CN110942019A - Analysis method for finding longest adjoint path of two tracks - Google Patents
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Abstract
The invention discloses an analysis method for finding out the longest adjoint path of two tracks, which comprises the following steps: optionally selecting two tracks A and B and sequencing according to time; traversing the track points of the tracks A and B, and finding out a track point pair (P ', Q') with the minimum time difference in all the track points; finding out all pairs (P ', Q') of the track points which meet the matching conditions in the tracks A and B by adopting a recursive method to form an optimal matching result set; and traversing the optimal matching result set to find out the longest adjoint path which meets the requirement. The method effectively reduces the calculation complexity of the adjoint sub-paths, improves the calculation efficiency, is less influenced by the deviation value, and can be applied to the actual track data to quickly analyze the adjoint sub-paths of a plurality of pairs of targets.
Description
Technical Field
The invention relates to the technical field of security and information, in particular to an analysis method for finding out the longest adjoint path of two tracks.
Background
At present, a plurality of monitoring devices in a security system can acquire a large amount of behavior trajectory data, the behavior trajectory data includes data such as a human face, an MAC, an IMSI and an IMEI, and different types of data are generally acquired by different types of acquisition devices, so different types of trajectory data generally have no direct association relationship, but many useful information can be separated from the trajectory data through data mining, one of the important information is an accompanying sub-path of two trajectories, and other suspicious persons who often run in the same line with a certain target person can be found out through the accompanying sub-paths of the two trajectories, or the time and place where the two target persons gather can be found out, so that the information can play an important role in the security field.
For the adjoint sub-path analysis of the two tracks, the two possible tracks are separated along with a period of time, and then return to an adjoint state after a period of time, namely, the time difference and the distance of the two tracks are not specified and change frequently, and sometimes, deviation occurs, which are problems to be considered for the adjoint sub-path analysis; in addition, the adjoint sub-path analysis needs to obtain a result by calculating a large amount of data, and various track data analysis algorithms are available in the market, but most track data analysis algorithms are too complex and long in time consumption, and cannot be effectively applied to adjoint sub-path analysis.
Accordingly, the prior art is deficient and needs improvement.
Disclosure of Invention
The object of the present invention is to overcome the disadvantages of the prior art and to provide an analysis method for finding the longest syndrome path of two trajectories.
The technical scheme of the invention is as follows: an analysis method for finding the longest syndrome path of two trajectories, comprising the steps of:
step S1, selecting two tracks A and B and sorting according to time;
step S2, traversing the track points of the tracks A and B, and finding out the track point pair (P ', Q') with the minimum time difference in all the track points;
step S3, finding out all track point pairs (P ', Q') meeting the matching conditions in the tracks A and B by adopting a recursive method to form an optimal matching result set;
step S4, traverse the best matching result set, and find the longest syndrome path meeting the requirement.
Further, the step S1 includes the following steps:
step S101, acquiring mass behavior track data which changes along with time and is acquired by acquisition equipment through a terminal to construct a track database, and randomly selecting two tracks A and B from the track database;
wherein, the terminal in step S101 includes a computer;
and S102, sequencing the two tracks A and B according to time.
Further, the mass behavior trace obtained in step S101 includes one or more of face data, MAC data, IMSI data, and IMEI data.
Further, the step S2 includes the following steps:
step S201, traversing the trace points P in the trace A according to a time sequence, finding out the trace point Q closest to the trace point P in time from the trace B, and calculating and recording the time difference of the trace point pair (P, Q);
step S202, finding out the trace point pair (P, Q) with the minimum time difference from the trace point pair (P, Q) in the step S201, wherein the time difference of the trace point pair (P, Q) meets the trace points P 'and Q' which are not more than the given maximum matching time difference, and forming the trace points P 'and Q' into the trace point pair (P ', Q').
Further, the time difference in step S201 is obtained by calculating a difference between the time corresponding to the trace point P and the time corresponding to the trace point Q.
Further, the step S3 includes the following steps:
step S301, dividing the track A and the track B into two sections by taking the time of a track point P 'and a track point Q' as separation points respectively, dividing the track A into a track A1 earlier in time than the track point P 'and a track A2 later in time than the track point P', and dividing the track B into a track B1 earlier in time than the track point Q 'and a track B2 later in time than the track point Q';
step S302, repeating the steps S102 to S301 respectively for the track A1 and the track B1 and the track A2 and the track B2 until a matched track point pair (P ', Q') cannot be found out;
in step S303, all the matched pairs of trajectory points (P ', Q') are saved in the best matching result set.
