技术领域Technical Field
本发明属于分布式光纤传感系统的信号处理技术领域，本发明涉及一种分布式光纤传感系统的远端扰动特征提取方法及系统。The present invention belongs to the technical field of signal processing of distributed optical fiber sensing systems, and the present invention relates to a method and system for extracting far-end disturbance features of a distributed optical fiber sensing system.
背景技术Background Art
光纤传感器凭借其不受电磁干扰、灵活性高以及容易组网的优势，在安防、监测与勘测领域中得到了广泛的应用。分布式光纤传感系统将光纤上的点均作为独立的传感单元，利用相位敏感(OTDR)技术，相比于传统的点式光纤传感系统，在测量范围、测量灵敏度以及响应能力上均有显著的提高。但同样的，分布式光纤传感系统的高灵敏度与快响应速度会造成系统对噪声与环境扰动的敏感性。同时，在长距离传输探测场景下，由于瑞利散射谱自身的衰落特性，将导致远端干扰信号被近端回波信号所淹没，从而影响对干扰信号的准确提取。Optical fiber sensors have been widely used in the fields of security, monitoring and surveying due to their advantages of being immune to electromagnetic interference, high flexibility and easy networking. Distributed optical fiber sensing systems use each point on the optical fiber as an independent sensing unit and use phase-sensitive (OTDR) technology. Compared with traditional point-type optical fiber sensing systems, they have significantly improved the measurement range, measurement sensitivity and response capability. However, the high sensitivity and fast response speed of distributed optical fiber sensing systems will make the system sensitive to noise and environmental disturbances. At the same time, in long-distance transmission detection scenarios, due to the fading characteristics of the Rayleigh scattering spectrum itself, the far-end interference signal will be submerged by the near-end echo signal, thereby affecting the accurate extraction of the interference signal.
申请号201810359506X公开了一种基于扰动信号特征提取的分布式光纤振动传感定位方法及装置，将待检测光缆划分为若干个区间，并在每个区间分别接收振动产生的脉冲峰信号后记录脉冲峰个数，其中，第i帧第j段光缆上的脉冲峰个数记为N(i，j)；分别统计光缆每个区间内脉冲峰个数的平均值和方差；在每个光缆区间内，将所述脉冲峰个数的平均值和方差和预设参数进行比较，根据比较结果确认当前区间的光缆是否存在扰动信号，以此类推，确认所述待检测光缆存在扰动信号的各个区间；获取存在扰动信号的光缆区间，再分别确认所述区间内扰动点位置。该方法基于统计得到，需要有一定量的数据。实际操作繁琐，在数据量较小时，往往无法得到准确的结果。Application No. 201810359506X discloses a distributed optical fiber vibration sensing positioning method and device based on disturbance signal feature extraction, which divides the optical cable to be detected into several intervals, and records the number of pulse peaks after receiving the pulse peak signal generated by vibration in each interval, wherein the number of pulse peaks on the jth section of the optical cable in the i-th frame is recorded as N(i, j); the average value and variance of the number of pulse peaks in each interval of the optical cable are counted respectively; in each optical cable interval, the average value and variance of the number of pulse peaks are compared with preset parameters, and the optical cable in the current interval is confirmed according to the comparison result whether there is a disturbance signal, and so on, each interval where the optical cable to be detected has a disturbance signal is confirmed; the optical cable interval with a disturbance signal is obtained, and then the position of the disturbance point in the interval is confirmed respectively. This method is based on statistics and requires a certain amount of data. The actual operation is cumbersome, and when the amount of data is small, accurate results are often not obtained.
申请号2017103708538公开了一种相位敏感光时域反射分布式光纤传感系统快速定位方法，构建多个光脉冲对应的瑞利散射光数字信号矩阵、在信号矩阵上间隔一定长度选择测试窗口和测试列、获得各个测试列的相位、根据相邻测试窗口测试列相位对扰动源区间粗略定位、提取包含扰动源的区间信号进行扰动精确定位。该方法没有解决由于瑞利散射谱自身的衰落特性，导致远端干扰信号被近端回波信号所淹没的技术问题，无法提取得到准确的扰动特征。Application No. 2017103708538 discloses a phase-sensitive optical time-domain reflectometry distributed optical fiber sensing system rapid positioning method, which constructs a Rayleigh scattered light digital signal matrix corresponding to multiple optical pulses, selects test windows and test columns at intervals of a certain length on the signal matrix, obtains the phase of each test column, roughly locates the disturbance source interval according to the phase of the adjacent test window test column, and extracts the interval signal containing the disturbance source for precise disturbance positioning. This method does not solve the technical problem that the far-end interference signal is submerged by the near-end echo signal due to the fading characteristics of the Rayleigh scattering spectrum itself, and cannot extract accurate disturbance features.
发明内容Summary of the invention
本发明的目的在于提供一种分布式光纤传感系统的远端扰动特征提取方法及系统，针对不同的瑞利散射谱关注区间，对原有的字典进行学习观测形成多态等效字典，随后基于多态等效字典，建立在多态等效字典级联下的联合稀疏表示模型，形成多回波散射谱的联合稀疏表示，最后，通过优化重构算法完成对远端扰动信号的识别与提取。提高了扰动信号特征提取的准确性。The purpose of the present invention is to provide a method and system for extracting far-end disturbance features of a distributed optical fiber sensing system. For different Rayleigh scattering spectrum focus intervals, the original dictionary is observed and learned to form a polymorphic equivalent dictionary. Then, based on the polymorphic equivalent dictionary, a joint sparse representation model under the cascade of polymorphic equivalent dictionaries is established to form a joint sparse representation of multi-echo scattering spectra. Finally, the identification and extraction of far-end disturbance signals are completed by optimizing the reconstruction algorithm. The accuracy of disturbance signal feature extraction is improved.
