Non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion

Abstract

The invention relates to the technical field of intelligent detection, and further relates to a non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion. The method comprises the steps that 1, acoustic sensors are arranged along a pipe network at equal intervals, each acoustic sensor collects an acoustic time domain signal generated by pipeline leakage, and then the frequency spectrum intensity of the acoustic time domain signal at the pipeline characteristic frequency is calculated; step 2, calculating a cross-correlation function between two different acoustic sensors according to the frequency spectrum intensity, and determining acoustic delay of the acoustic time domain signal between the different acoustic sensors by searching a peak value position of the cross-correlation function; step 3, according to the acoustic delay, calculating the position of a leakage point by using a sound wave propagation principle; according to the position of the leakage point, the water leakage amount is evaluated in combination with pipeline use data. According to the method, through multi-data fusion and intelligent analysis, effective combination of trenchless, high-sensitivity, accurate positioning and scientific quantitative evaluation is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent detection, and particularly relates to a non-excavation underground water supply and drainage pipeline leakage detection method based on multi-data fusion. Background Art

[0002] Underground water supply and drainage pipelines are an important part of urban infrastructure, undertaking key functions such as water supply, drainage, and sewage treatment. However, due to factors such as long-term underground burial, long service life, and complex environment, the pipeline system generally faces the problem of leakage. According to statistical data, the average leakage rate of urban water supply systems globally reaches 30% to 40%, and in some old urban areas, it is even as high as over 50%. Pipeline leakage not only causes waste of water resources and increased energy consumption, but may also lead to serious consequences such as ground subsidence and groundwater pollution, posing a great threat to the safe operation of cities and the ecological environment. Therefore, accurately and efficiently detecting and locating underground pipeline leakage points is of great significance for ensuring the safe operation of urban infrastructure and improving resource utilization efficiency.

[0003] Traditional underground pipeline leakage detection methods mainly include direct excavation inspection method, pressure test method, tracer method, and acoustic detection method, etc. The direct excavation inspection method is simple and intuitive in operation, but it is destructive, costly, inefficient, and causes serious interference to urban traffic and residents' lives. The pressure test method judges whether there is leakage by monitoring the pressure change in the pipe section. It has a certain detection sensitivity, but it is difficult to accurately locate the leakage point, and the detection effect on small leakage points is not good. The tracer method injects specific tracer substances (such as fluorescent dyes, radioactive isotopes, etc.) into the pipeline, and then monitors the leakage of these substances on the ground surface. Although it has high detection sensitivity, it is complex in operation, costly, and has the risk of environmental pollution. The acoustic detection method is one of the non-excavation leakage detection technologies that are widely applied at present. It mainly detects and locates based on the acoustic signals generated at the leakage point. This method has the advantages of no need for excavation, fast detection speed, and little environmental interference. Existing acoustic detection technologies mainly include the leak listening method, the correlation method, and the noise recording method, etc. The leak listening method uses professional equipment (such as an electronic leak listening rod) to listen to the water leakage sound on the ground or at pipeline accessory facilities. It is simple in operation, but its accuracy depends on the experience of the operator, and it is seriously interfered by environmental noise. The noise recording method judges the leakage point position by deploying multiple noise recorders in the pipeline system and analyzing the change in the intensity of the recorded acoustic signals. It has the advantage of high automation, but the positioning accuracy is limited. Summary of the Invention

[0004] The main objective of the present invention is to provide a non-excavation underground water supply and drainage pipeline leakage detection method based on multi-data fusion. Through multi-data fusion and intelligent analysis, this method effectively combines non-excavation, high sensitivity, precise positioning, and scientific quantitative assessment, providing a more efficient, accurate, economical, and comprehensive solution for underground pipeline leakage management.

[0005] To solve the above problems, the technical solution of the present invention is realized as follows:

[0006] A non-excavation underground water supply and drainage pipeline leakage detection method based on multi-data fusion, the method comprising:

[0007] Step 1: Deploy acoustic sensors at equal intervals along the pipeline. Each acoustic sensor collects the acoustic time-domain signal generated by pipeline leakage, and then calculates the spectral intensity of the acoustic time-domain signal at the pipeline characteristic frequency.

[0008] Step 2: According to the spectral intensity, calculate the cross-correlation function between two different acoustic sensors. By finding the peak position of the cross-correlation function, determine the acoustic delay of the acoustic time-domain signal between different acoustic sensors.

[0009] Step 3: According to the acoustic delay, use the acoustic wave propagation principle to calculate the leakage point location; according to the leakage point location, combined with pipeline usage data, evaluate the leakage volume.

[0010] Further, the pipeline usage data includes: the pipeline service time t service , in years; the pipeline wall thickness T, in m; the pipeline elastic modulus E, in Pa; the pipeline diameter D pipe , in m; the overburden depth H soil , in m; the overburden density ρ soil , in kg / m 3 ; the Poisson's ratio v of the pipeline material; the propagation speed c of the acoustic wave in the pipeline material pipe , in m / s; the pipeline internal pressure P int , in Pa.

[0011] Further, the pipeline characteristic frequency f is:

[0012]

[0013] Where ρ water is the density of water, in kg / m 3 .

[0014] Further, if the pipeline is a cast iron pipeline, the value range of the Poisson's ratio v is from 0.21 to 0.26; if the pipeline is a steel pipeline, the value range of the Poisson's ratio v is from 0.27 to 0.30; if the pipeline is a copper pipeline, the value range of the Poisson's ratio v is from 0.31 to 0.34; if the pipeline is a PVC pipe, the value range of the Poisson's ratio v is from 0.35 to 0.38; if the pipeline is a PE pipe, the value range of the Poisson's ratio v is from 0.39 to 0.42; if the pipeline is a PP pipe, the value range of the Poisson's ratio v is from 0.43 to 0.45; if the pipeline is an ABS pipe, the value range of the Poisson's ratio v is from 0.46 to 0.48; if the pipeline is a concrete pipe, the value range of the Poisson's ratio v is from 0.15 to 0.20; if the pipeline is a fiberglass pipe, the value range of the Poisson's ratio v is from 0.10 to 0.14.

[0015] Further, the spectral intensity S i (f) of the acoustic time-domain signal s i (t) collected by the i-th acoustic sensor at the pipeline characteristic frequency f is:

[0016]

[0017] where t0 is the starting time for collecting the acoustic time-domain signal, in s; t1 is the stopping time for collecting the acoustic time-domain signal, in s; t is the time variable, in s; z is the imaginary symbol; C soil is the propagation speed of the sound wave in the overburden, in m / s.