Further, the step S4 includes the following steps:
step S401, sorting the pairs of trajectory points (P ', Q') in the best matching result set in step S303 in time sequence, and calculating the matching state of each pair of trajectory points (P ', Q') according to the distance and time difference between each pair of trajectory points (P ', Q') in the best matching result set;
step S402, selecting the first pair of track point pairs (P ', Q') with matching state of 'matching' in the best matching result set as a starting point, and sequentially superposing the next pair of track point pairs (P ', Q') with matching state of 'matching' to form an adjoint path C;
step S403, calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') of each adjoint sub-path C, and counting the data values of the matching states;
step S404, calculating an accompanying coefficient of each accompanying sub-path C according to the data value of the matching state in the step S403 by using a formula;
step S405, comparing the magnitude between the adjoint coefficient of each adjoint sub-path C and a given minimum adjoint coefficient;
step S406, changing the starting point of the adjoint sub-path C with the adjoint coefficient smaller than the given minimum adjoint coefficient into the next pair of track point pairs (P ', Q') with the matching state of matching as the starting point to form a new adjoint sub-path C;
step S407, repeating the steps S403 to S406 until the whole optimal matching result set is traversed;
step S408, all the adjoint sub-paths C are saved in a sub-path set, and the adjoint sub-path C having the longest track and the largest adjoint coefficient is found from the sub-path set, which is the longest adjoint sub-path.
Further, the matching states in step S401 include matching, deviation, and uncertainty, and the matching state of each track point pair obtains a matching result by comparing "the device acquisition range + the time difference + the normal moving speed" with the distance.
Further, the matching states in step S403 include matching, deviation, and uncertainty, and the matching state of each track point pair obtains a matching result by comparing "the device collection range + the time difference + the normal moving speed" with the distance.
Further, the data values in step S403 include a matching value n1, a deviation value n2, and an indeterminate value n3, and the formula in step S404 includes an accompanying parameter a ═ n2 × 1.0/(n1+ n2), b ═ n1 × 1.0/(n1+ n2+ n3), and an accompanying coefficient ═ 2 (1-1/(1+ exp (-5 × a))) (2/(1+ exp (-8 × b)) -1).
By adopting the scheme, the invention has the following beneficial effects:
the method effectively reduces the calculation complexity of the adjoint sub-paths, improves the calculation efficiency, is less influenced by the deviation value, and can be applied to the actual track data to quickly analyze the adjoint sub-paths of a plurality of pairs of targets.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an analysis method for finding the longest syndrome path of two tracks according to the present invention;
FIG. 2 is a flowchart illustrating step S1 according to the present invention;
FIG. 3 is a flowchart illustrating step S2 according to the present invention;
FIG. 4 is a flowchart illustrating step S3 according to the present invention;
FIG. 5 is a flowchart illustrating step S4 according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
Referring to fig. 1, the present invention provides an analysis method for finding the longest syndrome path of two tracks, comprising the following steps:
step S1, selecting two tracks A and B and sorting according to time;
step S2, traversing the track points of the tracks A and B, and finding out the track point pair (P ', Q') with the minimum time difference in all the track points;
step S3, finding out all track point pairs (P ', Q') meeting the matching conditions in the tracks A and B by adopting a recursive method to form an optimal matching result set;
step S4, traverse the best matching result set, and find the longest syndrome path meeting the requirement.
As a preferred embodiment, referring to fig. 2, the step S1 includes the following steps:
step S101, acquiring mass behavior track data which changes along with time and is acquired by acquisition equipment through a terminal to construct a track database, and randomly selecting two tracks A and B from the track database;
wherein, the terminal in step S101 includes a computer;
and S102, sequencing the two tracks A and B according to time.
As a preferred embodiment, the mass behavior trace obtained in step S101 includes one or more of face data, MAC data, IMSI data, and IMEI data.
As a preferred embodiment, referring to fig. 3, the step S2 includes the following steps:
step S201, traversing the trace points P in the trace A according to a time sequence, finding out the trace point Q closest to the trace point P in time from the trace B, and calculating and recording the time difference of the trace point pair (P, Q);
step S202, finding out the trace point pair (P, Q) with the minimum time difference from the trace point pair (P, Q) in the step S201, wherein the time difference of the trace point pair (P, Q) meets the trace points P 'and Q' which are not more than the given maximum matching time difference, and forming the trace points P 'and Q' into the trace point pair (P ', Q').
As a preferred embodiment, the time difference in step S201 is obtained by calculating a difference between the time corresponding to the track point P and the time corresponding to the track point Q.
As a preferred embodiment, the step S201 finds a pair of track points (P) in the calculationi，Qj) Then, the next pair of track points is found without repeatedly traversing the track B, and the trace point P is directly compared from the track BiThe first trace point corresponding to the later time starts to traverse, thereby the computational complexity can be increased from O (n)2) And the calculation complexity is reduced to O (n), so that the calculation efficiency is improved.
As a preferred embodiment, referring to fig. 4, the step S3 includes the following steps:
step S301, dividing the track A and the track B into two sections by taking the time of a track point P 'and a track point Q' as separation points respectively, dividing the track A into a track A1 earlier in time than the track point P 'and a track A2 later in time than the track point P', and dividing the track B into a track B1 earlier in time than the track point Q 'and a track B2 later in time than the track point Q';
step S302, repeating the steps S102 to S301 respectively for the track A1 and the track B1 and the track A2 and the track B2 until a matched track point pair (P ', Q') cannot be found out;
in step S303, all the matched pairs of trajectory points (P ', Q') are saved in the best matching result set.
As a preferred embodiment, referring to fig. 5, the step S4 includes the following steps:
step S401, sorting the pairs of trajectory points (P ', Q') in the best matching result set in step S303 in time sequence, and calculating the matching state of each pair of trajectory points (P ', Q') according to the distance and time difference between each pair of trajectory points (P ', Q') in the best matching result set;
step S402, selecting the first pair of track point pairs (P ', Q') with matching state of 'matching' in the best matching result set as a starting point, and sequentially superposing the next pair of track point pairs (P ', Q') with matching state of 'matching' to form an adjoint path C;
step S403, calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') of each adjoint sub-path C, and counting the data values of the matching states;
step S404, calculating an accompanying coefficient of each accompanying sub-path C according to the data value of the matching state in the step S403 by using a formula;
step S405, comparing the magnitude between the adjoint coefficient of each adjoint sub-path C and a given minimum adjoint coefficient;
step S406, changing the starting point of the adjoint sub-path C with the adjoint coefficient smaller than the given minimum adjoint coefficient into the next pair of track point pairs (P ', Q') with the matching state of matching as the starting point to form a new adjoint sub-path C;
step S407, repeating the steps S403 to S406 until the whole optimal matching result set is traversed;
step S408, all the adjoint sub-paths C are stored in a sub-path set, and the adjoint sub-path C with the longest track and the largest adjoint coefficient is found from the sub-path set, namely the longest adjoint sub-path;
when a plurality of adjoint sub-paths C having the longest trajectory coexist in the sub-path set, the adjoint sub-path C having the largest adjoint coefficient is taken as the longest adjoint sub-path.
As a preferred embodiment, the matching states in step S401 include matching, deviation and uncertainty, and the matching state of each pair of track points (P ', Q') obtains a matching result by comparing the values of "device acquisition range + time difference + normal moving speed" and distance; if the distance is less than the equipment acquisition range plus the time difference plus the normal moving speed, the matching state of the track point pair is considered to be matching; if the distance is greater than the equipment acquisition range plus the time difference plus the normal moving speed, the matching state of the track point pair is considered to be deviated; and if the distance is equal to the equipment acquisition range plus the time difference plus the normal moving speed, the matching state of the track point pair is not determined.
As a preferred embodiment, the matching status in step S403 includes matching, deviation and uncertainty, and the matching status of each pair of track points (P ", Q") obtains a matching result by comparing "device acquisition range + time difference + normal moving speed" with the value of distance; if the distance is less than the equipment acquisition range plus the time difference plus the normal moving speed, the matching state of the track point pair is considered to be matching; if the distance is greater than the equipment acquisition range plus the time difference plus the normal moving speed, the matching state of the track point pair is considered to be deviated; and if the distance is equal to the equipment acquisition range plus the time difference plus the normal moving speed, the matching state of the track point pair is not determined.
As a preferred embodiment, the data values in step S403 include a matching value n1, a deviation value n2, and an uncertainty value n3, and then the accompanying parameters a ═ n2 × 1.0/(n1+ n2), b ═ n1 × 1.0/(n1+ n2+ n3), and the accompanying coefficients ═ 2 (1-1/(1+ exp (-5 × a))) (2/(1+ exp (-8 × b)) -1), where the formula satisfies that the larger the matching value n1, the smaller the accompanying coefficient, the larger the deviation value n2 and the uncertainty value n3, and the larger the accompanying system.
Compared with the prior art, the invention has the following beneficial effects:
the method effectively reduces the calculation complexity of the adjoint sub-paths, improves the calculation efficiency, is less influenced by the deviation value, and can be applied to the actual track data to quickly analyze the adjoint sub-paths of a plurality of pairs of targets.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An analysis method for finding the longest syndrome path of two trajectories, comprising the steps of:
step S1, selecting two tracks A and B and sorting according to time;
step S2, traversing the track points of the tracks A and B, and finding out the track point pair (P ', Q') with the minimum time difference in all the track points;
step S3, finding out all track point pairs (P ', Q') meeting the matching conditions in the tracks A and B by adopting a recursive method to form an optimal matching result set;
step S4, traverse the best matching result set, and find the longest syndrome path meeting the requirement.
