CN113220837A

CN113220837A - Network space behavior monitoring and analyzing method and system of entity activity participator

Info

Publication number: CN113220837A
Application number: CN202110515695.7A
Authority: CN
Inventors: 曾曦; 饶志宏; 陈天莹; 杨政; 李霄; 周伟中; 林青彪; 胡瑞雪
Original assignee: Shenzhen Wanglian Anrui Network Technology Co ltd
Current assignee: Shenzhen Wanglian Anrui Network Technology Co ltd
Priority date: 2021-05-12
Filing date: 2021-05-12
Publication date: 2021-08-06

Abstract

The invention discloses a method and a system for monitoring and analyzing network space behaviors of entity activity participants, and relates to the technical field of computer technology and data science. The activity participant and organization network information monitoring module is used as the input of the system, is responsible for collecting and monitoring data of the real space and the virtual space of character organization, and provides data base support for the activity participant network behavior analysis module, the activity participant and organization network behavior analysis module, the participant and organization holographic file management module and the participant and organization activity competition advantage and disadvantage analysis module. The invention quantitatively evaluates the influence on the real world entity activities by monitoring and analyzing the virtual network behaviors. The method breaks through the limitation that the existing product cannot analyze the influence of network space behaviors aiming at certain entity activities, and solves the problem of describing the influence factors of the entity activities on the real world and the virtual network.

Description

Network space behavior monitoring and analyzing method and system of entity activity participator

Technical Field

The invention relates to the technical field of computer technology and data science, in particular to a network space behavior monitoring and analyzing method and system for entity activity participants.

Background

With the development of science and technology, the combination of real life and network media is more and more compact, and the means for people to output ideas, convey viewpoints and show attitudes is more and more abundant. The entity activity range is from the spreading, collision and game of thought in the actual physical space to the network media, and forms the entity activity characteristic that the entity space and the network media are mutually blended and driven together. More and more activities such as comprehensive art, show, vote and star live broadcast are popularized in an online-offline combination mode, and influence of physical activities and participators is enlarged. However, internet propagation has the characteristics of freedom, openness, concealment, dispersion and penetration which cannot be realized by traditional media, so that netizens tend to express their own opinions through the channel, and if the network space behaviors of entity activity participants have strong social characteristics, great discussion potential can be formed in the network space, and the netizens pay attention, propagate and evaluate continuously, so that great negative effects can be brought to the entity activities or the participants. Therefore, it is highly desirable to perform multidimensional monitoring on the speech, heat, liveness, social image, interaction, etc. of the entity activity participants in the network space, and to analyze the overall network behavior of the activity participants in time, thereby improving the intelligence control and risk prevention abilities of the entity activity and the participants.

Prior art 1: the application numbers are: 201611078290.7, applicants' name: beijing Hongma media culture development Limited company, the invention and creation name is: the normalized modeling method for the artist popularity data supports investment decision of a performance sponsor on an artist by acquiring artist multi-platform popularity values at regular time and calculating the popularity values of the artist in a weighting manner, and saves decision time and cost. The artist evaluation method which has a single dimension and can not distinguish scenes is realized, and the real value brought by artists under different scene conditions and the influence on activity results can not be represented.

Prior art 2: the application numbers are: 201710157776.8, applicants' name: the name of the invention creation of the Alibara group holdings company is as follows: a monitoring method of network behavior data includes obtaining network behavior data of a user on line, determining a corresponding risk model, carrying out risk identification on the network behavior data by using the risk model to obtain an on-line risk identification result, and evaluating credit object network behavior. The network behavior analysis of a single model can be realized, and the influence of the network behavior on the actual specific activity is not involved.

Prior art 3: the application numbers are: 202010653575.9, applicants' name: the invention relates to a national computer network and information security management center, which is named as: the method comprises the steps of analyzing, counting and classifying different network behaviors based on network behavior data analysis, constructing a relational network, realizing relational line expansion analysis based on the network behavior characteristic data, and displaying line expansion results on a display screen. Only the analysis of the network behavior relation is realized, and the relation between the network media and the real events is not further dug deeply.

Prior art 4: the application numbers are: 201310512078.7, applicants' name: the invention relates to the university of national defense science and technology of the liberation army of Chinese people, and the name of the invention is as follows: the research and implementation of the user relationship analysis technology in the microblog public sentiment event researches the user relationship in the microblog public sentiment event, analyzes the public sentiment influence of the famous people in V, analyzes the support rate of topics in netizens and the like. Only a single platform, finite specific dimension for human analysis is implemented. Results that may be generated by personnel at the physical activity are not analyzed, nor are the organization advantages of personnel at a particular event.

The prior work can analyze human and tissue in different dimensions and degrees, but has the following problems:

1. existing research mainly aims at quantitative analysis and presentation of human and organization network behavior data, such as a popularity value of an analysis target or a popularity of a microblog event, so as to evaluate the popularity of a person and the degree to which an event is concerned. However, the behavior in the virtual network space may affect the result of a specific event in the real space, and the existing product cannot analyze the generation, development and effectiveness of the effect. For example, the existing product cannot monitor the network behaviors of players, brokerages, netizens and media of a show activity to analyze the influence of the network behaviors on the result of the show activity.