实现本发明目的的技术解决方案为：The technical solution to achieve the purpose of the present invention is:
一种分布式光纤传感系统的远端扰动特征提取方法，包括以下步骤：A method for extracting far-end disturbance features of a distributed optical fiber sensing system comprises the following steps:
S01：对光纤信道内信号瑞利散射谱的距离维区间进行划分，并针对各区间建立相应的字典学习模型，对原有的字典进行学习观测形成多态等效字典；S01: Divide the distance dimension interval of the Rayleigh scattering spectrum of the signal in the optical fiber channel, establish a corresponding dictionary learning model for each interval, and conduct learning observation on the original dictionary to form a polymorphic equivalent dictionary;
S02：基于多态等效字典，建立在多态等效字典级联下的联合稀疏表示模型，形成多回波散射谱的联合稀疏表示；S02: Based on the polymorphic equivalent dictionary, a joint sparse representation model under the cascade of polymorphic equivalent dictionaries is established to form a joint sparse representation of multi-echo scattering spectra;
S03：构建基于多态级联字典的联合优化重构算法，提取远端扰动信号特征。S03: Construct a joint optimization reconstruction algorithm based on polymorphic cascade dictionary to extract the characteristics of far-end disturbance signals.
优选的技术方案中，所述步骤S01中形成多态等效字典的方法包括：In a preferred technical solution, the method of forming a polymorphic equivalent dictionary in step S01 includes:
S11：针对所有的探测距离构造一个本征特征字典：S11: Construct an intrinsic feature dictionary for all detection distances:
Ψ＝[ψ1,ψ2,…,ψL]；Ψ=[ψ1 , ψ2 ,…, ψL ];
其中，ψl表示在第l个探测距离上的单位响应；Where ψl represents the unit response at the lth detection distance;
得到基态谱表示为：Get the ground state spectrum It is expressed as:
其中，η为基态特征系数；Among them, η is the ground state characteristic coefficient;
S12：针对第n个扰动事件ζn，其扰动特征空间为其扰动特征互补空间为基于得到对应的特征系数向量为：S12: For the nth disturbance event ζn , its disturbance feature space is Its disturbance feature complementary space is based on Get the corresponding characteristic coefficient vector for:
其中，ηl与分别表示η和的第l个元素；Among them, ηl and They represent η and The lth element of
S13：针对扰动事件ζn，构建扰动模态观测矩阵：S13: Construct the disturbance modal observation matrix for the disturbance event ζn :
其中，diag(z)表示以向量z为对角元素构成矩阵，β(n)为权系数向量；Wherein, diag(z) represents a matrix formed with vector z as diagonal element, and β(n) is a weight coefficient vector;
S14：基于上述扰动模态观测矩阵，对本征特征字典进行学习观测，形成扰动事件ζn所对应的模态字典：S14: Based on the above disturbance modal observation matrix, the intrinsic feature dictionary is learned and observed to form a modal dictionary corresponding to the disturbance event ζn :
Ψ(n)＝Φ(n)Ψ；Ψ(n) = Φ(n) Ψ;
将所有扰动事件N所对应的模态字典与本征特征字典进行级联组合，得到多态等效字典：The modal dictionary corresponding to all disturbance events N is cascaded with the intrinsic feature dictionary to obtain a polymorphic equivalent dictionary:
优选的技术方案中，所述步骤S02中形成多回波散射谱的联合稀疏表示的方法包括：In a preferred technical solution, the method for forming a joint sparse representation of multi-echo scattering spectra in step S02 includes:
将M个回波光脉冲组成的联合回波矩阵表示为：The joint echo matrix composed of M echo light pulses is expressed as:
其中，Pi表示第i个回波光脉冲对应的采样瑞利曲线，[·]T表示向量与矩阵的转置；Where,Pi represents the sampled Rayleigh curve corresponding to the i-th echo light pulse, [·]T represents the transpose of the vector and matrix;
基于多态等效字典，得到联合回波矩阵的联合稀疏表示模型为：Based on the polymorphic equivalent dictionary, the joint sparse representation model of the joint echo matrix is obtained as follows:
其中，Θ为联合特征矩阵，同时也是一个列稀疏矩阵，其非零列表征了相应多态字典所对应的扰动事件。Among them, Θ is the joint feature matrix, which is also a column sparse matrix, and its non-zero columns characterize the disturbance events corresponding to the corresponding polymorphic dictionary.
优选的技术方案中，所述步骤S03中提取远端扰动信号特征的方法包括：In a preferred technical solution, the method for extracting the far-end disturbance signal feature in step S03 includes:
S31：对联合回波矩阵进行向量化得到r＝vec(R)，进而得到扩展联合稀疏表示：S31: vectorize the joint echo matrix to obtain r=vec(R), and then obtain the extended joint sparse representation:
r＝Γθr＝Γθ
其中，I(N+1)×(N+1)表示维度为(N+1)×(N+1)的单位阵，表示克罗内克积，θ为重构特征向量；in, I(N+1)×(N+1) represents the unit matrix of dimension (N+1)×(N+1), represents the Kronecker product, θ is the reconstructed eigenvector;
S32：初始化重构特征向量θ(0)＝0，残差ε(0)＝r；索引集设置终止迭代门限ξ；t＝1，Θ0为空矩阵；S32: Initialize the reconstructed feature vector θ(0) = 0, the residual ε(0) = r; index set Set the termination iteration threshold ξ; t = 1, Θ0 is an empty matrix;
S33：将Γ中各模态字典Ψ(n)与第l列抽取组成字矩阵Γl，将每个Γl,l＝1,2…L与残差ε相乘，找出积为最大值所对应的索引为λt，即S33: Extract each modal dictionary Ψ(n) in Γ and the lth column to form a word matrix Γl , multiply each Γl , l＝1,2…L by the residual ε, and find the index λt corresponding to the maximum product, that is,
S34：更新索引集Λt＝Λt-1∪{λt}，记录找到的多态等效字典中与残差相关度最高的列组合，并重建原子集合为γt＝[γt-1,Γl]；S34: update the index set Λt =Λt-1 ∪{λt }, record the column combination with the highest correlation with the residual in the found polymorphic equivalent dictionary, and reconstruct the atomic set as γt =[γt-1 ,Γl ];
S35：求解得到θ(t)＝argmin||r-γtθ(t)||2，更新残差ε(t)＝r-γtθ(t)，t＝t+1；S35: Solve to obtain θ(t) = argmin||r-γt θ(t) ||2 , update the residual ε(t) = r-γt θ(t) , t = t+1;
S36：判断残差ε(t)是否小于ξ，如是，则迭代停止，输出重构特征向量θ；反之，则跳转至步骤S33继续执行；S36: Determine whether the residual ε(t) is less than ξ. If so, the iteration stops and the reconstructed feature vector θ is output; otherwise, jump to step S33 to continue execution;
S37：由重构特征向量θ重构得到联合特征矩阵Θ。S37: Reconstruct the joint feature matrix Θ from the reconstructed feature vector θ.
优选的技术方案中，所述步骤S03之后还包括，将重构得到的联合特征矩阵Θ的前L列置零，随后确定剩余的非零列，获得非零列索引向量υ，由索引向量υ指向扰动事件类型。In the preferred technical solution, after step S03, the first L columns of the reconstructed joint feature matrix Θ are set to zero, and then the remaining non-zero columns are determined to obtain the non-zero column index vector υ, and the index vector υ points to the disturbance event type.
本发明还公开了一种分布式光纤传感系统的远端扰动特征提取系统，包括：The present invention also discloses a remote disturbance feature extraction system of a distributed optical fiber sensing system, comprising:
多态等效字典构建模块，对光纤信道内信号瑞利散射谱的距离维区间进行划分，并针对各区间建立相应的字典学习模型，对原有的字典进行学习观测形成多态等效字典；The polymorphic equivalent dictionary construction module divides the distance dimension interval of the Rayleigh scattering spectrum of the signal in the optical fiber channel, establishes a corresponding dictionary learning model for each interval, and conducts learning observation on the original dictionary to form a polymorphic equivalent dictionary;
多回波散射谱的联合稀疏表示模块，基于多态等效字典，建立在多态等效字典级联下的联合稀疏表示模型，形成多回波散射谱的联合稀疏表示；The joint sparse representation module of multi-echo scattering spectrum is based on the polymorphic equivalent dictionary, and a joint sparse representation model is established under the cascade of polymorphic equivalent dictionaries to form a joint sparse representation of multi-echo scattering spectrum;
扰动信号特征提取模块，构建基于多态级联字典的联合优化重构算法，提取远端扰动信号特征。The disturbance signal feature extraction module constructs a joint optimization reconstruction algorithm based on a polymorphic cascade dictionary to extract the features of the far-end disturbance signal.
优选的技术方案中，所述形成多态等效字典的方法包括：In a preferred technical solution, the method for forming a polymorphic equivalent dictionary includes:
S11：针对所有的探测距离构造一个本征特征字典：S11: Construct an intrinsic feature dictionary for all detection distances:
Ψ＝[ψ1,ψ2,…,ψL]；Ψ=[ψ1 , ψ2 ,…, ψL ];
其中，ψl表示在第l个探测距离上的单位响应；Where ψl represents the unit response at the lth detection distance;
得到基态谱表示为：Get the ground state spectrum It is expressed as:
其中，η为基态特征系数；Among them, η is the ground state characteristic coefficient;
S12：针对第n个扰动事件ζn，其扰动特征空间为其扰动特征互补空间为基于得到对应的特征系数向量为：S12: For the nth disturbance event ζn , its disturbance feature space is Its disturbance feature complementary space is based on Get the corresponding characteristic coefficient vector for:
其中，ηl与分别表示η和的第l个元素；Among them, ηl and They represent η and The lth element of
S13：针对扰动事件ζn，构建扰动模态观测矩阵：S13: Construct the disturbance mode observation matrix for the disturbance event ζn :
其中，diag(z)表示以向量z为对角元素构成矩阵，β(n)为权系数向量；Wherein, diag(z) represents a matrix formed with vector z as diagonal element, and β(n) is a weight coefficient vector;
S14：基于上述扰动模态观测矩阵，对本征特征字典进行学习观测，形成扰动事件ζn所对应的模态字典：S14: Based on the above disturbance modal observation matrix, the intrinsic feature dictionary is learned and observed to form a modal dictionary corresponding to the disturbance event ζn :
Ψ(n)＝Φ(n)Ψ；Ψ(n) = Φ(n) Ψ;
将所有扰动事件N所对应的模态字典与本征特征字典进行级联组合，得到多态等效字典：The modal dictionary corresponding to all disturbance events N is cascaded with the intrinsic feature dictionary to obtain a polymorphic equivalent dictionary:
优选的技术方案中，所述多回波散射谱的联合稀疏表示的方法包括：In a preferred technical solution, the method for joint sparse representation of multi-echo scattering spectra includes:
将M个回波光脉冲组成的联合回波矩阵表示为：The joint echo matrix composed of M echo light pulses is expressed as:
其中，Pi表示第i个回波光脉冲对应的采样瑞利曲线，[·]T表示向量与矩阵的转置；Where,Pi represents the sampled Rayleigh curve corresponding to the i-th echo light pulse, [·]T represents the transpose of the vector and matrix;
基于多态等效字典，得到联合回波矩阵的联合稀疏表示模型为：Based on the polymorphic equivalent dictionary, the joint sparse representation model of the joint echo matrix is obtained as follows:
其中，Θ为联合特征矩阵，同时也是一个列稀疏矩阵，其非零列表征了相应多态字典所对应的扰动事件。Among them, Θ is the joint feature matrix, which is also a column sparse matrix, and its non-zero columns characterize the disturbance events corresponding to the corresponding polymorphic dictionary.
优选的技术方案中，所述提取远端扰动信号特征的方法包括：In a preferred technical solution, the method for extracting the characteristics of the far-end disturbance signal includes:
S31：对联合回波矩阵进行向量化得到r＝vec(R)，进而得到扩展联合稀疏表示：S31: vectorize the joint echo matrix to obtain r=vec(R), and then obtain the extended joint sparse representation:
r＝Γθr＝Γθ
其中，I(N+1)×(N+1)表示维度为(N+1)×(N+1)的单位阵，表示克罗内克积，θ为重构特征向量；in, I(N+1)×(N+1) represents the unit matrix of dimension (N+1)×(N+1), represents the Kronecker product, θ is the reconstructed eigenvector;
S32：初始化重构特征向量θ(0)＝0，残差ε(0)＝r；索引集设置终止迭代门限ξ；t＝1，Θ0为空矩阵；S32: Initialize the reconstructed feature vector θ(0) = 0, the residual ε(0) = r; index set Set the termination iteration threshold ξ; t = 1, Θ0 is an empty matrix;
S33：将Γ中各模态字典Ψ(n)与第l列抽取组成字矩阵Γl，将每个Γl,l＝1,2…L与残差ε相乘，找出积为最大值所对应的索引为λt，即S33: Extract each modal dictionary Ψ(n) in Γ and the lth column to form a word matrix Γl , multiply each Γl , l＝1,2…L by the residual ε, and find the index λt corresponding to the maximum product, that is,
S34：更新索引集Λt＝Λt-1∪{λt}，记录找到的多态等效字典中与残差相关度最高的列组合，并重建原子集合为γt＝[γt-1,Γl]；S34: update the index set Λt =Λt-1 ∪{λt }, record the column combination with the highest correlation with the residual in the found polymorphic equivalent dictionary, and reconstruct the atomic set as γt =[γt-1 ,Γl ];
S35：求解得到更新残差ε(t)＝r-γtθ(t)，t＝t+1；S35: Solved Update the residual ε(t) = r -γt θ(t) , t = t + 1;
S36：判断残差ε(t)是否小于ξ，如是，则迭代停止，输出重构特征向量θ；反之，则跳转至步骤S33继续执行；S36: Determine whether the residual ε(t) is less than ξ. If so, the iteration stops and the reconstructed feature vector θ is output; otherwise, jump to step S33 to continue execution;
S37：由重构特征向量θ重构得到联合特征矩阵Θ。S37: Reconstruct the joint feature matrix Θ from the reconstructed feature vector θ.
优选的技术方案中，还包括扰动事件类型识别模块，将重构得到的联合特征矩阵Θ的前L列置零，随后确定剩余的非零列，获得非零列索引向量υ，由索引向量υ指向扰动事件类型。The preferred technical solution also includes a disturbance event type identification module, which sets the first L columns of the reconstructed joint feature matrix Θ to zero, then determines the remaining non-zero columns, obtains the non-zero column index vector υ, and the index vector υ points to the disturbance event type.
本发明与现有技术相比，其显著优点为：Compared with the prior art, the present invention has the following significant advantages:
本发明可以解决针对分布式光纤传感系统中远端干扰信号因瑞利散射谱固有衰减而导致的被淹没问题。针对不同的瑞利散射谱关注区间，对原有的字典进行学习观测形成多态等效字典，随后基于多态等效字典，建立在多态等效字典级联下的联合稀疏表示模型，形成多回波散射谱的联合稀疏表示，最后，通过优化重构算法完成对远端扰动信号的识别与提取。大大提高了扰动信号特征提取的准确性，还可以对扰动事件类型进行识别。The present invention can solve the problem of the far-end interference signal being submerged due to the inherent attenuation of the Rayleigh scattering spectrum in the distributed optical fiber sensing system. For different Rayleigh scattering spectrum focus intervals, the original dictionary is learned and observed to form a polymorphic equivalent dictionary. Then, based on the polymorphic equivalent dictionary, a joint sparse representation model under the cascade of polymorphic equivalent dictionaries is established to form a joint sparse representation of multi-echo scattering spectra. Finally, the identification and extraction of the far-end disturbance signal is completed by optimizing the reconstruction algorithm. The accuracy of the disturbance signal feature extraction is greatly improved, and the type of disturbance event can also be identified.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为较佳实施例的分布式光纤传感系统的远端扰动特征提取方法的流程图；FIG1 is a flow chart of a method for extracting far-end disturbance features of a distributed optical fiber sensing system according to a preferred embodiment;
图2为较佳实施例的分布式光纤传感系统的远端扰动特征提取系统的原理框图。FIG2 is a principle block diagram of a remote disturbance feature extraction system of a distributed optical fiber sensing system of a preferred embodiment.
具体实施方式DETAILED DESCRIPTION
本发明的原理是：针对现有通用夹具和专用夹具的不足，设计一种简易装夹、不损伤精密零件、能完整检测各项形位误差，并且在同类型零件测量和检测对象尺寸存在偏差时具有通用性的夹具，可减少夹具拆卸和不必要的重复定位，从而减轻工人的劳动强度，提高测量效率。The principle of the present invention is: in view of the shortcomings of existing general fixtures and special fixtures, a fixture is designed that is easy to clamp, does not damage precision parts, can fully detect various form and position errors, and has universality when there are deviations in the measurement and detection object sizes of the same type of parts. It can reduce fixture disassembly and unnecessary repeated positioning, thereby reducing the labor intensity of workers and improving measurement efficiency.
实施例1：Embodiment 1:
如图1所示，一种分布式光纤传感系统的远端扰动特征提取方法，包括以下步骤：As shown in FIG1 , a method for extracting far-end disturbance features of a distributed optical fiber sensing system includes the following steps:
S01：对光纤信道内信号瑞利散射谱的距离维区间进行划分，并针对各区间建立相应的字典学习模型，对原有的字典进行学习观测形成多态等效字典；S01: Divide the distance dimension interval of the Rayleigh scattering spectrum of the signal in the optical fiber channel, establish a corresponding dictionary learning model for each interval, and conduct learning observation on the original dictionary to form a polymorphic equivalent dictionary;
S02：基于多态等效字典，建立在多态等效字典级联下的联合稀疏表示模型，形成多回波散射谱的联合稀疏表示；S02: Based on the polymorphic equivalent dictionary, a joint sparse representation model under the cascade of polymorphic equivalent dictionaries is established to form a joint sparse representation of multi-echo scattering spectra;
S03：构建基于多态级联字典的联合优化重构算法，提取远端扰动信号特征。S03: Construct a joint optimization reconstruction algorithm based on polymorphic cascade dictionary to extract the characteristics of far-end disturbance signals.
需要说明的是：对于已完成布线的光缆，其在未收扰动情况下的瑞利散射谱具有固定的分量组合，本发明将上述固定的分量组合称为光缆瑞利散射谱的基态谱，简称基态谱，若考虑光缆中分布式传感器的间隔为Δ，同时探测总长为LΔ，为方便后续内容中公式的简洁性，本发明中假设Δ＝1，上述假设不会影响Δ在其他取值情况下本发明内容的一般性。It should be noted that: for the optical cable that has been wired, its Rayleigh scattering spectrum in the absence of disturbance has a fixed component combination. The present invention refers to the above-mentioned fixed component combination as the ground state spectrum of the Rayleigh scattering spectrum of the optical cable, referred to as the ground state spectrum. If the interval of the distributed sensors in the optical cable is considered to be Δ, and the total detection length is LΔ, in order to facilitate the simplicity of the formulas in the subsequent content, it is assumed in the present invention that Δ=1. The above assumption will not affect the generality of the content of the present invention when Δ takes other values.
一较佳的实施例，步骤S01中形成多态等效字典的方法包括：In a preferred embodiment, the method for forming a polymorphic equivalent dictionary in step S01 includes:
S11：针对所有的探测距离构造一个本征特征字典：S11: Construct an intrinsic feature dictionary for all detection distances:
Ψ＝[ψ1,ψ2,…,ψL]；Ψ=[ψ1 , ψ2 ,…, ψL ];
其中，ψl表示在第l个探测距离上的单位响应；Where, ψl represents the unit response at the lth detection distance;
得到基态谱表示为：Get the ground state spectrum It is expressed as:
其中，η为基态特征系数；Among them, η is the ground state characteristic coefficient;
S12：针对第n个扰动事件ζn，其扰动特征空间为其扰动特征互补空间为基于得到对应的特征系数向量为：S12: For the nth disturbance event ζn , its disturbance feature space is Its disturbance feature complementary space is based on Get the corresponding characteristic coefficient vector for:
其中，ηl与分别表示η和的第l个元素；Among them, ηl and They represent η and The lth element of
S13：针对扰动事件ζn，构建扰动模态观测矩阵：S13: Construct the disturbance modal observation matrix for the disturbance event ζn :
其中，diag(z)表示以向量z为对角元素构成矩阵，β(n)为权系数向量；Wherein, diag(z) represents a matrix formed with vector z as diagonal element, and β(n) is a weight coefficient vector;
S14：基于上述扰动模态观测矩阵，对本征特征字典进行学习观测，形成扰动事件ζn所对应的模态字典：S14: Based on the above disturbance modal observation matrix, the intrinsic feature dictionary is learned and observed to form a modal dictionary corresponding to the disturbance event ζn :
Ψ(n)＝Φ(n)Ψ；Ψ(n) = Φ(n) Ψ;
将所有扰动事件N所对应的模态字典与本征特征字典进行级联组合，得到多态等效字典：The modal dictionary corresponding to all disturbance events N is cascaded with the intrinsic feature dictionary to obtain a polymorphic equivalent dictionary:
一较佳的实施例，步骤S02中形成多回波散射谱的联合稀疏表示的方法包括：In a preferred embodiment, the method for forming a joint sparse representation of multi-echo scattering spectra in step S02 includes:
将M个回波光脉冲组成的联合回波矩阵表示为：The joint echo matrix composed of M echo light pulses is expressed as:
其中，Pi表示第i个回波光脉冲对应的采样瑞利曲线，[·]T表示向量与矩阵的转置；Where,Pi represents the sampled Rayleigh curve corresponding to the i-th echo light pulse, [·]T represents the transpose of the vector and matrix;
基于多态等效字典，得到联合回波矩阵的联合稀疏表示模型为：Based on the polymorphic equivalent dictionary, the joint sparse representation model of the joint echo matrix is obtained as follows:
其中，Θ为联合特征矩阵，同时也是一个列稀疏矩阵，其非零列表征了相应多态字典所对应的扰动事件。Among them, Θ is the joint feature matrix, which is also a column sparse matrix, and its non-zero columns characterize the disturbance events corresponding to the corresponding polymorphic dictionary.
一较佳的实施例，步骤S03中提取远端扰动信号特征的方法包括：In a preferred embodiment, the method for extracting the far-end disturbance signal feature in step S03 includes:
S31：对联合回波矩阵进行向量化得到r＝vec(R)，进而得到扩展联合稀疏表示：S31: vectorize the joint echo matrix to obtain r=vec(R), and then obtain the extended joint sparse representation:
r＝Γθr＝Γθ
其中，I(N+1)×(N+1)表示维度为(N+1)×(N+1)的单位阵，表示克罗内克积，θ为重构特征向量；in, I(N+1)×(N+1) represents the unit matrix of dimension (N+1)×(N+1), represents the Kronecker product, θ is the reconstructed eigenvector;
S32：初始化重构特征向量θ(0)＝0，残差ε(0)＝r；索引集设置终止迭代门限ξ；t＝1，Θ0为空矩阵；S32: Initialize the reconstructed feature vector θ(0) = 0, the residual ε(0) = r; index set Set the termination iteration threshold ξ; t = 1, Θ0 is an empty matrix;
S33：将Γ中各模态字典Ψ(n)与第l列抽取组成字矩阵Γl，将每个Γl,l＝1,2…L与残差ε相乘，找出积为最大值所对应的索引为λt，即S33: Extract each modal dictionary Ψ(n) in Γ and the lth column to form a word matrix Γl , multiply each Γl , l＝1,2…L by the residual ε, and find the index λt corresponding to the maximum product, that is,
S34：更新索引集Λt＝Λt-1∪{λt}，记录找到的多态等效字典中与残差相关度最高的列组合，并重建原子集合为γt＝[γt-1,Γl]；S34: update the index set Λt =Λt-1 ∪{λt }, record the column combination with the highest correlation with the residual in the found polymorphic equivalent dictionary, and reconstruct the atomic set as γt =[γt-1 ,Γl ];
S35：求解得到θ(t)＝argmin||r-γtθ(t)||2，更新残差ε(t)＝r-γtθ(t)，t＝t+1；S35: Solve to obtain θ(t) = argmin||r-γt θ(t) ||2 , update the residual ε(t) = r-γt θ(t) , t = t+1;
S36：判断残差ε(t)是否小于ξ，如是，则迭代停止，输出重构特征向量θ；反之，则跳转至步骤S33继续执行；S36: Determine whether the residual ε(t) is less than ξ. If so, the iteration stops and the reconstructed feature vector θ is output; otherwise, jump to step S33 to continue execution;
S37：由重构特征向量θ重构得到联合特征矩阵Θ。S37: Reconstruct the joint feature matrix Θ from the reconstructed feature vector θ.
另一实施例中，步骤S03之后还包括，将重构得到的联合特征矩阵Θ的前L列置零，随后确定剩余的非零列，获得非零列索引向量υ，由索引向量υ指向扰动事件类型。从而可以对扰动事件类型进行识别。In another embodiment, after step S03, the first L columns of the reconstructed joint feature matrix θ are set to zero, and then the remaining non-zero columns are determined to obtain a non-zero column index vector υ, and the index vector υ points to the disturbance event type, so that the disturbance event type can be identified.
又一实施例中，如图2所示，本发明还公开了一种分布式光纤传感系统的远端扰动特征提取系统，包括：In another embodiment, as shown in FIG. 2 , the present invention further discloses a remote disturbance feature extraction system of a distributed optical fiber sensing system, comprising:
多态等效字典构建模块，对光纤信道内信号瑞利散射谱的距离维区间进行划分，并针对各区间建立相应的字典学习模型，对原有的字典进行学习观测形成多态等效字典；The polymorphic equivalent dictionary construction module divides the distance dimension interval of the Rayleigh scattering spectrum of the signal in the optical fiber channel, establishes a corresponding dictionary learning model for each interval, and conducts learning observation on the original dictionary to form a polymorphic equivalent dictionary;
多回波散射谱的联合稀疏表示模块，基于多态等效字典，建立在多态等效字典级联下的联合稀疏表示模型，形成多回波散射谱的联合稀疏表示；The joint sparse representation module of multi-echo scattering spectrum is based on the polymorphic equivalent dictionary, and a joint sparse representation model is established under the cascade of polymorphic equivalent dictionaries to form a joint sparse representation of multi-echo scattering spectrum;
扰动信号特征提取模块，构建基于多态级联字典的联合优化重构算法，提取远端扰动信号特征。The disturbance signal feature extraction module constructs a joint optimization reconstruction algorithm based on a polymorphic cascade dictionary to extract the features of the far-end disturbance signal.
具体的，分布式光纤传感系统的远端扰动特征提取系统的工作流程如下：Specifically, the workflow of the remote disturbance feature extraction system of the distributed optical fiber sensing system is as follows:
一、首先，建立分布式光纤传感系统回波瑞利谱曲线的特征字典，对于已完成布线的光缆，其在未收扰动情况下的瑞利散射谱具有固定的分量组合，本发明将上述固定的分量组合称为光缆瑞利散射谱的基态谱，若考虑光缆中分布式传感器的间隔为Δ，同时探测总长为LΔ，为方便后续内容中公式的简洁性，本发明中假设Δ＝1，上述假设不会影响Δ在其他取值情况下本发明内容的一般性。令ψl表示在第l个探测距离上的单位响应，则可以针对所有的探测距离构造一个本征特征字典：1. First, establish a feature dictionary of the echo Rayleigh spectrum curve of the distributed optical fiber sensing system. For the optical cable that has been wired, its Rayleigh scattering spectrum without receiving disturbance has a fixed component combination. The present invention refers to the fixed component combination as the ground state spectrum of the optical cable Rayleigh scattering spectrum. If the interval of the distributed sensor in the optical cable is Δ, and the total detection length is LΔ, for the convenience of the simplicity of the formula in the subsequent content, the present invention assumes Δ=1. The above assumption will not affect the generality of the content of the present invention under other values of Δ. Let ψl represent the unit response at the lth detection distance, then an intrinsic feature dictionary can be constructed for all detection distances:
Ψ＝[ψ1,ψ2,…,ψL] (1)Ψ＝[ψ1 ,ψ2 ,…,ψL ] (1)
此时基态谱可以表示为：At this time, the ground state spectrum It can be expressed as:
其中η为基态特征系数。由于基态谱是一个连续谱，因此上式所描述的不是一个理想的稀疏问题，因此可以采用模态分解等手段，从光缆的基态谱中，分解得到基态特征系数向量η。Where η is the ground state characteristic coefficient. is a continuous spectrum, so the above formula is not an ideal sparse problem. Therefore, modal decomposition and other methods can be used to obtain the ground state spectrum of the optical cable. In the decomposition, the ground state characteristic coefficient vector η is obtained.
本发明基于基态特征系数η，设计了一种多态字典学习观测方法，具体为：Based on the base state characteristic coefficient η, the present invention designs a polymorphic dictionary learning observation method, which is specifically:
令分布式光纤传感系统所关心的扰动事件类型数量共为N，则第n个扰动事件记为ζn。基于前期的扰动事件样本训练，可以刻画各类扰动事件的扰动特征分布情况，即扰动特征空间。针对扰动事件ζn，令表示其扰动特征空间，则进一步定义其扰动特征互补空间基于得到对应的特征系数向量具体为：Let the number of disturbance event types that the distributed optical fiber sensing system is concerned about be N, and the nth disturbance event is recorded as ζn . Based on the previous disturbance event sample training, the distribution of disturbance characteristics of various disturbance events can be characterized, that is, the disturbance feature space. For the disturbance event ζn , let Represents its perturbation feature space, and further defines its perturbation feature complementary space based on Get the corresponding characteristic coefficient vector Specifically:
其中，ηl与分别表示η和的第l个元素。Among them, ηl and They represent η and The lth element of .
进一步的，针对扰动事件ζn，构建扰动模态观测矩阵：Furthermore, for the disturbance event ζn , the disturbance mode observation matrix is constructed:
其中，diag(z)表示以向量z为对角元素构成矩阵，β(n)为权系数向量。基于上述扰动模态观测矩阵，即可对本征特征字典进行学习观测，形成扰动事件ζn所对应的模态字典：Wherein, diag(z) represents a matrix composed of vector z as diagonal elements, and β(n) is a weight coefficient vector. Based on the above disturbance modal observation matrix, the intrinsic feature dictionary can be learned and observed to form a modal dictionary corresponding to the disturbance event ζn :
Ψ(n)＝Φ(n)Ψ (5)Ψ(n) = Φ(n) Ψ (5)
将所有关注的扰动事件所对应的模态字典与本征特征字典进行级联组合，即可得到多态等效字典：By cascading the modal dictionaries corresponding to all the disturbance events of interest with the intrinsic feature dictionaries, we can obtain a polymorphic equivalent dictionary:
二、本发明随后在上述基础上对分布式光纤传感系统的多回波光脉冲进行联合优化表示。若Pi表示第i个回波光脉冲对应的采样瑞利曲线，则由M个回波光脉冲组成的联合回波矩阵可以表示为：Second, the present invention then performs a joint optimization representation of the multi-echo optical pulses of the distributed optical fiber sensing system on the basis of the above. IfPi represents the sampled Rayleigh curve corresponding to the i-th echo optical pulse, the joint echo matrix composed of M echo optical pulses can be expressed as:
其中，[·]T表示向量与矩阵的转置。Where [·]T represents the transpose of vectors and matrices.
基于式(6)所示的多态级联字典，可以得到联合回波矩阵的联合稀疏表示形式：Based on the polymorphic cascade dictionary shown in formula (6), the joint sparse representation of the joint echo matrix can be obtained:
其中，Θ为联合特征矩阵，同时也是一个列稀疏矩阵，其非零列表征了相应多态字典所对应的扰动事件。Among them, Θ is the joint feature matrix, which is also a column sparse matrix, and its non-zero columns characterize the disturbance events corresponding to the corresponding polymorphic dictionary.
三、基于上述联合稀疏表示模型，从联合回波矩阵中重构得到联合特征矩阵的过程可以概括为以下步骤：3. Based on the above joint sparse representation model, the process of reconstructing the joint feature matrix from the joint echo matrix can be summarized as the following steps:
步骤1：对联合回波矩阵进行向量化得到r＝vec(R)，进而得到扩展联合稀疏表示：Step 1: Vectorize the joint echo matrix to obtain r = vec(R), and then obtain the extended joint sparse representation:
r＝Γθ (9)r＝Γθ (9)
其中，I(N+1)×(N+1)表示维度为(N+1)×(N+1)的单位阵，表示克罗内克积。in, I(N+1)×(N+1) represents the unit matrix of dimension (N+1)×(N+1), represents the Kronecker product.
步骤2：初始化重构特征向量θ(0)＝0，残差ε(0)＝r；索引集设置终止迭代门限ξ；t＝1，Θ0为空矩阵；Step 2: Initialize the reconstructed feature vector θ(0) = 0, the residual ε(0) = r; index set Set the termination iteration threshold ξ; t = 1, Θ0 is an empty matrix;
步骤3：将Γ中各模态字典Ψ(n)与第l列抽取组成字矩阵Γl，将每个Γl,l＝1,2…L与残差ε相乘，找出积为最大值所对应的索引为λt，即Step 3: Extract each modal dictionary Ψ(n) in Γ and the lth column to form a word matrix Γl , multiply each Γl , l＝1,2…L with the residual ε, and find the index λt corresponding to the maximum product, that is,
步骤4：更新索引集Λt＝Λt-1∪{λt}，记录找到的多态级联字典中与残差相关度最高的列组合，并重建原子集合为γt＝[γt-1,Γl]；Step 4: Update the index set Λt = Λt-1 ∪{λt }, record the column combination with the highest residual correlation in the found polymorphic cascade dictionary, and reconstruct the atomic set as γt = [γt-1 ,Γl ];
步骤5：求解得到θ(t)＝argmin||r-γtθ(t)||2，更新残差ε(t)＝r-γtθ(t)，t＝t+1；Step 5: Solve to obtain θ(t) = argmin||r-γt θ(t) ||2 , update the residual ε(t) = r-γt θ(t) , t = t+1;
步骤6：判断残差ε(t)是否小于ξ，如是，则迭代停止，输出重构特征向量θ；反之，则跳转至步骤3继续上述步骤。Step 6: Determine whether the residual ε(t) is less than ξ. If so, the iteration stops and the reconstructed feature vector θ is output; otherwise, jump to step 3 to continue the above steps.
步骤7：由重构特征向量θ恢复得到联合特征矩阵Θ。Step 7: Restore the joint feature matrix Θ from the reconstructed feature vector θ.
另一实施例中，在得到联合特征矩阵Θ后，对Θ中数值进行分析，将Θ的前L列置零，随后确定剩余的非零列，获得非零列索引向量υ，由索引向量υ指向扰动事件类型。至此，本发明实现了包括远端扰动事件在内的多类型扰动事件的同步识别。In another embodiment, after obtaining the joint feature matrix Θ, the values in Θ are analyzed, the first L columns of Θ are set to zero, and then the remaining non-zero columns are determined to obtain the non-zero column index vector υ, and the index vector υ points to the disturbance event type. So far, the present invention realizes the synchronous identification of multiple types of disturbance events including remote disturbance events.
上述实施例为本发明优选地实施方式，但本发明的实施方式并不受上述实施例的限制，其他的任何未背离本发明的精神实质与原理下所作的改变、修饰、替代、组合、简化，均应为等效的置换方式，都包含在本发明的保护范围之内。The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.
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