CN111062823A - Social graph analysis method and device and storage medium - Google Patents
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Abstract
The application relates to a social graph analysis method, a social graph analysis device and a storage medium, and belongs to the technical field of data processing. The method is applied to a big data platform, and comprises the following steps: acquiring trajectory data of high-risk forecasters; calculating by using the identity of the high-risk forecaster as a vertex and the track data as an edge and using a Spark graph to obtain the social graph relationship of the high-risk forecaster; and storing the social graph relation into a document database. According to the embodiment of the application, by acquiring the track data of the high-risk forecaster, taking the identity of the high-risk forecaster as a vertex and taking the track data as an edge, the social graph relationship of the high-risk forecaster is obtained by utilizing the Spark graph to calculate, the abstract track data is converted into a specific relationship network graph, visual analysis is provided for business personnel, the working intensity in the daily case detection process is reduced, and the working efficiency is improved.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a social graph analysis method, a social graph analysis device and a storage medium.
Background
Drug crime is one of the prominent expression forms of new-period criminal crime, and is a prominent problem influencing the stability of social security and economic development at present. The development and spread of drug problems bring great harm to political, economic and social life, and the drug problems become an important factor influencing the security and stability of society.
With the advent of big data and the internet of things era, a large quantity of data is provided, more possibilities are provided for public security organs to obtain case detection clues, but on the other hand, the traditional business system has many defects in the aspect of bearing big data, the difficulty of discovering clues by business personnel is increased to a certain extent, and the requirements on analysis of the drug-related illegal crime are not met.
Disclosure of Invention
In view of this, an object of the present application is to provide a social graph analysis method, apparatus and storage medium, which are used to draw a social graph of a high-risk virus-related person by means of big data technology, convert abstract trajectory data into a specific relationship network graph, provide visual analysis for service personnel, improve capabilities of investigation, research and judgment, and information cue concatenation, reduce the intensity of checking work in a daily case detection process, and improve work efficiency.
The embodiment of the application is realized as follows:
in a first aspect, an embodiment of the present application provides a social graph analysis method, which is applied to a big data platform, and the method includes: acquiring trajectory data of high-risk forecasters; calculating by using the identity of the high-risk forecaster as a vertex and the track data as an edge and using a Spark graph to obtain the social graph relationship of the high-risk forecaster; and storing the social graph relation into a document database. According to the embodiment of the application, by acquiring the track data of the high-risk forecaster, taking the identity of the high-risk forecaster as a vertex and taking the track data as an edge, the social graph relationship of the high-risk forecaster is obtained by utilizing the Spark graph calculation, and by means of a big data technology, abstract track data is converted into a specific relationship network graph, so that visual analysis is provided for business personnel, the working intensity in the daily case detection process is reduced, and the working efficiency is improved.
With reference to a possible implementation manner of the embodiment of the first aspect, before acquiring trajectory data of high-risk predicted personnel, the method further includes: acquiring trajectory data of existing virus-related personnel; inputting the trajectory data of the existing virus-related personnel into a pre-trained high-risk prediction model for processing to obtain the high-risk prediction personnel. In the embodiment of the application, the track data of the confirmed virus-related personnel is obtained, the high-risk prediction personnel is obtained by predicting the high-risk prediction model trained in advance, and then the social graph of the high-risk virus-related personnel is drawn, so that the feasibility of the drawn social graph on the analysis of the illegal virus-related crimes is ensured, the existing virus-related personnel are excluded, and the phenomenon that the personnel is collected and monitored after a large amount of time is consumed in the analysis is avoided, and the extra labor cost is wasted.
With reference to a possible implementation manner of the embodiment of the first aspect, the acquiring trajectory data of the high-risk predicted personnel includes: acquiring daily data of high-risk forecasters; and filtering and cleaning the daily data to obtain the track data. According to the embodiment of the application, useless information is removed by filtering and cleaning the acquired daily data of the high-risk prediction personnel, so that the computing resources are saved, and the efficiency is improved.
With reference to one possible implementation manner of the embodiment of the first aspect, after storing the social-graph relationship in a document database, the method further includes: receiving a query request for querying a social graph of a target person, which is sent by a server communicating with the big data platform; and acquiring social graph relation data corresponding to the query request, and feeding back the social graph relation data to the server. In the embodiment of the application, a query function is introduced, so that a user can conveniently obtain social graph relation data of a target person to be queried, and visual analysis reference is provided for business persons.
