Intelligent health data processing method and device and storage medium

Abstract

The invention relates to the technical field of health data processing, in particular to an intelligent health data processing method and device and a storage medium, and the method comprises the steps: collecting the pure tone hearing threshold of a user under a standard frequency table, constructing a pure tone audiometry map of the user, extracting the hearing threshold features of the user, constructing the hearing sensitive state index of the user, and obtaining the hearing sensitive state index of the user; then determining a basic hearing state of the user; collecting health data of the user in real time; calibrating an otoacoustic emission amplitude and an ABR wave V amplitude of the user; constructing a peripheral-central function coupling index of the user; and constructing an occult hearing loss model based on the peripheral-central function coupling index and the hearing processing disorder index of the user so as to output the occult hearing loss state of the user, and performing hearing health early warning on the user by integrating the occult hearing loss state of the user and the basic hearing state. According to the invention, the efficiency of health data processing is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of health data processing technology, and in particular to an intelligent health data processing method, apparatus, and storage medium. Background Technology

[0002] Currently, occupational noise exposure has become a significant factor leading to hearing loss. Traditional hearing tests primarily rely on subjective methods such as pure-tone audiometry, which, while assessing overall hearing threshold levels, struggle to detect early or latent hearing function abnormalities, particularly lacking effective means to identify central auditory processing disorders and peripheral-central dysregulation. Furthermore, existing technologies often overlook the impact of individual noise exposure history on physiological hearing indicators, leading to biased assessment results and failing to meet the needs of precise health monitoring.

[0003] Meanwhile, hidden hearing loss is easily overlooked because it often appears normal in routine hearing tests. However, patients frequently experience symptoms such as difficulty recognizing speech in noisy environments and increased attentional burden, severely impacting their quality of life. Existing health data processing methods lack the ability to integrate and analyze multimodal physiological and behavioral data, making it difficult to construct dynamic and personalized hearing health assessment models. There is an urgent need for an intelligent processing solution that can integrate auditory physiological indicators, cognitive load, and noise exposure parameters to achieve early warning and tiered intervention for hearing risks. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent health data processing method, device, and storage medium to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, the present invention provides an intelligent health data processing method, comprising:

[0006] Based on the user's pure tone audiogram, the user's hearing threshold characteristics are extracted, and the user's hearing sensitivity index is constructed based on the hearing threshold characteristics to determine the user's basic hearing status.

[0007] Real-time collection of user health data and construction of dynamic calibration coefficients to calibrate the user's otoacoustic emission amplitude and ABR wave V amplitude, and then construction of the user's peripheral-central functional coupling index based on the calibrated otoacoustic emission amplitude and ABR wave V amplitude;

[0008] A hearing processing disorder index is constructed for users, and a hidden hearing loss model is built based on the user's peripheral-central functional coupling index and hearing processing disorder index, thereby outputting the user's hidden hearing loss status.

[0009] Optionally, the process of constructing a user's hearing sensitivity index is as follows:

[0010] Calculate the average pure-tone hearing threshold of the user, denoted as Te;

[0011] Set a hearing threshold YT. When Te is less than YT, the user's overall hearing is considered normal. At this time, calculate the offset ratio of pure tone hearing threshold Ti to Te at each standard frequency and record it as μi. Set μi=|Ti-Te| / Te. When μi is less than the offset threshold, mark the standard frequency corresponding to the pure tone hearing threshold as the damage frequency and use the damage frequency as the user's hearing threshold characteristic.

[0012] When Te is greater than or equal to YT, the user's overall hearing is determined to be abnormal, and a serious hearing loss alarm is sent to the user;

[0013] The continuous damage frequencies are combined into damage frequency intervals, and the average value of the pure tone hearing threshold within the damage frequency interval is calculated and denoted as He. Then, the user's hearing sensitivity index α is constructed by setting the damage threshold, and α is set as (He - damage threshold) / damage threshold.

[0014] Optionally, a hearing sensitivity threshold is set, and the hearing sensitivity index is compared with the hearing sensitivity threshold to determine the basic hearing status. When the hearing sensitivity index is less than the hearing sensitivity threshold, the user's basic hearing status is determined to be normal; when the hearing sensitivity index is greater than or equal to the hearing sensitivity threshold, the user's basic hearing status is determined to be noise-induced hearing loss.

