Intelligent pipeline leakage monitoring system and method based on audio frequency

Abstract

The invention relates to the technical field of pipeline safety monitoring, in particular to an intelligent pipeline leakage monitoring system and method based on audio frequency, and the system comprises an audio frequency signal collection module, a noise filtering and feature extraction module, a leakage recognition and positioning module, and a state evaluation and alarm module. Wherein the audio signal acquisition module is used for acquiring pipeline audio signals and outputting original audio signal data; the noise filtering and feature extraction module is used for carrying out filtering processing to suppress environmental noise and extracting feature vectors related to leakage; the leakage identifying and positioning module is used for identifying whether a leakage event exists or not and calculating the position of a leakage point relative to an adjacent sensor; and the state evaluation and alarm module is used for generating a leakage state evaluation result. According to the invention, by constructing a closed-loop monitoring process from audio frequency acquisition to alarm output, accurate identification, spatial positioning and graded evaluation of a pipeline leakage event are realized, and the response efficiency and practicability of the monitoring system are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of pipeline safety monitoring technology, and in particular to an intelligent pipeline leakage monitoring system and method based on audio frequency. Background Technology

[0002] In urban water supply, oil transportation, natural gas transmission, and chemical process pipeline systems, leaks can lead to energy waste, environmental pollution, and even safety accidents, causing serious economic and social losses. Traditional pipeline leak detection methods mostly rely on manual inspections, pressure monitoring, or flow comparisons. However, these methods are limited by high response delays, low spatial positioning accuracy, and the inability to reflect local leak conditions in real time, making it difficult to meet the actual needs of modern complex pipeline networks for high-precision, rapid response, and remote integrated monitoring. In recent years, acoustic monitoring technology has received widespread attention in the field of pipeline leak detection due to its advantages such as non-contact operation, high sensitivity, and real-time deployment.

[0003] However, most existing audio-based monitoring solutions only focus on single-point feature analysis or rough energy assessment, failing to achieve unified integration of multi-dimensional feature extraction, event level assessment, and precise spatial positioning of leakage signals. Furthermore, the various processing steps within these systems are often fragmented and isolated, lacking a closed-loop automated processing mechanism from audio acquisition to alarm output. This results in low leak detection accuracy, failing to meet the demands of rapid and reliable response in industrial applications. Therefore, there is an urgent need for an audio-based intelligent pipeline leak monitoring system and method to improve the timeliness and effectiveness of leak identification. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides an intelligent monitoring system and method for pipeline leakage based on audio frequency.

[0005] The audio-based intelligent pipeline leak monitoring system includes an audio signal acquisition module, a noise filtering and feature extraction module, a leak identification and location module, and a condition assessment and alarm module; among which: Audio signal acquisition module: used to acquire audio signals from the pipeline through acoustic sensors arranged along the pipeline and output raw audio signal data; Noise filtering and feature extraction module: used to receive raw audio signal data, filter it to suppress environmental noise, and extract feature vectors related to leakage; Leakage identification and location module: It is used to receive feature vectors, identify whether there is a leakage event, and after confirming the leakage, calculate the position of the leakage point relative to the adjacent sensors based on the signal strength and propagation attenuation model, and output the leakage identification result and location information; Status assessment and alarm module: It is used to generate leakage status assessment results based on the identification results and location information of the leakage event, and output alarm signals to the host computer or management terminal.

[0006] Optionally, the audio signal acquisition module includes a sensor deployment unit, a signal sampling unit, and a data output unit; wherein: Sensor deployment unit: used to deploy multiple acoustic sensors along the pipeline at preset intervals. The sensors are attached to the outside of the pipe wall and their installation position coordinates along the pipeline are recorded by positioning tags. Signal sampling unit: Used to control each acoustic sensor to synchronously collect the acoustic vibration of the inner wall of the pipe at a fixed sampling frequency, and generate a continuous time-domain audio signal sequence; Data output unit: used to timestamp the audio signal sequences collected by each sensor, encapsulate them in a unified format, and output the raw audio signal data.

