CN111933298A

CN111933298A - Crowd relation determination method, device, electronic equipment and medium

Info

Publication number: CN111933298A
Application number: CN202010817324.XA
Authority: CN
Inventors: 柯昆; 滕召荣
Original assignee: Yidu Cloud Beijing Technology Co Ltd
Current assignee: Yidu Cloud Beijing Technology Co Ltd
Priority date: 2020-08-14
Filing date: 2020-08-14
Publication date: 2020-11-13
Anticipated expiration: 2040-08-14
Also published as: CN111933298B

Abstract

The present disclosure provides a crowd relation determining method, a crowd relation determining apparatus, an electronic device, 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 the unique identification of each person in the crowd data; 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; and calculating social relationship data of each group to obtain contact relationship groups of each person through the social relationship data and the unique identification. The crowd relation determining method disclosed by the invention can overcome the problem of higher difficulty in searching for close contact users to a certain extent, and further improves the searching efficiency.

Description

Crowd relation determination method, 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 determining method, a crowd relationship determining apparatus, an electronic device, and a computer-readable medium.

Background

With the global outbreak of new coronavirus pneumonia epidemic, governments of various countries have begun to implement medical systems, especially the construction of real-time monitoring systems related to the epidemic, wherein the tracking of close contacts of patients is particularly important in the diffusion control of the epidemic. At present, social personnel who may be in close contact with a patient are generally screened through the activity track of the patient, but the difficulty in finding the personnel with the activity track coinciding with the patient is high, and the problem that inaccurate finding is easily caused by careless record of the activity track of the patient is also caused.

It is to be noted that the information disclosed in the above background section is only for enhancement of 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 present disclosure is directed to a method for determining a crowd relationship, a device for determining a crowd relationship, a computer readable medium, and an electronic device, which can overcome the problem of difficulty in finding a close contact user to a certain extent, and further improve the search efficiency.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to a first aspect of the present disclosure, there is provided a crowd relation determining method, including:

collecting crowd data of each scene, and determining the unique identification of each person in the crowd data;

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;

and calculating social relationship data of each group, and combining the social relationship data and the unique identification to obtain the contact population of each person.

In an exemplary embodiment of the present disclosure, the determining the social relationship data of the respective groups includes:

and determining the social relationship 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, the method further includes:

and filtering the crowd data according to a preset time limit so as to remove the crowd data which is not in the preset time limit.

In an exemplary embodiment of the disclosure, the crowd data includes a time field, and grouping the crowd data by the key field includes:

and when the scene features are activity places, grouping the crowd data through the key fields corresponding to the activity places and the time fields.

In an exemplary embodiment of the disclosure, grouping the crowd data by the time field and the key field corresponding to the activity place includes:

grouping the 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 an exemplary embodiment of the present disclosure, calculating the social relationship data of the respective groups 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 of the contact relation group of each person through the social relation data and the unique identifier includes:

acquiring social relationship data of a target user through the unique identifier of the target user;

and acquiring a 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, a crowd relation determining apparatus is provided, including a crowd collecting module, a crowd grouping module, and a crowd relation obtaining module, wherein:

and the crowd acquisition module is used for acquiring crowd data of each scene and determining the unique identification of each person in the crowd data.

And 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.

And the crowd relation acquisition module is used for calculating social relation data of each group so as to obtain the contact relation crowd of each person through the social relation data and the unique identifier.

In an exemplary embodiment of the disclosure, the crowd relation acquisition module is configured to: and determining the social relationship data of each group according to the scene characteristics of each group.

In an exemplary embodiment of the disclosure, the apparatus further includes a data filtering module, configured to filter the crowd data according to a preset time period, so as to remove the crowd data that is not within the preset time period.

In an exemplary embodiment of the disclosure, the crowd data includes a time field, and the crowd grouping module is specifically configured to: and when the scene features are activity places, grouping the crowd data through the key fields corresponding to the activity places and the time fields.

In an 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 to obtain a first group.

And the sorting unit is used for sorting the crowd data in the first group according to the time sequence of the time field.

And the second grouping unit is used for dividing the sorting according to a preset time interval so as to obtain a second group.

In an exemplary embodiment of the disclosure, the crowd relation 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 an exemplary embodiment of the present disclosure, the crowd relation acquiring module may include a data retrieving unit, and a target crowd acquiring unit, wherein:

and the data retrieval unit is used for acquiring the social relationship data of the target user through the unique identifier of the target user.

And the target crowd acquisition unit is used for acquiring the target crowd related to the target user through the social relationship 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 one 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 one of the above.

Exemplary embodiments of the present disclosure may have some or all of the following benefits:

in the method for determining the crowd relationship provided by the exemplary embodiment of the disclosure, on one hand, the collected data can be more comprehensive by collecting the crowd data of each scene, so that people are prevented from being overlooked when searching the contact crowd of the user, and the searching accuracy can 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 then the crowd with the contact relation with the user can be automatically found out through the unique identification of the user, so that the finding efficiency can be improved, and the finding 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 present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 schematically illustrates a flow diagram of a crowd relationship determination method according to one embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow diagram 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 apparatus according to one embodiment of the present disclosure;

FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different 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 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 disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. 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 their repetitive description 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 the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The technical solution of the embodiment of the present disclosure is explained in detail below:

the present exemplary embodiment provides a crowd relation determining method. Referring to fig. 1, the method may include the steps of:

step S110: the method comprises the steps of collecting crowd data of each scene, and determining unique identification of each person in the crowd data.

Step S120: and 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.

Step S130: and calculating social relationship data of each group to obtain contact relationship groups of each person through the social relationship data and the unique identification.

In the method for determining the crowd relationship provided by the above exemplary embodiment of the present disclosure, on one hand, the collected data can be more comprehensive by collecting the crowd data of each scene, so that people are prevented from being overlooked when searching for the contact crowd of the user, and the accuracy of searching can 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 then the crowd with the contact relation with the user can be automatically found out through the unique identification of the user, so that the finding efficiency can be improved, and the finding time can be shortened.

The above steps of the present exemplary embodiment will be described in more detail below.

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 every day in daily life, such as homes, companies, schools, subways, and the like. The crowd data refers to basic information registered by the user aiming at different scenes, and the crowd data under different scenes can contain different contents, for example, in a family scene, the crowd data can comprise the name, age, identification number, family address, family member and the like of the user; in a company scenario, the crowd data may include the company name, employee number, employee name, post, department, etc. where the user is located; in a school scenario, the user's school number, school name, class, etc. may be included in the demographic data. For example, a scene may be divided into a fixed place and an activity place according to the characteristics of the scene; the fixed location may be, for example, a company, school, community, etc., and the event location may include a vehicle, such as a subway, a flight, a cruise ship, a departure/entry, etc., and may also include a dining location, a play location, etc.; therefore, the crowd data may include various data such as user photos, flight numbers, entry and exit information, and the like according to actual situations, and this embodiment is not particularly limited thereto.

By acquiring crowd data of different scenes, data of different sources and different structures can be obtained. Exemplarily, the crowd data corresponding to the family scene can be obtained through census, new-born person information and the like; the crowd information corresponding to the family scene can be obtained through community personnel registration information; the crowd information of the company scene pair can be obtained through social security data; the information of the crowd corresponding to the activity place can be obtained through the entry and exit data.

After the crowd data of each scene is collected, the data can be collected, and each person in the crowd data is identified by using the effective certificate number in the crowd data, such as the data of an identity card number, a passport number and the like, so that a unique identifier is generated for each person code. By carrying out data processing on the crowd data through the unique identification, the crowd data can be distinguished and identified more easily in the subsequent data processing process. The unique identifier may be formed by a number, a combination of a number and a character, or formed by other methods, for example, by a combination of an alphanumeric character, and the like, which is not particularly limited in this embodiment. For example, the unique identifier of each person can be generated by sequentially increasing numbers, or the unique identifier of each person can be generated by a specific coding rule, and the like.

In step S120, key fields corresponding to the crowd data of each scene are obtained, and the crowd data are grouped by 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, for example, in a school scene, the key field may be a name of a school or a class, etc., in a subway scene, the key field may be a name of a subway or a name of an inbound or outbound station, etc. In this embodiment, key fields corresponding to different scenes may be predefined, and the key field corresponding to each scene may be obtained by obtaining a file that records the correspondence between the scenes and the key fields; or the key field can be marked in advance in the crowd data, so that the field with the mark in the crowd data is obtained as the key field according to the mark.

Grouping the crowd data through the key fields can divide the data with the same key fields into the same group, and divide the data with different key fields into different groups. Specifically, the field value of the key field in each piece of crowd data may be determined first, and then the groups with the same field value may be grouped. For example, the key field may be a school and a class, and students of the same school may be grouped into the same group through the key field, or students of the same school and the same class may be grouped into the same group through the key field. As another example, the key field may be flight number, passengers for the same flight number may be grouped into the same group, and so on. Grouping the crowd data can result in multiple groups, and people in the same group may have contact relationships.

In an exemplary embodiment, if the crowd data belongs to data corresponding to a scene of an event venue, the crowd data may be grouped by the key field as well as the time field. Illustratively, the key field corresponding to the event location may be an event location, such as a bus shift, flight number, etc. Since the event places are of a public nature, grouping may not be possible only by the event places, and thus may be done in a time plus event place manner. 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 to obtain a first group. The scene may specifically include an activity place, and when the scene is the activity place, the crowd data corresponding to the scene may be grouped according to the keyword, for example, the crowd data corresponding to the scene may be grouped according to the activity place, so as to obtain groups 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, and then the pieces of data in the first group are sorted according to the time sequence. The pieces of data in the first group after sorting may be arranged in order from morning to evening in time. Since the activity scene belongs to a common scene, it can be determined whether the same time period appears in the same place between different persons through time sequence, so that it can be determined whether there is a mutual contact relationship between persons. In the embodiment, the relationship between the users appearing in the activity place can be confirmed, and the relationship between the users and other users can be more comprehensively obtained according to the activity tracks of the users.

In step S230, the sorting is divided according to a preset time interval to obtain a second group. For example, the sorted first group may be divided from the data with larger difference before and after the time interval to obtain the second group. Specifically, after the data are arranged in time sequence, the time interval between every two adjacent data is calculated, and if the time interval between two adjacent data exceeds a preset value, the two adjacent data can be divided into smaller groups, so that the first group is divided into smaller groups. The preset value may be, for example, 1 hour, 2 hours, or the like, and may also 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 contact relationship groups of each person through the social relationship data and the unique identifier.

The social relationship data may include person-to-person relationships, with relationships possible in every two pieces of demographic data; the social relationship data may also include basic information of the persons having the relationship, such as name, identification number, unique identification, etc.; it may also include the type of relationship that exists, such as a colleague relationship, a classmate relationship, a parent-child relationship, etc. Illustratively, different scenes have different scene characteristics, and the relationship between data in each group can be obtained by using the scene characteristics. The scene characteristics can be the type of the scene, for example, the scene can be divided into families, schools, communities and the like; the relationship of the data of each group in different scenes can be determined as a corresponding scene type, for example, the group data of a family scene has a family member relationship, the data of a group in a company scene has a co-worker relationship, and the like. In addition, the corresponding relationship between the scene characteristics and the social relationship data can be predefined manually, for example, the scene characteristics are family scenes and can correspond to a primary contact relationship, the data in the group of the activity places can be a secondary contact relationship, and the like.

Because the data volume of the crowd data of each scene is large, the crowd data can be calculated through a big data analysis tool, and the relationship between every two people in each group is determined. For example, the spark calculation tool is used to determine the members in the same family, and the pairwise relationship between the members (such as father and son, brothers and sisters, and the like); people who are determined to be in the same work unit, have a colleague relationship if in the same department, and so on. Because the data volume of the crowd data is large, the calculation amount for calculating the relation between every two people is large, and time is consumed during spark calculation, the crowd data can be split and distributed to a plurality of operators for calculation, and therefore the calculation efficiency is improved. For example, one copy of the crowd data X is copied and recorded as Y, then the X is split into a plurality of copies (the data amount in the operator is recorded as L, if the relational data amount calculated by the operator is required to be not more than 100000, the split copies are L (L-1)/100000), then each set of data after the X is split is respectively matched with Y, a plurality of operators are generated again, and finally, pairwise calculation in the operators is performed. Practical experience has shown that the processing speed can be greatly increased by splitting the calculation.

There may be multiple pieces of data for the same person, e.g., one person has been at work with multiple companies, etc., so that data filtering may be performed on each group before performing calculations. For example, the crowd data may be filtered by the preset time limit, and data that is not in the preset time limit may be removed. The preset time limit may be the previous month, the previous two months, or the like of the current time point, or may also be the half month, the week, or the like of the current time point, and is determined according to actual requirements, which is not particularly limited in this embodiment.

In an exemplary embodiment, there is a chronological relationship between the data in the group for the event venue, so the temporal proximity relationship of the second group can be determined according to the time interval between the crowd data in the second group as described above. For example, the arrival mouth through which the patient with infectious disease has passed may have a droplet with infectious virus or a surface residue of the contacted object, and the time interval for calculating the time-adjacent relationship may be set in consideration of the survival time of the virus and the time interval for disinfecting the staff. The data in the second group may be grouped again according to the time interval, for example, according to one hour, and a field value of a 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 in 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 to be in a time-adjacent relationship. Furthermore, the time interval may include other time periods, such as 2 hours, 30 minutes, and the like, and the embodiment is not limited thereto.