[0015] Optionally, a dynamic calibration coefficient is constructed based on the user's working duration and the user's average working noise intensity. The expression for the dynamic calibration coefficient β is as follows:

[0016] β = 1 / exp[(LEX - L0) / L0];

[0017] LEX = ZQ + lg(t / 8);

[0018] In the formula, LEX represents the cumulative equivalent continuous A-weighted sound level, L0 represents the baseline exposure threshold, ZQ is the user's working average noise intensity, and t is the user's working duration;

[0019] The product of the dynamic calibration coefficient β and the user's otoacoustic emission amplitude and ABR wave V amplitude is used as the calibrated user's otoacoustic emission amplitude and ABR wave V amplitude, respectively.

[0020] Optionally, the process of constructing the user's peripheral-central functional coupling index is as follows:

[0021] PCI=a1×(Zdp-DP) / DP+a2×(ABRV-YV) / YV;

[0022] In the formula, a1 is the external auditory canal weight, a2 is the brainstem nerve weight, a1+a2=1, Zdp is the user's otoacoustic emission amplitude, DP is the otoacoustic emission amplitude threshold, ABRV is the ABR wave V amplitude, and YV is the preset V wave amplitude.

[0023] Optionally, the process of constructing a user's hearing processing disorder index is as follows:

[0024] The system collects the user's speech recognition accuracy, pupil dilation percentage, and average response time in auditory tasks under standard signal-to-noise ratio. Based on the collected results, it constructs the user's auditory processing disorder index, which is denoted as γ. The formula is γ=[TF×FT] / CV, where CV represents the user's speech recognition accuracy, TF represents the user's pupil dilation percentage, and FT represents the user's average response time.

[0025] Optionally, a concealed hearing loss model is constructed based on the user's peripheral-central functional coupling index and auditory processing disorder index: when b1×PCI+b2×γ×β is less than the concealed hearing loss index threshold, the user's hearing loss status is determined to be normal; when b1×PCI+b2×γ×β is greater than or equal to the concealed hearing loss index threshold, the user's hearing loss status is determined to be abnormal.

[0026] Where b1 is the damage weight, b2 is the cognitive compensation weight, and b1+b2=1.

[0027] Optionally, if the user's occult hearing loss is normal and their basic hearing is normal, the user's hearing risk level is determined to be no risk, and no hearing health warning is issued to the user; if the user's occult hearing loss is abnormal and their basic hearing is normal, the user's hearing risk level is determined to be level two risk, and a cognitive compensation warning is sent to the user.

[0028] If a user's basic hearing status is noise-induced hearing loss, the user's hearing risk level is determined to be Level 1, and a serious damage alarm is sent to the user.

[0029] According to another aspect of this application, an intelligent health data processing device is provided, comprising:

[0030] The data processing unit is used to collect the user's pure tone hearing threshold under the standard frequency table and to construct the user's pure tone audiogram based on the pure tone hearing threshold under the standard frequency table.

[0031] The basic state determination unit is used to extract the user's hearing threshold characteristics based on the user's pure tone audiogram, and construct the user's hearing sensitivity index based on the hearing threshold characteristics, thereby determining the user's basic hearing status.

[0032] The dynamic analysis unit is used to collect users' health data in real time and construct dynamic calibration coefficients to calibrate the user's otoacoustic emission amplitude and ABR wave V amplitude. Then, based on the calibrated otoacoustic emission amplitude and ABR wave V amplitude, the user's peripheral-central functional coupling index is constructed.

[0033] The hidden hearing loss analysis unit is used to construct the user's auditory processing impairment index, and to construct a hidden hearing loss model based on the user's peripheral-central functional coupling index and auditory processing impairment index, thereby outputting the user's hidden hearing loss status.

[0034] The integrated early warning unit is used to provide users with hearing health warnings by combining the user's hidden hearing loss status with their basic hearing status.

[0035] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program is used to control the electronic device in which the computer-readable storage medium is located to perform the intelligent health data processing method during runtime.

