and negatively correlated with the warning strength of the warning strategy with this wearing comfort score, the strength of the warning prompt can match the user's actual wearing discomfort level, rather than using a fixed prompt method, thereby effectively improving the accuracy and rationality of headphone wearing warnings and enhancing the user experience.

[0051] Based on the first embodiment described above, a second embodiment of the headphone wearing comfort warning method of this application is proposed. For the second embodiment, please refer to... Figure 2 If the first warning level is a level three warning level, after step S40, the headphone wearing comfort warning method may also include steps S50-S60: Step S50: After the preset duration of the warning message is output, check whether the earphone and the target user's device are in a connected state. Step S60: If yes, disconnect the connection between the headset and the user device.

[0052] It should be noted that the preset duration refers to a pre-set time length, calculated from the time the warning message is output, used to reserve a time window for the user to respond to the warning message and take adjustment measures. It can be a default duration or can be flexibly set by the user according to the actual situation. This embodiment does not make a specific limitation on it. User equipment refers to an external electronic device belonging to the target user that establishes a communication connection with the headset, such as a mobile phone, tablet computer, or computer. This embodiment does not make a specific limitation on it.

[0053] Detecting whether the headset and the target user's device are connected essentially means checking whether a valid communication link is maintained between them, such as whether a Bluetooth connection remains established. Disconnecting the headset from the user device actively terminates the established communication link, preventing further data transmission.

[0054] In this embodiment, a preset buffer period is reserved after the highest level warning prompt is output, giving the target user a certain response time to adjust the wearing status or take a break. If the headphones are still connected to the user's device after the buffer period expires, it indicates that the user may not have responded to the warning prompt in time. At this time, the connection can be automatically disconnected to forcibly interrupt the user's continued use of the headphones, thereby achieving forced intervention when the wearing discomfort is severe and protecting the user's ear health.

[0055] Based on the first and / or second embodiments described above, a third embodiment of the headphone wearing comfort warning method of this application is proposed. In the third embodiment, please refer to... Figure 3 Before step S20, the headphone wearing comfort warning method may also include steps S01 to S04: Step S01: Determine whether the value of each wear data in the multidimensional wear data is less than or equal to the preset safety threshold corresponding to each wear data. It should be noted that the preset safety threshold is the safety limit value set for each piece of wearing data in the multidimensional wearing data. It can be a default value or it can be flexibly set by the user according to the actual situation. This embodiment does not make specific limitations on it.

[0056] Step S02: If the values ​​of each wearing data are less than or equal to the preset safety threshold corresponding to each wearing data, then the step of inputting the multi-dimensional wearing data into the pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment is executed. Understandably, if the values ​​of each wearing data point are less than or equal to the preset safety threshold corresponding to each wearing data point, it indicates that the wearing data of each dimension are within the safe range and there is no abnormal deterioration of any single indicator. At this time, the comprehensive comfort assessment stage can be entered according to the normal procedure without additional intervention.

[0057] Step S03: If there are wearing data that are greater than a preset safety threshold in each wearing data, then the wearing data that are greater than the preset safety threshold are regarded as abnormal data, and a second warning strategy is generated based on the abnormal data, wherein the warning intensity of the second warning strategy is positively correlated with the value of the abnormal data. It should be noted that abnormal data refers to wearing data in the multi-dimensional wearing data that exceeds its corresponding preset safety threshold. The second early warning strategy is an early warning execution plan generated based on abnormal data. It is independent of the first early warning strategy generated based on comfort scores and is specifically used to deal with scenarios where a single indicator is abnormal. The warning strength of the second early warning strategy is positively correlated with the value of the abnormal data; that is, the larger the value of the abnormal data, the higher the warning strength of the second early warning strategy, and the smaller the value of the abnormal data, the lower the warning strength of the second early warning strategy.

[0058] In one feasible implementation, step S03 may include steps S031 to S032: Step S031: Determine the second warning level corresponding to the abnormal data; It should be noted that the second warning level refers to the warning level indicator determined based on the magnitude of the abnormal data, which is used to distinguish the severity of the abnormality of a single indicator.

[0059] In one feasible implementation, step S031 may include steps S301 to S304: Step S301: Determine the percentage by which the value of the abnormal data is greater than the preset safety threshold corresponding to the abnormal data; It should be noted that the percentage refers to the extent to which the value of the abnormal data exceeds its corresponding preset safety threshold, and it is used to quantify the degree to which the abnormal data exceeds the standard.

