CN109144984A - Crime Stock discrimination method, equipment and storage medium - Google Patents
Crime Stock discrimination method, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a kind of crime Stock discrimination method, equipment and storage medium, the crime Stock discrimination method is the following steps are included: obtain social networks data according to the data acquisition instruction in the data configuration request received；The social networks data are pre-processed according to the data prediction rule in the configuring request, and generate social networks network according to pretreated social networks data；The request of crime Stock discrimination is received, Community Group is obtained from the social networks network, and according to the crime population characteristic information in crime Stock discrimination request, crime Community Group is identified from the Community Group.The present invention is according to implicit weak rigidity relationship, more relationships and is associated with the data such as spectrum by force, produces social networks network, can quickly can identify crime Community Group from social networks network according to a small amount of suspect's information.
Description
Technical Field
The invention relates to the field of social group identification, in particular to a criminal group identification method, criminal group identification equipment and a storage medium.
Background
With the rapid popularization and promotion of Web2.0, social networks such as Facebook, Twitter, QQ, microblog and WeChat are rapidly developed, the interactivity and self-media attributes of the Internet are more and more obvious, and the social relationship between people is more complicated. In real life, people show strong association more, and social networks show weak association more.
At present, the main technical methods for detecting the relationship between criminal members and the relationship intimacy degree thereof are divided into two types, and the network formed by criminals who are caught at the same time or implement crimes in the same area is used for identification; the identification is made using a network formed by communication or transactions between persons. However, the existing criminal group identification method considers more single relations, focuses more on explicit strong association relations and focuses less on implicit weak association relations, needs to collect a large amount of data, but is difficult in data collection and cannot measure the criminal group.
Therefore, the existing criminal group identification method has the problems of low scale and efficiency of data processing, difficulty in data acquisition and incapability of accurately measuring the criminal group.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a criminal group identification method, equipment and a storage medium, and aims to solve the technical problems that the scale and the efficiency of data processing are low, data acquisition is difficult, and the criminal group cannot be accurately measured.
In order to achieve the above object, the present invention provides a criminal group identification method, including the steps of:
acquiring social relationship data according to a data acquisition instruction in the received data configuration request;
preprocessing the social relationship data according to a data preprocessing rule in the configuration request, and generating a social relationship network according to the preprocessed social relationship data;
receiving a crime group identification request, acquiring a community group from the social relationship network, and identifying the crime community group from the community group according to crime group characteristic information in the crime group identification request.
Optionally, the step of preprocessing the social relationship data according to the data preprocessing rule in the configuration request, and generating a social relationship network according to the preprocessed social relationship data includes:
cleaning the social relationship data according to the network cleaning configuration in the configuration request to obtain first social relationship data;
identifying the first social relationship data according to the multi-source mapping strategy in the configuration request to obtain second social relationship data;
and generating a social relationship network according to the second social relationship data and the weight strategy configuration in the configuration request.
Optionally, the step of obtaining a community group from the social relationship network includes:
initializing the social relationship network to convert nodes in the social relationship network into a node community;
calculating the directional modularity of each node community in the social relationship network;
calculating modularity increment of each community node and adjacent community nodes according to the directional modularity, and updating the node communities in the social relationship network according to the modularity increment;
compressing all the nodes in the same node community in the updated social relationship network into new nodes, and converting the new nodes into new node communities to obtain community groups.
Optionally, after the step of identifying a criminal community group from the community groups according to the criminal community characteristic information in the criminal community identification request, the criminal community identification method further includes:
and identifying a crime core member from the crime community group, marking the crime core member in the social relationship network, and displaying the marked social relationship network.
Optionally, the step of identifying a criminal core member from the criminal community group comprises:
determining a crime core member sample according to crime core member information in the crime group identification request;
counting the connection weight of each non-criminal core member and the criminal core member sample, and marking the non-criminal core member with the maximum connection weight as a criminal core member;
and counting the sum of the connection weights of each non-criminal core member and all criminal core members, and marking the non-criminal core member with the largest sum of the connection weights as the criminal core member until the number of the criminal core members reaches the number of the criminal core members in the criminal group identification request.
Optionally, the criminal group identification method further includes:
inquiring identity information and position information of each crime core member, and marking in the crime community group;
determining a to-be-acted public security organization and a to-be-arrested crime core member according to the geographical position distribution of the public security organization and the position information;
and transmitting the identity information and the position information of the criminal core member to be arrested to the public security organization to be acted through the network.
Optionally, before the step of obtaining social relationship data according to the data obtaining instruction in the received data configuration request, the crime group identification method further includes:
acquiring fingerprint information from a received data configuration request, and verifying the fingerprint information;
and when the fingerprint information passes the verification, executing the step of acquiring the social relationship data according to the data acquisition instruction in the received data configuration request.
Optionally, the criminal group identification method further includes:
receiving a query request of a criminal community group, and acquiring identification information of the criminal community group in the query request;
and displaying the criminal community group when the criminal community group associated with the identification information is inquired.
The present invention also provides a criminal group identification apparatus, including: a memory, a processor and a criminal group identification program stored on the memory and executable on the processor, the criminal group identification program when executed by the processor implementing the steps of the criminal group identification method as described above.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a criminal group identification program which, when executed by a processor, implements the steps of the criminal group identification method as described above.
The invention acquires the social relationship data according to the data acquisition instruction in the received data configuration request, then preprocessing the social relationship data according to the data preprocessing rule in the configuration request, generating a social relationship network according to the preprocessed social relationship data, acquiring a community group from the social relationship network when a crime group identification request is received, and identifies the criminal community group from the community group according to the characteristic information of the criminal community in the criminal community identification request, the scheme identifies the criminal community group according to the implicit data of weak association relationship, multiple relationship, strong association optical system and the like, the method can generate a social relationship network, can quickly identify the criminal community group from the social relationship network according to a small amount of criminal suspect information, and provides a visual display social relationship network so that a user can track the criminal community group in a multi-dimensional mode.
Drawings
FIG. 1 is a schematic diagram of an apparatus architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a first embodiment of the criminal group identification method of the present invention;
FIG. 3 is a detailed flowchart of the step of preprocessing the social relationship data according to the data preprocessing rule in the configuration request and generating a social relationship network according to the preprocessed social relationship data in FIG. 2;
FIG. 4 is a schematic diagram illustrating a detailed flow of the step of community group discovery for the social networking service to obtain community groups shown in FIG. 2;
FIG. 5 is a flow chart illustrating a crime group identification method according to a second embodiment of the present invention;
FIG. 6 is a schematic flow chart illustrating a detailed process of the step of identifying a crime core member from the criminal community group shown in FIG. 5;
FIG. 7 is a schematic diagram illustrating a social relationship network visually in an embodiment of the present invention;
FIG. 8 is a flow chart illustrating a third embodiment of the criminal group identification method of the present invention;
fig. 9 is a flowchart illustrating a criminal group identification method according to a fourth embodiment of the present invention;
fig. 10 is a flowchart illustrating a criminal group identification method according to a fifth embodiment of 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
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: the method comprises the steps of obtaining social relationship data according to a data obtaining instruction in a received data configuration request, preprocessing the social relationship data according to a data preprocessing rule in the configuration request, generating a social relationship network according to the preprocessed social relationship data, obtaining a community group from the social relationship network when a crime group identification request is received, and identifying the crime community group from the community group according to crime group characteristic information in the crime group identification request.
In the prior art, a single relation is considered more, an explicit strong association relation is concerned more, and an implicit weak association relation is concerned less, so that a large amount of data needs to be collected, but the data collection is difficult, and the measurement cannot be performed on a criminal group.
The invention provides a solution, which can generate a social relationship network according to implicit data such as weak association relationship, multiple relationships, strong association optical system and the like, can quickly identify a criminal community group from the social relationship network according to a small amount of criminal suspect information, and provides a visual display social relationship network so as to facilitate a user to track the criminal community group in multiple dimensions.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
The device of the embodiment of the invention can be a PC, and can also be a mobile device with a display function, such as a smart phone, a tablet computer, a portable computer and the like.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the device may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, WiFi modules, and so forth. Such as light sensors, motion sensors, and other sensors. In particular, the light sensor may include an ambient light sensor that adjusts the brightness of the display screen based on the ambient light level and a proximity sensor that turns off the display screen and/or backlight when the device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the device is stationary, and can be used for applications of recognizing the device posture (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; of course, the device may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which are not described herein again.
Those skilled in the art will appreciate that the configuration of the apparatus shown in fig. 1 is not intended to be limiting of the apparatus and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is one type of criminal group identification storage medium, may include therein an operating system, a network communication module, a user interface module, and a criminal group identification program.
In the device shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to invoke the criminal group identification program stored in the memory 1005 and perform the following steps:
acquiring social relationship data according to a data acquisition instruction in the received data configuration request;
preprocessing the social relationship data according to a data preprocessing rule in the configuration request, and generating a social relationship network according to the preprocessed social relationship data;
receiving a crime group identification request, acquiring a community group from the social relationship network, and identifying the crime community group from the community group according to crime group characteristic information in the crime group identification request.
Further, the step of preprocessing the social relationship data according to the data preprocessing rule in the configuration request and generating a social relationship network according to the preprocessed social relationship data includes:
cleaning the social relationship data according to the network cleaning configuration in the configuration request to obtain first social relationship data;
identifying the first social relationship data according to the multi-source mapping strategy in the configuration request to obtain second social relationship data;
and generating a social relationship network according to the second social relationship data and the weight strategy configuration in the configuration request.
Further, the step of obtaining community groups from the social relationship network includes:
initializing the social relationship network to convert nodes in the social relationship network into a node community;
calculating the directional modularity of each node community in the social relationship network;
calculating modularity increment of each community node and adjacent community nodes according to the directional modularity, and updating the node communities in the social relationship network according to the modularity increment;
compressing all the nodes in the same node community in the updated social relationship network into new nodes, and converting the new nodes into new node communities to obtain community groups.
Further, the processor 1001 may call the criminal group identification program stored in the memory 1005, and further perform the steps of:
and identifying a crime core member from the crime community group, marking the crime core member in the social relationship network, and displaying the marked social relationship network.
Further, the step of identifying a criminal core member from the criminal community group comprises:
determining a crime core member sample according to crime core member information in the crime group identification request;
counting the connection weight of each non-criminal core member and the criminal core member sample, and marking the non-criminal core member with the maximum connection weight as a criminal core member;
and counting the sum of the connection weights of each non-criminal core member and all criminal core members, and marking the non-criminal core member with the largest sum of the connection weights as the criminal core member until the number of the criminal core members reaches the number of the criminal core members in the criminal group identification request.
Further, the processor 1001 may call the criminal group identification program stored in the memory 1005, and further perform the steps of:
inquiring identity information and position information of each crime core member, and marking in the crime community group;
determining a to-be-acted public security organization and a to-be-arrested crime core member according to the geographical position distribution of the public security organization and the position information;
and transmitting the identity information and the position information of the criminal core member to be arrested to the public security organization to be acted through the network.
Further, the processor 1001 may call the criminal group identification program stored in the memory 1005, and further perform the steps of:
acquiring fingerprint information from a received data configuration request, and verifying the fingerprint information;
and when the fingerprint information passes the verification, acquiring social relationship data according to a data acquisition instruction in the received data configuration request.
Further, the processor 1001 may call the criminal group identification program stored in the memory 1005, and further perform the steps of:
receiving a query request of a criminal community group, and acquiring identification information of the criminal community group in the query request;
and displaying the criminal community group when the criminal community group associated with the identification information is inquired.
The specific embodiment of the criminal group identification apparatus of the present invention is substantially the same as the specific embodiments of the criminal group identification method described below, and will not be described herein again.
The invention provides a criminal group identification method.
Referring to fig. 2, fig. 2 is a flowchart illustrating a criminal group identification method according to a first embodiment of the present invention.
In this embodiment, the criminal group identification method includes:
step S101, obtaining social relationship data according to a data obtaining instruction in a received data configuration request;
the criminal group identification method is applied to criminal group identification equipment, the criminal group identification equipment comprises mobile terminal equipment, fixed terminal equipment and the like, and the mobile terminal equipment comprises a smart phone, a tablet computer and the like. The criminal group identification equipment comprises a user graphical interface, a basic supporting unit, a data preprocessing unit and a criminal group identification unit, wherein the user graphical interface is mainly used for graphical user operation, including data acquisition configuration, data preprocessing configuration, monitoring, visual display and the like; the basic supporting unit mainly provides crime group identification basic services, including providing computing power clusters, data acquisition synchronous services, monitoring services and the like; the data preprocessing unit is mainly used for integrating different types of social relationship data, cleaning data, setting network weight and the like; the criminal group identification unit is mainly used for carrying out community group discovery on a social relationship network, and identifying potential criminal community groups through characteristics of criminal suspects and criminal community groups.
The data acquisition configuration is used for configuring and acquiring different types of network relationship data, data synchronization strategies, data synchronization tasks and the like in the social relationship network; the data preprocessing unit comprises a multi-source mapping strategy, a network cleaning configuration and a network relation weight configuration, wherein the multi-source mapping strategy is used for identifying identities of the same node in different types of networks so as to fuse different types of networks into the same network, the network cleaning configuration is used for cleaning social relation data according to crime characteristic information such as occurrence time and regions for identifying crime groups, reducing the scale of the network so as to identify the community crime groups more quickly and accurately, and the network relation weight configuration is used for adjusting the weight relation among the nodes in the network.
The method comprises the steps that a user triggers a data configuration request through a user graphical interface in crime group identification equipment, the crime group identification equipment receives the triggered data configuration request, acquires a data acquisition instruction in the data configuration request, and simultaneously acquires social relationship data stored in a data warehouse according to the data acquisition instruction, wherein the social relationship data comprises social network data and communication network data, and the social network data comprises data of social software such as a WeChat platform, a microblog platform and a QQ platform. The criminal group identification device realizes data interaction with social software through an interface, and realizes collection of data of various network types.
Step S102, preprocessing the social relationship data according to the data preprocessing rule in the configuration request, and generating a social relationship network according to the preprocessed social relationship data;
the criminal group identification device obtains a data preprocessing rule in the configuration request, preprocesses the social relationship data stored in the data warehouse according to the data preprocessing rule, stores the preprocessed social relationship data in the data warehouse, and generates a social relationship network according to the preprocessed social relationship data. The configuration request comprises a data acquisition instruction, a data preprocessing rule, a multi-source mapping strategy, a network cleaning configuration, a weight strategy configuration and the like, and the social relationship network is a directed weighting network.
Specifically, referring to fig. 3, fig. 3 is a detailed flowchart of step S102 shown in fig. 2, where step S102 includes:
step S1021, cleaning the social relationship data according to the network cleaning configuration in the configuration request to acquire first social relationship data;
step S1022, identify the first social relationship data according to the multi-source mapping policy in the configuration request, so as to obtain second social relationship data;
and step S1023, configuring and generating a social relationship network according to the second social relationship data and the weight strategy in the configuration request.
The criminal group identification equipment firstly acquires a network cleaning configuration in the configuration request to clean the social relationship data so as to acquire first social relationship data, wherein the network cleaning configuration comprises criminal characteristic information such as occurrence time, regions and the like of a criminal group; and then acquiring a multi-source mapping strategy in the configuration request to identify the first social relationship data so as to acquire second social relationship data, wherein the multi-source mapping strategy is used for identifying identities of the same node in different types of networks, fusing different types of networks into the same network, finally acquiring weight strategy configuration in the configuration request, and generating a social relationship network according to the second social relationship data and the weight strategy configuration, wherein the weight strategy configuration comprises weight relationships among all nodes in the social relationship network.
In specific implementation, the network cleaning configuration, the multi-source mapping policy and the weight policy configuration may all be configured through a graphical user interface, for example, the network cleaning configuration is configured such that the cleaning condition is crime characteristic information such as occurrence time and region of a crime group, the multi-source mapping policy is to merge the same identity node in different types of networks into social network data, and the weight policy is configured such that the weight relationship between nodes in the social network does not change.