With reference to one possible implementation manner of the embodiment of the first aspect, the trajectory data includes: at least one of call data, short message data, logistics data, trip data and trip and stay data. In the embodiment of the application, data information of multiple dimensions is obtained to ensure that the finally depicted social graph of the high-risk virus-involved personnel has reference value as much as possible.
In a second aspect, an embodiment of the present application further provides a social graph analysis method, applied to a server, where the method includes: responding to a query operation aiming at a target person input by a user, and sending a first query request for querying a social graph of the target person to a big data platform communicated with the server; receiving first social graph relation data returned by the big data platform in response to the first query request; and generating and displaying the social graph of the target person based on the first social graph relation data. In the embodiment of the application, the server responds to the query operation aiming at the target person and input by the user, sends the first query request for querying the social graph of the target person to the big data platform, and generates and displays the social graph of the target person based on the first social graph relation data returned by the big data platform responding to the first query request, so that business persons can intuitively realize the requirement on analysis of the offending criminals of the classified drugs based on the displayed social graph.
With reference to one possible implementation manner of the embodiment of the second aspect, after generating and presenting the social graph of the target person based on the first social-graph relationship data, the method further includes: sending a second query request for querying the social graph of the social persons to the big data platform in response to the query operation input by the user and aiming at the social persons except the target person in the social graph; receiving second social graph relationship data returned by the big data platform in response to the second query request; and merging and de-duplicating the first social graph relation data and the second social graph relation data, and generating and displaying a secondary social graph of the target person. In the embodiment of the application, the server can further respond to the query operation of the user for the social persons except the target person in the displayed social graph, send a second query request for querying the social graph of the social person to the big data platform, and generate and display a second-level social graph of the target person based on second social graph relation data returned by the big data platform in response to the second query request after the second query request is deduplicated with the first social graph relation data.
In a third aspect, an embodiment of the present application further provides a social graph analysis apparatus, which is applied to a big data platform; the device comprises: the device comprises an acquisition module, a calculation module and a storage module; the acquisition module is used for acquiring the trajectory data of the high-risk forecaster; the calculation module is used for calculating and obtaining the social graph relation of the high-risk forecaster by using the identity of the high-risk forecaster as a vertex and the track data as an edge and utilizing a Spark graph; and the storage module is used for storing the social graph relation into a document database.
In a fourth aspect, an embodiment of the present application further provides a social graph analysis apparatus, applied to a server, where the apparatus includes: the device comprises a sending module, a receiving module and a generating and displaying module; the sending module is used for responding to a query operation aiming at a target person input by a user and sending a first query request for querying a social graph of the target person to a big data platform communicated with the server; the receiving module is used for receiving first social graph relation data returned by the big data platform in response to the first query request; and the generating and displaying module is used for generating and displaying the social graph of the target person based on the first social graph relation data.
In a fifth aspect, this embodiment of the present application further provides a storage medium, on which a computer program is stored, where the computer program is executed by a computer to perform the foregoing first aspect embodiment and/or the method provided in connection with any one of the possible implementations of the first aspect embodiment, or to perform the foregoing second aspect embodiment and/or the method provided in connection with any one of the possible implementations of the second aspect embodiment.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts. The foregoing and other objects, features and advantages of the application will be apparent from the accompanying drawings. Like reference numerals refer to like parts throughout the drawings. The drawings are not intended to be to scale as practical, emphasis instead being placed upon illustrating the subject matter of the present application.
Fig. 1 shows a flow chart diagram of a social graph analysis method applied to a big data platform according to an embodiment of the present application.
Fig. 2 is a schematic diagram illustrating a social graph relationship of a target person according to an embodiment of the present disclosure.
Fig. 3 shows a flowchart of a social graph analysis method applied to a server according to an embodiment of the present application.
FIG. 4 is a diagram illustrating a secondary social-graph relationship of a target person provided by an embodiment of the present application.
Fig. 5 shows a module schematic diagram of a social graph analysis apparatus applied to a big data platform according to an embodiment of the present application.
Fig. 6 shows a module schematic diagram of a social graph analysis apparatus applied to a server according to an embodiment of the present application.
Fig. 7 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, relational terms such as "first," "second," and the like may be used solely in the description herein to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Further, the term "and/or" in the present application is only one kind of association relationship describing the associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone.
