Detection system for construction near pipeline based on sound wave analysis

Abstract

The invention discloses a pipeline near construction detection system based on sound wave analysis. According to the invention, the quality-effect jump of pipeline safety protection is realized through a multi-level intelligent analysis architecture. The sound wave feature extraction unit captures microscopic vibration fingerprints of mechanical operation, in combination with the deep learning analysis capability of the mode recognition unit, more than ten construction behavior features can be accurately recognized, and mechanical excavation and natural geological activities which are difficult to distinguish by a traditional monitoring means are subjected to millisecond-level discrimination. The construction strength evaluation unit constructs a three-dimensional risk thermodynamic diagram through a sound wave energy propagation model, the forced vibration strength of different pipe sections is quantified in real time, when heavy equipment enters a pipeline safety buffer zone, the system can trigger early warning before making contact with a pipeline physical structure, and a key time window is won for emergency disposal. The full-chain analysis capability from feature perception to risk prediction enables the hidden damage risk of the underground pipe network to have a visual prevention and control means for the first time.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas pipeline construction detection, and specifically relates to a construction detection system near a pipeline based on acoustic wave analysis. Background Technique

[0002] The detection of construction near natural gas pipelines is a crucial safety measure, aiming to ensure that no damage is caused to the pipelines or safety accidents are triggered during construction operations around the pipelines. This detection generally includes comprehensive monitoring and analysis of the geological conditions, pipeline alignment, burial depth, protection measures, and construction activities at the construction site. By using advanced detection equipment and technologies, such as ground penetrating radar, acoustic wave detection, and ground penetrating radar, it is possible to monitor in real time the soil disturbance, pipeline stress changes, etc. during the construction process. Such detection helps to timely discover potential risk factors, ensure the stable operation of natural gas pipelines, prevent accidents such as leakage and explosion, and thus ensure public safety and environmental protection.

[0003] However, traditional methods lack the ability to dynamically suppress environmental noise, are vulnerable to interference such as wind, rain, and traffic, resulting in false alarms, and are unable to accurately distinguish construction activities from natural geological vibrations. The passive monitoring system lacks a risk prediction mechanism and only alarms after physical contact occurs, unable to achieve damage prevention. Summary of the Invention

[0004] The purpose of the present invention is to provide a construction detection system near a pipeline based on acoustic wave analysis in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: A construction detection system near a pipeline based on acoustic wave analysis, the system includes: an acoustic wave acquisition module, a signal processing module, an acoustic wave identification and early warning module, a data storage and analysis module, and a user interface and interaction module;

[0006] Inside the acoustic wave identification and early warning module, there are provided an acoustic wave feature extraction unit, an acoustic wave pattern recognition unit, a construction activity intensity evaluation unit, an early warning and report generation unit, and a self-learning and optimization unit.

[0007] In a preferred embodiment, the high-sensitivity microphone array deployed along the pipeline by the acoustic wave acquisition module adopts a distributed topology, and a set of four-channel acoustic sensors is configured every 50 meters. The sensor housing is built-in with a double-layer vibration isolation device to effectively isolate the interference noise generated by the vibration of the pipeline itself. The adaptive sampling system designed for the complex field environment can dynamically adjust the sampling frequency according to the background noise intensity, and automatically switch to the 4000Hz high-frequency acquisition mode under rainstorm or strong wind conditions to ensure the complete capture of the transient impact signals generated by construction machinery. The sensor node is built-in with a self-check circuit, starts the impedance calibration process every morning at zero o'clock, verifies the microphone sensitivity offset value by transmitting a standard test acoustic wave, and triggers a remote operation and maintenance alarm when the deviation exceeds 5%.

[0008] In a preferred embodiment, after preprocessing the original acoustic wave data by wavelet threshold denoising, the signal processing module uses an improved empirical mode decomposition algorithm to separate the environmental noise and the construction feature signals. The core innovation lies in constructing a construction machinery acoustic fingerprint feature library, converting 17 types of typical construction acoustic waves such as the hydraulic hammering of excavators and the high-frequency vibration of drilling machines into 128-dimensional Mel-frequency cepstral coefficient templates. A sliding time window mechanism is introduced in the processing flow, and the signal is jointly analyzed in the time-frequency domain every 200 milliseconds. When it is detected that the energy increase in a specific frequency band within 3 consecutive windows exceeds the baseline value by 20dB, the feature extraction thread is automatically triggered, and the effective signal segment is sent to the downstream recognition module.

[0009] In a preferred embodiment, the acoustic wave feature extraction unit uses a wavelet transform and adaptive filtering fusion algorithm to extract the key features of acoustic waves. For the complex sound field environment around the pipeline, the system first separates the high-frequency impact signal and the low-frequency background noise through multi-scale wavelet decomposition, and then uses a dynamic threshold filter to enhance the energy of a specific frequency band. The time-varying window function is innovatively introduced to analyze transient signals, and 12 physical parameters such as the rising edge slope and spectral attenuation coefficient of mechanical shock waves are converted into a standardized feature vector. When a continuous pulse group is detected in the frequency band from 30Hz to 800Hz, the transient feature locking mechanism is automatically triggered, and the multi-path reflection interference is eliminated through phase alignment technology to ensure the spatio-temporal consistency of the feature parameters, providing a highly robust input for subsequent pattern recognition.

[0010] In a preferred embodiment, the acoustic wave pattern recognition unit classifies the types of construction activities (such as excavation, drilling, etc.) in real time through a deep neural network. The input is the acoustic wave spectrum feature (100-dimensional vector), and the output is the probability of the construction category. The core innovation lies in the incremental dynamic loss function, combined with the elastic weight consolidation (EWC) technology, which protects the old knowledge from being forgotten when continuously learning new data. The formula is as follows:

[0011]

[0012] Where L交叉熵 Denotes the standard cross - entropy loss, which measures the classification error.

[0013] λ represents the hyperparameter that adjusts the weights of old and new knowledge.

[0014] F i Denotes the Fisher information of the parameter θi on historical data, quantifying its importance; θ i -θ 旧,i Denotes the deviation between the new parameter and the old parameter.

[0015] In a preferred embodiment, the construction activity intensity assessment unit constructs a dynamic assessment system based on the acoustic wave energy time - domain integration model, and quantifies the construction intensity by calculating the energy accumulation value of the characteristic frequency band per unit time. The system establishes a three - level assessment standard. When the energy value in the frequency band of 200Hz to 500Hz lasts for 30 seconds and exceeds the baseline level by 20dB, it is determined as a medium - risk. At the same time, the operation intensity of mechanical equipment is predicted by combining the fluctuation frequency of the signal envelope. The innovative assessment algorithm integrates the acoustic propagation characteristics of pipeline materials, incorporates the acoustic attenuation coefficient of carbon steel pipelines into the intensity correction formula, making the assessment results of different pipe segments physically comparable, and finally outputs a standardized risk index of 0 - 10 levels.

[0016] In a preferred embodiment, the early warning and report generation unit adopts multi - dimensional event correlation analysis technology. When the acoustic features and the vibration sensor data form a coupled event in the spatial coordinates, a three - level progressive early warning is automatically generated. The core algorithm constructs a spatio - temporal cube model, fuses and calculates the sound source azimuth angle, propagation delay and pipeline topology data, and accurately marks the relative distance between the construction point and the pipeline. The report generation system embeds a knowledge graph engine, automatically associates the disposal plans of historical similar events, and generates a customized emergency plan by combining the current meteorological conditions and soil humidity parameters, realizing the end - to - end automation output from the original data to the decision - making suggestions.

[0017] In a preferred embodiment, the self - learning and optimization unit deploys a dual - channel online learning framework. The main channel absorbs new data in real - time through an incremental neural network, and the secondary channel uses a generative adversarial network to simulate extreme working conditions. The innovative concept drift detection mechanism continuously monitors the change in the feature space distribution. When it is found that the KL divergence between the existing pattern library and the new data set exceeds the threshold, the model reconstruction process is automatically started. The optimization process introduces a transfer learning strategy, migrating the parameter of the feature extractor that has been verified effective in the urban utility tunnel monitoring scenario to the wild pipeline scenario, significantly shortening the model convergence time in the new environment and forming the ability of cross - regional knowledge sharing.

[0018] In a preferred embodiment, the data storage and analysis module adopts a hierarchical storage architecture. The real-time data stream is first written into the memory database for transient analysis of a 15-second window, and the feature vector is compressed and stored in the time series database for long-term retention. The data analysis engine has a built-in pattern growth algorithm, which intelligently predicts the development trend of construction activities by comparing the acoustic wave evolution trajectory in historical events. When it is detected that the sound pressure level in a specific direction presents a linear growth pattern of 3dB per hour, a potential risk evolution map is automatically generated, and the remaining safety time is calculated in combination with the pipeline stress distribution model, providing a quantitative disposal window for decision makers.

[0019] In a preferred embodiment, the user interface and interaction module deeply integrate the acoustic monitoring data with the pipeline digital twin through a three-dimensional geographic visualization platform, and the operator can rotate and view the real-time soundprint heat map of any pipe section through touch gestures. When an early warning event occurs, the system automatically retrieves multimodal data from the 30 minutes before and after to generate an interactive timeline, and supports dragging and comparing the sound wave spectrum and vibration waveform curve. The original intelligent report generator can extract the trend of key parameter changes, automatically mark the suspected construction machinery type and azimuth information, output emergency response plan proposals that meet industry standards, and ensure the traceability of instruction transmission through digital signature technology.

[0020] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0021] 1. In the present invention, a leap in the quality and efficiency of pipeline safety protection is achieved through a multi-level intelligent analysis architecture. The acoustic feature extraction unit captures the microscopic vibration fingerprint of mechanical operations. Combined with the deep learning analysis capability of the pattern recognition unit, it can accurately identify more than ten types of construction behavior characteristics, and make millisecond-level distinctions between mechanical excavation and natural geological activities that are difficult to distinguish by traditional monitoring methods. The construction intensity assessment unit constructs a three-dimensional risk heat map through an acoustic wave energy propagation model, and quantifies the forced vibration intensity of different pipe sections in real time. When heavy equipment enters the pipeline safety buffer zone, the system can trigger an early warning before contacting the physical structure of the pipeline, thereby gaining a critical time window for emergency response. This full-chain analysis capability from feature perception to risk prediction enables the invisible damage risk of underground pipeline networks to have a visual prevention and control method for the first time.

[0022] 2. In the present invention, the early warning and reporting unit deeply integrates geographical information and engineering knowledge graphs. The automatically generated disposal plan not only includes the accurate coordinates of the construction points, but also can recommend the optimal intervention measures according to the pipeline material and burial depth parameters. The self-learning unit enables the system to have the environmental adaptability across climate zones by continuously absorbing monitoring data from multiple regions. Whether it is the low-frequency vibration attenuation in frozen soil areas or the acoustic wave refraction distortion in wet soil, the system can dynamically adjust the analysis strategy. This intelligent evolution characteristic transforms the pipeline protection from passive response to growth-type defense. While reducing the false alarm rate by 80%, it promotes the formation of a smart pipeline network ecological system that can be synchronized and upgraded with the urban development. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is the overall system block diagram of the present invention;

[0024] Figure 2 It is the system block diagram of the acoustic wave recognition and early warning module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] Refer to Figure 1-2 ,

[0027] A construction detection system near pipelines based on acoustic wave analysis, the system includes: an acoustic wave acquisition module, a signal processing module, an acoustic wave recognition and early warning module, a data storage and analysis module, and a user interface and interaction module;

[0028] Inside the acoustic wave recognition and early warning module, there are an acoustic wave feature extraction unit, an acoustic wave pattern recognition unit, a construction activity intensity evaluation unit, an early warning and report generation unit, and a self-learning and optimization unit.

[0029] The highly sensitive microphone array deployed along the pipeline by the acoustic wave acquisition module adopts a distributed topology structure, and a set of four-channel acoustic sensors is configured every 50 meters. The double-layer vibration isolation device is built into the sensor housing to effectively isolate the interference noise generated by the pipeline's own vibration. The adaptive sampling system designed for the complex outdoor environment can dynamically adjust the sampling frequency according to the background noise intensity, and automatically switch to the 4000Hz high-frequency acquisition mode under rainstorm or strong wind conditions to ensure the complete capture of the transient impact signals generated by construction machinery. The self-check circuit is built into the sensor node, and the impedance calibration process is started every morning. The sensitivity offset value of the microphone is verified by transmitting a standard test acoustic wave. When the deviation exceeds 5%, a remote operation and maintenance alarm is triggered.

[0030] After the signal processing module preprocesses the original acoustic wave data through wavelet threshold denoising, it uses an improved empirical mode decomposition algorithm to separate environmental noise from construction feature signals. The core innovation lies in constructing a construction machinery acoustic fingerprint feature library, which converts 17 types of typical construction acoustic waves, such as excavator hydraulic hammering and drill rig high-frequency vibration, into 128-dimensional Mel-frequency cepstral coefficient templates. A sliding time window mechanism is introduced in the processing flow. Every 200 milliseconds, the signal is jointly analyzed in the time-frequency domain. When it is detected that the energy increase in a specific frequency band within 3 consecutive windows exceeds the baseline value by 20 dB, the feature extraction thread is automatically triggered, and the effective signal segment is sent to the downstream recognition module.

[0031] The acoustic wave feature extraction unit uses a fusion algorithm of wavelet transform and adaptive filtering to extract the key features of acoustic waves. For the complex acoustic field environment around the pipeline, the system first separates high-frequency impact signals and low-frequency background noise through multi-scale wavelet decomposition, and then uses a dynamic threshold filter to enhance the energy of a specific frequency band. Innovatively, a time-varying window function is introduced to analyze transient signals, and 12 physical parameters, such as the rising edge slope and spectral attenuation coefficient of mechanical shock waves, are converted into standardized feature vectors. When a continuous pulse group is detected in the frequency band from 30 Hz to 800 Hz, the transient feature locking mechanism is automatically triggered, and the multi-path reflection interference is eliminated through phase alignment technology to ensure the spatio-temporal consistency of the feature parameters, providing highly robust input for subsequent pattern recognition.

[0032] The acoustic wave pattern recognition unit classifies the types of construction activities (such as excavation, drilling, etc.) in real time through a deep neural network. The input is the acoustic wave spectrum feature (a 100-dimensional vector), and the output is the probability of the construction category. The core innovation lies in the incremental dynamic loss function, combined with the Elastic Weight Consolidation (EWC) technology, which protects old knowledge from being forgotten when continuously learning new data. The formula is as follows:

[0033]

[0034] where L 交叉熵 represents the standard cross-entropy loss, which measures the classification error.

[0035] λ represents a hyperparameter that adjusts the weights of old and new knowledge (e.g., \lambda = 0.5λ = 0.5).

[0036] F i represents the Fisher information of the parameter θi on historical data, quantifying its importance; θ i -θ 旧,i represents the deviation between the new parameter and the old parameter.

[0037] The construction activity intensity assessment unit constructs a dynamic assessment system based on the acoustic wave energy time-domain integral model, and quantifies the construction intensity by calculating the energy accumulation value in the characteristic frequency band per unit time. The system establishes a three-level assessment standard. When the energy value in the frequency band of 200 Hz to 500 Hz is continuously more than 20 dB above the baseline level for 30 seconds, it is determined as a medium risk. At the same time, the operating intensity of mechanical equipment is predicted by combining the fluctuation frequency of the signal envelope. The innovative assessment algorithm integrates the acoustic propagation characteristics of pipeline materials, incorporates the acoustic attenuation coefficient of carbon steel pipelines into the intensity correction formula, makes the assessment results of different pipeline sections physically comparable, and finally outputs a standardized risk index of 0-10 levels.

[0038] The early warning and report generation unit adopts multi-dimensional event correlation analysis technology. When the acoustic characteristics and vibration sensor data form a coupling event in the spatial coordinates, a three-level progressive early warning is automatically generated. The core algorithm constructs a spatio-temporal cube model, fuses and calculates the sound source azimuth angle, propagation time delay and pipeline topology data, and accurately marks the relative distance between the construction point and the pipeline. The report generation system embeds a knowledge graph engine, automatically associates the disposal plans of historical similar events, and generates a customized emergency plan by combining the current meteorological conditions and soil humidity parameters, realizing the end-to-end automatic output from the original data to the decision-making suggestions.

[0039] The self-learning and optimization unit deploys a dual-channel online learning framework. The main channel absorbs new data in real time through an incremental neural network, and the secondary channel uses a generative adversarial network to simulate extreme working conditions. The innovative concept drift detection mechanism continuously monitors the change in the feature space distribution. When it is found that the KL divergence between the existing pattern library and the new data set exceeds the threshold, the model reconstruction process is automatically started. The optimization process introduces a transfer learning strategy, transfers the parameter of the feature extractor verified effective in the urban pipe gallery monitoring scenario to the wild pipeline scenario, greatly shortens the model convergence time in the new environment, and forms the ability of cross-regional knowledge sharing.

[0040] The data storage and analysis module adopts a hierarchical storage architecture. The real-time data stream is first written into the in-memory database for transient analysis of a 15-second window, and the feature vectors are stored in the time-series database for long-term retention after compression. The data analysis engine is built with a pattern growth algorithm, and the development trend of construction activities is intelligently predicted by comparing the acoustic wave evolution trajectories in historical events. When it is detected that the sound pressure level at a specific azimuth shows a linear growth pattern of 3 dB per hour, a potential risk evolution map is automatically generated, and the remaining safety time is calculated by combining the pipeline stress distribution model, providing a quantitative disposal window for decision-makers.

[0041] The user interface and interaction module deeply integrates acoustic monitoring data with the digital twin of the pipeline through a three-dimensional geographic visualization platform. Operators can rotate and view the real-time soundprint heat map of any pipe section through touch gestures. When an early warning event occurs, the system automatically retrieves multimodal data from the 30 minutes before and after to generate an interactive timeline, supporting drag-and-drop comparison of sound wave spectra and vibration waveform curves. The original intelligent report generator can extract the trend of key parameter changes, automatically mark the suspected construction machinery type and azimuth information, output emergency response plan proposals that meet industry standards, and ensure the traceability of instruction transmission through digital signature technology.

[0042] From the above we can know:

[0043] In the present invention, a leap in the quality and efficiency of pipeline safety protection is achieved through a multi-level intelligent analysis architecture. The acoustic feature extraction unit captures the microscopic vibration fingerprint of mechanical operations, and combined with the deep learning analysis capability of the pattern recognition unit, it can accurately identify more than ten types of construction behavior characteristics, and make millisecond-level distinctions between mechanical excavation and natural geological activities that are difficult to distinguish by traditional monitoring methods. The construction intensity assessment unit constructs a three-dimensional risk heat map through an acoustic wave energy propagation model, and quantifies the forced vibration intensity of different pipe sections in real time. When heavy equipment enters the pipeline safety buffer zone, the system can trigger an early warning before contacting the physical structure of the pipeline, thereby gaining a critical time window for emergency response. This full-chain analysis capability from feature perception to risk prediction enables the invisible damage risk of underground pipeline networks to have a visual prevention and control method for the first time.

[0044] In the present invention, the early warning and reporting unit deeply integrates geographic information and engineering knowledge graphs, and the automatically generated disposal plan not only contains the precise coordinates of the construction point, but also recommends the optimal intervention measures based on the pipeline material and burial depth parameters. The self-learning unit continuously absorbs multi-regional monitoring data, enabling the system to adapt to the environment across climate zones. Whether it is the attenuation of low-frequency vibrations in frozen areas or the refraction distortion of sound waves in moist soil, the system can dynamically adjust the analysis strategy. This intelligent evolutionary feature enables pipeline protection to shift from passive response to growth-based defense, while reducing the false alarm rate by 80%, and promoting the formation of a smart pipe network ecosystem that can be upgraded in sync with urban development.

[0045] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0046] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A construction detection system near a pipeline based on acoustic wave analysis, characterized in that: The system includes: an acoustic wave acquisition module, a signal processing module, an acoustic wave recognition and early warning module, a data storage and analysis module, and a user interface and interaction module; Inside the acoustic wave recognition and early warning module, there are an acoustic wave feature extraction unit, an acoustic wave pattern recognition unit, a construction activity intensity evaluation unit, an early warning and report generation unit, and a self-learning and optimization unit; The acoustic wave acquisition module captures the acoustic wave signals around the pipeline in real time through a distributed microphone array, and transmits the original data stream to the signal processing module for noise reduction and feature enhancement; After the signal processing module extracts the standardized spectrum features using wavelet transform technology, it inputs the preprocessed data into the core analysis unit of the acoustic wave recognition and early warning module. The acoustic wave recognition and early warning module analyzes the construction activity features through a deep neural network, generates a risk level by linking the intensity evaluation algorithm, and while triggering an early warning instruction, synchronously writes the structured feature vector into the time-series database of the data storage and analysis module; The data storage and analysis module conducts pattern mining and trend deduction on historical events, and outputs a risk evolution map to the user interface and interaction module; The user interface and interaction module dynamically displays the acoustic fingerprint heat map of the entire pipeline through a three-dimensional geographic information platform, and receives the feedback instructions from the operator to reverse-optimize the recognition threshold.

2. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, wherein: The high-sensitivity microphone array deployed along the pipeline by the acoustic wave acquisition module adopts a distributed topology structure, and a set of four-channel acoustic sensors is configured every 50 meters; a double-layer vibration isolation device is built into the sensor housing to effectively isolate the interference noise generated by the vibration of the pipeline itself.

3. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, wherein: After the signal processing module preprocesses the original acoustic wave data through wavelet threshold noise reduction, it uses an improved empirical mode decomposition algorithm to separate the environmental noise and the construction feature signal; the core innovation lies in constructing a construction machinery acoustic fingerprint feature library, which converts 17 types of typical construction acoustic waves such as the hydraulic hammering of an excavator and the high-frequency vibration of a drilling machine into 128-dimensional Mel-frequency cepstral coefficient templates.

4. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, wherein: The acoustic wave feature extraction unit uses a fusion algorithm of wavelet transform and adaptive filtering to extract the key features of the acoustic wave; aiming at the complex acoustic field environment around the pipeline, the system first separates the high-frequency impact signal and the low-frequency background noise through multi-scale wavelet decomposition, and then uses a dynamic threshold filter to enhance the energy of a specific frequency band.

5. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, characterized in that: The acoustic wave pattern recognition unit classifies the construction activity types in real time through a deep neural network; the input is the acoustic wave spectrum feature, and the output is the probability of the construction category; the core innovation lies in the incremental dynamic loss function, combined with the elastic weight consolidation technology, to protect the old knowledge from being forgotten when continuously learning new data. The formula is as follows: where L 交叉熵 represents the standard cross-entropy loss, which measures the classification error; λ represents the hyperparameter that adjusts the weights of new and old knowledge; F i represents the Fisher information of the parameter θi on historical data, quantifying its importance; θ i -θ 旧,i Indicates the deviation between the new parameter and the old parameter.

6. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, characterized in that: The construction activity intensity evaluation unit constructs a dynamic evaluation system based on the acoustic wave energy time-domain integration model, and quantifies the construction intensity by calculating the energy accumulation value of the characteristic frequency band within a unit time; the system establishes a three-level evaluation standard. When it is monitored that the energy value in the frequency band of 200 Hz to 500 Hz exceeds the baseline level by 20 dB for 30 seconds continuously, it is determined as a medium risk, and at the same time, the operation intensity of the mechanical equipment is predicted by combining the fluctuation frequency of the signal envelope.

7. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, wherein: The early warning and report generation unit adopts multi-dimensional event correlation analysis technology. When acoustic features and vibration sensor data form a coupled event in spatial coordinates, a three-level progressive early warning is automatically generated; a spatio-temporal cube model is constructed to fuse and calculate the sound source azimuth, propagation delay, and pipeline topology data to accurately label the relative distance between the construction point and the pipeline; the report generation system embeds a knowledge graph engine, automatically associates the disposal solutions of historical similar events, and generates a customized emergency plan in combination with the current meteorological conditions and soil humidity parameters to achieve end-to-end automated output from raw data to decision-making suggestions.

8. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, wherein: The self-learning and optimization unit deploys a dual-channel online learning framework. The main channel absorbs new data in real time through an incremental neural network, and the secondary channel uses a generative adversarial network to simulate extreme working conditions.

9. The construction detection system near a pipeline based on acoustic wave analysis according to claim 1, wherein: The data storage and analysis module adopts a hierarchical storage architecture. The real-time data stream is first written into an in-memory database for transient analysis with a 15-second window, and the feature vectors are stored in a time-series database for long-term retention after compression; the data analysis engine is built with a pattern growth algorithm to intelligently predict the development trend of construction activities by comparing the acoustic wave evolution trajectories in historical events.

10. A pipeline near construction detection system based on acoustic wave analysis according to claim 1, characterized in that: The user interface and interaction module deeply integrates acoustic monitoring data with the pipeline digital twin through a three-dimensional geographic visualization platform. Operators can rotate and view the real-time acoustic fingerprint heat map of any pipe segment through touch gestures; when an early warning event occurs, the system automatically retrieves multi-modal data for the previous and next 30 minutes to generate an interactive timeline.

Images