CN111933298B - Crowd relation determining method and device, electronic equipment and medium - Google Patents

Crowd relation determining method and device, electronic equipment and medium Download PDF

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CN111933298B
CN111933298B CN202010817324.XA CN202010817324A CN111933298B CN 111933298 B CN111933298 B CN 111933298B CN 202010817324 A CN202010817324 A CN 202010817324A CN 111933298 B CN111933298 B CN 111933298B
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group
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scene
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CN111933298A (en
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柯昆
滕召荣
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Yidu Cloud Beijing Technology Co Ltd
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Abstract

The present disclosure provides a crowd relationship determination method, a crowd relationship determination device, an electronic apparatus, and a computer-readable medium; relates to the technical field of data processing. The crowd relation determining method comprises the following steps: collecting crowd data of each scene, and determining unique identification of each person in the crowd data; acquiring key fields respectively corresponding to crowd data of each scene, and grouping the crowd data through the key fields to obtain a plurality of groups; and calculating social relation data of each group to obtain contact relation groups of each person through the social relation data and the unique identification. The crowd relation determining method can overcome the problem of great difficulty in searching for close contact users to a certain extent, and further improves searching efficiency.

Description

Crowd relation determining method and device, electronic equipment and medium
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a crowd relationship determination method, a crowd relationship determination device, an electronic apparatus, and a computer readable medium.
Background
Along with the outbreak of new coronavirus pneumonia epidemic situation in the world, governments in various countries begin to build medical systems, especially real-time monitoring systems related to epidemic situations, wherein the tracking of the intimate contact person of patients is particularly important in the aspect of the diffusion control of epidemic situations. At present, various social personnel in close contact with the patient can be screened through the moving track of the patient, but the searching difficulty of the personnel with the moving track coincident with the patient is high, and the problem that inaccurate searching is easily caused by overlooking of the record of the moving track of the patient is also caused.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure aims to provide a crowd relation determining method, a crowd relation determining device, a computer readable medium and electronic equipment, which can overcome the problem of great difficulty in searching for closely contacted users to a certain extent, thereby improving the searching efficiency.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a crowd relationship determination method, including:
collecting crowd data of each scene, and determining unique identification of each person in the crowd data;
acquiring key fields respectively corresponding to crowd data of each scene, and grouping the crowd data through the key fields to obtain a plurality of groups;
and calculating social relation data of each group, and combining the social relation data and the unique identification to obtain the contact crowd of each person.
In an exemplary embodiment of the disclosure, the determining social relationship data for each group includes:
and determining social relation data of each group according to the scene characteristics of each group.
In an exemplary embodiment of the present disclosure, before determining the social relationship data of each group, further comprising:
and filtering the crowd data according to preset timeliness to remove the crowd data which are not in the preset timeliness.
In an exemplary embodiment of the present disclosure, the crowd data includes a time field, and grouping the crowd data by the key field includes:
and when the scene characteristic is an activity place, grouping the crowd data through the key field and the time field corresponding to the activity place.
In an exemplary embodiment of the present disclosure, grouping the crowd data through the key field and the time field corresponding to the event venue includes:
grouping crowd data corresponding to the activity places according to the key fields to obtain a first group;
sorting the crowd data in the first group according to the time sequence of the time field;
and dividing the sorting according to a preset time interval to obtain a second group.
In one exemplary embodiment of the present disclosure, computing social relationship data for each group includes:
and determining the time adjacent relation of the second group according to the time interval between the crowd data in the second group.
In an exemplary embodiment of the disclosure, the obtaining the contact relationship population of each person through the social relationship data and the unique identification includes:
acquiring social relationship data of a target user through the unique identification of the target user;
and acquiring target crowd related to the target user through the social relation data of the target user.
According to a second aspect of the present disclosure, there is provided a crowd relationship determination apparatus, including a crowd collecting module, a crowd grouping module, and a crowd relationship obtaining module, wherein:
the crowd collecting module is used for collecting crowd data of each scene and determining unique identification of each person in the crowd data.