2. The prior research on the network space behavior monitoring of people and organizations mainly aims at depicting the network performance of the target, and cannot analyze the advantages and disadvantages of the people and the organizations from the aspect of specific activities, for example, only the support degree of players in a show activity can be analyzed, but the advantages and disadvantages of the players in the show activity in the aspect of improving the ranking cannot be respectively analyzed.

3. The existing system does not research a method for intelligently generating a strategy suggestion of an activity event, and the main method for generating the strategy suggestion of the event is to compile a strategy report by manually arranging online and offline data and combining with artificial intelligence analysis. Online and offline information of an event cannot be dynamically monitored, and generation of a countermeasure proposal cannot be intelligently and dynamically completed for a specific event.

4. The existing research results mainly aim at monitoring the entity space of the character organization, are single in consideration dimension, cannot monitor and analyze the character organization in the scene of combining the entity space and the network media, do not organically combine the entity space and the network media, and cannot support the analysis of the influence of the network behavior on the real events.

Disclosure of Invention

In order to overcome the problems in the related art, the embodiments of the present disclosure provide a method and a system for monitoring and analyzing cyberspace behaviors of entity activity participants. The technical scheme is as follows:

the network space behavior monitoring and analyzing system of the entity activity participant comprises:

the activity participant and organization network information monitoring module is used as the input of the system, is responsible for collecting and monitoring data of a real space and a virtual space of character organization, and provides data base support for the activity participant network behavior analysis module, the activity participant and organization network behavior analysis module, the participant and organization holographic file management module and the participant and organization activity competition advantage and disadvantage analysis module;

the activity participant network behavior analysis module is used for realizing character network behavior analysis by utilizing the network behavior data provided by the activity participants and the organization network information monitoring module;

the activity participation organization network behavior analysis module is used for realizing the network behavior analysis of the organization by utilizing the activity participants and the network behavior data provided by the organization network information monitoring module;

the participant and organization holographic archive management module is used for performing online and offline association and holographic archive generation on the data provided by the activity participant organization network information monitoring module;

and the participant and organization activity competitive advantage and disadvantage analysis module is used for realizing comparative analysis, advantage and disadvantage analysis and strategy suggestion generation based on the original data provided by the monitoring of the activity participant and organization network information, the network behavior analysis of the activity participant and the analysis results of the activity participation organization network behavior, and the online and offline incidence relation and the holographic file of the participant and organization holographic file management.

In one embodiment, the activity participant and organization network information monitoring module comprises: the system comprises an activity participant network information monitoring module and an activity participation organization network information monitoring module; wherein the content of the first and second substances,

the activity participant network information monitoring module comprises: the basic data collection unit is used for collecting basic dimension data of the human beings; the activity participation offline activity information collecting unit monitors the actual offline behavior of the person; the activity participant network interaction information monitoring unit is used for monitoring the global social platform and the instant messaging interaction information; the activity participant social platform information monitoring unit is used for monitoring basic information of a character social platform and information of a friend information social platform; the news media report information monitoring unit of the activity participants monitors global news media; the online propaganda information monitoring unit of the activity participant monitors the online propaganda event information of the participant;

the activity participation organization network information monitoring module comprises: the organization basic data collection unit is used for collecting basic information data of organization creation time, originators, registration information, organization purposes, related fields, addresses, company LOGO and official websites; the organization relation acquisition unit is used for acquiring member information, member relation information and inter-organization relation information in the organization and character and organization relation information data; the organization offline activity collection unit is used for collecting the event data actually participating, initiating and pushing under the organization line; the social platform propaganda information monitoring unit is used for monitoring propaganda videos, propaganda articles, propaganda posts, propaganda advertisements and propaganda forum information of the social platform; the organization network speech information monitoring unit is used for monitoring the organization whole network speech information; and the organization online activity monitoring unit is used for monitoring online activity information of the organization participating, organizing and pushing on the social platform and the news website.

In one embodiment, the activity participant network behavior analysis module comprises:

the participant network propaganda strength analysis unit is used for calculating the investment strength data of participants in network propaganda in activity based on posting information, news report information and comment interaction related information of the participants;

the participant network support degree analysis unit is used for analyzing the condition that participants are supported by netizens on each social platform and news website;

the participant network activity degree analysis unit analyzes the active behavior of the personnel in the network space and quantitatively evaluates the activity condition of the people;

the participator network popularity value analysis unit integrates supported factors of people on the network, such as praise, forwarding and commented, calculates the change situation of people popularity value of people, and presents the change trend;

the participant network exposure degree analysis unit analyzes the exposure degree of the personnel on each big news media and the social platform based on the reference condition of the characters by the news media and the exposure times of the characters on the social platform actively, and comprehensively analyzes the exposure condition of the characters in the whole network;

the participant network interaction analysis unit is used for analyzing the interaction condition of the participants in the activity according to the participant network interaction data;

the participant network propaganda influence analyzing unit analyzes the propaganda influence of the participants according to the influence factors of the social platform and the news media and the reading and forwarding number factors of the participants posted on the social platform;

and the participant network propaganda content analysis unit is used for performing fusion analysis on online and offline propaganda contents of participants in a certain activity, drawing a propaganda activity timeline, displaying the online and offline propaganda contents and calculating the propaganda content quality.

