WO2020228044A1 - 一种快速管道漏磁数据多尺度异常区域推荐系统及方法 - Google Patents

一种快速管道漏磁数据多尺度异常区域推荐系统及方法 Download PDF

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WO2020228044A1
WO2020228044A1 PCT/CN2019/087346 CN2019087346W WO2020228044A1 WO 2020228044 A1 WO2020228044 A1 WO 2020228044A1 CN 2019087346 W CN2019087346 W CN 2019087346W WO 2020228044 A1 WO2020228044 A1 WO 2020228044A1
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abnormal
window
scale
area
magnetic flux
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刘金海
付明芮
臧东
卢森骧
汪刚
马大中
徐可馨
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Northeastern University China
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    • G—PHYSICS
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
    • G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
    • G01N27/83—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields
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  • the invention relates to the technical field of pipeline detection, in particular to a system and method for recommending multi-scale abnormal regions of rapid pipeline magnetic flux leakage data.
  • pipeline transportation is called the five major transportation modes along with railway, highway, waterway and aviation.
  • pipeline service time due to the influence of pipeline material problems, external damage and medium corrosion, the pipeline condition gradually deteriorates, and there is a potential risk of damage and leakage. Once a leak occurs, it will not only cause air pollution, but also a violent explosion.
  • an oil spill accident occurred in the Bohai Bay.
  • the accident leaked 385 cubic meters of crude oil, causing a total of 5,500 square kilometers of seawater pollution. Therefore, in order to ensure the safety of energy transportation and ecological environment, the pipeline must be regularly inspected and maintained.
  • Non-destructive testing is widely used as an important means of pipeline safety maintenance.
  • magnetic flux leakage detection as a non-destructive testing method is widely used in nearly 90% of in-service pipelines.
  • a complete magnetic flux leakage data analysis process includes 5 parts, namely: data preprocessing, abnormal area recommendation, abnormal identification, defect size inverse estimation and defect safety assessment.
  • the data preprocessing part completes the filtering of the base value correction of the original data; the abnormal area recommendation part obtains the position of the abnormal area; the abnormal recognition part completes the classification and recognition of the abnormal position, such as defects, valves, meters, etc.; the defect size estimation part realizes the defect signal To the size of the mapping, and the defect safety assessment part is to calculate the safety level of the defect to determine whether it needs repair.
  • the recommendation of abnormal areas is a key and challenging issue in the magnetic flux leakage data analysis process.
  • a good anomaly area recommendation algorithm not only has position accuracy and edge accuracy, but also has the ability to be fast.
  • the recommendation for abnormal regions is based on the traditional exhaustive search algorithm, without considering the influence of the sampling of candidate regions on the efficiency of the algorithm.
  • the huge search space ultimately wastes a lot of time. At the same time, minor abnormalities are prone to missed detection due to noise.
  • the technical problem to be solved by the present invention is to provide a fast pipeline magnetic flux leakage data multi-scale abnormal area recommendation system and method for the above-mentioned shortcomings of the prior art.
  • the present invention has obvious rapidity and is particularly suitable for huge pipeline data sets.
  • the present invention provides a fast multi-scale abnormal area recommendation system for pipeline magnetic flux leakage data, including an input and output module, a multi-scale window division module, an abnormal area estimation module, and a boundary precision module;
  • the input and output module is used for inputting the magnetic leakage signal and outputting the abnormal target location area of the pipeline, and outputting the magnetic leakage signal to the multi-scale window division module;
  • the multi-scale window division module is used to complete the acquisition of the multi-scale candidate abnormal window, and output the abnormal window to the abnormal area estimation module;
  • the abnormal area estimation module is used to estimate the position of the abnormal area, obtain an abnormal estimation set, and output the set to the boundary precision module;
  • the boundary precision module is used to describe in detail the boundary of each window in the abnormality estimation set to obtain the abnormal recommendation area, and output the abnormal recommendation area to the input and output module after merging.
  • the present invention provides a method for recommending a multi-scale abnormal area of fast pipeline magnetic flux leakage data, which is implemented by the described system of recommending a multi-scale abnormal area of rapid pipeline magnetic flux leakage data, including the following steps:
  • Step 1 Obtain the magnetic flux leakage signal D of a section of pipeline, and divide it into a multi-scale window, and divide it into N scale levels L 1 , L 2 ,...L N , and perform abnormal edge extraction on the N scale levels.
  • Obtain the abnormal form set W ⁇ W 1 , W 2 ,..., W k ,...W N ⁇ ; where, Is the set of windows under the kth scale level, and b is the number of windows included in the kth scale level;
  • Step 2.2 Estimate the score of the abnormal window set W'; for the abnormal area, there will be multiple windows stacked together to form multiple "back" glyphs, and each window corresponds to a measurement window intersection
  • W k′ represents the current window
  • m is the number of contour windows contained in the current window
  • represents the window area, that is, the number of data points contained in the window; Respectively represent the h-1th window and the hth window under the kth scale level;
  • step 1 The specific steps of step 1 are as follows:
  • Step 1.1 For a section of pipeline magnetic flux leakage signal D, divide the minimum and maximum values of the signal into N scale levels L 1 , L 2 ,...L N , then the value of the k-th scale level is:
  • Step 1.2 For the scale level k, slice the received value L k of the scale level and the current signal D to obtain a binary matrix D k , namely:
  • D i,j are the data points in the i-th row and j-th column in the pipeline magnetic flux leakage signal D;
  • Step 1.3 Perform abnormal edge extraction on the binary matrix D k .
  • Step 1.3.2 Use the above horizontal template fx and vertical template fy to filter the binary matrix D k in two directions respectively to obtain the filtered binary matrix D k,x and D k,y ; the binary matrix
  • the edge matrix of D k is E k :
  • Step 1.3.3 Obtain the abnormal edges, and then regularize the abnormal edges to form a rectangular window to obtain the current scale set abnormal window set W k ;
  • the present invention provides a fast pipeline magnetic flux leakage data multi-scale abnormal area recommendation system and method.
  • the multi-scale window combination is obtained.
  • the abnormal candidate area is finally obtained.
  • the present invention extracts data at multiple data levels.
  • the extraction process embodies from coarse to fine.
  • the present invention can not only find abnormalities with larger size and obvious signals.
  • small abnormalities can be found, and sufficient abnormal candidate regions can be provided;
  • the present invention deals with multi-scale windows, expresses signal characteristics in windows, and finally determines the target.
  • the invention has obvious rapidity, especially suitable for the huge data set of the pipeline.
  • FIG. 1 is a block diagram of a system for recommending a multi-scale abnormal area of fast pipeline magnetic flux leakage data according to an embodiment of the present invention
  • FIG. 2 is a flowchart of a method for recommending a multi-scale abnormal area of fast pipeline magnetic flux leakage data according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of magnetic flux leakage data provided by an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of multi-scale slice division provided by an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a multi-scale abnormal edge provided by an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of multi-scale abnormal edge regularization provided by an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of a score estimation window provided by an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a maximum containment relationship combination window provided by an embodiment of the present invention.
  • FIG. 9 is a schematic diagram of an accurate boundary window provided by an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of the largest peripheral window provided by an embodiment of the present invention.
  • FIG. 11 is a schematic diagram of a defect signal target area provided by an embodiment of the present invention.
  • the present invention provides a fast multi-scale abnormal area recommendation system for pipeline magnetic flux leakage data, as shown in FIG. 1, including an input and output module, a multi-scale window division module, an abnormal area estimation module, and a boundary precision module;
  • the input and output module is used for inputting the magnetic leakage signal and outputting the abnormal target location area of the pipeline, and outputting the magnetic leakage signal to the multi-scale window division module;
  • the multi-scale window division module is used to complete the acquisition of the multi-scale candidate abnormal window and output the abnormal window to the abnormal area estimation module;
  • the abnormal area estimation module is used to estimate the position of the abnormal area, obtain an abnormal estimation set, and output the set to the boundary precision module.
  • the boundary precision module is used to describe in detail the boundary of each window in the abnormality estimation set to obtain the abnormal recommendation area, and output the abnormal recommendation area to the input and output module after merging.
  • the present invention provides a fast pipeline magnetic flux leakage data multi-scale abnormal area recommendation method, which is implemented by the fast pipeline magnetic flux leakage data multi-scale abnormal area recommendation system, as shown in Fig. 2, including the following steps:
  • Step 1 Obtain the magnetic flux leakage signal D of a section of pipeline, and divide it into a multi-scale window, and divide it into N scale levels L 1 , L 2 ,...L N , and perform abnormal edge extraction on the N scale levels.