Referring to fig. 1, steps included in a social graph analysis method applied to a big data platform according to an embodiment of the present application will be described with reference to fig. 1.
Step S101: and acquiring the trajectory data of the high-risk forecaster.
With the development of the internet of things technology, the third-party public security system can realize the convergence of various data resources through an information resource service platform (a big data platform) responsible for data collection or a data convergence platform based on a security domain, a smart city, a community and other means, and obtain daily data representing the activity track of specific personnel, such as conversation data, logistics data, trip and stay data. In order to reduce the data storage amount, optionally, after the third-party public security system obtains the daily data of a specific person, the daily data can be filtered and cleaned, and useless data can be removed. Of course, in order to ensure the integrity of the data, the third-party public security system can also directly store the daily data of the specific personnel after obtaining the daily data.
The big data platform (for example, hadoop) acquires data representing the activity track of high-risk forecaster such as conversation data, logistics data, trip data and trip and stay data of the high-risk forecaster by butting a third-party public security system. As an implementation mode, the data representing the activity track of the high-risk forecaster, which is acquired by the big data platform from the third-party public security system, is daily data which is filtered and cleaned. As another embodiment, the data representing the activity track of the high-risk forecaster, which is acquired by the big data platform (e.g., hadoop) from the third-party public security system, may also be original daily data, and in order to save computing resources and improve computing efficiency and accuracy of a computing result, as an embodiment, after acquiring the daily data of the high-risk forecaster, the big data platform (e.g., hadoop) performs preprocessing such as filtering and cleaning on the daily data, and as track data after preprocessing, for example, the acquired daily data is filtered and cleaned by mapreduce or hive and then stored in a distributed file system (HDFS).
It should be noted that the trajectory data may be at least one of call data, short message data, logistics data, travel data, and travel and stay data. The trajectory data of different high-risk forecasters may be different, and the dimensions covered by the trajectory data of some high-risk forecasters are complete and can simultaneously comprise call data, short message data, logistics data, trip data and trip and stay data; some may be more singular and may only have data for a call.
Wherein, the appointed personnel comprise high-risk prediction personnel, existing virus-related personnel and the like. The high-risk forecaster is obtained by forecasting based on the trajectory data of the existing virus-related personnel. As an implementation manner, the high-risk forecaster can be obtained by forecasting by using a pre-trained high-risk poison-related forecasting model, namely, trajectory data of existing high-risk poison-related forecasters is input into the pre-trained high-risk poison-related forecasting model for processing, so that the high-risk forecaster can be obtained. The high-risk prediction model can be a common neural network model, and is trained by using the trajectory data of the existing virus-related personnel in advance, so that the correlation among the existing virus-related personnel can be learned, and the trained natural model can be obtained for subsequent use.
Optionally, the big data platform (for example, hadoop) may obtain a list of the people involved in the virus by interfacing with a third-party public security system, then obtain daily data such as daily call data, logistics data, trip entrance data and the like of the people involved in the virus, store the obtained raw data into the HDFS after performing mapreduce or hive filtering and cleaning, introduce the obtained trajectory data of the people involved in the virus into a pre-trained high-risk prediction model, obtain the latest high-risk predictor, store the latest high-risk predictor in the HDFS, and obtain and store the trajectory data of the high-risk predictor. That is, in this embodiment, before acquiring trajectory data of a high risk forecaster, the method further comprises: acquiring trajectory data of existing virus-related personnel; inputting the trajectory data of the existing virus-related personnel into a pre-trained high-risk prediction model for processing to obtain high-risk prediction personnel.
When various data are stored, classified storage can be performed, that is, the data of the same type are stored in the same database, and the data of different types are stored in different databases, for example, the obtained basic information of the high-risk forecaster is stored in the mysql database, the corresponding social graph data is stored in the mongodb database, and the basic information of the social people involved in the corresponding social graph relationship network is stored in the elastic search database.
Step S102: and calculating by using the identity of the high-risk forecaster as a vertex and the track data as an edge and utilizing a Spark graph to obtain the social graph relationship of the high-risk forecaster.
After the trajectory data of the high-risk forecaster is obtained, the social graph relationship of the high-risk forecaster is obtained by taking the identity (such as an 18-bit identity card number) of the high-risk forecaster as a vertex and the trajectory data of the high-risk forecaster as an edge and calculating by using a Spark graph x, and the abstract trajectory data is converted into a specific relationship network graph to provide visual analysis for service personnel.
Because the offender of the drug-related law violation usually adopts various concealing and disguising modes for evading legal sanctions, but the criminal is objective and has more obvious characteristics, namely: the drug addict has higher relapse rate, the relation between recessive and dominant drug-involved persons is fixed when the drug addicts enter the post for multiple times, the membership relations are mutually crossed, the process from drug trafficking to drug addicts is carried out layer by layer, and the higher-level analysis and mining function of illegal criminal offence is realized by analyzing the social graph relation of high-risk forecasters. For example, the communication with the high-risk forecaster is frequent, or the communication with a plurality of high-risk forecasters is carried out simultaneously, or a certain high-risk forecaster enters a specific place, or a certain person is seen in a certain place in a certain fixed time period, and the like, the persons are likely to be the toxic-related persons, and the places are likely to be the toxic-related places, so that the attention can be paid.
For example, with the identity card number of the high-risk forecaster a as a Vertex (Vertex RDD), and with the corresponding trajectory data (including call, short message, logistics, trip and check-in) as an Edge (Edge RDD), calculating by using a Spark graph, to obtain the social graph relationship of the high-risk forecaster a, assuming that an agent having a call record with the agent a in the relationship network includes B, C, D and an agent having a logistics with the agent a includes B, E, the trip location of the agent a includes SZ city and CD city, and the check-in location includes a hotel located in SZ city. For ease of understanding, reference may be made to the social-graph relationships illustrated in FIG. 2. It should be noted that the trajectory data of different high-risk predictors may be different, the dimensions covered by the trajectory data of some high-risk predictors are relatively complete, some high-risk predictors are relatively single, and accordingly, the obtained relationship networks of the social graph relationships are different. For example, the social graph relationship of the high-risk forecaster B is calculated by taking the identification number of the high-risk forecaster B as a Vertex (Vertex RDD), taking the corresponding track data (including only calls and logistics) as an Edge (Edge RDD), and using a Spark graph x map, for example, an agent having a call record with B in the relationship network includes A, C, F, and an agent having a logistics communication with B includes A, Q. Further, the acquired trajectory data is trajectory data within a preset time, such as the trajectory data of the day of acquisition, or the trajectory data of the first three days, and the like. Wherein, the longer the preset time period is, the more track data are obtained, and the more complex the relationship network is.
Step S103: and storing the social graph relation into a document database.
After the social graph relation of the high-risk forecaster is obtained, the social graph relation is stored in a document database, for example, the mongodb database, so that subsequent analysis and query are facilitated. For example, the data format stored is as follows:
{
pictureId：“123456”，sfzh：“123456”，
Version：“20190827”，
edge：[
{from“123456”to“432154”，
Type：call，time：“2017-07-01”}，
{from：“123456”to“432145”，
Type：call，time：“2017-07-01”}
]}
the "123456", "432154", and "432145" all represent identification numbers, and the "20190827" represents a version number (may be creation time as a version number). Wherein pictureId is a vertex, edge is association data, from is an initiator, to is a receiver, Version is a Version number, and Type is a Type including call (call), short message (message), logistics (logistics), trip (go), place (place), and the like. Wherein the above only shows data comprising only call records.
In order to facilitate the query of social spectrogram data of high-risk predictors, the big data platform further provides a data interface (such as an http interface) communicated with the server, and the server can obtain the spectrogram data of the target person to be queried through the http interface. Optionally, the method further comprises: receiving a query request for querying a social graph of a target person, which is sent by a server communicating with the big data platform; and acquiring social graph relation data corresponding to the query request, and feeding back the social graph relation data to the server. In this embodiment, when receiving an inquiry request for inquiring the map data of a target person sent by a server, the big data platform parses the inquiry request, obtains the identification information (e.g., an identification number) of the target person carried in the inquiry request, searches the social graph relationship data corresponding to the identification information from the database, and feeds back the found social graph relationship data corresponding to the target person to the server.
Referring to fig. 3, steps included in a social graph analysis method applied to a server according to an embodiment of the present application will be described with reference to fig. 3.
Step S201: and responding to a query operation aiming at a target person input by a user, and sending a first query request for querying a social graph of the target person to a big data platform communicated with the server.