The crowd grouping module is used for acquiring key fields corresponding to the crowd data of each scene respectively, and grouping the crowd data through the key fields to obtain a plurality of groups.
The crowd relation acquisition module is used for calculating social relation data of each group so as to acquire contact relation crowd of each person through the social relation data and the unique identification.
In one exemplary embodiment of the present disclosure, the crowd relationship acquisition module is configured to: and determining social relation data of each group according to the scene characteristics of each group.
In an exemplary embodiment of the present disclosure, the apparatus further includes a data filtering module, configured to filter the crowd data according to a preset age, so as to remove crowd data that is not within the preset age.
In an exemplary embodiment of the present disclosure, the crowd data includes a time field, and the crowd grouping module is specifically configured to: and when the scene characteristic is an activity place, grouping the crowd data through the key field and the time field corresponding to the activity place.
In one exemplary embodiment of the present disclosure, the crowd grouping module may include a first grouping unit, a ranking unit, and a second grouping unit, wherein:
and the first grouping unit is used for grouping the crowd data corresponding to the activity place according to the key field so as to obtain a first group.
And the ordering unit is used for ordering the crowd data in the first group according to the time sequence of the time field.
The second grouping unit is used for dividing the sorting according to a preset time interval to obtain a second group.
In one exemplary embodiment of the present disclosure, the crowd relationship acquisition module may be configured to: and determining the time adjacent relation of the second group according to the time interval between the crowd data in the second group.
In one exemplary embodiment of the present disclosure, the crowd-relationship acquisition module may include a data retrieval unit, and a target crowd acquisition unit, wherein:
and the data retrieval unit is used for acquiring social relationship data of the target user through the unique identification of the target user.
The target crowd acquisition unit is used for acquiring target crowd related to the target user through social relation data of the target user.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of any of the above via execution of the executable instructions.
According to a fourth aspect of the present disclosure, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of any of the above.
Exemplary embodiments of the present disclosure may have some or all of the following advantages:
in the crowd relation determining method provided by the example embodiment of the present disclosure, on one hand, the collected data may be more comprehensive by collecting crowd data of each scene, so that personnel omission is avoided when searching for a contact crowd of a user, and the searching accuracy may be improved; on the other hand, the contact relation crowd of each person can be obtained by calculating the social relation data of each group, and further, the crowd with the contact relation with the user can be automatically searched through the unique identification of the user, so that the searching efficiency can be improved, and the searching time can be shortened.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a flow chart of a crowd relationship determination method according to one embodiment of the disclosure;
FIG. 2 schematically illustrates a flow chart of a crowd relationship determination method according to another embodiment of the disclosure;
FIG. 3 schematically illustrates a block diagram of a crowd relationship determination device in accordance with one embodiment of the present disclosure;
fig. 4 shows a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The following describes the technical scheme of the embodiments of the present disclosure in detail:
the present exemplary embodiment provides a crowd relationship determination method. Referring to fig. 1, the method may include the steps of:
step S110: crowd data of each scene is collected, and unique identification of each person in the crowd data is determined.
Step S120: and acquiring key fields respectively corresponding to the crowd data of each scene, and grouping the crowd data through the key fields to obtain a plurality of groups.
Step S130: and calculating social relation data of each group to obtain contact relation groups of each person through the social relation data and the unique identification.
In the crowd relation determining method provided by the above-mentioned example embodiment of the present disclosure, on one hand, the collected data may be more comprehensive by collecting crowd data of each scene, so as to avoid personnel omission when searching for a contact crowd of a user, and improve the searching accuracy; on the other hand, the contact relation crowd of each person can be obtained by calculating the social relation data of each group, and further, the crowd with the contact relation with the user can be automatically searched through the unique identification of the user, so that the searching efficiency can be improved, and the searching time can be shortened.
Next, the above steps of the present exemplary embodiment will be described in more detail.
In step S110, crowd data of each scene is collected, and a unique identifier of each person in the crowd data is determined.
People come in and go out of various scenes such as families, companies, schools, subways, and the like every day in daily life. Crowd data refers to basic information registered by a user aiming at different scenes, wherein the crowd data can contain different contents in different scenes, for example, in a home scene, the crowd data can comprise the name, the age, the identity card number, the home address, the family members and the like of the user; in a corporate scenario, crowd data may include company name, employee number, employee name, post, department, etc. where the user is located; in a school scenario, crowd data may include a user's number, school name, class, etc. For example, scenes may be divided into fixed venues and event venues according to their features; the fixed location may be, for example, a company, school, community, etc., and the activity location may include vehicles such as subways, flights, cruise ships, entry/exit, etc., and may also include dining sites, play sites, etc.; therefore, according to the actual situation, the crowd data may include various data, such as a user photo, a flight number, entry and exit information, and the embodiment is not limited thereto.
By acquiring crowd data of different scenes, data of different structures from different sources can be obtained. By way of example, crowd data corresponding to a home scene can be obtained through census, neonatal information, and the like; crowd information corresponding to a family scene can also be obtained through community personnel registration information; crowd information of a company scene pair can be obtained through social security data; crowd information corresponding to the event venue and the like can be acquired through the entry and exit data.
After the crowd data of each scene is collected, the data can be summarized, each person in the crowd data is identified by using the effective certificate numbers in the crowd data, such as the data of an identity card number, a passport number and the like, and then a unique identifier is generated for each person. The unique identification is used for carrying out data processing on the crowd data, so that the crowd data can be distinguished and identified more easily in the subsequent data processing process. The unique identifier may be formed by a digital component, a digital plus character component, or other means, such as an alphanumeric component, and the present embodiment is not limited thereto. For example, the unique identification of each person may be generated by means of sequentially increasing numbers, or the unique identification of each person may be generated by means of a specific encoding rule, or the like.
In step S120, key fields corresponding to the crowd data of each scene are obtained, and the crowd data are grouped according to the key fields to obtain a plurality of groups.
The crowd data may include a plurality of fields, and the key field may be one or more fields associated with a scene therein, for example, in a school scene, the key field may be a school name or a class, etc., and in a subway scene, the key field may be a subway name or an in-out name, etc. In this embodiment, key fields corresponding to different scenes may be predefined, and by obtaining a file recording a correspondence between a scene and a key field, a key field corresponding to each scene may be obtained; or the key fields can be marked in advance in the crowd data, so that the marked fields in the crowd data are obtained as the key fields according to the marks.
Grouping crowd data by key fields can group the same data of the key fields into the same group, and different data of the key fields are grouped into different groups. Specifically, the field values of the key field in each piece of crowd data may be determined first, and then the same field values may be grouped. For example, the key field may be a school and a class, and then students of the same school may be grouped together by the key field, or students of the same school and the same class may be grouped together. As another example, the key field may be a flight number, passengers of the same flight number may be grouped into the same group, and so on. Grouping the crowd data may result in multiple groups, and people in the same group may have contact relationships.
In an exemplary embodiment, if crowd data belongs to data corresponding to a scene of an event venue, the crowd data may be grouped by a key field and a time field. For example, the key field corresponding to the activity place may be the activity place, such as a bus shift, a flight number, and the like. Since the event sites have a common nature, grouping may not be possible by the event sites alone, and thus grouping may be by means of time plus event sites. Specifically, the method may include step S210, step S220, and step S230, as shown in fig. 2.
In step S210, the crowd data corresponding to the activity place is grouped according to the key field, so as to obtain a first group. The scene may specifically include an activity place, and when the scene is an activity place, crowd data corresponding to the scene may be first grouped according to a keyword, for example, crowd data corresponding to the scene is grouped according to an activity place, so as to obtain a group with the same activity place.
In step S220, the crowd data in the first group is sorted according to the time sequence of the time field. The field value corresponding to the time field of each piece of crowd data in the first group can be obtained through query, for example, the field value of the time field can be 6:08, 12:00, 12:30, and the like, so that the pieces of data in the first group are ordered according to the time sequence. The pieces of data in the first group after sorting may be arranged in an order from early to late in time. Since the activity scene belongs to the public scene, whether the same time period occurs in the same place between different people can be determined through the time sequence, so that whether the relationship of mutual contact exists between people can be determined. In this embodiment, the relationship between users appearing in the activity place can be confirmed, and the relationship between the user and other users can be obtained more comprehensively according to the activity track of the user.
In step S230, the sorting is divided according to a preset time interval to obtain a second group. For example, the first group after sorting may be divided from data with larger difference before and after the time interval, to obtain the second group. Specifically, after each piece of data is arranged according to the time sequence, the time interval between every two adjacent pieces of data is calculated respectively, if the time interval between two adjacent pieces of data exceeds a preset value, the two adjacent pieces of data can be divided, and the first group is divided into smaller groups. The preset value may be, for example, 1 hour, 2 hours, or the like, or may include 5 hours, 6 hours, or the like, and the embodiment is not limited thereto.
In step S130, social relationship data of each group is calculated to obtain a contact relationship crowd of each person through the social relationship data and the unique identification.
Social relationship data may include person-to-person relationships, where there may be relationships in every two pieces of crowd data; social relationship data may also include basic information of the person in which the relationship exists, such as name, identification card number, unique identification, etc.; types of relationships that exist, such as colleague relationships, classmate relationships, parent-child relationships, and the like, may also be included. By way of example, different scenes have different scene features, with which the relationship between the data in the individual packets can be derived. Scene features may be types of scenes, e.g., scenes may be categorized into families, schools, communities, etc.; the data of each group in different scenes may determine their relationship as a corresponding scene type, e.g., there is a family membership between group data of family scenes, there is a colleague relationship between data in groups of company scenes, etc. In addition, the correspondence between the scene features and the social relationship data may be predefined manually, for example, the scene features may correspond to a primary contact relationship of a home scene, the data in the group of the activity place may be a secondary contact relationship, and so on.
Because the crowd data of each scene has more data volume, the crowd data can be calculated through a big data analysis tool, and the relation between every two people in each group is determined. For example, members in the same family are determined through a spark computing tool, and the relationship between the members (such as parent-child, sibling and sister relationships) is determined; people who determine the same work unit, have a colleague relationship if in the same department, and so on. Because the crowd data per se has larger data volume, the calculated amount for calculating the relation between every two is very large, and the calculation is very time-consuming in spark calculation, the data can be split and distributed to a plurality of operators for calculation, and therefore the calculation efficiency is improved. For example, the crowd data X is copied and recorded as Y, then the X is split into a plurality of parts (the data amount in the operators is recorded as L, if the relation data amount calculated by the operators is required to be not more than 100000, the split parts are L (L-1)/100000), then each group of data after the X split is matched with Y respectively, a plurality of operators are regenerated, and finally the two-by-two calculation in the operators is performed. Practical experience shows that the processing speed can be greatly improved through split calculation.
The same person may have multiple pieces of data, e.g., one person was at the discretion of multiple companies, etc., so that each group may be data filtered before the calculation is performed. By way of example, crowd data may be filtered through preset timelines to remove data that is not in preset timelines. The preset aging may be a month before, or the like at the current time point, or may be a month half, a week, or the like at the current time point, which is determined according to actual requirements, and this embodiment is not particularly limited.
In an exemplary embodiment, for an event venue, there is a time-sequential relationship between the data in the groups, so that the time-adjacent relationship of the second group can be determined according to the time interval between the people group data in the second group. For example, when an infectious disease patient has passed through an entrance, there is a possibility that a droplet with infectious virus or a residue on the surface of a contacted object remains, and the time interval for calculating the time adjacency may be set in consideration of the virus survival time and the disinfection time interval of the worker. The data between the second groups may be grouped again according to the time interval, for example, the data are grouped according to one hour, the field value of the time field of the first piece of crowd data is determined from the first piece of crowd data in the second group, for example, the field value is a, the data within one hour of a may be divided into the same group to obtain a third group, and the data in the same third group may be determined as a time adjacent relation between every two data. In addition, the time interval may include other durations, such as 2 hours, 30 minutes, etc., to which the present embodiment is not limited.
