CN112182055A

CN112182055A - Suspect determination method, apparatus, device and storage medium

Info

Publication number: CN112182055A
Application number: CN201910588586.0A
Authority: CN
Inventors: 林征; 李洋; 李纯懿; 陈勇; 简映光; 徐韬; 张煜
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Guizhou Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Guizhou Co Ltd
Priority date: 2019-07-02
Filing date: 2019-07-02
Publication date: 2021-01-05

Abstract

The embodiment of the application provides a suspect determination method, a suspect determination device, suspect determination equipment and a suspect determination storage medium. The method comprises the following steps: determining persons appearing in a case-sending area within a case-sending time period of a case to be processed as persons to be analyzed according to the track information of a plurality of persons, wherein the track information of the persons comprises position points recorded in the interaction process of user terminals of the persons and a base station and the recording time of the position points; calculating a distance factor and a time factor of each to-be-analyzed person according to the to-be-analyzed track information of each to-be-analyzed person, wherein the to-be-analyzed track information of the to-be-analyzed person is track information of the to-be-analyzed person in a case area in a case time period; and determining the suspect among the persons to be analyzed according to the distance factor and the time factor of each person to be analyzed. The method and the device for determining the suspect improve efficiency and accuracy of determining the suspect and reduce labor cost.

Description

Suspect determination method, apparatus, device and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for determining a suspect.

Background

In modern society, criminal behaviors occur occasionally, and police department usually determines the suspect according to the related video information of the cameras distributed throughout the street when tracking the criminal suspect, namely determines the suspect by adopting a manual investigation mode, so that the suspect determining speed is low, the efficiency is low, the labor cost is high, and the suspect determining process is greatly influenced by human factors due to different experiences of each detector, so that the accuracy of determining the suspect is reduced to a certain extent.

Disclosure of Invention

One or more embodiments of the present disclosure provide a method, an apparatus, a device, and a storage medium for determining a suspect, so as to solve the problems of slow speed, low efficiency, low accuracy, and high labor cost in determining a suspect in the prior art.

To solve the above technical problem, one or more embodiments of the present specification are implemented as follows:

in one aspect, one or more embodiments of the present specification provide a suspect determination method, including:

determining the personnel appearing in a case-sending area within a case-sending time period of a case to be processed as the personnel to be analyzed according to the track information of a plurality of personnel, wherein the track information of the personnel comprises position points recorded in the interaction process of a user terminal of the personnel and a base station and the recording time of the position points;

respectively calculating a distance factor of each to-be-analyzed person according to the distance between each position point in the to-be-analyzed track information of each to-be-analyzed person and the case issuing place of the to-be-processed case, wherein the to-be-analyzed track information of the to-be-analyzed person is track information of the to-be-analyzed person in the case issuing area in the case issuing time period;

respectively calculating the time factor of each person to be analyzed according to the recording time of each position point in the track information to be analyzed of each person to be analyzed;

and determining a suspect in the personnel to be analyzed according to the distance factor and the time factor of each personnel to be analyzed.

Optionally, the determining, according to the trajectory information of the plurality of persons, the person appearing in the case occurrence area within the case occurrence time period of the case to be processed as the person to be analyzed includes:

determining the persons appearing in the case-taking region within the case-taking time period of the case to be processed as candidate persons according to the track information of the plurality of persons;

screening out permanent people from the candidate people according to the historical track information of the candidate people;

determining the candidate who screens out the residents as the person to be analyzed.

Optionally, the calculating the time factor of each person to be analyzed according to the recording time of each position point in the trajectory information to be analyzed of each person to be analyzed includes:

calculating the staying time of each person to be analyzed in the case-sending area within the case-sending time period according to the recording time of each position point in the track information to be analyzed of each person to be analyzed;

respectively calculating the time factor of each person to be analyzed according to the stay time of each person to be analyzed in the case area in the case time period and by combining a time factor calculation formula, wherein the time factor calculation formula is as follows:

wherein, P_{j, time}Time factor for jth said person to be analyzed, t_jThe j-th staying time length, t, of the person to be analyzed in the case region in the case time period_Region(s)The total duration of the time period for the case.

Optionally, the method further includes:

calculating the prior probability of each person to be analyzed;

the determining a suspect among the persons to be analyzed according to the distance factor and the time factor of each person to be analyzed includes:

and determining a suspect in the personnel to be analyzed according to the distance factor, the time factor and the prior probability of each personnel to be analyzed.

Optionally, the calculating the prior probability of each person to be analyzed includes:

determining a first probability of each person to be analyzed according to the suspicion probability of suspects appearing in the historical case-sending area;

determining a second probability of each of the persons to be analyzed in a forepart database, wherein the forepart database comprises the probability of each of the forepart persons committing again;

calculating a third probability of each person to be analyzed according to the personal information of each person to be analyzed;

and determining the probability with the maximum value among the first probability, the second probability and the third probability of each person to be analyzed as the prior probability of each corresponding person to be analyzed.

Optionally, the method further includes:

calculating the abnormal probability of each person to be analyzed according to the historical track information of each person to be analyzed and the case planning period of the case to be processed;

the determining a suspect among the persons to be analyzed according to the distance factor, the time factor, and the prior probability of each person to be analyzed includes:

and determining a suspect in the personnel to be analyzed according to the distance factor, the time factor, the prior probability and the abnormal probability of each personnel to be analyzed.

Optionally, the determining a suspect in the to-be-analyzed person according to the distance factor, the time factor, the prior probability, and the abnormal probability of each to-be-analyzed person includes:

respectively multiplying the prior probability, the distance factor, the time factor and the abnormal probability of each person to be analyzed to obtain the suspicion probability of each person to be analyzed;

sequencing the personnel to be analyzed according to the sequence of the suspicion probability from large to small;

and determining the first N persons to be analyzed as the suspects.

