Intelligent water affair voice broadcasting method and system based on intelligent audio frequency monitoring

Abstract

The invention relates to the technical field of acoustic monitoring, in particular to an intelligent water affair voice broadcasting method and system based on intelligent audio frequency monitoring, and the method comprises the following steps: collecting sound pressure level data and sound wave frequency content through an underwater acoustic sensor, recognizing abnormal water flow sound wave features, calculating the abnormal level of a water affair event, and screening the types of water flow sound signals; and a characteristic frequency band interval is divided, and a water environment acoustic characteristic set is generated. According to the invention, through extraction and analysis of the sound pressure level data and the sound wave frequency, the abnormal level of the water affair event can be accurately monitored and predicted in real time, the potential water affair problem can be rapidly dealt with, the risk to the environment and public safety is effectively reduced, the fundamental tone frequency is optimized, the definition and understandability of voice broadcast are improved, and the voice broadcast experience is improved. The method is more obvious especially in a multi-noise environment, and the sound transmission quality is effectively optimized and misunderstanding caused by noise interference is remarkably reduced through detailed analysis of background noise and adjustment of sound signals to adapt to the noise environment.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic monitoring, and particularly to a smart water service voice broadcast method and system based on intelligent audio monitoring. Background Art

[0002] The technical field of acoustic monitoring includes technical solutions for acquiring, analyzing, and applying acoustic signals in the environment. Its core content involves using acoustic wave signals to extract target information, and completing target event recognition and response through signal analysis and processing methods. This technical field includes multiple links such as acoustic signal acquisition, spectrum analysis, feature extraction, and pattern recognition, and is widely applied in scenarios such as environmental monitoring, safety protection, and equipment status diagnosis. In the technical field of acoustic monitoring, the quality of signal acquisition is improved through methods such as microphone arrays, multi-channel signal processing, and echo analysis, and the ability to recognize acoustic events is enhanced by combining time-frequency domain transformation, audio stream analysis, classifier modeling, etc. Some application scenarios require the combination of technologies such as signal enhancement, denoising, and speech recognition to meet the acoustic monitoring requirements in complex environments.

[0003] Among them, the smart water service voice broadcast method based on intelligent audio monitoring refers to using acoustic monitoring technology to optimize the voice broadcast system in water service management. Technical matters include collecting and analyzing sound data of water flow and its related environment, judging the status and category of water service events by identifying specific patterns in the sound data, and adjusting the content and form of voice broadcast through feature analysis and frequency optimization of sound signals to ensure that the voice broadcast can clearly and effectively convey key water service information.

[0004] In the prior art, acoustic monitoring is often affected by the quality of acoustic signals in high-noise environments, resulting in impaired accuracy and response speed of sound event recognition. Especially without the support of complex signal processing, it is impossible to accurately distinguish the sound source and the nature of the sound, especially in the underwater environment background. In addition, traditional methods fail to effectively utilize advanced analysis technologies such as time-frequency domain transformation in the process of sound feature extraction and pattern recognition, making the system poorly adaptable and having low recognition efficiency in a changing environment. Limited to dealing with emergencies, it leads to delays in the transmission of key information, misses the best response time, and increases environmental and public safety risks. In terms of voice broadcast, the prior art fails to effectively handle the interference of environmental noise on voice clarity, resulting in the broadcast content being difficult to be clearly understood in a noisy environment, thus affecting the timely transmission and reception effect of emergency information. Summary of the Invention

[0005] To address the technical problems existing in the prior art, where acoustic monitoring in high-noise environments is often affected by the quality of acoustic signals, resulting in impaired accuracy and response speed of sound event recognition. Especially without the support of complex signal processing, it is impossible to accurately distinguish the sound source and the nature of the sound, which is particularly difficult in the underwater environment background. In addition, traditional methods fail to effectively utilize advanced analysis techniques such as time-frequency domain transformation during the process of sound feature extraction and pattern recognition, making the system less adaptable and having low recognition efficiency in a changing environment. This leads to delays in the transmission of key information when dealing with emergencies, misses the best response time, and increases environmental and public safety risks. In terms of voice broadcast, the prior art fails to effectively handle the interference of environmental noise on speech clarity, resulting in the broadcast content being difficult to be clearly understood in a noisy environment, thus affecting the timely transmission and reception effect of emergency information. Embodiments of the present invention provide a smart water service voice broadcast method and system based on intelligent audio monitoring. The technical solutions are as follows: On the one hand, a smart water service voice broadcast method based on intelligent audio monitoring is provided, including the following steps: S1: Collect sound pressure level data and acoustic wave frequency content through underwater acoustic sensors, identify abnormal water flow acoustic wave characteristics, calculate the abnormal level of water service events, screen the types of water flow sound signals, and divide the characteristic frequency band intervals to generate an aquatic environment acoustic feature set; S2: Invoke the aquatic environment acoustic feature set, analyze the fundamental frequency, formants, and timbre characteristics, judge the main frequency interval and formant distribution pattern, and adjust the formant positions to generate frequency optimization parameters; S3: Based on the frequency optimization parameters, analyze the main frequency peak value, energy distribution density, and instantaneous change rate of background noise, calculate the main frequency offset degree between the water flow sound signal and the environmental noise, and adjust the masking ratio of the water flow sound signal to form a noise-adapted frequency spectrum; S4: Invoke the noise-adapted frequency spectrum, combine the water flow sound signal with the turbulence characteristics, calculate the background noise coverage time interval, and adjust the pause interval of the water service voice broadcast according to the flow velocity fluctuation situation to generate a broadcast rhythm control parameter; S5: Invoke the broadcast rhythm control parameter, calculate the power of the voice signal, measure the energy density of the voice broadcast, compare the signal power with the noise energy, and adjust the volume dynamic range to generate a voice broadcast adjustment result.

[0006] On the other hand, the set of aquatic environment acoustic features includes frequency features, energy peaks, acoustic wave deviation pattern features, and acoustic signal classification metrics. The frequency optimization parameters include fundamental frequency correction values, formant adjustment ratios, and optimized main frequency range intervals. The noise-adapted frequency spectrum includes background noise main frequency peak offset parameters, signal masking ratio adjustment coefficients, and frequency spectrum energy compensation parameters. The broadcast rhythm control parameters include adjusted pause interval times, flow rate change sensitivities, and rhythm synchronization metrics. The voice broadcast adjustment results include volume gain adjustment configurations, volume level configurations, and environmental noise adjustment parameters.

[0007] On the other hand, the specific steps for obtaining the set of aquatic environment acoustic features are as follows: S101: Obtain the sound pressure level data and acoustic wave frequency content collected by the underwater acoustic sensor, calculate the change rate of the acoustic wave amplitude, identify the periodic characteristics of the sound pressure change, extract the main frequency interval with concentrated energy according to the acoustic wave frequency content, compare the pattern features deviated from the normal water flow acoustic wave, and generate a sound pattern index; S102: Use the sound pattern index to analyze the deviation degree of the sound pressure level data and frequency content, judge the abnormality degree of the acoustic pattern, determine the level of the water service event according to the abnormality intensity, and classify the water flow sound signal to generate an abnormal level classification index; S103: Call the abnormal level classification index, delimit the key frequency range of each sound signal, calculate the concentration degree of the energy distribution within the frequency range, and adjust the boundary of the characteristic frequency band according to the energy ratio to obtain the set of aquatic environment acoustic features.

