A method for measuring user behavior consistency degree based on a complex correspondence system, which is applied to internet payment platform security. An entire solution is divided into three stages: at a first stage, analyzing complex correspondence relation characteristics according to an existing user behavior model; at a second stage, establishing a behavior profile according to user behavior characteristics and establishing user behavior relation matrixes; and at a third stage, completing user behavior matrix decomposition according to the complex correspondence characteristics of a user, calculating a user behavior consistency degree and detecting a degree of consistency between user behaviors and expected behaviors. The internal behavior relation of the user is more elaborately analyzed, the user behavior relation profile is established, the complex correspondence relations are distinguished and classified, and user behavior consistency measurement and analysis architecture based on the complex correspondence relations are given.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for measuring a user behavior consistency degree based on a complex correspondence system, characterized in that an entire solution is divided into three stages: a first stage comprising the following specific implementation steps: step 1-1: subdividing cross order relations based on an existing workflow net, and refining behavior profile relations; step 1-2: analyzing complex correspondence relations, classifying the complex correspondence relations and determining behavior characteristics of each class; and step 1-3: simultaneously analyzing transitive dependency relations between user activities according to indirect relations between users; wherein the steps 1-1, 1-2 and 1-3 are performed in parallel; a second stage comprising the following specific implementation steps: step 2-1: determining correlations between five classes of correspondence relations according to the classification of the complex correspondence relations completed in step 1-2 and the behavior characteristics of each class; step 2-2: establishing user extended behavior profile relations according to the behavior profile relations refined in step 1-1; step 2-3: converting user behavior relations into matrix elements based on the step 2-2 in combination with the step 1-3 according to a formula a ij = { 2 ?  + a j 1 ?   a j 0 0 ?   a j 0 1 ?   a j 0 2 ?   a j 0 3 ?   a j - 1 ?   a j   ( i , j = 1 , 2 , …  , n )   ?  indicates text missing or illegible when filed  (wherein aij denotes elements in behavior relation matrix); and step 2-4: establishing a user behavior relation matrix graph based on the steps 2-2 and 2-3, wherein an establishment step thereof is as follow (from matrix MD1→MD2→MD3→MD4 . . . →MDn→MD): a third stage comprising the following specific implementation steps: step 3-1: decomposing user behavior relation matrixes according to the five user complex correspondence relation classes determined in the step 2-1 and the behavior relation matrix graph established in the step 2-4; and step 3-2: calculating behavior consistency between a user model and an expected model according to correspondence relations between an actual model and the expected model of a user, calculation formula: consistency   degree = area   of   consistent   behavior   relation   matrixes total   area   of   behavior   relation   matrixes wherein consistent behavior relations show consistent portions of user activities, area of behavior matrixes is used for depicting entire consistent behavior relations thereof, a higher consistency value represents that user behaviors and expected behaviors are more consistent, a lower consistency value represents that the user behavior and the expected behaviors are more inconsistent, and when consistency is particularly low, the user behaviors are suspected as illegal behaviors. 2. The method for measuring a user behavior consistency degree based on a complex correspondence system according to claim 1, characterized in that, in the step 3-1 of decomposing the user behavior relation matrixes, a solution algorithm of elements in the behavior relation matrix graph thereof is as follow: input: two workflow nets N1(P1,T1;F1) and N2(P21,T2; F2), wherein they have transition sets of correspondence relations A={a1, a2, . . . , an}, B={b1, b2, . . . , bm}, aij={0|aiaj)}{1|(ai{tilde over (→)}aj)(ai{tilde over (→)}−1aj)}{2|ai+aj)}{3|ai∥+aj} (i=1, 2, . . . , n), bij={0|bibj){1|(bi{tilde over (→)}jb)}(bi{tilde over (→)}−1bj)}{2|(bi+bj)}{3|bi∥+bj} (i=1, 2, . . . , m), behavior matrixes MDA0 and MDB0 for ordering; output: elements aij (i, j=1, 2, . . . , n) and bij (i, j=1, 2, . . . , m) in behavior relation matrix graphs MDA and MDB; (1) firstly determining elements aii (i=1, 2, . . . , n) of diagonals in MDA, sequentially judging whether ai (i=1, 2, . . . , n) is in a ring structure or not, and if ai is not in the ring structure, outputting aii=2 and executing step (2); or else, outputting aii=0 and executing step (2); (2) then determining values of ai,i+1 and ai+1,i(i=1, 2, . . . , n−1), in the net N1, sequentially calculating behavior relations between ai and ai+1, then converting the behavior relations into an integer p, outputting ai,i+1=ai+1,i=p, and executing step (3); (3) then determining values of ai,i+2 and ai+2,i (i=1, 2, . . . , n−2); if ai,i+1≠ai+1,i+2, outputting ai,i+2=ai+2,i=min{ai,j+1, ai+1,i+2}; or else, if ai,i+1=ai+1.1+2=1, outputting ai,i+2=ai+2,i=1; or else, if ai,i+1=ai+1,i+2≠1, judging behavior relations between ai and ai+2 and converting the behavior relations into a relation value q, outputting ai,i+2=ai+2,i=q, and executing step (4); (4) similarly, determining ai,i+h and ai+h,1 (i=1, 2, . . . , n−h) (h=3, . . . , n−1), outputting ai,i+h=ai+h,i, and ending the algorithm till the last element a1n; similarly, calculating elements bij (i, j=1, 2, . . . , m) in MDB according to the solution algorithm of the elements in the behavior relation matrix graph to obtain a matrix MDB. 3. The method for measuring a user behavior consistency degree based on a complex correspondence system according to claim 1, characterized in that, in the step 3-2 of calculating the behavior consistency between the user model and the expected model, a solution algorithm of a consistency degree thereof is as follow: input: two workflow nets N1=(P1, T1; F1) and N2 (P21, T2; F2), wherein relation matrixes MDA0 and MDB0 thereof are solved through the solution algorithm of the elements in the behavior relation matrix graph in the step 3-1; output: a consistency degree BP (1) firstly and respectively dividing MDA0 and MDB0 into p and q corresponding sets according to correspondence relations of transition sets in MDA0 and MDB0, sequentially marking MDA0 as {a1, a2, . . . , am}, {am+1, am+2, . . . , a1} . . . {as+1, . . . , an}, and executing step (2); (2) firstly taking and marking first m order square matrixes in MDA0 as a module 1 according to a first set {a1, a2, . . . am}, corresponding to MDB0, in MDA0, and executing step (3); (3) taking and marking an m×(1−m) order matrix consisting of 1→(m) rows and (m+1)→(1) columns in MDA0 and a transposed matrix thereof as a module 2 according to a second set {am+1, am+2, . . . , a1}, corresponding to MDB0, in MDA0, and executing step (4); (4) following the previous step till a pth set {as+1 , . . . , an}, corresponding to MDB0, in MDA0, taking and marking an m×(n−s) order matrix consisting of 1→(m) rows and (s+1)→(n) columns in MDA0 and a transposed matrix thereof as a module p, and executing step (5); (5) taking and marking a (1−m) order matrix consisting of (m+1)→(1) rows and (m+1)→(1) columns in MDA0 and a transposed matrix thereof as a module p+1 according to a second set {am+1, am+2 , . . . , a1}, corresponding to MDB0, in MDA0, and executing step (6); (6) following step (4), marking a (1−m)×(n−s) order matrix consisting of (m+1)→(1) rows and (s+1)→(n) columns in MDA0 and a transposed matrix thereof as a module p+2, and executing step (7); (7) performing operation in this way till a pth set {as+1 , . . . , an}, corresponding to MDB0, in MDA0, taking and marking a (n−s) order matrix consisting of s+1→n rows and s+1→n columns as a module p  ( p + 1 ) 2 , and executing step (8); (8) if p=q, similarly also decomposing MDB0 into p  ( p + 1 ) 2 corresponding modules, marking the modules as module 1, 2, . . . p  ( p + 1 ) 2 , and executing step (10); or else, if p≠q, also decomposing non-repetitive correspondence relations in MDB0 into p  ( p + 1 ) 2 corresponding modules, and executing step (9); (9) locking repetitive corresponding transition sets, sequentially marking areas consisting of the repetitive corresponding sets as module p  ( p + 1 ) 2 + 1 = 1 , ?   p  ( p + 1 ) 2 + 2 = 2 ,  …  , p  ( p + 1 ) 2 + ( q - p ) = ( q - p )  ?  ,  ?  indicates text missing or illegible when filed  and executing step (10); and (10) sequentially checking matrix elements in module 1, 2,. . . , p  ( p + 1 ) 2 in MDA0, finding out ai, ai and different elements bi, bj in the same module of MDB0, if p=q, outputting a consistency degree BP, and ending the algorithm, and if p≠q, locking module 1c, 2c, . . . , (q−p)c, outputting a consistency degree BP, and ending the algorithm.