In order to facilitate the query of the social spectrogram data of the high-risk prediction personnel, the server can recommend a list of the high-risk prediction personnel to the client corresponding to the server periodically, so that the user can query the social spectrogram relation data of each high-risk prediction personnel in the list. For example, the server may recommend a list of high-risk predictors to a corresponding Web page, and the user may browse the list by accessing the Web page at the specified address, and then may input the identification information of the high-risk predictors shown in the list in the query input box of the Web page display interface, so as to browse the corresponding social graph relationship data, or may browse the social graph relationship data corresponding to the user by directly clicking a certain person in the list.
When a user inputs the identification information of a certain target person in a query input box of a client, such as a Web page, or directly clicks the certain target person in a list, a server responds to a query operation aiming at the target person input by the user and sends a first query request for querying the social graph of the target person to a big data platform communicated with the server. The first query request carries identification information of a target person, where the identification information may be an identification number, a mobile phone number for real-name authentication, or an account number for other real-name authentication (e.g., a paypal account number).
The big data platform provides a data interface (such as an http interface) for communicating with the server, and the server can obtain the map data of the target person to be queried through the http interface.
Step S202: and receiving first social graph relation data returned by the big data platform in response to the first query request.
After receiving a first query request sent by a server, a big data platform analyzes the first query request, obtains identification information (for example, an identification number) of a target person carried in the first query request, searches social graph relation data corresponding to the identification information from a database, and feeds back the found social graph relation data (first social graph relation data) corresponding to the target person to the server.
Step S203: and generating and displaying the social graph of the target person based on the first social graph relation data.
After receiving first social graph relation data returned by the big data platform in response to the first query request, the server generates and displays a social graph of the target person based on the first social graph relation data, so that the user can see the social graph of the target person on the client, wherein the representation is shown in fig. 2.
The client serves as a medium for interaction between the user and the server, the user can send a query request to the server through the client, and the server can display corresponding data to the user through the client to further complete man-machine interaction. The client is installed on a user terminal, such as a computer, a smart phone, a tablet and other terminals.
The first-level graph only shows the social relationship of the target person, so that the mining of the drug-related group is facilitated, the social graphs of other persons except the target person in the first-level graph can be further obtained to obtain a second-level graph of the target person, and the second-level graph not only shows the social relationship of the target person, but also shows the social situations of persons closely related to the target person. The user can further obtain the attributes of the member information in the social graph and the graph data corresponding to the member to obtain the secondary social graph of the target person. For example, a social graph of member B in the social graph of A (i.e., a secondary graph of A) is obtained for the target person. That is, after step S203, the user may input the identification information of the social persons other than the target person in the social graph of the target person in the query input box of the client (e.g., a Web page or other APP), or directly click on the social persons other than the target person in the social graph, and the server responds to the query operation input by the user for the social persons other than the target person in the social graph, sends a second query request for querying the social graph of the social person to the big data platform, and receives second social graph relationship data returned by the big data platform in response to the second query request; and merging and de-duplicating the first social graph relation data and the second social graph relation data, and generating and displaying a secondary social graph of the target person. The second query request carries identity information of the social contact person, where the identity information may be an identity card number, a mobile phone number authenticated by a real name, or an account number authenticated by another real name (e.g., a paypal account number).
For convenience of understanding, the graph data of the target person a shown in fig. 2 is taken as an example, and it is assumed that the user inputs a query operation for the social graph of the social person B at this time, the server sends a second query request for obtaining the social graph of the social person B to the big data platform in response to the query operation. After receiving the second query request, the big data platform analyzes the second query request, obtains the identification information (e.g., identification number) of the social person B carried in the second query request, searches the social graph relationship data corresponding to the identification information from the database, and feeds back the found social graph relationship data (second social graph relationship data) corresponding to the second query request to the server. After receiving the second social-graph relationship data, the server merges and deduplicates the first social-graph relationship data and the second social-graph relationship data, generates and displays a secondary social graph of the target person a, and the displayed graph is shown in fig. 4. In the exemplary diagram shown in fig. 4, only the social graph of the social person B is shown, and the graph data of the social person C, the social person D, and the social person E may also be simultaneously shown. The principle of obtaining the respective graph data of the social person C, the social person D, and the social person E is similar to the principle of obtaining the graph data of the social person B, and is not described again.