In one embodiment, the activity participation organization network behavior analysis module comprises:

participating in the network publicity analysis unit of the organization: calculating the investment of the organization in the aspect of network propaganda based on the related information of organization offline propaganda activity data, organization online propaganda data, organization network posting information, news report information and comment interaction;

and the participating organization network support degree analysis unit: analyzing the support condition of the participating organization on each social platform and news website;

participating in the organization of network publicity content analysis unit: analyzing and presenting the publicity content organized on the network;

the ginseng network people gas value analysis unit: and comprehensively analyzing organizations, organization members and activity related personnel participating in related activities.

In one embodiment, the participant and organization holographic archive management module comprises:

the online and offline information association unit of the participator supports retrieval of association relation, online and offline association relation viewing of the participator, online and offline inspection of association relation expansion of association relation, inspection of association relation according to time dimension, inspection of association relation of single activity and generation and push of new association relation;

the online and offline information association unit of the participating organization supports association relation retrieval, online and offline association relation full-network viewing of participants, online and offline association relation expanding viewing of organization personnel association relation, association relation viewing of organization members, viewing of association relation according to time dimension, and generation and pushing of new association relation;

the character holographic file supports automatic completion of character related data, file one-key generation and file export according to character basic information, work information, family information, education information, event information, social relations, network virtual identity information, social platform information and news website information;

the holographic file generation and management unit participates in organization, the holographic file includes basic organization information, member organization information, relationship organization information, event organization information, social platform organization information and news media organization information, the automatic generation and completion of the file according to organization and related character data are supported, and the one-key generation and file export of the file are supported.

In one embodiment, the participant and organization activity competitive advantage/disadvantage analysis module comprises:

the participant information comparison and analysis unit is used for comparing and analyzing the network propaganda strength, the network support degree, the network activity degree, the network popularity value, the network exposure degree, the network interaction amount, the network propaganda influence and the network propaganda content comparison of the participants in the same activity;

the participating organization comparison and analysis unit is used for comparing the performance conditions of participating in the organization in the same activity;

the participant and tissue advantage analysis unit supports the advantage comparison analysis of the participants and the tissues;

and the participant and organization strategy suggestion unit provides cooperation strategy suggestions and countermeasure suggestions based on the participant and organization advantage analysis results and the strategy knowledge base.

Another objective of the present invention is to provide a method for implementing the cyberspace behavior monitoring and analyzing system of the entity activity participant, which is applied to an information data processing terminal, and the cyberspace behavior monitoring and analyzing method of the entity activity participant comprises the following steps:

step one, monitoring activity participants and organization information in depth;

step two, monitoring the information breadth of activity participants and organizations;

step three, information management and study and judgment;

step four, the activity participants and the organization are in holographic association;

analyzing activities of participants and the network behaviors of organizations;

and step six, analyzing the advantages and disadvantages of activity competition.

In one embodiment, the activity participant and organization information depth monitoring process comprises:

(1) determining activity participants and organizations to be subjected to depth monitoring to form a depth monitoring target library;

(2) extracting the deep monitoring account information on the person and the organization network platform according to the basic information of the deep monitoring target;

(3) determining a platform range needing deep monitoring according to the characteristics of a deep monitoring account, and generating a deep monitoring platform list;

(4) according to the platform characteristics, a monitoring field capable of comprehensively monitoring the behavior state of the target network is constructed;

(5) researching a data acquisition mechanism of each platform and constructing a platform mechanism feature library;

(6) establishing a target network behavior feature library based on target basic information research, analyzing the target network activity rule, and acquiring target network behavior features;

(7) generating a data depth monitoring strategy by combining target network behavior characteristics, target monitoring fields and platform mechanism characteristics, and supporting the configuration of the strategy;

(8) respectively collecting data which are regularly updated, periodically collected in full amount and collected in real time according to the depth monitoring type;

(9) obtaining original depth monitoring data;

the activity participant and organization information breadth monitoring process comprises the following steps:

(1) determining the range of a data breadth monitoring platform, and analyzing the characteristics of the type of the data platform;

(2) carrying out subdivision monitoring according to different platform types;

(3) monitoring the breadth of the social platform, extracting the commonality characteristics of the social platform, and using the commonality characteristics as the basis for establishing a monitoring strategy;

(4) according to the importance degree, the updating speed and the monitoring strategy factors of the social platform, respectively making monitoring strategies of each platform aiming at different social platforms;

(5) monitoring the breadth of the social platform to complete the monitoring of basic information, posting information and comment information of the social account;

(6) the news media breadth monitoring sets the monitoring frequency of different news media according to the importance degree and the message sending rule factors of the news media;

(7) carrying out related posts and comment related information on news networks and forum websites;

(8) the instant messaging breadth monitoring establishes an instant messaging data acquisition mechanism according to the characteristics of an instant messaging platform;

(9) monitoring related message information according to an instant messaging breadth monitoring mechanism;

(10) the social platform, the news media and the communication platform breadth monitoring data are converged and integrated to form an original breadth monitoring database.