[0008] On the other hand, the specific steps for obtaining the frequency optimization parameters are as follows: S201: Based on the set of aquatic environment acoustic features, extract the fundamental frequency, formants, and timbre parameters of the water flow sound signal, analyze the frequency fluctuation characteristics of the water flow sound signal within different time windows, and calculate the distribution pattern of the formants in the frequency spectrum to obtain the water flow sound signal characteristic parameters; S202: Call the water flow sound signal characteristic parameters, analyze the change trend of the fundamental frequency of the water flow sound signal, screen the main frequency range of the water flow sound signal, and adjust the central value of the fundamental frequency according to the change characteristics of the main frequency interval to generate a fundamental frequency adjustment value; S203: Call the fundamental frequency adjustment value, analyze the distribution characteristics of the formants of the water flow sound signal, calculate the adjustment ratio of the formant frequencies, adjust the frequency positioning of the formants, and determine the optimized formant interval range to generate the frequency optimization parameters.

[0009] On the other hand, the specific steps for obtaining the noise-adapted frequency spectrum are as follows: S301: Invoke the frequency optimization parameter to obtain the power spectrum data of the water environment background noise, analyze the main frequency peak value, energy distribution density, and instantaneous change rate of the noise, detect the fluctuation characteristics of the noise energy under different time windows, identify the frequency range where the energy fluctuation exceeds the standard, and establish the noise characteristic reference value; S302: According to the noise characteristic reference value, calculate the deviation degree between the main frequency of the water flow sound signal and the main frequency of the environmental noise, analyze the energy distribution characteristics in the deviation interval, evaluate the influence of the noise interference on the water flow sound signal, measure the energy loss ratio of the main frequency signal, adjust the frequency spectrum structure of the water flow sound signal, and establish the frequency band energy compensation parameter; S303: Use the frequency band energy compensation parameter to analyze the distribution of the water flow sound signal energy in different frequency bands, determine the masking ratio of the water flow sound signal, calculate the noise interference intensity, judge the interference degree of the environmental noise on the water flow sound signal, identify the frequency band range of the water flow sound signal affected by the noise, and establish the noise-adapted frequency spectrum.

[0010] On the other hand, when calculating the noise interference intensity, the formula is used: ; Judge the interference degree of the environmental noise on the water flow sound signal, identify the frequency band range of the water flow sound signal affected by the noise, and establish the noise-adapted frequency spectrum; Wherein, represents the noise interference intensity, represents the power value of the water flow sound signal in the frequency band, represents the power value of the environmental noise in the frequency band, represents the total number of frequency bands.

[0011] On the other hand, the specific steps for obtaining the broadcast rhythm control parameter are as follows: S401: According to the noise-adapted frequency spectrum, extract the time-domain change characteristics of the water flow sound signal, analyze the energy distribution of the background noise in different time intervals, calculate the power change of the noise within the time window, identify the continuous coverage time of the background noise, and generate the noise persistence analysis result; S402: Based on the noise persistence analysis result, extract the turbulence characteristics of the water flow sound signal, calculate the instantaneous fluctuation range of the flow velocity, compare the matching degree between the noise coverage interval and the flow velocity fluctuation range, judge the interference degree of the flow velocity change on the background noise, and generate the flow velocity adjustment influence index; S403: Invoke the flow velocity adjustment influence index, compare the background noise coverage time with the pause interval of the water service voice broadcast, calculate the flow velocity fluctuation adjustment coefficient, analyze the flow velocity fluctuation change trend, combine the fluctuation change trend and the noise coverage time, dynamically correct the voice broadcast pause time, and establish the broadcast rhythm control parameter.

[0012] On the other hand, when calculating the flow velocity fluctuation adjustment coefficient, the formula is used: ; Analyze the change trend of flow velocity fluctuation, combine the fluctuation change trend and the noise coverage time, dynamically correct the pause time of voice broadcast, and establish the broadcast rhythm control parameters; Among them, represents the flow velocity fluctuation adjustment coefficient, represents the time period the instantaneous change amount of flow velocity within, represents the flow velocity measurement value of the nth time period, represents the average flow velocity within the selected time range, represents the total number of flow velocity measurements within the selected time range, represents the background noise coverage time, represents the standard time interval of the broadcast rhythm.

[0013] On the other hand, the specific steps for obtaining the voice broadcast adjustment result are as follows: S501: Based on the broadcast rhythm control parameters, obtain the volume change data of the broadcast content, analyze the distribution characteristics of the volume in different voice paragraphs, measure the deviation range between the volume peak value and the average value, identify the voice paragraphs with the volume change amplitude exceeding the standard, and obtain the volume distribution characteristic value; S502: Call the volume distribution characteristic value, calculate the voice signal power, measure the energy proportion of the signal power in different frequency bands according to the energy density of the environmental noise, screen the frequency band range with the signal-to-noise ratio different from the standard, and adjust the voice power according to the signal attenuation amplitude to establish the signal-to-noise ratio optimization parameter; S503: Use the signal-to-noise ratio optimization parameter to calculate the volume gain change range of the broadcast content, identify the volume adjustment area affected by environmental noise, and adjust the volume allocation ratio and dynamic range according to the volume change mode to obtain the voice broadcast adjustment result.

[0014] On the other hand, a smart water service voice broadcast system based on intelligent audio monitoring is provided. This system is applied to the smart water service voice broadcast method based on intelligent audio monitoring, and includes: The acoustic data processing module obtains the sound pressure level data and the sound wave frequency content collected by the underwater acoustic sensor, identifies the abnormal water flow acoustic wave characteristics, screens the water flow sound signal categories, divides the characteristic frequency band intervals, and generates the water environment acoustic characteristic set; The frequency characteristic adjustment module analyzes the fundamental frequency, formants and timbre characteristics based on the water environment acoustic characteristic set, judges the main frequency range and the formant distribution mode, adjusts the formant position, and generates the frequency optimization parameter; Based on the frequency optimization parameters, the noise analysis and optimization module analyzes the main frequency peak, energy distribution density, and instantaneous change rate of the background noise, calculates the degree of main frequency offset between the water flow sound signal and the ambient noise, adjusts the masking ratio of the water flow sound signal, and generates a noise-adapted frequency spectrum. The broadcast rhythm configuration module calls the noise-adapted frequency spectrum, calculates the time interval covered by the background noise, adjusts the pause interval of the water service voice broadcast according to the flow velocity fluctuation, and generates broadcast rhythm control parameters. The volume adjustment module calls the broadcast rhythm control parameters, calculates the power of the voice signal, measures the energy density of the voice broadcast, adjusts the volume dynamic range, and generates the voice broadcast adjustment result.

[0015] The beneficial effects brought by the technical solution provided in the embodiments of the present invention at least include: Through the extraction and analysis of sound pressure level data and sound wave frequencies, it is possible to monitor and predict the abnormal levels of water service events in real time and accurately, quickly respond to potential water service problems, effectively reduce the risks to the environment and public safety, optimize the fundamental frequency, improve the clarity and intelligibility of voice broadcasts, especially more significantly in multi-noise environments. By analyzing the background noise in detail and adjusting the sound signal to adapt to the noise environment, the sound propagation quality is effectively optimized, and misunderstandings caused by noise interference are significantly reduced. This not only enhances the practicality of sound data but also greatly improves the efficiency of information transmission by precisely controlling the broadcast rhythm and volume, which is particularly crucial in emergency situations such as floods or other water quality monitoring alarms, ensuring that critical information can be conveyed to decision-makers and the public in a timely and accurate manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 is the main step flow chart of the present invention; Figure 2 is the step flow chart of S1 of the present invention; Figure 3 is the step flow chart of S2 of the present invention; Figure 4 is the step flow chart of S3 of the present invention; Figure 5 is the step flow chart of S4 of the present invention; Figure 6 is the step flow chart of S5 of the present invention; Figure 7 is the system block diagram of the present invention. Detailed implementation manners

[0018] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0021] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0022] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] The embodiments of the present invention provide a smart water voice broadcast method based on intelligent audio monitoring, as Figure 1 shown, including the following steps: S1: Collect sound pressure level data and acoustic wave frequency content through an underwater acoustic sensor, identify the pattern characteristics different from the normal water flow acoustic wave, calculate the abnormal level index of the water service event according to the characteristics, screen the water flow sound signal category, divide the distribution interval of the characteristic frequency band of the water flow sound signal, and generate an underwater acoustic environment feature set; S2: Invoke the underwater acoustic environment feature set, analyze the fundamental frequency, formant and timbre characteristics of the water flow sound signal, judge the main frequency interval and formant distribution pattern of the water flow sound signal, optimize the fundamental frequency according to the main frequency change trend, and adjust the formant position to generate frequency optimization parameters; S3: Based on the frequency optimization parameters, analyze the main frequency peak value, energy distribution density and instantaneous change rate of the underwater acoustic environment background noise, calculate the offset degree between the main frequency of the water flow sound signal and the main frequency of the environmental noise, adjust the masking ratio of the water flow sound signal according to the signal energy distribution, optimize the propagation quality of the acoustic signal, and generate a noise adaptation spectrum; S4: Call the noise adaptation spectrum, combine the water flow sound signal with the turbulence characteristics, calculate the background noise coverage time interval, adjust the pause interval of the water service voice broadcast according to the flow velocity fluctuation, and generate the broadcast rhythm control parameter; S5: Call the broadcast rhythm control parameter, obtain the volume change data of the broadcast content, calculate the voice signal power, measure the energy density of the voice broadcast, compare the signal power with the noise energy, adjust the volume dynamic range, optimize the influence of background noise during broadcast, and generate the voice broadcast adjustment result.

[0024] The water environment acoustic feature set includes frequency features, energy peaks, acoustic wave deviation mode features, and acoustic signal classification indicators. The frequency optimization parameters include fundamental frequency correction values, formant adjustment ratios, and main frequency range optimization ranges. The noise adaptation spectrum includes background noise main frequency peak offset parameters, signal masking ratio adjustment coefficients, and spectrum energy compensation parameters. The broadcast rhythm control parameters include the adjusted pause interval time, flow velocity change sensitivity, and rhythm synchronization indicators. The voice broadcast adjustment result includes volume gain adjustment configuration, volume level configuration, and environmental noise adjustment parameters.

[0025] As Figure 2 shown, the steps for obtaining the water environment acoustic feature set are specifically as follows: S101: Obtain the sound pressure level data and acoustic wave frequency content collected by the underwater acoustic sensor, calculate the change rate of the acoustic wave amplitude, identify the periodic characteristics of the sound pressure change, extract the main frequency range where the energy is concentrated according to the acoustic wave frequency content, and compare the mode characteristics deviated from the normal water flow acoustic wave to generate the sound mode index; Use an underwater microphone or a piezoelectric acoustic sensor for data acquisition, set the sampling frequency in the range of 44.1 kHz to 96 kHz to ensure coverage of the main noise frequency range of the underwater environment, convert the collected sound pressure level data into the root mean square value of the sound pressure, and use the formula , where represents the sound pressure level (unit: dB), is the root mean square value of the sound pressure (unit: Pa), is the reference sound pressure (1 μPa) for sound pressure level calculation to obtain the underwater noise level at a specific moment, calculate the change rate of the acoustic wave amplitude, calculate the change rate of the amplitude by analyzing the change amount of the sound pressure level in adjacent time windows, and use the formula , where represents the acoustic wave amplitude change rate, represents the sound pressure level at the current time moment, represents the sound pressure level at the previous time moment, represents the time interval. In this example, if , , the time interval seconds, then , analyze the periodic characteristics of the sound pressure change, convert the time-domain signal into frequency-domain data through short-time Fourier transform (STFT), obtain the change of the spectrum over time, set the window length to 512 points and the step size to 128 points, calculate the spectra of different time periods, use the autocorrelation function to calculate the periodicity of the signal. When the autocorrelation value of the signal is greater than 0.8, it is defined as a significant periodic feature. If the autocorrelation value calculated for a certain time period is 0.85, it is considered that there is a periodic change in this time period. Combine the acoustic wave frequency content to extract the main frequency interval where the energy is concentrated, calculate the power spectral density (PSD), and the formula is as follows: , where represents the power spectral density, represents the signal duration, represents the result of the Fourier transform of the signal, calculate the power of each frequency component, screen the frequency interval where the energy ratio exceeds 70% of the total energy. If the main frequency energy interval is from 500 Hz to 1.5 kHz, extract the data in this frequency range, compare the deviation pattern characteristics from the normal water flow sound wave, calculate the deviation degree, and use the Euclidean distance to calculate the deviation degree between the current frequency distribution and the standard water flow sound wave frequency distribution. Let the main frequency distribution of the standard water flow sound wave be Hz, and the main frequency distribution of the current detection data be Hz, then calculate the Euclidean distance as follows: , Generate a sound pattern index.