[0007] Optionally, the noise filtering and feature extraction module includes a frequency band filtering unit, a time-frequency transformation unit, and a feature extraction unit; wherein: Frequency band filtering unit: Used to receive the raw audio signal data output by the audio signal acquisition module, and use a bandpass filter to perform frequency domain filtering on the signal to filter out low-frequency mechanical vibration noise and high-frequency electromagnetic interference in the background environment, while retaining leakage-related audio components located in the preset frequency band; Time-frequency transformation unit: used to perform short-time Fourier transform processing on the filtered audio signal to generate the time-frequency distribution spectrum of the audio signal; Feature extraction unit: used to extract the location of leakage-related abrupt change points, high-frequency duration energy values ​​and spectral slope features from the time-frequency spectrum, and output them in the form of feature vectors.

[0008] Optionally, the feature extraction unit includes: The mutation point detection subunit is used to extract high-energy mutation points along the time axis in the time-frequency spectrum, calculate the spectral energy difference sequence between adjacent time frames, and base the detection on a set mutation threshold. Determine the location of the energy mutation; High-frequency band identification subunit: used to identify high-frequency band signals that continuously appear in the time-frequency spectrum and set the frequency threshold. Statistical greater than The average energy, and screening for those that meet the requirement of a duration not less than a set length. Continuous high-frequency band; Spectral envelope analysis subunit: used to extract the changing trend of the spectral envelope in consecutive frames. It constructs the envelope change curve by normalizing the envelope energy, calculates its change rate using the first derivative, and outputs the spectral slope characteristics. Feature output: Used to concatenate the output mutation point location, high-frequency duration energy value and spectral slope features to form a fixed-dimensional feature vector.

[0009] Optionally, the leak identification and location module includes a leak event identification unit, a signal strength analysis unit, and a leak location calculation unit; wherein: Leakage event identification unit: It is used to receive the feature vector output by the feature extraction unit, sequentially obtain the high energy mutation amplitude, the average energy value of the continuous high frequency band and the spectral slope value in the feature vector, and compare them with the preset leakage identification threshold respectively; when any feature value exceeds the corresponding threshold, a leakage confirmation signal is output. Signal strength analysis unit: used to receive the filtered audio signals from each sensor in the audio signal acquisition module, calculate the signal strength value corresponding to each sensor, and record the corresponding spatial position coordinates of the sensor; Leakage location calculation unit: After receiving a leakage confirmation signal, it is used to deduce the relative position coordinates of the leakage point based on the signal strength values ​​of multiple adjacent sensors and their spatial positions through a propagation attenuation model, and output the identification result of the leakage event and its corresponding spatial position information.

[0010] Optionally, the signal strength analysis unit includes: Envelope Extraction Subunit: Used to receive the filtered audio signals from each sensor in the audio signal acquisition module, extract their instantaneous amplitude envelopes through Hilbert transform, and generate the signal envelope sequence of each sensor channel; Intensity calculation subunit: Used to calculate the average energy of the entire signal envelope sequence of each sensor to obtain the audio signal intensity value of the corresponding sensor. ; Position mapping subunit: used to retrieve preset sensor installation layout information, obtain the three-dimensional spatial coordinate value corresponding to each sensor, and bind and store it with the calculated signal strength value.

[0011] Optionally, the leak location calculation unit includes: Sensor pair filtering subunit: After receiving a leak confirmation signal, it filters out multiple adjacent sensors with signal strength values ​​higher than a set threshold from all sensors to form an effective sensing pair, and retains their spatial location coordinates and corresponding strength values. Strength difference calculation subunit: used for any pair of sensors and The difference in the audio signal intensity values ​​between the two sensors is calculated, and based on the energy attenuation relationship of sound waves propagating in the pipe medium, the distance difference between the leak point and the two sensors is derived. ; Location inversion derivation subunit: used for multiple sets The relative spatial coordinates of the leak point are obtained by inverting the corresponding spatial coordinates using the least squares method. ; Output results: The spatial coordinates of the leak point obtained by inversion are used as the location result of the leak event, and are output together with the corresponding leak confirmation information to form the final leak identification result and spatial location information.

[0012] Optionally, the status assessment and alarm module includes a leakage level determination unit, a status generation unit, and an alarm output unit; wherein: Leakage Level Determination Unit: Based on the leakage identification results and spatial location information output by the leakage identification and location module, combined with the corresponding signal strength value and multi-source sensor coverage, it calculates the leakage impact range and intensity level, classifies the leakage event according to the preset evaluation criteria, and outputs the leakage level label. Status generation unit: used to generate standardized leak status assessment results based on leak level labels and leak location coordinates, including event number, occurrence time, leak level, spatial coordinates, and current handling status marker; Alarm output unit: Used to send alarm information to the host computer or management terminal through the industrial communication interface after the leakage status assessment results are generated.

[0013] Optionally, the leakage level determination unit includes: Intensity index calculation subunit: Used to obtain the spatial location of the leak point output by the leak identification and location module, extract the signal strength values ​​of several sensors near the spatial location, and calculate the average intensity index of the leak area. ; Influence range estimation subunit: used to analyze signals whose strength values ​​exceed a preset strength threshold. The number of sensors and their spatial distribution range are used to calculate the radius of influence of the leak. ; Hierarchical decision generation sub-unit: used to generate decisions based on calculations. and The value is matched against a preset leakage level assessment standard to determine the level of the leakage event and output the corresponding leakage level label. .

[0014] The audio-frequency-based intelligent pipeline leakage monitoring method, implemented by the aforementioned audio-frequency-based intelligent pipeline leakage monitoring system, includes the following steps: S1: Multiple acoustic sensors are deployed at preset locations along the pipeline to collect raw audio signal data during pipeline operation and mark the spatial location information of each sensor. S2: Bandpass filtering is performed on the original audio signal obtained in S1 to suppress low-frequency mechanical interference and high-frequency electromagnetic noise. Then, the high-energy mutation point, the average energy value of the continuous high-frequency band and the spectral slope value are calculated to generate the feature vector. S3: The feature vector generated in S2 is compared with the preset leakage identification threshold in turn. If any feature dimension exceeds the corresponding threshold, a leakage confirmation signal is output and the current detection frame is determined to be in a leakage state. S4: After confirming the leakage status, extract the filtered signals from multiple sensors that are spatially adjacent to the leakage point in S1, calculate the corresponding signal strength values, and call their spatial coordinate information as subsequent positioning inputs; S5: Based on the signal strength values ​​and spatial positions of multiple adjacent sensors obtained in S4, a sound wave propagation attenuation model is constructed to derive the distance difference from the leak point to each sensor, and the relative spatial position coordinates of the leak point are calculated by using the least squares method. S6: Receives the leakage confirmation signal and leakage point coordinates output by S3 and S5, calculates the leakage level by combining the average leakage intensity and the radius of influence, encapsulates the leakage status assessment results, and outputs alarm information to the host computer or management terminal through the communication interface.

[0015] The beneficial effects of this invention are: This invention constructs a complete audio monitoring and processing flow, which sequentially realizes audio signal acquisition, feature extraction, leakage event identification, spatial positioning and status assessment. It organically combines multi-source sensor information with time-frequency analysis, propagation modeling and level judgment mechanism, thereby improving the accuracy and response efficiency of leakage detection.

[0016] This invention, after a leakage event occurs, can invert the specific location of the leakage point based on the differences in signal strength and spatial layout of multiple sensors, and output structured status information and multi-level alarm signals according to the leakage intensity and impact range matched with preset grading standards, thereby realizing automatic closed-loop control from perception to response, effectively improving the real-time monitoring and early warning capabilities for sudden leakage accidents. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a pipeline leakage intelligent monitoring system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the intelligent pipeline leakage monitoring method according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0021] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1 As shown, the audio-based intelligent pipeline leakage monitoring system includes an audio signal acquisition module, a noise filtering and feature extraction module, a leakage identification and location module, and a status assessment and alarm module; wherein: Audio signal acquisition module: used to acquire audio signals from the pipeline through acoustic sensors arranged along the pipeline and output raw audio signal data; Noise filtering and feature extraction module: used to receive raw audio signal data, filter it to suppress environmental noise, and extract feature vectors related to leakage; Leakage identification and location module: It is used to receive feature vectors, identify whether there is a leakage event, and after confirming the leakage, calculate the position of the leakage point relative to the adjacent sensors based on the signal strength and propagation attenuation model, and output the leakage identification result and location information; Status assessment and alarm module: It is used to generate leakage status assessment results based on the identification results and location information of leakage events, and output alarm signals to the host computer or management terminal.

[0023] The audio signal acquisition module includes a sensor deployment unit, a signal sampling unit, and a data output unit; wherein: Sensor deployment unit: used to deploy multiple acoustic sensors along the pipeline at a preset interval. The sensors are attached to the outside of the pipe wall and their installation position coordinates along the pipeline are recorded by positioning tags for spatial reference during subsequent leak location. Signal sampling unit: Used to control each acoustic sensor to synchronously collect the acoustic vibration of the inner wall of the pipe at a fixed sampling frequency, and generate a continuous time-domain audio signal sequence. The sampling frequency is set to be greater than twice the maximum frequency component of the leakage audio signal to meet the requirements of the Nyquist sampling theorem. Data output unit: This unit is used to timestamp the audio signal sequences collected by each sensor, encapsulate them in a unified format, and output the raw audio signal data. By refining the structure of the audio signal acquisition module, this unit clarifies the functional division of each link in sensor deployment, sampling, and data output, which can effectively improve the spatial resolution and temporal synchronization of audio acquisition and provide high-quality raw signal data support for subsequent feature extraction and leakage localization.

[0024] The noise filtering and feature extraction module includes a frequency band filtering unit, a time-frequency transformation unit, and a feature extraction unit; wherein: Frequency band filtering unit: Used to receive the raw audio signal data output by the audio signal acquisition module, and use a bandpass filter to perform frequency domain filtering on the signal to filter out low-frequency mechanical vibration noise and high-frequency electromagnetic interference in the background environment, while retaining leakage-related audio components located in the preset frequency band; Time-frequency transformation unit: used to perform short-time Fourier transform processing on the filtered audio signal to generate a time-frequency distribution spectrum of the audio signal, reflecting the changes in signal intensity in both time and frequency dimensions; Feature extraction unit: used to extract the location of leakage-related abrupt change points, high-frequency duration energy values ​​and spectral slope features from the time-frequency spectrum, and output them as feature vectors as input data for subsequent leakage identification and localization modules; the above unit can effectively suppress external interference noise and enhance the identifiability of leakage feature signals by introducing bandpass filtering and time-frequency transformation mechanisms. The extracted multi-dimensional audio features have stronger stability and discriminative power, thereby improving the accuracy and reliability of subsequent leakage identification.

[0025] The feature extraction unit includes: The mutation point detection subunit is used to extract high-energy mutation points along the time axis in the time-frequency spectrum, calculate the spectral energy difference sequence between adjacent time frames, and base the detection on a set mutation threshold. The formula for determining the location of the energy mutation is: ,in, For the first The spectral energy abrupt change value of the frame; For the first Frame at frequency Energy at the location; For the first Frame at frequency Energy at the location; , The minimum and maximum frequency limits are set. High-frequency band identification subunit: used to identify high-frequency band signals that continuously appear in the time-frequency spectrum and set the frequency threshold. Statistical greater than The average energy, and screening for those that meet the requirement of a duration not less than a set length. The formula for calculating the average energy of a continuous high-frequency band is: ,in, For the first The average energy of the frame in the high-frequency band; For the first Frame at frequency Energy at the location; Indicates from arrive The number of frequency points between them; Spectral envelope analysis subunit: Used to extract the variation trend of the spectral envelope in consecutive frames. It constructs the envelope variation curve by normalizing the envelope energy, calculates its rate of change using the first derivative, and outputs the spectral slope characteristics. The specific calculation formula is as follows: ,in, For the first The first time derivative of the total energy of the frame spectrum; This indicates the operation of differentiation with respect to time; Feature output: This feature vector is formed by concatenating the output abrupt change location, high-frequency sustained energy value, and spectral slope features into a fixed-dimensional feature vector, which serves as the input for subsequent identification. Through the above sub-units, the energy abrupt change, sustained high frequency, and spectral change trend are quantified and extracted, and output as a unified feature vector. This can construct a highly discriminative audio feature expression method, enhance the modeling ability of pipeline leakage sound features, and improve the accuracy of subsequent identification and location.

[0026] The leak identification and location module includes a leak event identification unit, a signal strength analysis unit, and a leak location calculation unit; wherein: Leakage event identification unit: It is used to receive the feature vector output by the feature extraction unit, sequentially obtain the high energy mutation amplitude, the average energy value of the continuous high frequency band and the spectral slope value in the feature vector, and compare them with the preset leakage identification threshold respectively; when any feature value exceeds the corresponding threshold, a leakage confirmation signal is output. Signal strength analysis unit: used to receive the filtered audio signals from each sensor in the audio signal acquisition module, calculate the signal strength value corresponding to each sensor, and record the corresponding spatial position coordinates of the sensor as the input basis for subsequent positioning calculations; Leakage location calculation unit: After receiving a leakage confirmation signal, it is used to deduce the relative position coordinates of the leakage point based on the signal strength values ​​of multiple adjacent sensors and their spatial positions through a propagation attenuation model, and output the identification result of the leakage event and its corresponding spatial position information. By combining audio feature recognition with physical model calculation, the above unit can not only accurately determine whether a leakage exists, but also realize the spatial location of the leakage point based on the intensity attenuation law, thereby significantly improving the leakage detection accuracy and practicality of the system.