In one embodiment, the information management and adjudication comprises:

(1) registering data sources aiming at different data sources, wherein the data sources comprise two parts of data sources and data target addresses;

(2) aiming at three scenes of an offline file, an online streaming data and a database file, registration is completed through FTP configuration, Kafka configuration and database configuration;

(3) performing real-time integration on the basis of the configured data source, and supporting quality analysis on the data while integrating;

(4) integrating and constructing activity participants and organization standard databases in real time based on data;

(5) based on a standard database, performing cross-space multi-dimensional reconstruction on data to form an activity participant and organization multi-dimensional database, and supporting management on data dimensions;

(6) extracting relationship information, and constructing a virtual reality whole-network association relationship by extracting whole-network relationship elements;

(7) supporting the push of the doubt of the association relationship, and researching and judging the doubt relationship to generate a credible relationship information base;

(8) behavior data extraction is carried out, and virtual and actual behavior information is extracted;

(9) classifying the information of the virtual and actual behaviors to deduce the similar behaviors;

(10) studying and judging the same kind of behaviors to form a credible behavior information base;

(11) extracting online and offline basic information, and performing contradiction analysis on the basic information;

(12) and (4) pushing out basic information with contradiction, studying and judging, and forming a credible basic information base.

In one embodiment, the activity participant and tissue holographic association comprises:

(1) respectively extracting real identity associated elements, virtual identity associated elements, offline event associated elements and online event associated elements based on activity participants and organization data;

(2) constructing a real identity incidence relation, a virtual identity incidence relation, an offline event incidence relation and an online event incidence relation;

(3) constructing a virtual-real mapping incidence relation according to the real and virtual identity incidence relation;

(4) constructing an event context relation according to the online and offline event incidence relation;

(5) constructing the association relationship of the whole network activity participants and the organization by combining the virtual-real mapping association relationship and the event context relationship;

(6) drawing an association relation and judging the association relation;

(7) forming a credible association relation library;

the activity participant and organization network behavior analysis comprises the following steps:

(1) by extracting activity participants and organization data, mining and analyzing the offline information of the human organization, the social platform information of the human organization and the news media information of the human organization based on different data dimensions;

(2) based on the social platform information of the character organization, respectively constructing the behavior characteristics of the social platform according to different platforms;

(3) performing active behavior analysis mining and passive behavior analysis mining according to two aspects of active behavior and passive behavior;

(4) integrating active and passive mining results of the social platform, and performing character organization monitoring analysis of social dimensions, such as analysis of the support degree and the heat degree of the social platform;

(5) establishing a news media behavior feature library through character organization news media data analysis;

(6) analyzing and mining the news media based on the news media behavior feature library to realize character organization monitoring analysis of news media dimensions;

(7) forming an offline behavior feature library by mining offline information of the human tissue;

(8) performing comprehensive analysis on the character organization whole-network behaviors by combining the line descending characteristics, the social platform behavior characteristics and the news media behavior characteristics;

the activity competition advantage and disadvantage analysis comprises:

(1) determining an analysis target, extracting the analysis target and analyzing a surrounding theme or event;

(2) extracting online elements and offline elements of the target, wherein the online elements comprise a social platform and news media virtual world data;

(3) fusing online and offline data to generate element recommendation;

(4) based on different analysis topics, extracting historical data related to the topics;

(5) generating a topic related knowledge base and a topic related inference knowledge base based on the topic historical data;

(6) fusing a major and minor factor library and a subject knowledge library, analyzing the major and minor of the target under the subject, and generating the major and minor of the target;

(7) and (4) combining the inference knowledge base and the advantage and disadvantage information to carry out target inference analysis and generate related inference.

The technical scheme provided by the embodiment of the invention has the following beneficial effects:

1. the invention quantitatively evaluates the influence on the real world entity activities by monitoring and analyzing the virtual network behaviors. The method breaks through the limitation that the existing product cannot analyze the influence of network space behaviors aiming at certain entity activities, and solves the problem of describing the influence factors of the entity activities on the real world and the virtual network.

2. The invention realizes the advantage and disadvantage analysis of related persons and organizations of the entity event, takes the expected result of a specific activity as a starting point, and analyzes the advantages and disadvantages of related persons, organizations, media and the like related to the current event. The limitation that the prior product can not be used for analyzing the advantages and disadvantages is broken through.

3. The invention realizes intelligent and dynamic generation of the strategy suggestions of the entity activity participants and organizations, breaks through the mode of the conventional strategy suggestion generation, lowers the threshold of the strategy suggestion generation and improves the efficiency of the strategy suggestion generation. The problem that the strategy suggestion can only be edited and written by manpower is solved.

4. The invention monitors and analyzes the human and the organization to cover the real space and the network media, monitors the human and the organization network space behaviors from two aspects of depth and breadth, relates to a plurality of unique covering platforms, and realizes the organic combination of the entity space and the network media. The problem that the existing monitoring mode is not enough to support the analysis of the influence of the network behavior on the real events is solved.

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.

FIG. 1 is a diagram of a cyberspace behavior monitoring and analyzing system provided by the present invention.

FIG. 2 is a flow chart of the present invention for monitoring the depth of activity participants and organization information.

FIG. 3 is a flow chart of activity participant and organization information breadth monitoring provided by the present invention.

FIG. 4 is a flow chart of information management and study provided by the present invention.

FIG. 5 is a flow chart of holographic association of activity participants and organizations provided by the present invention.

FIG. 6 is a flow chart of activity participant and organization network behavior analysis provided by the present invention.

FIG. 7 is a flow chart of the competitive advantage analysis of the activity provided by the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.

It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. As used herein, the terms "vertical," "horizontal," "left," "right," and the like are for purposes of illustration only and are not intended to represent the only embodiments.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

In view of the defects of the prior art, the invention provides a network space behavior monitoring and analyzing method and system for entity activity participants.

The network space behavior monitoring and analyzing system comprises: the system comprises an activity participant and organization network information monitoring module, an activity participant network behavior analysis module, an activity participant and organization network behavior analysis module, a participant and organization holographic file management module and a participant and organization activity competitive advantage and disadvantage analysis module.

1. The activity participant and organization network information monitoring module comprises: the system comprises an activity participant network information monitoring module and an activity participation organization network information monitoring module.

Wherein, activity participant network information monitoring module includes:

a basic data collection unit: the data of the basic dimensionality of the people are collected, and the data comprise identity information, family information, education information, work information, social relation information and the like of the people.

The activity participating person offline activity information collecting unit: the behavior of the person which actually occurs offline is monitored, wherein the behavior comprises important event information, personal activities, group activities, special activities and the like.

Activity participant network interaction information monitoring unit: the method comprises the steps of monitoring global social platforms and instant messaging interaction information, wherein the information comprises account posting information, comment information, message information and the like, and covers mainstream platforms such as Facebook, twitter, microblog, YouTube, tremble, telegram, continuous login, flute, neck English, WhatsApp, Instagram, WeChat and the like.

Activity participant social platform information monitoring unit: and monitoring social platform information such as basic information of the human social platform, friend information and the like.

The news media report information monitoring unit of the activity participant: the method is characterized by monitoring global news media, mainly monitoring information of the activity participants in each media news, and netizen reading amount, comment amount, praise amount, forwarding amount, comment content and the like of media news reports.

Activity participant online publicity information monitoring unit: and monitoring the propaganda event information of the participators on the network, such as advertisements, propaganda manuscripts, online recourse activities, online voting support activities and the like.

The activity participation organization network information monitoring module comprises:

organization basic data collection unit: and collecting basic information such as organization creation time, an originator, registration information, organization purpose, related fields, addresses, company LOGO, official websites and the like.

An organization relation acquisition unit: collecting member information, member relation information, relation information between organizations, character and organization relation information and the like in the organizations.

Organizing an offline activity collection unit: and collecting related events actually participated, initiated and pushed under the organization line, including an activity initiator, an activity event, an activity place, an activity target and the like, and covering event information of different fields, different subjects and different people related to the organization.

The social platform publicity information monitoring unit is organized: and monitoring the information of the social platform organization such as propaganda videos, propaganda articles, propaganda posts, propaganda advertisements and propaganda forums.

Organizing a network speech information monitoring unit: monitoring the whole-network speech information of the organization, wherein the speech information comprises basic account information, friend information, posting information, comment information and the like of the organization, and covers mainstream platforms such as Facebook, twitter, microblog, YouTube, tremble, telegram, WhatsApp, Instagram, WeChat and the like.

Organizing an online activity monitoring unit: monitoring online activity information of an organization participating, organizing and pushing on a social platform and a news website, such as online voting, online opinion collection, online activity holding and the like.

2. The activity participant network behavior analysis module comprises:

participant network propaganda strength analysis unit: and calculating the investment of the participators in network propaganda in a certain activity based on the posting information, news report information, comment interaction and other related information of the participators. Including promotional capital investment, promotional manpower investment, promotional platform investment, promotional media investment, promotional account investment, etc.

The participant network support degree analysis unit: and analyzing the situation that the participator is supported by the netizens on each social platform and news website. And quantitatively analyzing the network support of the participants based on data such as praise, forwarding, forward comment and the like. The method comprises the dimensions of support degree trend presentation, platform support degree distribution, support degree contrast analysis and the like.

The participant network activity degree analysis unit: and analyzing the active behavior of the personnel in the network space, and quantitatively evaluating the activity condition of the person. And analyzing the change condition of the activity of the personnel, analyzing the activity of each platform and drawing the activity trend based on the factors representing the activity state of the personnel in the personnel network behavior data.

The participant network human gas value analysis unit: and synthesizing supported factors of people on the network, such as praise, forwarding and commented, calculating the change situation of the popularity of the people, and presenting the change trend.

Participant network exposure analysis unit: and analyzing the exposure condition of the character in the whole network comprehensively based on the condition that the character is referred to by the news media and the exposure times of the character on the social platform actively, and analyzing the exposure degree of the analyst on each big news media and the social platform.

Participant network interaction analysis unit: and analyzing the interaction condition of the participants in the activity according to the network interaction data of the participants, such as interaction information that posts are commented and referred.

Participant network publicity influence analysis unit: and analyzing the propaganda influence of the participator according to the influence factors of the social platform and the news media and the reading number, the forwarding number and other factors of the participator posted on the social platform.

Participant network promotion content analysis unit: and performing fusion analysis on online and offline propaganda contents of participants in a certain activity, drawing a propaganda activity timeline, displaying the online and offline propaganda contents, and calculating the quality of the propaganda contents.

3. The activity participation organization network behavior analysis module comprises:

participating in the network publicity analysis unit of the organization: and calculating the investment of the organization in the aspect of network propaganda based on related information such as organization offline propaganda activity data, organization online propaganda data, organization network posting information, news report information, comment interaction and the like.

And the participating organization network support degree analysis unit: and analyzing the support condition of the participating organization on each social platform and news website. And quantitatively analyzing the network support of the participants based on data such as praise, forwarding, forward comment and the like. Support degree change analysis, support degree key event analysis, event support degree evolution analysis and the like.

Participating in the organization of network publicity content analysis unit: the method has the advantages that the propaganda content organized on the internet is analyzed and presented, analysis and checking according to time dimension are supported, and cluster analysis of propaganda events, similarity analysis of propaganda content, comparison analysis of similar propaganda content effects and the like are supported.

The ginseng network people gas value analysis unit: and comprehensively analyzing organizations, organization members and activity related personnel participating in related activities. The method comprises the steps of popularity value ranking of participants, popularity value change trend analysis, popularity value activity correlation degree analysis and the like.

4. The participant and organization holographic archive management module comprises:

the online and offline information association unit of the participator: the method supports retrieval of incidence relation, online and offline incidence relation viewing of participants, online and offline incidence relation viewing of incidence relation, online and offline viewing of incidence relation, viewing of incidence relation according to time dimension, viewing of single activity incidence relation, pushing of new incidence relation generation and the like.

And the online and offline information association unit participating in organization: the method supports retrieval of incidence relation, online and offline viewing of incidence relation of participants, online and offline viewing of incidence relation of organization personnel, viewing of incidence relation of organization members according to time dimension, pushing generation of new incidence relation and the like.

The participant holographic file generation and management unit: the character holographic file supports automatic completion of character related data, file one-key generation, file export and the like according to character basic information, work information, family information, education information, event information, social relations, network virtual identity information, social platform information, news website information and the like.

Participating in organizing the holographic file generation and management unit: the organization holographic file comprises organization basic information, organization member information, organization relation information, organization event information, organization social platform information, organization news media information and the like, and supports automatic generation and completion according to organization and related character data, one-key generation of the file, file export and the like.

5. The competitive advantage and disadvantage analysis module for the participators and the organizational activities comprises:

the participant information comparison and analysis unit: and comparing and analyzing the network propaganda strength, the network support degree, the network activity degree, the network popularity value, the network exposure degree, the network interaction amount, the network propaganda influence force and the network propaganda content comparison of the same activity of the participants.

Participating in a tissue comparison analysis unit: comparing the performance of the participating organization in the same activity, including participating in organization propaganda force transmission, organization support degree, organization propaganda content comparison and related personnel comparison in the organization.

And the participant and organization dominance analysis unit: and supporting the advantage contrastive analysis of the participators and organizations, including the advantage contrastive analysis of personnel support, capital, vermicelli, propaganda, time, regional, event fermentation and the like.

Participant and organization countermeasure proposal unit: and providing cooperative countermeasure suggestions and countermeasure suggestions based on the participant and organization advantage analysis results and on the basis of the countermeasure knowledge base. Including capital countermeasures, publicity countermeasures, time countermeasures, regional countermeasures, and the like.

The invention also provides a network space behavior monitoring and analyzing method of the entity activity participator, wherein:

1. the process for monitoring the depth of the information of the activity participants and the organization comprises the following steps:

(1) and determining activity participants and organizations to be subjected to depth monitoring to form a depth monitoring target library.

(2) And extracting the deep monitoring account information on the person and the organization network platform according to the basic information of the deep monitoring target.

(3) And determining a platform range needing deep monitoring according to the characteristics of the deep monitoring account number, and generating a deep monitoring platform list.

(4) And according to the data characteristics of each platform, constructing a monitoring field table capable of comprehensively monitoring the behavior state of the target network.

(5) Researching the anti-crawler mechanism of each platform, establishing a data acquisition mechanism in a targeted manner, and constructing a characteristic library of the data crawling mechanism of each platform.

(6) Researching the internet surfing behavior of the corresponding account based on the basic information of the target account, constructing a target network behavior feature library by a label portrait method, and performing statistical analysis on the activity rule of the target network to obtain the behavior feature of the target network.

(7) And formulating a generated data deep monitoring strategy by combining target network behavior characteristics, target monitoring fields and platform mechanism characteristics, wherein the generated data deep monitoring strategy comprises the contents of collecting account details, collecting evaluation rates, collecting fields, used account resources and the like, and the strategy configuration is supported.

(8) And starting an acquisition server, issuing an acquisition strategy, and acquiring data which is regularly updated, periodically acquired in full amount and acquired in real time according to the depth monitoring type.

(9) And the collected data is transmitted back to the system for storage, and the original depth monitoring data is obtained.

2. The process for monitoring the activity participant and the organization information breadth comprises the following steps:

(1) and determining the range of the data breadth monitoring platform, and analyzing the characteristics of the type of the data platform.

(2) And carrying out subdivision monitoring according to different platform types.

(3) And (3) monitoring the breadth of the social platform, and extracting the commonality characteristics of the social platform to be used as the basis for making a monitoring strategy.

(4) And respectively formulating monitoring strategies of each platform aiming at different social platforms according to factors such as importance degree, updating speed and monitoring strategies of the social platforms.

(5) And monitoring the social platform in a wide range, and finishing monitoring basic information, posting information, comment information and the like of the social account.

(6) The news media breadth monitoring sets the monitoring frequency of different news media according to factors such as the importance degree and the text sending rule of the news media.

(7) And carrying out related posts and comment related information on news networks and forum websites.

(8) The instant messaging breadth monitoring establishes an instant messaging data acquisition mechanism according to the characteristics of an instant messaging platform.

(9) And finishing the monitoring of the related message information according to an instant messaging breadth monitoring mechanism.

3. The information management and study and judgment method comprises the following steps:

(1) and performing data source registration aiming at different data sources, wherein the data sources comprise two parts of data sources and data target addresses.

(2) And aiming at three scenes of an offline file, an online streaming data and a database file, the registration is completed in an FTP configuration mode, a Kafka configuration mode and a database configuration mode.

(3) And performing real-time integration on the basis of the configured data source, performing offline file extraction through DataX, performing streaming file extraction through flash, and performing database extraction through JDBC. Meanwhile, the logs are stored, and data quality statistical analysis is carried out on the basis of the logs.

(4) Based on data real-time integration, an activity participant and organization standard database is constructed through data cleaning, conversion and duplication removal, including a vacancy filtering algorithm, a date format conversion algorithm and an identity card.

(5) Based on a standard database, extracting character organization entities based on a Conditional Random Field (CRF) technology, performing cross-space multi-dimensional reconstruction on data through a label extraction and portrait technology, forming a multi-dimensional database of activity participants and organizations, and managing data dimensions.

(6) And extracting the relationship information through a Sonwball algorithm, and constructing the virtual reality whole-network association relationship through extracting the whole-network relationship elements.

(7) And finding out conflicts in the relational data through a data comparison algorithm, pushing the association relation doubted by the data conflicts, and manually studying and judging the doubted relation to generate a credible relational information base.

(8) And extracting behavior data and extracting virtual and real behavior information based on posting, comment data and a real space activity data table of the social platform.

(9) And clustering the virtual and real behavior data through a DBSCAN clustering algorithm, and deducing the similar behaviors.

(10) And manually studying and judging the similar behaviors obtained by clustering to form a credible behavior information base.

(11) And extracting online and offline basic information, and performing data comparison on the basic information.

(12) And (4) pushing out basic information with contradiction, manually studying and judging, and forming a credible basic information base.

4. The holographic association process of activity participants and organizations comprises the following steps:

(1) and respectively extracting real identity associated elements, virtual identity associated elements, offline event associated elements and online event associated elements based on activity participants and organization data.

(2) And constructing a real identity incidence relation, a virtual identity incidence relation, an offline event incidence relation and an online event incidence relation.

(3) And constructing a virtual-real mapping incidence relation according to the real and virtual identity incidence relation.

(4) And forming an event database of the person and the organization according to the online and offline event data, constructing a mapping relation between the person, the organization and the event, constructing entity, relation and attribute triplets, and forming an event context relation in a graph form.

(5) And combining the virtual space social account association relationship with the structural relationship data between the real space character organization, forming a virtual and real space mapping association relationship of the character organization based on the normalization processing of virtual and real identity alignment, and combining the event association relationship to jointly construct an association relationship between the activity participants and the organization across the virtual space and the real space.

(6) Drawing the incidence relation in a map mode, judging the incidence relation in a manual mode, reserving the correct relation, and modifying and deleting the error relation.

(7) And forming a credible association relation library.

5. The analysis process of activity participators and organization network behaviors is as follows:

(1) by extracting activity participants and organization data, respectively carrying out data modeling development and extraction on offline information of the human organization, social platform information of the human organization and news media information of the human organization based on different data dimensions, and constructing a Facebook data processing model, a Twitter data processing model, an Instagram data processing model, a Lihkg data processing model, a Reddit data processing model, a Telegram data processing model, a Patreon data processing model, a Mewe data processing model and a YouTube data processing model.

(2) Based on the social platform information of character organization, respectively constructing social platform behavior characteristic models according to different platforms, and constructing a Facebook user characteristic model, a Facebook posting information model, a Facebook comment information model, a Facebook friend information model, a Twitter user information model, a Twitter posting information model, a Twitter comment information model, a Telegram channel information model and the like.

(3) Performing active behavior analysis mining and passive behavior analysis mining according to two aspects of active behavior and passive behavior, wherein the active behavior analysis comprises statistics and calculation of Facebook active posting, commenting, forwarding, praise, Twitter active posting, commenting, forwarding, praise and the like; passive behavioral analysis includes Facebook forwarded, commented on, praised, Twitter forwarded, commented on, praised, etc.

(4) And (3) integrating active and passive analysis results of social platforms such as Facebook, Twitter, Instagram, patreon, mewe and the like, and calculating the support, heat, volume and the like of the character organization by using statistical calculation and weighted average and other modes to realize monitoring and analysis of the character organization basic information and behavior information of social dimensions.

(5) The method comprises the steps of organizing news media data models through characters, organizing and processing the news media data, and processing and developing the data through a news media basic information data model, a news post data model and a news comment data model to form a news media behavior feature library.

(6) News reports and comment information related to characters and organizations are extracted through key word extraction, semantic extraction, feature extraction and other modes based on a news media behavior feature library, news media data are analyzed through a classification algorithm, a clustering algorithm, a theme extraction algorithm, a statistical analysis algorithm, a weighted average algorithm and the like, and character organization monitoring analysis of news media dimensions is achieved.

(7) The character organization behavior characteristic library of the entity space is constructed by editing, summarizing, comparing and removing the information under the character organization line, and comprises character basic information, education information, work information, family information and the like.

(8) The combination line descending is characteristic data, social platform behavior characteristic data and news media behavior characteristic data, correlation, fusion and summarization are carried out on the human organization behaviors in a mode of cross virtual and real space identity alignment, cross platform identity classification and the like, and comprehensive analysis and presentation are carried out in a mode of lists, trend graphs, comparison graphs, thermodynamic diagrams, word clouds and the like through a mode of statistical calculation and the like.

6. The analysis process of the advantages and disadvantages of the activity competition is as follows:

(1) determining an analysis target, extracting basic information data of a character and an organization of the analysis target, and acquiring the subject event information to be analyzed in a data import and front-end input mode.

(2) Extracting virtual space online data of target characters and organizations from open source network information data such as a social platform, a news media and the like by combining event topics, wherein the virtual space online data comprises Facebook postings, Facebook comments, Twitter postings, Twitter comments, news media reports, news media comments and the like; and extracting offline data of the character organization from the entity space real database of the character organization, wherein the offline data comprises basic information, actual relation information and the like of the character organization.

(3) Based on fused online and offline data, an event topic classification system model is constructed firstly, classification strategies of various events are researched, a semantic-based classification model and a merit and disadvantage integral judgment model are combined to realize automatic merit and disadvantage classification of target topic time, and generation of merit and disadvantage elements including dimensions of character elements, time elements, economic elements, public opinion elements, event elements and the like is completed through manual proofreading and confirmation.

(4) Based on different analysis topics, similar topic historical events are extracted through a topic event discovery model based on similarity.

(5) Based on the topic historical data, a semantic-based ontology modeling technology is adopted, and a topic-related knowledge base and a topic-related inference knowledge base are constructed and generated through automatic knowledge extraction and manual configuration.

(6) And (3) fusing a major and minor element library and a subject knowledge library, supplementing and expanding the figure major and minor, the time major and minor, the economic major and minor, the public opinion major and the time major and minor and the like of the target under the subject, and generating a target major and minor analysis material through manual adjustment.

(7) And (4) combining the inference knowledge base and the advantages and disadvantages information, adopting inference association based on labels, performing label contrast mapping on the advantages and disadvantages and the inferences, generating related inference recommendations, and forming final inference after manual verification.

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 should be limited only by the attached claims.

Claims

1. A cyberspace behavior monitoring and analysis system of a physical activity participant, the cyberspace behavior monitoring and analysis system of the physical activity participant comprising:

2. The cyberspace behavior monitoring and analysis system of physical activity participants according to claim 1, wherein said activity participant and organization network information monitoring module includes: the system comprises an activity participant network information monitoring module and an activity participation organization network information monitoring module; wherein the content of the first and second substances,

3. The cyberspace behavior monitoring and analysis system of physical activity participants according to claim 1, wherein said activity participant cyberspace behavior analysis module includes:

4. The cyberspace behavior monitoring and analysis system of physical activity participants according to claim 1, wherein said activity participation organization cyberspace behavior analysis module includes:

5. The cyberspace behavioral monitoring and analysis system of physical activity participants according to claim 1, wherein said participant and organization holographic archive management module includes:

6. The cyberspace behavior monitoring and analysis system of physical activity participants according to claim 1, wherein said participant and organizational activity competitive advantage analysis module comprises:

7. A method for realizing the network space behavior monitoring and analyzing system of the entity activity participator in any claim 1 to 6, which is applied to the information data processing terminal, and the network space behavior monitoring and analyzing method of the entity activity participator comprises the following steps: