Human health risk assessment and management system by noise

Abstract

The invention discloses a noise-to-human health risk assessment and management system, relates to the technical field of noise health risk management, and aims to solve the technical problem that existing noise health risk assessment cannot be dynamically associated with quantitative assessment. The construction module is used for constructing a three-dimensional evaluation model taking the noise exposure level as the X axis, the human body physiological index fluctuation degree as the Y axis and the health hazard index as the Z axis according to the noise exposure level and the human body physiological index data set; the noise exposure level acquisition module is responsible for acquiring noise exposure data; and the health hazard index acquisition module is responsible for acquiring Z values corresponding to health hazard indexes, and inputting the acquired noise exposure data and the health hazard indexes as X-axis and Z-axis coordinates into the three-dimensional evaluation model respectively. The method has the advantages of realizing dynamic association quantitative evaluation of the health risk and improving the accuracy of the evaluation result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of noise health risk management, more particularly to a noise human health risk assessment and management system. BACKGROUND

[0002] At present, in the existing noise health risk assessment technology, there is a key problem: it is difficult to realize the dynamic correlation quantitative evaluation of noise exposure level and human physiological index fluctuation. The existing evaluation method is mostly based on static noise data or single physiological index for risk judgment, which cannot comprehensively consider the change of noise intensity with time, the real-time fluctuation difference of multiple physiological indexes (such as blood pressure, heart rate, blood oxygen saturation, etc.) of human body, and the deviation degree of individual physiological index from group benchmark level, resulting in insufficient accuracy of health risk assessment results, and difficulty in accurately reflecting the real-time influence of noise on human health, thereby affecting the timeliness and effectiveness of subsequent risk intervention. In view of this, we propose a noise human health risk assessment and management system. SUMMARY

[0003] The purpose of the present application is to provide a noise human health risk assessment and management system to solve the technical problem that the existing noise health risk assessment cannot be dynamically correlated and quantitatively evaluated.

[0004] To solve the above technical problems, the present application provides the following technical scheme: a noise human health risk assessment and management system, comprising: A human health risk assessment model establishment module is used to construct a three-dimensional evaluation model with noise exposure level as X-axis, human physiological index fluctuation degree as Y-axis, and health hazard index as Z-axis according to noise exposure level and human physiological index data set; A noise exposure level acquisition module is responsible for collecting noise exposure data; A health hazard index acquisition module is responsible for collecting Z values corresponding to health hazard indexes, and inputs the collected noise exposure data and health hazard indexes as X-axis and Z-axis coordinates into the three-dimensional evaluation model, respectively, to obtain physiological gap and health gap between human physiological index standard value and measured value after model operation; A health hazard degree determination module is used to receive physiological gap and health gap output by the three-dimensional evaluation model, and simultaneously combine risk coefficient calculated by the evaluation module to comprehensively determine the final hazard grade of human health through weight rules set by the weight distribution module; An evaluation module includes a monitoring module and a control module, the monitoring module is used to track real-time changes of relevant data, and the control module is responsible for calculating real-time second risk coefficient; An early warning module is used to generate early warning information according to health risk grade and related indexes, and a warning module is used to execute early warning operation; The alarm module is configured to execute a graded alarm operation according to the risk coefficient output by the evaluation module, the final health level, and the instruction of the early warning module.

[0005] Preferably, the weight distribution module calculates the final health level to comprehensively reflect the joint influence of the physiological-health gap and the risk coefficient. The physiological-health gap comprehensive value is obtained by weighting the physiological gap and the health gap. The control module calculates a real-time second risk coefficient to dynamically reflect the change in health risk. The physiological gap reflects the difference in physiological indicator fluctuation between the noise exposure and the non-noise exposure. The health gap reflects the deviation degree of the individual physiological indicator from the group benchmark level.

[0006] Preferably, the human physiological indicators include blood pressure, heart rate, blood oxygen saturation, respiratory rate, and body temperature. The human physiological indicator dataset includes measurement data of blood pressure, heart rate, blood oxygen saturation, respiratory rate, and body temperature, and the model establishment module constructs a three-dimensional evaluation model through multi-step analysis. The measurement values of the physiological indicators of the subjects under different noise exposure levels are collected, and the noise exposure level is taken as the X-axis coordinate. The statistical analysis is performed on each group of physiological indicator data, the standard deviation is calculated as the Y-axis coordinate, and the health hazard indicator is calculated as the Z-axis coordinate. The value rule of the Y-axis data is set for different noise exposure levels: when the noise exposure level is lower than 30 dBA, the Y-axis is the standard deviation of the physiological indicator under the non-noise exposure; when the noise exposure level reaches or exceeds 30 dBA, the Y-axis is the physiological gap reflecting the fluctuation difference of the physiological indicator. .

[0007] Preferably, the measurement values of the physiological indicators of the subjects and the corresponding benchmark values are obtained by mean calculation to eliminate the influence of single measurement error.

[0008] Preferably, the system can run on both PC and mobile phone terminals to adapt to different use scenarios. When the subject is at the noise source position, the system runs on the PC terminal, the PC is directly connected with the noise sensor to realize real-time collection of noise data. When the subject is away from the noise source, the system runs through the mobile phone APP, the noise sensor is connected with the mobile phone to ensure effective transmission of noise data. The noise sensing device is composed of a sound level meter, an audio collector, a voltage regulator, and a signal transmitting device. The voltage regulator is used to adjust the working parameters of the sound level meter, and the signal transmitting device is responsible for converting the noise waveform collected by the sound level meter into a digital signal and transmitting it to the corresponding terminal.

[0009] Preferably, the mobile phone APP comprises multiple functional modules, which cooperatively realize noise health risk assessment and management: Noise exposure intensity detection module: Fourier algorithm is adopted to process the received digital signal to generate a time series of noise exposure intensity to reflect the change characteristics of noise over time; Signal processing module: with noise time domain signal display, frequency domain signal display and Fourier transform function, it is connected with the acoustic parameter acquisition module to provide support for subsequent parameter calculation; Human physiological index acquisition and analysis module: physiological indexes such as blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature are collected, the mean values of various indexes are calculated, and the corresponding frequency spectrum is generated through Fourier algorithm; Database and risk assessment module: the database stores various collected and calculated data, and the risk assessment module divides the risk level based on these data and displays the fluctuation degree of physiological indexes and health hazards through the display module.

[0010] Preferably, the acoustic parameter acquisition module comprises multiple functional units to realize different acoustic parameter processing: Loudness module: the loudness of sound pressure level signal is calculated through formula to reflect the subjective feeling of human ear to noise; Sound pressure level module: the sound pressure level of noise time domain signal is calculated to objectively quantify the noise intensity; Physiological index waveform display module: time domain signal waveform and frequency spectrum signal waveform of blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature are displayed to intuitively present the change characteristics of physiological indexes.

[0011] Preferably, a fixed risk coefficient is preset in the monitoring module of the evaluation module The control module updates the risk coefficient according to the following rules to dynamically adjust the sensitivity of risk assessment: The difference between the second risk coefficient and the third risk coefficient is calculated in real time, and when the difference is greater than or equal to the fixed risk coefficient A new risk coefficient is generated, and the third risk coefficient before the generation date is deleted; The evaluation module comprises a verification module to evaluate the newly generated risk coefficient, calculate a verification index The warning module triggers basic warning, moderate warning or emergency warning according to the size of value.

[0012] Preferably, the alarm module executes corresponding grading response strategy according to different health risk levels triggered by the warning module to realize precise risk intervention: Basic warning: Triggered when the noise exposure intensity is in the range of 20-30 dBA, noise reduction suggestions are pushed and real-time noise spectrum curve and physiological index fluctuation curve are displayed; Moderate warning: Triggered when the noise exposure intensity is in the range of 30-55 dBA, a red warning window will pop up on the mobile APP and be accompanied by vibration. Emergency warning: Triggered when the noise exposure intensity reaches or exceeds 55 dBA, the phone will ring continuously and the light will flash.

[0013] A method for assessing and managing the health risks of noise includes the following steps: S1: Construct a three-dimensional assessment model. Based on the noise exposure level and human physiological index dataset, establish a three-dimensional assessment model with the noise exposure level as the X-axis, the fluctuation of human physiological indexes as the Y-axis, and the health hazard indexes as the Z-axis. S2: Collect relevant data, obtain noise exposure data as the X-axis coordinate, collect the Z-values ​​corresponding to health hazard indicators as the Z-axis coordinate, input the two into the three-dimensional assessment model, and obtain the physiological gap and health gap. S3: Determine the health hazard level. Receive the physiological and health gaps output by the three-dimensional assessment model, combine them with the risk coefficient calculated by the assessment module, and determine the final health hazard level through the weighting rules set by the weighting allocation module. S4: Dynamically update the risk coefficient, track relevant data changes in real time, calculate the real-time second risk coefficient, and update the risk coefficient according to preset rules; S5: Generate early warning information based on the level of health hazard and related indicators; S6: Execute graded alarms. Based on the risk coefficient, the final health hazard level, and the warning information, execute the corresponding graded alarm operation.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a three-dimensional assessment model with noise exposure level as the X-axis, human physiological index fluctuation as the Y-axis, and health hazard index as the Z-axis. Combined with a dynamic risk coefficient update mechanism, it realizes a multi-dimensional dynamic correlation quantitative assessment of noise exposure and human physiological index fluctuation, accurately reflects the real-time risk of noise to human health, and effectively solves the problem of the inability to dynamically correlate and quantify the assessment in the prior art.

[0015] 2. This invention also ensures efficient acquisition and transmission of noise data and physiological index data in different scenarios by adapting to both PC and mobile APP terminals and combining the flexible data transmission design of the noise sensing device, thus solving the problem of untimely and incomplete data collection in dynamic correlation assessment.

[0016] 3. This invention also transforms precise risk assessment results into specific and actionable intervention measures through a tiered early warning and targeted alarm response strategy, thus solving the problems of unclear risk intervention measures and ambiguous response levels after dynamic quantitative assessment. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0018] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0019] Example 1, such as Figure 1 As shown, the present invention provides a noise risk assessment and management system for human health, comprising: The human health risk assessment model building module is used to construct a three-dimensional assessment model based on noise exposure level and human physiological indicator dataset, with noise exposure level as the X-axis, human physiological indicator fluctuation degree as the Y-axis, and health hazard indicator as the Z-axis. It realizes quantitative assessment of health risks through multi-dimensional parameter correlation. The noise exposure level acquisition module is responsible for collecting noise exposure data; The health hazard index acquisition module is responsible for collecting the Z-values ​​corresponding to the health hazard indexes. The collected noise exposure data and health hazard indexes are used as X-axis and Z-axis coordinates, respectively, and input into the three-dimensional evaluation model. After model calculation, the physiological and health gaps between the standard values ​​and measured values ​​of human physiological indicators are obtained. The health hazard determination module receives the physiological and health gaps output by the three-dimensional assessment model. It also combines the risk coefficients calculated by the assessment module with the weighting rules set by the weighting allocation module to comprehensively determine the final hazard level to human health. The assessment module includes a monitoring module and a control module. The monitoring module is used to track relevant data changes in real time, while the control module is responsible for calculating the real-time second risk coefficient to dynamically reflect the changing trend of health risks. The early warning module is used to generate early warning information based on the health risk level and related indicators, and to link the alarm module to execute the early warning operation. The alarm module is used to execute tiered alarm operations based on the risk coefficient, final health level output by the assessment module, and instructions from the early warning module, to ensure timely transmission of risk information.

[0020] In an embodiment of the present invention, the weight allocation module calculates the final health level using a formula to comprehensively reflect the combined impact of the physiological-health gap and the risk coefficient. The formula for calculating the final health level is: ; wherein, represents a physiological-health gap comprehensive value, which is a value obtained by comprehensively considering the physiological gap and the health gap, and is used for subsequent calculation of the final health level; represents a real-time second risk coefficient, which dynamically reflects the health risk of the human body under the current situation affected by noise; The formula is used to comprehensively evaluate the final health level of the human body in the noise environment. By giving the physiological-health gap comprehensive value a weight of 0.7, and the real-time second risk coefficient a weight of 0.3, and then weighting and summing the two, the comprehensive influence of the physiological-health gap and the real-time risk coefficient on the final health level is embodied, and the importance of the physiological-health gap is highlighted; The physiological-health gap comprehensive value is obtained by weighting the physiological gap and the health gap, and the calculation formula is: ; wherein, represents a physiological gap, which reflects the difference degree of the physiological index fluctuation under the noise exposure and the non-noise exposure, represents a health gap, which reflects the deviation degree of the individual physiological index from the group reference level; The formula is used to obtain a value that can comprehensively reflect the physiological gap and the health gap, wherein the physiological gap has a weight of 0.6, has a weight of 0.4, and the influence of the two gaps on health is comprehensively considered by weighting and summing, and the role of the physiological gap is highlighted; The control module calculates the real-time second risk coefficient by using the formula, so as to dynamically reflect the change of the health risk: ; wherein, and represent weight coefficients, which are used to give different importance degrees to the daily exercise frequency standardized value and the health hazard degree quantitative value respectively when calculating the real-time second risk coefficient, wherein, ,

[0021] represents a daily exercise frequency standardized value, which reflects the proportional relationship between the actual exercise frequency of the individual and the recommended exercise frequency, and the value range is 0-1; represents a health hazard degree quantitative value, which is a quantitative representation of the health hazard degree, and the value range is 0-100, and is obtained by weighting the physiological gap and the health gap; The formula is used to dynamically calculate the real-time second risk coefficient, and is a weight coefficient, and each daily exercise frequency normalized value and health hazard degree quantitative value are weighted and summed to obtain a real-time second risk coefficient, so as to reflect the comprehensive influence of exercise and health hazard degree on risk; The physiological gap reflects the difference in physiological index fluctuation under noise exposure and non-noise exposure, and the calculation formula is: ; Wherein, represents the standard deviation of the physiological index under noise exposure, reflecting the fluctuation of the human physiological index under the noise environment; represents the standard deviation of the physiological index under non-noise exposure, reflecting the normal fluctuation of the human physiological index under non-noise environment; The formula calculates the absolute difference between the standard deviation of the physiological index under noise exposure and the standard deviation of the physiological index under non-noise exposure , and then divides the standard deviation of the physiological index under non-noise exposure to obtain the physiological gap, so as to reflect the influence degree of noise on the physiological index fluctuation; The health gap reflects the deviation degree of individual physiological index from the group reference level, and the calculation formula is: ; Wherein, represents the measured value of the individual physiological index, that is, the actual measured value of the individual physiological index; represents the group reference value, which is the average level reference value of a certain physiological index in the group; represents the standard deviation of the group reference value, reflecting the dispersion degree of the physiological index in the group.

[0022] In the embodiments of the present application, the human physiological index covers blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature.

[0023] The human physiological index data set contains measurement data of blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature, and the model establishing module constructs a three-dimensional evaluation model through multi-step analysis: The measurement values of various physiological indexes of the subjects under different noise exposure levels are collected, and the noise exposure level is taken as the X-axis coordinate; The standard deviation of each group of physiological index data is calculated as the Y-axis coordinate, and the health hazard index is calculated as the Z-axis coordinate, wherein the number of subjects involved is represented by ; The following rules were set for the Y-axis data to reflect different noise exposure levels: when the noise exposure level is below 30 dBA, the Y-axis represents the standard deviation of physiological indicators under no noise exposure conditions; when the noise exposure level reaches or exceeds 30 dBA, the Y-axis represents the physiological difference reflecting the fluctuation of physiological indicators. ; The formula for calculating the standard deviation of physiological indicators is: ; in, The sample standard deviation of a physiological indicator is a statistic that describes the dispersion of a set of physiological indicator measurements. The larger the value, the greater the difference between the measurements and the more obvious the fluctuation. It indicates the number of measurements, that is, the total number of times a certain physiological indicator is measured, reflecting the sample size of the data; Indicates the first The measurement value, that is, the value at the th measurement point. The specific values ​​of the physiological indicators obtained in this measurement; The mean of the measured values ​​is the average of the sum of all measured values ​​divided by the number of measurements, and it reflects the central tendency of this set of measured values. This formula is used to calculate the dispersion of a set of physiological index measurements. First, it calculates the dispersion of each measurement. The mean of this set of measurements The differences were calculated, and these differences were squared to eliminate positive and negative effects. Then, all squared differences were summed and divided by the number of measurements. Subtract 1 to get the sample variance. Finally, take the square root of the sample variance to get the sample standard deviation. Through this series of calculations, the fluctuation of physiological index measurements around the mean can be quantitatively reflected, providing data support for subsequent assessment of the impact of noise on human physiological indicators. By clearly defining human physiological indicators that are closely related to health, such as blood pressure and heart rate, and by including measurement data for these indicators in the dataset, comprehensive and crucial raw data support is provided for the entire health risk assessment module, ensuring the relevance and specificity of the assessment. In terms of model construction, a three-dimensional assessment model was constructed through multi-step analysis. The noise exposure level, the standard deviation of physiological indicators, and the health hazard indicators were used as the X, Y, and Z axis coordinates, respectively, realizing the organic combination of multi-dimensional parameters. This three-dimensional model can more intuitively and comprehensively present the relationship between noise exposure level and changes in human physiological indicators, which helps to clearly discover the potential patterns. For the setting of the Y-axis data value rule, considering the difference in the influence of different noise exposure levels on the human body, when the noise exposure level is lower than 30dBA, the standard deviation of the physiological index under the condition of no noise exposure is taken as the Y-axis data, and when reaching or exceeding 30dBA, the physiological gap , so that the model can flexibly adjust the parameters according to the different noise intensities, and the adaptability of the model to different noise environments and the accuracy of the evaluation are improved; The application of the physiological index standard deviation calculation formula can accurately quantify the dispersion degree of the physiological index measurement value. This quantification result is not only an important data source for the Y-axis of the three-dimensional model, but also can provide reliable quantitative basis for subsequent analysis of the influence of noise on the fluctuation of physiological indexes, which helps to more scientifically judge the risk degree of noise on human health, and lays a solid foundation for the system to realize accurate health risk assessment and management.

[0024] In the embodiments of the present application, the measurement values and the corresponding reference values of each physiological index of the subjects are obtained by mean calculation, so as to eliminate the influence of single measurement error; The calculation formula of the blood pressure measurement value and the reference value is: , ; Among them, represents the individual blood pressure measurement mean value, which is the value obtained by averaging the multiple blood pressure measurement results of a certain individual, reflecting the average level of the blood pressure of the individual; represents the measurement times, that is, the total number of measurements of a certain physiological index of the individual; represents the blood pressure measurement value of the i th time, which is the specific blood pressure value obtained in the i th measurement; represents the group blood pressure reference value, which is the value obtained by averaging the individual blood pressure measurement mean values of multiple subjects, reflecting the average level of the blood pressure of the group, as a reference standard; represents the number of subjects, that is, the total number of people participating in data collection; represents the blood pressure measurement mean value of the j th subject, which is the value obtained by averaging the multiple blood pressure measurement results of the j th subject; represents the blood pressure measurement value of the i th time of the j th subject, which is the specific blood pressure value obtained in the i th measurement of the j th subject; The formula is used to calculate the measurement values and the corresponding reference values of each physiological index of the subjects. Taking blood pressure as an example, the calculation logic of the individual blood pressure measurement mean value is to add and sum the multiple blood pressure measurement values , and then divide by the measurement times ​​​, the errors in single measurement can be eliminated by averaging, and the actual level of the individual's physiological indicator can be more accurately reflected; Group blood pressure reference value The calculation logic is: first, the mean value of each subject's blood pressure measurement is obtained , the sum of these mean values is summed up, and then divided by the number of subjects Thus, the average level of the physiological indicator at the group level is obtained as a reference standard for measuring whether the individual indicator is normal; The measurement values and reference values of heart rate, blood oxygen saturation, respiratory rate and body temperature are calculated in the same way as described above, respectively represented as: , ; , ; Among them, The individual heart rate measurement mean value is the value obtained by averaging the multiple heart rate measurement results of a certain individual, reflecting the average level of the individual's heart rate; The first heart rate measurement value is the specific heart rate value obtained in the first measurement; The group heart rate reference value is the value obtained by averaging the individual heart rate measurement mean values of multiple subjects, reflecting the average level of the group heart rate, as a reference standard; The first heart rate measurement value is the specific heart rate value obtained in the first measurement; The group heart rate reference value is the value obtained by averaging the individual heart rate measurement mean values of multiple subjects, reflecting the average level of the group heart rate, as a reference standard; The individual blood oxygen saturation measurement mean value is the value obtained by averaging the multiple blood oxygen saturation measurement results of a certain individual, reflecting the average level of the individual's blood oxygen saturation; The first blood oxygen saturation measurement value is the specific blood oxygen saturation value obtained in the first measurement; The group blood oxygen saturation reference value is the value obtained by averaging the individual blood oxygen saturation measurement mean values of multiple subjects, reflecting the average level of the group blood oxygen saturation, as a reference standard; The first blood oxygen saturation measurement value is the specific blood oxygen saturation value obtained in the first measurement; The group blood oxygen saturation reference value is the value obtained by averaging the individual blood oxygen saturation measurement mean values of multiple subjects, reflecting the average level of the group blood oxygen saturation, as a reference standard; The first blood oxygen saturation measurement value is the specific blood oxygen saturation value obtained in the first measurement; The first blood oxygen saturation measurement value is the specific blood oxygen saturation value obtained in the first measurement;​​​​ From the perspective of data accuracy, by measuring multiple physiological indicators of the subjects and taking the average to obtain individual measurement values, random errors that may exist in single measurement can be effectively eliminated, and the obtained individual physiological indicator data is closer to the true level, providing a reliable individual data basis for subsequent risk assessment; In the determination of the group reference value, by taking the average of the individual measurement values of multiple subjects and combining the authoritative white paper published by the National Health Commission, the group reference value is more representative and authoritative. This reference value provides a scientific reference standard for judging whether the individual physiological indicators deviate from the normal range, which helps to accurately identify the abnormal changes of individual physiological indicators in a noisy environment; The unified average calculation method is applied to multiple physiological indicators such as blood pressure and heart rate, ensuring the consistency and standardization of data processing, and facilitating unified analysis and comparison of various indicators by the system. At the same time, the standard deviation of physiological indicators is statistically analyzed by SPSS software, further ensuring the reliability and scientificity of the data, providing strong support for the accuracy and effectiveness of the entire noise risk assessment module on human health, making the evaluation results more convincing and valuable; The standard deviation of physiological indicators is statistically analyzed by SPSS software to ensure the reliability of the data; the group reference value is determined by referring to the "China Residents Physiological Indicator White Paper" 2023 version published by the National Health Commission.

[0025] In the embodiments of the present application, the system can run on both PC and mobile phone terminals, adapting to different use scenarios: When the subject is at the noise source location, the system runs on the PC terminal, and the PC is directly connected with the noise sensor to realize real-time collection of noise data; When the subject is away from the noise source, the system runs through the mobile phone APP, and the noise sensor is connected with the mobile phone to ensure effective transmission of noise data; The noise sensing device is composed of a sound level meter, an audio collector, a voltage regulator and a signal transmitting device. The voltage regulator is used to adjust the working parameters of the sound level meter, and the signal transmitting device is responsible for converting the noise waveform collected by the sound level meter into a digital signal and transmitting it to the corresponding terminal.

[0026] In the embodiments of the present application, the mobile phone APP contains multiple functional modules, which cooperatively realize noise health risk assessment and management: Noise exposure intensity detection module: Fourier algorithm is used to process the received digital signal to generate a time series of noise exposure intensity, reflecting the characteristics of noise change over time, and the formula is ; Signal processing module: with noise time domain signal display, frequency domain signal display and Fourier transform function, connected with acoustic parameter acquisition module, providing support for subsequent parameter calculation; Human physiological index acquisition and analysis module: collect physiological indexes such as blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature, calculate the mean value of each index, and generate the corresponding frequency spectrum through Fourier algorithm; Database and risk assessment module: the database stores various collected and calculated data, and the risk assessment module divides the risk level based on these data, and displays the fluctuation degree and health hazard of physiological indexes through the display module.

[0027] The Fourier transform formula of noise signal is: ; Among them, represents the time series of noise exposure intensity, which is a function of time , describes the change of noise exposure intensity with time; represents the amplitude, which corresponds to the sound pressure level in the time series of noise exposure intensity, reflecting the strength of the noise; represents the frequency, which is the number of vibrations per second of the noise signal, determining the pitch of the noise; represents the time, which is an independent variable describing the change of noise signal; represents the phase angle, which is used to determine the starting position of the sine function at time, affecting the initial state of the signal; represents the frequency spectrum of the noise signal, which is the result of Fourier transform, describing the distribution of the noise signal at different frequencies; represents the complex exponential function, which is the kernel function in Fourier transform, used to realize the conversion from time domain to frequency domain, where is the imaginary unit; From the perspective of noise signal processing, the time series formula generated by the noise exposure intensity detection module can accurately depict the characteristics of noise changing with time, allowing the staff to intuitively understand the fluctuation of noise in the time domain, providing a basis for judging the stability of noise; The Fourier transform function of the signal processing module converts the time domain signal into the frequency domain signal, successfully realizing the analysis and conversion of noise signal from time dimension to frequency dimension. This makes the characteristics of different frequency components in the noise appear, which helps to identify specific frequency noise that is more harmful to human health, providing an important reference for subsequent targeted protective measures; The human physiological index acquisition and analysis module collects multiple physiological indexes and generates a frequency spectrum, and in combination with frequency spectrum analysis of noise signals, an association between noise frequency and physiological index change can be established, so that the mechanism of the influence of noise on human health can be further explored; The database and the risk assessment module work together to realize effective storage of data and scientific division of risk levels, and through the display module, relevant information is displayed intuitively, so that users can clearly understand the physiological index fluctuation and health hazard situation of themselves, the practicability and ease of use of the system are improved, and comprehensive and efficient technical support is provided for the assessment and management of noise health risks.

[0028] In the embodiment of the present application, the acoustic parameter acquisition module comprises a plurality of functional units, each of which realizes different acoustic parameter processing: Loudness module: the loudness of the sound pressure level signal is calculated by formula to reflect the subjective feeling of the human ear to the noise, and the formula is: Sound pressure level module: the time domain signal of the noise is processed to calculate the sound pressure level of the noise to objectively quantify the noise intensity; Physiological index waveform display module: the time domain signal waveform and the frequency spectrum signal waveform of blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature are displayed to intuitively present the change characteristics of the physiological indexes.

[0029] The calculation formula of the sound pressure level is: Among them, represents the loudness of the sound pressure level signal, reflecting the subjective feeling degree of the human ear to the noise; represents the whisper loudness, which is a basic reference value for calculating the loudness; , , are constant coefficients, respectively , ; , which is used to calculate the loudness in combination with the frequency ; represents the sound pressure level, which is a physical quantity for objectively quantifying the noise intensity; represents the effective sound pressure, which is the root mean square value of the sound pressure in a period of time, reflecting the effective value of the sound pressure; represents the reference sound pressure, and the value is Pa, which is the reference value for calculating the sound pressure level; ​​From the perspective of noise parameter quantization, the loudness module calculates the loudness through a specific formula, considers the influence of frequency on the subjective perception of the human ear, can more accurately reflect the actual perception of the human ear to noise, provides a key parameter for evaluating the influence of noise on human comfort, and makes the evaluation more in line with the subjective experience of the human body; The sound pressure level calculated by the sound pressure level module objectively quantifies the intensity of the noise, provides a clear physical quantity basis for judging whether the noise is over-standard and the over-standard degree, and facilitates objective evaluation and management of the noise; The physiological index waveform display module directly displays the time domain and frequency spectrum waveforms of each physiological index, so that users and relevant personnel can clearly observe the changes of the physiological index with time and frequency; in combination with acoustic parameters such as loudness and sound pressure level, the correlation between different acoustic parameters and changes in physiological indexes can be deeply analyzed, which helps to reveal the rules of the influence of noise on the human body; Overall, the various functional units of the acoustic parameter acquisition module work together to not only achieve comprehensive acquisition of subjective and objective parameters of noise, but also assist in analyzing the influence of noise on the human body through physiological index waveform display, thereby providing strong support for the system to accurately evaluate the health risk of noise and develop effective protection and intervention measures, and improving the scientificity and practicality of the system.

[0030] In the embodiments of the present application, the monitoring module of the evaluation module is pre-set with a fixed risk coefficient The control module updates the risk coefficient according to the following rules to dynamically adjust the sensitivity of risk assessment: The difference between the second risk coefficient and the third risk coefficient is calculated in real time, and when the difference is greater than or equal to the fixed risk coefficient A new risk coefficient is generated, and the third risk coefficient before the generation date is deleted; The evaluation module includes a verification module that evaluates the newly generated risk coefficient, calculates a verification index The warning module triggers a basic warning, a moderate warning, or an emergency warning according to the value of ; The third risk coefficient is the real-time second risk coefficient of the previous period, and the calculation formula is: ; The calculation formula of the verification index is: ; The noise exposure correction coefficient is determined by the ratio of the noise exposure level to the reference level: ; wherein, represents the verification index, which is used to evaluate the newly generated risk coefficient, and the value determines the level of the triggered warning; represents a noise exposure correction coefficient, reflecting the deviation degree of the real-time noise exposure level relative to the noise reference level; represents a physiological index deviation degree, reflecting the deviation degree of the individual physiological index relative to the reference value; represents the real-time noise exposure level, i.e. the noise intensity measured at the current time; represents the noise reference level, taking the value of 30dBA, which is the reference standard for measuring whether the real-time noise exposure level deviates; represents a fixed risk coefficient, which is a preset threshold for judging whether to generate a new risk coefficient; From the risk coefficient updating mechanism, by presetting the fixed risk coefficient , and taking whether the difference between the second risk coefficient and the third risk coefficient reaches the threshold as the judgment standard for generating a new risk coefficient, the dynamic updating of the risk coefficient is realized. This mechanism can timely reflect the changes of health risks, avoid the evaluation lag caused by the long-term invariability of the risk coefficient, and improve the sensitivity of the system to the changes of risks; The calculation of the verification index comprehensively considers the noise exposure correction coefficient and the physiological index deviation degree, and through reasonable weight distribution, it highlights the influence of the external factor of noise exposure and also takes into account the role of the internal factor of physiological index, making the evaluation of the new risk coefficient more comprehensive and objective. Based on the verification index, different levels of early warning modules are triggered, which can take corresponding intervention measures according to the different risk levels, realizing the precision and grading of early warning; The setting and updating of the third risk coefficient provide historical reference basis for the comparison and updating of the risk coefficient, enabling the system to dynamically adjust the risk assessment based on historical data, further improving the accuracy and reliability of risk assessment. Overall, this technical solution enhances the dynamic monitoring and evaluation ability of the system for noise health risks through a scientific risk coefficient updating mechanism and verification method, provides a strong guarantee for timely and effective early warning and intervention, and improves the practicality and safety of the system.

[0031] In the embodiments of the present application, the alarm module executes corresponding grading response strategies according to different health risk levels triggered by the early warning module, to realize precise risk intervention: Basic early warning: triggered when the noise exposure intensity is in the range of 20-30dBA, push noise reduction suggestions, and display real-time noise spectrum curve and physiological index fluctuation curve; Moderate warning: triggered when the noise exposure intensity is in the range of 30-55 dBA, the phone APP pops up a red warning window with vibration prompt, showing the location of the nearest noise source and the avoidance route, and sending a reminder message to 1 preset emergency contact; Emergency warning: triggered when the noise exposure intensity reaches or exceeds 55 dBA, the phone rings continuously with flashing lights, automatically dials 2 preset emergency contacts and sends a help message containing positioning information, and synchronously pushes the user's real-time physiological index data to the nearby community medical station.

[0032] The risk coefficient threshold calculation formula of each level of warning is: , , ; Among them, represents the risk coefficient threshold of the basic warning, when the real-time risk coefficient reaches or exceeds this value, the basic warning is triggered; represents the risk coefficient threshold of the moderate warning, when the real-time risk coefficient reaches or exceeds this value, the moderate warning is triggered; represents the risk coefficient threshold of the emergency warning, when the real-time risk coefficient reaches or exceeds this value, the emergency warning is triggered; , and represent the threshold coefficient, which is used to distinguish the risk degree of different warning levels, for example, it can take values such as 1.2, 1.5, 2.0, etc., the values increase in turn, reflecting the upgrade of the warning level; represents the first risk coefficient, which is the initial risk coefficient preset by the system, serving as the basic reference value for the threshold calculation of each level of warning; Through this formula, the first risk coefficient is associated with the risk degree of each level of warning, so that the threshold setting is based on the initial risk assessment benchmark, and the risk level difference of different warning levels is reflected through the difference of the threshold coefficient, thus providing clear judgment basis for the graded response of the alarm module; From the accuracy of the graded response, by setting different risk coefficient thresholds and corresponding noise exposure intensity ranges, the alarm module can take differentiated response measures according to the difference of the risk degree. The basic warning reminds the user to pay attention to the noise impact in a mild way, the moderate warning helps the user to avoid risks through more eye-catching prompts and auxiliary information, and the emergency warning takes strong warning and linkage measures to ensure the safety of the user, realizing the progressive response from slight prompt to emergency intervention, and improving the pertinence and effectiveness of risk response. The threshold value calculation formula of the risk coefficient of each level of early warning is determined based on the first risk coefficient and a threshold coefficient, so that the threshold setting has a clear reference basis and adjustability. By changing the threshold coefficient, the risk assessment requirements in different scenarios can be flexibly adapted, thereby enhancing the adaptability and flexibility of the system. Meanwhile, the early warning level is corresponded to the noise exposure intensity range, so that the early warning is more intuitive and easy to understand, and it is convenient for users and relevant personnel to quickly understand the severity of the risk. The hierarchical response strategy of the alarm module in the technical scheme not only can timely deliver risk information to the user, but also can provide practical response suggestions and auxiliary measures, such as route avoidance guidance, contact with emergency contacts, etc., thereby effectively improving the ability of the user to respond to noise health risks. The function of linking the community medical station can provide timely medical support for the user in an emergency, further guaranteeing the health and safety of the user, and significantly improving the practicability and reliability of the entire system.

[0033] Embodiment 2, a noise health risk assessment and management method, comprising the following steps: S1: constructing a three-dimensional evaluation model, establishing a three-dimensional evaluation model with noise exposure level as X-axis, human physiological index fluctuation degree as Y-axis, and health hazard index as Z-axis according to noise exposure level and human physiological index data set; S2: collecting relevant data, obtaining noise exposure data as X-axis coordinates, collecting Z values corresponding to health hazard indexes as Z-axis coordinates, inputting the two into the three-dimensional evaluation model to obtain physiological gap and health gap; S3: determining the health hazard grade, receiving the physiological gap and health gap output by the three-dimensional evaluation model, combining the risk coefficient calculated by the evaluation module, and comprehensively determining the final health hazard grade through the weight rules set by the weight distribution module; S4: dynamically updating the risk coefficient, tracking the changes of relevant data in real time, calculating the real-time second risk coefficient, and updating the risk coefficient according to the preset rules; S5: generating early warning information, generating early warning information according to the health hazard grade and related indexes; S6: executing hierarchical alarm, executing corresponding hierarchical alarm operation according to the risk coefficient, the final health hazard grade and the early warning information.

[0034] The embodiments of the present application are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of the present application according to the above embodiments, and make different inferences and changes, as long as they do not deviate from the spirit of the present application, and are within the protection scope of the present application.

Claims

1. A noise-to-human-health risk assessment and management system, characterized in that, The system comprises: a human health risk assessment model establishment module, configured to establish a three-dimensional assessment model with noise exposure level as X-axis, physiological index fluctuation as Y-axis, and health hazard index as Z-axis according to noise exposure level and human physiological index data set; a noise exposure level acquisition module, configured to collect noise exposure data; a health hazard index acquisition module, configured to collect Z values corresponding to health hazard indexes, and input the collected noise exposure data and health hazard indexes as X-axis and Z-axis coordinates into the three-dimensional assessment model, and obtain physiological gap and health gap between human physiological index standard value and measured value through model operation; a health hazard degree determination module, configured to receive physiological gap and health gap output by the three-dimensional assessment model, and determine the final harm level of human health by combining the risk coefficient calculated by the assessment module and the weight rules set by the weight distribution module; an assessment module, comprising a monitoring module and a control module, wherein the monitoring module is configured to track changes in relevant data in real time, and the control module is configured to calculate real-time second risk coefficient; a warning module, configured to generate warning information according to health risk level and related indexes, and link to an alarm module to execute warning operation; an alarm module, configured to execute graded alarm operation according to the risk coefficient output by the assessment module, the final health level and the instruction of the warning module.

2. The noise risk to human health assessment and management system according to claim 1, wherein, The weight distribution module calculates the final health level to comprehensively reflect the combined influence of physiological-health gap and risk coefficient; The physiological-health gap comprehensive value is obtained by weighting the physiological gap and the health gap; The control module calculates the real-time second risk coefficient to dynamically reflect the change of health risk; The physiological gap reflects the difference in physiological index fluctuation between noise exposure and non-noise exposure; The health gap reflects the deviation degree of individual physiological index from the group benchmark level.

3. The noise-to-human-health risk assessment and management system of claim 2, wherein, The human physiological index includes blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature; The human physiological index data set includes measurement data of blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature, and the model establishment module constructs the three-dimensional assessment model through multi-step analysis: Collect the physiological index measurement values of the subjects under different noise exposure levels, and take the noise exposure level as X-axis coordinate; Statistically analyze each group of physiological index data, calculate the standard deviation as Y-axis coordinate, and calculate the health hazard index as Z-axis coordinate; Set the value rules of Y-axis data for different noise exposure levels: when the noise exposure level is lower than 30dBA, the Y-axis is the standard deviation of physiological index under non-noise exposure; When the noise exposure level reaches or exceeds 30 dBA, the Y axis is a physiological gap reflecting the difference in fluctuation of physiological indicators .

4. The noise-to-human-health risk assessment and management system of claim 3, wherein, The measurement values and corresponding benchmark values of the physiological indexes of the subjects are obtained by mean value calculation to eliminate the influence of single measurement error.

5. A noise-to-human-health risk assessment and management system according to claim 4, wherein, The system can run on both PC and mobile phone terminals, and is suitable for different use scenarios: When the subject is at the noise source position, the system runs on the PC terminal, the PC is directly connected with the noise sensor to realize real-time collection of noise data; When the subject is away from the noise source, the system runs through the mobile phone APP, the noise sensor is connected with the mobile phone to ensure effective transmission of noise data; The noise sensing device is composed of a sound level meter, an audio collector, a voltage regulator and a signal transmitting device, the voltage regulator is used for adjusting the working parameters of the sound level meter, and the signal transmitting device is responsible for converting the noise waveform collected by the sound level meter into a digital signal and transmitting it to the corresponding terminal.

6. The noise-to-human-health risk assessment and management system of claim 5, wherein, The mobile phone APP includes multiple functional modules, which cooperatively realize noise health risk assessment and management: Noise exposure intensity detection module: Fourier algorithm is used to process the received digital signal to generate a time series of noise exposure intensity to reflect the characteristics of noise change over time; Signal processing module: with noise time domain signal display, frequency domain signal display and Fourier transform function, connected with acoustic parameter acquisition module to provide support for subsequent parameter calculation; Human physiological index acquisition and analysis module: collects physiological indexes such as blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature, calculates the mean value of each index, and generates the corresponding frequency spectrum through Fourier algorithm; Database and risk assessment module: the database stores various collected and calculated data, and the risk assessment module divides the risk level based on these data, and displays the fluctuation degree of physiological indexes and health hazards through the display module.

7. A noise-to-human-health risk assessment and management system according to claim 6, wherein, The acoustic parameter acquisition module includes multiple functional units, which realize different acoustic parameter processing respectively: Loudness module: calculate the loudness of sound pressure level signal through formula to reflect the subjective feeling of human ear to noise; Sound pressure level module: process the noise time domain signal to calculate the sound pressure level of noise to objectively quantify the noise intensity; Physiological index waveform display module: display the time domain signal waveform and frequency spectrum signal waveform of blood pressure, heart rate, blood oxygen saturation, respiratory rate and body temperature to intuitively present the change characteristics of physiological indexes.

8. The noise-to-human-health risk assessment and management system of claim 7, wherein, a preset fixed risk coefficient in a monitoring module of the evaluation module The control module updates the risk coefficient according to the following rules to dynamically adjust the sensitivity of the risk assessment: calculating a difference between the second risk coefficient and the third risk coefficient in real time, and when the difference is greater than or equal to a fixed risk coefficient generating a new risk coefficient and deleting the third risk coefficient before the generation date; The evaluation module includes a verification module, which assesses the newly generated risk coefficients and calculates the verification index using a formula. The early warning module is based on The magnitude of the value triggers a basic warning, a moderate warning, or an emergency warning, respectively.

9. The noise-to-human-health risk assessment and management system of claim 8, wherein, The alarm module executes corresponding grading response strategies according to different health risk levels triggered by the early warning module to realize precise risk intervention: Basic early warning: triggered when the noise exposure intensity is in the range of 20-30dBA, push noise reduction suggestions, and display real-time noise spectrum curve and physiological index fluctuation curve; Moderate early warning: triggered when the noise exposure intensity is in the range of 30-55dBA, the mobile phone APP pops up a red warning window with vibration prompt; Emergency early warning: triggered when the noise exposure intensity reaches or exceeds 55dBA, the mobile phone continuously rings with flashing light.

10. A method applied to a noise risk to human health assessment and management system as claimed in claim 9, characterized in that, The method comprises the following steps: S1: build a three-dimensional evaluation model, establish a three-dimensional evaluation model with noise exposure level as X axis, human physiological index fluctuation degree as Y axis and health hazard index as Z axis according to noise exposure level and human physiological index data set; S2: collect relevant data, obtain noise exposure data as X axis coordinate, collect Z value corresponding to health hazard index as Z axis coordinate, input the two into the three-dimensional evaluation model to obtain physiological gap and health gap; S3: determine the health hazard level, receive the physiological gap and health gap output by the three-dimensional evaluation model, combine the risk coefficient calculated by the evaluation module, and determine the final health hazard level through the weight rules set by the weight distribution module. S4: dynamically updating the risk coefficient, tracking the change of the related data in real time, calculating a real-time second risk coefficient, and updating the risk coefficient according to a preset rule; S5: generating early warning information according to the health hazard level and the related indexes; S6: performing a graded alarm according to the risk coefficient, the final health hazard level, and the early warning information.

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