Step S103, receiving a crime group identification request, acquiring a community group from the social relationship network, and identifying a crime community group from the community group according to crime group characteristic information in the crime group identification request;
the user triggers a criminal group identification request through a user graphical interface, wherein the criminal group identification request carries criminal group characteristic information, criminal core member number and the like, and the criminal group characteristic information comprises criminal core member information, criminal group basic characteristics and the like. The criminal group identification device receives a triggered criminal group identification request, acquires a community group from the social relationship network according to the criminal group identification request, and identifies the criminal community group from the community group according to the criminal group characteristic information in the criminal identification request.
Specifically, referring to fig. 4, fig. 4 is a detailed flowchart of step S103 shown in fig. 2, where the step S103 includes:
step S1031, initializing the social relationship network to convert nodes in the social relationship network into a node community;
step S1032, calculating the directional modularity of each node community in the social relationship network;
step S1033, calculating modularity increment of each community node and adjacent community nodes according to the directional modularity, and updating node communities in the social relationship network according to the modularity increment;
step S1034, compressing all the nodes in the same node community in the updated social relationship network into new nodes, and converting the new nodes into a new node community to obtain a community group.
The criminal group identification device initializes a social relationship network to convert a node in the social relationship network into a node community (each node in the social relationship network is a node community), calculates a directional modularity of each node community in the social relationship network, calculates a modularity increment between each community node and an adjacent community node according to the directional modularity, and updates the node community in the social relationship network according to the modularity increment, namely, calculates an adjacent community node with a maximum positive modularity increment from adjacent community nodes (at least one adjacent community node) of each community node, and transfers the node from the node community to which the node community node belongs if the adjacent community node with the maximum positive modularity increment cannot be found, and repeating the step S1033 and the step S1034 without transferring the node community until the modularity cannot be increased due to the transfer of any node in the social relationship network, finally compressing all the nodes in the same node community in the updated social relationship network into new nodes, converting the new nodes into a new node community to obtain a community group, and identifying the crime community group from the community group according to the crime group characteristic information in the crime identification request. The directional modularity is defined on the basis of the traditional modularity, and the specific form is as follows:
wherein,denotes the sum of the weights of all sides, A'ijRepresenting the weight of node j to node i,represents the sum of the weights of all edges terminating at node i,represents the sum of the weights of all edges starting at node j, ciIndicating the community to which node i belongs. Namely, the directional modularity of each node community in the social relationship network can be quickly calculated through the definition of the directional modularity.
In this embodiment, the present invention obtains social relationship data according to a data obtaining instruction in a received data configuration request, then preprocesses the social relationship data according to a data preprocessing rule in the configuration request, and generates a social relationship network according to the preprocessed social relationship data, then obtains a community group from the social relationship network when a crime group identification request is received, and identifies a crime community group from the community group according to crime group feature information in the crime group identification request, according to the implicit data such as weak association relationship, multiple relationship, and strong association optical system, the present invention can generate the social relationship network, can quickly identify the crime community group from the social relationship network according to a small amount of crime suspect information, and further provides a visual display social relationship network, so that the user can track the criminal community group in multiple dimensions.
Further, referring to fig. 5, a second embodiment of the criminal group identification method of the present invention is proposed based on the above-described first embodiment, and is different from the foregoing embodiments in that after step S103, the criminal group identification method further includes:
and step S104, identifying a crime core member from the crime community group, marking the crime core member in the social relationship network, and displaying the marked social relationship network.
After the criminal community identification device identifies the criminal community from the community, the criminal community identification device identifies a criminal core member from the criminal community, marks the criminal core member in the social relationship network, and displays the marked social relationship network.
Specifically, referring to fig. 6, fig. 6 is a detailed flowchart of step S104 shown in fig. 5, where the step S104 includes:
step S1041, determining a crime core member sample according to the crime core member information in the crime group identification request;
step S1042, counting the connection weight of each non-criminal core member and the criminal core member sample, and marking the non-criminal core member with the maximum connection weight as a criminal core member;
step S1043, counting the sum of the connection weights of each non-criminal core member and all criminal core members, and marking the non-criminal core member with the largest sum of the connection weights as the criminal core member until the number of the criminal core members reaches the number of the criminal core members in the criminal group identification request.
The criminal group identification equipment firstly determines a criminal core member sample according to criminal core member information in a criminal group identification request, then counts the connection weight of each non-criminal core member and the criminal core member sample, marks the non-criminal core member with the largest connection weight as a criminal core member, records the number of the criminal core members, counts the sum of the connection weights of each non-criminal core member and all criminal core members, marks the non-criminal core member with the largest sum of the connection weights as the criminal core member, and records the number of the criminal core members in the criminal group identification request until the recorded number of the criminal core members reaches the number of the criminal core members in the criminal group identification request.
In a specific implementation, when the crime group identification device counts the connection weight or the sum of the connection weights, the crime group identification device acquires the number of non-crime core members with the largest connection weight or the largest sum of the connection weights, if the number of the non-crime core members with the largest connection weight or the largest sum of the connection weights is unique, the non-crime core members with the largest connection weight are marked as crime core members, if the number of the non-crime core members with the largest connection weight or the largest sum of the connection weights is not unique, the number of adjacent crime core members of the non-crime core members with the largest connection weight or the largest sum of the connection weights is acquired, if the number of the non-crime core members with the largest number of the adjacent crime core members is unique, the non-crime core members with the largest number of the adjacent crime core members are marked as crime core, then randomly selecting one of the non-crime core members with the largest number of adjacent crime core members, and marking the selected non-crime core member as a crime core member.
Fig. 7 is a schematic diagram illustrating a social relationship network visually in an embodiment of the present invention, as shown in fig. 6, the social relationship network includes nine community groups, two of the community groups are crime groups, and seven of the community groups are other community groups, the selected crime group includes 17 persons and 3 core persons, and the other crime group includes 16 persons and 3 core persons.
In this embodiment, after the criminal community group is identified, the criminal core member is further found from the criminal community group, and is displayed in the social relationship network, so that the public security organization can conveniently and quickly acquire the information of the criminal core member.
Further, referring to fig. 8, a third embodiment of the criminal group identification method of the present invention is proposed based on the above first or second embodiment, and is different from the foregoing embodiment in that the criminal group identification method further includes:
step S105, inquiring identity information and position information of each crime core member, and marking in the crime community group;
step S106, determining a to-be-acted public security organization and a to-be-apprehended criminal core member according to the geographical position distribution of the public security organization and the position information;
and step S107, transmitting the identity information and the position information of the criminal core member to be arrested to the public security organization to be acted through the network.
The criminal group identification device firstly inquires identity information and position information of each criminal core member, marks the identity information and the position information in the criminal community group, then determines a to-be-acted public security organization and the criminal core members to be arrested according to geographical position distribution of the public security organization and the position information, namely selects the criminal core members closest to the public security organization, limits arrest quantity, and finally transmits the identity information and the position information of the to-be-arrested criminal core members to the to-be-acted public security organization through a network, so that the to-be-acted public security organization can know the identity information and the position information of the to-be-arrested criminal core members.
In the embodiment, the invention can accurately and quickly issue the arrest task to the corresponding public security agency through the geographical position distribution of the public security agency and the identity information and the position information of the criminal core member, thereby effectively improving the efficiency.
Further, referring to fig. 9, a fourth embodiment of the criminal group identification method of the present invention is proposed based on the first, second, or third embodiment described above, and is different from the foregoing embodiment in that, before step S101, the criminal group identification method further includes:
step S108, acquiring fingerprint information from the received data configuration request, and verifying the fingerprint information;
when the fingerprint information passes the verification, step S101 is executed, that is, social relationship data is obtained according to the data obtaining instruction in the received data configuration request.
The criminal group identification equipment acquires fingerprint information in a data configuration request when receiving the data configuration request triggered from the outside, verifies the fingerprint information, and acquires social relationship data according to a data acquisition instruction of the data configuration request when the fingerprint information passes the verification. In specific implementation, user authority is set, and the criminal group identification equipment responds to the operation of the user only when the criminal group identification equipment has the operation authority.
In the embodiment, the security of the criminal group identification equipment can be effectively ensured by verifying the fingerprint information, and the criminal group identification equipment is prevented from being stolen by others.
Further, referring to fig. 10, a fifth embodiment of the criminal group identification method of the present invention is proposed based on the first, second, third or fourth embodiment described above, and is different from the foregoing embodiment in that after step S103, the criminal group identification method further includes:
step S109, receiving a query request of a criminal community group, and acquiring identification information of the criminal community group in the query request;
and step S110, when the criminal community group associated with the identification information is inquired, displaying the criminal community group.
The criminal community identification device acquires identification information of a criminal community in an inquiry request when receiving the inquiry request of an externally triggered criminal community, and displays the criminal community when inquiring the criminal community associated with the identification information.
In the embodiment, the identification information is associated with the criminal community group, so that the corresponding criminal community group can be conveniently and rapidly inquired according to the identification information, and the operation of a user is facilitated.
Furthermore, an embodiment of the present invention also provides a crime group identification storage medium, on which a crime group identification program is stored, which when executed by a processor implements the steps of:
acquiring social relationship data according to a data acquisition instruction in the received data configuration request;
preprocessing the social relationship data according to a data preprocessing rule in the configuration request, and generating a social relationship network according to the preprocessed social relationship data;
receiving a crime group identification request, acquiring a community group from the social relationship network, and identifying the crime community group from the community group according to crime group characteristic information in the crime group identification request.
Further, the step of preprocessing the social relationship data according to the data preprocessing rule in the configuration request and generating a social relationship network according to the preprocessed social relationship data includes:
cleaning the social relationship data according to the network cleaning configuration in the configuration request to obtain first social relationship data;
identifying the first social relationship data according to the multi-source mapping strategy in the configuration request to obtain second social relationship data;
and generating a social relationship network according to the second social relationship data and the weight strategy configuration in the configuration request.
Further, the step of obtaining community groups from the social relationship network includes:
initializing the social relationship network to convert nodes in the social relationship network into a node community;
calculating the directional modularity of each node community in the social relationship network;
calculating modularity increment of each community node and adjacent community nodes according to the directional modularity, and updating the node communities in the social relationship network according to the modularity increment;
compressing all the nodes in the same node community in the updated social relationship network into new nodes, and converting the new nodes into new node communities to obtain community groups.
Further, the criminal group identification program when executed by the processor further implements the steps of:
and identifying a crime core member from the crime community group, marking the crime core member in the social relationship network, and displaying the marked social relationship network.
Further, the step of identifying a criminal core member from the criminal community group comprises:
determining a crime core member sample according to crime core member information in the crime group identification request;
counting the connection weight of each non-criminal core member and the criminal core member sample, and marking the non-criminal core member with the maximum connection weight as a criminal core member;
and counting the sum of the connection weights of each non-criminal core member and all criminal core members, and marking the non-criminal core member with the largest sum of the connection weights as the criminal core member until the number of the criminal core members reaches the number of the criminal core members in the criminal group identification request.
Further, the criminal group identification program when executed by the processor further implements the steps of:
inquiring identity information and position information of each crime core member, and marking in the crime community group;
determining a to-be-acted public security organization and a to-be-arrested crime core member according to the geographical position distribution of the public security organization and the position information;
and transmitting the identity information and the position information of the criminal core member to be arrested to the public security organization to be acted through the network.
Further, the criminal group identification program when executed by the processor further implements the steps of:
acquiring fingerprint information from a received data configuration request, and verifying the fingerprint information;
and when the fingerprint information passes the verification, acquiring social relationship data according to a data acquisition instruction in the received data configuration request.
Further, the criminal group identification program when executed by the processor further implements the steps of:
receiving a query request of a criminal community group, and acquiring identification information of the criminal community group in the query request;
and displaying the criminal community group when the criminal community group associated with the identification information is inquired.
The specific embodiment of the computer-readable storage medium of the present invention is substantially the same as the specific embodiments of the criminal group identification method described above, and will not be described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A criminal group identification method, comprising the steps of:
acquiring social relationship data according to a data acquisition instruction in the received data configuration request;
preprocessing the social relationship data according to a data preprocessing rule in the configuration request, and generating a social relationship network according to the preprocessed social relationship data;
receiving a crime group identification request, acquiring a community group from the social relationship network, and identifying the crime community group from the community group according to crime group characteristic information in the crime group identification request.
2. The criminal group identification method of claim 1, wherein the step of preprocessing the social relationship data according to the data preprocessing rule in the configuration request and generating a social relationship network according to the preprocessed social relationship data comprises:
cleaning the social relationship data according to the network cleaning configuration in the configuration request to obtain first social relationship data;
identifying the first social relationship data according to the multi-source mapping strategy in the configuration request to obtain second social relationship data;
and generating a social relationship network according to the second social relationship data and the weight strategy configuration in the configuration request.
3. The crime group identification method of claim 1, wherein the step of obtaining a community group from the social relationship network comprises:
initializing the social relationship network to convert nodes in the social relationship network into a node community;
calculating the directional modularity of each node community in the social relationship network;
calculating modularity increment of each community node and adjacent community nodes according to the directional modularity, and updating the node communities in the social relationship network according to the modularity increment;
compressing all the nodes in the same node community in the updated social relationship network into new nodes, and converting the new nodes into new node communities to obtain community groups.
4. The criminal group identification method according to claim 1, wherein after the step of identifying a criminal community group from the community groups based on the criminal group characteristic information in the criminal group identification request, the criminal group identification method further comprises:
and identifying a crime core member from the crime community group, marking the crime core member in the social relationship network, and displaying the marked social relationship network.
5. The criminal group identification method of claim 4, wherein the step of identifying a criminal core member from the criminal community group comprises:
determining a crime core member sample according to crime core member information in the crime group identification request;
counting the connection weight of each non-criminal core member and the criminal core member sample, and marking the non-criminal core member with the maximum connection weight as a criminal core member;
and counting the sum of the connection weights of each non-criminal core member and all criminal core members, and marking the non-criminal core member with the largest sum of the connection weights as the criminal core member until the number of the criminal core members reaches the number of the criminal core members in the criminal group identification request.
6. The criminal group identification method of any one of claims 1-5, further comprising:
inquiring identity information and position information of each crime core member, and marking in the crime community group;
determining a to-be-acted public security organization and a to-be-arrested crime core member according to the geographical position distribution of the public security organization and the position information;
and transmitting the identity information and the position information of the criminal core member to be arrested to the public security organization to be acted through the network.
7. The method of any one of claims 1-5, wherein prior to the step of obtaining social relationship data based on the data acquisition instructions in the received data configuration request, the method further comprises:
acquiring fingerprint information from a received data configuration request, and verifying the fingerprint information;
and when the fingerprint information passes the verification, executing the step of acquiring the social relationship data according to the data acquisition instruction in the received data configuration request.
8. The criminal group identification method according to any one of claims 1 to 5, wherein after the step of identifying a criminal community group from the community groups based on the criminal group characteristic information in the criminal group identification request, the criminal group identification method further comprises:
receiving a query request of a criminal community group, and acquiring identification information of the criminal community group in the query request;
and displaying the criminal community group when the criminal community group associated with the identification information is inquired.
9. A criminal group identification apparatus characterized by comprising: a memory, a processor, and a criminal group identification program stored on the memory and executable on the processor, the criminal group identification program when executed by the processor implementing the steps of the criminal group identification method of any of claims 1-8.
10. A computer storage medium having stored thereon a criminal group identification program, which when executed by a processor implements the steps of the criminal group identification method of any one of claims 1-8.
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| CN201710504137.4A CN109144984A (en) | 2017-06-27 | 2017-06-27 | Crime Stock discrimination method, equipment and storage medium |
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Application publication date: 20190104 |