[0036] Compared with existing technologies, the beneficial effects of this invention are as follows: This solution integrates pure-tone hearing threshold, otoacoustic emissions, ABR wave V amplitude, and cognitive behavioral data to construct a hearing sensitivity index, a peripheral-central functional coupling index, and a hearing processing disorder index. It innovatively establishes a model for occult hearing loss, enabling accurate identification of early or functional hearing abnormalities that are difficult to detect with traditional hearing tests. Simultaneously, the introduction of a dynamic calibration mechanism based on individual noise exposure history significantly improves the personalization and accuracy of the assessment. Furthermore, through comprehensive early warning of multi-dimensional hearing status, it effectively supports tiered intervention, greatly enhancing the intelligence level and clinical practical value of hearing health data processing. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the intelligent health data processing method in this embodiment.

[0039] Figure 2 This is a flowchart illustrating the method for determining the basic hearing status in this embodiment.

[0040] Figure 3 This is a schematic diagram of the intelligent health data processing device provided in this embodiment.

[0041] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this embodiment. Detailed Implementation

[0042] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0043] It should be noted that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of this application, first can also be referred to as second, and similarly, second can also be referred to as first.

[0044] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0045] Specifically, the intelligent health data processing method provided in this application is applied to the health data processing of people exposed to occupational noise, specifically in the process of monitoring hearing loss and identifying hearing processing disorders in people exposed to occupational noise, thus providing an early warning for their health. This invention aims to build an intelligent health processing system that covers multiple population groups, adapts to multiple data types, and is scientifically reproducible. It integrates multi-source heterogeneous health data from different groups (such as occupational and special environment populations) through a standardized process, and uses modular algorithms and parameterized models to generate risk assessments. This system ensures that the analysis process and results can be independently verified, realizing standardization and reproducibility from data to decision.

[0046] To apply the above-mentioned application scenarios, this application provides an intelligent health data processing method, the flowchart of which can be found in the document. Figure 1 As shown, it includes:

[0047] Step S101: Collect the user's pure tone hearing threshold under the standard frequency table, and construct the user's pure tone audiogram based on the pure tone hearing threshold under the standard frequency table.

[0048] It is worth noting that the "user's pure tone hearing threshold under the standard frequency table" collected in this application is the result of the user's most recent test of the pure tone hearing threshold under the standard frequency table, and is not collected in real time; the setting of the standard frequency table can be freely set by those skilled in the art. In this application, it is simply adopted that 250Hz is used as the initial standard frequency, and 250Hz is used as the unit of measurement up to 8 kHz as the standard frequency table; the pure tone hearing threshold is the minimum sound intensity of the same frequency that the user can hear; the pure tone audiometry graph described in this application is a line graph.

[0049] Specifically, by collecting users' pure-tone hearing thresholds at standard frequencies and constructing pure-tone audiograms, it is possible to systematically and visually present users' hearing sensitivity at different frequencies, providing a reliable data foundation for subsequent accurate identification of hearing impairment frequencies and assessment of overall hearing status, and significantly improving the scientific rigor and individualization of hearing health assessment.

[0050] Please continue reading. Figure 1 As shown, the intelligent health data processing method further includes:

[0051] Step S102: Extract the user's hearing threshold features based on the user's pure tone audiogram, and construct the user's hearing sensitivity index based on the hearing threshold features to determine the user's basic hearing status.

[0052] Please see Figure 2 The diagram shown is a flowchart illustrating the basic hearing status determination method provided in this application, including:

[0053] Step S201: Extract the user's hearing threshold features based on the user's pure tone audiogram, and construct the user's hearing sensitivity index based on the hearing threshold features.

[0054] Specifically, in step S201, the process of constructing the user's hearing sensitivity index is as follows:

[0055] Calculate the average pure-tone hearing threshold of the user, denoted as Te;

[0056] Set a hearing threshold YT. When Te is less than YT, the user's overall hearing is considered normal. At this time, calculate the offset ratio of pure tone hearing threshold Ti to Te at each standard frequency and record it as μi. Set μi=|Ti-Te| / Te. When μi is less than the offset threshold, mark the standard frequency corresponding to the pure tone hearing threshold as the damage frequency and use the damage frequency as the user's hearing threshold characteristic.