  • Obtain the abnormal window set W ⁇ W 1 , W 2 ,..., W k ,...W N ⁇ . among them, Is the set of windows under the kth scale level, and b is the number of windows included in the kth scale level;
  • N 40;
  • Step 1.1 For a section of pipeline magnetic flux leakage signal D, as shown in Figure 3, divide the minimum and maximum values of the signal into N scale levels L 1 , L 2 ,...L N , then the k-th scale level The numerical size of is:
  • the pipeline magnetic flux leakage signal D is divided into 40 scale levels
  • Step 1.2 For the scale level k, the scale level collected value L k and the current signal D are sliced, as shown in Figure 4, to obtain a binary matrix D k , namely:
  • D i,j are the data points in the i-th row and j-th column in the pipeline magnetic flux leakage signal D;
  • Step 1.3 Perform abnormal edge extraction on the binary matrix D k .
  • Step 1.3.2 Use the above horizontal template fx and vertical template fy to filter the binary matrix D k in two directions respectively to obtain the filtered binary matrix D k,x and D k,y ; the binary matrix
  • the edge matrix of D k is E k :
  • Step 1.3.3 Obtain abnormal edges, as shown in Figure 5, and then regularize the abnormal edges to form a rectangular window, as shown in Figure 6, to obtain the current scale set abnormal window set W k .
  • Step 2.2 Estimate the score of the abnormal window set W′; in practice, after the source data is divided into multiple scales and levels, for the abnormal area, there will be multiple windows stacked together to form multiple "backs" Font. Therefore, in order to characterize this feature, each window corresponds to a value S(W k′ ) that measures the degree of window overlap, traverse the abnormal window set W′, and calculate the value of the window overlap degree of each window , The value is equivalent to the score of the abnormal form, the formula is as follows:
  • W k′ represents the current window
  • m is the number of contour windows contained in the current window
  • represents the window area, that is, the number of data points contained in the window; Respectively represent the h-1th window and the hth window under the kth scale level;
  • the magnetic leakage signal defect will generate three magnetic leakage signal area windows.
  • the middle window corresponds to the peak position, and the two windows correspond to the valley position.
  • the three windows are combined to obtain The final target location area is shown in Figure 11;

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Abstract

一种快速管道漏磁数据多尺度异常区域推荐系统及方法，涉及管道检测技术领域。步骤如下：步骤1：获取一段管道的漏磁信号，并将其进行多尺度窗体划分，对N个尺度层级进行异常边缘提取，得到异常窗体集合；步骤2：对异常窗体集合进行异常区域估计，得到异常估计集合；步骤3：边界精确；根据异常估计集合，将W k"中的所有窗体进行相邻窗体的面积比，遍历异常估计集合W"，去除面积比小于λ的窗体，并选取当前集合内交叠窗体中最外围窗体作为异常推荐区域；该方法能够发现尺寸较大，信号明显的异常，同时能够发现较小的异常，能够提供充分的异常候选区域；具有明显的快速性，特别适应管道庞大的数据集。

Description

一种快速管道漏磁数据多尺度异常区域推荐系统及方法 技术领域
本发明涉及管道检测技术领域，尤其涉及一种快速管道漏磁数据多尺度异常区域推荐系统及方法。
背景技术
管道运输以其高效率以及安全可靠的特点，与铁路、公路，水路和航空并称五大运输方式。随着管道在役时间的增长，因管道材质问题、外损伤以及介质腐蚀的影响，管道状况逐渐恶化，存在潜在的破损和泄漏风险。一旦发生泄漏，不但会造成大气污染，也极易引发剧烈爆炸。2011年渤海湾发生溢油事故，根据国家海洋局统计该事故泄漏原油385立方米，共造成5500平方公里海水污染。因此，为了确保能源运输和生态环境安全，必须对管道定期进行安检和维护。
无损检测(Non-destructive testing,NDT)作为管道安全维护的一种重要手被广泛应用。其中，漏磁检测作为无损检测方法的一种被广泛应用于接近90％的在役管道中。一个完整的漏磁数据分析过程包括5个部分，即：数据预处理、异常区域推荐、异常识别、缺陷尺寸反估计和缺陷安全评估。数据预处理部分完成原始数据的基值校正的滤波；异常区域推荐部分获得异常区域的位置；异常识别部分完成对异常位置的分类识别，如缺陷，阀门、仪表等；缺陷尺寸估计部分实现缺陷信号到尺寸的映射，而缺陷安全评估部分是计算缺陷的安全等级，确定是否需要维修。
异常区域推荐是漏磁数据分析流程中关键且有挑战的问题。一个良好的异常区域推荐算法不但具备位置准确性和边缘准确性，同时要具备快速能力。在实际应用中，针对异常区域的推荐都是基于传统穷举搜索算法，没有考虑候选区域采样问题对算法效率的影响，搜索空间的巨大最终浪费大量时间。同时，受噪声影响，微小异常很容易发生漏检。
发明内容
本发明要解决的技术问题是针对上述现有技术的不足，提供一种快速管道漏磁数据多尺度异常区域推荐系统及方法，本发明具有明显的快速性，特别适应管道庞大的数据集。
为解决上述技术问题，本发明所采取的技术方案是：
一方面，本发明提供一种快速管道漏磁数据多尺度异常区域推荐系统，包括输入输出模块、多尺度窗体划分模块、异常区域估计模块、边界精确模块；
所述输入输出模块用于输入漏磁信号和输出管道异常的目标位置区域，将漏磁信号输出至多尺度窗体划分模块；
所述多尺度窗体划分模块用于完成多尺度候选异常窗体的获取，并将异常窗体输出至异常区域估计模块；
所述异常区域估计模块用于估计异常区域位置，得到异常估计集合，并将该集合输出至边界精确模块；
所述边界精确模块用于对异常估计集合中每个窗体的边界进行详细刻画，得到异常推荐区域，将异常推荐区域进行合并后输出至输入输出模块。
另一方面，本发明提供一种快速管道漏磁数据多尺度异常区域推荐方法，通过所述的一种快速管道漏磁数据多尺度异常区域推荐系统实现，包括如下步骤：
步骤1：获取一段管道的漏磁信号D，并将其进行多尺度窗体划分，划分为N个尺度层级L 1,L 2,...L N，对N个尺度层级进行异常边缘提取，得到异常窗体集合W＝{W 1，W 2，...，W k，…W N}；其中，
Figure PCTCN2019087346-appb-000001
为第k个尺度层级下窗体集合，b为第k个尺度层级包含的窗体数；
步骤2：对步骤1得到的异常窗体集合进行异常区域估计，得到异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}；
步骤2.1：将异常窗体集合中的窗体进行预处理，所述预处理为去除边界未闭合窗体，得到处理后异常窗体集合W′＝{W 1′，W 2′，...，W k′，…W N′}；
步骤2.2：对异常窗体集合W′进行得分估计；对于异常区域而言，会有多个窗体套叠在一起，形成多个“回”字形，每一个窗体都对应一个度量窗体交叠程度的值S(W k′)，遍历异常窗体集合W′，计算每一个窗体的窗体交叠程度值，将该值等效为异常窗体的得分，公式如下：
Figure PCTCN2019087346-appb-000002
其中，W k′表示当前窗体，m为当前窗口内部包含的等高线窗体数目；
Figure PCTCN2019087346-appb-000003
分别表示第k个窗体内的窗体数目和第k个窗体外的窗体数目；
步骤2.3：异常区域的获取，选取出窗体的分数大于σ的窗体为异常窗体，其中0≤σ≤1，且判断同一尺度层级下是否存在并列窗体，若存在则将该尺度下的并列窗体全部删除，得到异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}；
步骤3：边界精确；根据步骤2中得到的异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}， 将W k″中的所有窗体进行相邻窗体的面积比，遍历异常估计集合W″，去除面积比小于λ的窗体，其中0≤λ≤1，并选取当前集合内交叠窗体中最外围窗体作为异常推荐区域；
面积比的公式如下：
Figure PCTCN2019087346-appb-000004
其中，
Figure PCTCN2019087346-appb-000005
为第k个尺度层级下第h-1窗体和第h窗体的面积比；||*||表示窗体面积，即窗体包含的数据点个数；
Figure PCTCN2019087346-appb-000006
分别代表第k个尺度层级下的第h-1窗体和第h窗体；
所述步骤1的具体步骤如下：
步骤1.1：对于一段管道漏磁信号D，将信号的最小值和最大值之间划分N个尺度等级L 1,L 2,...L N，则第k个尺度级的数值大小为：
Figure PCTCN2019087346-appb-000007
步骤1.2：对于尺度等级k，将该尺度等级收数值L k与当前信号D做切片处理，得到二值矩阵D k，即：
Figure PCTCN2019087346-appb-000008
其中D i,j是管道漏磁信号D中第i行、第j列的数据点；
步骤1.3：对二值矩阵D k进行异常边缘提取。
步骤1.3.1：建立两个正交方向的模板：水平模板fx和竖直模板fy，即：fy＝[-11]；fx＝fy T，fx表示fy的转置。
步骤1.3.2：利用上述的水平模板fx和竖直模板fy对二值矩阵D k分别进行两个方向的滤波，得到滤波后的二值矩阵D k,x和D k,y；二值矩阵D k的边缘矩阵为E k：
Figure PCTCN2019087346-appb-000009
步骤1.3.3：获取异常边缘，然后将异常边缘规则化形成矩形窗体，得到当前尺度集异常窗体集合W k；
步骤1.4：重复步骤1至步骤3，获得所有尺度等级的异常窗体集合 W＝{W 1，W 2，...，W k，…W N}。
采用上述技术方案所产生的有益效果在于：本发明提供的一种快速管道漏磁数据多尺度异常区域推荐系统及方法，通过对漏磁数据进行多尺度划分，进而得到多尺度下窗体组合，通过对窗体的系列删除和合并行为，最终得到异常候选区域。本发明从多尺度角度出发，对数据进行多个数据层级的异常提取，提取过程体现了由粗到细，相比于一般的异常提取算法，本发明不但能够发现尺寸较大，信号明显的异常，同时能够发现较小的异常，能够提供充分的异常候选区域；本发明是针对多尺度窗体进行处理，将信号特征用窗体表示，最终确定目标，相比于一般的完全基于信号的异常提取算法，该发明具有明显的快速性，特别适应管道庞大的数据集。
附图说明
图1为本发明实施例提供的快速管道漏磁数据多尺度异常区域推荐系统框图；
图2为本发明实施例提供的快速管道漏磁数据多尺度异常区域推荐方法流程图；
图3为本发明实施例提供的漏磁数据示意图；
图4为本发明实施例提供的多尺度切片划分示意图；
图5为本发明实施例提供的多尺度异常边缘示意图；
图6为本发明实施例提供的多尺度异常边缘规则化示意图；
图7为本发明实施例提供的分数估计窗体示意图；
图8为本发明实施例提供的最大包含关系组合窗体示意图；
图9为本发明实施例提供的边界精确窗体示意图；
图10为本发明实施例提供的最大外围窗体示意图；
图11为本发明实施例提供的缺陷信号目标区域示意图。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
本实施例如下所述。
一方面，本发明提供一种快速管道漏磁数据多尺度异常区域推荐系统，如图1所示，包括输入输出模块、多尺度窗体划分模块、异常区域估计模块、边界精确模块；
所述输入输出模块用于输入漏磁信号和输出管道异常的目标位置区域，将漏磁信号输出至多尺度窗体划分模块；
所述多尺度窗体划分模块用于完成多尺度候选异常窗体的获取，并将异常窗体输出至异 常区域估计模块；
所述异常区域估计模块用于估计异常区域位置，得到异常估计集合，并将该集合输出至边界精确模块。
所述边界精确模块用于对异常估计集合中每个窗体的边界进行详细刻画，得到异常推荐区域，将异常推荐区域进行合并后输出至输入输出模块。
另一方面，本发明提供一种快速管道漏磁数据多尺度异常区域推荐方法，通过所述的一种快速管道漏磁数据多尺度异常区域推荐系统实现，如图2所示，包括如下步骤：
步骤1：获取一段管道的漏磁信号D，并将其进行多尺度窗体划分，划分为N个尺度层级L 1,L 2,...L N，对N个尺度层级进行异常边缘提取，得到异常窗体集合W＝{W 1，W 2，...，W k，…W N}。其中，
Figure PCTCN2019087346-appb-000010
为第k个尺度层级下窗体集合，b为第k个尺度层级包含的窗体数；
本实施例中N＝40；
步骤1.1：对于一段管道漏磁信号D，如图3所示，将信号的最小值和最大值之间划分N个尺度等级L 1,L 2,...L N，则第k个尺度级的数值大小为：
Figure PCTCN2019087346-appb-000011
本实施例中将管道漏磁信号D划分为40个尺度等级；
步骤1.2：对于尺度等级k，将该尺度等级收数值L k与当前信号D做切片处理，如图4所示，得到二值矩阵D k，即：
Figure PCTCN2019087346-appb-000012
其中D i,j是管道漏磁信号D中第i行、第j列的数据点；
步骤1.3：对二值矩阵D k进行异常边缘提取。
步骤1.3.1：建立两个正交方向的模板：水平模板fx和竖直模板fy，即：fy＝[-1 1]；fx＝fy T，fx表示fy的转置。
步骤1.3.2：利用上述的水平模板fx和竖直模板fy对二值矩阵D k分别进行两个方向的滤波，得到滤波后的二值矩阵D k,x和D k,y；二值矩阵D k的边缘矩阵为E k：
Figure PCTCN2019087346-appb-000013
步骤1.3.3：获取异常边缘，如图5所示，然后将异常边缘规则化形成矩形窗体，如图6所示，得到当前尺度集异常窗体集合W k。
步骤1.4：重复步骤1至步骤3，获得所有尺度等级的异常窗体集合W＝{W 1，W 2，...，W k，…W N}。
步骤2：是对步骤1得到的异常窗体集合进行异常区域估计，得到异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}。
步骤2.1：将异常窗体集合中的窗体进行预处理，所述预处理为去除边界未闭合窗体，得到处理后异常窗体集合W′＝{W 1′，W 2′，...，W k′，…W N′}；我们定义，一个异常窗体存在的充要条件是：异常边缘点能够构成首尾相连接的闭合区域。
步骤2.2：对异常窗体集合W′进行得分估计；实际中，源数据经过多个尺度层级划分后，对于异常区域而言，会有多个窗体套叠在一起，形成多个“回”字形。因此，为了表征这一特性，每一个窗体都对应一个度量窗体交叠程度的值S(W k′)，遍历异常窗体集合W′，计算每一个窗体的窗体交叠程度值，将该值等效为异常窗体的得分，公式如下：
Figure PCTCN2019087346-appb-000014
其中，W k′表示当前窗体，m为当前窗口内部包含的等高线窗体数目；
Figure PCTCN2019087346-appb-000015
分别表示第k个窗体内的窗体数目和第k个窗体外的窗体数目。
步骤2.3：异常区域的获取，选取出窗体的分数大于σ的窗体为异常窗体，其中0≤σ≤1，如图7所示，且判断同一尺度层级下是否存在并列窗体，若存在则将该尺度下的并列窗体全部删除，如图8所示，得到异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}；
本实施例中σ＝0.7；
选取最大包含关系组合窗体，去除干扰窗体，即：每一个窗体内部如果存在窗体，则内部的窗体必须属于完全包含关系。
步骤3：边界精确；如图9所示，根据步骤2中得到的异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}，将集合中的每个经过上述步骤，对于任意一个目标区域而言，会有多个窗体覆盖，为了获得精确边界，我们提出了空间相关比，来衡量相邻窗体的面 积比，将W k″中的所有窗体进行相邻窗体的面积比，遍历异常估计集合W″，去除面积比小于λ的窗体，其中0≤λ≤1，本实施例中λ＝0.5；并选取去除面积比小于λ的窗体后的集合内交叠窗体中最外围窗体作为异常推荐区域，如图10所示；
面积比的公式如下：
Figure PCTCN2019087346-appb-000016
其中，
Figure PCTCN2019087346-appb-000017
为第k个尺度层级下第h-1窗体和第h窗体的面积比；||*||表示窗体面积，即窗体包含的数据点个数；
Figure PCTCN2019087346-appb-000018
分别代表第k个尺度层级下的第h-1窗体和第h窗体；
本实施例中为漏磁信号缺陷，漏磁信号缺陷会产生三个漏磁信号区域窗体，中间窗体对应峰位置，两侧窗体对应谷位置，将三个窗体进行合并处理，得到最终目标位置区域，如图11所示；
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明权利要求所限定的范围。

Claims (3)

  1. 一种快速管道漏磁数据多尺度异常区域推荐系统，其特征在于：包括输入输出模块、多尺度窗体划分模块、异常区域估计模块、边界精确模块；
    所述输入输出模块用于输入漏磁信号和输出管道异常的目标位置区域，将漏磁信号输出至多尺度窗体划分模块；
    所述多尺度窗体划分模块用于完成多尺度候选异常窗体的获取，并将异常窗体输出至异常区域估计模块；
    所述异常区域估计模块用于估计异常区域位置，得到异常估计集合，并将该集合输出至边界精确模块；
    所述边界精确模块用于对异常估计集合中每个窗体的边界进行详细刻画，得到异常推荐区域，将异常推荐区域进行合并后输出至输入输出模块。
  2. 一种快速管道漏磁数据多尺度异常区域推荐方法，通过权利要求1所述的一种快速管道漏磁数据多尺度异常区域推荐系统实现，其特征在于：包括如下步骤：
    步骤1：获取一段管道的漏磁信号D，并将其进行多尺度窗体划分，划分为N个尺度层级L 1,L 2,...L N，对N个尺度层级进行异常边缘提取，得到异常窗体集合W＝{W 1，W 2，...，W k，…W N}；其中，
    Figure PCTCN2019087346-appb-100001
    为第k个尺度层级下窗体集合，b为第k个尺度层级包含的窗体数；
    步骤2：对步骤1得到的异常窗体集合进行异常区域估计，得到异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}；
    步骤2.1：将异常窗体集合中的窗体进行预处理，所述预处理为去除边界未闭合窗体，得到处理后异常窗体集合W′＝{W 1′，W 2′，...，W k′，…W N′}；
    步骤2.2：对异常窗体集合W′进行得分估计；对于异常区域而言，有多个窗体套叠在一起，形成多个“回”字形，每一个窗体都对应一个度量窗体交叠程度的值S(W k′)，遍历异常窗体集合W′，计算每一个窗体的窗体交叠程度值，将该值等效为异常窗体的得分，公式如下：
    Figure PCTCN2019087346-appb-100002
    其中，W k′表示当前窗体，m为当前窗口内部包含的等高线窗体数目；
    Figure PCTCN2019087346-appb-100003
    分别表示第k个窗体内的窗体数目和第k个窗体外的窗体数目；
    步骤2.3：异常区域的获取，选取出窗体的分数大于σ的窗体为异常窗体，其中0≤σ≤1，且判断同一尺度层级下是否存在并列窗体，若存在则将该尺度下的并列窗体全部删除，得到 异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}；
    步骤3：边界精确；根据步骤2中得到的异常估计集合W″＝{W 1″，W 2″，...，W k″，…W N″}，将W k″中的所有窗体进行相邻窗体的面积比，遍历异常估计集合W″，去除面积比小于λ的窗体，其中0≤λ≤1，并选取当前集合内交叠窗体中最外围窗体作为异常推荐区域；
    面积比的公式如下：
    Figure PCTCN2019087346-appb-100004
    其中，
    Figure PCTCN2019087346-appb-100005
    为第k个尺度层级下第h-1窗体和第h窗体的面积比；||*||表示窗体面积，即窗体包含的数据点个数；
    Figure PCTCN2019087346-appb-100006
    分别代表第k个尺度层级下的第h-1窗体和第h窗体。
  3. 根据权利要求2所述的一种快速管道漏磁数据多尺度异常区域推荐方法，其特征在于：所述步骤1的具体步骤如下：
    步骤1.1：对于一段管道漏磁信号D，将信号的最小值和最大值之间划分N个尺度等级L 1,L 2,...L N，则第k个尺度级的数值大小为：
    Figure PCTCN2019087346-appb-100007
    步骤1.2：对于尺度等级k，将该尺度等级收数值L k与当前信号D做切片处理，得到二值矩阵D k，即：
    Figure PCTCN2019087346-appb-100008
    其中D i,j是管道漏磁信号D中第i行、第j列的数据点；
    步骤1.3：对二值矩阵D k进行异常边缘提取；
    步骤1.3.1：建立两个正交方向的模板：水平模板fx和竖直模板fy，即：fy＝[-1 1]；fx＝fy T，fx表示fy的转置；
    步骤1.3.2：利用上述的水平模板fx和竖直模板fy对二值矩阵D k分别进行两个方向的滤波，得到滤波后的二值矩阵D k,x和D k,y；二值矩阵D k的边缘矩阵为E k：
    Figure PCTCN2019087346-appb-100009
    步骤1.3.3：获取异常边缘，然后将异常边缘规则化形成矩形窗体，得到当前尺度集异常窗体集合W k；
    步骤1.4：重复步骤1至步骤3，获得所有尺度等级的异常窗体集合W＝{W 1，W 2，...，W k，…W N}。
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