In an exemplary embodiment, after determining the relationship of the crowd data, a plurality of data pairs with relationship may be obtained, and all relationships may be summarized to obtain social relationship data. For example, after determining the relationship between two pieces of data, the unique identifiers, relationship types, associated key fields, time, and the like of two persons corresponding to the two pieces of data are stored according to 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 raw crowd data that have a relationship, as well as the type of relationship, the time of association, and so on.

In other embodiments of the present disclosure, the method may further include other processing procedures, for example, different writing methods may exist for the same concept in crowd data of different scenes, such as different details of addresses, and thus the crowd data may be standardized and processed into a uniform format; alternatively, the irregular data in the crowd data may be adjusted, for example, error correction processing is performed, null data is deleted, or the like, or normalization processing is performed on the crowd data, which all belong to the protection scope of the present disclosure.

After the social relationship data is obtained, when a 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 then obtaining a target crowd related to the target user through the social relation data of the target user. Specifically, a target data record containing the unique identifier of the target user can be found out from all social relationship data corresponding to the crowd data, and then the unique identifier of another person who has a relationship with the target user is output from the found target data record, so that the crowd who has contact with the target user is obtained. Through the implementation mode, the close contact persons can be tracked more conveniently, the close contact persons with various dimensions can be found out through the unique identification of the target user, and if the detailed data of the close contact persons are needed, the detailed data can also be directly extracted from the original crowd data without inquiring other databases again, so that the data can be traced or verified more conveniently and quickly.

Further, in the present exemplary embodiment, a crowd relation determining apparatus is also provided, which is configured to execute the crowd relation determining method of the present disclosure. The device can be applied to a server or terminal equipment.

Referring to fig. 3, the crowd relation determining apparatus 300 may include: a crowd collection module 310, a crowd grouping module 320, and a crowd relationship acquisition module 330, wherein:

and the crowd acquisition module 310 is configured to acquire crowd data of each scene and determine a unique identifier of each person in the crowd data.

And a crowd grouping module 320, configured to obtain key fields corresponding to the crowd data of each scene, and group the crowd data through the key fields to obtain multiple groups.

The crowd relation obtaining module 330 is configured to calculate social relation data of each group, so as to obtain contact relation crowd of each person through the social relation data and the unique identifier.

In an exemplary embodiment of the disclosure, the crowd relation acquisition module 330 is configured to: and determining the social relationship data of each group according to the scene characteristics of each group.

In an exemplary embodiment of the disclosure, the crowd data includes a time field, and the crowd grouping module 320 is specifically configured to: and when the scene features are activity places, grouping the crowd data through the key fields corresponding to the activity places and the time fields.

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:

In an exemplary embodiment of the disclosure, the crowd relation 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 an exemplary embodiment of the disclosure, the crowd relation acquiring module 330 may include a data retrieving unit, and a target crowd acquiring unit, wherein:

For details that are not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the crowd relationship determining method described above for the details that are not disclosed in the embodiments of the apparatus of the present disclosure.

The crowd relationship determining method provided by the embodiment of the disclosure is generally executed by a server having a computing processing function, and accordingly, the crowd relationship determining apparatus is generally disposed in the server. However, it is easily understood by those skilled in the art that the method for determining a crowd relationship provided in the present disclosure may also be executed by a terminal device, such as a computer, a tablet computer, a mobile phone, and the like, and accordingly, the device for determining a crowd relationship may also be disposed in the terminal device, which is not particularly limited in the present exemplary embodiment.

It should be noted that the computer system 400 of the electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.

As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with 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 necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via 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 section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and 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. A driver 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 mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.

In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. 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 illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program, when executed by a Central Processing Unit (CPU)401, performs various functions defined in the methods and apparatus of the present application.

It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 present 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 contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 flowchart 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 described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as 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 the 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, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

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 variations, 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 will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining a crowd relationship, comprising:

and calculating social relationship data of each group to obtain contact relationship groups of each person through the social relationship data and the unique identification.

2. The method of claim 1, wherein computing the social relationship data for each group comprises:

3. The method of claim 1, wherein prior to determining the social relationship data for each group, further comprising:

4. The method of claim 2, wherein the crowd data includes a time field, and wherein grouping the crowd data by the key field comprises:

5. The method of claim 4, wherein grouping the demographic data by the time field and the corresponding key field of the arena comprises:

6. The method of claim 4, wherein computing social relationship data for each group comprises:

7. The method of claim 1, wherein obtaining the contact-related population of each person through the social relationship data and the unique identifier comprises:

8. A crowd relationship determination device, comprising:

the crowd acquisition module is used for acquiring crowd data of each scene and determining the 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;

9. 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 1-7 via execution of the executable instructions.

10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 7.