In an exemplary embodiment, after determining the relationship of crowd data, a plurality of data pairs with relationships can be obtained, and all relationships are summarized to obtain social relationship data. For example, after determining the relationship between two pieces of data, unique identifiers, relationship types, associated key fields, time and the like of two persons corresponding to the two pieces of data respectively may be stored in a uniform format, so as to obtain social relationship data. In addition, social relationship data may also be generated in other formats, such as storing two pieces of original crowd data in which a relationship exists, as well as relationship type, associated time, and so forth.
In other embodiments of the present disclosure, the method may further include other processing procedures, for example, different writing methods may exist in the same concept in crowd data of different scenes, for example, the details of the addresses are different, so that the crowd data may be standardized and processed to a uniform format; alternatively, the irregular data in the crowd data may be adjusted, for example, error correction processing may be performed, null data may be deleted, or normalization processing may be performed on the crowd data, which are all within the scope of the disclosure.
After social relationship data is obtained, when the close contact person of a target user needs to be tracked, the social relationship data of the target user can be obtained through the unique identification of the target user; and further obtaining target crowd related to the target user through social relationship data of the target user. Specifically, a target data record containing a unique identifier of a target user can be searched out from all social relationship data corresponding to crowd data, and then a unique identifier of another person in a relationship with the target user is output from the searched target data record, so that crowd in contact with the target user is obtained. According to the method and the device for searching the data, the intimate contact persons can be tracked more conveniently, the intimate contact persons in various dimensions can be found out through the unique identification of the target user, and if detailed data of the intimate contact persons are needed, the detailed data of the intimate contact persons can be directly extracted from the original crowd data without querying other databases again, so that the data can be tracked or verified more conveniently and rapidly.
Further, in this exemplary embodiment, a crowd relation determining device is further provided, which is configured to execute the crowd relation determining method disclosed in the disclosure. The device can be applied to a server or terminal equipment.
Referring to fig. 3, the crowd relationship determination device 300 may include: a crowd collecting module 310, a crowd grouping module 320, and a crowd relationship obtaining module 330, wherein:
the crowd collecting module 310 is configured to collect crowd data of each scene, and determine a unique identifier of each person in the crowd data.
The crowd grouping module 320 is configured to obtain key fields corresponding to the crowd data of each scene, and group the crowd data through the key fields to obtain a plurality of groups.
The crowd relation obtaining module 330 is configured to calculate social relation data of each group, so as to obtain a contact relation crowd of each person through the social relation data and the unique identifier.
In one exemplary embodiment of the present disclosure, crowd relationship acquisition module 330 is configured to: and determining social relation data of each group according to the scene characteristics of each group.
In an exemplary embodiment of the present disclosure, the apparatus further includes a data filtering module, configured to filter the crowd data according to a preset age, so as to remove crowd data that is not within the preset age.
In an exemplary embodiment of the present disclosure, the crowd data includes a time field, and the crowd grouping module 320 is specifically configured to: and when the scene characteristic is an activity place, grouping the crowd data through the key field and the time field corresponding to the activity place.
In an exemplary embodiment of the present disclosure, the crowd grouping module 320 may include a first grouping unit, a ranking unit, and a second grouping unit, wherein:
and the first grouping unit is used for grouping the crowd data corresponding to the activity place according to the key field so as to obtain a first group.
And the ordering unit is used for ordering the crowd data in the first group according to the time sequence of the time field.
The second grouping unit is used for dividing the sorting according to a preset time interval to obtain a second group.
In one exemplary embodiment of the present disclosure, crowd relationship acquisition module 330 may be configured to: and determining the time adjacent relation of the second group according to the time interval between the crowd data in the second group.
In one exemplary embodiment of the present disclosure, the crowd-relationship acquisition module 330 may include a data retrieval unit, and a target crowd acquisition unit, wherein:
and the data retrieval unit is used for acquiring social relationship data of the target user through the unique identification of the target user.
The target crowd acquisition unit is used for acquiring target crowd related to the target user through social relation data of the target user.
Since each functional module of the crowd-related determining device of the exemplary embodiment of the present disclosure corresponds to a step of the exemplary embodiment of the crowd-related determining method described above, for details not disclosed in the embodiment of the device of the present disclosure, please refer to the embodiment of the crowd-related determining method described above in the present disclosure.
The crowd relation determining method provided by the embodiment of the disclosure is generally executed by a server having a calculation processing function, and accordingly, the crowd relation determining device is generally disposed in the server. However, it is easily understood by those skilled in the art that the crowd relation determining method provided in the embodiment of the present disclosure may also be executed by a terminal device, for example, a computer, a tablet computer, a mobile phone, etc., and the crowd relation determining apparatus may also be disposed in the terminal device, which is not particularly limited in the present exemplary embodiment.
Fig. 4 shows a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
It should be noted that the computer system 400 of the electronic device shown in fig. 4 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present disclosure.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output portion 407 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
In particular, according to embodiments of the present disclosure, the processes described below with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. The computer program, when executed by a Central Processing Unit (CPU) 401, performs the various functions defined in the methods and apparatus of the present application.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by one of the electronic devices, cause the electronic device to implement the methods described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 1 and 2, and so on.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (6)

1. A crowd relationship determination method, comprising:
collecting crowd data of each scene, and determining unique identification of each person in the crowd data; the crowd data comprises a time field;
acquiring key fields corresponding to crowd data of each scene respectively, and grouping the crowd data corresponding to the activity places according to the key fields when the scene features are the activity places so as to acquire a first group; wherein the key field is one or more fields associated with the scene;
sorting the crowd data in the first group according to the time sequence of the time field;
dividing the sequencing according to a preset time interval to obtain a second group;
calculating social relation data of each group to obtain contact relation groups of each person through the social relation data and the unique identification;
the calculating social relation data of each group to obtain contact relation groups of each person through the social relation data and the unique identification includes:
determining a time adjacent relation of the second group according to the time interval between crowd data in the second group, and acquiring social relation data of a target user through the unique identification of the target user;
and acquiring target crowd related to the target user through the social relation data of the target user.
2. The method of claim 1, wherein the computing social relationship data for each group comprises:
and determining social relation data of each group according to the scene characteristics of each group.
3. The method of claim 1, further comprising, prior to determining social relationship data for each group:
and filtering the crowd data according to preset timeliness to remove the crowd data which are not in the preset timeliness.
4. A crowd relationship determination apparatus, comprising:
the crowd acquisition module is used for acquiring crowd data of each scene and determining unique identification of each person in the crowd data; the crowd data comprises a time field;
the crowd grouping module is used for acquiring key fields corresponding to crowd data of each scene respectively, and grouping the crowd data corresponding to the activity places according to the key fields when the scene features are the activity places so as to acquire a first group; wherein the key field is one or more fields associated with the scene; sorting the crowd data in the first group according to the time sequence of the time field; dividing the sequencing according to a preset time interval to obtain a second group;
the crowd relation acquisition module is used for calculating social relation data of each group so as to acquire contact relation crowd of each person through the social relation data and the unique identification; the calculating social relation data of each group to obtain contact relation groups of each person through the social relation data and the unique identification includes: determining a time adjacent relation of the second group according to the time interval between crowd data in the second group, and acquiring social relation data of a target user through the unique identification of the target user; and acquiring target crowd related to the target user through the social relation data of the target user.
5. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of any of claims 1-3 via execution of the executable instructions.
6. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1-3.
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