[0060] Step S302: If the percentage is less than the first preset percentage threshold, then the second warning level is determined to be the first warning level. Step S303: If the percentage is greater than or equal to the first preset percentage threshold and less than the second preset percentage threshold, then the second warning level is determined to be a level 2 warning level. Step S304: If the percentage is greater than or equal to the second preset percentage threshold, then the second warning level is determined to be a level three warning level.

[0061] It should be noted that the first preset percentage threshold is a pre-set percentage boundary value used to divide the level 1 warning level and the level 2 warning level. It can be a default value or it can be flexibly set by the user according to the actual situation. This embodiment does not impose specific limitations on it. The second preset percentage threshold is a pre-set percentage boundary value used to divide the level 2 warning level and the level 3 warning level. It can be a default value or it can be flexibly set by the user according to the actual situation. This embodiment does not impose specific limitations on it.

[0062] This implementation calculates the percentage of abnormal data exceeding a safety threshold and compares this percentage with two preset percentage thresholds to quantify the degree of abnormality of a single indicator into an early warning level. This approach provides an objective and quantifiable basis for determining the early warning level, rather than relying solely on a binary judgment of "whether it exceeds the standard." This allows for a more precise differentiation of individual anomalies of varying degrees, providing a basis for generating differentiated early warning strategies that match the severity of the anomalies, thus ensuring the accuracy and rationality of the subsequently generated early warning strategies.

[0063] This embodiment does not specifically limit the implementation of step S031. For example, in other feasible implementations, the difference between the value of the abnormal data and the preset safety threshold corresponding to the abnormal data can be calculated to obtain the deviation amount; then, based on the preset mapping relationship between the deviation amount and the warning level, the second warning level can be determined.

[0064] In addition, it should be noted that when verifying the safety threshold of the wear data in each dimension of the multidimensional wear data, there may be multiple abnormal wear data in multiple dimensions of the multidimensional wear data. In this case, the warning level corresponding to each abnormal data can be determined first, and then the highest warning level can be used as the second warning level.

[0065] Step S032: Based on the preset mapping relationship between warning levels and warning strategies, obtain the warning strategy corresponding to the second warning level, and use it as the second warning strategy.

[0066] In this implementation, by first mapping abnormal data to warning levels and then obtaining corresponding warning strategies based on those levels, the severity of individual indicator anomalies can be expressed hierarchically, rather than using the same warning method for all anomalies. This further improves the accuracy and rationality of warning systems. Simultaneously, the decoupling of warning levels and warning strategies through the mapping relationship facilitates independent adjustments to the level classification rules and strategy configurations for individual anomaly warnings, enhancing the flexibility of the warning system.

[0067] This embodiment does not specifically limit the implementation of step S03. For example, in other feasible implementations, the positive correlation between the warning intensity of the second warning strategy and the value of the abnormal data can also be utilized. Specifically, the value of the abnormal data can be directly mapped to the warning intensity, and then the warning strategy associated with the warning intensity can be used as the second warning strategy.

[0068] Step S04: Output early warning information based on the second early warning strategy.

[0069] This embodiment establishes a system where, before inputting multi-dimensional wearing data into the comfort prediction model, each wearing data point undergoes a single safety threshold assessment. This allows for rapid interception of severe anomalies in individual indicators. Specifically, when the value of a certain dimension of wearing data exceeds its corresponding safety threshold, an alert is triggered directly without waiting for the model's overall score, thus shortening the response time from the occurrence of an anomaly to the user receiving the notification. Simultaneously, the alert strength of the second alert strategy is positively correlated with the value of the abnormal data, ensuring a stronger alert for more severe anomalies. This complements the first alert strategy based on the overall score, constructing a two-layer alert system combining rapid single-item response and comprehensive score evaluation. This not only guarantees immediate alerts in abnormal situations but also ensures the accuracy of the comprehensive comfort assessment under normal conditions.

[0070] Based on the first, second, and / or third embodiments described above, a fourth embodiment of the headphone wearing comfort warning method of this application is proposed. In this fourth embodiment, please refer to... Figure 4 The headphone wearing comfort warning method may also include steps S101~S102: Step S101: Based on multi-dimensional wearing data, generate wearing adjustment suggestions for the target user; It should be noted that the wearing adjustment suggestions refer to the specific operation prompts automatically generated based on the wearing status reflected by multi-dimensional wearing data, which are used to guide the target user to improve wearing comfort. The content points to specific adjustment actions that the user can perform.

[0071] When generating wearing adjustment suggestions for a target user based on multi-dimensional wearing data, in one feasible implementation, the main dimensions causing decreased wearing comfort can be identified based on the numerical distribution of wearing data in each dimension of the multi-dimensional wearing data, and adjustment suggestions can be generated for those dimensions. For example, if the wearing pressure data is too high, an adjustment suggestion can be generated suggesting that the user replace the ear tips with smaller ones or adjust the wearing angle of the headphones; if the ear skin conductivity data is too high, an adjustment suggestion can be generated suggesting that the user remove the headphones periodically to allow them to breathe. In another feasible implementation, the multi-dimensional wearing data can also be comprehensively analyzed. When multiple dimensions of wearing data simultaneously show adverse trends but have not yet triggered warning thresholds, preventative wearing adjustment suggestions can be generated in advance. For example, when the wearing temperature and humidity continue to rise but have not yet exceeded the standard, an adjustment suggestion can be generated suggesting that the user suspend the use of headphones for a period of time to achieve preemptive intervention. This embodiment does not specifically limit the implementation method of step S101.

[0072] Step S102: Control the target user's device to display wearing adjustment suggestions.

[0073] In this embodiment, by referencing multi-dimensional wearing data, wearing adjustment suggestions are automatically generated and displayed. This extends the warning from simply "informing the user of a problem" to "guiding the user on how to solve the problem." This allows users to not only know that their current wearing comfort has decreased, but also to clearly know what specific actions they should take to improve their wearing condition. This reduces the trial-and-error cost for users to figure out how to adjust the situation on their own, helps users correct bad wearing habits in a timely manner, and fundamentally improves the comfort and health protection of users who wear headphones for a long time.

[0074] This application also provides a control device, which may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the headphone wearing comfort warning method in the above embodiments.

[0075] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a control device suitable for implementing the embodiments of this application. Figure 5 The control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0076] like Figure 5As shown, the control device may include a processing unit 101 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory 102 or a program loaded from storage device 103 into random access memory 104. Random access memory 104 also stores various programs and data required for the operation of the control device. The processing unit 101, read-only memory 102, and random access memory 104 are interconnected via bus 105. Input / output interface 106 is also connected to bus 105. Typically, the following systems can be connected to input / output interface 106: input devices 107 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 108 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 103 including, for example, magnetic tapes, hard disks, etc.; and communication devices 109. Communication device 109 allows the control device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagram shows control equipment with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0077] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 103, or installed from read-only memory 102. When the computer program is executed by processing device 101, it performs the functions defined in the methods of the embodiments of this application.

[0078] The control device provided in this application, employing the headphone wearing comfort warning method described in the above embodiments, can improve the accuracy and rationality of headphone wearing warnings, thereby enhancing the user experience. Compared with the prior art, the beneficial effects of the control device provided in this application are the same as those of the headphone wearing comfort warning method provided in the above embodiments, and other technical features of the control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0079] It should be understood that various parts of the embodiments of this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0080] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the above claims.

[0081] This application also provides an earphone, please refer to... Figure 6 The earphone may include an earphone body, multiple pressure sensors, bioelectric sensors, motion sensors, temperature and humidity sensors, environmental sensors, and control devices as described in the above embodiments. Each pressure sensor, bioelectric sensor, motion sensor, temperature and humidity sensor, environmental sensor, and control device is located in the earphone body, and each pressure sensor is arranged in a distributed array in the ear contact area of ​​the earphone body.

[0082] Compared with the prior art, the beneficial effects of the headphones provided in this application embodiment are the same as the beneficial effects of the headphone wearing comfort warning method provided in the above embodiment, and other technical features of the headphones are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0083] This application also provides a computer-readable storage medium storing a computer program that can run on a processor. The computer program is used to execute the headphone wearing comfort warning method in the above embodiments.

[0084] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0085] The aforementioned computer-readable storage medium may be included in the control device; or it may exist independently and not assembled into the control device.

[0086] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the control device, the control device causes the control device to: dynamically acquire multi-dimensional wearing data of the target user while wearing the headphones, the multi-dimensional wearing data including at least wearing pressure, wearing temperature, wearing humidity, ear skin conductivity data, wearing scenario information, and headphone micro-motion indicators; input the multi-dimensional wearing data into a pre-trained comfort prediction model to generate a wearing comfort score for the target user at the current moment; if the wearing comfort score is less than a first preset score threshold, generate a first warning strategy based on the wearing comfort score, wherein the warning intensity of the first warning strategy is negatively correlated with the wearing comfort score; and output warning prompt information based on the first warning strategy.

[0087] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0089] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0090] The computer-readable storage medium provided in this application embodiment stores computer-readable program instructions for executing the above-described headphone wearing comfort warning method, which can improve the accuracy and rationality of headphone wearing warnings, thereby enhancing the user experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the headphone wearing comfort warning method provided in the above-described embodiments, and will not be repeated here.

[0091] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the headphone wearing comfort warning method described above.

[0092] The computer program product provided in this application can improve the accuracy and rationality of headphone wearing warnings, thereby enhancing the user experience. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the headphone wearing comfort warning method provided in the above embodiments, and will not be repeated here.

[0093] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for providing early warning of headphone wearing comfort, characterized in that, The method includes: Dynamically acquire multi-dimensional wearing data of the target user when wearing headphones. The multi-dimensional wearing data includes at least wearing pressure, wearing temperature, wearing humidity, ear skin electrical conductivity data, wearing scenario information, and headphone micro-motion indicators. The multidimensional wearing data is input into a pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment. If the wearing comfort score is less than a first preset score threshold, a first warning strategy is generated based on the wearing comfort score, wherein the warning intensity of the first warning strategy is negatively correlated with the wearing comfort score; The warning message is output based on the first warning strategy.

2. The method as described in claim 1, characterized in that, The step of generating a first warning strategy based on the wearing comfort score includes: Determine the first warning level corresponding to the wearing comfort score; Based on the preset mapping relationship between warning levels and warning strategies, the warning strategy corresponding to the first warning level is obtained and used as the first warning strategy.

3. The method as described in claim 2, characterized in that, The step of determining the first warning level corresponding to the wearing comfort score includes: If the wearing comfort score is less than the first preset score threshold and greater than or equal to the second preset score threshold, then the first warning level is determined to be a level one warning level. If the wearing comfort score is less than the second preset score threshold and greater than or equal to the third preset score threshold, then the first warning level is determined to be a level two warning level. If the wearing comfort score is less than the third preset score threshold, then the first warning level is determined to be a level three warning level.

4. The method as described in claim 3, characterized in that, When the first warning level is the third-level warning level, after the step of outputting the warning prompt information based on the first warning strategy, the method further includes: After a preset duration of outputting the warning message, the system checks whether the earphone and the target user's device are in a connected state. If so, disconnect the connection between the headset and the user equipment.

5. The method as described in claim 1, characterized in that, Before the step of inputting the multidimensional wearing data into the pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment, the method further includes: Determine whether the value of each wear data in the multidimensional wear data is less than or equal to the preset safety threshold corresponding to each wear data; If the values ​​of each of the wearing data are less than or equal to the preset safety threshold corresponding to each of the wearing data, then the step of inputting the multidimensional wearing data into the pre-trained comfort prediction model to generate the wearing comfort score of the target user at the current moment is executed. If any of the wearing data exceeds a preset safety threshold, the wearing data exceeding the preset safety threshold is considered abnormal data, and a second warning strategy is generated based on the abnormal data, wherein the warning intensity of the second warning strategy is positively correlated with the value of the abnormal data. The warning message is output based on the second warning strategy.

6. The method as described in claim 5, characterized in that, The step of generating a second early warning strategy based on the abnormal data includes: Determine the second early warning level corresponding to the abnormal data; Based on the preset mapping relationship between warning levels and warning strategies, the warning strategy corresponding to the second warning level is obtained and used as the second warning strategy.

7. The method as described in claim 6, characterized in that, The step of determining the second early warning level corresponding to the abnormal data includes: Determine the percentage by which the value of the abnormal data is greater than the preset security threshold corresponding to the abnormal data; If the percentage is less than the first preset percentage threshold, then the second warning level is determined to be a Level 1 warning level; If the percentage is greater than or equal to the first preset percentage threshold and less than the second preset percentage threshold, then the second warning level is determined to be a level two warning level. If the percentage is greater than or equal to the second preset percentage threshold, then the second warning level is determined to be a Level III warning level.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Based on the multi-dimensional wearing data, wearing adjustment suggestions are generated for the target user; The user device of the target user is controlled to display the wearing adjustment suggestions.

9. A control device, characterized in that, The control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the headphone wearing comfort warning method as described in any one of claims 1 to 8.

10. An earphone, characterized in that, The earphone includes an earphone body, multiple pressure sensors, a bioelectric sensor, a motion sensor, a temperature and humidity sensor, an environmental sensor, and a control device as described in claim 9. Each of the pressure sensors, the bioelectric sensor, the motion sensor, the temperature and humidity sensor, the environmental sensor, and the control device is disposed in the earphone body, and each of the pressure sensors is arranged in a distributed array in the ear contact area of ​​the earphone body.

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