In another aspect, one or more embodiments of the present specification provide a suspect determination apparatus, including:

the system comprises a first determining module, a second determining module and a processing module, wherein the first determining module is used for determining the personnel appearing in a case-sending area in a case-sending time period of a case to be processed as the personnel to be analyzed according to track information of a plurality of personnel, and the track information of the personnel comprises position points recorded in the interaction process of a user terminal of the personnel and a base station and recording time of the position points;

the first calculation module is used for calculating a distance factor of each to-be-analyzed person according to the distance between each position point in the to-be-analyzed track information of each to-be-analyzed person and the case issuing place of the to-be-processed case, wherein the to-be-analyzed track information of the to-be-analyzed person is track information of the to-be-analyzed person in the case issuing area in the case issuing time period;

the second calculation module is used for calculating the time factor of each person to be analyzed according to the recording time of each position point in the track information to be analyzed of each person to be analyzed;

and the second determination module is used for determining a suspect in the personnel to be analyzed according to the distance factor and the time factor of each personnel to be analyzed.

Optionally, the first determining module includes:

the first determining unit is used for determining the persons appearing in the case-starting area in the case-starting time period of the case to be processed as candidate persons according to the track information of a plurality of persons;

the screening unit is used for screening out the normally living people in the candidate people according to the historical track information of the candidate people;

a second determination unit configured to determine the candidate person who screens out the standing person as the person to be analyzed.

Optionally, the second computing module includes:

the first calculation unit is used for calculating the stay time of each to-be-analyzed person in the case-sending time period in the case-sending area according to the recording time of each position point in the to-be-analyzed track information of each to-be-analyzed person;

a second calculating unit, configured to calculate a time factor of each to-be-analyzed person according to a retention time of each to-be-analyzed person in the scenario time period in the scenario area and by combining a time factor calculation formula, where the time factor calculation formula is:

wherein, P_{j, time}Is as followsTime factors of j of the persons to be analyzed, t_jThe j-th staying time length, t, of the person to be analyzed in the case region in the case time period_Region(s)The total duration of the time period for the case.

Optionally, the apparatus further comprises:

the third calculation module is used for calculating the prior probability of each person to be analyzed;

the second determining module is specifically configured to determine a suspect among the persons to be analyzed according to the distance factor, the time factor, and the prior probability of each person to be analyzed.

Optionally, the third computing module includes:

the third determining unit is used for determining the first probability of each person to be analyzed according to the suspicion probability of suspicions appearing in the historical case area;

a fourth determining unit, configured to determine a second probability of each of the persons to be analyzed in a predecessor database, where the predecessor database includes a probability that each of the predecessors makes a crime again;

a third calculating unit, configured to calculate a third probability of each of the persons to be analyzed according to the personal information of each of the persons to be analyzed;

a fifth determining unit, configured to determine, as the prior probability of each to-be-analyzed person, a probability with a largest value among the first probability, the second probability, and the third probability of each to-be-analyzed person.

Optionally, the apparatus further comprises:

the fourth calculation module is used for calculating the abnormal probability of each person to be analyzed according to the historical track information of each person to be analyzed and the case working period of the case to be processed;

the second determining module is specifically configured to determine a suspect among the persons to be analyzed according to the distance factor, the time factor, the prior probability, and the abnormal probability of each person to be analyzed.

Optionally, the second determining module includes:

the fourth calculation unit is used for respectively multiplying the prior probability, the distance factor, the time factor and the abnormal probability of each person to be analyzed to obtain the suspicion probability of each person to be analyzed;

the sequencing unit is used for sequencing the personnel to be analyzed according to the sequence of the suspicion probability from large to small;

and the sixth determining unit is used for determining the first N persons to be analyzed as the suspect.

In yet another aspect, one or more embodiments of the present specification provide a suspect determination apparatus, including:

a processor; and

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

In yet another aspect, one or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed, implement the following:

By adopting the technical scheme of one or more embodiments of the specification, the to-be-analyzed person is determined according to the track information of a plurality of persons, the case issue time period and the case issue area of the to-be-processed case, then the distance factor and the time factor of each to-be-analyzed person are calculated according to the to-be-analyzed track information of each to-be-analyzed person, and finally the suspect is determined in the to-be-analyzed person according to the distance factor and the time factor of each to-be-analyzed person, wherein the track information of the persons comprises the position point recorded in the interaction process of the user terminal of the person and the base station and the recording time of the position point, and the to-be-analyzed track information of the to-be-analyzed person is the track information of the to-be-analyzed person in the case issue. On one hand, the method for automatically determining the suspect is provided, the steps are simple and easy to execute, the speed and the efficiency for determining the suspect are greatly improved, meanwhile, the labor cost is greatly reduced, and the accuracy for determining the suspect is improved due to the fact that the influence of human factors is avoided; on the other hand, the base station can acquire the position point and the interaction time (namely the recording time of the position point) of the user terminal interacting with the base station in real time, so that the accuracy and the effectiveness of the acquired position point and interaction time of the user terminal are effectively guaranteed, the accuracy and the effectiveness of the track information of the personnel are guaranteed, and the accuracy of determining the suspect is further improved.

Drawings

In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, 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 described in one or more embodiments of the present specification, and other drawings can be obtained by those skilled in the art without inventive exercise.

Fig. 1 is a schematic flow chart of a suspect determination method according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram of a trajectory to be analyzed of a person to be analyzed in a planning area according to an embodiment of the present application;

fig. 3 is a schematic diagram of a trajectory to be analyzed of a person to be analyzed according to an embodiment of the present application;

FIG. 4 is a schematic flow chart illustrating a calculation of a prior probability of each person to be analyzed according to an embodiment of the present disclosure;

FIG. 5 is a schematic flow chart illustrating a process for determining a person to be analyzed according to an embodiment of the present disclosure;

fig. 6 is a schematic diagram illustrating a suspect determination apparatus according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of suspect determination equipment provided in an embodiment of the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments of the present disclosure without making any creative effort shall fall within the protection scope of one or more of the embodiments of the present disclosure.