[0027] The signal strength analysis unit includes: Envelope Extraction Subunit: Used to receive the filtered audio signals from each sensor in the audio signal acquisition module, extract their instantaneous amplitude envelopes through Hilbert transform, and generate the signal envelope sequence of each sensor channel; Intensity calculation subunit: Used to calculate the average energy of the entire signal envelope sequence of each sensor to obtain the audio signal intensity value of the corresponding sensor. The specific calculation formula is as follows: ;in, For the first Signal strength values ​​of each sensor; For the first The filtered audio signal envelope of each sensor; The duration of the signal analysis time window; Position mapping subunit: used to retrieve preset sensor installation layout information, obtain the three-dimensional spatial coordinate value corresponding to each sensor, and bind and store it with the calculated signal strength value for subsequent positioning model calls.

[0028] The steps for extracting the instantaneous amplitude envelope using the Hilbert transform are as follows: First, the audio signal acquisition module obtains the first... The time-domain audio signal filtered by each sensor is denoted as... And use it as the input signal for the Hilbert transform; Then, to Perform a Hilbert transform to obtain its corresponding Hilbert transform components. Further construct analytical signals Its expression is: ,in, For the first The analytical signals of each sensor; This is the filtered audio signal; To The orthogonal components obtained after performing the Hilbert transform; The imaginary unit; Next, analyze the signal. By taking the modulus and calculating its instantaneous amplitude envelope, the signal envelope sequence is obtained. Its expression is: ; Finally, the calculated As input to the subsequent strength calculation subunit, it is used to perform energy integration operations and complete the signal strength calculation; The above steps extract the instantaneous amplitude envelope through Hilbert transform, which can effectively characterize the energy fluctuation characteristics of the audio signal in a local time period, avoid amplitude distortion caused by frequency component superposition or short-term phase disturbance, and provide an accurate basis for robust assessment of signal strength and subsequent leakage location.

[0029] The leak location calculation unit includes: Sensor pair filtering subunit: After receiving a leak confirmation signal, it filters out multiple adjacent sensors with signal strength values ​​higher than a set threshold from all sensors to form an effective sensing pair, and retains their spatial location coordinates and corresponding strength values. Strength difference calculation subunit: used for any pair of sensors and The difference in the audio signal intensity values ​​between the two sensors is calculated, and based on the energy attenuation relationship of sound waves propagating in the pipe medium, the distance difference between the leak point and the two sensors is derived. The calculation formula is as follows: ,in, Leakage point to sensor With sensors The difference in distance between them; For sensors and Received signal strength value; Let be the attenuation coefficient of sound waves propagating in the pipe medium, and be a preset constant; Location inversion derivation subunit: used for multiple sets The relative spatial coordinates of the leak point are obtained by inverting the corresponding spatial coordinates using the least squares method. That is, to find the point that minimizes the error function, which is defined as: ,in, Positioning residual error function; Let be the spatial location vector of the leak point to be determined; For sensors and The spatial coordinate vector; Indicates Euclidean distance; Output results: The spatial coordinates of the leak point obtained by inversion are used as the location result of the leak event, and are output together with the corresponding leak confirmation information to form the final leak identification result and spatial location information; The above sub-unit realizes spatial inversion positioning by constructing the correspondence between signal strength difference and spatial distance difference, and combining the joint constraints of multiple pairs of sensors, which can significantly improve the leak positioning accuracy, has strong adaptability and anti-interference ability, and is particularly suitable for industrial pipeline scenarios with multiple deployments and high positioning accuracy requirements.

[0030] The status assessment and alarm module includes a leakage level determination unit, a status generation unit, and an alarm output unit; wherein: Leakage Level Determination Unit: Based on the leakage identification results and spatial location information output by the leakage identification and location module, combined with the corresponding signal strength value and multi-source sensor coverage, it calculates the leakage impact range and intensity level, classifies the leakage event according to the preset evaluation criteria, and outputs the leakage level label. Status generation unit: used to generate standardized leak status assessment results based on leak level labels and leak location coordinates, including event number, occurrence time, leak level, spatial coordinates, and current handling status marker; Alarm output unit: After the leakage status assessment results are generated, the alarm information is sent to the host computer or management terminal through the industrial communication interface. It supports Modbus, RS485 or Ethernet TCP protocol transmission formats, and sets multi-level response flags according to the alarm level to complete the event triggering report. The above unit can realize rapid response and remote management of leakage events of different severities by clearly classifying the leakage level and standardizing the status output. Combined with the alarm mechanism adapted to the communication protocol, it effectively improves the practicality and deployment flexibility of the system in the industrial environment.

[0031] The leakage level determination unit includes: Intensity index calculation subunit: Used to obtain the spatial location of the leak point output by the leak identification and location module, extract the signal strength values ​​of several sensors near the spatial location, and calculate the average intensity index of the leak area. The calculation formula is as follows: ,in, This represents the average intensity index of the leak area. For the first [leak point] whose distance from the leak point is within the threshold range Signal strength values ​​of each sensor; The number of sensors required to meet the spatial threshold conditions; Influence range estimation subunit: used to analyze signals whose strength values ​​exceed a preset strength threshold. The number of sensors and their spatial distribution range are used to calculate the radius of influence of the leak. Its formula is: ,in, Radius of the leakage impact; For the first Spatial position vectors of each sensor; This is the spatial location vector of the leak point; The distance is Euclidean. The preset signal strength threshold; This indicates that the condition is met. Of all the sensors, its location relative to the leak point is... The index number of the sensor with the largest distance; Hierarchical decision generation sub-unit: used to generate decisions based on calculations. and The value is matched against a preset leakage level assessment standard to determine the level of the leakage event and output the corresponding leakage level label. The aforementioned units, through quantitative analysis combining signal strength and spatial distribution information, can accurately assess the actual impact of leakage events and, in conjunction with preset level standards, form clear risk classification labels, thereby supporting more targeted alarm strategies and subsequent intervention measures.

[0032] Table 1 Leakage Level Assessment Mapping Table

[0033] In Table 1 above, the average intensity index is used to measure the overall energy level of the leakage sound source, with the unit being dB; the influence radius represents the spatial scale that the leakage sound energy can effectively cover; the level label represents the discrete level in the output result, used to identify the urgency and priority of the leakage event; by establishing a leakage level assessment mapping table, a joint quantitative assessment of the intensity and spatial impact of the leakage event can be achieved, making the level label determination objective, adjustable, and real-time executable, providing a clear basis for subsequent alarm classification output and event response.

[0034] like Figure 2 As shown, the intelligent pipeline leakage monitoring method based on audio frequency, implemented by the aforementioned intelligent pipeline leakage monitoring system based on audio frequency, includes the following steps: S1: Multiple acoustic sensors are deployed at preset locations along the pipeline to collect raw audio signal data during pipeline operation and mark the spatial location information of each sensor. S2: Bandpass filtering is performed on the original audio signal obtained in S1 to suppress low-frequency mechanical interference and high-frequency electromagnetic noise. Then, the high-energy mutation point, the average energy value of the continuous high-frequency band and the spectral slope value are calculated to generate the feature vector. S3: The feature vector generated in S2 is compared with the preset leakage identification threshold in turn. If any feature dimension exceeds the corresponding threshold, a leakage confirmation signal is output and the current detection frame is determined to be in a leakage state. S4: After confirming the leakage status, extract the filtered signals from multiple sensors that are spatially adjacent to the leakage point in S1, calculate the corresponding signal strength values, and call their spatial coordinate information as subsequent positioning inputs; S5: Based on the signal strength values ​​and spatial positions of multiple adjacent sensors obtained in S4, a sound wave propagation attenuation model is constructed to derive the distance difference from the leak point to each sensor, and the relative spatial position coordinates of the leak point are calculated by using the least squares method. S6: Receives the leakage confirmation signal and leakage point coordinates output by S3 and S5, calculates the leakage level by combining the average leakage intensity and the radius of influence, encapsulates the leakage status assessment results, and outputs alarm information to the host computer or management terminal through the communication interface.

[0035] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0036] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent pipeline leakage monitoring system based on audio frequency, characterized in that, It includes an audio signal acquisition module, a noise filtering and feature extraction module, a leak detection and location module, and a status assessment and alarm module; among which: Audio signal acquisition module: used to acquire audio signals from the pipeline through acoustic sensors arranged along the pipeline and output raw audio signal data; Noise filtering and feature extraction module: used to receive raw audio signal data, filter it to suppress environmental noise, and extract feature vectors related to leakage; Leakage identification and location module: It is used to receive feature vectors, identify whether there is a leakage event, and after confirming the leakage, calculate the position of the leakage point relative to the adjacent sensors based on the signal strength and propagation attenuation model, and output the leakage identification result and location information; Status assessment and alarm module: It is used to generate leakage status assessment results based on the identification results and location information of the leakage event, and output alarm signals to the host computer or management terminal.

2. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 1, characterized in that, The audio signal acquisition module includes a sensor deployment unit, a signal sampling unit, and a data output unit; wherein: Sensor deployment unit: used to deploy multiple acoustic sensors along the pipeline at preset intervals. The sensors are attached to the outside of the pipe wall and their installation position coordinates along the pipeline are recorded by positioning tags. Signal sampling unit: Used to control each acoustic sensor to synchronously collect the acoustic vibration of the inner wall of the pipe at a fixed sampling frequency, and generate a continuous time-domain audio signal sequence; Data output unit: used to timestamp the audio signal sequences collected by each sensor, encapsulate them in a unified format, and output the raw audio signal data.

3. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 1, characterized in that, The noise filtering and feature extraction module includes a frequency band filtering unit, a time-frequency transformation unit, and a feature extraction unit; wherein: Frequency band filtering unit: Used to receive the raw audio signal data output by the audio signal acquisition module, and use a bandpass filter to perform frequency domain filtering on the signal to filter out low-frequency mechanical vibration noise and high-frequency electromagnetic interference in the background environment, while retaining leakage-related audio components located in the preset frequency band; Time-frequency transformation unit: used to perform short-time Fourier transform processing on the filtered audio signal to generate the time-frequency distribution spectrum of the audio signal; Feature extraction unit: used to extract the location of leakage-related abrupt change points, high-frequency duration energy values ​​and spectral slope features from the time-frequency spectrum, and output them in the form of feature vectors.

4. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 1, characterized in that, The feature extraction unit includes: The mutation point detection subunit is used to extract high-energy mutation points along the time axis in the time-frequency spectrum, calculate the spectral energy difference sequence between adjacent time frames, and base the detection on a set mutation threshold. Determine the location of the energy mutation; High-frequency band identification subunit: used to identify high-frequency band signals that continuously appear in the time-frequency spectrum and set the frequency threshold. Statistical greater than The average energy, and screening for those that meet the requirement of a duration not less than a set length. Continuous high-frequency band; Spectral envelope analysis subunit: used to extract the changing trend of the spectral envelope in consecutive frames. It constructs the envelope change curve by normalizing the envelope energy, calculates its change rate using the first derivative, and outputs the spectral slope characteristics. Feature output: Used to concatenate the output mutation point location, high-frequency duration energy value and spectral slope features to form a fixed-dimensional feature vector.

5. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 1, characterized in that, The leak identification and location module includes a leak event identification unit, a signal strength analysis unit, and a leak location calculation unit; wherein: Leakage event identification unit: It is used to receive the feature vector output by the feature extraction unit, sequentially obtain the high energy mutation amplitude, the average energy value of the continuous high frequency band and the spectral slope value in the feature vector, and compare them with the preset leakage identification threshold respectively; when any feature value exceeds the corresponding threshold, a leakage confirmation signal is output. Signal strength analysis unit: used to receive the filtered audio signals from each sensor in the audio signal acquisition module, calculate the signal strength value corresponding to each sensor, and record the corresponding spatial position coordinates of the sensor; Leakage location calculation unit: After receiving a leakage confirmation signal, it is used to deduce the relative position coordinates of the leakage point based on the signal strength values ​​of multiple adjacent sensors and their spatial positions through a propagation attenuation model, and output the identification result of the leakage event and its corresponding spatial position information.

6. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 5, characterized in that, The signal strength analysis unit includes: Envelope Extraction Subunit: Used to receive the filtered audio signals from each sensor in the audio signal acquisition module, extract their instantaneous amplitude envelopes through Hilbert transform, and generate the signal envelope sequence of each sensor channel; Intensity calculation subunit: Used to calculate the average energy of the entire signal envelope sequence of each sensor to obtain the audio signal intensity value of the corresponding sensor. ; Position mapping subunit: used to retrieve preset sensor installation layout information, obtain the three-dimensional spatial coordinate value corresponding to each sensor, and bind and store it with the calculated signal strength value.

7. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 6, characterized in that, The leak location calculation unit includes: Sensor pair filtering subunit: After receiving a leak confirmation signal, it filters out multiple adjacent sensors with signal strength values ​​higher than a set threshold from all sensors to form an effective sensing pair, and retains their spatial location coordinates and corresponding strength values. Strength difference calculation subunit: used for any pair of sensors and The difference in the audio signal intensity values ​​between the two sensors is calculated, and based on the energy attenuation relationship of sound waves propagating in the pipe medium, the distance difference between the leak point and the two sensors is derived. ; Location inversion derivation subunit: used for multiple sets The relative spatial coordinates of the leak point are obtained by inverting the corresponding spatial coordinates using the least squares method. ; Output results: The spatial coordinates of the leak point obtained by inversion are used as the location result of the leak event, and are output together with the corresponding leak confirmation information to form the final leak identification result and spatial location information.

8. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 1, characterized in that, The status assessment and alarm module includes a leakage level determination unit, a status generation unit, and an alarm output unit; wherein: Leakage Level Determination Unit: Based on the leakage identification results and spatial location information output by the leakage identification and location module, combined with the corresponding signal strength value and multi-source sensor coverage, it calculates the leakage impact range and intensity level, classifies the leakage event according to the preset evaluation criteria, and outputs the leakage level label. Status generation unit: used to generate standardized leak status assessment results based on leak level labels and leak location coordinates, including event number, occurrence time, leak level, spatial coordinates, and current handling status marker; Alarm output unit: Used to send alarm information to the host computer or management terminal through the industrial communication interface after the leakage status assessment results are generated.

9. The intelligent pipeline leakage monitoring system based on audio frequency according to claim 8, characterized in that, The leakage level determination unit includes: Intensity index calculation subunit: Used to obtain the spatial location of the leak point output by the leak identification and location module, extract the signal strength values ​​of several sensors near the spatial location, and calculate the average intensity index of the leak area. ; Influence range estimation subunit: used to analyze signals whose strength values ​​exceed a preset strength threshold. The number of sensors and their spatial distribution range are used to calculate the radius of influence of the leak. ; Hierarchical decision generation sub-unit: used to generate decisions based on calculations. and The value is matched against a preset leakage level assessment standard to determine the level of the leakage event and output the corresponding leakage level label. .

10. An intelligent monitoring method for pipeline leakage based on audio frequency, implemented by the intelligent monitoring system for pipeline leakage based on audio frequency as described in any one of claims 1-9, characterized in that, Includes the following steps: S1: Multiple acoustic sensors are deployed at preset locations along the pipeline to collect raw audio signal data during pipeline operation and mark the spatial location information of each sensor. S2: Bandpass filtering is performed on the original audio signal obtained in S1 to suppress low-frequency mechanical interference and high-frequency electromagnetic noise. Then, the high-energy mutation point, the average energy value of the continuous high-frequency band and the spectral slope value are calculated to generate the feature vector. S3: The feature vector generated in S2 is compared with the preset leakage identification threshold in turn. If any feature dimension exceeds the corresponding threshold, a leakage confirmation signal is output and the current detection frame is determined to be in a leakage state. S4: After confirming the leakage status, extract the filtered signals from multiple sensors that are spatially adjacent to the leakage point in S1, calculate the corresponding signal strength values, and call their spatial coordinate information as subsequent positioning inputs; S5: Based on the signal strength values ​​and spatial positions of multiple adjacent sensors obtained in S4, a sound wave propagation attenuation model is constructed to derive the distance difference from the leak point to each sensor, and the relative spatial position coordinates of the leak point are calculated by using the least squares method. S6: Receives the leakage confirmation signal and leakage point coordinates output by S3 and S5, calculates the leakage level by combining the average leakage intensity and the radius of influence, encapsulates the leakage status assessment results, and outputs alarm information to the host computer or management terminal through the communication interface.

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