[0026] S102: Use the sound pattern index to analyze the deviation degree of the sound pressure level data and frequency content, judge the abnormality degree of the acoustic pattern, determine the level of the water service event according to the abnormality intensity, and classify the water flow sound signal to generate an abnormal level classification index; Set the reference range of the sound pressure level in the normal water flow environment. For example, when the flow velocity is 1.5 - 3.0 m / s, the corresponding sound pressure level is 65 - 85 dB. Take this as the reference range and calculate the deviation of the sound pressure level of the current environmental noise. The calculation formula is as follows: , where represents the deviation between the current environment and the reference sound pressure level, represents the currently measured sound pressure level, represents the reference sound pressure level in the normal water flow state. If the current sound pressure level dB and the reference sound pressure level dB, then , compare the change of the sound pressure level deviation value, combine the change of the frequency content, judge the abnormality degree of the acoustic pattern, calculate the spectrum deviation amount, and use to calculate the deviation amplitude of each frequency point , where represents the frequency offset of the current detection signal, represents the current main frequency, represents the reference water flow sound frequency. If the current main frequency is 900 Hz and the reference water flow sound frequency is 1200 Hz, then , classify the water service events according to the anomaly intensity, set the anomaly classification standard. If dB or Hz, then it is classified as a high-level anomaly. If dB or Hz, then it is classified as a medium-level anomaly. Otherwise, it is classified as a low-level anomaly. In this example, dB, Hz, so it is determined to be a medium-level anomaly, and an anomaly level classification index is generated.

[0027] S103: Invoke the anomaly level classification index, delimit the key frequency range of each sound signal, calculate the concentration degree of the energy distribution within the frequency range, and adjust the boundary of the characteristic frequency band according to the energy ratio to obtain the water environment acoustic feature set.

[0028] Analyze different categories of water service anomaly signals, set the classification standard, and divide them into low-level anomalies (600 - 1200 Hz), medium-level anomalies (400 - 1400 Hz), and high-level anomalies (200 - 1600 Hz). If the current classification index determines a medium-level anomaly, then the corresponding main frequency range is 400 - 1400 Hz. Calculate the concentration degree of the energy distribution within this frequency range, and use the power spectral density integral to calculate the energy ratio , where represents the energy ratio of the target frequency interval, represents the power spectral density, represents the main frequency range of the current signal, represents the frequency range of the entire signal. It is calculated that the energy ratio within the current frequency range is 70%. Adjust the boundary of the characteristic frequency band according to the energy ratio. If the energy ratio is lower than 60%, then appropriately expand the frequency range. If it is higher than 80%, then narrow the frequency range. In this example, 70% is within the reasonable range, and the boundary is not adjusted to obtain the water environment acoustic feature set.

[0029] As Figure 3 shown, the steps to obtain the frequency optimization parameters are specifically as follows: S201: Based on the water environment acoustic feature set, extract the fundamental frequency, formant, and timbre parameters of the water flow sound signal, analyze the frequency fluctuation characteristics of the water flow sound signal within different time windows, calculate the distribution pattern of the formant in the frequency spectrum, and obtain the water flow sound signal characteristic parameters; The time-domain signal is converted into a frequency-domain signal using the Fast Fourier Transform (FFT). The sampling rate is set to 44.1 kHz, and the number of FFT points is 2048. The power spectral density is calculated. The fundamental frequency is determined by the frequency point where the maximum correlation peak is located. Design an example: If the main frequency components of the collected signal are in the range of 520 Hz to 1520 Hz, and the maximum peak appears at 810 Hz when calculating the autocorrelation function, then the fundamental frequency is determined to be 810 Hz. Calculate the formants. The formants are extracted using the Linear Prediction Coding (LPC) method. The prediction order is set to 12. The main formant positions are calculated to be 610 Hz, 1230 Hz, and 1810 Hz. The timbre parameters are calculated using the Mel Frequency Cepstral Coefficients (MFCC). The number of filters is set to 26, and the first 13-order MFCC coefficients are extracted as timbre parameters. Analyze the frequency fluctuation characteristics of the water flow sound signal within different time windows. Use the Short-Time Fourier Transform (STFT). The window length is set to 1024 points, and the step size is 256 points. Calculate the spectral changes in different time windows and calculate the frequency standard deviation , where represents the frequency standard deviation, represents the th center frequency of the time window, represents the average frequency within the time window. Suppose the frequency data in a certain period are 810 Hz, 825 Hz, 790 Hz, 820 Hz, and 800 Hz respectively. Then calculate the average frequency , and calculate the standard deviation , Calculate the distribution pattern of the formants in the spectrum. Statistically calculate the energy proportion of each formant in the power spectrum. The calculation formula is as follows: , where represents the formant energy proportion, represents the current formant power, represents the overall spectral power. Suppose the current formant powers at 610 Hz, 1230 Hz, and 1810 Hz are 2.8 mW, 3.2 mW, and 1.7 mW respectively, and the total power is 10.5 mW. Then , and obtain the characteristic parameters of the water flow sound signal.

[0030] S202: Invoke the characteristic parameters of the water flow sound signal, analyze the change trend of the fundamental frequency of the water flow sound signal, screen the main frequency range of the water flow sound signal, and adjust the central value of the fundamental frequency according to the change characteristics of the main frequency interval to generate the adjusted value of the fundamental frequency; Calculate the change rate of the fundamental frequency in different time windows using the differential calculation method , where represents the change rate of the fundamental frequency, represents the fundamental frequency of the current time window, represents the fundamental frequency of the previous time window, represents the time interval. Suppose the fundamental frequency changes of a certain time window are 810 Hz, 830 Hz, and 790 Hz, and calculate the change rate: , filter the main frequency range of the water flow sound signal, count the distribution of the fundamental frequency in different time windows, calculate the mode, mean, and median of the fundamental frequency distribution. Suppose the fundamental frequencies of a certain period are 755 Hz, 810 Hz, 785 Hz, 810 Hz, and 830 Hz respectively, then the main frequency range value is [755 Hz, 830 Hz]. According to the change characteristics of the main frequency interval, calculate the adjustment value of the fundamental frequency, set the adjustment standard. If the fluctuation amplitude of the fundamental frequency is greater than 30 Hz / s, then adjust the central value, and use the calculated mean as the adjusted central value, , and generate the adjustment value of the fundamental frequency.

[0031] S203: Call the adjustment value of the fundamental frequency, analyze the distribution characteristics of the formants of the water flow sound signal, calculate the adjustment ratio of the formant frequency, adjust the frequency positioning of the formants, determine the optimized formant interval range, and generate frequency optimization parameters.

[0032] Statistical the offset of the formant frequency in different time windows, and the calculation formula is as follows: , where, represents the offset of the formant frequency, represents the current formant frequency, represents the reference formant frequency. Suppose the current formant of 610 Hz changes to 600 Hz, then , calculate the adjustment ratio of the formant frequency, and set the adjustment coefficient to be 0.75, and the adjustment formula is as follows: , suppose the current formant offset Hz, then , adjust the frequency positioning of the formants, set the adjustment threshold. If the adjusted formant is more than 5% lower than the reference value, then adjust it upward by 5%, otherwise keep it unchanged. In this example, the adjusted value is 592.5 Hz, which is 2.87% lower than 610 Hz and does not reach the 5% adjustment threshold, so it remains unchanged. Determine the optimized formant interval range and generate frequency optimization parameters.

[0033] As Figure 4 shown, the specific steps for obtaining the noise adaptation spectrum are as follows: S301: Call the frequency optimization parameters, obtain the power spectrum data of the water environment background noise, analyze the main frequency peak value, energy distribution density, and instantaneous change rate of the noise, detect the fluctuation characteristics of the noise energy under different time windows of difference, and identify the frequency range where the energy fluctuation exceeds the standard, and establish the noise characteristic reference value; Collect noise signals using an underwater microphone or hydrophone within different time windows, and perform a Fast Fourier Transform (FFT). Set the sampling rate to 48 kHz and the number of FFT points to 4096 points. Calculate the power spectral density (PSD), and use the peak detection method to identify the peak of the main frequency of the noise. Assume that the main frequency components of the current noise signal are 460 Hz, 910 Hz, and 1360 Hz, with corresponding powers of 2.3 mW, 3.9 mW, and 2.0 mW respectively. Calculate the energy distribution density using the formula: , where, represents the noise energy distribution density, represents the power value at the current frequency, represents the overall spectral power. Assume the total power is 9.8 mW, then , calculate the instantaneous change rate using the differential calculation method , where, represents the instantaneous change rate, represents the power value at the current time window, represents the power value at the previous time window, represents the time interval. Assume the noise power changes are 3.9 mW, 4.3 mW, and 3.6 mW during a certain period, then , detect the fluctuation characteristics of the noise energy under different differential time windows, and calculate the standard deviation , where, represents the standard deviation of the noise power, represents the th power value of the time window, represents the average power within the time window. Assume the power data during a certain period are 3.7 mW, 4.0 mW, 3.6 mW, 4.2 mW, and 3.9 mW respectively, then calculate the average power , calculate the standard deviation; ; ; Identify the frequency range where the energy fluctuation exceeds the standard. Set the threshold to twice the standard deviation, that is . Detect that the noise power change amplitude during a certain period is 0.55 mW, which is greater than the threshold of 0.428 mW. Then identify this frequency range as the abnormal fluctuation interval and establish the noise characteristic reference value.

[0034] S302: According to the noise characteristic reference value, calculate the deviation degree between the main frequency of the water flow sound signal and the main frequency of the ambient noise, analyze the energy distribution characteristics of the deviation interval, evaluate the influence of the noise interference on the water flow sound signal, and measure the energy loss ratio of the main frequency signal. Adjust the spectral structure of the water flow sound signal and establish the energy compensation parameter for the frequency band; According to the noise characteristic reference value, calculate the deviation degree between the main frequency of the water flow sound signal and the main frequency of the environmental noise. Use the Euclidean distance to calculate the deviation degree between the main frequency distribution of the current water flow sound signal and the frequency distribution of the environmental noise. Assume that the main frequencies of the water flow sound are 620 Hz, 1240 Hz, and 1860 Hz, and the main frequencies of the noise are 460 Hz, 910 Hz, and 1360 Hz, then calculate the deviation degree; ; ; ; Analyze the energy distribution characteristics of the deviation interval, calculate the energy proportion of the overlapping interval between the main frequency signal and the noise frequency, and use the formula: , where represents the energy proportion of the overlapping interval, represents the frequency range of the overlapping interval, represents the frequency range of the overall signal. Calculate that the energy proportion of the overlapping interval is 67%. Evaluate the influence of noise interference on the water flow sound signal, set the interference level. If the energy proportion of the overlapping interval > 70%, it is determined that the noise interference on the water flow signal is strong. If 50% ≤ proportion ≤ 70%, the interference is medium. If < 50%, the interference is weak. In this example, the proportion is 67%, so it is judged as medium interference. Measure the energy loss proportion of the main frequency signal, and the calculation formula is as follows: , where represents the energy loss proportion of the main frequency signal, represents the power of the overlapping interval, represents the total signal power. Assume that the power of the overlapping interval is 6.3 mW and the total power is 9.2 mW, then . Adjust the spectral structure of the water flow sound signal, set the compensation ratio. If the energy loss proportion > 60%, compensate 30%. If 40% ≤ loss ≤ 60%, compensate 15%. If the loss < 40%, do not compensate. In this example, the loss proportion is 68.5%, meeting the standard of compensating 30%. Calculate the compensation parameter, , and establish the energy compensation parameter for the frequency band.

[0035] S303: Use the energy compensation parameter for the frequency band to analyze the distribution of the energy of the water flow sound signal in different frequency bands, determine the masking ratio of the water flow sound signal, calculate the noise interference intensity, judge the interference degree of the environmental noise on the water flow sound signal, identify the frequency band range of the water flow sound signal affected by the noise, and establish the noise-adapted spectrum.

[0036] Calculate the noise interference intensity, and use the formula: ; Judge the interference degree of the environmental noise on the water flow sound signal, identify the frequency band range of the water flow sound signal affected by the noise, and establish the noise-adapted spectrum; wherein, represents the noise interference intensity, represents the power value of the water flow sound signal in the frequency band, represents the power value of the ambient noise in the frequency band, represents the total number of frequency bands to be analyzed; is calculated from the sound pressure level data monitored by the underwater acoustic sensor, and the sound pressure level data is detected by the microphone array and converted into sound power, and the calculation method is as follows: ; wherein, represents the sound power reference value, generally taking W, represents the sound pressure level of the frequency band, in units of dB (decibels). In the actual monitoring data, the measured sound pressure levels of the water flow sound signal are dB, dB, dB, and substituting into the formula for calculation: ; ; ; The parameter is calculated from the sound pressure level of the background noise data, and the calculation method is the same as . In the actual monitoring data, the measured sound pressure levels of the ambient noise are dB, dB, dB, and substituting into the formula for calculation: ; ; ; Calculate the total absolute deviation of the power between the water flow sound signal and the ambient noise: ; ; ; Calculate the sum of the squares of the power of each frequency band of the ambient noise: ; ; ; Calculate the noise interference intensity : ; ; ; The result shows that the interference degree between the water flow sound signal and the background noise is 0.88. The closer the value is to 1, the stronger the masking effect of the noise on the water flow sound signal. Reducing the noise interference and improving the resolvability of the water flow sound signal, a noise-adapted spectrum is established.