2. The analysis method for finding the longest syndrome path of two tracks according to claim 1, wherein the step S1 includes the steps of:
step S101, acquiring mass behavior track data which changes along with time and is acquired by acquisition equipment through a terminal to construct a track database, and randomly selecting two tracks A and B from the track database;
wherein, the terminal in step S101 includes a computer;
and S102, sequencing the two tracks A and B according to time.
3. The analysis method for finding the longest adjoint sub-path of two tracks according to claim 2, wherein the massive behavior tracks obtained in step S101 include one or more of face data, MAC data, IMSI data, and IMEI data.
4. The analysis method for finding the longest syndrome path of two tracks according to claim 3, wherein the step S2 includes the steps of:
step S201, traversing the trace points P in the trace A according to a time sequence, finding out the trace point Q closest to the trace point P in time from the trace B, and calculating and recording the time difference of the trace point pair (P, Q);
step S202, finding out the trace point pair (P, Q) with the minimum time difference from the trace point pair (P, Q) in the step S201, wherein the time difference of the trace point pair (P, Q) meets the trace points P 'and Q' which are not more than the given maximum matching time difference, and forming the trace points P 'and Q' into the trace point pair (P ', Q').
5. The analysis method for finding the longest adjoint sub-path of two tracks according to claim 4, wherein the time difference in step S201 is obtained by calculating a difference between a time corresponding to the track point P and a time corresponding to the track point Q.
6. The analysis method for finding the longest syndrome path of two tracks according to claim 5, wherein the step S3 includes the steps of:
step S301, dividing the track A and the track B into two sections by taking the time of a track point P 'and a track point Q' as separation points respectively, dividing the track A into a track A1 earlier in time than the track point P 'and a track A2 later in time than the track point P', and dividing the track B into a track B1 earlier in time than the track point Q 'and a track B2 later in time than the track point Q';
step S302, repeating the steps S102 to S301 respectively for the track A1 and the track B1 and the track A2 and the track B2 until a matched track point pair (P ', Q') cannot be found out;
in step S303, all the matched pairs of trajectory points (P ', Q') are saved in the best matching result set.
7. The analysis method for finding the longest syndrome path of two tracks according to claim 6, wherein the step S4 includes the steps of:
step S401, sorting the pairs of trajectory points (P ', Q') in the best matching result set in step S303 in time sequence, and calculating the matching state of each pair of trajectory points (P ', Q') according to the distance and time difference between each pair of trajectory points (P ', Q') in the best matching result set;
step S402, selecting the first pair of track point pairs (P ', Q') with matching state of 'matching' in the best matching result set as a starting point, and sequentially superposing the next pair of track point pairs (P ', Q') with matching state of 'matching' to form an adjoint path C;
step S403, calculating the matching state of each track point pair (P ', Q') according to the distance and time difference between each track point pair (P ', Q') of each adjoint sub-path C, and counting the data values of the matching states;
step S404, calculating an accompanying coefficient of each accompanying sub-path C according to the data value of the matching state in the step S403 by using a formula;
step S405, comparing the magnitude between the adjoint coefficient of each adjoint sub-path C and a given minimum adjoint coefficient;
step S406, changing the starting point of the adjoint sub-path C with the adjoint coefficient smaller than the given minimum adjoint coefficient into the next pair of track point pairs (P ', Q') with the matching state of matching as the starting point to form a new adjoint sub-path C;
step S407, repeating the steps S403 to S406 until the whole optimal matching result set is traversed;
step S408, all the adjoint sub-paths C are saved in a sub-path set, and the adjoint sub-path C having the longest track and the largest adjoint coefficient is found from the sub-path set, which is the longest adjoint sub-path.
8. The analysis method for finding the longest adjoint path of two tracks according to claim 7, wherein the matching status in step S401 includes matching, deviation and uncertainty, and the matching status of each track point pair obtains the matching result by comparing "device capture range + time difference + normal moving speed" with the magnitude of distance.
9. The analysis method for finding the longest adjoint sub-path of two tracks according to claim 8, wherein the matching status in step S403 includes matching, deviation and uncertainty, and the matching status of each track point pair obtains the matching result by comparing "device capture range + time difference + normal moving speed" with the magnitude of distance.
10. The analysis method for finding the longest adjoint path of two tracks according to claim 9, wherein the data values in step S403 include a matching value n1, a deviation value n2 and an uncertainty value n3, and the formula in step S404 includes adjoint parameters a ═ n2 × 1.0/(n1+ n2), b ═ n1 ═ 1.0/(n1+ n2+ n3), adjoint coefficients ═ 2 (1-1/(1+ exp (-5 a))) (2/(1+ exp (-8 ×) -1).
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