A DAS signal localization method based on adaptive tensor decomposition and dynamic correction

Abstract

This invention relates to a DAS signal localization method based on adaptive tensor decomposition and dynamic correction. The method includes converting phase difference data into strain rate data, constructing a three-dimensional tensor based on the time-domain signal of spatial points, solving an optimization problem after constraint modeling, and extracting the denoised DAS signal. It also involves calculating the initial fault location, calculating the actual optical path and apparent location after temperature changes based on thermal expansion and thermo-optic effects, establishing a dynamic correction algorithm, adaptively calibrating the system coordinate reference, and calculating the final localization result. This invention uses unsupervised tensor decomposition to separate the original signal into a low-rank background field, a sparse event field, and a structured noise field, overcoming the dependence on labeled data and insufficient separation of complex noise. By establishing a temperature-optical path coupling physical model and a dynamic correction algorithm with real-time cross-correlation calibration, it accurately compensates for localization drift caused by environmental factors, achieving high-fidelity denoising and precise localization of cable line events in complex environments.

Description

Technical Field

[0001] This invention relates to the field of DAS (Distributed Acoustic Sensing) signal processing technology, and particularly to a DAS signal localization method based on adaptive tensor decomposition and dynamic correction. Background Technology

[0002] DAS technology uses the entire optical fiber as a continuous sensor and combines phase-sensitive optical time-domain reflectometry with intelligent algorithms to achieve high-sensitivity, long-distance monitoring and meter-level accuracy event location of cable lines. It has effectively promoted the transformation of cable operation and maintenance from "passive emergency repair" to "proactive early warning" and has become an efficient means of cable line monitoring.

[0003] However, DAS-based cable monitoring technology still faces systemic bottlenecks in signal denoising and event localization. In terms of denoising: traditional methods rely on the assumption that signal and noise are separable in the transform domain, resulting in poor performance with complex ambient noise and potential waveform distortion; sparse representation-based methods have high computational complexity, making them unsuitable for real-time processing; supervised deep learning models suffer from weak generalization ability in unknown noise scenarios due to a lack of "clean" labeled data; and unsupervised models struggle to accurately model complex noise in real-world environments. In terms of localization: mainstream technologies treat denoising and localization as separate, sequential modules, leading to distortion or residual noise generated during denoising directly transmitting and amplifying localization errors, especially under strong noise conditions, making stable, high-precision localization and accurate direction determination for weak events difficult. Furthermore, existing algorithms generally lack deep embedding of the physical laws governing fiber optic sensing, resulting in poor physical interpretability and insufficient reliability under complex operating conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a DAS signal localization method based on adaptive tensor decomposition and dynamic correction, thereby solving the aforementioned problems in the prior art.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] The DAS signal localization method based on adaptive tensor decomposition and dynamic correction includes the following steps:

[0007] Step S01: Convert the phase difference data acquired by the DAS system into strain rate data. Based on the time-domain signal of each spatial point, construct a three-dimensional tensor and perform constraint modeling of three subspaces with different manifold structures, including a low-rank background field, a sparse event field, and a structured residual noise field. Based on the constraint modeling, solve the optimization problem and extract the denoised DAS signal.

[0008] Step S02: Obtain the optical round-trip time based on the denoised DAS signal. At the initial temperature Lower the calibration system and calculate the initial fault location. ;

[0009] Calculate the actual optical path length after temperature change based on the thermal expansion and thermo-optic effects of optical fibers. And calculate the apparent location observed by the system. A dynamic correction algorithm is established for real-time monitoring and modeling of fixed reference points to adaptively calibrate the system coordinate reference and calculate the final positioning result. .

[0010] The beneficial effects of this invention are as follows: This invention separates the original signal into a low-rank background field, a sparse event field, and a structured noise field through unsupervised tensor decomposition, overcoming the problems of dependence on labeled data and insufficient separation of complex noise; furthermore, by establishing a temperature-optical path coupling physical model and a dynamic correction algorithm with real-time cross-correlation calibration, it accurately compensates for the positioning drift caused by environmental factors, and finally achieves high-fidelity denoising and stable sub-meter-level accurate positioning of cable line events in complex environments.

[0011] Based on the above technical solution, the present invention can be further improved as follows.

[0012] Furthermore, step S01 specifically includes the following steps:

[0013] Step S11: Convert the phase difference data acquired by the DAS system into strain rate data. ;

[0014] Step S12: For each spatial point time domain signal Perform continuous wavelet transform, select Construct a three-dimensional tensor from the coefficient vectors corresponding to each scale. ,in For the number of spatial points, For time points, The wavelet scale number;

[0015] Step S13: Based on 3D tensor constraints, model the space into three subspaces with different manifold structures. ,in The background field is of low rank. For sparse event fields, For structured residual noise field;

[0016] Step S14: Based on the constraint modeling in Step S13, the complete optimization problem is obtained. The augmented Lagrangian function is constructed, and the augmented Lagrangian function is solved iteratively based on the ADMM penalty parameter to obtain the final decomposition result. ;

[0017] Step S15: Obtain the sparse event field from the converged solution obtained in the last iteration. and for each spatial point ,Will The denoised DAS signal is obtained by performing an inverse continuous wavelet transform along the scale dimension.

[0018] The further beneficial effects of adopting the above are: the present invention constructs an adaptive tensor quantum space decomposition-based DAS signal denoising method, which breaks through the technical bottlenecks of DAS technology such as dependence on labeled data, insufficient separation of complex mixed noise, and physical unreliability of results by a new paradigm of unsupervised tensor decomposition and physical constraints.

[0019] Furthermore, step S13 specifically includes the following steps:

[0020] Step S131: For The matrix is ​​obtained by performing a modulo-1 tensor expansion. The weighted tensor nuclear norm is defined as:

[0021] ;

[0022] in, For the first A singular value, Introducing space-time total variation regularization terms:

[0023] ;

[0024] Step S132: ... Divided into multiple overlapping 3D blocks, each block having a size of [size missing]. For the first Each block is vectorized as follows:

[0025] ;

[0026] Define the group sparse norm as:

[0027] ;

[0028] in, For the first Covariance matrix estimation of elements within each block The defined spatiotemporal neighborhood group is used; finally, the maximum measurable strain of the DAS system is introduced. Set the implementation range constraint, that is ;

[0029] Step S133: For Each modulus expansion matrix Apply nuclear norm constraints The weight satisfy .

[0030] Furthermore, step S14 specifically includes the following steps:

[0031] Step S141: Combining the constraints of step S13, we obtain the complete optimization problem:

[0032] ;

[0033] In the formula, These are the coefficients of the total variation regularization term; The event field regularization coefficient; This is the noise field regularization coefficient; These are constraints;

[0034] Step S142: Construct the augmented Lagrangian function:

[0035] ;

[0036] In the formula, For ADMM penalty parameters; For Lagrange multipliers; For the inner product of Frobenius;

[0037] Step S143: Using ADMM iteration, solve for the augmented Lagrangian function in step S142 and output the final decomposition result. .

[0038] Furthermore, step S02 specifically includes the following steps:

[0039] Step S21: Obtain the optical round-trip time based on the denoised DAS signal. At the initial temperature Lower the calibration system and calculate the initial fault location. :

[0040] ;

[0041] In the formula: At the speed of light, m / s; Initial temperature The initial effective refractive index;

[0042] Step S22: Introduce the thermal expansion effect and thermo-optic effect of the optical fiber to optimize the calculation of light from the initial fault location. The optical path experienced Based on optical path Calculate the actual round-trip time of light Apparent location is calculated using a formula. ;

[0043] Step S23: Real-time monitoring and modeling of the fixed reference point, establishing a dynamic correction algorithm, and dynamically correcting... The value;

[0044] Step S24: Global Correction Then, calculate the distance from the fault point to the monitoring point. :

[0045] .

[0046] The further beneficial effects of adopting the above are: the present invention constructs a DAS signal precise positioning method with multi-information dynamic correction, which effectively overcomes the influence of temperature changes on fiber positioning accuracy in long-distance complex environments, and greatly improves the reliability and accuracy of long-term monitoring of distributed fiber optic sensing systems.

[0047] Furthermore, step S22 specifically includes the following steps:

[0048] Step S221: Introduce the thermal expansion effect of the optical fiber and set the optical fiber to a temperature field. Among them These are the distance coordinates along the fiber optic cable. For time; for a infinitesimal element At temperature The inherent length under temperature change At that time, the differential change in its physical length is:

[0049] ;

[0050] From monitoring points =0 to the initial fault location measured by the fiber optic cable physical length for:

[0051] ;

[0052] In the formula, The coefficient of thermal expansion is the local linear thermal expansion coefficient.

[0053] Step S222: Introducing the thermo-optical effect of the optical fiber, the effective refractive index distribution is as follows:

[0054] ;

[0055] In the formula, The thermo-optic coefficient of the optical fiber;

[0056] Step S223: Light from the initial fault location The optical path optimization experienced is as follows:

[0057] ;

[0058] Integral variable These are the optical fiber material coordinates at the current temperature, compared to the intrinsic coordinates at the reference temperature. The relationship is Optical path length using intrinsic coordinates Represented as:

[0059] ;

[0060] Step S224: The actual round-trip time of light after temperature change. Apparent location observed by the system for:

[0061] .

[0062] The further beneficial effect of adopting the above is that the present invention comprehensively considers the thermo-optical effect and the optical fiber thermal expansion effect, thereby effectively improving the positioning accuracy.

[0063] Furthermore, step S23 specifically includes the following steps:

[0064] Step S231: After initial calibration, in the spatiotemporal data of the DAS, based on the initial fault location... Centered on a small segment of space, extract Rayleigh scattering backscattering signals or reflection event signals within that segment. , serving as the fingerprint template for this reference point;

[0065] Step S232: For each newly acquired DAS data frame within the spatial window Calculate the complex cross-correlation coefficients using a sliding window near the expected location:

[0066] ;

[0067] In the formula, For sampling points within the template window, and For signal and data The mean of all data in each. This is the position offset;

[0068] Find the cross-correlation function peak position ;

[0069] Step S233: At the peak position Nearby, set , , The subpixel offset fitted using a quadratic function is:

[0070] .

[0071] The further beneficial effects of adopting the above are: the present invention establishes a dynamic correction algorithm by real-time monitoring and modeling of fixed reference points, and adaptively calibrates the system coordinate reference. Attached Figure Description

[0072] Figure 1 This is a flowchart of the method of the present invention;

[0073] Figure 2 This is a flowchart of the DAS signal denoising method adapted to Zhang quantum space decomposition of the present invention;

[0074] Figure 3 This is a flowchart of the DAS signal precise positioning method with multi-information dynamic correction according to the present invention. Detailed Implementation

[0075] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0076] Example 1

[0077] like Figures 1 to 3 As shown, the DAS signal localization method based on adaptive tensor decomposition and dynamic correction includes the following steps:

[0078] Step S01: Convert the phase difference data acquired by the DAS system into strain rate data. Based on the time-domain signal of each spatial point, construct a three-dimensional tensor and perform constraint modeling of three subspaces with different manifold structures, including a low-rank background field, a sparse event field, and a structured residual noise field. Based on the constraint modeling, solve the optimization problem and extract the denoised DAS signal.

[0079] Step S02: Obtain the optical round-trip time based on the denoised DAS signal. At the initial temperature Lower the calibration system and calculate the initial fault location. ;

[0080] Calculate the actual optical path length after temperature change based on the thermal expansion and thermo-optic effects of optical fibers. And calculate the apparent location observed by the system. A dynamic correction algorithm is established for real-time monitoring and modeling of fixed reference points to adaptively calibrate the system coordinate reference and calculate the final positioning result. .

[0081] Existing methods cannot accurately model and separate DAS signals under complex field noise, resulting in poor denoising effect, signal distortion, and lack of physical reliability in the output. Traditional technical architectures separate denoising and positioning tasks and cannot overcome the time-varying drift of system parameters caused by factors such as ambient temperature, resulting in a decrease in positioning accuracy over time in long-distance and complex environments, making it difficult to achieve long-term stable sub-meter level absolute coordinate perception.

[0082] This invention uses unsupervised tensor decomposition to separate the original signal into a low-rank background field, a sparse event field, and a structured noise field, overcoming the problems of dependence on labeled data and insufficient separation of complex noise. Furthermore, by establishing a temperature-optical path coupling physical model and a dynamic correction algorithm with real-time cross-correlation calibration, it accurately compensates for positioning drift caused by environmental factors, ultimately achieving high-fidelity denoising and stable sub-meter-level accurate positioning of cable line events in complex environments.

[0083] Example 2

[0084] like Figure 2 As shown, this embodiment is a further improvement on embodiment 1, as detailed below:

[0085] This invention constructs an adaptive tensor quantum space decomposition-based DAS signal denoising method. Through a novel paradigm that integrates unsupervised tensor decomposition with physical constraints, it overcomes the technical bottlenecks of DAS technology, such as its dependence on labeled data, insufficient separation of complex mixed noise, and physical unreliability of the results. The flowchart is shown below. Figure 2 As shown.

[0086] Step S01 specifically includes the following steps:

[0087] Step S11: Raw Data Acquisition: Convert the phase difference data acquired by the DAS system into strain rate data. ;

[0088] Step S12: Construction of 3D tensors: For each spatial point time domain signal Perform continuous wavelet transform, select Construct a three-dimensional tensor from the coefficient vectors corresponding to each scale. ,in For the number of spatial points, For time points, The wavelet scale number;

[0089] Step S13: Hierarchical subspace modeling: Based on 3D tensor constraints, model the subspace into three subspaces with different manifold structures. ,in The background field is of low rank. For sparse event fields, For structured residual noise field;

[0090] Step S13 specifically includes the following steps:

[0091] Step S131: Low-rank background field Constraint modeling:

[0092] Low-rank background fields are mainly caused by temperature drift and slow system variation, exhibiting global low-rank and local smoothness in the three dimensions of time, space, and frequency. Therefore, this invention uses weighted tensor kernel norms to model low-rank background fields. First, [the following is a partial translation of the original text, which is incomplete and requires further context]. The matrix is ​​obtained by performing a modulo-1 tensor expansion. Then, the weighted tensor nuclear norm is defined as:

[0093] ;

[0094] in, For the first A singular value, Finally, a space-time total variational regularization term is introduced to implement local smoothing constraints:

[0095] ;

[0096] Step S132: Sparse Event Field Constraint modeling:

[0097] The sparse event field corresponds to the external vibration of the cable, and has the characteristics of being transient and spatially continuous. Therefore, this invention uses a set of sparse norms to model the sparse event field. First, the sparse event field is modeled using the set of sparse norms. Divided into multiple overlapping 3D blocks, each block having a size of [size missing]. Then for the first Each block is vectorized as follows:

[0098] ;

[0099] The sparse norm of a set is then defined as:

[0100] ;

[0101] in, For the first Covariance matrix estimation of elements within each block The defined spatiotemporal neighborhood group is used; finally, the maximum measurable strain of the DAS system is introduced. Set the implementation range constraint, that is ;

[0102] Step S133: Structure the residual noise field Constraint modeling:

[0103] The structured residual noise field mainly consists of polarization fading noise, which exhibits low-rank properties in specific modes (usually spatial dimensions). Therefore, this invention employs multimodal low-rank constraints to model the structured residual noise field, i.e., for Each modulus expansion matrix Apply nuclear norm constraints The weight satisfy .

[0104] Step S14: Based on the constraint modeling in Step S13, the complete optimization problem is obtained. The augmented Lagrangian function is constructed, and the augmented Lagrangian function is solved iteratively based on the ADMM penalty parameter to obtain the final decomposition result. ;

[0105] Step S14 specifically includes the following steps:

[0106] Step S141: Combining the constraints of step S13, we obtain the complete optimization problem:

[0107] ;

[0108] In the formula, These are the coefficients of the total variation regularization term; The event field regularization coefficient; This is the noise field regularization coefficient; These are constraints;

[0109] Step S142: Construct the augmented Lagrangian function:

[0110] ;

[0111] In the formula, For ADMM penalty parameters; For Lagrange multipliers; For the inner product of Frobenius;

[0112] Step S143: Using ADMM iteration, solve for the augmented Lagrangian function in step S142 and output the final decomposition result. .

[0113] Step S15: Denoising signal extraction and output: Obtain the sparse event field from the converged solution obtained in the last iteration. and for each spatial point ,Will Perform inverse continuous wavelet transform along the scale dimension (3rd dimension) to obtain the denoised DAS signal.

[0114] Example 3

[0115] like Figure 3As shown, this embodiment is a further improvement on embodiment 1, as detailed below:

[0116] This invention constructs a multi-information dynamic correction method for precise positioning of DAS signals, effectively overcoming the impact of temperature changes on fiber optic positioning accuracy in long-distance complex environments, and significantly improving the reliability and accuracy of long-term monitoring in distributed fiber optic sensing systems. The flowchart is shown below. Figure 3 As shown.

[0117] Step S02 specifically includes the following steps:

[0118] Step S21: Obtain the optical round-trip time based on the denoised DAS signal. At the initial temperature Lower the calibration system and calculate the initial fault location. :

[0119] ;

[0120] In the formula: At the speed of light, m / s; Initial temperature The initial effective refractive index;

[0121] Step S22: Introduce the thermal expansion effect and thermo-optic effect of the optical fiber to optimize the calculation of light from the initial fault location. The optical path experienced Based on optical path Calculate the actual round-trip time of light Apparent location is calculated using a formula. ;

[0122] Step S22 specifically includes the following steps:

[0123] Because cable tunnels are long and have complex and variable environments, the internal optical fibers are in non-uniform temperature fields in different sections. Therefore, this invention takes into account both the thermo-optical effect and the thermal expansion effect of optical fibers, thereby effectively improving positioning accuracy.

[0124] Step S221: Introduce the thermal expansion effect of the optical fiber and set the optical fiber to a temperature field. Among them These are the distance coordinates along the fiber optic cable. For time; for a infinitesimal element At temperature The inherent length under temperature change At that time, the differential change in its physical length is:

[0125] ;

[0126] Therefore, from the monitoring point =0 to the initial fault location measured by the fiber optic cable physical length for:

[0127] ;

[0128] In the formula, The coefficient of thermal expansion is the local linear thermal expansion coefficient.

[0129] Step S222: Introducing the thermo-optical effect of the optical fiber, the effective refractive index distribution is as follows:

[0130] ;

[0131] In the formula, The thermo-optic coefficient of the optical fiber;

[0132] Step S223: Light from the initial fault location The optical path optimization experienced is as follows:

[0133] ;

[0134] Integral variable These are the optical fiber material coordinates at the current temperature, compared to the intrinsic coordinates at the reference temperature. The relationship is Therefore, the optical path is expressed in inherent coordinates. Represented as:

[0135] ;

[0136] Step S224: The actual round-trip time of light after temperature change. However, the system still uses the constant obtained from calibration when calculating the position; therefore, the apparent position observed by the system is... for:

[0137] .

[0138] Step S23: Real-time monitoring and modeling of the fixed reference point, establishing a dynamic correction algorithm, and dynamically correcting... The value;

[0139] Step S23 specifically includes the following steps:

[0140] To ensure the absolute accuracy of long-term measurements, this invention establishes a dynamic correction algorithm through real-time monitoring and modeling of fixed reference points, and adaptively calibrates the system coordinate reference.

[0141] Step S231: Establish reference point template: After initial calibration, in the spatiotemporal data of DAS, based on the initial fault location... Centered on a small segment of space, extract Rayleigh scattering backscattering signals or reflection event signals within that segment. , serving as the fingerprint template for this reference point;

[0142] Step S232: Calculate the normalized cross-correlation coefficient: For each newly acquired DAS data frame within the spatial window Calculate the complex cross-correlation coefficients using a sliding window near the expected location:

[0143] ;

[0144] In the formula, For sampling points within the template window, and For signal and data The mean of all data in each. This is the position offset;

[0145] Further find the cross-correlation function peak position ;

[0146] Step S233: Subpixel-level peak positioning: at the peak position Nearby, set , , The subpixel offset fitted using a quadratic function is:

[0147] .

[0148] Step S24: Establish the final localization result: global correction Then, calculate the distance from the fault point to the monitoring point. :

[0149] .

[0150] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A DAS signal localization method based on adaptive tensor decomposition and dynamic correction, characterized in that, Includes the following steps: Step S01: Convert the phase difference data acquired by the DAS system into strain rate data. Based on the time domain signal of each spatial point, construct a three-dimensional tensor and perform constraint modeling of three subspaces with different manifold structures, including the low-rank background field, the sparse event field, and the structured residual noise field. Based on the aforementioned constraints, model the problem, solve the optimization problem, and extract the denoised DAS signal. Step S02: Based on the denoised DAS signal, obtain the optical round-trip time. At the initial temperature Lower the calibration system and calculate the initial fault location. ; Calculate the actual optical path length after temperature change based on the thermal expansion and thermo-optic effects of optical fibers. , For time, calculate the apparent location observed by the system. A dynamic correction algorithm is established for real-time monitoring and modeling of fixed reference points to adaptively calibrate the system coordinate reference and calculate the final positioning result. .

2. The DAS signal localization method based on adaptive tensor decomposition and dynamic correction according to claim 1, characterized in that, Step S01 specifically includes the following steps: Step S11: Convert the phase difference data acquired by the DAS system into strain rate data. ; Step S12: For each spatial point time domain signal Perform continuous wavelet transform, select Construct a three-dimensional tensor from the coefficient vectors corresponding to each scale. ,in For the number of spatial points, For time points, The wavelet scale number; Step S13: Based on 3D tensor constraints, model the space into three subspaces with different manifold structures. ,in The background field is of low rank. For sparse event fields, For structured residual noise field; Step S14: Based on the constraint modeling in step S13, the complete optimization problem is obtained. An augmented Lagrangian function is constructed, and the augmented Lagrangian function is solved iteratively based on the ADMM penalty parameter to obtain the final decomposition result. ; Step S15: Obtain the sparse event field from the converged solution obtained in the last iteration. and for each spatial point ,Will The denoised DAS signal is obtained by performing an inverse continuous wavelet transform along the scale dimension.

3. The DAS signal localization method based on adaptive tensor decomposition and dynamic correction according to claim 2, characterized in that, Step S13 specifically includes the following steps: Step S131: For The matrix is ​​obtained by performing a modulo-1 tensor expansion. The weighted tensor nuclear norm is defined as: ； in, For the first A singular value, , Introducing space-time total variation regularization terms: ； Step S132: ... Divided into multiple overlapping 3D blocks, each block having a size of [size missing]. For the first Each block is vectorized as follows: ； Define the group sparse norm as: ； in, For the first Covariance matrix estimation of elements within each block The defined spatiotemporal neighborhood group is used; finally, the maximum measurable strain of the DAS system is introduced. Set the implementation range constraint, that is ; Step S133: For Each modulus expansion matrix Apply nuclear norm constraints The weight satisfy .

4. The DAS signal localization method based on adaptive tensor decomposition and dynamic correction according to claim 3, characterized in that, Step S14 specifically includes the following steps: Step S141: Combining the constraints of step S13, the complete optimization problem is obtained: ； In the formula, These are the coefficients of the total variation regularization term; The event field regularization coefficient; This is the noise field regularization coefficient; These are constraints; Step S142: Construct the augmented Lagrangian function: ； In the formula, For ADMM penalty parameters; For Lagrange multipliers; For the inner product of Frobenius; Step S143: Using ADMM iteration, solve for the augmented Lagrangian function in step S142, and output the final decomposition result. .

5. The DAS signal localization method based on adaptive tensor decomposition and dynamic correction according to claim 1, characterized in that, Step S02 specifically includes the following steps: Step S21: Based on the denoised DAS signal, obtain the optical round-trip time. At the initial temperature Lower the calibration system and calculate the initial fault location. : ； In the formula: At the speed of light, m / s; Initial temperature The initial effective refractive index; Step S22: Introduce the thermal expansion effect and thermo-optic effect of the optical fiber to optimize the calculation of light from the initial fault location. The optical path experienced Based on the optical path Calculate the actual round-trip time of light Calculate apparent location using formula ; Step S23: Real-time monitoring and modeling of the fixed reference point, establishing a dynamic correction algorithm, and dynamically correcting... The value; Step S24: Global Correction Then, calculate the distance from the fault point to the monitoring point. : 。 6. The DAS signal localization method based on adaptive tensor decomposition and dynamic correction according to claim 5, characterized in that, Step S22 specifically includes the following steps: Step S221: Introduce the thermal expansion effect of the optical fiber and set the optical fiber to a temperature field. Among them These are the distance coordinates along the fiber optic cable. For time; for a infinitesimal element At temperature The inherent length under temperature change At that time, the differential change in its physical length is: ； From monitoring points =0 to the initial fault location measured by the fiber optic cable physical length for: ； In the formula, The coefficient of thermal expansion is the local linear thermal expansion coefficient. Step S222: Introducing the thermo-optical effect of the optical fiber, the effective refractive index distribution is as follows: ； In the formula, The thermo-optic coefficient of the optical fiber; Step S223: Light from the initial fault location The optical path optimization experienced is as follows: ； Integral variable These are the optical fiber material coordinates at the current temperature, compared to the intrinsic coordinates at the reference temperature. The relationship is Optical path length using intrinsic coordinates Represented as: ； Step S224: The actual round-trip time of light after temperature change. Apparent location observed by the system for: 。 7. The DAS signal localization method based on adaptive tensor decomposition and dynamic correction according to claim 6, characterized in that, Step S23 specifically includes the following steps: Step S231: After initial calibration, in the spatiotemporal data of the DAS, based on the initial fault location... Centered on a small segment of space, extract Rayleigh scattering backscattering signals or reflection event signals within that segment. , serving as the fingerprint template for this reference point; Step S232: For each newly acquired DAS data frame within the spatial window Calculate the complex cross-correlation coefficients using a sliding window near the expected location: ； In the formula, For sampling points within the template window, and For signal and data The mean of all data in each. This is the position offset; Find the cross-correlation function peak position ; Step S233: At the peak position Nearby, set , , The subpixel offset fitted using a quadratic function is: 。

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