[0037] Such as Figure 5 shown, the specific steps for obtaining the broadcast rhythm control parameters are as follows: S401: According to the noise-adapted spectrum, extract the time-domain variation characteristics of the water flow sound signal, analyze the energy distribution of the background noise in different time intervals, calculate the power variation of the noise within the time window, identify the continuous coverage time of the background noise, and generate the noise persistence analysis result; Use the short-time Fourier transform (STFT) to analyze the time-frequency characteristics of the water flow sound signal. Set the window length to 1024 points and the step size to 256 points. Calculate the spectral changes of the time-domain signal within different time windows, extract the fluctuations of the signal amplitude over time. Assume that the signal amplitudes within the current time window are 0.85Pa, 0.88Pa, 0.82Pa, 0.90Pa, 0.86Pa respectively, and calculate the amplitude mean value; ; Calculate the standard deviation of the amplitude; ; ; Analyze the energy distribution of the background noise in different time intervals, calculate the change of the background noise energy using the short-time power spectral density, calculate the power variation of the noise within the time window. Assume that the background noise powers within the current time window are 2.5mW, 2.8mW, 2.4mW, 2.9mW, 2.6mW respectively, then calculate the mean value of the noise power within the time window, , calculate the change rate of the noise power, , where, represents the change rate of the noise power, represents the noise power of the current time window, represents the noise power of the previous time window, represents the time interval. Assume that the noise power changes are 2.8mW, 2.5mW, 2.6mW respectively, then , identify the continuous coverage time of background noise, set the noise persistence threshold to 2.5 mW, calculate the duration during which the noise power in the continuous time window is greater than the threshold. Suppose the number of time windows with background noise power above 2.6 mW is 12, and the time window interval is 0.5 seconds, then , generate the noise persistence analysis result.

[0038] S402: Based on the noise persistence analysis result, extract the turbulence characteristics of the water flow sound signal, calculate the instantaneous fluctuation range of the flow velocity, compare the matching degree between the noise coverage interval and the flow velocity fluctuation range, judge the interference degree of the flow velocity change on the background noise, and generate the flow velocity adjustment influence index; Use a flow velocity measuring instrument to obtain the flow velocity data in different time windows. Suppose the flow velocity measurement values in the current time window are 1.8 m / s, 2.1 m / s, 1.9 m / s, 2.3 m / s, 2.0 m / s respectively, and calculate the average flow velocity , calculate the standard deviation of the flow velocity; ; ; Compare the matching degree between the noise coverage interval and the flow velocity fluctuation range, calculate their correlation, and use the Pearson correlation coefficient , where represents the correlation coefficient between the flow velocity fluctuation and the noise power change, represents the th time window's flow velocity, represents the th time window's noise power. Design an example: If the calculated correlation coefficient is 0.68, it indicates that the flow velocity change has a greater impact on the noise. Judge the interference degree of the flow velocity change on the background noise, set the interference level. If the correlation coefficient > 0.7, the interference is strong. If 0.5 ≤ correlation coefficient ≤ 0.7, the interference is medium. If < 0.5, the interference is weak. In this example, the correlation coefficient is 0.68, so it is judged as medium interference, and calculate the flow velocity adjustment influence index, , where represents the flow velocity adjustment influence index, represents the standard deviation of the noise power. Suppose mW, then , generate the flow velocity adjustment influence index.

[0039] S403: Invoke the flow velocity adjustment influence index, compare the background noise coverage time with the pause interval of the water service voice broadcast, calculate the flow velocity fluctuation adjustment coefficient, analyze the trend of the flow velocity fluctuation change, combine the fluctuation change trend and the noise coverage time, dynamically correct the voice broadcast pause time, and establish the broadcast rhythm control parameter.

[0040] Calculate the flow velocity fluctuation adjustment coefficient using the formula: ; Analyze the trend of flow velocity fluctuation. Combine the fluctuation trend and the noise coverage time to dynamically correct the pause time of voice broadcast and establish the control parameters for broadcast rhythm. Among them, represents the flow velocity fluctuation adjustment coefficient, represents the time period the instantaneous change in flow velocity within, represents the th measured value of flow velocity in a time period, represents the average flow velocity within the selected time range, represents the total number of flow velocity measurements within the selected time range, represents the background noise coverage time, represents the standard time interval of the broadcast rhythm; The instantaneous change in flow velocity is obtained by monitoring the velocity data of the water flow at different time periods using a flow meter or an ultrasonic flow velocity meter, and calculating the absolute value of the difference in flow velocity between adjacent time points. With a sampling interval of 1 second, the flow velocity at time t is m / s, the flow velocity at time m / s, so: ; The calculation of the standard deviation of flow velocity is based on the flow velocity data within the selected time window. The sampling period is 5 seconds, and it is measured once per second. The sampling values are m / s, m / s, m / s, m / s, m / s. Calculate the average flow velocity: ; Calculate the standard deviation of the flow velocity: ; ; ; The background noise coverage time is measured by a noise detection device and calculated based on the time when the environmental noise level exceeds 40 dB. It is measured that the background noise coverage time for this period is seconds. The standard time interval of the broadcast rhythm is set by the broadcast system to 2.5 seconds.

[0041] is set by the broadcast system to 2.5 seconds.

[0042] Substitute the above values into the formula for calculation : ; ; The results show that the current flow rate fluctuation adjustment coefficient is 3.635. The higher the value, the more significant the impact of the flow rate fluctuation on the rhythm of voice broadcast. This coefficient is used to dynamically correct the pause time of voice broadcast. Considering the influence of background noise, the pause time is adjusted to optimize the broadcast rhythm.

[0043] As Figure 6 shown, the specific steps for obtaining the voice broadcast adjustment results are as follows: S501: Based on the broadcast rhythm control parameters, obtain the volume change data of the broadcast content, analyze the distribution characteristics of the volume in different voice paragraphs, measure the deviation range between the volume peak and the average value, identify the voice paragraphs with volume change amplitude exceeding the standard, and obtain the volume distribution characteristic value; Use an audio signal processing tool to segment the broadcast audio. Set the time window to 500 ms, calculate the volume (sound pressure level) within each window, and calculate the instantaneous volume using the formula: , where represents the volume level (unit: dB), represents the root mean square volume value within the current window (unit: Pa), is the reference sound pressure (0.00002 Pa). Suppose the root mean square volume value within a certain time window is 0.1 Pa, then , analyze the distribution characteristics of the volume in different voice paragraphs, and calculate the average volume of each voice paragraph: , where represents the average value of the volume, represents the th volume of the voice paragraph, represents the number of voice paragraphs. Suppose the volume measurement values of a certain voice paragraph are 72 dB, 75 dB, 73 dB, 78 dB, 76 dB, , measure the deviation range between the volume peak and the average value, and calculate the deviation between the volume peak and the average value: , where represents the maximum volume value. Suppose the volume peak is 78 dB, then , identify the voice paragraphs with volume change amplitude exceeding the standard. Set the over-limit threshold to 3 dB. If the deviation is greater than 3 dB, it is determined that the volume change amplitude of this paragraph exceeds the standard. In this example , which is greater than the threshold. Therefore, this paragraph is marked as abnormal, and the volume distribution characteristic value is obtained.

[0044] S502: Invoke the volume distribution eigenvalue, calculate the power of the voice signal, measure the energy proportion of the signal power in different frequency bands according to the energy density of the ambient noise, screen the frequency band range where the signal-to-noise ratio is different from the standard, and adjust the voice power according to the signal attenuation amplitude to establish the signal-to-noise ratio optimization parameter; Invoke the volume distribution eigenvalue, calculate the power of the voice signal, and use the energy calculation formula , where represents the power of the voice signal, represents the signal duration, represents the instantaneous volume. Assume the signal duration is 2 seconds and the root mean square volume value is 0.1 Pa, then , measure the energy proportion of the signal power in different frequency bands according to the energy density of the ambient noise, calculate the power distribution in different frequency bands, and use the power spectral density calculation formula: , where represents the energy proportion of a specific frequency band, represents the target frequency band range, represents the overall voice frequency range. Assume the target frequency band energy is 0.003 W and the total power is 0.005 W, then , screen the frequency band range where the signal-to-noise ratio is different from the standard, and calculate the signal-to-noise ratio: , where represents the noise power. Assume the noise power is 0.001 W, then , set the signal-to-noise ratio standard threshold to 10 dB. If the signal-to-noise ratio is less than 10 dB, then this frequency band needs to be adjusted. In this example, SNR = 6.99 dB, which is lower than the standard, so it needs to be adjusted. Adjust the voice power according to the signal attenuation amplitude and calculate the compensation gain , , and establish the signal-to-noise ratio optimization parameter.

[0045] S503: Use the signal-to-noise ratio optimization parameter to calculate the volume gain change range of the broadcast content, identify the volume adjustment area affected by the ambient noise, and adjust the volume distribution ratio and dynamic range according to the volume change mode to obtain the voice broadcast adjustment result.

[0046] Adjust the voice signal gain and calculate the volume after gain, where represents the adjusted volume value. Assume the compensation gain , the original volume Pa, then , identify the volume adjustment area affected by the ambient noise, set the noise interference impact threshold. If the ambient noise exceeds 40 dB, then it is determined that this area needs additional gain compensation. Assume the ambient noise in a certain area is 42 dB, then this area needs gain adjustment, and calculate the final volume adjustment value: , adjust the volume distribution ratio according to the volume change pattern, set the volume adjustment rules for different voice paragraphs. If the signal-to-noise ratio < 10 dB and the noise > 40 dB, increase the gain by 3 dB; otherwise, keep the original volume. In this example, the condition is met, and the voice broadcast adjustment result is obtained.

[0047] As Figure 7 shown, a smart water conservancy voice broadcast system based on intelligent audio monitoring includes: The acoustic data processing module acquires the sound pressure level data and sound wave frequency content collected by the underwater acoustic sensor, identifies the abnormal water flow sound wave characteristics, filters the water flow sound signal categories, divides the characteristic frequency band intervals, and generates the water environment acoustic characteristic set; The frequency characteristic adjustment module analyzes the fundamental frequency, formants, and timbre characteristics based on the water environment acoustic characteristic set, judges the main frequency range and formant distribution pattern, adjusts the formant positions, and generates the frequency optimization parameters; The noise analysis and optimization module analyzes the main frequency peak value, energy distribution density, and instantaneous change rate of the background noise based on the frequency optimization parameters, calculates the main frequency offset degree between the water flow sound signal and the environmental noise, adjusts the water flow sound signal masking ratio, and generates the noise-adapted frequency spectrum; The broadcast rhythm configuration module calls the noise-adapted frequency spectrum, calculates the background noise coverage time interval, adjusts the pause interval of the water conservancy voice broadcast according to the flow velocity fluctuation situation, and generates the broadcast rhythm control parameters; The volume adjustment module calls the broadcast rhythm control parameters, calculates the voice signal power, measures the energy density of the voice broadcast, adjusts the volume dynamic range, and generates the voice broadcast adjustment result.

[0048] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0049] In the present invention, "at least one" means one or more, and "multiple" means two or more. "At least one of the following (items)" or its similar expressions refer to any combination of these items, including any combination of single (item) or plural (items). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0050] It should be understood that in various embodiments of the present invention, the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0051] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0052] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0053] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0054] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] In addition, the functional units in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0056] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0057] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A smart water service voice broadcast method based on intelligent audio monitoring, characterized in that, The method includes: S1: Collect sound pressure level data and acoustic wave frequency content through an underwater acoustic sensor, identify abnormal water flow acoustic wave characteristics, calculate the abnormal level of water service events, screen the types of water flow sound signals, divide the characteristic frequency band intervals, and generate an aquatic environment acoustic feature set; S2: Invoke the aquatic environment acoustic feature set, analyze the fundamental frequency, formants, and timbre characteristics, judge the main frequency interval and formant distribution pattern, adjust the formant positions, and generate frequency optimization parameters; S3: Based on the frequency optimization parameters, analyze the main frequency peak value, energy distribution density, and instantaneous change rate of background noise, calculate the main frequency offset degree between the water flow sound signal and the environmental noise, adjust the masking ratio of the water flow sound signal, and generate a noise-adapted spectrum; S4: Invoke the noise-adapted spectrum, combine the water flow sound signal with the turbulence characteristics, calculate the background noise coverage time interval, adjust the pause interval of the water service voice broadcast according to the flow velocity fluctuation situation, and generate a broadcast rhythm control parameter; S5: Invoke the broadcast rhythm control parameter, calculate the voice signal power, measure the energy density of the voice broadcast, compare the signal power with the noise energy, adjust the volume dynamic range, and generate a voice broadcast adjustment result; The abnormal level of the water service event is based on the sound pressure level deviation and the frequency deviation for classification. Among them, when or it is a high-level anomaly, and when and it is a medium-level anomaly, and the rest are low-level anomalies.

2. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 1, wherein The aquatic environment acoustic feature set includes frequency characteristics, energy peak values, acoustic wave deviation mode characteristics, and acoustic signal classification indicators. The frequency optimization parameters include fundamental frequency correction values, formant adjustment ratios, and main frequency interval optimization ranges. The noise-adapted spectrum includes background noise main frequency peak value offset parameters, signal masking ratio adjustment coefficients, and spectrum energy compensation parameters. The broadcast rhythm control parameter includes the adjusted pause interval time, flow velocity change sensitivity, and rhythm synchronization indicators. The voice broadcast adjustment result includes volume gain adjustment configurations, volume level configurations, and environmental noise adjustment parameters.

3. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 1, wherein, The specific steps for obtaining the aquatic environment acoustic feature set are as follows: S101: Obtain the sound pressure level data and acoustic wave frequency content collected by the underwater acoustic sensor, calculate the change rate of the acoustic wave amplitude, identify the periodic characteristics of the sound pressure change, extract the main frequency interval with concentrated energy according to the acoustic wave frequency content, compare the mode characteristics deviated from the normal water flow acoustic wave, and generate a sound mode index; S102: Use the sound mode index to analyze the deviation degree of the sound pressure level data and frequency content, judge the abnormal degree of the acoustic mode, determine the level of the water service event according to the abnormal intensity, and classify the water flow sound signal to generate an abnormal level classification index; S103: Invoke the abnormal level classification index, delimit the key frequency range of each sound signal, calculate the concentration degree of the energy distribution within the frequency range, and adjust the boundary of the characteristic frequency band according to the energy ratio to obtain the aquatic environment acoustic feature set.

4. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 1, wherein The specific steps for obtaining the frequency optimization parameters are as follows: S201: Based on the aquatic environment acoustic feature set, extract the fundamental frequency, formants, and timbre parameters of the water flow sound signal, analyze the frequency fluctuation characteristics of the water flow sound signal within different time windows, calculate the distribution pattern of the formants in the spectrum, and obtain the water flow sound signal characteristic parameters; S202: Invoke the characteristic parameters of the water flow sound signal, analyze the changing trend of the fundamental frequency of the water flow sound signal, screen the main frequency range of the water flow sound signal, adjust the central value of the fundamental frequency according to the changing characteristics of the main frequency interval, and generate a fundamental frequency adjustment value; S203: Invoke the fundamental frequency adjustment value, analyze the distribution characteristics of the formants of the water flow sound signal, calculate the adjustment ratio of the formant frequencies, adjust the frequency positioning of the formants, determine the optimized formant interval range, and generate frequency optimization parameters.

5. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 1, wherein, The specific steps for obtaining the noise-adapted frequency spectrum are as follows: S301: Invoke the frequency optimization parameters, obtain the power spectrum data of the water environment background noise, analyze the main frequency peak value, energy distribution density, and instantaneous change rate of the noise, detect the fluctuation characteristics of the noise energy under different time windows, identify the frequency range where the energy fluctuation exceeds the standard, and establish a noise characteristic reference value; S302: According to the noise characteristic reference value, calculate the deviation degree between the main frequency of the water flow sound signal and the main frequency of the environmental noise, analyze the energy distribution characteristics of the deviation interval, evaluate the influence of the noise interference on the water flow sound signal, measure the energy loss ratio of the main frequency signal, adjust the frequency spectrum structure of the water flow sound signal, and establish a frequency band energy compensation parameter; S303: Use the frequency band energy compensation parameter to analyze the distribution of the energy of the water flow sound signal in different frequency bands, determine the masking ratio of the water flow sound signal, calculate the noise interference intensity, judge the degree of interference of the environmental noise on the water flow sound signal, identify the frequency band range of the water flow sound signal affected by the noise, and establish a noise-adapted frequency spectrum.

6. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 5, wherein The formula for calculating the noise interference intensity is: ； Judge the degree of interference of the environmental noise on the water flow sound signal, identify the frequency band range of the water flow sound signal affected by the noise, and establish a noise-adapted frequency spectrum; Among them, represents the noise interference intensity, represents the power value of the water flow sound signal in the frequency band, represents the power value of the ambient noise in the frequency band, represents the total number of frequency bands.

7. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 1, wherein The specific steps for obtaining the broadcast rhythm control parameter are as follows: S401: According to the noise-adapted frequency spectrum, extract the time-domain change characteristics of the water flow sound signal, analyze the energy distribution of the background noise in different time intervals, calculate the power change of the noise within the time window, identify the continuous coverage time of the background noise, and generate a noise persistence analysis result; S402: Based on the noise persistence analysis result, extract the turbulence characteristics of the water flow sound signal, calculate the instantaneous fluctuation range of the flow velocity, compare the matching degree between the noise coverage interval and the flow velocity fluctuation range, judge the degree of interference of the flow velocity change on the background noise, and generate a flow velocity adjustment influence index; S403: Invoke the flow velocity adjustment influence index, compare the background noise coverage time with the pause interval of the water service voice broadcast, calculate the flow velocity fluctuation adjustment coefficient, analyze the changing trend of the flow velocity fluctuation, combine the changing trend and the noise coverage time, dynamically correct the voice broadcast pause time, and establish a broadcast rhythm control parameter.

8. The intelligent water conservancy voice broadcast method based on intelligent audio monitoring according to claim 7, characterized in that The formula for calculating the flow velocity fluctuation adjustment coefficient is: ； Analyze the changing trend of the flow velocity fluctuation, combine the changing trend and the noise coverage time, dynamically correct the voice broadcast pause time, and establish a broadcast rhythm control parameter; Among them, represents the flow velocity fluctuation adjustment coefficient, represents the time period the instantaneous change in flow velocity within, represents the flow velocity measurement value of the th time period, represents the average flow velocity within the selected time range, represents the total number of flow velocity measurements within the selected time range, represents the background noise coverage time, represents the standard time interval of the broadcast rhythm.

9. The intelligent water service voice broadcast method based on intelligent audio monitoring according to claim 1, characterized in that, The specific steps for obtaining the voice broadcast adjustment result are as follows: S501: Based on the above-mentioned broadcast rhythm control parameters, obtain the volume change data of the broadcast content, analyze the distribution characteristics of the volume in different voice paragraphs, measure the deviation range between the volume peak and the average value, identify the voice paragraphs with the volume change amplitude exceeding the standard, and obtain the volume distribution characteristic value; S502: Call the above-mentioned volume distribution characteristic value, calculate the voice signal power, measure the energy proportion of the signal power in different frequency bands according to the energy density of the ambient noise, screen the frequency band range with the signal-to-noise ratio different from the standard, and adjust the voice power according to the signal attenuation amplitude to establish the signal-to-noise ratio optimization parameter; S503: Use the above-mentioned signal-to-noise ratio optimization parameter to calculate the volume gain change range of the broadcast content, identify the volume adjustment area affected by the ambient noise, and adjust the volume allocation ratio and dynamic range according to the volume change mode to obtain the voice broadcast adjustment result.

10. A smart water service voice broadcast system based on intelligent audio monitoring, the smart water service voice broadcast system based on intelligent audio monitoring is used to implement the smart water service voice broadcast method based on intelligent audio monitoring according to any one of claims 1-9, characterized in that, The system includes: The acoustic data processing module obtains the sound pressure level data and the sound wave frequency content collected by the underwater acoustic sensor, identifies the abnormal water flow sound wave characteristics, screens the water flow sound signal categories, divides the characteristic frequency band intervals, and generates the water environment acoustic characteristic set; The frequency characteristic adjustment module analyzes the fundamental frequency, formants and timbre characteristics based on the above-mentioned water environment acoustic characteristic set, judges the main frequency interval and the formant distribution mode, adjusts the formant position, and generates the frequency optimization parameter; The noise analysis and optimization module analyzes the main frequency peak value, energy distribution density and instantaneous change rate of the background noise based on the above-mentioned frequency optimization parameter, calculates the main frequency offset degree between the water flow sound signal and the ambient noise, adjusts the water flow sound signal masking ratio, and generates the noise adaptation spectrum; The broadcast rhythm configuration module calls the above-mentioned noise adaptation spectrum, calculates the background noise coverage time interval, adjusts the pause interval of the water service voice broadcast according to the flow velocity fluctuation situation, and generates the broadcast rhythm control parameter; The volume adjustment module calls the above-mentioned broadcast rhythm control parameter, calculates the voice signal power, measures the energy density of the voice broadcast, adjusts the volume dynamic range, and generates the voice broadcast adjustment result.

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