[0018] Further, the cross-correlation function R ij (τ) between the i-th acoustic sensor and the j-th acoustic sensor is:

[0019]

[0020] where τ is the time delay variable, in s; f max is the upper limit of the characteristic frequency; f min is the lower limit of the characteristic frequency; is the conjugate of the spectral intensity S i (f) of the acoustic time-domain signal s j (t) collected by the j-th acoustic sensor at the pipeline characteristic frequency f.

[0021] Further, the acoustic delay Δt is:

[0022]

[0023] Further, the leakage point location L leak is:

[0024]

[0025] Among them, the position L of the leakage point leak represents the distance measured along the pipeline axis direction from the reference starting point, and the reference starting point is the position of the first acoustic sensor; g is the acceleration due to gravity, with the unit of m / s 2 ; v soil is the Poisson's ratio of the overburden; L total is the distance between the acoustic sensor and other adjacent acoustic sensors.

[0026] Furthermore, the leakage water volume Q leak is:

[0027]

[0028] Among them, L i is the position of the i-th acoustic sensor along the pipeline axis direction; t ref is the reference service time of the pipeline; pH is the overburden pH value; Cl - is the chloride ion concentration in the overburden, with the unit of mg / kg; pH ref is the reference pH value; is the reference chloride ion concentration.

[0029] The non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion of the present invention has the following beneficial effects: First, this method breaks through the limitations of traditional acoustic detection technology. By calculating the spectral intensity of the acoustic time-domain signal at the characteristic frequency of the pipeline, the signal-to-noise ratio and the accuracy of feature extraction are greatly improved. Especially in complex underground environments and under different pipe material conditions, this method can effectively identify and extract the leakage signal features, making the detection results more reliable and stable. Second, this method innovatively introduces the concept of pipeline characteristic frequency and establishes a complete calculation model. By considering parameters such as the acoustic wave propagation speed, diameter, wall thickness, elastic modulus, and Poisson's ratio of the pipeline material, the characteristic frequencies of different pipelines are accurately calculated, laying a foundation for subsequent spectral analysis and signal processing. Especially for pipelines of different materials, a detailed range of Poisson's ratio values is provided, enabling the detection system to adapt to various pipe network environments. Third, this method significantly improves the accuracy of leakage location by constructing a cross-correlation function considering environmental factors. Compared with traditional methods, this cross-correlation function introduces environmental parameters such as overburden depth, overburden density, and acoustic wave propagation speed, which can effectively compensate for the changes in the propagation characteristics of acoustic waves in complex underground environments and reduce the positioning error. Fourth, this method not only realizes the precise positioning of leakage but also can accurately evaluate the leakage volume, providing a quantitative basis for pipe network maintenance. The leakage volume evaluation model comprehensively considers pipeline physical parameters, acoustic signal characteristics, service time, and environmental factors. Especially, the influence of the pH value and chloride ion concentration of the overburden on pipeline aging is innovatively introduced, making the evaluation results more comprehensive and accurate. Finally, this method realizes non-excavation detection throughout the process, minimizing the interference to urban operation and residents' lives. The detection process is efficient and fast, which can greatly reduce the detection cost and time and improve the efficiency of pipe network maintenance. At the same time, this method is applicable to various pipe materials and burial conditions and has a wide application prospect. Brief Description of the Drawings

[0030] Figure 1 It is a schematic flow chart of the non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion provided by an embodiment of the present invention. Detailed Embodiments

[0031] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Example 1, refer to Figure 1: A non-excavation underground water supply and drainage pipeline leakage detection method based on multi-data fusion, the method comprising:

[0033] Step 1: Acoustic sensors are deployed at equal intervals along the pipeline. Each acoustic sensor collects the acoustic time-domain signal generated by pipeline leakage, and then calculates the spectral intensity of the acoustic time-domain signal at the pipeline characteristic frequency.

[0034] Step 2: According to the spectral intensity, calculate the cross-correlation function between two different acoustic sensors. By finding the peak position of the cross-correlation function, determine the acoustic delay of the acoustic time-domain signal between different acoustic sensors.

[0035] Step 3: According to the acoustic delay, use the acoustic wave propagation principle to calculate the leakage point location; according to the leakage point location, combined with the pipeline usage data, evaluate the leakage volume.

[0036] Underground water supply and drainage pipelines are an important part of urban infrastructure, and their safe and stable operation is crucial for ensuring residents' lives and industrial production. However, due to various factors such as pipeline aging, corrosion, and external force damage, pipeline leakage occurs from time to time. Traditional leakage detection methods, such as listening rods and correlation meters, often rely on manual experience, have low efficiency, and have poor detection effects for pipelines buried deep or with complex environmental noise. Excavation detection is not only costly, time-consuming, and laborious, but also has a significant impact on traffic and the environment. Therefore, developing an efficient, accurate, and non-excavation intelligent detection method for underground pipeline leakage has important practical significance and application value. The non-excavation underground water supply and drainage pipeline leakage detection method based on multi-data fusion is a technical solution proposed to address this challenge. It uses modern sensing technology, signal processing technology, and data fusion ideas to achieve rapid positioning and quantitative evaluation of underground pipeline leakage.

[0037] The core of this method lies in the utilization of acoustic principles. When pressurized fluid (usually water) inside the pipeline jets out from the leakage point, it will have complex interactions with the pipe wall and the surrounding medium (such as soil), generating vibrations and sound waves. These sound waves will propagate along multiple paths, including the pipe wall, the internal fluid, and the external soil. The first step of this method is to evenly deploy a series of highly sensitive acoustic sensors along the pipeline network to be detected at predetermined and equal distances. These sensors, like a doctor's stethoscope, are placed close to or in contact with the pipeline (usually installed at contact points such as valves and fire hydrants, or approaching the pipe body through drilling), and are specifically used to capture the weak acoustic signals that may be generated by pipeline leakage. The purpose of the even deployment is to form an effective monitoring network to ensure that the signals can be received by multiple sensors, providing the necessary data redundancy and spatial information for subsequent positioning calculations. Each sensor works independently, real-time collecting the acoustic vibration signals at its location and converting these continuously changing vibrations into a time-domain signal data stream. These original time-domain signals contain various information, including both the target leakage signals and may be mixed with environmental noises (such as vehicle driving and ground construction) and the operating noises of the pipeline system itself (such as water flow sounds and pump station vibrations). Therefore, it is often difficult to effectively identify leakage by directly analyzing the original time-domain signals.

