WO2017071127A1 - 一种基于分支进程的模型一致性分析方法及系统 - Google Patents

一种基于分支进程的模型一致性分析方法及系统 Download PDF

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WO2017071127A1
WO2017071127A1 PCT/CN2016/071042 CN2016071042W WO2017071127A1 WO 2017071127 A1 WO2017071127 A1 WO 2017071127A1 CN 2016071042 W CN2016071042 W CN 2016071042W WO 2017071127 A1 WO2017071127 A1 WO 2017071127A1
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behavior
relationship
branch process
consistency
brtdg
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PCT/CN2016/071042
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French (fr)
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蒋昌俊
陈闳中
闫春钢
丁志军
王咪咪
赵培海
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同济大学
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    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16ZINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00Subject matter not provided for in other main groups of this subclass

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  • the invention relates to a consistency analysis method and system, in particular to a model consistency analysis method and system based on a branch process.
  • this patent research is based on a model consistency analysis method and system developed by the branch process. Therefore, the behavior of the user in the transaction process is consistently determined to determine the legitimacy of the user.
  • the object of the present invention is to provide a model consistency analysis method and system based on a branch process, which is used to solve the behavior consistency measurement problem of the ring structure model and the duplicate name activity pair in the prior art. .
  • the present invention provides a method for analyzing a model consistency based on a branch process, which includes the following steps: S1, constructing a user transaction PN machine model according to a user behavior running track; S2, respectively The branch process of establishing the user transaction PN machine model expands the BPU 1 and the branch process of the expected model expands the BPU 2 ; S3, from the behavior operation perspective, analyzes the behavior dependency relationship between the transitions in the branch process expansion in step S2, and Determining the behavior dependency relationship R i ; S4 , constructing a three-dimensional map of the behavior relationship of the two models according to the behavior dependency relationship between the transitions determined according to step S3 and the branch process expansion; S5, comparing and analyzing the behavior of the two models Relational 3D graphs, calculate user behavior consistency, and detect how consistent user behavior is with expected behavior. .
  • the behavior dependency relationship R i is divided into four categories: a selection relationship SR, a sequence relationship OR, a concurrency relationship CR, and a reciprocal relationship IOR.
  • the step S5 further includes the following steps: the step S5 further includes the following steps: S51, acquiring all the elements in the three-dimensional three-dimensional map; S52, obtaining the elements in the three-dimensional three-dimensional map by analyzing and comparing; S53, adopting The following formula calculates the degree of consistency between the user model and the expected model:
  • the formula is passed in step S53 Calculate behavioral consistency
  • V 1 ⁇ , V 2 ⁇ - a set of identical points in two three-dimensional maps
  • E 1 ⁇ , E 2 ⁇ - a set of uniform vectors in two three-dimensional maps
  • the present invention provides a model consistency analysis system based on a branch process, comprising: a model building module, configured to construct a user transaction PN machine model according to a user behavior running track;
  • a branch process expansion module a branch process for establishing a user transaction PN machine model, a BPU 1 , and a branch process of the expected model to expand the BPU 2 ;
  • a dependency determination module configured to expand the module from the behavior operation perspective The behavior dependency relationship between the transitions in the established branch process expansion is analyzed, and the behavior dependency relationship R i is determined;
  • the three-dimensional graph construction module is configured to determine the behavior dependency relationship between the transitions determined by the module according to the dependency relationship determination module And the branch process established by the branch process expansion module expands a three-dimensional map of the behavior relationship of the two models respectively;
  • the consistency analysis module is configured to compare and analyze the behavior of the two models constructed by the three-dimensional graph construction module. Relational 3D graphs, calculate user behavior consistency, and detect how consistent user behavior is with expected behavior.
  • the behavior dependency relationship R i is divided into four categories: a selection relationship SR, a sequence relationship OR, a concurrency relationship CR, and a reciprocal relationship IOR.
  • the consistency analysis module further includes: an element acquisition module, configured to acquire all the elements in the two-dimensional three-dimensional map; and an analysis comparison module, configured to analyze and compare the elements in the element acquisition module to obtain a two-dimensional three-dimensional map.
  • the medium-consistent element; the consistency calculation module is configured to receive the output result of the element acquisition module and the analysis and comparison module, and calculate the consistency between the user model and the expected model by using the following formula:
  • the consistency calculation module receives the output result of the element acquisition module and the analysis and comparison module, and then passes the formula. Calculate behavioral consistency,
  • V 1 ⁇ , V 2 ⁇ - a set of coincident points in two three-dimensional maps
  • E 1 ⁇ , E 2 ⁇ - a set of uniform vectors in two three-dimensional maps
  • a branch process-based model consistency analysis method and system of the present invention has the following beneficial effects:
  • a method for proposing a three-dimensional map of behavioral relationships transforming the behavioral relationship between model pairs into points and vectors in three-dimensional space, shortening the calculation time
  • FIG. 1 is a schematic flow chart showing the consistency analysis method of the present invention.
  • FIG. 2 is a schematic diagram showing a model of a user transaction PN machine of the present invention.
  • Figure 3 shows a schematic representation of the intended model of the present invention.
  • FIG. 4 is a schematic diagram showing the development of a branch process of the PN machine model of the present invention.
  • Figure 5 shows a schematic diagram of the branching process of the expected model of the present invention.
  • Figure 6 is a flow chart showing the element acquisition algorithm in the three-dimensional map of the present invention.
  • Fig. 7 is a three-dimensional view showing the behavior relationship of the PN machine model of the present invention.
  • Figure 8 shows a three-dimensional map of the behavioral relationship of the intended model of the present invention.
  • Figure 9 is a flow chart showing the calculation of the degree of consistency of the present invention.
