Phase compensation method and device, electronic equipment and storage medium

Abstract

The invention relates to the field of optical fiber sensing, in particular to a phase compensation method and device, electronic equipment and a storage medium. The phase compensation method comprises the following steps: acquiring a first temperature measurement value and a first phase measurement value; determining a first temperature effect value based on a preset temperature effect calculation model and the first temperature measurement value, wherein the first temperature effect value is used for representing the influence of the first temperature measurement value on the phase drift; calculating a posterior drift estimation value at the first moment based on the first temperature effect value to obtain a first posterior drift estimation value; phase compensation is performed on the first phase measurement value based on the first posteriori drift estimation value. According to the invention, the phase drift of the DAS signal caused by temperature can be compensated, and the detection precision of the DAS system is improved.

Description

Technical Field

[0001] This application relates to the field of fiber optic sensing, specifically to a phase compensation method, device, electronic device, and storage medium. Background Technology

[0002] Distributed Acoustic Sensing (DAS) is a novel fiber optic sensing technology that uses optical fibers as a sensing array. By analyzing the phase change of backscattered Rayleigh light along the length of the sensing fiber, it records acoustic vibrations within the fiber's spatial range, enabling real-time continuous monitoring of vibration signals.

[0003] During low-frequency detection, fluctuations in the ambient temperature of the optical fiber can cause low-frequency interference, which can introduce aliasing noise into the low-frequency signal to be detected, affecting the detection accuracy of the DAS system. Summary of the Invention

[0004] In view of the above, embodiments of this application provide a phase compensation method, apparatus, electronic device, and storage medium that can compensate for phase drift caused by temperature and improve the detection accuracy of the DAS system.

[0005] In a first aspect, embodiments of this application provide a phase compensation method, including: Acquire the first temperature measurement value and the first phase measurement value; Wherein, the first phase measurement value is the distributed acoustic wave sensing system at the ... The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at specific times; the... This indicates the time order of the first phase measurement value among the phase measurements acquired by the distributed acoustic wave sensing system; the first temperature measurement value is the time order of the phase measurements acquired by the distributed acoustic wave sensing system. At any given time, the measured temperature of the environment in which the optical fiber is located; A first temperature effect value is determined based on a preset temperature effect calculation model and the first temperature measurement value. The first temperature effect value is used to represent the influence of the first temperature measurement value on the phase drift. Calculate the first temperature effect value based on the first temperature effect value. The posterior drift estimate at time t is used to obtain the first posterior drift estimate; Phase compensation is performed on the first phase measurement based on the first posterior drift estimate.

[0006] In some embodiments, the first temperature effect value is used to calculate the... The posterior drift estimate at time t is used to obtain the first posterior drift estimate, which includes: When the time order of the first phase measurement value among the phase measurements is greater than or equal to two, a second posterior drift estimate is obtained, and the second posterior drift estimate is the first... The posterior drift estimate at time step; The first temperature effect value and the second posterior drift estimate are weighted and summed using a preset target smoothness weight to obtain the first prior drift estimate. The first posterior drift estimate is determined based on the first prior drift estimate.

[0007] In some embodiments, the first temperature effect value and the second posterior drift estimate are weighted and summed using a preset target smoothness weight to obtain a first prior drift estimate, including: The first prior drift estimate is calculated using the first formula, where the first formula is: ; Among them, the The first prior drift estimate; The preset target smoothness weight; The first temperature effect value; This is the second posterior drift estimate.

[0008] In some embodiments, determining the first posterior drift estimate based on the first prior drift estimate includes: Get the The posterior error covariance at time t; Wherein, if the first phase measurement value is the second phase measurement value to be compensated, the first... The posterior error covariance at time t is the preset initial error covariance; When the first phase measurement value has a time order greater than two among the phase measurement values, the first... The posterior error covariance at time t is based on the first... The prior error covariance at time t and the first The gain coefficient at time t is determined; Based on the first The sum of the posterior error covariance at time step and the preset target process noise covariance determines the first prior error covariance, which is the sum of the posterior error covariance at time step step. The prior error covariance at time t; A first gain coefficient is determined based on the first prior error covariance and the preset target observation noise covariance. The first gain coefficient is the first... Gain coefficient at time step; The first posterior drift estimate is determined based on the first gain coefficient and the first prior drift estimate.

[0009] In some embodiments, determining a first gain coefficient based on the first prior error covariance and a preset target observation noise covariance includes: The first gain coefficient is determined using the third formula, the first prior error covariance, and the preset target observation noise covariance. The third formula is as follows: ; Among them, the For the first gain coefficient, the Let the first prior error covariance be the... The preset target observation noise covariance.

[0010] In some embodiments, the steps for determining the target smoothness weight, the target process noise covariance, and the target observation noise covariance include: Acquire samples of each phase measurement value and samples of each temperature measurement value that are time-aligned with the samples of each phase measurement value; A global search is performed in the parameter search space based on a preset objective function to obtain global candidate parameters. Wherein, the parameter search space represents the range of values ​​for the target smoothness weight, the target process noise covariance, and the target observation noise covariance; The global candidate parameters include candidate smoothness weights, candidate process noise covariance, and observation noise covariance. The objective function represents the weighted sum of the mean penalty, correlation penalty, smoothness penalty, and signal feature penalty. The mean penalty term is the mean of each compensated phase measurement sample; The correlation penalty term represents the correlation between the posterior drift estimate corresponding to each phase measurement sample and the temperature measurement sample; The smoothness penalty term is the smoothness of the phase drift curve, which represents the curve of the posterior drift estimate corresponding to each phase measurement sample changing over time. The signal feature penalty term is determined based on the frequency of vibration of the compensated phase measurement value sample; Local optimization is performed on the global candidate parameters to obtain the target smoothness weight, the target process noise covariance, and the target observation noise covariance.

[0011] In some embodiments, the first temperature effect value is obtained based on a preset temperature effect calculation model and a first temperature measurement value, including: ; Among them, the The first temperature effect value, the The first temperature measurement value, the The preset temperature secondary influence coefficient, the The preset temperature linear influence coefficient, the This is a preset constant.

[0012] Secondly, embodiments of this application also provide a phase compensation device, comprising: The data acquisition module is used to acquire the first temperature measurement value and the first phase measurement value; Wherein, the first phase measurement value is the distributed acoustic wave sensing system at the ... The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at specific times; the... The first phase measurement value represents the time order of the phase measurement values ​​collected by the distributed acoustic wave sensing system; the first temperature measurement value is the temperature measurement value of the ambient temperature where the optical fiber is located at the k-th time. The effect calculation module is used to determine a first temperature effect value based on a preset temperature effect calculation model and the first temperature measurement value. The first temperature effect value is used to represent the influence of the first temperature measurement value on the phase drift. The drift estimation module is used to calculate the first temperature effect value. The posterior drift estimate at time t is used to obtain the first posterior drift estimate; The phase compensation module is used to perform phase compensation on the first phase measurement value based on the first posterior drift estimate.

[0013] Thirdly, embodiments of this application also provide an electronic device, the electronic device including a processor and a memory, the memory being used to store instructions, and the processor being used to call the instructions in the memory, causing the electronic device to perform the phase compensation method as described above.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the phase compensation method described above.

[0015] In the phase compensation method of this application embodiment, the electronic device can acquire a first phase measurement value and a first temperature measurement value acquired synchronously, so as to ensure the accuracy of the correlation between temperature and phase measurement values ​​with strict time alignment. Then, a first temperature effect value is determined by using a preset temperature effect calculation model and the first temperature measurement value, thereby quantifying the correlation between temperature and phase drift. Based on the first temperature effect value, a first posterior drift estimate is calculated, and the first phase measurement value is compensated by the first posterior drift estimate, effectively eliminating the interference of ambient temperature fluctuations on the phase measurement signal, ensuring that the phase measurement value can be effectively compensated under different temperature conditions, and improving the detection accuracy of the DAS system. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps of a phase compensation method provided according to an embodiment of this application.

[0017] Figure 2 This is a flowchart of the sub-steps of step 103 provided according to an embodiment of this application.

[0018] Figure 3 This is a flowchart illustrating the steps for performing phase compensation on various temperature measurements according to an embodiment of this application.

[0019] Figure 4 This is a flowchart illustrating the steps for calculating target parameters according to an embodiment of this application.

[0020] Figure 5 This is a schematic diagram of the device structure of a phase compensation device provided according to an embodiment of this application.

[0021] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0022] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0023] The following description sets forth many specific details to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0025] It should be further noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0026] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0027] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0028] The technical terms used in the embodiments of this application are described below.

[0029] Kalman filtering is an algorithm that uses the state equations of a linear system to optimally estimate the system state using system input and output observation data. Since the observation data includes noise and interference from the system, the optimal estimation can also be viewed as a filtering process. The phase compensation method in this application can be based on the Kalman filtering algorithm.

[0030] The following uses the standard Kalman filter framework to illustrate the state equation and observation equation of the standard Kalman filter.

[0031] The state equation for a standard Kalman filter is shown below: ; in, for The state vector at any given time, for example, in an embodiment of this application, It can be used as a posterior drift estimate; This is the state transition matrix; To control the input matrix; To control the input vector, for example, in an embodiment of this application, This can be the first temperature effect value; The noise is a process noise and follows a normal distribution. .

[0032] The observation equation for the standard Kalman filter is shown below: ; in, for The observed value at a given time, for example, in an embodiment of this application, This can be the first phase measurement value; The observation matrix; To observe the noise, it follows a normal distribution. .

[0033] The above uses the standard Kalman filter framework as an example. In practical applications, the embodiments of this application may also use optimized versions of Kalman filters such as extended Kalman filter and unscented Kalman filter. The embodiments of this application do not limit this.

[0034] The phase compensation method, apparatus, electronic device, and computer-readable storage medium provided in the embodiments of this application are described below.

[0035] The phase compensation method in this application embodiment includes: acquiring a first temperature measurement value and a first phase measurement value; wherein, the first phase measurement value is the value of the distributed acoustic wave sensing system at the first... The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at specific times; the... This indicates the time order of the first phase measurement value among the phase measurements acquired by the distributed acoustic wave sensing system; the first temperature measurement value is the time order of the phase measurements acquired by the distributed acoustic wave sensing system. At a given time, the ambient temperature of the optical fiber is measured; based on a preset temperature effect calculation model and the first temperature measurement, a first temperature effect value is determined, which represents the influence of the first temperature measurement on the phase drift; the first temperature effect value is then used to calculate the... The posterior drift estimate at time t is used to obtain the first posterior drift estimate; phase compensation is then performed on the first phase measurement based on the first posterior drift estimate.

[0036] In the phase compensation method of this application embodiment, the electronic device can acquire a first phase measurement value and a first temperature measurement value acquired synchronously, so as to ensure the accuracy of the correlation between temperature and phase measurement values ​​with strict time alignment. Then, a first temperature effect value is determined by using a preset temperature effect calculation model and the first temperature measurement value, thereby quantifying the correlation between temperature and phase drift. Based on the first temperature effect value, a first posterior drift estimate is calculated, and the first phase measurement value is compensated by the first posterior drift estimate, effectively eliminating the interference of ambient temperature fluctuations on the phase measurement signal, ensuring that the phase measurement value can be effectively compensated under different temperature conditions, and improving the detection accuracy of the DAS system.

[0037] The phase compensation method of this application can be applied to one or more electronic devices. The electronic device is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, processors, microprogrammed control units (MCUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc. For example, the electronic device can be the signal processing and control unit of a DAS system.

[0038] The following is combined with Figure 1 This application describes a phase compensation method provided in one embodiment, which can be applied to the aforementioned electronic device.

[0039] refer to Figure 1 As shown, Figure 1 This is a flowchart illustrating the steps of a phase compensation method according to an embodiment of this application. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The phase compensation method may include: Step 101: Obtain the first temperature measurement value and the first phase measurement value.

[0040] The first phase measurement value is the DAS system at the [missing value]. The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at any given time.

[0041] This indicates the temporal order of the first phase measurement value among all phase measurements acquired by the distributed acoustic wave sensing system.

[0042] The first temperature measurement value is at the time... At any given time, the temperature measurement of the environment in which the optical fiber is located.

[0043] For example, a DAS system can emit laser pulses into an optical fiber. When the laser propagates within the fiber, it generates backscattered Rayleigh light, which the optical detection module of the DAS system can detect (referred to as the DAS signal).

[0044] The DAS system can extract the phase of the DAS signal (referred to as DAS phase), and the extracted DAS phase can be recorded as the observed phase data: .

[0045] The observed phase data is a one-dimensional time series with a length of n. Each element of the observed phase data is a phase measurement value acquired by the DAS system and arranged in chronological order. The phase measurement values ​​are real values, and the unit can be radians or degrees.

[0046] This embodiment will be in the [number]th [section]. The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at each moment, is called the first phase measurement (i.e., In the aforementioned observed phase data, From 0 to Integers.

[0047] The ambient temperature of the optical fiber can be simultaneously acquired during the acquisition time of each phase measurement in the observed phase data to obtain temperature data. .

[0048] The temperature data is also a one-dimensional time series, including temperature measurements of the environment in which the optical fiber is located. Temperature and phase measurements are acquired synchronously. The data length of the temperature data is... The data length is the same as that of the phase data. That is, the observed phase data and temperature data are of equal length and time-aligned.

[0049] This embodiment will be in the [number]th [section]. At that moment, the temperature measurement of the ambient temperature where the optical fiber is located is called the first temperature measurement value (denoted as ). ).

[0050] In some embodiments, after acquiring temperature data and observation phase data, the temperature data and observation phase data can be filtered, for example, by mean filtering, median filtering, SG filtering, etc.

[0051] When using a low-pass filter, the cutoff frequency of the low-pass filter can be 1 Hz (HZ), but it is not limited to this. Filtering can reduce high-frequency interference that is unrelated to low-frequency trend signals.

[0052] After filtering, the electronic device can perform step 102.

[0053] Step 102: Determine the first temperature effect value based on the preset temperature effect calculation model and the first temperature measurement value.

[0054] The first temperature effect value is the quantified value of the influence of the first temperature measurement value on the phase drift.

[0055] In some embodiments, the preset temperature effect calculation model may be as follows: ; in, This is the first temperature effect value. This is the first temperature measurement value (i.e., the temperature measurement value at time k). This is the preset temperature secondary influence coefficient. The preset temperature linear influence coefficient, This is a preset constant.

[0056] The above , and Calibration can be performed using a polynomial fitting algorithm, but this application does not limit the specific implementation of this method.

[0057] For each time step The temperature effect values ​​can be calculated using the temperature effect calculation model described above.

[0058] The temperature effect calculation model in this application uses a quadratic function formula to quantify the nonlinear relationship between temperature and phase drift effect. Compared with a linear model, it can more accurately fit the complex influence of temperature changes on phase drift in the actual environment. , and This enables accurate calculation of temperature effect values ​​in different temperature ranges, providing reliable basic data for subsequent drift estimation and phase compensation, and effectively improving the compensation accuracy for phase drift caused by temperature factors.

[0059] Step 103, calculate the first temperature effect value. The posterior drift estimate at time t is used to obtain the first posterior drift estimate.

[0060] In some embodiments, step 103 may include: When the time order of the first phase measurement value among the acquired phase measurements is equal to 1 (that is, ... When this happens, the computer device can set the first posterior drift estimate to a preset initial drift estimate. For example, setting the first posterior drift estimate to... That is, denoted as .

[0061] When the time order of the first phase measurement value among the various phase measurements is greater than or equal to two, the computer calculates the first phase measurement value based on the first temperature effect value. The steps to obtain the first posterior drift estimate from the posterior drift estimate at time step time may include: The computer device acquires the second posterior drift estimate, which is the second posterior drift estimate of the first... The posterior drift estimate at time t. For example, when When =1, the second posterior drift estimate can be ;when When =2, the second posterior drift estimate can be .

[0062] Then, the computer device can use a preset target smoothness weight to adjust the first temperature effect value. Second posterior drift estimate We perform a weighted summation to obtain the first prior drift estimate. Based on the first prior drift estimate Determine the first posterior drift estimate .

[0063] Among them, based on the first prior drift estimate Determine the first posterior drift estimate This may include: the first prior drift estimate As the first posterior drift estimate .

[0064] In other embodiments, the electronic device determines the first posterior drift estimate based on the first prior drift estimate in the following manner: First, obtain the number Posterior error covariance at time 1 ;wherein, the first phase measurement value is the second phase measurement value to be compensated (i.e. In the case of ), the first Posterior error covariance at time 1 For example, the preset initial error covariance, The initial error covariance can be configured according to actual application requirements, and this application embodiment does not limit it.

[0065] The first phase measurement value has a time order greater than two among the various phase measurements (i.e., ... At that time, the first Posterior error covariance at time 1 According to the Prior error covariance at time Passing the exam Gain coefficient at time step Sure.

[0066] Then, the computer device can be based on the first Posterior error covariance at time 1 The sum of the noise covariance of the target process and the preset target value Determine the first prior error covariance, the first prior error covariance is the... Prior error covariance at time .

[0067] Next, the computer device is based on the first prior error covariance. and the preset target observation noise covariance Determine the first gain coefficient The first gain coefficient is the Gain coefficient at time step; based on the first gain coefficient and the first prior drift estimate Determine the first posterior drift estimate, for example, based on the first phase measurement. First gain coefficient and the first prior drift estimate Determine the first posterior drift estimate.

[0068] For example, refer to Figure 2 As shown, step 103 may include: Step 1031: Obtain the second posterior drift estimate.

[0069] When the time order of the first phase measurement value among the phase measurements is greater than or equal to two, the second posterior drift estimate is the first... The posterior drift estimate at time t, and the second posterior drift estimate can be denoted as . .

[0070] Step 1032: The first temperature effect value and the second posterior drift estimate are weighted and summed using a preset target smoothness weight to obtain the first prior drift estimate.

[0071] In some embodiments, a preset target smoothness weight is used. The first temperature effect value Second posterior drift estimate We perform a weighted summation to obtain the first prior drift estimate. This can be achieved in the following ways: The first prior drift estimate is calculated using the first formula, where the first formula is: ; This is the first prior drift estimate; The preset target smoothness weights; This is the first temperature effect value; For the first The posterior drift estimate at time (i.e., the second posterior drift estimate).

[0072] The embodiments of this application can realize the temporal correlation and smooth transition of phase drift estimation results at adjacent time points, avoid drift estimation deviation caused by temperature effect value fluctuation at a single time point, further improve the accuracy of posterior drift estimation value, and thus enhance the continuity and consistency of phase compensation effect.

[0073] Step 1031 above can also be called the prior estimation step of the phase drift value.

[0074] Step 1033, obtain the first... The posterior error covariance at time t.

[0075] Wherein, the first phase measurement value is the second phase measurement value to be compensated (i.e., In the case of ), the first The posterior error covariance at time t is a preset initial error covariance, for example, the preset initial error covariance. .

[0076] The first phase measurement value has a time order greater than two among the phase measurements (i.e., When ), the first Posterior error covariance at time 1 According to the first The prior error covariance at time t and the first The gain coefficient at time step is determined. For example, the formula can be shown below: ; in, For the first The posterior error covariance at time t; For the first Gain coefficient at time step; For the first The prior error covariance at time t.

[0077] Step 1034, based on the first The sum of the posterior error covariance at time step and the preset target process noise covariance determines the first prior error covariance.

[0078] The first prior error covariance is the... The prior error covariance at time t.

[0079] For example, the steps to calculate the first prior error covariance can be referenced using the following formula: ; in, The first prior error covariance, For the first The posterior error covariance at time t; This is the sum of the pre-defined target process noise covariance.

[0080] Step 1035: Determine the first gain coefficient based on the first prior error covariance and the preset target observation noise covariance.

[0081] The first gain coefficient is the... Gain coefficient at time step.

[0082] Further, step 1035 can be achieved as follows: the first gain coefficient is determined using the third formula, the first prior error covariance, and the preset target observation noise covariance. The third formula is: ; in, The first gain coefficient, The first prior error covariance, The target observation noise covariance is preset.

[0083] The first gain coefficient in this embodiment can be dynamically adjusted according to the changes in the first prior error covariance and the target observation noise covariance, which is beneficial to achieve accurate correction of the first prior drift estimate, improve the fitting degree of the first posterior drift estimate to the actual phase drift, and thus enhance the phase compensation effect.

[0084] Step 1036: Determine the first posterior error covariance based on the first gain coefficient and the first prior error covariance.

[0085] In some embodiments, when there are additional phase measurements to be supplemented after the first phase measurement in the observed phase data (i.e., there are...) Step 1036 can be executed to facilitate subsequent actions. Calculate the corresponding prior error covariance and gain coefficient, etc.

[0086] In some embodiments, the formula for calculating the first posterior error covariance is as follows: ; in, The first posterior error covariance; The first gain coefficient; This is the first prior drift estimate.

[0087] Step 1037: Determine the first posterior drift estimate based on the first gain coefficient and the first prior drift estimate.

[0088] In some embodiments, step 1037 can be implemented by the following formula: ; in, This is the first posterior drift estimate. This is the first prior drift estimate. The first gain coefficient; This is the first phase measurement value.

[0089] Steps 101 to 103 above describe the calculation process of the posterior drift estimate (i.e., the first posterior drift estimate) corresponding to the first temperature measurement value. In practical applications, the posterior drift estimate of each temperature measurement value can be determined sequentially according to the time sequence of the collected observation phase data.

[0090] For example, refer to Figure 3 As shown, after acquiring a series of phase measurements, a Kalman filter can be used to iteratively process each phase measurement. For example, the system can be initialized first, setting the initial drift estimate. Set the initial error covariance Setting the target process noise covariance 1. Set the target observation noise covariance and setting target smoothness weights .

[0091] Then, targeting Calculate the prior estimate of the drift. Calculate the gain coefficient Updated posterior error covariance Next, Increment by 1 until all phase measurements have been processed (i.e., ...). Step 104 can be executed.

[0092] Step 104: Perform phase compensation on the first phase measurement based on the first posterior drift estimate.

[0093] In some embodiments, step 104 may include: the computer device subtracting a first posterior drift estimate from the first phase measurement to obtain a compensated first phase measurement.

[0094] In some embodiments, step 104 can be implemented by the following formula: ; in, The first phase measurement value after compensation; This is the first phase measurement value; This is the first posterior drift estimate.

[0095] After obtaining the compensated phase measurement values, these compensated phase measurement values ​​can be high-pass filtered. The cutoff frequency of the high-pass filter can be 0.0005Hz. The cutoff frequency can be adjusted according to the frequency of the target signal, but is not limited to this. The low-frequency trend that was not completely removed in step 104 can be removed by high-pass filtering.

[0096] In the phase compensation method of this application embodiment, the electronic device can acquire a first phase measurement value and a first temperature measurement value acquired synchronously, so as to ensure the accuracy of the correlation between temperature and phase measurement values ​​with strict time alignment. Then, a first temperature effect value is determined by using a preset temperature effect calculation model and the first temperature measurement value, thereby quantifying the correlation between temperature and phase drift. Based on the first temperature effect value, a first posterior drift estimate is calculated, and the first phase measurement value is compensated by the first posterior drift estimate, effectively eliminating the interference of ambient temperature fluctuations on the phase measurement signal, ensuring that the phase measurement value can be effectively compensated under different temperature conditions, and improving the detection accuracy of the DAS system.

[0097] The target smoothness weight, target process noise covariance, and target observation noise covariance in steps 101 to 103 above can be set empirically, determined based on a search algorithm, or obtained from the acquired observation phase data. And for each observation phase data After phase compensation, the observation phase data and the synchronously acquired temperature data are used as sample data to further optimize the set target smoothness weight, target process noise covariance and target observation noise covariance, so as to serve as the parameters used for the next batch of phase compensation of the observation phase data.

[0098] For example, refer to Figure 4 As shown, the steps for determining the target smoothness weights, target process noise covariance, and target observation noise covariance (also known as optimization steps) may include: Step 401: Obtain samples of each phase measurement value and samples of each temperature measurement value that are time-aligned with the samples of each phase measurement value.

[0099] The sample of phase measurements, along with the samples of temperature measurements, can be configured as needed. For example, if further optimization of the target smoothness weight, target process noise covariance, and target observation noise covariance is required, the observation phase data can be configured accordingly. As samples of each phase measurement, the temperature data As a sample of temperature measurement values.

[0100] Step 402: Perform a global search in the parameter search space based on the preset objective function to obtain global candidate parameters.

[0101] The parameter search space represents the range of values ​​for the target smoothness weight, the target process noise covariance, and the target observation noise covariance.

[0102] For example, the parameter search space can be as follows: Target process noise covariance ; Target observation noise covariance ; Target smoothness weight .

[0103] Global candidate parameters include candidate smoothness weights, candidate process noise covariance, and observation noise covariance.

[0104] The objective function represents the weighted sum of the mean penalty, correlation penalty, smoothness penalty, and signal feature penalty.

[0105] For example, the objective function is shown below; ; in, The vector of parameters to be optimized; All of these are preset weights, for example, ; This is a mean penalty term; This is a relevance penalty item; This is a relevance penalty item; This is a penalty term for signal characteristics.

[0106] Mean penalty term The mean of each compensated phase measurement sample, with a mean penalty term. This can be expressed by the following formula: ; in, This represents the number of phase measurement samples. This is the sample of the kth phase measurement value after compensation.

[0107] The purpose of the mean penalty term is to ensure that the mean of each compensated phase measurement sample is close to 0.

[0108] Relevance penalty item This indicates the correlation between the posterior drift estimate corresponding to each phase measurement sample and the temperature measurement sample, with a correlation penalty term. This can be expressed by the following formula: ; in, This represents the number of phase measurement samples. For the first The posterior drift estimate corresponding to each sample of phase measurements; The mean of the posterior drift estimates corresponding to each phase measurement sample; For the first A sample of temperature measurements; This is the sample mean of each temperature measurement.

[0109] The correlation penalty term strengthens the correlation between temperature-induced phase drift and temperature by calculating the correlation between the estimated baseline and temperature, and applies a correlation penalty to emphasize the correlation between the baseline and temperature. The smaller the value, the higher the correlation.

[0110] Smoothness penalty The smoothness of the phase drift curve, which represents the change of the posterior drift estimate corresponding to each phase measurement sample over time, is denoted by a smoothness penalty term. This can be expressed by the following formula: .

[0111] in, This represents the number of phase measurement samples. For the first The posterior drift estimate corresponding to each sample of phase measurements; For the first The posterior drift estimate corresponding to each sample of phase measurements; For the first -1 posterior drift estimate corresponding to a phase measurement sample.

[0112] Smoothness penalty To ensure the smoothness of the drift curve, the mean of the second difference of the estimated baseline is calculated. The smaller the value, the smoother the estimated baseline.

[0113] Signal feature penalty term Determined based on the frequency of vibration of the compensated phase measurement samples. Smoothness penalty term. In order to preserve the original signal vibration characteristics, the zero-crossing rate feature can currently be used to represent the vibration signal characteristics.

[0114] Signal feature penalty term This can be expressed by the following formula: ; Among them, the zero-crossing rate The calculation formula is: ; This represents the number of phase measurement samples. For indicator functions, To prevent division by zero for small constants; This is the sample of the k-th phase measurement after compensation; This is the (k-1)th phase measurement sample after compensation.

[0115] In step 402, a global search algorithm (such as differential evolution algorithm) can be used to find global candidate parameters within an acceptable time range, and then step 403 is executed.

[0116] For example, the global search algorithm (differential evolution algorithm) for finding global candidate parameters can be expressed by the following formula: ; in For parameter search space; The objective function is as described above.

[0117] After obtaining the global candidate parameters, step 403 can be executed.

[0118] Step 403: Perform local optimization on the global candidate parameters to obtain the target smoothness weight, target process noise covariance, and target observation noise covariance.

[0119] Using the global candidate parameters obtained by the global search algorithm as initial parameters, a local optimization algorithm, such as (quasi-Newton method, L-BFGS-B algorithm), is used to calculate the local optimum. The expression can be as follows: ; The objective function is as described above.

[0120] The above can be optimized using boundary constraints to ensure that the parameters are within a physically reasonable range.

[0121] In other embodiments, the target smoothness weight, target process noise covariance, and target observation noise covariance may also be optimized using a genetic algorithm or a greedy algorithm, etc. This application does not limit this.

[0122] This application's embodiments comprehensively consider multiple dimensions of indicators, such as the stability of the compensated phase mean, the correlation between the drift estimate and temperature, the smoothness of the drift curve, and the vibration characteristics of the compensated signal. This ensures that the optimized parameters can adapt to the actual application requirements of the distributed acoustic wave sensing system, maximize the overall effect of phase compensation, and guarantee the performance stability of the system under different operating conditions.

[0123] Based on the same idea as the phase compensation method in the above embodiments, this application also provides a phase compensation device, which can be used to perform the above phase compensation method. For ease of explanation, the structural schematic diagram of the cable operating parameter prediction device embodiment only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0124] like Figure 5 As shown, the phase compensation device includes a data acquisition module 501, an effect calculation module 502, a temperature measurement module 503, and a drift estimation module 504. In some embodiments, the above modules can be programmable software instructions stored in memory and executable by a processor. It is understood that in other embodiments, the above modules can also be program instructions or firmware embedded in the processor.

[0125] The data acquisition module 501 is used to acquire the first temperature measurement value and the first phase measurement value.

[0126] The first phase measurement value is the value of the distributed acoustic wave sensing system at the [number]th phase. The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at specific times; the... The first phase measurement value represents the time order of the phase measurements collected by the distributed acoustic wave sensing system; the first temperature measurement value is the ambient temperature of the optical fiber at the k-th time.

[0127] The effect calculation module 502 is used to determine a first temperature effect value based on a preset temperature effect calculation model and the first temperature measurement value. The first temperature effect value is used to represent the influence of the first temperature measurement value on the phase drift. Drift estimation module 503 is used to calculate the first temperature effect value based on the first temperature effect value. The posterior drift estimate at time t is used to obtain the first posterior drift estimate; Phase compensation module 504 is used to perform phase compensation on the first phase measurement value based on the first posterior drift estimate.

[0128] In some embodiments, the first temperature effect value is used to calculate the... The posterior drift estimate at time t is used to obtain the first posterior drift estimate, which includes: When the time order of the first phase measurement value among the phase measurements is greater than or equal to two, a second posterior drift estimate is obtained, and the second posterior drift estimate is the first... The posterior drift estimate at time step; The first temperature effect value and the second posterior drift estimate are weighted and summed using a preset target smoothness weight to obtain the first prior drift estimate. The first posterior drift estimate is determined based on the first prior drift estimate.

[0129] In some embodiments, the first temperature effect value and the second posterior drift estimate are weighted and summed using a preset target smoothness weight to obtain a first prior drift estimate, including: The first prior drift estimate is calculated using the first formula, where the first formula is: ; Among them, the The first prior drift estimate; The preset target smoothness weight; The first temperature effect value; This is the second posterior drift estimate.

[0130] In some embodiments, determining the first posterior drift estimate based on the first prior drift estimate includes: Get the The posterior error covariance at time t; Wherein, if the first phase measurement value is the second phase measurement value to be compensated, the first... The posterior error covariance at time t is the preset initial error covariance; When the first phase measurement value has a time order greater than two among the phase measurement values, the first... The posterior error covariance at time t is based on the first... The prior error covariance at time t and the first The gain coefficient at time t is determined; Based on the first The sum of the posterior error covariance at time step and the preset target process noise covariance determines the first prior error covariance, which is the sum of the posterior error covariance at time step step. The prior error covariance at time t; A first gain coefficient is determined based on the first prior error covariance and the preset target observation noise covariance. The first gain coefficient is the first... Gain coefficient at time step; The first posterior drift estimate is determined based on the first gain coefficient and the first prior drift estimate.

[0131] In some embodiments, determining a first gain coefficient based on the first prior error covariance and a preset target observation noise covariance includes: The first gain coefficient is determined using the third formula, the first prior error covariance, and the preset target observation noise covariance. The third formula is as follows: ; Among them, the For the first gain coefficient, the Let the first prior error covariance be the... The preset target observation noise covariance.

[0132] In some embodiments, the steps for determining the target smoothness weight, the target process noise covariance, and the target observation noise covariance include: Acquire samples of each phase measurement value and samples of each temperature measurement value that are time-aligned with the samples of each phase measurement value; A global search is performed in the parameter search space based on a preset objective function to obtain global candidate parameters. Wherein, the parameter search space represents the range of values ​​for the target smoothness weight, the target process noise covariance, and the target observation noise covariance; The global candidate parameters include candidate smoothness weights, candidate process noise covariance, and observation noise covariance. The objective function represents the weighted sum of the mean penalty, correlation penalty, smoothness penalty, and signal feature penalty. The mean penalty term is the mean of each compensated phase measurement sample; The correlation penalty term represents the correlation between the posterior drift estimate corresponding to each phase measurement sample and the temperature measurement sample; The smoothness penalty term is the smoothness of the phase drift curve, which represents the curve of the posterior drift estimate corresponding to each phase measurement sample changing over time. The signal feature penalty term is determined based on the frequency of vibration of the compensated phase measurement value sample; Local optimization is performed on the global candidate parameters to obtain the target smoothness weight, the target process noise covariance, and the target observation noise covariance.

[0133] In some embodiments, the first temperature effect value is obtained based on a preset temperature effect calculation model and a first temperature measurement value, including: ; Among them, the The first temperature effect value, the The first temperature measurement value, the The preset temperature secondary influence coefficient, the The preset temperature linear influence coefficient, the This is a preset constant.

[0134] Figure 6 This is a schematic diagram of an embodiment of the electronic device of this application.

[0135] The electronic device 100 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and executable on the processor 30. When the processor 30 executes the computer program 40, it implements the steps described in the phase compensation method embodiments above, for example... Figure 1 Steps 101 to 104 are shown.

[0136] For example, computer program 40 can also be divided into one or more modules / units, which are stored in memory 20 and executed by processor 30. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 40 in electronic device 100.

[0137] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. It may include more or fewer components than shown in the diagram, or combine certain components, or different components. For example, the electronic device 100 may also include input / output devices, network access devices, buses, etc.

[0138] Processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors, single-chip microcomputers, or any conventional processor.

[0139] The memory 20 can be used to store computer programs 40 and / or modules / units. The processor 30 implements various functions of the electronic device 100 by running or executing the computer programs and / or modules / units stored in the memory 20 and by calling data stored in the memory 20. The memory 20 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0140] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the electronic device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and other division methods may be used in actual implementation.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into the same processing unit, or each unit can exist physically separately, or two or more units can be integrated into the same unit. The integrated units described above can be implemented in hardware or in the form of hardware plus software functional modules.

[0143] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and not restrictive in all respects. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or electronic devices recited in the electronic device claims may also be implemented by the same unit or electronic device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A phase compensation method, characterized in that, include: Acquire the first temperature measurement value and the first phase measurement value; Wherein, the first phase measurement value is the distributed acoustic wave sensing system at the ... The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at specific times; the... This indicates the time order of the first phase measurement value among the phase measurements acquired by the distributed acoustic wave sensing system; the first temperature measurement value is the time order of the phase measurements acquired by the distributed acoustic wave sensing system. At any given time, the measured temperature of the environment in which the optical fiber is located; A first temperature effect value is determined based on a preset temperature effect calculation model and the first temperature measurement value. The first temperature effect value is used to represent the influence of the first temperature measurement value on the phase drift. Calculate the first temperature effect value based on the first temperature effect value. The posterior drift estimate at time t is used to obtain the first posterior drift estimate; Phase compensation is performed on the first phase measurement based on the first posterior drift estimate.

2. The phase compensation method as described in claim 1, characterized in that, The calculation based on the first temperature effect value is the first The posterior drift estimate at time t is used to obtain the first posterior drift estimate, which includes: When the time order of the first phase measurement value among the phase measurements is greater than or equal to two, a second posterior drift estimate is obtained, and the second posterior drift estimate is the first... The posterior drift estimate at time step; The first temperature effect value and the second posterior drift estimate are weighted and summed using a preset target smoothness weight to obtain the first prior drift estimate. The first posterior drift estimate is determined based on the first prior drift estimate.

3. The phase compensation method as described in claim 2, characterized in that, The step of weighting and summing the first temperature effect value and the second posterior drift estimate using a preset target smoothness weight to obtain the first prior drift estimate includes: The first prior drift estimate is calculated using the first formula, where the first formula is: ； Among them, the The first prior drift estimate; The preset target smoothness weight; The first temperature effect value; This is the second posterior drift estimate.

4. The phase compensation method as described in claim 2, characterized in that, Determining the first posterior drift estimate based on the first prior drift estimate includes: Get the The posterior error covariance at time t; Wherein, if the first phase measurement value is the second phase measurement value to be compensated, the first... The posterior error covariance at time t is the preset initial error covariance; When the first phase measurement value has a time order greater than two among the phase measurement values, the first... The posterior error covariance at time t is based on the first... The prior error covariance at time t and the first The gain coefficient at time t is determined; Based on the first The sum of the posterior error covariance at time step and the preset target process noise covariance determines the first prior error covariance, which is the sum of the posterior error covariance at time step step. The prior error covariance at time t; A first gain coefficient is determined based on the first prior error covariance and the preset target observation noise covariance. The first gain coefficient is the first... Gain coefficient at time step; The first posterior drift estimate is determined based on the first gain coefficient and the first prior drift estimate.

5. The phase compensation method as described in claim 4, characterized in that, The step of determining the first gain coefficient based on the first prior error covariance and the preset target observation noise covariance includes: The first gain coefficient is determined using the third formula, the first prior error covariance, and the preset target observation noise covariance. The third formula is as follows: ； Among them, the For the first gain coefficient, the Let the first prior error covariance be the... The preset target observation noise covariance.

6. The phase compensation method as described in claim 4, characterized in that, The steps for determining the target smoothness weight, the target process noise covariance, and the target observation noise covariance include: Acquire samples of each phase measurement value and samples of each temperature measurement value that are time-aligned with the samples of each phase measurement value; A global search is performed in the parameter search space based on a preset objective function to obtain global candidate parameters. Wherein, the parameter search space represents the range of values ​​for the target smoothness weight, the target process noise covariance, and the target observation noise covariance; The global candidate parameters include candidate smoothness weights, candidate process noise covariance, and observation noise covariance. The objective function represents the weighted sum of the mean penalty, correlation penalty, smoothness penalty, and signal feature penalty. The mean penalty term is the mean of each compensated phase measurement sample; The correlation penalty term represents the correlation between the posterior drift estimate corresponding to each phase measurement sample and the temperature measurement sample; The smoothness penalty term is the smoothness of the phase drift curve, which represents the curve of the posterior drift estimate corresponding to each phase measurement sample changing over time. The signal feature penalty term is determined based on the frequency of vibration of the compensated phase measurement value sample; Local optimization is performed on the global candidate parameters to obtain the target smoothness weight, the target process noise covariance, and the target observation noise covariance.

7. The phase compensation method according to any one of claims 1 to 6, characterized in that, The process of obtaining the first temperature effect value based on a preset temperature effect calculation model and a first temperature measurement value includes: ； Among them, the The first temperature effect value, the The first temperature measurement value, the The preset temperature secondary influence coefficient, the The preset temperature linear influence coefficient, the This is a preset constant.

8. A phase compensation device, characterized in that, include: The data acquisition module is used to acquire the first temperature measurement value and the first phase measurement value; Wherein, the first phase measurement value is the distributed acoustic wave sensing system at the ... The phase of the backscattered Rayleigh light generated within the optical fiber, acquired at specific times; the... The first phase measurement value represents the time order of the phase measurement values ​​collected by the distributed acoustic wave sensing system; the first temperature measurement value is the temperature measurement value of the ambient temperature where the optical fiber is located at the k-th time. The effect calculation module is used to determine a first temperature effect value based on a preset temperature effect calculation model and the first temperature measurement value. The first temperature effect value is used to represent the influence of the first temperature measurement value on the phase drift. The drift estimation module is used to calculate the first temperature effect value. The posterior drift estimate at time t is used to obtain the first posterior drift estimate; The phase compensation module is used to perform phase compensation on the first phase measurement value based on the first posterior drift estimate.

9. An electronic device, the electronic device comprising a processor and a memory, characterized in that, The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to perform the phase compensation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the phase compensation method as described in any one of claims 1 to 7.

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