Currently, in the power-on state, the ue may send an interactive request for position location to surrounding base stations at a frequency of milliseconds. The base station responds to the interactive request of the position location sent by the user terminal, locates the position point of the user terminal (namely the position of the user terminal) and the interactive time between the user terminal and the base station, records the position point, and determines the interactive time as the recording time of the position point, namely, the position point recorded by the user terminal and the base station in the interactive process and the recording time of the position point can be obtained. And summarizing the position points recorded by the user terminal and the base station in each interaction process and the recording time of the position points to obtain the track information of the personnel using the user terminal.

Next, a process of determining a location point of the user terminal and an interaction time during an interaction between the user terminal and the base station will be described.

When the ue interacts with the base station, the following two parameters TOA (Time of Arrival) and AOA (Angle of Arrival) can be obtained, and the location of the ue relative to the base station and the interaction Time between the ue and the base station can be calculated according to the two parameters, and then the location point of the ue can be calculated according to the location of the ue relative to the base station and the location of the base station itself.

Therefore, the trajectory information of the person corresponding to each user terminal can be acquired through the method, and the acquired trajectory information of each person is stored in the database. It should be noted that the user terminal may be a mobile phone, a tablet computer, and the like, and this is not particularly limited in this exemplary embodiment.

Based on the above conditions, an embodiment of the present application provides a method for determining a suspect, and fig. 1 is a schematic flow chart of the method for determining a suspect provided in the embodiment of the present application, where an execution subject of the method for determining a suspect may be, for example, a terminal device or a server, where the terminal device may include, for example, a personal computer, and the server may be, for example, an independent server or a server cluster composed of multiple servers, and this is not particularly limited in this exemplary embodiment. As shown in fig. 1, the method may include the steps of:

step S110, determining the personnel appearing in the case-sending area within the case-sending time period of the case to be processed as the personnel to be analyzed according to the track information of the plurality of personnel, wherein the track information of the personnel comprises position points recorded in the interaction process of the user terminal of the personnel and the base station and the recording time of the position points.

In the embodiment of the present application, the cases to be processed may be theft cases, robbery cases, corner-selling cases, and the like, and this exemplary embodiment is not particularly limited to this. The time period of the incident of the case to be treated can be determined by the investigator. The case region of the case to be processed may be determined according to the case place of the case to be processed, for example, the case region may be a range with a radius of 1000 meters and centered on the case place of the case to be processed, or may be a rectangular region with a preset area and centered on the case place of the case to be processed, where the preset area may be determined according to an investigation range, and this is not particularly limited in the embodiment of the present application.

The process of determining whether a person is a person to be analyzed may include: judging whether each position point recorded in the trajectory information of the person is in a case area, if not, determining that the person is not the person to be analyzed, if so, determining that the position point in the case area in the trajectory information of the person is a candidate position point, then, judging whether the recording time of each candidate position point is in a case time period, if not, determining that the person is not the person to be analyzed, and if so, determining that the person is the person to be analyzed. Repeating the above process can judge whether other persons are to-be-analyzed persons.

It should be noted that the above process for determining the person to be analyzed is only exemplary and is not intended to limit the present invention.

The trajectory information of a plurality of persons can be directly obtained from the database, and since the trajectory information of the persons stored in the database includes all the collected position points of the persons and the recording time of each position point, in order to reduce the calculation workload and improve the calculation efficiency, the trajectory information of each person here can be the trajectory information of each person within a preset time period, wherein the preset time period includes the case issuing time period of the case to be processed.

The plurality of persons may be all persons already stored in the database, and in order to reduce the calculation workload and improve the calculation efficiency, the plurality of persons may be persons collected by a base station capable of interacting with a user terminal in a case area of a case to be processed.

Step S120, respectively calculating a distance factor of each to-be-analyzed person according to the distance between each position point in the to-be-analyzed track information of each to-be-analyzed person and the case issuing place of the to-be-processed case, wherein the to-be-analyzed track information of the to-be-analyzed person is track information of the to-be-analyzed person in the case issuing area in the case issuing time period.

In the embodiment of the present application, the process of obtaining the trajectory information to be analyzed of one person to be analyzed is as follows: matching each position point recorded in the track information of the person to be analyzed with a case area, determining the position point in the case area as a candidate position point, matching the recording time of each candidate position point with a case time period, determining the candidate position point with the recording time in the case time period as a target position point, and collecting the target position point and the recording time of the target position point to obtain the track information to be analyzed of the person to be analyzed.

Since the principle of obtaining the to-be-analyzed trajectory information of each to-be-analyzed person is the same, the principle of obtaining the to-be-analyzed trajectory information of each other to-be-analyzed person is not repeated here. It should be noted that the above process of acquiring the trajectory information to be analyzed of the person to be analyzed is only exemplary and is not intended to limit the present invention. Fig. 2 is a schematic diagram of a trajectory to be analyzed of a person to be analyzed in a planning area according to an embodiment of the present application. As can be seen from fig. 2, the hair style region is a rectangular region, and the hair style region includes the trajectories to be analyzed of 5 persons to be analyzed.

Next, a process of calculating a distance from each position point in the trajectory information to be analyzed of each person to be analyzed to the case occurrence place of the case to be processed will be described.

As shown in FIG. 3, if the coordinates of the location of the case are (x)₀，y₀) The number of the position points in the to-be-analyzed trajectory information of the jth to-be-analyzed person is n_jAnd the distance from the ith position point in the to-be-analyzed track information of the jth to-be-analyzed person to the pattern ground is as follows:

wherein l_j，iThe distance from the ith position point in the to-be-analyzed track information of the jth to-be-analyzed person to the hair-position is, and the coordinate of the ith position point in the to-be-analyzed track information of the jth to-be-analyzed person is (x)_j，i，y_j，i)，1≤i≤n_jAnd i is an integer.

Based on this, the process of calculating the distance factor of each person to be analyzed according to the distance from each position point in the trajectory information to be analyzed of each person to be analyzed to the case location of the case to be processed may include:

firstly, calculating the minimum distance between a position point recorded in the to-be-analyzed track information of each to-be-analyzed person and a pattern place, wherein a specific calculation formula is as follows:

wherein,

the minimum distance between the position point recorded in the to-be-analyzed track information of the jth person to be analyzed and the pattern ground.

Then, summing the distances between the position points recorded in the to-be-analyzed trajectory information of each to-be-analyzed person and the case place respectively to obtain the trajectory sum of each to-be-analyzed person, wherein a specific calculation formula is as follows:

wherein l_jIs the sum of trajectories of the jth person to be analyzed, l_j，iThe distance between the ith position point in the to-be-analyzed track information of the jth to-be-analyzed person and the hair-position, n_jThe number of the position points in the information of the trajectory to be analyzed of the jth person to be analyzed.

Then, summing the track sums of all the persons to be analyzed to obtain the track sum of all the persons to be analyzed, wherein a specific calculation formula is as follows:

wherein l_{General assembly}For all persons to be analyzedSum of trajectories of the members, l_jJ is more than or equal to 1 and less than or equal to m, m is an integer and is the number of the personnel to be analyzed.

Next, calculating a standard deviation of the trajectory of each person to be analyzed, wherein a specific calculation formula is as follows:

wherein: s_jIs the standard deviation of the trajectory of the jth person to be analyzed, n_jThe number of position points in the trajectory information to be analyzed, l, for the jth person to be analyzed_jIs the sum of the trajectories of the jth person to be analyzed.

Next, calculating the standard deviation of the trajectories of all the persons to be analyzed, wherein the specific calculation formula is as follows:

wherein s is_{General assembly}Standard deviation of trajectories for all persons to be analyzed, n_jThe number of position points in the trajectory information to be analyzed of the jth person to be analyzed, m is the number of persons to be analyzed, l_{General assembly}J is more than or equal to 1 and less than or equal to m, and m is an integer.

And finally, calculating the distance factor of each person to be analyzed, wherein a specific calculation formula is as follows:

wherein, P_{j, distance}Distance factor, s, for the jth person to be analyzed_{General assembly}Standard deviation of trajectories for all persons to be analyzed, s_jAs the standard deviation of the trajectory of the jth person to be analyzed,

for the jth person to be analyzedAnd analyzing the minimum distance between the position points recorded in the track information and the hair-cutting place.

Step S130, respectively calculating a time factor of each person to be analyzed according to the recording time of each position point in the track information to be analyzed of each person to be analyzed.

In this embodiment of the application, the staying time of each to-be-analyzed person in the planning area in the planning time period may be calculated according to the recording time of each position point in the to-be-analyzed trajectory information of each to-be-analyzed person. Specifically, the process of calculating the stay time of a person to be analyzed in the case area in the case time period includes: sequencing the recording time of each position point in the to-be-analyzed track information of the to-be-analyzed personnel according to the sequence of time from first to last; and determining the difference between the first recorded time and the last recorded time as the stay time of the person to be analyzed in the case area in the case time period. Since the calculation principle of the stay time of each to-be-analyzed person in the planning area in the planning time period is the same, the calculation of the stay time of each other to-be-analyzed person in the planning area in the planning time period is not described here. It should be noted that the above-mentioned manner of calculating the stay time of the person to be analyzed in the solution area in the solution time period is only exemplary and is not used to limit the present invention.

Then, respectively calculating the time factor of each person to be analyzed according to the stay time of each person to be analyzed in the case area in the case time period and by combining a time factor calculation formula, wherein the time factor calculation formula is as follows:

wherein, P_{j, time}Time factor for jth person to be analyzed, t_jThe dwell time of the jth person to be analyzed in the case region in the case time period, t_Region(s)The total length of the arming period.

Step S140, determining a suspect among the persons to be analyzed according to the distance factor and the time factor of each person to be analyzed.

In the embodiment of the application, the suspicion probability of each to-be-analyzed person can be calculated according to the distance factor and the time factor of each to-be-analyzed person, and then the suspicion person is determined in the to-be-analyzed person according to the suspicion probability of each to-be-analyzed person. The suspected probability may be calculated as:

P_j＝P_{j, distance}*P_{j, time}

Wherein, P_jThe suspicion probability, P, of the jth person to be analyzed_{j, time}Time factor, P, for the jth person to be analyzed_{j, distance}Is the distance factor of the jth person to be analyzed.

It should be noted that the above-mentioned manner of calculating the suspicion probability is merely exemplary, and is not intended to limit the present invention, for example, the time factor and the distance factor of each person to be analyzed may be weighted and summed, and the calculation result may be determined as the suspicion probability of each corresponding person to be analyzed.

The method for determining the suspect among the persons to be analyzed according to the suspect probability of each person to be analyzed may include:

in the first mode, the suspicion probability of each to-be-analyzed person is respectively compared with the preset probability, and the to-be-analyzed person with the suspicion probability greater than the preset probability is determined as the suspect, wherein the size of the preset probability can be set according to requirements, and the exemplary embodiment is not specially limited to this.

And in a second mode, the to-be-analyzed persons are ranked according to the sequence of the suspicion probability from large to small, the first N to-be-analyzed persons are determined as suspicions, wherein the value of N can be set according to requirements, and this is not particularly limited in this exemplary embodiment.

In summary, the method for automatically determining the suspect is provided, the steps are simple and easy to execute, the speed and efficiency of determining the suspect are greatly improved, the labor cost is greatly reduced, and the accuracy of determining the suspect is improved due to the fact that the influence of human factors is avoided; in addition, the base station can acquire the position point and the interaction time (namely the recording time of the position point) of the user terminal interacting with the base station in real time, so that the accuracy and the effectiveness of the acquired position point and the interaction time of the user terminal are effectively guaranteed, the accuracy and the effectiveness of the track information of the personnel are guaranteed, and the accuracy rate of determining the suspect is further improved.

In order to further improve the accuracy of determining a suspect, a priori probability of each person to be analyzed may be calculated, and on this basis, the determining a suspect among the persons to be analyzed according to the distance factor and the time factor of each person to be analyzed may include: and determining a suspect in the personnel to be analyzed according to the distance factor, the time factor and the prior probability of each personnel to be analyzed.

In this embodiment of the present application, a process of calculating a prior probability of each person to be analyzed is described first, and fig. 4 is a schematic flow chart of calculating a prior probability of each person to be analyzed provided in this embodiment of the present application, and as described in fig. 4, the method may include the following steps:

and S410, determining a first probability of each person to be analyzed according to the suspicion probability of the suspicion person appearing in the historical case area.

In an embodiment of the present application, the process of determining the first probability of the person to be analyzed may include: comparing the identification information (e.g., an identity number) of the person to be analyzed with the identification information (e.g., an identity number) of each suspect appearing in the history case issue area, determining the suspicion probability of the suspect identical to the identification information of the person to be analyzed as the first probability of the person to be analyzed if the suspect identical to the identification information of the person to be analyzed exists in the suspicions appearing in the history case issue area, and if the suspect identical to the identification information of the person to be analyzed does not exist in the suspicions appearing in the history case issue area, determining the first probability of the person to be analyzed as zero. And repeating the process to obtain the first probability of each other person to be analyzed.

And step S420, determining a second probability of each person to be analyzed in a forepart database, wherein the forepart database comprises the probability of each forepart committing a case again.

In the embodiment of the present application, since there is a predecessor database for predecessors, and the predecessor database stores the probability of each predecessor committing a case again, the specific probability of a predecessor committing a case again can be determined according to the current situation of a predecessor (e.g., whether there is fixed income, fixed residence, psychological state, etc.).

The process of determining a second probability of a person to be analyzed may comprise: comparing the identification information (such as an identification card number) of the person to be analyzed with the identification information (such as an identification card number) of each president in the president database, if the president database has the president with the identification information of the person to be analyzed, determining the probability that the president makes a case again as the second probability of the person to be analyzed, and if the president database does not have the president with the identification information of the person to be analyzed, determining the second probability of the person to be analyzed as zero. And repeating the process to obtain the second probability of each other person to be analyzed.

And step S430, calculating a third probability of each person to be analyzed according to the personal information of each person to be analyzed.

In the embodiment of the present application, the personal information of each person to be analyzed may include native place, age, whether there is a fixed address, whether there is a stable work, academic information, marital status, and the like. Firstly, a probability calculation model is constructed according to a deep learning network, and the construction process of the specific probability calculation model is as follows: the method comprises the steps of obtaining personal information of a plurality of persons, marking the personal information of each person corresponding to a prisoner probability of each person, inputting the personal information of each person marking the prisoner probability into a deep learning network for training, and determining the deep learning network after training as a probability calculation model. Based on this, the process of calculating the probability of a crime for a person to be analyzed may include: inputting the personal information of the person to be analyzed into a probability calculation model to output a crime probability of the person to be analyzed, and determining the crime probability of the person to be analyzed as a third probability of the person to be analyzed. And repeating the process to obtain the third probability of each other person to be analyzed.

It should be noted that the above-mentioned manner for calculating the third probability of the person to be analyzed is only exemplary, and is not intended to limit the present invention, that is, the third probability of the person to be analyzed may also be calculated by other manners.

Step S440, determining a probability with the largest value among the first probability, the second probability and the third probability of each person to be analyzed as a prior probability of each corresponding person to be analyzed.

In an embodiment of the present application, the process of determining the prior probability of a person to be analyzed according to the first probability, the second probability, and the third probability of the person to be analyzed may include: and sequencing the first probability, the second probability and the third probability of the personnel to be analyzed according to a descending order, and determining the probability arranged at the first position as the prior probability of the personnel to be analyzed, namely determining the probability with the maximum value among the first probability, the second probability and the third probability as the prior probability of the personnel to be analyzed. Repeating the above process to obtain the prior probability of other persons to be analyzed.

For example, if the first probability of the person a to be analyzed is 0.8, the second probability is 0, and the third probability is 0.2, the first probability is determined as the prior probability of the person a to be analyzed, that is, the prior probability of the person a to be analyzed is 0.8.

According to the distance factor, the time factor and the prior probability of each person to be analyzed, the process of determining the suspect among the persons to be analyzed may include:

firstly, respectively calculating the suspicion probability of each person to be analyzed according to the distance factor, the time factor and the prior probability of each person to be analyzed, wherein the specific calculation formula of the suspicion probability is as follows:

P_j＝P_{j, distance}*P_{j, time}*P_{j, prior}

Wherein, P_jThe suspicion probability, P, of the jth person to be analyzed_{j, time}Time factor, P, for the jth person to be analyzed_{j, distance}Distance factor, P, for the jth person to be analyzed_{j, prior}Is the prior probability of the jth person to be analyzed.

Then, the manner of determining the suspect among the persons to be analyzed according to the suspect probability of each person to be analyzed may include the following two:

first, the suspicion probability of each to-be-analyzed person is compared with a preset probability, and the to-be-analyzed person whose suspicion probability is greater than the preset probability is determined as the suspect, where a numerical value of the preset probability may be set as required, which is not specially limited in the present exemplary embodiment.

Secondly, according to the sequence of the suspicion probability from large to small, the staff to be analyzed are sorted, and the first N staff to be analyzed are determined as suspicions, wherein the value of N may be set as required, which is not particularly limited in the present exemplary embodiment.

Therefore, when the suspect is determined among the persons to be analyzed, the prior probability of the persons to be analyzed is increased, the dimensionality of data is increased, and the accuracy of determining the suspect is further improved.

In order to further improve the accuracy of determining the suspect, the abnormal probability of each person to be analyzed can be calculated according to the historical track information of each person to be analyzed and the case working period of the case to be processed. Based on this, the determining a suspect among the persons to be analyzed according to the distance factor, the time factor, and the prior probability of each of the persons to be analyzed may include: and determining a suspect in the personnel to be analyzed according to the distance factor, the time factor, the prior probability and the abnormal probability of each personnel to be analyzed.

In the embodiment of the present application, the process of calculating the abnormality probability of each person to be analyzed may include:

the method comprises the steps of firstly, obtaining historical track information of each to-be-analyzed person, wherein the historical track information of the to-be-analyzed person is track information of the to-be-analyzed person N days before a case sending time period, wherein the value of N can be determined according to a case working period of a to-be-processed case, and the case working period of the to-be-processed case can be obtained by counting a large amount of case data of the same type as the to-be-processed case by the to-be-analyzed person. For example, if the type of the case to be analyzed is a theft case and the case-stealing operation period is once a day, the value of N may be set to 5, and if the type of the case to be analyzed is population sales, the case-selling operation period is once every three days, the value of N may be set to 15.

Then, the similarity between the historical track information of each person to be analyzed and the planning cycle of the case to be processed is calculated respectively.

The following description will be given by way of example, if the case to be processed has a case operation period of once a day, the historical track information of the person to be analyzed is divided according to the day to obtain track information of each day, whether the place where the person to be analyzed goes each day has a case is judged according to the track information of each day and by combining a case database, the number of days in which the case occurs is counted, and the ratio of the number of days in which the case occurs to the total number of days corresponding to the historical track information is determined as the similarity between the historical track information of the person to be analyzed and the case operation period of the case to be processed. The case database comprises information of all cases, and the information of each case comprises case time, case place and the like.

And finally, determining the similarity between the historical track information of each person to be analyzed and the planning period of the case to be processed as the abnormal probability of each person to be analyzed.

It should be noted that the above calculation method of the abnormal probability of the person to be analyzed is only exemplary, and is not intended to limit the present invention.

According to the distance factor, the time factor, the prior probability and the abnormal probability of each person to be analyzed, the process of determining the suspect in the persons to be analyzed may include:

firstly, respectively multiplying the prior probability, the distance factor, the time factor and the abnormal probability of each person to be analyzed to obtain the suspicion probability of each person to be analyzed, wherein a specific calculation formula is as follows:

P_j＝P_{j, distance}*P_{j, time}*P_{j, prior}*P_{j, abnormality}

Wherein, P_jThe suspicion probability, P, of the jth person to be analyzed_{j, time}Time factor, P, for the jth person to be analyzed_{j, distance}Distance factor, P, for the jth person to be analyzed_{j, prior}Is the prior probability, P, of the jth person to be analyzed_{j, abnormality}The abnormality probability of the jth person to be analyzed.

And then, sequencing the to-be-analyzed persons according to the sequence of the suspicion probability from large to small, and finally determining the first N to-be-analyzed persons as suspicions. The value of N may be set as required, and this is not particularly limited in this exemplary embodiment.

It should be noted that, after the suspicion probability of each to-be-analyzed person is calculated, the suspicion probability of each to-be-analyzed person may be compared with a preset probability, and the to-be-analyzed person whose suspicion probability is greater than the preset probability may be determined as the suspect, where a numerical value of the preset probability may be determined according to a requirement, which is not particularly limited in the present exemplary embodiment.

Therefore, when the suspect is determined among the to-be-analyzed persons, the abnormal probability of the to-be-analyzed persons is increased, the dimensionality of data is increased, and the accuracy of determining the suspect is further improved.

In order to further improve the efficiency of determining the suspect, as shown in fig. 5, the determining, as the person to be analyzed, the person appearing in the case occurrence area within the case occurrence time period of the case to be processed according to the trajectory information of the plurality of persons may include:

step S510, determining the persons appearing in the case-taking area in the case-taking time period of the case to be processed as candidate persons according to the track information of the plurality of persons. In the embodiment of the present application, since the implementation principle of step S510 is the same as that of step S110, it is not described herein again.

And S520, screening out the standing persons from the candidate persons according to the historical track information of the candidate persons.

In this embodiment of the application, the historical trajectory information of the candidate may be the trajectory information of the candidate in the last 15 days, or may also be the trajectory information of the candidate in the last 30 days, and the like, which is not particularly limited in this exemplary embodiment.

The permanent population in the hair care area includes persons living in the hair care area, persons working in the hair care area (for example, persons working in property, persons working in business in the hair care area, and the like), and the like. Because the people who live in the case area and the people who work at night in the case area are in the case area most of the night, whether the candidate people are in the case area in the night time period can be judged according to the historical track information of the candidate people, and whether the candidate people are the permanent population can be judged. Next, a process of determining whether the candidate is a standing person will be described.

Selecting a night time period (for example, from 0 o 'clock to 4 o' clock in the morning), where the historical trajectory information of the candidate is the trajectory information of the candidate in the last N days, and under this condition, the process of determining whether the candidate is a standing population may include:

and dividing the historical track information of the candidate personnel by taking days as units to obtain N pieces of candidate track information. And calculating the number of days of the candidate in the distribution area in the night time period according to the N candidate track information. And calculating the ratio of the number of days of the candidate in the case area to N in the night time period, judging whether the ratio is greater than a preset value, and if the ratio is greater than the preset value, determining the candidate as the constant population. The preset value may be 0.8, 0.9, or the like, which is not particularly limited in this exemplary embodiment.

The process of calculating the number of days in the distribution area of the candidate person in the night time period according to the N candidate trajectory information may include: according to the recording time of the position point in each candidate track information, the position point of the recording time in the night time period in each candidate track information is determined as the corresponding target position point in each candidate track information, whether the target position point in each candidate track information is in the case area or not is judged, the candidate track information of the target position point in the case area is determined as the target track information, the number of the target track information is counted, and the number of the target track information is determined as the number of days of the candidate people in the case area in the night time period.

Since the principle of determining whether each candidate is a standing person is the same, the principle of determining whether another candidate is a standing person will not be described here.

Since the people who go to work in the case area are in the case area in most of the daytime, whether the candidate is in the case area in the preset time period of the daytime can be judged according to the historical track information of the candidate to judge whether the candidate is a permanent population. Next, a process of determining whether the candidate is a standing person will be described.

Selecting a preset time period in the daytime (for example, from 9 am to 6 pm), where the historical trajectory information of the candidate is the trajectory information of the candidate in the last N days, and the process of determining whether the candidate is a standing population may include:

and dividing the historical track information of the candidate personnel by taking days as units to obtain N pieces of candidate track information. And calculating the number of days of the candidate in the development area in a preset time period according to the N candidate track information. And calculating the ratio of the number of days of the candidate in the case area to N within a preset time period, judging whether the ratio is greater than a preset value, and if so, determining the candidate as the constant population. The preset value may be 0.8, 0.9, or the like, which is not particularly limited in this exemplary embodiment.

The process of calculating the number of days of the candidate in the encounter area within the preset time period according to the N candidate trajectory information may include: according to the recording time of the position point in each candidate track information, the position point of the recording time in each candidate track information within the preset time period is determined as the corresponding target position point in each candidate track information, whether the target position point in each candidate track information is in the case area or not is judged, the candidate track information of the target position point in the case area is determined as the target track information, the number of the target track information is counted, and the number of the target track information is determined as the number of days of the candidate personnel within the case area within the preset time period.

It should be noted that, since the principle of determining whether each candidate is a standing person is the same, the principle of determining whether other candidates are standing persons will not be described here.

Through the method, the standing persons in the candidate persons can be determined, and the standing persons can be screened out from the candidate persons.

And step S530, determining the candidate people with the standing people screened out as the people to be analyzed. In the embodiment of the present application, the candidate who screens out the permanent person is determined as the person to be analyzed, and since the principle of determining the suspect among the persons to be analyzed has been described above, the details are not described here.

In conclusion, due to the fact that the persons who live normally are screened, the number of the persons to be analyzed is reduced, and therefore the efficiency of determining the suspect is improved.

It should be noted that if the suspect is not determined among the persons to be analyzed determined in step S530, the suspect may be determined among the persons who live identified in step S520, and the principle of determining the suspect among the persons who live is the same as the principle of determining the suspect among the persons to be analyzed, and therefore, the details are not described here.

Corresponding to the above suspect determination method, based on the same technical concept, an embodiment of the present application further provides a suspect determination apparatus, fig. 6 is a schematic composition diagram of the suspect determination apparatus provided in the embodiment of the present application, the apparatus is used for executing the above suspect determination method, and as shown in fig. 6, the apparatus 600 may include: a first determination module 601, a first calculation module 602, a second calculation module 603, and a second determination module 604, wherein:

a first determining module 601, configured to determine, according to trajectory information of multiple persons, the person appearing in a case occurrence area within a case occurrence time period of a case to be processed as a person to be analyzed, where the trajectory information of the person includes a position point recorded in an interaction process between a user terminal of the person and a base station and recording time of the position point;

a first calculating module 602, configured to calculate a distance factor of each to-be-analyzed person according to a distance between each position point in to-be-analyzed trajectory information of each to-be-analyzed person and a scenario issuing place of the to-be-processed scenario, where the to-be-analyzed trajectory information of the to-be-analyzed person is trajectory information of the to-be-analyzed person in the scenario issuing area within the scenario issuing time period;

a second calculating module 603, configured to calculate a time factor of each to-be-analyzed person according to the recording time of each position point in the to-be-analyzed trajectory information of each to-be-analyzed person;

a second determining module 604, configured to determine a suspect among the persons to be analyzed according to the distance factor and the time factor of each person to be analyzed.

Optionally, the first determining module may include:

Optionally, the second calculating module 603 may include:

Optionally, the apparatus 600 may further include:

Optionally, the third computing module may include:

Optionally, the apparatus 600 may further include:

Optionally, the second determining module 604 may include:

The suspect determination device in the embodiment of the application determines the to-be-analyzed person only according to the track information of a plurality of persons, then calculates the distance factor and the time factor of the to-be-analyzed person according to the to-be-analyzed track information of the to-be-analyzed person, and determines the suspect according to the distance factor and the time factor of the to-be-analyzed person, thereby providing a way for automatically determining the suspect, and the steps are simple and easy to execute, greatly improving the speed and efficiency for determining the suspect, greatly reducing the labor cost, and improving the accuracy for determining the suspect due to the avoidance of the influence of human factors; here, because the base station can acquire the position point and the interaction time (namely the recording time of the position point) of the user terminal interacting with the base station in real time, the accuracy and the effectiveness of the acquired position point and the interaction time of the user terminal are effectively ensured, so that the accuracy and the effectiveness of the track information of the personnel are ensured, and the accuracy rate of determining the suspect is further improved.

Based on the same technical concept, the embodiment of the present application further provides a suspect determination device, and fig. 7 is a schematic structural diagram of the suspect determination device provided in the embodiment of the present application, where the suspect determination device is used to execute the suspect determination method.

As shown in fig. 7, the suspect determination device may have a relatively large difference due to different configurations or performances, and may include one or more processors 701 and a memory 702, where one or more stored applications or data may be stored in the memory 702. Memory 702 may be, among other things, transient storage or persistent storage. The application stored in memory 702 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a device for suspect determination. Still further, processor 701 may be configured to communicate with memory 702 to execute a series of computer-executable instructions in memory 702 on the suspect determination device. The suspect determination apparatus may also include one or more power supplies 703, one or more wired or wireless network interfaces 704, one or more input-output interfaces 705, one or more keyboards 706, and the like.

In one particular embodiment, the suspect determination apparatus comprises a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may comprise one or more modules, and each module may comprise a series of computer-executable instructions for the suspect determination apparatus, and execution of the one or more programs by one or more processors comprises computer-executable instructions for:

Optionally, when executed, the determining, according to trajectory information of a plurality of persons, the person who appears in the encounter area within the encounter time period of the case to be processed as the person to be analyzed includes:

Optionally, when executed, the calculating, according to the recording time of each position point in the trajectory information to be analyzed of each person to be analyzed, the time factor of each person to be analyzed includes:

Optionally, the computer executable instructions, when executed, further comprise:

calculating the prior probability of each person to be analyzed;

Optionally, when executed, the calculating the prior probability of each person to be analyzed includes:

Optionally, when executed, the determining a suspect among the persons to be analyzed according to the distance factor, the time factor, the prior probability, and the abnormal probability of each person to be analyzed includes:

and determining the first N persons to be analyzed as the suspects.

According to the suspect determination device in the embodiment of the application, the suspect to be determined is determined only according to the track information of a plurality of persons, then the distance factor and the time factor of the person to be analyzed are calculated according to the track information to be analyzed of the person to be analyzed, and the suspect can be determined according to the distance factor and the time factor of the person to be analyzed, so that a method for automatically determining the suspect is provided, the steps are simple and easy to execute, the speed and the efficiency for determining the suspect are greatly improved, the labor cost is greatly reduced, and the accuracy for determining the suspect is improved due to the fact that the influence of human factors is avoided; here, because the base station can acquire the position point and the interaction time (namely the recording time of the position point) of the user terminal interacting with the base station in real time, the accuracy and the effectiveness of the acquired position point and the interaction time of the user terminal are effectively ensured, so that the accuracy and the effectiveness of the track information of the personnel are ensured, and the accuracy rate of determining the suspect is further improved.

Based on the same technical concept, embodiments of the present application further provide a storage medium for storing computer-executable instructions, where in a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, and the like, and when the computer-executable instructions stored in the storage medium are executed by a processor, the following processes may be implemented:

Optionally, when executed by the processor, the determining, as the person to be analyzed, the person appearing in the encounter area within the encounter time period of the case to be processed according to the trajectory information of the plurality of persons includes:

Optionally, when executed by a processor, the calculating the time factor of each person to be analyzed according to the recording time of each position point in the trajectory information to be analyzed of each person to be analyzed includes:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, further include:

calculating the prior probability of each person to be analyzed;

Optionally, the computer executable instructions stored in the storage medium, when executed by the processor, calculate the prior probability of each of the persons to be analyzed includes:

Optionally, when executed by a processor, the determining a suspect among the persons to be analyzed according to the distance factor, the time factor, the prior probability, and the anomaly probability of each of the persons to be analyzed includes:

and determining the first N persons to be analyzed as the suspects.

When the computer executable instruction stored in the storage medium in the embodiment of the application is executed by the processor, the person to be analyzed is determined only according to the track information of a plurality of persons, then the distance factor and the time factor of the person to be analyzed are calculated according to the track information to be analyzed of the person to be analyzed, and the suspect can be determined according to the distance factor and the time factor of the person to be analyzed, so that a method for automatically determining the suspect is provided, the steps are simple and easy to execute, the speed and the efficiency for determining the suspect are greatly improved, the labor cost is greatly reduced, and the accuracy for determining the suspect is improved due to the fact that the influence of human factors is avoided; here, because the base station can acquire the position point and the interaction time (namely the recording time of the position point) of the user terminal interacting with the base station in real time, the accuracy and the effectiveness of the acquired position point and the interaction time of the user terminal are effectively ensured, so that the accuracy and the effectiveness of the track information of the personnel are ensured, and the accuracy rate of determining the suspect is further improved.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage media, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage media.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for determining a suspect, comprising:

2. The suspect determination method according to claim 1, wherein the determining the persons appearing in the case occurrence area within the case occurrence time period of the case to be processed as the persons to be analyzed according to the trajectory information of the plurality of persons comprises:

3. The suspect determination method according to claim 1, wherein the calculating the time factor of each of the persons to be analyzed respectively according to the recording time of each of the position points in the trajectory information to be analyzed of each of the persons to be analyzed comprises:

calculating the staying time of each person to be analyzed in the case-sending time period in the case-sending area according to the recording time of each position point in the track information to be analyzed of each person to be analyzed;

wherein, P_{j, time}Time factor for jth said person to be analyzed, t_jThe jth person to be analyzed is in the case sending time periodDwell time in the hair field, t_Region(s)The total duration of the time period for the case.

4. The suspect determination method of claim 1, further comprising:

calculating the prior probability of each person to be analyzed;

5. The suspect determination method of claim 4, wherein said calculating a prior probability for each of said persons to be analyzed comprises:

6. The suspect determination method of claim 4, further comprising:

7. The method according to claim 6, wherein the determining a suspect among the persons to be analyzed according to the distance factor, the time factor, the prior probability, and the anomaly probability of each of the persons to be analyzed comprises:

and determining the first N persons to be analyzed as the suspects.

8. A suspect determination apparatus, comprising:

9. A suspect determination apparatus, comprising:

a processor; and

10. A storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed, implement the following: