CN111159601B - Social contact method for visually displaying community based on feature data - Google Patents
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Abstract
The invention discloses a social contact method for visually displaying communities based on characteristic data, which comprises the steps of establishing communities and a public database connected with the communities for each user, establishing label information in the communities and issuing community dynamics by the users, wherein data generated by the users in the communities and interaction data among the users are recorded in the public database; transmitting the acquired data in the public database to a big data refining module for refining to obtain refined data; transmitting the fine data to an artificial intelligence module and processing the fine data; taking each community as a unit, carrying out comprehensive scoring on each item of characteristic data of the users in the community and the users in other communities, and recommending the community to the user to be recommended until the matching is successful; the visualization module is used for visually displaying the data of all users in the community, recommending the users in the community to the users to be recommended and matching, and quickly and accurately matching people with a suitable friend-making object.
Description
Technical Field
The invention belongs to the technical field of internet, and relates to a social contact method for visually displaying a community based on characteristic data.
Background
With the development of internet technology, computer technology, internet of things technology and various modern intelligent terminal technologies, a big data era has come. Large enough for businesses, governments, media departments, small enough for individuals to take "readings" on a daily basis. Various complicated and complicated data information flood the eyeballs of people. This requires an efficient method to extract useful information from a large amount of information and to generate some kind of correlation result in real time for the decision maker to make a correct decision. The data visualization technology is the application of the visualization technology in the aspect of big data, and the process of converting data information into a visual form is adopted, so that the data presentation effect is enhanced. The user can observe and analyze the data in a more intuitive interaction mode, so that the relevance among the data is found, and the man-machine interaction is realized. At present, data visualization technology is used in various aspects of people's life. From the life social contact of people, such as some friend making software, friends and the like can be recommended to users according to user preferences and user data. However, the background of the big data era also puts higher demands on visualization technology. The updating and development speed of data information requires that the visualization technology can generate data relevance in real time. The traditional visualization method is very complicated in the face of increasingly complex data, and even cannot process the data efficiently in time, so that real and efficient interactive communication between people is seriously influenced.
Disclosure of Invention
The invention aims to provide a social contact method for visually displaying communities based on characteristic data, aiming at overcoming the defects of the prior art, and quickly and accurately matching people with proper communities and matching proper friend-making objects through the communities. In order to achieve the purpose, the invention adopts the following technical scheme:
a social method for displaying communities based on feature data visualization comprises the following steps:
the method comprises the following steps: establishing a community and a public database connected with the communities for each user, establishing label information in the community and issuing community dynamics by the user, and recording data generated by the user in the community and interaction data among the users in the public database;
step two: transmitting the acquired data in the public database to a big data refining module for refining to obtain refined data;
step three: transmitting the fine data to an artificial intelligence module and processing the fine data; the artificial intelligence module comprises a perception module, an operation module and a cognition module, and the perception module identifies the precise data and induces the precise data into various characteristic data; the operation module is used for operating the summarized characteristic data and calculating the matching degree of the characteristic data between every two users; the cognitive module is used for carrying out classified storage and real-time analysis on the precise data which cannot be summarized in the characteristic data until the perception module can summarize the precise data stored in the cognitive module;
step four: taking each community as a unit, carrying out comprehensive scoring on the feature data of the users in the community and the feature data of the users to be recommended in other communities, and recommending the communities to the users to be recommended until the matching is successful;
step five: the visualization module visually displays data of all users in the community through size visualization, color visualization, position visualization, network visualization and time visualization, and recommends the users in the community to the users to be recommended and performs matching; size visualization: the higher the matching degree of the feature data is, the larger the size of the feature data is displayed; and (3) position visualization: displaying a location distribution of a user; network visualization: displaying a personal network of a user; time visualization: the change of the characteristic data of the user is displayed according to the time axis.
Further, the feature data includes: growth, background, personality, donation, education, financial and value views.
Further, the user can freely select the feature data, and other users with the highest matching degree of the feature data are recommended to the user until the user selects the object which is desired to be matched.
Further, the data generated in the community can be set by the user to be open or hidden for other specific users, and the data set to be completely hidden in the community by the user cannot be acquired by the public database.
Furthermore, if the user is disassociated from the original matching object, the matching needs to be performed again, and the operation module can perform calculation through the last stored and processed precise data, so that the user does not need to perform secondary input.
Furthermore, the perception module analyzes the location based service of the LBS for the user, and further obtains the characteristic data of the user.
Further, interactive data between users are collected and transmitted through the motion sensing device.
Further, the friend degree is calculated according to the interaction frequency in the interaction data between every two users.
Furthermore, the matching degree of each item of feature data is displayed in the color visualization, and the higher the friend degree is, the darker the color for displaying the matching degree of each item of feature data is.
Further, the fine data comprises basic data, behavior data and deep data.
The invention has the beneficial effects that:
a social method for displaying communities based on feature data visualization comprises the following steps:
the method comprises the following steps: establishing a community and a public database connected with the communities for each user, establishing label information in the community and issuing community dynamics by the user, and recording data generated by the user in the community and interaction data among the users in the public database; the function is as follows: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring data of a user comprehensively, and taking a community and a public database which absorbs data from the community as original sources of the data;
step two: transmitting the acquired data in the public database to a big data refining module for refining to obtain refined data, wherein the refined data comprises basic data, behavior data and deep data; the method is used for carrying out primary processing and refining on huge original data to obtain fine data;
step three: transmitting the fine data to an artificial intelligence module and processing the fine data;
the artificial intelligence module comprises a perception module, an operation module and a cognition module, and the perception module identifies the precise data and induces the precise data into various characteristic data; the operation module is used for operating the summarized characteristic data and calculating the matching degree of the characteristic data between every two users; the cognitive module is used for carrying out classified storage and real-time analysis on the precise data which cannot be summarized in the characteristic data until the perception module can summarize the precise data stored in the cognitive module; the function is as follows: the operation module is used for carrying out quantitative processing on the matching degree between various items of feature data of different users, so that each user can know the matching degree between the user and other users from multiple dimensions, and the users can conveniently and rapidly and accurately screen and match in a huge user group;
step four: taking each community as a unit, carrying out comprehensive scoring on the feature data of the users in the community and the feature data of the users to be recommended in other communities, recommending the communities to the users to be recommended until the matching is successful, and having the functions of: in order to further facilitate the users to match the objects quickly and accurately, the users need to be helped to match a proper community, the feature data of the users in the community and the feature data of the users to be recommended are subjected to comprehensive grading, so the feature data of the users in different communities are concentrated on the comprehensive grading, the selection process of the users is further simplified, the users can select the proper community from the comprehensive grading for matching, then the matching degree of the feature data of each user in the community and the calculated matching degree of the feature data of each user in the community is further checked, and finally whether the users enter the community is determined, so that the process of grasping the matching from the whole to the part is realized;
step five: the visualization module visually displays data of all users in the community through size visualization, color visualization, position visualization, network visualization and time visualization, and recommends the users in the community to the users to be recommended and performs matching; size visualization: the higher the matching degree of the feature data is, the larger the size of the feature data is displayed; and (3) position visualization: displaying a location distribution of a user; network visualization: displaying a personal network of a user; time visualization: the change of the characteristic data of the user is displayed according to the time axis. The function is as follows: the characteristic data of the users are converted into another visual mode, and the users newly entering the community can comprehensively and finely know the users in the community.
The characteristic data includes: the long-term, background, personality, donation, education, financial and value views, the effect of which lies in: the characteristic data is mainly divided into seven dimensions, and the characteristics of any user are comprehensively shown from the aspects of growth, background, character, donation, education, finance and value.
Firstly, a user freely selects characteristic data in the characteristic data, and other users with the highest matching degree of the characteristic data are recommended to the user until the user selects an object which is required to be matched; the function is as follows: more independent selection rights are left for the users, because the feature data are more than one item, the degree of importance of each user to the feature data of the object needing matching is different, for example, some users emphasize the matching degree of education in the feature data, the users select the object with high education matching degree in the feature data preferentially, the object with low education matching degree is screened firstly, then the screening of other feature data is carried out in the screened object group until the most suitable object is screened, and the personalization degree of the client for selecting the matching object is increased.
The data generated in the community can be set by the user to be open or hidden for other specific users, and the data set to be completely hidden in the community by the user cannot be acquired by the public database. The method has the advantages that the maximum privacy protection is provided for the user, the using function of the community is expanded, the user can upload relatively private data to the community, the complete hiding is set, and the community is used as a cloud storage.
If the user is in a relationship with the original matching object, the matching is needed again, the operation module can calculate through the last stored and processed precise data, and the user does not need to input for the second time.
The sensing module analyzes the Location Based Service (LBS) of the user, and further obtains the characteristic data of the user as a supplement of the precise data.
Interactive data between users are collected and transmitted through the motion sensing equipment, and interactive authenticity is enhanced.
And calculating the friend degree according to the interaction frequency in the interaction data between every two users.
Displaying the matching degree of each item of feature data in the color visualization, wherein the higher the friend degree is, the darker the color for displaying the matching degree of each item of feature data is; the function is as follows: therefore, the user can better know the interaction condition with the user successfully matched recently.
Drawings
FIG. 1 is a schematic diagram of a social method for displaying communities based on feature data visualization.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly, e.g., as being fixedly connected, detachably connected, or integrated; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, a social method for visually displaying communities based on feature data includes the following steps:
the method comprises the following steps: establishing a community and a public database connected with the communities for each user, establishing label information in the community and issuing community dynamics by the user, and recording data generated by the user in the community and interaction data among the users in the public database;
specifically, the tag information may be interests, commodities, stories, animals, and the like; the community dynamics can be pictures, characters, small videos, live broadcasts and the like, the community dynamics is issued in each corresponding label information, the data of the user is comprehensively collected, and the community and a public database which absorbs the data from the community are used as original sources of the data;
step two: transmitting the acquired data in the public database to a big data refining module for refining to obtain refined data, wherein the refined data comprises basic data, behavior data and deep data;
specifically, the basic data comprises basic and quantifiable information such as height, academic calendar, marital situation, home address, work address and engaged industry; the behavior data comprises information recorded in time and space in a community and a public database, wherein the dimensionality of the time can be access time, access frequency, residence time, interaction time between users and the like; the spatial dimension can be geographical distribution, access source, user group and the like, and the more analysis dimensions of user behaviors, the more accurate user information can be mined, and the more user requirements and characteristics can be known; the data of the highest layer relating to the character aspect of the deep layer of the user are: the character of the user is analyzed by using the existing data by using the testing methods of the nine-style personality, the DISC, the MBTI and the like, wherein the existing data comprises the analysis and the extraction of the data of the user on the aspects of the character, such as feeling, emotion, judgment, perception, intuition and the like. The big data refining module is used for carrying out primary processing and refining on huge original data to obtain fine data, and the burden of further processing the data in the follow-up process is simplified.
Step three: transmitting the fine data to an artificial intelligence module and processing the fine data;
the artificial intelligence module comprises a perception module, an operation module and a cognition module, and the perception module identifies the precise data and induces the precise data into various characteristic data; the operation module is used for operating the summarized characteristic data and calculating the matching degree of the characteristic data between every two users; the cognitive module is used for classifying, storing and analyzing the precise data which cannot be accurately summarized into each item of characteristic data in real time until the perception module can accurately summarize the precise data stored in the cognitive module;
specifically, the feature data includes: growth, background, personality, dedication, education, financial and value views; for convenience, A, B, C, D, E, F, G is also used to represent the seven feature data, which can also be referred to as AG feature data, and the feature data is divided into seven dimensions, so that the features of any user can be fully shown from the viewpoints of long-term, background, character, contribution, education, finance and value; the operation module quantifies the matching degree of each item of feature data of different users, so that each user can know the matching degree of each item of feature data with other users from multiple dimensions, and the users can conveniently and accurately screen and match in a huge user group; on the other hand, since the existing part of the essence data cannot be accurately summarized into the feature data, for example, it cannot be determined whether the user is oriented to the outside or the inside according to the existing essence data, the cognitive module needs to continuously store and analyze the acquired essence data in real time, or push related contents to the user to further identify whether the character is oriented to the inside or the outside, that is, the cognitive module has the capability of learning and refining the essence data.
Step four: taking each community as a unit, carrying out comprehensive scoring on the feature data of the users in the community and the feature data of the users to be recommended in other communities, and recommending the communities to the users to be recommended until the matching is successful;
specifically, in order to further facilitate users to match objects quickly and accurately, the users need to be assisted to match a suitable community, after the intelligent operation module performs matching degree operation on feature data of any two users, the feature data of the users in the community and the feature data of the users to be recommended are fed back to the intelligent operation module in batches by taking the community as a unit, and the feature data of the users in the community and the feature data of the users to be recommended are subjected to comprehensive scoring; therefore, the feature data of the users in different communities are concentrated on the comprehensive score, the selection process of the users is further simplified, the users can select a proper community for matching from the comprehensive score, then the matching degree of each feature data of each user in the community and the calculated matching degree is further checked, and the process of grasping the matching from the whole to the part is realized;
step five: the visualization module visually displays data of all users in the community through size visualization, color visualization, position visualization, network visualization and time visualization, and recommends the users in the community to the users to be recommended and performs matching; size visualization: the higher the matching degree of the feature data is, the larger the size of the feature data is displayed; and (3) position visualization: displaying a location distribution of a user; network visualization: displaying a personal network of a user; time visualization: the change of the characteristic data of the user is displayed according to the time axis. In the embodiment, the user checks the data of other users through the visualization module and autonomously selects whether to match.
The position visualization is used for displaying the distribution of users in a specific area on a map in real time, and after the users are matched with a community, the users can enter the community to view other users in any specific area selected manually or recommended by the users, and browse the visualized data of the users.
The network visually displays the personal network of any user, other users can view the user on the personal network node of the user under the condition of having the authority, and know the characteristic data and the like of the user on the node, the practical function is that other users interested in a certain user can know the personal network of the user to further the user, the known characteristic data comprises background, education, characters and the like, for example, the user is interested in the user A and the user B, the user C is an object frequently interacted with the user B, and the user A can view various characteristic data of the user C to know the user B laterally. The visualization module enables the characteristic data of the users to be converted into another visualization mode, and the users can conveniently know each other comprehensively and finely.
Another method for recommending objects to a user is as follows: the characteristic data is selected freely by the user, and the object with the highest matching degree of the characteristic data is recommended to the user until the user selects the object which is required to be matched, wherein the object can be a community or a user in the community.
Specifically, because the feature data is more than one item, the degree of importance of each user to the feature data of the object to be matched is different, for example, some users emphasize the matching degree of education and growth in the feature data, the user selects the object with high education matching degree in the feature data preferentially, the object with low education matching degree is screened firstly, then the user or the community with high growth matching degree is recommended in the screened object group, and finally the screening of other feature data is carried out until the most suitable object is screened, so that the personalization degree of the matched object selected by the client is increased.
In an embodiment, the data generated in the community can be set by the user to be open or hidden for other specific users, and the data set to be completely hidden in the community by the user cannot be acquired by the public database. The user can manage own community by himself, the attribute of the data in the community is set, the attribute can be set for a specific object, for example, several users in the community can not browse the information set as the hidden attribute, the maximum privacy protection is provided for the user with the community, in addition, in order to expand the using function of the community, the user can upload the relatively private data to the community and set as complete hiding, at the moment, anyone except the user can not browse the data set as complete hiding, and the community is used as a cloud storage.
In the embodiment, if the user is disassociated from the original matching object, the matching needs to be performed again, and the operation module performs calculation through the last stored and processed precise data, so that the user does not need to perform secondary input.
In some embodiments, the sensing module performs Location Based Services (LBS) analysis on the user, and further obtains feature data of the user, specifically, Location Based Services (LBS) have penetrated into various fields in life as a new way for obtaining information, a location dimension is added to the obtained information, for example, characteristics of the user in places such as social contact, entertainment trip, shopping and the like are regularly recorded and analyzed, and data such as taste, consumption habits, social contact style and the like of the user are analyzed, and the data are analyzed by the sensing module as a supplement of fine data and summarized into corresponding feature data.
And calculating the friend degree according to the interaction frequency in the interaction data between every two users, and displaying the matching degree of each item of feature data in the color visualization according to the friend degree. When using color visualization to show the degree of friends between users, special care is taken to ensure that the user can distinguish between 45% and 55% of the data points in order to ensure that the user has a clear distinction between closer degrees of friends. The friend degree is changed in real time, for example, the interaction frequency with a user is obviously reduced recently, the color effect displayed by the matching degree of each feature data becomes lighter.
In the embodiment, interactive data between users are collected and transmitted through the motion sensing device, so that interactive reality is enhanced, for example, the VR and AR technologies are utilized to enable two people to play games, socialize and chat in a virtual world, and the reality, interestingness and flexibility of communication interaction are enhanced.
Claims (7)
1. A social method for displaying communities based on feature data visualization is characterized by comprising the following steps:
the method comprises the following steps: establishing a community and a public database connected with the communities for each user, establishing label information in the community and issuing community dynamics by the user, and recording data generated by the user in the community and interaction data among the users in the public database;
step two: the acquired data in the public database are transmitted to a big data refining module to be refined to obtain refined data, and the refined data are the data obtained by processing and refining the data in the public database by the big data refining module;
step three: transmitting the fine data to an artificial intelligence module and processing the fine data;
the artificial intelligence module comprises a perception module, an operation module and a cognition module, and the perception module identifies the precise data and induces the precise data into various characteristic data; the operation module is used for operating the summarized characteristic data and calculating the matching degree of the characteristic data between every two users; the cognitive module carries out classified storage and real-time analysis on the precise data which cannot be accurately summarized into each item of characteristic data until the perception module can further summarize the precise data stored in the cognitive module;
step four: taking each community as a unit, carrying out comprehensive scoring on the feature data of the users in the community and the feature data of the users to be recommended in other communities, and recommending the communities to the users to be recommended until the matching is successful;
step five: the visualization module visually displays data of all users in the community through size visualization, color visualization, position visualization, network visualization and time visualization, and recommends the users in the community to the users to be recommended and performs matching; size visualization: the higher the matching degree of the feature data is, the larger the size of the feature data is displayed; and (3) position visualization: displaying a location distribution of a user; network visualization: displaying a personal network of a user; time visualization: the change of the characteristic data of the user is displayed according to the time axis.
2. The social method based on the characteristic data visualization display community as claimed in claim 1, wherein if the user is disconnected from the original matching object, the matching is required again, and the operation module performs the calculation through the processed precise data stored last time without the need of the user for a second input.
3. The social method for visually displaying communities based on feature data as claimed in claim 1, wherein the perception module performs LBS-based location services analysis on the users to further obtain the feature data of the users.
4. The social method for visually displaying communities based on feature data as claimed in claim 1, wherein the interaction data between users is collected and transmitted through a motion sensing device.
5. The social method for visually displaying communities according to characteristics data as claimed in claim 1, wherein the friend degree is calculated according to the interaction frequency in the interaction data between every two users.
6. The social method for displaying communities based on feature data visualization of claim 5, wherein the matching degree of each feature data is displayed in the color visualization, and the higher the friend degree is, the darker the color is for displaying the matching degree of each feature data.
7. The social method for visually presenting communities according to characteristics data as claimed in claim 1, wherein the fine data comprises basic data, behavior data and deep data.
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