The problem of overlarge social data amount needs to be considered in the display, the social graph data of the target person needing to be displayed is few, the social graph data can be directly displayed, but the data amount is overlarge, the problems of page blocking and the like can occur if the social graph data are directly displayed, the filtering condition can be increased, and if the danger degree is adopted as the filtering condition, people with low danger degree and little display significance are filtered; or adopting a time filtering condition, and only including the latest social data; or filtering out travel and/or check-in information in the relationship network; through the filtering conditions, some data with less important relative significance can be filtered, and the problem of page jamming caused by overlarge data volume is solved.
As an implementation mode, the risk value of the social persons in the relationship network can be determined through the communication frequency with the target person, and the higher the communication frequency with the target person is, the higher the corresponding score value is.
As shown in fig. 5, an embodiment of the present application further provides a social graph analysis apparatus 100 applied to a big data platform, including: an acquisition module 110, a calculation module 120, and a storage module 130.
The obtaining module 110 is configured to obtain trajectory data of high-risk forecasted personnel.
And the calculating module 120 is configured to calculate and obtain the social graph relationship of the high-risk forecaster by using the identity of the high-risk forecaster as a vertex and the trajectory data as an edge and using a Spark graph x map.
A storage module 130, configured to store the social graph relationship in a document database.
Optionally, the obtaining module 110 is specifically configured to: acquiring daily data of high-risk forecasters; and filtering and cleaning the daily data to obtain the track data.
Optionally, the obtaining module 110 is further configured to obtain trajectory data of existing toxic-related personnel before obtaining trajectory data of high-risk predicted personnel. Accordingly, the social graph analysis apparatus 100 further includes: and an input module. The input is processed by inputting the trajectory data of the existing virus-related personnel into a pre-trained high-risk prediction model, so that the high-risk prediction personnel are obtained.
Optionally, the social graph analysis apparatus 100 further includes: a receiving module, configured to receive, after the storage module 130 stores the social graph relationship in a document database, a query request for querying a social graph of a target person, where the query request is sent by a server in communication with the big data platform; accordingly, the obtaining module 110 is further configured to obtain social-graph relationship data corresponding to the query request, and feed back the social-graph relationship data to the server.
The social graph analysis apparatus 100 provided in the embodiment of the present application has the same implementation principle and technical effect as the aforementioned method embodiment applied to the social graph analysis method in the big data platform, and for brief description, reference may be made to corresponding contents in the previous method embodiment for what is not mentioned in the apparatus embodiment.
As shown in fig. 6, an embodiment of the present application further provides a social graph analysis apparatus 200 applied to a server, including: a sending module 210, a receiving module 220, and a generating and presenting module 230.
The sending module 210 is configured to send, to a big data platform in communication with the server, a first query request for querying a social graph of a target person in response to a query operation for the target person input by a user.
A receiving module 220, configured to receive first social-graph relationship data returned by the big data platform in response to the first query request.
And a generating and displaying module 230, configured to generate and display a social graph of the target person based on the first social graph relationship data.
Optionally, the sending module 210 is further configured to, after the generating and presenting module 230 generates and presents the social graph of the target person based on the first social-graph relationship data, respond to the query operation input by the user for the social people in the social graph except for the target person, and send a second query request for querying the social graph of the social people to the big data platform; correspondingly, the receiving module 220 is further configured to receive second social-graph relationship data returned by the big data platform in response to the second query request; the generating and displaying module 230 is further configured to generate and display a secondary social graph of the target person after merging and deduplicating the first social graph relationship data and the second social graph relationship data.
The social graph analysis apparatus 200 provided in the embodiment of the present application has the same implementation principle and technical effect as the aforementioned method embodiment applied to the social graph analysis method in the server, and for brief description, reference may be made to corresponding contents in the previous method embodiment where no mention is made in part of the apparatus embodiment.
As shown in fig. 7, fig. 7 is a block diagram illustrating a structure of an electronic device 300 according to an embodiment of the present disclosure. The electronic device 300 includes: a data interface 310, a memory 320, a communication bus 330, and a processor 340.
The components of the data interface 310, the memory 320, and the processor 340 are electrically connected to each other through one or more communication buses 330 or signal lines. The data interface 310 is used for transceiving data. The memory 320 is used for storing a computer program, such as a software functional module shown in fig. 5, namely the social graph analysis apparatus 100, or a software functional module shown in fig. 6, namely the social graph analysis apparatus 200. The social graph analysis apparatus 100 or 200 includes at least one software function module, which may be stored in the memory 320 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the electronic device 300. When the processor 340 runs the software functional module shown in fig. 5, the processor 340 is configured to obtain trajectory data of the high-risk forecaster; the social graph relation of the high-risk forecaster is calculated by taking the identity of the high-risk forecaster as a vertex and the track data as an edge and utilizing a SparkGraphx graph; and further for storing the social-graph relationship in a document database. When the processor 340 runs the software functional module shown in fig. 6, the processor 340 is configured to send a first query request for querying a social graph of a target person to a big data platform in communication with the server in response to a query operation for the target person input by a user; and further configured to receive first social-graph relationship data returned by the big data platform in response to the first query request; and the social graph of the target person is generated and displayed based on the first social graph relation data.
The Memory 320 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The electronic device 300 is a big data platform or a server. The server is not limited to a web server, a database server, a cloud server, and the like. Big data platforms include, but are not limited to, hadoop platforms, Lambda platforms.
The present embodiment also provides a non-volatile computer-readable storage medium (hereinafter, referred to as a storage medium), where the storage medium stores a computer program, and when the computer program is executed by the electronic device 300, the computer program executes the social graph analysis method shown in fig. 1, or executes the social graph analysis method shown in fig. 3.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a notebook computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A social graph analysis method is applied to a big data platform, and comprises the following steps:
acquiring trajectory data of high-risk forecasters;
calculating by using the identity of the high-risk forecaster as a vertex and the track data as an edge and using a Spark graph to obtain the social graph relationship of the high-risk forecaster;
and storing the social graph relation into a document database.
2. The method of claim 1, wherein prior to obtaining trajectory data for high risk forecaster personnel, the method further comprises:
acquiring trajectory data of existing virus-related personnel;
inputting the trajectory data of the existing virus-related personnel into a pre-trained high-risk prediction model for processing to obtain the high-risk prediction personnel.
3. The method of claim 1, wherein obtaining trajectory data for high risk forecaster personnel comprises:
acquiring daily data of high-risk forecasters;
and filtering and cleaning the daily data to obtain the track data.
4. The method of claim 1, wherein after storing the social-graph relationship in a document database, the method further comprises:
receiving a query request for querying a social graph of a target person, which is sent by a server communicating with the big data platform;
and acquiring social graph relation data corresponding to the query request, and feeding back the social graph relation data to the server.
5. The method of any of claims 1-4, wherein the trajectory data comprises: at least one of call data, short message data, logistics data, trip data and trip and stay data.
6. A social graph analysis method is applied to a server, and the method comprises the following steps:
responding to a query operation aiming at a target person input by a user, and sending a first query request for querying a social graph of the target person to a big data platform communicated with the server;
receiving first social graph relation data returned by the big data platform in response to the first query request;
and generating and displaying the social graph of the target person based on the first social graph relation data.
7. The method of claim 6, wherein after generating and presenting the social graph of the target person based on the first social-graph relationship data, the method further comprises:
sending a second query request for querying the social graph of the social persons to the big data platform in response to the query operation input by the user and aiming at the social persons except the target person in the social graph;
receiving second social graph relationship data returned by the big data platform in response to the second query request;
and merging and de-duplicating the first social graph relation data and the second social graph relation data, and generating and displaying a secondary social graph of the target person.
8. The social graph analysis device is applied to a big data platform; the device comprises:
the acquisition module is used for acquiring the trajectory data of the high-risk forecaster;
the calculation module is used for calculating and obtaining the social graph relation of the high-risk forecaster by using the identity of the high-risk forecaster as a vertex and the track data as an edge and utilizing a Spark graph;
and the storage module is used for storing the social graph relation into a document database.
9. A social graph analysis device applied to a server, the device comprising:
the sending module is used for responding to a query operation aiming at a target person input by a user and sending a first query request for querying a social graph of the target person to a big data platform communicated with the server;
the receiving module is used for receiving first social graph relation data returned by the big data platform in response to the first query request;
and the generating and displaying module is used for generating and displaying the social graph of the target person based on the first social graph relation data.
10. A storage medium having stored thereon a computer program which, when executed by a computer, performs the method of any one of claims 1-5 or performs the method of any one of claims 6-7.
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