[0038] In order to extract useful leakage information from complex mixed signals, signal processing is required. What makes this method unique is that instead of analyzing the signals across the entire frequency band, it first focuses on a specific frequency - the pipeline characteristic frequency. This characteristic frequency is not arbitrarily selected but is closely related to the physical properties of the pipeline itself, such as the pipeline material (which determines the sound wave propagation speed in the pipe wall, the elastic modulus, and Poisson's ratio of the material), the pipeline diameter, wall thickness, and the density of the medium (water) inside the pipeline. It can be considered that the pipeline structure itself is like a filter or resonator, and the energy generated by leakage may be more concentrated or prominent at certain structure-related frequencies. By calculating or through experience to determine this characteristic frequency, the focus of the analysis can be concentrated on the frequency band most likely to carry leakage information. Calculating the spectral intensity at a specific frequency essentially uses signal processing techniques such as Fourier transform to analyze the energy magnitude of the original time-domain signal at this characteristic frequency point. This step not only effectively extracts the signal components related to the pipeline structure response but also initially plays a role in suppressing noise in other frequency bands. More importantly, when calculating the spectral intensity, this method introduces the concept of multi-data fusion, taking the geometric dimensions of the pipeline (diameter, wall thickness), material properties (elastic modulus), operating parameters (internal pressure), and external environment information (cover depth, density, sound wave propagation speed in the soil), etc. as adjustment factors or weights to correct or standardize the calculated spectral intensity. This means that the obtained spectral intensity value is not only a manifestation of the acoustic signal energy but also indirectly reflects the relative significance of this energy level under the current pipeline, pressure, and soil conditions. This makes the spectral intensity measured at different locations and under different conditions more comparable, laying a foundation for subsequent analysis.

[0039] After obtaining the preliminarily processed and fused spectral intensity information at each sensor location, the next step is to determine the time difference of the leakage signal arriving at different sensors, that is, the acoustic delay. This is a crucial step in achieving leakage location. This method uses cross-correlation function analysis technology. Cross-correlation is a classic method for measuring the similarity and time lag relationship between two signals. The specific operation is to select the spectral intensity signals calculated from two different sensors (usually a pair of adjacent or sensors separated by a certain distance) (which can be understood as a narrowband signal near the characteristic frequency point or its representative value), and calculate the cross-correlation function between them. The value of the cross-correlation function changes with the time shift amount (delay time) of one signal relative to the other signal. When the similar components in the two signals (here mainly referring to the signal components from the same leakage source) are aligned in time, the cross-correlation function reaches a peak. The abscissa (time delay) corresponding to this peak precisely represents the time difference required for the leakage acoustic wave to propagate to these two sensors. For example, if the cross-correlation peak appears at a positive delay time, it means the signal arrives at the first sensor first; if it appears at a negative delay time, it means the signal arrives at the second sensor first. The magnitude of the peak also reflects the correlation degree and intensity of the signal. When calculating the cross-correlation function, the idea of data fusion can also be introduced, such as using parameters related to the pipeline, soil, and pressure to weight or correct the cross-correlation calculation process to improve the robustness and accuracy of the calculation. By systematically calculating the cross-correlation functions between multiple sensor pairs on the pipeline network and finding the peaks, a series of acoustic delay data can be obtained.

[0040] After obtaining the accurate acoustic delay, the third step is to use this time difference information to calculate the specific location of the leakage point. The basic principle is similar to that of satellite navigation systems or earthquake hypocenter location, which belongs to the positioning method based on the time difference of signal arrival. Suppose the leakage point is located on the pipeline between two sensors. Given the distance between the two sensors, the time difference (i.e., acoustic delay) of the sound wave propagating from the leakage point to these two sensors, and the effective propagation speed of the sound wave in the propagation medium, the exact position of the leakage point relative to these two sensors can be calculated through simple geometric relationships or algebraic equations. However, in the actual situation of underground pipelines, the propagation path and speed of sound waves are complex. The sound wave may mainly propagate along the pipe wall, or through the water body inside the pipeline, or through the surrounding soil, or even a combination of these paths. The propagation speed of each path is different and is affected by various factors such as pipe material, size, internal pressure, water properties, and soil type, density, and water content. Therefore, for precise positioning, in-depth multi-data fusion must be carried out again. It is necessary to comprehensively consider the propagation speed of sound waves in the pipe material, in water, and in soil, as well as the properties of these media (such as pipe elastic modulus, Poisson's ratio, soil density, Poisson's ratio, overburden depth, etc.). This method uses the principle of sound wave propagation, combines the previously calculated acoustic delay, and various physical parameters provided in the pipeline usage data to establish a physical model or calculation formula that more conforms to the actual propagation situation to calculate the position of the leakage point. This calculation process reflects the high integration of acoustic measurement data with the physical model of the pipeline system and environmental parameters.

[0041] Merely locating the leakage point is not enough. Evaluating the amount of leakage is equally important for making decisions on whether immediate repair is needed and how to repair it. The last part of this method is to combine the located position of the leakage point and the pipeline usage data to evaluate the leakage volume. The principle of evaluating the leakage volume is usually based on the assumption that there is a certain correlation between the severity of the leakage (i.e., the amount of leakage) and the intensity of the acoustic signal generated by the leakage. Generally speaking, a larger leak will generate a stronger acoustic signal under the same pressure. However, the sound wave will attenuate during propagation, and the signal intensity received by sensors farther away from the leakage point will be weaker. Therefore, the amount of leakage cannot be simply judged by the directly received signal intensity. This method takes this into account. It uses the previously calculated spectral intensity values of each sensor (especially the intensity values of those sensors closest to the leakage point), and combines the determined position of the leakage point L leak and the position L of each sensor i , to calculate the propagation distance of the signal |L i -L leak |. Then, according to the material and size (diameter D) of the pipeline pipe, factors such as wall thickness T and elastic modulus E are used to establish an acoustic attenuation model to compensate for the measured spectral intensity and estimate the sound intensity at the leakage source. In addition, the leakage rate is also directly related to the internal pressure P of the pipeline. The greater the pressure, the greater the leakage rate for the same leakage opening. The service time t of the pipeline int is directly related. The greater the pressure, the greater the leakage rate for the same leakage opening. The service time t of the pipeline service as well as the chemical properties of the surrounding soil environment (such as pH value, chloride ion concentration Cl - ) will also affect the corrosion condition of the pipeline and the formation and development of the leakage opening, and thus affect the relationship between the leakage rate and the sound signal intensity. This method creatively takes these factors into consideration. Through a comprehensive model, it integrates acoustic measurement data (spectral intensity, position information), pipeline physical data (size, material, pressure), pipeline historical data (service time), and environmental chemical data (soil pH, chloride ion concentration), and finally gives a quantitative evaluation of the leakage rate Q leak . The fusion of such multi-dimensional data makes the evaluation of the leakage rate no longer rely solely on the sound intensity, but is based on a comprehensive understanding of the physical process and influencing factors of the entire leakage event, thereby improving the accuracy and reliability of the evaluation.

[0042] Furthermore, the pipeline usage data includes: the service time t of the pipeline service , in years; the wall thickness T of the pipeline, in m; the elastic modulus E of the pipeline, in Pa; the diameter D of the pipeline pipe , in m; the covering depth H soil , in m; the covering density ρ soil , in kg / m 3 ; the Poisson's ratio v of the pipeline material; the propagation speed c of sound waves in the pipeline material pipe , in m / s; the internal pressure P of the pipeline int , in Pa.

[0043] The service time t of the pipeline service is obtained by consulting the pipeline network laying archive materials. This parameter reflects the aging degree of the pipeline and directly affects the fatigue state of the pipeline material and the possibility of leakage. As the service time prolongs, the pipeline material will deteriorate to varying degrees, resulting in a decrease in the wall strength and thus an increase in the leakage risk. In non-destructive detection, this parameter is used as an important input for the leakage evaluation model to correct the calculation of the leakage rate. The wall thickness T and diameter D of the pipeline pipeIt can usually be obtained from the pipeline network design drawings or measured on-site at the exposed points of the pipeline using an ultrasonic thickness gauge. For pipe segments completely buried underground, ground penetrating radar technology can be used for non-destructive detection to obtain it. These two parameters are closely related to the structural strength of the pipeline and affect the propagation characteristics of sound waves in the pipeline and the spectral characteristics of leakage signals. The elastic modulus E and Poisson's ratio v of the pipeline are important parameters describing the mechanical properties of the pipeline material and are usually obtained by querying from standard material manuals according to the pipeline material type. For pipelines with a long service life, these parameters may change and need to be corrected through sampling tests or using on-site dynamic response tests. In leakage detection, these parameters affect the propagation speed and modal characteristics of sound waves, and thus affect the accuracy of leakage location. The overburden depth H soil and the overburden density ρ soil can be obtained through geological exploration or estimated using pipeline network construction records and formation data. These parameters affect the intensity of the leakage signals received by surface acoustic sensors. The thicker and denser the overburden layer, the more obvious the signal attenuation. In non-excavation detection methods, these parameters are used to compensate for signal attenuation to ensure accurate analysis of the leakage location. The propagation speed c of sound waves in the pipeline material pipe is the core parameter for leakage location calculation and can be measured through theoretical calculation or on-site calibration. Theoretical calculation is based on the elastic modulus and density of the pipeline material, while on-site calibration determines it by conducting percussion tests on the pipeline at known locations and measuring the sound wave propagation time. This parameter directly determines the conversion relationship between acoustic delay and leakage location and is a key factor in leakage location accuracy. The internal pressure P of the pipeline int is a real-time parameter reflecting the operating state of the pipeline network and is usually obtained through real-time monitoring by pressure sensors installed at key nodes of the pipeline network. The fluctuation of the internal pressure will affect the acoustic signal characteristics generated by leakage. Under a higher internal pressure, the frequency and intensity of the leakage acoustic signals will increase accordingly. In the leakage detection system, this parameter is used to adjust the leakage identification threshold and estimate the leakage volume.

[0044] Furthermore, the characteristic frequency f of the pipeline is:

[0045]

[0046] where ρ water is the density of water, with the unit of kg / m 3 .

[0047] According to the thin-walled cylinder theory, when the cylinder vibrates radially, its deformation is related to the change in internal pressure. For the radial vibration of the cylinder, its governing equation can be derived through the Lagrange equation. When establishing the Lagrange equation of the pipeline vibration, the kinetic energy and potential energy of the pipeline need to be considered. The kinetic energy of the pipeline is mainly determined by the mass and vibration velocity of the pipeline wall, while the potential energy consists of the elastic potential energy of the pipeline wall. For the radial vibration of the pipeline wall, its kinetic energy can be expressed as where ρ pipe is the density of the pipeline material, v r is the radial vibration velocity, and L is the length of the pipeline. The elastic potential energy of the pipeline wall can be expressed as where u r is the radial displacement.

[0048] When considering fluid-structure interaction, the internal fluid exerts pressure on the pipeline wall, and this pressure will cause the pipeline wall to generate radial vibration. According to the fluid dynamics theory, the pressure fluctuation of the internal fluid can be expressed as where ρ water is the density of water, c water is the sound wave propagation speed in water, and u is the displacement vector of the fluid. Combining the fluid pressure with the elastic deformation of the pipeline wall, a fluid-structure coupling equation is established. In a simplified one-dimensional model, the wave equation of the pipeline radial vibration can be obtained:

[0049] Performing variational analysis on this wave equation, assuming the displacement u r has the form of u r = Asin(kx - ωt), where k is the wave number and ω is the angular frequency. Substituting this expression into the wave equation, the characteristic equation can be obtained: From the characteristic equation, the relationship between the angular frequency ω and the wave number k can be obtained: Considering the relationship between the wave number k and the wavelength λ and the relationship between the sound wave propagation speed c pipe in the pipeline and the wavelength and frequency c pipe = λf, the expression of the frequency f can be further derived. Expressing the wave number k as and substituting it into the expression of the angular frequency ω (ω = 2πf), we get: Simplifying the above expression, we get:

[0050] In the pipeline vibration analysis, the mass effect of the pipeline wall is usually much smaller than that of the fluid, that is, ρ pipe << ρ water , so it can be approximated as ρ pipe + ρ water ≈ ρ water .

[0051] Further simplify the above expression to obtain: Solve for the frequency f: Considering the cyclic boundary conditions and the geometric constraints of the pipe, there is a relationship between the wavelength λ and the pipe diameter D pipe : λ = 2D pipe . This means that c pipe = λf = 2D pipe f, so Combining the above two expressions for the frequency f, we can obtain: Rearranging the above equation gives: Solve for c pipe , and we get: Substitute to obtain the final expression for the characteristic frequency of the pipe: After further mathematical transformation and simplification, and considering the empirical correction factor in practical applications, the final expression for the characteristic frequency f of the pipe is:

[0052] Furthermore, if the pipe is a cast iron pipe, the value range of the Poisson's ratio ν is from 0.21 to 0.26; if the pipe is a steel pipe, the value range of the Poisson's ratio v is from 0.27 to 0.30; if the pipe is a copper pipe, the value range of the Poisson's ratio ν is from 0.31 to 0.34; if the pipe is a PVC pipe, the value range of the Poisson's ratio ν is from 0.35 to 0.38; if the pipe is a PE pipe, the value range of the Poisson's ratio v is from 0.39 to 0.42; if the pipe is a PP pipe, the value range of the Poisson's ratio v is from 0.43 to 0.45; if the pipe is an ABS pipe, the value range of the Poisson's ratio v is from 0.46 to 0.48; if the pipe is a concrete pipe, the value range of the Poisson's ratio v is from 0.15 to 0.20; if the pipe is a fiberglass pipe, the value range of the Poisson's ratio v is from 0.10 to 0.14.

[0053] The Poisson's ratio ν is a dimensionless parameter that describes the relationship between the lateral deformation and the axial deformation of a material. Due to differences in molecular structure and internal composition, different pipeline materials exhibit different ranges of Poisson's ratio values. For metal pipes such as cast iron pipes, the Poisson's ratio ranges from 0.21 to 0.26. The Poisson's ratio of steel pipes is slightly higher, ranging from 0.27 to 0.30, while that of copper pipes is between 0.31 and 0.34. The relatively low Poisson's ratios of these metal materials indicate that they have relatively small lateral deformations when subjected to axial forces, which is related to the lattice structure of the metal materials and the strength of the internal molecular bonds. Due to their high molecular structure characteristics, plastic pipes usually have relatively high Poisson's ratios. The Poisson's ratio of PVC pipes ranges from 0.35 to 0.38, that of PE pipes is 0.39 to 0.42, that of PP pipes is 0.43 to 0.45, and that of ABS pipes is 0.46 to 0.48. The relatively high Poisson's ratios of these plastic pipes mean that they will produce greater lateral deformations when subjected to axial forces, which is related to the molecular chain structure of the plastic materials and the relatively weak intermolecular forces. Inorganic materials such as concrete pipes and fiberglass pipes have relatively low Poisson's ratios. The Poisson's ratio of concrete pipes ranges from 0.15 to 0.20, and that of fiberglass pipes is even lower, only 0.10 to 0.14. The relatively low Poisson's ratios of these materials indicate that they have very small lateral deformations when subjected to axial forces, which is closely related to their brittle characteristics and internal microstructures. During the intelligent detection of pipeline leakage, accurate Poisson's ratio parameters are crucial for calculating the characteristic frequencies of pipelines, the acoustic wave propagation velocity, and leakage location. When the acoustic sensor collects the leakage signal, the system needs to select the corresponding range of Poisson's ratio according to the type of pipeline material to ensure the accuracy of subsequent cross-correlation function calculations and leakage location calculations. Different Poisson's ratio values will significantly affect the propagation mode and velocity of acoustic waves in the pipeline, thereby affecting the final leakage location accuracy.

[0054] Furthermore, the spectral intensity S i (f) of the acoustic time-domain signal s i (t) collected by the i-th acoustic sensor at the characteristic frequency f of the pipeline is:

[0055]

[0056] where t0 is the starting time for collecting the acoustic time-domain signal, with the unit of s; t1 is the stopping time for collecting the acoustic time-domain signal, with the unit of s; t is the time variable, with the unit of s; z is the imaginary symbol; C soil is the propagation velocity of the acoustic wave in the overburden soil, with the unit of m / s.

[0057] First of all, for the time-domain signal s i (t) collected by the i-th acoustic sensor, it is necessary to transform it from the time domain to the frequency domain through Fourier transform. Fourier transform is a basic mathematical tool for analyzing the frequency characteristics of time-varying signals, and its standard form is where \(j\) is the imaginary unit and \(\omega\) is the angular frequency. In practical applications, signals can only be collected within a finite time window \([t_0, t_1]\), so the Fourier transform is modified to Considering the angular frequency \(\omega = 2\pi f\), and to maintain symbol consistency, the imaginary unit in the formula is represented as \(z\), obtaining the preliminary spectrum expression of the acoustic signal \(s\) i (t) at frequency \(f\) This expression describes the energy distribution of the signal at a specific frequency \(f\), but it has not yet considered the influence of the pipeline system and the surrounding environment on the acoustic wave propagation. In the scenario of pipeline leakage, the acoustic waves generated at the leakage point propagate through the pipeline wall and the surrounding soil to the acoustic sensor, and during this process, they will be affected by various factors resulting in energy attenuation. To accurately reflect the spectral intensity of the actual leakage signal, it is necessary to correct the preliminary spectrum and introduce the influence factor of the transmission path. According to the acoustic wave propagation theory, when acoustic waves propagate from the leakage point to the sensor, their energy attenuation is affected by the pipeline geometric dimensions, material properties, and the properties of the surrounding environmental medium.

[0058] First, consider the pipeline size factor. According to the acoustic wave propagation theory in a circular pipe, the acoustic wave energy is proportional to the cube of the pipeline diameter, expressed as This is because the larger the pipeline diameter, the smaller the divergence effect of the acoustic wave during propagation, and the slower the energy attenuation. Second, the internal pressure \(P\) of the pipeline int will affect the initial energy of the acoustic wave generated by the leakage. The higher the pressure, the greater the acoustic wave energy at the leakage point. Therefore, the spectral intensity is proportional to the internal pressure. When considering the pipeline material properties, the elastic modulus \(E\) and the wall thickness \(T\) are two key parameters. According to the theory of elasticity, the greater the elastic modulus of the material, the smaller the energy loss of the acoustic wave during propagation; while the greater the wall thickness, the more significant the energy attenuation when the acoustic wave penetrates the pipeline wall. Considering these two factors comprehensively, the spectral intensity is proportional to Finally, it is necessary to consider the influence of the overburden environment on the acoustic wave propagation. The greater the overburden depth \(H\) soil , the longer the propagation distance of the acoustic wave and the more obvious the energy attenuation; the greater the overburden density \(\rho\) soil , the greater the impedance of the acoustic wave in the soil and the more energy loss; and the propagation speed \(C\) of the acoustic wave in the overburden soil reflects the acoustic properties of the soil and affects the energy attenuation of the acoustic wave. According to the acoustic wave propagation theory in a non-uniform medium, the spectral intensity is proportional to Comprehensively considering all the above factors, the complete expression of the spectral intensity is obtained: This formula can be further strictly derived through the wave equation theory. Considering the wave equation in the pipeline system where \(p\) is the sound pressure and \(c\) is the speed of sound, is the Laplace operator. Applying the wave equation to the pipeline in the cylindrical coordinate system and considering the boundary conditions: the elastic boundary of the pipe wall and the impedance boundary of the soil, the expression of the energy change during the propagation of sound waves can be obtained. Solving the wave equation by the analytical solution method and substituting the results into the energy relation E = ∫p 2 dt, the relationship between the acoustic wave energy and the pipeline parameters and environmental parameters can be deduced. Combining the energy conservation characteristics of the Fourier transform, the expression of the spectral intensity is finally obtained, which is consistent with the formula obtained by physical analysis in the previous text. This strict derivation starting from the wave equation verifies the theoretical rationality of the spectral intensity formula. In practical applications, this formula enables the system to calculate the standardized spectral intensity based on the collected original acoustic signals and known pipeline parameters, thus providing accurate frequency-domain data for subsequent cross-correlation analysis and leakage location. By this method, the interference of pipeline parameters and environmental factors on spectral analysis is excluded, and the accuracy and reliability of leakage detection are improved.

[0059] Furthermore, the cross-correlation function R ij (τ) between the i-th acoustic sensor and the j-th acoustic sensor is:

[0060]

[0061] where τ is the time delay variable, with the unit of s; f max is the upper limit of the characteristic frequency; f min is the lower limit of the characteristic frequency; is the conjugate of the spectral intensity S i (f) of the acoustic time-domain signal s j (t) collected by the j-th acoustic sensor at the pipeline characteristic frequency f.

[0062] In digital signal processing, the cross-correlation function of two discrete-time signals x[n] and y[n] is defined as where n represents the time delay. Extending this definition to continuous-time domain signals, the cross-correlation function of two continuous signals x(t) and y(t) can be expressed as where τ is the time delay variable. In the frequency domain, according to the correlation theorem, the cross-correlation function can be calculated by the Fourier transform: where X(f) and Y(f) are the Fourier transforms of x(t) and y(t) respectively, and Y * (f) represents the conjugate of Y(f), represents the inverse Fourier transform. The definition of the inverse Fourier transform is Therefore, the cross-correlation function can be expressed as In the application of pipeline network leakage detection, what is concerned is the acoustic signals s collected by the i-th acoustic sensor and the j-th acoustic sensori (t) and s j The cross - correlation of (t) within a specific frequency range.

[0063] Since the leakage signal is mainly concentrated in the pipeline characteristic frequency range [f min , f max , the integration range is limited to this interval, and we get where S i (f) and S j (f) are the spectral intensities of s i (t) and s j (t) at frequency f respectively, is the conjugate of S j (f), and z is the imaginary symbol. This formula represents the basic cross - correlation calculation. However, in actual leakage detection, the influence of environmental and pipeline conditions on the cross - correlation function also needs to be considered. When the leakage sound wave propagates from the leakage point to two sensors at different positions, the signal will be affected by the attenuation and phase change of the propagation path. To compensate for these effects and obtain a more accurate time - delay estimate, a correction factor needs to be introduced. First, the propagation speed C soil of the sound wave in the overburden directly affects the propagation time of the sound wave, and the cross - correlation function needs to be proportional to C soil for standardized comparison under different soil conditions. Second, the internal pressure P int of the pipeline will affect the initial energy of the leakage signal, and the cross - correlation function needs to be inversely proportional to P int to eliminate the influence of pressure fluctuations on the cross - correlation calculation.

[0064] Finally, the pipeline diameter D pipe and the wall thickness T also affect the propagation characteristics of the sound wave in the pipeline. The geometric mean of these two parameters is inversely proportional to the cross - correlation function. Considering these factors, the expression of the corrected cross - correlation function is obtained: The physical meaning of this formula can be further understood through the solution of the wave equation. Consider the sound wave propagation equation in the pipeline where p is the sound pressure, c is the sound speed, is the Laplace operator. When the sound wave propagates from the leakage point to the sensor, its propagation characteristics are affected by the pipeline and soil parameters. By solving the wave equation, the relationship between the sound pressure p and the propagation distance r and time t can be obtained: p where A is the amplitude, ω is the angular frequency, and k is the wave number. The signals received by the two sensors can be expressed as and where r i and r j are the distances from the leakage point to the two sensors. The cross - correlation function of these two signals is related to is directly proportional to. Considering that the distance difference (r i -r j ) and the time delay τ are related as (r i -r j ) = cτ, the phase term in the cross-correlation function can be written as e j2πfτ . The amplitude term is affected by pipeline parameters and soil conditions and can be represented by the correction factor derived previously. In this way, through the analytical solution of the wave equation, the same cross-correlation function expression as before can be obtained. In practical applications, the calculation of the cross-correlation function R ij (τ) requires first obtaining the spectral intensities S i (f) and S j (f) of the two sensors, and then performing frequency-domain multiplication and integration within the characteristic frequency range [f min , f max , and finally applying the correction factor. This calculation method can effectively eliminate the interference of environmental and pipeline conditions, improve the accuracy and stability of cross-correlation analysis, and lay a foundation for subsequent acoustic delay determination and leak point location.

[0065] Furthermore, the acoustic delay Δt is as follows:

[0066]

[0067] Furthermore, the leak point location L leak is:

[0068]

[0069] where the leak point location L leak represents the distance measured along the pipeline axis from the reference starting point, and the reference starting point is the location of the first acoustic sensor; g is the acceleration due to gravity with the unit of m / s 2 ; ν soil is the Poisson's ratio of the overburden; L total is the distance between the acoustic sensor and the adjacent other acoustic sensors.

[0070] The determination of the acoustic delay Δt is based on cross-correlation function analysis. The cross-correlation function R ij (τ) describes the variation relationship of the similarity between the signals collected by the i-th sensor and the j-th sensor with the time delay τ. Starting from the signal processing theory, when two signals originate from the same sound source but have different propagation paths, the cross-correlation function reaches its maximum value at a specific time delay, and this time delay corresponds to the time difference for the sound wave to propagate from the sound source to the two sensors. Therefore, the acoustic delay Δt can be obtained by solving the cross-correlation function R ijDetermined by the time delay corresponding to the maximum value of (τ), i.e., Δt = arg max τ R ij (τ). The arg max operation here represents finding the value of the independent variable when the function attains its maximum value. Mathematically, it can be obtained by solving the condition that the derivative of the cross-correlation function with respect to τ equals zero, i.e., and Since the cross-correlation function is a complex integral expression, in practical applications, numerical methods are usually employed to calculate its maximum point, such as applying interpolation algorithms after discrete sampling or using the fast Fourier transform to improve the calculation efficiency.

[0071] Once the acoustic delay Δt is determined, the next step is to derive the formula for calculating the leakage point location L leak This derivation is based on the geometric relationship of sound wave propagation and wave theory. Suppose two adjacent acoustic sensors are located at L i and L j on the pipeline axis respectively, and the distance between the sensors is L total = |L j - L i |. When a leakage occurs at the position L leak on the pipeline, the sound waves generated by the leakage point will propagate to the two sensors respectively, and the propagation times are t i and t j respectively. The acoustic delay Δt is the difference between these two propagation times, Δt = |t i - t j |. According to the sound wave propagation principle, the propagation time is proportional to the propagation distance, i.e., and where v eff is the effective propagation speed of sound waves in the pipeline system. Considering the complex propagation path of sound waves in the underground pipeline system, which involves the influence of pipeline materials, internal fluids, and surrounding soil, the effective propagation speed v eff needs to consider multiple factors. According to wave dynamics theory, the wave speed in a multiphase medium is affected by the elastic properties and density of the medium. For a buried pipeline system, the propagation path of sound waves from the leakage point to the sensor mainly includes two parts: wall propagation and soil propagation. The wall propagation speed is mainly determined by the elastic modulus and density of the pipeline material, while the soil propagation speed is affected by the covering depth, soil density, and soil elastic properties. According to elastic wave theory, the sound wave propagation speed C soil in the soil is related to the elastic properties and density of the soil and can be expressed as where E soil is the elastic modulus of the soil, ρ soil is the soil density, vsoil is the Poisson's ratio of the soil. For the underground pipeline system, due to the overburden depth H soil existing, the propagation of sound waves in the soil is also affected by the static pressure of the soil, and the static pressure increases with the increase of the overburden depth, causing the sound wave propagation speed to change. Considering the complexity of the underground pipeline system, the effective propagation speed v eff can be expressed as where g is the acceleration due to gravity. This expression reflects the comprehensive influence of soil properties, pipeline properties, and internal pressure on the propagation of sound waves. Substituting the effective propagation speed into the relationship between acoustic delay and propagation distance, we can obtain After further transformation, we get This indicates that the effective propagation speed can be determined by the sensor spacing and the measured acoustic delay. For the determination of the leakage point location L leak , the specific positional relationship between the leakage point and the two sensors needs to be considered. Let the reference starting point be the position L i of the first acoustic sensor. The leakage point location L leak represents the distance measured along the pipeline axis direction from the reference starting point. According to the time difference of the acoustic signals collected by the two sensors, the relative position of the leakage point can be deduced. When the leakage point is between the two sensors, there is |L leak -L i |-|L leak -L j | = v eff ·Δt. Considering that L j = L i +L total , and assuming L i ≤L leak ≤L j , then there is (L leak -L i )-(L j -L leak ) = v eff ·Δt, that is, 2L leak -L i -L j = v eff ·Δt. Substituting L j = L i +L total , we get 2L leak -2L i -L total = v eff ·Δt, that is Since L i is the reference starting point, it can be set to zero, then Substituting the effective propagation speed v effSubstituting the expression, we get This is the calculation formula for the position of the leakage point. It takes into account the geometric characteristics of the pipeline system, soil characteristics, and acoustic wave propagation characteristics, and can accurately locate the position of the leakage point based on the measured acoustic delay. In practical applications, it is necessary to pay attention to correcting the installation position deviation of the acoustic sensor and the complexity of the acoustic wave propagation path to improve the positioning accuracy.

[0072] Furthermore, the leakage flow rate Q leak is:

[0073]

[0074] where L i is the position of the i-th acoustic sensor along the pipeline axis; t ref is the reference service time of the pipeline; pH is the pH value of the overburden; Cl - is the chloride ion concentration in the overburden, with the unit of mg / kg; pH ref is the reference pH value; is the reference chloride ion concentration.

[0075] First of all, starting from the basic principles of fluid mechanics, the flow rate at the leakage point of the pipeline can be expressed by the orifice flow formula, that is where Q is the flow rate, C d is the flow coefficient, A is the orifice area, P is the pressure difference, and ρ is the fluid density. In the scenario of underground pipeline network leakage, the pressure difference is mainly determined by the internal pressure P of the pipeline int . Therefore, the leakage flow rate is proportional to the square root of the internal pressure of the pipeline. Considering that it is difficult to directly measure the orifice area in practical engineering, an indirect method needs to be used for estimation. The intensity distribution of the acoustic signal is an important basis for estimating the orifice area. When the pipeline leaks, the acoustic wave generated by the leakage point will propagate in the pipeline and be collected by the acoustic sensors deployed along the line. According to acoustic theory, the acoustic wave energy is related to the intensity of the flow disturbance in the medium, and the intensity of the flow disturbance is related to the leakage area and the flow rate. Therefore, the scale of the leakage can be inferred by analyzing the spectral characteristics of the acoustic signal. In frequency domain analysis, the spectral intensity S i (f) reflects the energy distribution of the acoustic signal collected by the i-th sensor at a specific frequency f. For the acoustic signal caused by leakage, the distribution characteristics of its spectral intensity are directly related to the scale of the leakage. In particular, the maximum spectral intensity max i |S i (f)| measured by all sensors and the minimum spectral intensity min i |S i(f)| The ratio can reflect the relative scale of leakage. This is because the larger the leakage scale, the stronger the acoustic wave energy generated, and the more significant the difference in spectral intensity between sensors. According to the acoustic wave energy attenuation theory, the acoustic wave energy decays exponentially with the propagation distance, and the attenuation coefficient is related to the medium characteristics.

[0076] In an actual pipe network, the spectral intensity ratio can be used as an indicator of the leakage scale. It is proportional to the hole area and thus also proportional to the leakage volume. Considering from the perspective of the physical characteristics of the pipeline, the diameter D of the pipeline pipe has a significant impact on the leakage volume. According to the pipeline flow formula in fluid mechanics, under the same pressure condition, the flow rate is proportional to the 2.5th power of the pipe diameter. This is because the larger-diameter pipeline has stronger flow capacity, and the same proportion of leakage will result in a larger leakage volume. Therefore, the term is introduced in the leakage volume calculation formula. The structural strength of the pipeline also affects the development of leakage and the leakage volume. The compressive capacity of the pipeline is jointly determined by the elastic modulus E of the material and the wall thickness T. The higher the elastic modulus, the stiffer the material and the smaller the deformation under the same pressure; the larger the wall thickness, the higher the overall strength of the pipeline. Therefore, the leakage volume is proportional to , indicating that the lower the pipeline strength, the larger the leakage volume under the same conditions. In practical applications, the distance between the acoustic sensor and the leakage point also affects the accuracy of the measurement results. According to the acoustic wave propagation theory, the acoustic wave energy decays exponentially with the propagation distance, and the attenuation rate is determined by the medium characteristics. For an underground pipe network, the energy attenuation of the acoustic wave during propagation can be approximately expressed as where α is the attenuation coefficient, and the empirical value is about 0.5, |L i -L leak | is the distance from the i-th sensor to the leakage point. This attenuation factor needs to be applied in the leakage volume estimation to compensate for the measurement deviation caused by the distance factor. The aging state of the pipeline has an important impact on the development of leakage and the leakage volume.

[0077] The longer the service time t of the pipeline service , the higher the degree of material aging and the faster the development of leakage. At the same time, environmental factors such as the pH value of the overburden and the chloride ion concentration Cl - will also accelerate the corrosion and aging of the pipeline material. According to the material corrosion kinetics model, the corrosion rate of the material is closely related to the acidity and alkalinity of the environment and the chloride ion concentration. Acidic environment (low pH value) and high chloride ion concentration environment will accelerate the corrosion of metal pipelines and the degradation of non-metal pipelines. Integrating these factors into the aging influence factor, it can be expressed as where t ref is the reference service time, pH ref is the reference pH value, is the reference chloride ion concentration. This expression describes the increasing trend of the pipeline leakage degree with the extension of service time and the enhancement of environmental corrosivity. When the service time approaches zero or the environmental corrosivity is very low, the aging influence factor approaches zero, indicating almost no aging influence; when the service time is very long or the environmental corrosivity is very high, the aging influence factor approaches 1, indicating that the aging influence reaches the maximum. Considering all the above factors comprehensively, the leakage water volume Q leak The calculation formula can be expressed as: This formula theoretically comprehensively considers the influences of acoustic characteristics, pipeline physical characteristics, positional relationship and aging state on the leakage water volume, and realizes the accurate estimation of the leakage water volume. In practical applications, it is necessary to calibrate and verify each parameter in the formula through experimental data to ensure the accuracy and reliability of the estimation results. Through this method of multi-data fusion, a comprehensive assessment of the leakage situation of the underground pipe network can be achieved without excavation, providing a scientific basis for pipe network maintenance and repair.

[0078] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; 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 make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A trenchless underground water supply and drainage network leakage detection method based on multi-data fusion, characterized in that: The method comprises: Step 1: Deploy acoustic sensors at equal intervals along the pipeline network. Each acoustic sensor collects the acoustic time domain signal generated by pipeline leakage, and then calculates the spectrum intensity of the acoustic time domain signal at the characteristic frequency of the pipeline; Step 2: Calculate the cross-correlation function between two different acoustic sensors based on the spectrum intensity, and determine the acoustic delay of the acoustic time domain signal between different acoustic sensors by finding the peak position of the cross-correlation function; Step 3: Calculate the location of the leakage point based on acoustic delay and the principle of sound wave propagation; assess the amount of water leakage based on the location of the leakage point and the pipeline usage data.

2. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion according to claim 1 is characterized in that: Pipeline usage data include: Pipeline service time t service , in years; pipeline wall thickness T, in meters; pipeline elastic modulus E, in Pa; pipeline diameter D pipe , unit is m; cover depth H soil , unit is m; cover soil density ρ soil , unit is kg / m 3 ; Poisson's ratio v of the pipe material; The propagation speed of sound waves in the pipe material c pipe , unit is m / s; internal pressure of the pipeline P int , unit is Pa.

3. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion as claimed in claim 2 is characterized in that: The pipeline characteristic frequency f is: Among them, ρ water is the density of water in kg / m 3 .

4. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion as claimed in claim 3 is characterized in that: If the pipeline is a cast iron pipe, the value range of Poisson's ratio v is 0.21 to 0.26; if the pipeline is a steel pipe, the value range of Poisson's ratio v is 0.27 to 0.30; if the pipeline is a copper pipe, the value range of Poisson's ratio v is 0.31 to 0.34; if the pipeline is a PVC pipe, the value range of Poisson's ratio v is 0.35 to 0.38; if the pipeline is a PE pipe, the value range of Poisson's ratio v is 0.39 to 0.42; if the pipeline is a PP pipe, the value range of Poisson's ratio ν is 0.43 to 0.45; if the pipeline is an ABS pipe, the value range of Poisson's ratio ν is 0.46 to 0.48; if the pipeline is a concrete pipe, the value range of Poisson's ratio v is 0.15 to 0.20; if the pipeline is a glass fiber reinforced plastic pipe, the value range of Poisson's ratio v is 0.10 to 0.

14.

5. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion as claimed in claim 4 is characterized in that: The acoustic time domain signal s collected by the i-th acoustic sensor i (t) Spectral intensity S at the pipeline characteristic frequency f i (f) is: Wherein, t0 is the start time of collecting acoustic time domain signals, in seconds; t1 is the stop time of collecting acoustic time domain signals, in seconds; t is the time variable, in seconds; z is the imaginary number symbol; C soil It is the propagation speed of sound waves in the covering soil, in m / s.

6. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion as claimed in claim 5, characterized in that: The cross-correlation function R between the i-th acoustic sensor and the j-th acoustic sensor ij (τ) is: Where τ is the time delay variable, in seconds; f max is the upper limit of characteristic frequency; f min is the lower limit of characteristic frequency; is the acoustic time domain signal s collected by the jth acoustic sensor i (t) Spectral intensity S at the pipeline characteristic frequency f j (f) conjugation.

7. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion according to claim 6 is characterized in that: The acoustic delay Δt is:

8. The trenchless underground water supply and drainage network leakage detection method based on multi-data fusion as claimed in claim 7, characterized in that: Leakage point location L leak for: Among them, the leakage point location L leak It represents the distance measured along the pipeline axis from the reference starting point, where the reference starting point is the position of the first acoustic sensor; g is the gravitational acceleration in m / s 2 ; ν soil is the Poisson's ratio of the covering soil; L total is the distance between the acoustic sensor and other adjacent acoustic sensors.

9. The method for detecting leakage of a trenchless underground water supply and drainage network based on multi-data fusion according to claim 8, characterized in that: Water leakage Q leak for: Among them, L i is the position of the ith acoustic sensor along the pipeline axis; t ref is the reference service time of the pipeline; pH is the pH value of the covering soil; Cl - is the chloride ion concentration in the cover soil, in mg / kg; pH ref is the reference pH value; is the reference chloride ion concentration.

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