[0057] When Te is greater than or equal to YT, the user's overall hearing is determined to be abnormal, and a serious hearing loss alarm is sent to the user;

[0058] The continuous damage frequencies are combined into damage frequency intervals, and the average value of the pure tone hearing threshold within the damage frequency interval is calculated and denoted as He. Then, the user's hearing sensitivity index α is constructed by setting the damage threshold, and α is set as (He - damage threshold) / damage threshold.

[0059] For example, the damage threshold in this application is 34dB and the offset threshold is 10%.

[0060] Please continue reading. Figure 2 As shown, the method for determining the basic hearing status also includes:

[0061] Step S202, determining the user's basic hearing status based on the user's hearing sensitivity index, the process is as follows:

[0062] A hearing sensitivity threshold is set, and the hearing sensitivity index is compared with the hearing sensitivity threshold to determine the basic hearing status. When the hearing sensitivity index is less than the hearing sensitivity threshold, the user's basic hearing status is determined to be normal; when the hearing sensitivity index is greater than or equal to the hearing sensitivity threshold, the user's basic hearing status is determined to be noise-induced hearing loss.

[0063] For example, the hearing sensitivity threshold described in this application is 0.174.

[0064] Specifically, by extracting hearing threshold features based on pure-tone audiometry and constructing a hearing sensitivity index, it is possible to effectively distinguish between normal and abnormal overall hearing states, and further identify local frequency damage intervals, thereby achieving sensitive capture of early or mild hearing changes and avoiding the problem of traditional methods relying solely on average hearing thresholds while ignoring frequency-specific damage.

[0065] Please continue reading. Figure 1 As shown, the intelligent health data processing method further includes:

[0066] Step S103: Collect the user's health data in real time, and construct a dynamic calibration coefficient to calibrate the user's otoacoustic emission amplitude and ABR wave V amplitude. Then, construct the user's peripheral-central functional coupling index based on the calibrated otoacoustic emission amplitude and ABR wave V amplitude. The user's health data includes: the user's otoacoustic emission amplitude, ABR wave V amplitude, the user's working time, and the user's average working noise level in decibels.

[0067] Specifically, the real-time acquisition process of the user's otoacoustic emission amplitude and ABR wave V amplitude described in this application can be performed in real time using a head-mounted hearing device.

[0068] Specifically, in step S103, the process of constructing dynamic calibration coefficients and calibrating the user's health data is as follows:

[0069] A dynamic calibration coefficient is constructed based on the user's working hours and the user's average working noise intensity. The expression for the dynamic calibration coefficient β is as follows:

[0070] β = 1 / exp[(LEX - L0) / L0];

[0071] LEX = ZQ + lg(t / 8);

[0072] In the formula, LEX represents the cumulative equivalent continuous A-weighted sound level, L0 represents the baseline exposure threshold, ZQ is the user's working average noise intensity, and t is the user's working duration;

[0073] The product of the dynamic calibration coefficient β and the user's otoacoustic emission amplitude and ABR wave V amplitude is used as the calibrated user's otoacoustic emission amplitude and ABR wave V amplitude, respectively.

[0074] For example, the baseline exposure threshold in this application is 85 dB(A), and the cumulative equivalent continuous A-weighted sound level is the user's standardized noise exposure dose during working days.

[0075] Specifically, in step S103, the process of constructing the user's peripheral-central functional coupling index is as follows:

[0076] PCI=a1×(Zdp-DP) / DP+a2×(ABRV-YV) / YV;

[0077] In the formula, a1 is the external auditory canal weight, a2 is the brainstem nerve weight, a1+a2=1, Zdp is the user's otoacoustic emission amplitude, DP is the otoacoustic emission amplitude threshold, ABRV is the ABR wave V amplitude, YV is the preset V wave amplitude, and PCI is the user's peripheral-central functional coupling index.

[0078] For example, in this application, the value of the otoacoustic emission amplitude threshold is the average otoacoustic emission amplitude of a healthy population of the same age as the user, and the preset V wave amplitude is 0.5μV.

[0079] Specifically, dynamic calibration coefficients are constructed by introducing actual work noise exposure parameters of users (such as working hours and average noise intensity) to perform personalized calibration of otoacoustic emissions and ABR wave V amplitude, effectively eliminating the influence of environmental noise interference on physiological indicators and improving the accuracy and timeliness of peripheral and central auditory function assessment. By fusing the calibrated otoacoustic emission amplitude and ABR wave V amplitude, a peripheral-central functional coupling index is constructed, which can comprehensively reflect the collaborative working state of the cochlea and auditory brainstem pathway, providing key physiological evidence for identifying hidden hearing loss and making up for the limitations of single indicator assessment.

[0080] Please refer to Figure 1 for further details. The intelligent health data processing method also includes:

[0081] Step S104: Construct the user's auditory processing disorder index, and construct a hidden hearing loss model based on the user's peripheral-central functional coupling index and auditory processing disorder index, and then output the user's hidden hearing loss status.

[0082] Specifically, in step S104, the process of constructing the user's hearing processing impairment index is as follows:

[0083] The system collects the user's speech recognition accuracy, pupil dilation percentage, and average response time in auditory tasks under standard signal-to-noise ratio. Based on the collected results, it constructs the user's auditory processing disorder index, which is denoted as γ. The formula is γ=[TF×FT] / CV, where CV represents the user's speech recognition accuracy, TF represents the user's pupil dilation percentage, and FT represents the user's average response time.

[0084] Specifically, the speech recognition accuracy rate of the user in the auditory task under standard signal-to-noise ratio described in this application is the recognition accuracy rate of the user in a speech recognition task, which consists of multiple sub-tasks. The percentage of pupil dilation of the user is the average percentage of pupil dilation of the user during the recognition of each sub-task in the speech recognition task. The average response time of the user is the average response time of the user during the recognition of each sub-task in the speech recognition task.

[0085] It is worth noting that in this application, the average response time of the task is measured in seconds (s), and the numerical values ​​of CV and TF are expressed as dimensionless percentages. At the same time, the user's hearing processing disorder index mentioned in this application is a dimensionless number that does not take into account its physical meaning.

[0086] Specifically, in step S104, the process of determining the user's concealed hearing loss status is as follows:

[0087] A model for concealed hearing loss is constructed based on the user's peripheral-central functional coupling index and auditory processing disorder index: when b1×PCI+b2×γ×β is less than the concealed hearing loss index threshold, the user's hearing loss status is determined to be normal; when b1×PCI+b2×γ×β is greater than or equal to the concealed hearing loss index threshold, the user's hearing loss status is determined to be abnormal.

[0088] Where b1 is the damage weight, b2 is the cognitive compensation weight, and b1+b2=1.

[0089] For example, the threshold value of the concealed hearing loss index in this application is 0.3; meanwhile, the values ​​of the damage weight and the cognitive compensation weight in this application are 0.75 and 0.25, respectively.

[0090] Specifically, by combining speech recognition accuracy, pupil dilation percentage, and task reaction time, an auditory processing disorder index is constructed. This index quantifies users' auditory processing ability from the perspective of cognitive load and auditory perception efficiency, effectively revealing central auditory processing abnormalities that traditional hearing tests cannot detect, and improving the detection rate of hidden hearing problems.

[0091] Please continue reading. Figure 1As shown, the intelligent health data processing method further includes:

[0092] Step S105: Provide the user with a hearing health warning by combining the user's hidden hearing loss status and basic hearing status.

[0093] Specifically, in step S105, the process of issuing a hearing health warning to the user is as follows:

[0094] When a user's hidden hearing loss is normal and their basic hearing is normal, the user's hearing risk level is determined to be no risk, and no hearing health warning is issued to the user; when a user's hidden hearing loss is abnormal and their basic hearing is normal, the user's hearing risk level is determined to be level two risk, and a cognitive compensation warning is sent to the user.

[0095] If a user's basic hearing status is noise-induced hearing loss, the user's hearing risk level is determined to be Level 1, and a serious damage alarm is sent to the user.

[0096] Specifically, the Level 1 risk and Level 2 risk mentioned in this application refer to the risk of irreversible or severe hearing loss and the risk of reversible hearing loss without causing severe hearing loss, respectively.

[0097] Specifically, by integrating basic hearing status with latent hearing loss status, a three-level risk warning mechanism (no risk, level two risk, and level one risk) is implemented. This not only promptly alerts users to potential hearing damage but also distinguishes between reversible compensation warnings and irreversible severe damage alarms, thereby improving the targeting and intervention efficiency of health management.

[0098] Please see Figure 3 As shown, it is a structural schematic diagram of the intelligent health data processing device provided in this application, including:

[0099] The data processing unit is used to collect the user's pure tone hearing threshold under the standard frequency table and to construct the user's pure tone audiogram based on the pure tone hearing threshold under the standard frequency table.

[0100] The basic state determination unit is used to extract the user's hearing threshold characteristics based on the user's pure tone audiogram, and construct the user's hearing sensitivity index based on the hearing threshold characteristics, thereby determining the user's basic hearing status.

[0101] The dynamic analysis unit is used to collect users' health data in real time and construct dynamic calibration coefficients to calibrate the user's otoacoustic emission amplitude and ABR wave V amplitude. Then, based on the calibrated otoacoustic emission amplitude and ABR wave V amplitude, the user's peripheral-central functional coupling index is constructed.

[0102] The hidden hearing loss analysis unit is used to construct the user's auditory processing impairment index, and to construct a hidden hearing loss model based on the user's peripheral-central functional coupling index and auditory processing impairment index, thereby outputting the user's hidden hearing loss status.

[0103] The integrated early warning unit is used to provide users with hearing health warnings by combining the user's hidden hearing loss status with their basic hearing status.

[0104] The intelligent health data processing device provided in this application embodiment can execute the intelligent health data processing method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0105] From a hardware perspective, to enable the intelligent health data processing method to function in a computer, this application also provides a computer-readable storage medium. Please refer to [link to relevant documentation]. Figure 4 As shown, it is disposed within an electronic device, the electronic device comprising:

[0106] The system comprises a processor 1, a memory 2, a communication interface 3, and a bus 4; wherein the processor 1 and the memory 2, and the memory 2 and the communication interface 3, transmit data via the bus 4; the processor is used to process data in the memory and generate commands, the memory is used to store data, the communication interface is used to receive and send data, and the bus is used to realize data transmission between the processor, the memory, and the communication interface.

[0107] In this embodiment, the intelligent health data processing method can be implemented as a runnable computer program. When the computer program is loaded into the processor or into the memory and processed by the processor via the bus, one or more steps of the intelligent health data processing method can be executed.

[0108] Optionally, the processor described in this embodiment can be connected to the computer program through hardware programming or other means to complete the data execution process of the intelligent health data processing method.

[0109] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for intelligent health data processing, the method comprising: The application comprises the following steps: According to the user's pure tone audiogram, the user's hearing threshold characteristics are extracted, and the user's hearing sensitivity state index is constructed according to the hearing threshold characteristics, and then the user's basic hearing state is determined; Real-time collection of user health data and construction of dynamic calibration coefficient to calibrate the user's otoacoustic emission amplitude and ABR wave V amplitude, and then construct the user's peripheral-central function coupling index according to the calibrated otoacoustic emission amplitude and ABR wave V amplitude; Constructing the user's auditory processing disorder index, and constructing the hidden hearing loss model based on the user's peripheral-central function coupling index and auditory processing disorder index, and then outputting the user's hidden hearing loss state.

2. The intelligent health data processing method of claim 1, wherein, The process of constructing the user's hearing sensitivity state index is as follows: Calculate the average value of the user's pure tone threshold, denoted as Te; Set the hearing threshold value YT, when Te is less than YT, determine that the user's overall hearing is normal, at this time, calculate the offset ratio of the pure tone threshold Ti under each standard frequency and Te, and record it as μi, set μi=|Ti-Te| / Te, when μi is less than the offset threshold, mark the standard frequency corresponding to the pure tone threshold as the damage frequency, and take the damage frequency as the user's hearing threshold characteristic; When Te is greater than or equal to YT, it is determined that the user's overall hearing is abnormal, and a hearing damage alarm is sent to the user; Combine the continuous damage frequency into a damage frequency interval, and calculate the average value of the pure tone threshold in the damage frequency interval, denoted as He, and then set the damage threshold to construct the user's hearing sensitivity state index α, set α=(He-damage threshold) / damage threshold.

3. The intelligent health data processing method of claim 2, wherein, Set the hearing sensitivity threshold, and compare the hearing sensitivity state index with the hearing sensitivity threshold to determine the basic hearing state, when the hearing sensitivity state index is less than the hearing sensitivity threshold, it is determined that the user's basic hearing state is normal; When the hearing sensitivity state index is greater than or equal to the hearing sensitivity threshold, it is determined that the user's basic hearing state is noise-induced hearing loss.

4. The intelligent health data processing method of claim 3, wherein, Based on the user's working time and the user's average working noise intensity, the dynamic calibration coefficient is constructed, and the expression of the dynamic calibration coefficient β is as follows: β=1 / exp[(LEX-L0) / L0]; LEX=ZQ+lg(t / 8); In the formula, LEX represents the cumulative equivalent continuous A-weighted sound level, L0 represents the reference exposure threshold, ZQ is the user's average working noise intensity, and t is the user's working time; The product of the dynamic calibration coefficient β and the user's otoacoustic emission amplitude and ABR wave V amplitude is taken as the calibrated user's otoacoustic emission amplitude and ABR wave V amplitude.

5. The intelligent health data processing method of claim 4, wherein, The process of constructing the user's peripheral-central function coupling index is as follows: PCI=a1×(Zdp-DP) / DP+a2×(ABRV-YV) / YV; In the formula, a1 is the external ear canal weight, a2 is the brainstem nerve weight, a1+a2=1, Zdp is the user's otoacoustic emission amplitude, DP is the otoacoustic emission amplitude threshold, ABRV is the ABR wave V amplitude, and YV is the preset V wave amplitude.

6. The intelligent health data processing method of claim 5, wherein, The process of constructing the user's auditory processing disorder index is as follows: The language recognition accuracy of the user in the hearing task under the standard signal-to-noise ratio, the pupil diameter expansion percentage of the user, and the task average reflection time are collected, and a hearing processing disorder index of the user is constructed according to the collection results, and the hearing processing disorder index of the user is denoted as γ, and γ is set as [TF×FT] / CV; in the formula, CV represents the language recognition accuracy of the user, TF represents the pupil diameter expansion percentage of the user, and FT represents the task average reflection time of the user.

7. The intelligent health data processing method of claim 6, wherein, Based on the peripheral-central function coupling index and the hearing processing disorder index of the user, an occult hearing loss model is constructed: when b1×PCI+b2×γ×β is less than an occult hearing loss index threshold, the hearing loss state of the user is determined to be normal; when b1×PCI+b2×γ×β is greater than or equal to the occult hearing loss index threshold, the hearing loss state of the user is determined to be abnormal; Wherein, b1 is a damage weight, b2 is a cognitive compensation weight, and b1+b2=1.

8. The intelligent health data processing method of claim 7, wherein, When the occult hearing loss state of the user is normal and the basic hearing state is normal, it is determined that the hearing risk level of the user is no risk, and no hearing health warning is sent to the user; When the occult hearing loss state of the user is abnormal and the basic hearing state is normal, it is determined that the hearing risk level of the user is a secondary risk, and a cognitive compensation warning is sent to the user; When the basic hearing state of the user is noise-induced hearing loss, it is determined that the hearing risk level of the user is a primary risk, and a serious damage alarm is sent to the user.

9. An intelligent health data processing apparatus applied to the intelligent health data processing method according to any one of claims 1-8, characterized in that, It comprises: A data processing unit is configured to collect the pure tone threshold of the user under a standard frequency table, and construct a pure tone audiogram of the user based on the pure tone threshold under the standard frequency table; A basic state determination unit is configured to extract the hearing threshold characteristics of the user based on the pure tone audiogram of the user, and construct a hearing sensitivity state index of the user based on the hearing threshold characteristics, and then determine the basic hearing state of the user; A dynamic analysis unit is configured to collect the health data of the user in real time, and construct a dynamic calibration coefficient to calibrate the otoacoustic emission amplitude and ABR wave V amplitude of the user, and then construct a peripheral-central function coupling index of the user based on the calibrated otoacoustic emission amplitude and ABR wave V amplitude; An occult loss analysis unit is configured to construct a hearing processing disorder index of the user, and construct an occult hearing loss model based on the peripheral-central function coupling index and the hearing processing disorder index of the user, and then output the occult hearing loss state of the user; A comprehensive warning unit is configured to comprehensively warn the user of the hearing health based on the occult hearing loss state and the basic hearing state of the user.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent health data processing method of any one of claims 1-8 when running.

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