  • Figure 10 is a block diagram showing the structure of the consistency analysis system of the present invention.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • FIG. 1 is a flow chart of a model consistency analysis method based on a branch process, including the following steps: constructing a user transaction PN machine model according to a user behavior running track; respectively establishing a branch process of the user transaction PN machine model to expand the BPU 1 , and The branch process of the expected model is expanded to BPU 2 ; from the perspective of behavioral operation, the behavioral dependencies between the transitions in the above-mentioned branch process expansion are analyzed, and the behavior dependency relationship R i is determined; according to the behavioral dependency relationship between the determined transitions And the branch process expands to construct a three-dimensional graph of the behavior relationship of the two models respectively; compare and analyze the three-dimensional map of the behavior relationship of the two models, calculate the consistency of the user behavior, and detect the degree of consistency between the user behavior and the expected behavior.
  • the specific analysis method is as follows:
  • the user transaction PN machine model N 1 as shown in FIG. 2 is constructed, and the expected model is N 2 as shown in FIG. 3; the two models are branched and expanded, and respectively obtained as shown in FIG. 4 and FIG.
  • the branch process shown in FIG. 5 expands BPU 1 and BPU 2.
  • BPU 1 has the same name transitions t 1 , t 2 , t 4 , t 6 .
  • behavioral dependencies Four kinds of behavioral dependencies are set: selection relation SR, order relationship OR, concurrency relationship CR, and reversal relationship IOR. From the perspective of behavioral operation, the behavioral dependence relationship between the transitions in the above-mentioned branch process development is analyzed and determined. Which of the above four types of behavioral dependencies belongs to.
  • the change is taken as the x-axis and the y-axis of the coordinate axis, and the behavioral dependency relationship Ri between the transitions is taken as the z-axis of the coordinate axis, and the behavioral relationship is shown in the three-dimensional map BRTDG 1 , and the transition in L 2 is taken as the x-axis and y of the coordinate axis.
  • the axis, with the behavioral dependency Ri between the transitions as the z-axis of the coordinate axis outputs a behavioral relationship of the three-dimensional graph BRTDG 2 .
  • the algorithm shown in FIG. 6 is used to obtain all the elements in the two-dimensional three-dimensional map. Taking one of the three-dimensional maps as an example, if the transitions t i and t j satisfy only one behavior dependency, then what kind of judgment is satisfied?
  • the obtained three-dimensional diagram of the behavioral relationship of the PN machine model is shown in Fig. 7.
  • the marked points in the figure form a point set V 1
  • the vector with the arrow forms the vector set E 1
  • the behavioral relationship of the expected model is shown in the figure
  • the marked points in the figure form a point set V 2
  • the vectors with arrows form a vector set E 2 .
  • the behavioral relationship of the two branch processes is obtained.
  • the total number of elements in the three-dimensional graph is 98, the number of identical elements is 90, and the inconsistent elements are as shown in FIG. 7 and FIG. With a total of eight vectors, it can be seen that the consistency of the models N 1 and N 2 is
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • Figure 8 shows the structure diagram of the model consistency analysis system based on the branch process.
  • the system includes a model building module, a branch process expansion module, a dependency determination module, a three-dimensional graph building module, and an element acquisition module, an analysis comparison module, and a consistent
  • the consistency analysis module formed by the degree calculation module.
  • the model building module is configured to construct a user transaction PN machine model according to the user behavior running track;
  • the branch process expansion module is used to establish a branch process of the user transaction PN machine model to expand the BPU 1 , and the branch process of the expected model to expand the BPU 2 a dependency determining module, configured to analyze behavioral dependencies between transitions in a branch process established by the branch process expansion module from a behavioral operation perspective, and determine a behavior dependency relationship Ri;
  • a three-dimensional graph building module And a three-dimensional graph for constructing behavior relationships of the two models according to the behavior dependency relationship between the transitions determined by the dependency determination module and the branch process expansion established by the branch process expansion module;
  • the consistency analysis module is used for comparison A three-dimensional graph of the behavioral relationship of the two models constructed by the three-dimensional graph building module is analyzed, and the degree of consistency of the user behavior is calculated, and the degree of consistency between the user behavior and the expected behavior is detected, which in turn includes acquiring all the elements in the three-dimensional map of the two relationships.
  • An element acquisition module for entering elements in the element acquisition module Comparative Analysis of acquired two-dimensional diagram elemental analysis consistent comparison module, for acquiring module receiving element and an analysis result of the comparison module outputs the degree of matching calculated matching degree computation module. Calculate the formula based on the consistency of the branch process:
  • the model building module constructs a user transaction PN machine model N 1 as shown in FIG. 2 according to the user behavior running track, and the expected model is N 2 as shown in FIG. 3; the branch process expansion module performs branch process development on the two models.
  • the branch processes shown in FIG. 4 and FIG. 5 are respectively expanded to expand BPU 1 and BPU 2 .
  • the model N 1 contains a ring structure, there is a change in the name of the BPU 1.
  • the algorithm shown in FIG. 6 is used to obtain all the elements in the two-dimensional three-dimensional map. Taking one of the three-dimensional maps as an example, in the element acquisition module, if only one behavior dependency is satisfied for the transitions t i and t j .
  • the obtained three-dimensional diagram of the behavioral relationship of the PN machine model is shown in Fig. 7.
  • the marked points in the figure form a point set V 1
  • the vector with the arrow forms the vector set E 1
  • the behavioral relationship of the expected model is shown in the figure
  • the marked points in the figure form a point set V 2
  • the vectors with arrows form a vector set E 2 .
  • the point set obtained by the element acquisition module and the vector set input analysis comparison module are completed in the analysis and comparison module to complete the analysis and comparison of the elements in the element acquisition module, and obtain the elements in the two-dimensional three-dimensional map, the specific algorithm
  • the consistency calculation module receives the element acquisition module and analyzes the output result of the comparison module, and then passes the formula. Calculate the degree of behavioral consistency.
  • the behavioral relationship of the two branch processes is obtained.
  • the total number of elements in the three-dimensional map is 98, the number of identical elements is 90, and the inconsistent elements are as shown in FIG. 7 and FIG. With a total of eight vectors, it can be seen that the consistency of the models N 1 and N 2 is
  • This method can effectively distinguish when the user transaction model process has a loop structure, which greatly increases the accuracy.
  • the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

Abstract

一种基于分支进程的模型一致性分析方法及系统,其系统包括模型构建模块、分支进程展开模块、依赖关系确定模块、三维图构建模块以及一致性分析模块。分析方法为:根据用户行为运行轨迹构建用户交易PN机模型;分别建立用户交易PN机模型以及预期模型的分支进程展开BPU 1、BPU 2;从行为运行角度对分支进程展开中的变迁间行为依赖关系进行分析,确定其行为依赖关系R i;根据确定的行为依赖关系分别构建两个模型的行为关系三维图;比较分析两行为关系三维图,计算用户行为一致性度,检测用户行为与预期行为的一致程度。本分析方法及系统能对用户交易过程中的行为一致进行判定,以判定用户的合法性。

Description

一种基于分支进程的模型一致性分析方法及系统 技术领域
本发明涉及一种一致性分析方法及系统,特别是涉及一种基于分支进程的模型一致性分析方法及系统。
背景技术
随着互联网的飞速发展以及计算机科学技术的不断进步,网上支付平台的应用越来越广泛,越来越多的人通过网络交易和支付方式开展业务活动,网络交易的发展前景十分广阔。
在网络交易过程中,为了实时对交易行为进行监控和分析,针对每个捕捉到的用户交易行为的踪迹,我们构建基于PN机的动态交易行为模型,以此来分析交易行为模型与预期的交易流程行为的一致性。计算其行为一致性度,我们认为行为一致性度较小的行为为非正常行为,为此需要解决基于一致性度分析的交易流程行为实时监控技术。
现有技术中就两个模型之间的行为一致性有过一些研究,一类是只研究一致性质,即研究一致、非一致,不涉及度量的概念;一类从度量角度研究一致性,如仅仅从结构角度研究网模型间相似度。行为轮廓虽然结合了网的结构和动态行为关系,但是仅仅从模型的大致轮廓出发研究一致性度,对于模型的一致性检测不够精确,且对于含有环结构的模型,并未涉及。而且仅仅通过分析两个网的变迁之间的行为关系,又不能从整体角度进行网的一致性分析。
为此,本专利研究基于分支进程展开的模型一致性分析方法及系统。从而对用户交易过程中的行为一致进行判定,以此来判定用户的合法性。
发明内容
鉴于以上所述现有技术的缺点,本发明的目的在于提供一种基于分支进程的模型一致性分析方法及系统,用于解决现有技术中环结构模型及重名活动对的行为一致性测度问题。
为实现上述目的及其他相关目的,本发明提供一种基于分支进程的模型一致性分析方法,其特征在于,包括如下步骤:S1,根据用户行为运行轨迹,构建用户交易PN机模型;S2,分别建立用户交易PN机模型的分支进程展开BPU1、以及预期模型的分支进程展开BPU2;S3,从行为运行角度,对步骤S2中的分支进程展开中的变迁间的行为依赖关系进行分析,并确定其行为依赖关系Ri;S4,根据步骤S3确定的变迁间的所述行为依赖关系以及所述分支进程展开分别构建两个模型的行为关系三维图;S5,比较分析两模型的所述行为关系三维图, 计算用户行为一致性度,检测用户行为与预期行为的一致程度。。
优选地,所述行为依赖关系Ri分为四类:选择关系SR、顺序关系OR、并发关系CR、逆顺关系IOR。
优选地,步骤S4还具体包括如下步骤:步骤S4还具体包括如下步骤:S41,分别获取分支进程展开BPU1、BPU2的多重变迁集L1={t11,t12,…,t1n}、L2={t21,t22,…,t2m};S42,以L1中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG1,同样以L2中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG2
优选地,步骤S5还具体包括如下步骤:步骤S5还具体包括如下步骤:S51,获取两关系三维图中的所有元素;S52,通过分析比较获取两关系三维图中相一致的元素;S53,采用如下公式计算用户模型与预期模型的一致性度:
Figure PCTCN2016071042-appb-000001
优选地,步骤S51还具体包括如下步骤:S511,分析L1中的变迁t11、t12…t1n变迁间的行为依赖关系,令ti=t1i,i=j=1,执行步骤S512;S512,若ti和tj只满足一种行为依赖关系,那么就形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;否则执行步骤S513;S513,若ti和tj先后满足关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij;否则,执行步骤S514;S514,执行i=i+1,若i≤n,则返回步骤S512;若i>n,则执行步骤S515;S515,执行i=1,j=j+1,若j≤n,则返回步骤S512;若j>n,则执行步骤S516;S516,根据步骤S512形成点集V1={v11,v12,…,v1n},根据步骤S513形成向量集E1={e11,e12,…,e1s},执行步骤S517;S517,令ti=t2i,i=j=1,重复步骤S512-S516,分析L2中的变迁t21、t22...t2n变迁间的行为依赖关系,形成点集V2={v21,v22,…,v2m}以及向量集E2={e21,e22,…,e2t}。
优选地,步骤S52还具体包括如下步骤:S521,逐一将通过步骤S516得到的BRTDG1中的点与通过步骤S517得到的BRTDG2中的每一点进行比较,若对于两个图上的点v1s、v2s,其中v1s=(x1i,y1j,z1t)、v2s=(x2i,y2j,z2t)且v1s∈V1,v2s∈V2,满足z1t=z2t,则输出V1=V1∪v1s,V2=V2∪v2s,V1 =V1,V2 =V2,否则,输出V1=V1,V2=V2,V1 =V1\{v1s},V2 =V2\{v2s};S522,逐一将通过步骤S516得到的BRTDG1中的向量与通过步骤S517得到的BRTDG2中的每一向量进行比较,若对于两个图上的向量e1s、e2s,其中e1s=v1i→v1j、e2s=v2i→v2j且 v1i∈V1,v1j∈V1,v2i∈V2,v2j∈V2,满足e1s=e2s,则输出E1=E1∪e1s,E2=E2∪e2s,E1 =E1,E2 =E2,否则,输出E1=E1,E2=E2,E1 =E1\{e1s},E2 =E2\{e2s}。
优选地,步骤S53中通过公式
Figure PCTCN2016071042-appb-000002
计算行为一致性度,
其中:Db-一致性度,
V1 、V2 ~-两个三维图中一致点的集合,
E1 、E2 -两个三维图中一致向量的集合,
V1、V2-点集,
E1、E2-向量集。
为实现上述目的及其他相关目的,本发明提供一种基于分支进程的模型一致性分析系统,其特征在于,包括:模型构建模块,用于根据用户行为运行轨迹,构建用户交易PN机模型;
分支进程展开模块,用于建立用户交易PN机模型的分支进程展开BPU1、以及预期模型的分支进程展开BPU2;依赖关系确定模块,用于从行为运行角度,对所述分支进程展开模块所建立的分支进程展开中的变迁间的行为依赖关系进行分析,并确定其行为依赖关系Ri;三维图构建模块,用于根据所述依赖关系确定模块所确定的变迁间的所述行为依赖关系以及所述分支进程展开模块所建立的所述分支进程展开分别构建两个模型的行为关系三维图;一致性分析模块,用于比较分析所述三维图构建模块所构建的两模型的所述行为关系三维图,计算用户行为一致性度,检测用户行为与预期行为的一致程度。
优选地,所述行为依赖关系Ri分为四类:选择关系SR、顺序关系OR、并发关系CR、逆顺关系IOR。
优选地,三维图构建模块具体执行如下操作步骤:分别获取分支进程展开BPU1、BPU2的多重变迁集L1={t11,t12,…,t1n}、L2={t21,t22,…,t2m};以L1中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG1,同样以L2中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG2
优选地,一致性分析模块还具体包括:元素获取模块,用于获取两关系三维图中的所有元素;分析比较模块,用于对所述元素获取模块中的元素进行分析比较获取两关系三维图中相一致的元素;一致度计算模块,用于接收所述元素获取模块以及所述分析比较模块的输出结果,并采用如下公式计算用户模型与预期模型的一致性度:
Figure PCTCN2016071042-appb-000003
优选地,元素获取模块具体执行如下操作步骤:步骤一,分析L1中的变迁t11、t12…t1n变迁间的行为依赖关系,令ti=t1i,i=j=1,执行步骤二;步骤二,若ti和tj只满足一种行为依赖关系,那么就形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;否则执行步骤三;步骤三,若ti和tj先后满足关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij;否则,执行步骤四;步骤四,执行i=i+1,若i≤n,则返回步骤二;若i>n,则执行步骤五;步骤五,执行i=1,j=j+1,若j≤n,则返回步骤二;若j>n,则执行步骤六;步骤六,根据步骤二形成点集V1={v11,v12,…,v1n},根据步骤三形成向量集E1={e11,e12,…,e1s},执行步骤七;步骤七,令ti=t2i,i=j=1,重复前述步骤二至六,分析L2中的变迁t21、t22...t2n变迁间的行为依赖关系,形成点集V2={v21,v22,…,v2m}以及向量集E2={e21,e22,…,e2t}。
优选地,分析比较模块具体执行如下操作步骤:步骤八,逐一将通过步骤六得到的BRTDG1中的点与通过步骤七得到的BRTDG2中的每一点进行比较,若对于两个图上的点v1s、v2s,其中v1s=(x1i,y1j,z1t)、v2s=(x2i,y2j,z2t)且v1s∈V1,v2s∈V2,满足z1t=z2t,则输出V1=V1∪v1s,V2=V2∪v2s,V1 =V1,V2 =V2,否则,输出V1=V1,V2=V2,V1 =V1\{v1s},V2 =V2\{v2s};步骤九,逐一将通过步骤六得到的BRTDG1中的向量与通过步骤七得到的BRTDG2中的每一向量进行比较,若对于两个图上的向量e1s、e2s,其中e1s=v1i→v1j、e2s=v2i→v2j且v1i∈V1,v1j∈V1,v2i∈V2,v2j∈V2,满足e1s=e2s,则输出E1=E1∪e1s,E2=E2∪e2s,E1 =E1,E2 =E2,否则,输出E1=E1,E2=E2,E1 =E1\{e1s},E2 =E2\{e2s}。
优选地,一致度计算模块接收所述元素获取模块以及所述分析比较模块的输出结果后,通过公式
Figure PCTCN2016071042-appb-000004
计算行为一致性度,
其中:Db-一致性度,
V1 、V2 -两个三维图中一致点的集合,
E1 、E2 -两个三维图中一致向量的集合,
V1、V2-点集,
E1、E2-向量集。
如上所述,本发明的一种基于分支进程的模型一致性分析方法及系统,具有以下有益效果:
1.利用分支进程技术,更精确地获取用户行为模式一致性度;
2.提出行为关系三维图的方法,将模型对间的行为关系转化为三维空间中的点和向量,缩短了计算时间;
3.区别于以往只考虑结构的方法,从理论上给出了一种动态的一致性度测量方法;
4.区分了含有环结构的情况,提高了精确度,解决了存在环结构的用户模型一致性测量问题。
附图说明
图1显示为本发明的一致性分析方法流程示意图。
图2显示为本发明的用户交易PN机模型示意图。
图3显示为本发明的预期模型示意图。
图4显示为本发明的PN机模型的分支进程展开示意图。
图5显示为本发明的预期模型的分支进程展开示意图。
图6显示为本发明的三维图中的元素获取算法流程图。
图7显示为本发明的PN机模型的行为关系三维图。
图8显示为本发明的预期模型的行为关系三维图。
图9显示为本发明的一致度计算流程图。
图10显示为本发明的一致性分析系统结构示意图。
具体实施方式
以下通过特定的具体实例说明本发明的实施方式,本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用,本说明书中的各项细节也可以基于不同观点与应用,在没有背离本发明的精神下进行各种修饰或改变。
请参阅图1-图10。需要说明的是,本实施例中所提供的图示仅以示意方式说明本发明的基本构想,遂图式中仅显示与本发明中有关的组件而非按照实际实施时的组件数目、形状及 尺寸绘制,其实际实施时各组件的型态、数量及比例可为一种随意的改变,且其组件布局型态也可能更为复杂。
实施方式一:
如图1所示为基于分支进程的模型一致性分析方法流程图,包括如下步骤:根据用户行为运行轨迹,构建用户交易PN机模型;分别建立用户交易PN机模型的分支进程展开BPU1、以及预期模型的分支进程展开BPU2;从行为运行角度,对上述分支进程展开中的变迁间的行为依赖关系进行分析,并确定其行为依赖关系Ri;根据确定的变迁间的所述行为依赖关系以及所述分支进程展开分别构建两个模型的行为关系三维图;比较分析两模型的所述行为关系三维图,计算用户行为一致性度,检测用户行为与预期行为的一致程度。具体分析方法如下:
根据用户行为运行轨迹,构建如图2所示的用户交易PN机模型N1,而预期模型如图3所示的N2;对这两个模型进行分支进程展开,分别获取如图4、图5所示的分支进程展开BPU1、BPU2,如图2、图4所示,由于模型N1含有环结构,其BPU1中存在重名变迁,有可能在网中的两个变迁有不只一种行为关系,从图中4可看出BPU1存在重名变迁t1、t2、t4、t6。设置了四类行为依赖关系:选择关系SR、顺序关系OR、并发关系CR、逆顺关系IOR,从行为运行角度,对上述所建立的分支进程展开中的变迁间的行为依赖关系进行分析,确定属于上述四类行为依赖关系中的哪一种。
分别获取分支进程展开BPU1、BPU2的多重变迁集L1={t11,t12,…,t1n}、L2={t21,t22,…,t2m};以L1中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG1,同样以L2中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG2
采用如图6所示的算法来获取两关系三维图中的所有元素,以其中的一个三维图为例,对于变迁ti和tj,若只满足一种行为依赖关系,则判断满足何种行为依赖关系,确定后形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;若不只满足一种行为依赖关系,则判断ti和tj先后满足哪两种关系,对于确定的先后关系Ri、Rj,则形成两个点vij=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij
为实现不同变迁间关系的确定以及所有元素的获取,采用循环执行步骤,具体为:步骤一,分析L1中的变迁t11、t12…t1n变迁间的行为依赖关系,令ti=t1i,i=j=1,执行步骤二;
步骤二,若ti和tj只满足一种行为依赖关系,那么就形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;否则执行步骤三;
步骤三,若ti和tj先后满足关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij;否则,执行步骤四;
步骤四,执行i=i+1,若i≤n,则返回步骤二;若i>n,则执行步骤五;
步骤五,执行i=1,j=j+1,若j≤n,则返回步骤二;若j>n,则执行步骤六;
步骤六,根据步骤二形成点集V1={v11,v12,…,v1n},根据步骤三形成向量集E1={e11,e12,…,e1s},执行步骤七;
步骤七,令ti=t2i,i=j=1,重复前述步骤二至六,分析L2中的变迁t21、t22...t2n变迁间的行为依赖关系,形成点集V2={v21,v22,…,v2m}以及向量集E2={e21,e22,…,e2t}。
所获得的PN机模型的行为关系三维图如图7所示,图中的标出的点形成点集V1,带有箭头的向量形成向量集E1;预期模型的行为关系三维图如图8所示,图中的标出的点形成点集V2,带有箭头的向量形成向量集E2
对上述获得的点集以及向量集进行分析比较,并获取两关系三维图中相一致的元素,其具体的算法如图9所示:逐一将前述得到的BRTDG1中的点以及BRTDG2中的每一点进行比较,若对于两个图上的点v1s、v2s,其中v1s=(x1i,y1j,z1t)、v2s=(x2i,y2j,z2t)且v1s∈V1,v2s∈V2,满足z1t=z2t,则输出V1=V1∪v1s,V2=V2∪v2s,V1 =V1,V2 =V2,否则,输出V1=V1,V2=V2,V1 =V1\{v1s},V2 =V2\{v2s};逐一将前述得到的BRTDG1中的向量与BRTDG2中的每一向量进行比较,若对于两个图上的向量e1s、e2s,其中e1s=v1i→v1j、e2s=v2i→v2j且v1i∈V1,v1j∈V1,v2i∈V2,v2j∈V2,满足e1s=e2s,则输出E1=E1∪e1s,E2=E2∪e2s,E1 =E1,E2 =E2,否则,输出E1=E1,E2=E2,E1 =E1\{e1s},E2 =E2\{e2s}。
如图9所示,由上述获取的分析结果,通过公式
Figure PCTCN2016071042-appb-000005
计算行为一致性度。
对于本例中,根据以上算法,得出两个分支进程的行为关系三维图的总共元素个数为98, 一致的元素个数为90,不一致的元素如图7、图8中加粗的点与向量,共8个,故可知模型N1和N2的一致性度为
Figure PCTCN2016071042-appb-000006
实施方式二:
如图8所示为基于分支进程的模型一致性分析系统结构图,系统包括模型构建模块、分支进程展开模块、依赖关系确定模块、三维图构建模块、以及由元素获取模块、分析比较模块、一致度计算模块所构成的一致性分析模块。其中,模型构建模块,用于根据用户行为运行轨迹,构建用户交易PN机模型;分支进程展开模块,用于建立用户交易PN机模型的分支进程展开BPU1、以及预期模型的分支进程展开BPU2;依赖关系确定模块,用于从行为运行角度,对所述分支进程展开模块所建立的分支进程展开中的变迁间的行为依赖关系进行分析,并确定其行为依赖关系Ri;三维图构建模块,用于根据所述依赖关系确定模块所确定的变迁间的行为依赖关系以及所述分支进程展开模块所建立的分支进程展开分别构建两个模型的行为关系三维图;一致性分析模块,用于比较分析所述三维图构建模块所构建的两模型的行为关系三维图,计算用户行为一致性度,检测用户行为与预期行为的一致程度,其又包括用于获取两关系三维图中的所有元素的元素获取模块、用于对元素获取模块中的元素进行分析比较获取两关系三维图中相一致的元素的分析比较模块、用于接收元素获取模块以及分析比较模块的输出结果计算一致度的一致度计算模块。基于分支进程一致性度计算公式:
Figure PCTCN2016071042-appb-000007
其各模块的分析过程如下:
模型构建模块,根据用户行为运行轨迹,构建如图2所示的用户交易PN机模型N1,而预期模型如图3所示的N2;分支进程展开模块对这两个模型进行分支进程展开,分别获取如图4、图5所示的分支进程展开BPU1、BPU2。如图2、图4所示,由于模型N1含有环结构,其BPU1中存在重名变迁,有可能在网中的两个变迁有不只一种行为关系,从图4中可看出BPU1存在重名变迁t1、t2、t4、t6。设置四类行为依赖关系:选择关系SR、顺序关系OR、并发关系CR、逆顺关系IOR,依赖关系确定模块,从行为运行角度,对上述所建立的分支进程展开中的变迁间的行为依赖关系进行分析,确定属于上述四类行为依赖关系中的哪一种。
在三维图构建模块中,分别获取分支进程展开BPU1、BPU2的多重变迁集L1={t11,t12,…,t1n}、L2={t21,t22,…,t2m};以L1中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG1,同样以L2中的变迁作 为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG2
采用如图6所示的算法来获取两关系三维图中的所有元素,以其中的一个三维图为例,在元素获取模块中,对于变迁ti和tj,若只满足一种行为依赖关系,则判断满足何种行为依赖关系,确定后形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;若不只满足一种行为依赖关系,则判断ti和tj先后满足哪两种关系,对于确定的先后关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij
在元素获取模块的执行操作中,为实现不同变迁间关系的确定以及所有元素的获取,采用循环执行步骤,具体为:步骤一,分析L1中的变迁t11、t12…t1n变迁间的行为依赖关系,令ti=t1i,i=j=1;
步骤二,若ti和tj只满足一种行为依赖关系,那么就形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;否则执行步骤三;
步骤三,若ti和tj先后满足关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij;否则,执行步骤四;
步骤四,执行i=i+1,若i≤n,则返回步骤二;若i>n,则执行步骤五;
步骤五,执行i=1,j=j+1,若j≤n,则返回步骤二;若j>n,则执行步骤六;
步骤六,根据步骤二形成点集V1={v11,v12,…,v1n},根据步骤三形成向量集E1={e11,e12,…,e1s},执行步骤七;
步骤七,令ti=t2i,i=j=1,重复前述步骤二至六,分析L2中的变迁t21、t22...t2n变迁间的行为依赖关系,形成点集V2={v21,v22,…,v2m}以及向量集E2={e21,e22,…,e2t}。
所获得的PN机模型的行为关系三维图如图7所示,图中的标出的点形成点集V1,带有箭头的向量形成向量集E1;预期模型的行为关系三维图如图8所示,图中的标出的点形成点集V2,带有箭头的向量形成向量集E2
将元素获取模块获得的点集以及向量集输入分析比较模块中,在分析比较模块中完成对 元素获取模块中的元素的分析比较,并获取两关系三维图中相一致的元素,其具体的算法如图9所示:逐一将前述元素获取模块得到的BRTDG1中的点以及BRTDG2中的每一点进行比较,若对于两个图上的点v1s、v2s,其中v1s=(x1i,y1j,z1t)、v2s=(x2i,y2j,z2t)且v1s∈V1,v2s∈V2,满足z1t=z2t,则输出V1=V1∪v1s,V2=V2∪v2s,V1 =V1,V2 =V2,否则,输出V1=V1,V2=V2,V1 =V1\{v1s},V2 =V2\{v2s};逐一将前述元素获取模块得到的BRTDG1中的向量与BRTDG2中的每一向量进行比较,若对于两个图上的向量e1s、e2s,其中e1s=v1i→v1j、e2s=v2i→v2j且v1i∈V1,v1j∈V1,v2i∈V2,v2j∈V2,满足e1s=e2s,则输出E1=E1∪e1s,E2=E2∪e2s,E1 =E1,E2 =E2,否则,输出E1=E1,E2=E2,E1 =E1\{e1s},E2 =E2\{e2s}。
如图9所示,一致度计算模块接收元素获取模块以及分析比较模块的输出结果后,通过公式
Figure PCTCN2016071042-appb-000008
计算行为一致性度。
对于本例中,根据以上算法,得出两个分支进程的行为关系三维图的总共元素个数为98,一致的元素个数为90,不一致的元素如图7、图8中加粗的点与向量,共8个,故可知模型N1和N2的一致性度为
Figure PCTCN2016071042-appb-000009
一致度值越高代表该用户行为与预期行为越一致,一致度值越低代表该用户行为与预期行为越不一致,当一致度值特别低时,我们怀疑该用户行为为非法行为或者预期模型的构建存在问题。
该方法在用户交易模型过程出现循环结构的情况下,可以进行有效区分,从而大大增加了精度。
经过实验证明,该方法在准确率和计算时间上都优于先前的研究。
综上所述,本发明有效克服了现有技术中的种种缺点而具高度产业利用价值。
上述实施例仅例示性说明本发明的原理及其功效,而非用于限制本发明。任何熟悉此技术的人士皆可在不违背本发明的精神及范畴下,对上述实施例进行修饰或改变。因此,举凡所属技术领域中具有通常知识者在未脱离本发明所揭示的精神与技术思想下所完成的一切等效修饰或改变,仍应由本发明的权利要求所涵盖。

Claims (14)

  1. 一种基于分支进程的模型一致性分析方法,其特征在于,包括如下步骤:
    S1,根据用户行为运行轨迹,构建用户交易PN机模型;
    S2,分别建立用户交易PN机模型的分支进程展开BPU1、以及预期模型的分支进程展开BPU2
    S3,从行为运行角度,对步骤S2中的分支进程展开中的变迁间的行为依赖关系进行分析,并确定其行为依赖关系Ri
    S4,根据步骤S3确定的变迁间的所述行为依赖关系以及所述分支进程展开分别构建两个模型的行为关系三维图;
    S5,比较分析两模型的所述行为关系三维图,计算用户行为一致性度,检测用户行为与预期行为的一致程度。
  2. 根据权利要求1所述的基于分支进程的模型一致性分析方法,其特征在于:所述行为依赖关系Ri分为四类:选择关系SR、顺序关系OR、并发关系CR、逆顺关系IOR。
  3. 根据权利要求1或2所述的基于分支进程的模型一致性分析方法,其特征在于:步骤S4还具体包括如下步骤:
    S41,分别获取分支进程展开BPU1、BPU2的多重变迁集L1={t11,t12,…,t1n}、L2={t21,t22,…,t2m};
    S42,以L1中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG1,同样以L2中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG2
  4. 根据权利要求3所述的基于分支进程的模型一致性分析方法,其特征在于:步骤S5还具体包括如下步骤:
    S51,获取两关系三维图中的所有元素;
    S52,通过分析比较获取两关系三维图中相一致的元素;
    S53,采用如下公式计算用户模型与预期模型的一致性度:
    Figure PCTCN2016071042-appb-100001
  5. 根据权利要求4所述的基于分支进程的模型一致性分析方法,其特征在于:步骤S51还具体包括如下步骤:
    S511,分析L1中的变迁t11、t12…t1n变迁间的行为依赖关系,令ti=t1i,i=j=1,执行步骤S512;
    S512,若ti和tj只满足一种行为依赖关系,那么就形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;否则执行步骤S513;
    S513,若ti和tj先后满足关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij;否则,执行步骤S514;
    S514,执行i=i+1,若i≤n,则返回步骤S512;若i>n,则执行步骤S515;
    S515,执行i=1,j=j+1,若j≤n,则返回步骤S512;若j>n,则执行步骤S516;
    S516,根据步骤S512形成点集V1={v11,v12,…,v1n},根据步骤S513形成向量集E1={e11,e12,…,e1s},执行步骤S517;
    S517,令ti=t2i,i=j=1,重复步骤S512-S516,分析L2中的变迁t21、t22...t2n变迁间的行为依赖关系,形成点集V2={v21,v22,…,v2m}以及向量集E2={e21,e22,…,e2t}。
  6. 根据权利要求5所述的基于分支进程的模型一致性分析方法,其特征在于:步骤S52还具体包括如下步骤:
    S521,逐一将通过步骤S516得到的BRTDG1中的点与通过步骤S517得到的BRTDG2中的每一点进行比较,若对于两个图上的点v1s、v2s,其中v1s=(x1i,y1j,z1t)、v2s=(x2i,y2j,z2t)且v1s∈V1,v2s∈V2,满足z1t=z2t,则输出V1=V1∪v1s,V2=V2∪v2s,V1 =V1,V2 =V2,否则,输出V1=V1,V2=V2,V1 =V1\{v1s},V2 =V2\{v2s};
    S522,逐一将通过步骤S516得到的BRTDG1中的向量与通过步骤S517得到的BRTDG2中的每一向量进行比较,若对于两个图上的向量e1s、e2s,其中e1s=v1i→v1j、e2s=v2i→v2j且v1i∈V1,v1j∈V1,v2i∈V2,v2j∈V2,满足e1s=e2s,则输出E1=E1∪e1s,E2=E2∪e2s, E1 =E1,E2 =E2,否则,输出E1=E1,E2=E2,E1 =E1\{e1s},E2 =E2\{e2s}。
  7. 根据权利要求6所述的基于分支进程的模型一致性分析方法,其特征在于:步骤S53中通过公式
    Figure PCTCN2016071042-appb-100002
    计算行为一致性度,
    其中:Db-一致性度,
    Figure PCTCN2016071042-appb-100003
    -两个三维图中一致点的集合,
    Figure PCTCN2016071042-appb-100004
    -两个三维图中一致向量的集合,
    V1、V2-点集,
    E1、E2-向量集。
  8. 一种基于分支进程的模型一致性分析系统,其特征在于,包括:
    模型构建模块,用于根据用户行为运行轨迹,构建用户交易PN机模型;
    分支进程展开模块,用于建立用户交易PN机模型的分支进程展开BPU1、以及预期模型的分支进程展开BPU2
    依赖关系确定模块,用于从行为运行角度,对所述分支进程展开模块所建立的分支进程展开中的变迁间的行为依赖关系进行分析,并确定其行为依赖关系Ri
    三维图构建模块,用于根据所述依赖关系确定模块所确定的变迁间的所述行为依赖关系以及所述分支进程展开模块所建立的所述分支进程展开分别构建两个模型的行为关系三维图;
    一致性分析模块,用于比较分析所述三维图构建模块所构建的两模型的所述行为关系三维图,计算用户行为一致性度,检测用户行为与预期行为的一致程度。
  9. 根据权利要求8所述的基于分支进程的模型一致性分析系统,其特征在于:所述行为依赖关系Ri分为四类:选择关系SR、顺序关系OR、并发关系CR、逆顺关系IOR。
  10. 根据权利要求8或9所述的基于分支进程的模型一致性分析系统,其特征在于:三维图构建模块具体执行如下操作步骤:
    分别获取分支进程展开BPU1、BPU2的多重变迁集L1={t11,t12,…,t1n}、 L2={t21,t22,…,t2m};
    以L1中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG1,同样以L2中的变迁作为坐标轴的x轴和y轴,以变迁间的行为依赖关系Ri作为坐标轴的z轴,输出行为关系三维图BRTDG2
  11. 根据权利要求10所述的基于分支进程的模型一致性分析系统,其特征在于:一致性分析模块还具体包括:
    元素获取模块,用于获取两关系三维图中的所有元素;
    分析比较模块,用于对所述元素获取模块中的元素进行分析比较获取两关系三维图中相一致的元素;
    一致度计算模块,用于接收所述元素获取模块以及所述分析比较模块的输出结果,并采用如下公式计算用户模型与预期模型的一致性度:
    Figure PCTCN2016071042-appb-100005
  12. 根据权利要求11所述的基于分支进程的模型一致性分析系统,其特征在于:元素获取模块具体执行如下操作步骤:
    步骤一,分析L1中的变迁t11、t12…t1n变迁间的行为依赖关系,令ti=t1i,i=j=1,执行步骤二;
    步骤二,若ti和tj只满足一种行为依赖关系,那么就形成一个新的点vij=(ti,tj,Rm),并输出BRTDG=BRTDG∪vij;否则执行步骤三;
    步骤三,若ti和tj先后满足关系Rm、Rn,则形成两个点vij1=(ti,tj,Rm),vij2=(ti,tj,Rn),并形成向量eij=vij1→vij2,输出BRTDG=BRTDG∪eij;否则,执行步骤四;
    步骤四,执行i=i+1,若i≤n,则返回步骤二;若i>n,则执行步骤五;
    步骤五,执行i=1,j=j+1,若j≤n,则返回步骤二;若j>n,则执行步骤六;
    步骤六,根据步骤二形成点集V1={v11,v12,…,v1n},根据步骤三形成向量集E1={e11,e12,…,e1s},执行步骤七;
    步骤七,令ti=t2i,i=j=1,重复前述步骤二至六,分析L2中的变迁t21、t22...t2n变迁间 的行为依赖关系,形成点集V2={v21,v22,…,v2m}以及向量集E2={e21,e22,…,e2t}。
  13. 根据权利要求12所述的基于分支进程的模型一致性分析系统,其特征在于:分析比较模块具体执行如下操作步骤:
    步骤八,逐一将通过步骤六得到的BRTDG1中的点与通过步骤七得到的BRTDG2中的每一点进行比较,若对于两个图上的点v1s、v2s,其中v1s=(x1i,y1j,z1t)、v2s=(x2i,y2j,z2t)且v1s∈V1,v2s∈V2,满足z1t=z2t,则输出V1=V1∪v1s,V2=V2∪v2s,V1 =V1,V2 =V2,否则,输出V1=V1,V2=V2,V1 =V1\{v1s},V2 =V2\{v2s};
    步骤九,逐一将通过步骤六得到的BRTDG1中的向量与通过步骤七得到的BRTDG2中的每一向量进行比较,若对于两个图上的向量e1s、e2s,其中e1s=v1t→v1j、e2s=v2i→v2j且v1i∈V1,v1j∈V1,v2i∈V2,v2j∈V2,满足e1s=e2s,则输出E1=E1∪e1s,E2=E2∪e2s,E1 =E1,E2 =E2,否则,输出E1=E1,E2=E2,E1 =E1\{e1s},E2 =E2\{e2s}。
  14. 根据权利要求13所述的基于分支进程的模型一致性分析系统,其特征在于:一致度计算模块接收所述元素获取模块以及所述分析比较模块的输出结果后,通过公式
    Figure PCTCN2016071042-appb-100006
    计算行为一致性度,
    其中:Db-一致性度,
    Figure PCTCN2016071042-appb-100007
    -两个三维图中一致点的集合,
    Figure PCTCN2016071042-appb-100008
    -两个三维图中一致向量的集合,
    V1、V2-点集,
    E1、E2-向量集。
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