CN103854227A - Interpersonal relationship analyzing system and method - Google Patents

Abstract

A system and method for analyzing human relationship, the method comprising: acquiring preset photos in each time zone from the memory of an electronic device; The photos of two users; calculate the distance between the first user and the second user in each photo identified in each time period, so as to obtain the interpersonal relationship measurement between the first user and the second user in each time period value; according to the measured value of human relationship in each time period, a trend graph of human relationship between the first user and the second user is drawn. The present invention can analyze the interpersonal relationship between users in each time zone according to the photos of the users.

Description

Human relationship analysis system and method

technical field

The present invention relates to a data analysis system and method, in particular to an interpersonal relationship analysis system and method.

Background technique

The rise of social networking has made many users willing to share pictures on the Internet. Existing social networking systems such as Facebook or Google+ can allow users to upload pictures, and provide the function of automatic face recognition of friends. Information (such as name) tags (Tag) in the photo. Community users can use tags to know which friends are in which photos, but it cannot provide information on the strength of the relationship between community users and a friend in a picture. Furthermore, if the user wants to know the trend of the interpersonal relationship with the friend (such as which years the relationship is particularly good), the user may not be able to recall it immediately, and must manually go to the photo album to select, and this process requires a lot of time. time.

Contents of the invention

In view of the above, it is necessary to provide an interpersonal relationship analysis system and method, which can analyze the interpersonal relationship between users in each time zone according to the user's photos, and draw the interpersonal relationship between users into an interpersonal relationship trend graph.

An interpersonal relationship analysis system, applied to electronic devices, the system includes: a photo acquisition module, used to obtain preset photos in each time period from the memory of the electronic device; The photos containing the first user and the second user are identified in the photos in each time segment; the interpersonal relationship analysis module is used to calculate the first user and the second user in each photo identified in each time segment The distance between the first user and the second user in each time zone is obtained; the interpersonal relationship display module is used to draw the first user and the second user in each time zone according to the interpersonal relationship measure value The interpersonal relationship trend graph of the second user, the interpersonal relationship trend graph including the change curve of the interpersonal relationship measurement value of the first user and the second user in each time segment.

A method for analyzing interpersonal relationships, applied to an electronic device, the method comprising: a photo acquisition step, obtaining preset photos in each time zone from a memory of the electronic device; The photos containing the first user and the second user are identified in the photos; the interpersonal relationship analysis step 1 is to calculate the distance between the first user and the second user in each photo identified in each time segment, so as to obtain The interpersonal relationship measurement value of the first user and the second user in each time zone; the interpersonal relationship display step is to draw the interpersonal relationship trend of the first user and the second user according to the interpersonal relationship measurement value in each time zone , the interpersonal relationship trend graph includes the change curve of the interpersonal relationship measurement value of the first user and the second user in each time segment.

Compared with the prior art, the described interpersonal relationship analysis system and method can analyze the interpersonal relationship between users in each time zone according to the user's photos, and draw the interpersonal relationship between users into an interpersonal relationship trend graph , which is convenient for community users to view the development trend of the relationship between users.

Description of drawings

Fig. 1 is a schematic diagram of the operating environment of the interpersonal relationship analysis system of the present invention.

Fig. 2 is a functional block diagram of the interpersonal relationship analysis system of the present invention.

Fig. 3 is a flowchart of a preferred embodiment of the interpersonal relationship analysis method of the present invention.

FIG. 4 is a trend diagram of the interpersonal relationship between the first user and the second user drawn by the present invention.

FIG. 5 is a schematic diagram of shifting time grids in the plotted interpersonal relationship trend graph.

Fig. 6 is a schematic diagram of multi-person query.

Fig. 7 is a schematic diagram of the relationship strength between the first user and the second user in different time periods.

Fig. 8 is a schematic diagram of the number of photos of the first user and the second user in different time periods.

Description of main component symbols

electronic device 2 display screen 20 input device twenty two memory twenty three Human Relationship Analysis System twenty four processor 25 Relationship Trend Chart 30 time grid 32 Data receiving module 240 Photo acquisition module 241 face recognition module 242 Human Relationship Analysis Module 243 Human Relationship Demonstration Module 244

Detailed ways

As shown in FIG. 1 , it is a schematic diagram of the operating environment of the interpersonal relationship analysis system of the present invention. The human relationship analysis system 24 runs in the electronic device 2 . The electronic device 2 also includes a display device 20 , an input device 22 , a memory 23 and a processor 25 connected via a data bus. The electronic device 2 can be a computer, a mobile phone, a PDA (Personal Digital Assistant, personal digital assistant), etc.

The memory 23 is used to store the program codes of the interpersonal relationship analysis system 24, user photos and other materials. The display device 20 is used to display information such as the user's photo and interpersonal relationship trend graph, for example, the display device 20 may be a liquid crystal display screen of a computer, a touch screen of a mobile phone, and the like. The input device 22 is used to input various data set by the user, for example, the input device 22 includes a keyboard, a mouse, and the like.

The interpersonal relationship analysis system 24 is used to analyze the interpersonal relationship between users in each time zone according to the user's photos, and draw the interpersonal relationship between users into an interpersonal relationship trend graph, which is displayed on the display device 20. The specific process Described below.

In this embodiment, the interpersonal relationship analysis system 24 can be divided into one or more modules, and the one or more modules are stored in the memory 23 and configured to be operated by one or more processors ( In this embodiment, one processor 25) is executed to complete the present invention. For example, as shown in FIG. 2 , the human relationship analysis system 24 is divided into a data receiving module 240 , a photo acquisition module 241 , a face recognition module 242 , a human relationship analysis module 243 and a human relationship display module 244 . The module referred to in the present invention is a program segment that completes a specific function, and is more suitable than a program to describe the execution process of software in the electronic device 2 . The specific functions of each module will be described below with reference to FIG. 3 .

As shown in FIG. 3 , it is a flow chart of a preferred embodiment of the interpersonal relationship analysis method of the present invention.

In step S10, the data receiving module 240 receives the keyword of the second user input by the first user and the size of the time period for analyzing the interpersonal relationship. The keyword of the second user may be the name of the second user. In this embodiment, the first user is the user himself. Referring to FIG. 4 , the first user (me) can input the name (Celine) of the second user on a social networking site (such as Facebook) in the search box. The size of the time period may be one week, one month, one quarter, and the like.

In step S11 , the photo acquisition module 241 acquires all photos in each time period from the photo album in the memory 23 . In this embodiment, all photos in the photo album include information about a shooting time. Specifically, if there is EXIF (Exchangeable image file format) information in a photo, the time recorded in the EXIF information is set as the shooting time of the photo. If there is no EXIF information in a photo, the time when the photo is uploaded to the social networking site (such as the memory 23 ) is set as the shooting time of the photo.

For example, assuming that the first user sets the size of the time zone as one month, the photo obtaining module 241 obtains all photos of each month from the photo book of the memory 23 according to the shooting time of each photo. For example, the photo acquisition module 241 acquires 10 photos in January 2012, 15 photos in February and so on. In other embodiments, the size of the time segment may also be set as a default value (such as 1 month), and no setting is required by the user.

In step S12, the face recognition module 242 identifies photos containing the first user and the second user from all photos in each time segment. For example, the face recognition module 242 recognizes that 6 of the 10 photos in January 2012 include the first user and the second user, and 8 of the 15 photos in February include the first user and the second user.

Specifically, the face recognition module 242 recognizes the face blocks in each photo in each time zone, and combines the identified face blocks with the face photos of the first user and the second user Comparison is performed to determine whether the photo contains the first user and the second user. Wherein, the face photos of the first user and the second user may be avatars of the first user and the second user on social networking sites.

If there is a first face block matched with the first user's face photo in the photo, and a second face block matched with the second user's face photo, then the face recognition module 242 determines that the photo contains the first user and the second user.

In step S13, the interpersonal relationship analysis module 243 calculates the distance between the first user and the second user in each photo identified in each time zone, so as to obtain the distance between the first user and the second user in each time zone. Interpersonal Measures. For example, the human relationship analysis module 243 calculates that the measured value of human relationship between the first user and the second user in January 2012 is 80, and the measured value of human relationship in February 2012 is 90. The higher the interpersonal relationship measurement value in a time period, the better the relationship between the first user and the second user in the time period.

Specifically, the interpersonal relationship analysis module 243 sequentially acquires each photo in each time segment, and calculates the number of people U included in each photo according to the number of face blocks included in each photo.

The human relationship analysis module 243 calculates the distance D between the first user and the second user in each photo. The distance refers to the degree of adjacency between people in the photo. For example, if two people are next to each other in a certain photo, the distance between the two people is 1, and if there are n people between the two people, the distance between the two people is n+1.

The interpersonal relationship analysis module 243 calculates the relationship between the first user and the second user in each photo by using the number of people U in each photo and the distance D between the first user and the second user according to a preset relationship algorithm Strength E. The relation algorithm is E=1/f(U, D), for example, E=1/(U*D).

After all the photos in each time zone have been processed, the interpersonal relationship analysis module 243 sums up the relationship strength E calculated for each photo in the time zone to obtain the relationship between the first user and the second user in each time zone. The measure of interpersonal relationship within a segment is calculated as described in formula (1).

${\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; Tt</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; Tt</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Wherein, E _Tt (a, b) represents the interpersonal relationship measurement value between the first user a and the second user b within the time interval T _t .

P _Tt represents the total number of photos including the first user and the second user within the time period T _t .

U _n represents the number of people included in the nth photo within the time period T _t .

D _n (a, b) represents the distance between the first user a and the second user b in the nth photo within the time interval T _t .

Step S14, the interpersonal relationship display module 244 draws the interpersonal relationship trend graph 30 of the first user and the second user according to the interpersonal relationship measurement value in each time zone, and displays the interpersonal relationship trend graph 30 on the display device 20 . Referring to FIG. 4 , the interpersonal relationship trend graph 30 includes a variation curve L1 of the interpersonal relationship measurement value of the first user and the second user in each time segment. Wherein, the horizontal axis of the interpersonal relationship trend graph 30 represents time, and each point in the horizontal axis represents a time interval T _t , for example, T _t1 represents January 2004, namely [2004-01-01, 2004-01- 31], the vertical axis represents the interpersonal relationship measure E _Tt between the first user and the second user in each time segment. From the curve L1 in FIG. 4 , it can be seen intuitively the development trend of the relationship between the first user and the second user, for example, when the relationship is the best and when it starts to gradually become estranged.

In other embodiments, the interpersonal relationship trend graph 30 may also include a movable time grid 32, the time grid 32 includes a plurality of time segments and each time segment includes the first user and The photo of the second user. Referring to FIG. 4 , the time grid 32 includes a time segment T _t1 to a time segment T _t1-n . Referring to FIG. 5 , the moved time grid 32 includes a time segment T _t2 to a time segment T _t2-n . When the time grid 32 moves, the interpersonal relationship display module 244 displays the photos identified in each time segment in the time grid 32 under the interpersonal relationship trend graph 30 in a predetermined order (such as the shooting time sequence). . In other embodiments, the width of the time grid 32 can be enlarged or reduced, and the minimum can become a straight line (ie, a time segment).

In other embodiments, the data receiving module 240 may also receive keywords (or even more keywords) of the second user and the third user input by the first user, wherein the first user is the user himself. Referring to FIG. 6 , the first user (me) can input the name of the second user (Celine) and the name of the third user (Mandy) in the search box. Two relationship curves will be displayed in the interpersonal relationship trend graph 30, wherein the first relationship curve L1 represents the interpersonal relationship trend between the first user and the second user, and the second relationship curve L2 represents the interpersonal relationship between the first user and the third user. relationship trends.

It should be noted that, in other embodiments, after the data receiving module 240 receives the keywords of the second user and the third user input by the first user, only the second user and the third user may be displayed in the interpersonal relationship trend graph 30 . Three user relationship curves.

In other embodiments, step S13 may also be: the interpersonal relationship analysis module 243 determines the number of photos containing the first user and the second user identified in each time zone, and determines the A measure of relationships over time. The greater the number of identified photos in a time period, the higher the determined interpersonal relationship measurement value, which means the better the relationship between the first user and the second user in this time period.

However, the accuracy of the relationship measure determined by the number of photos is less than the accuracy of the relationship measure determined by the relationship strength (ie, user distance). For example, as shown in FIG. 7 , the interpersonal relationship measurement value E Tt _{-1 of the time period T t- 1} is smaller than the interpersonal relationship measurement value E _{Tt-2 of the time period T t -2} . However, as shown in FIG. 8 , the number of photos P _Tt-1 in the time period T _t- 1 is greater than the number of photos P _{Tt-2 in the time period T t- 2} . The bars in Figure 8 represent the number of pictures, and the higher the bar, the greater the number of pictures.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements can be made without departing from the spirit and scope of the technical solutions of the present invention.

Claims (20)

1. an interpersonal relation analytic system, is applied to electronic installation, it is characterized in that, this system comprises:

Photo acquisition module, for obtaining the photo in default each time section from the storer of electronic installation;

Face recognition module, identifies for the photo in each time section the photo that comprises first user and the second user;

Interpersonal relation analysis module, for calculating respectively every photo first user identifying in each time section and the second user's distance, thereby obtains first user and the interpersonal relation metric of the second user in each time section; And

Interpersonal relation display module, for drawing out first user and the second user's interpersonal relation trend map according to the interpersonal relation metric in each time section, this interpersonal relation trend map comprises the change curve of first user and the interpersonal relation metric of the second user in each time section.

2. interpersonal relation analytic system as claimed in claim 1, is characterized in that, every photo in this storer all comprises the information of a shooting time.

3. interpersonal relation analytic system as claimed in claim 2, it is characterized in that, if have EXIF information in a photo, be set as to the shooting time of this photo the time of recording in EXIF information, if there is no EXIF information in a photo, this photo upload is set as to the shooting time of this photo to the time of storer.

4. interpersonal relation analytic system as claimed in claim 1, is characterized in that, identifies the photo that comprises first user and the second user and comprise the photo of described face recognition module in each time section:

Identify the face block in each photo in each time section, the above-mentioned face block identifying and first user and the second user's human face photo is compared;

If there is the first face block mating with the human face photo of first user in a photo, and the second face block mating with the second user's human face photo, judge that this photo comprises first user and the second user.

5. interpersonal relation analytic system as claimed in claim 1, is characterized in that, described interpersonal relation analysis module obtains first user and the interpersonal relation metric of the second user in each time section comprises:

Obtain successively every photo in each time section, calculate according to the face number of blocks comprising in every photo the number U that every photo comprises;

Calculate the distance B between first user and the second user in every photo;

According to default ralation method E=1/f(U, a D), utilize number U in every photo and first user and the second user's distance B, calculate the relationship strength E between first user and the second user in every photo; And

Add up every relationship strength E that photo calculates in each time section, to obtain first user and the second user interpersonal relation metric in each time section.

6. interpersonal relation analytic system as claimed in claim 5, it is characterized in that, distance B between described first user and the second user is determined according to following methods: if be separated by n people between first user and the second user, determine that the distance between first user and the second user is n+1.

7. interpersonal relation analytic system as claimed in claim 5, is characterized in that, described ralation method is E=1/(U*D).

8. interpersonal relation analytic system as claimed in claim 1, it is characterized in that, described interpersonal relation trend map also comprises a movably time grid, in the time that this time, grid moved, according to predetermined order, the photo identifying in each time section of this time grid is presented to the below of interpersonal relation trend map.

9. interpersonal relation analytic system as claimed in claim 8, is characterized in that, the width of described time grid can expand or dwindle.

10. interpersonal relation analytic system as claimed in claim 1, is characterized in that, described interpersonal relation analysis module also for:

According to the number of pictures that comprises first user and the second user identifying in each time section, determine first user and the second user interpersonal relation metric in each time section.

11. 1 kinds of interpersonal relation analytical approachs, are applied to electronic installation, it is characterized in that, the method comprises:

Photo obtaining step obtains the photo in default each time section from the storer of electronic installation;

Recognition of face step, identifies the photo that comprises first user and the second user the photo in each time section;

Interpersonal relation analytical procedure one, calculates respectively the distance of first user and the second user in every the photo identifying in each time section, thereby obtains first user and the interpersonal relation metric of the second user in each time section; And

Interpersonal relation is shown step, draw out first user and the second user's interpersonal relation trend map according to the interpersonal relation metric in each time section, this interpersonal relation trend map comprises the change curve of first user and the interpersonal relation metric of the second user in each time section.

12. interpersonal relation analytical approachs as claimed in claim 11, is characterized in that, every photo in this storer all comprises the information of a shooting time.

13. interpersonal relation analytical approachs as claimed in claim 12, it is characterized in that, if have EXIF information in a photo, be set as to the shooting time of this photo the time of recording in EXIF information, if there is no EXIF information in a photo, this photo upload is set as to the shooting time of this photo to the time of storer.

14. interpersonal relation analytical approachs as claimed in claim 11, is characterized in that, described recognition of face step comprises:

Identify the face block in each photo in each time section, the above-mentioned face block identifying and first user and the second user's human face photo is compared;

If there is the first face block mating with the human face photo of first user in a photo, and the second face block mating with the second user's human face photo, judge that this photo comprises first user and the second user.

15. interpersonal relation analytical approachs as claimed in claim 11, is characterized in that, described interpersonal relation analytical procedure one comprises:

Obtain successively every photo in each time section, calculate according to the face number of blocks comprising in every photo the number U that every photo comprises;

Calculate the distance B between first user and the second user in every photo;

According to default ralation method E=1/f(U, a D), utilize number U in every photo and first user and the second user's distance B, calculate the relationship strength E between first user and the second user in every photo; And

Add up every relationship strength E that photo calculates in each time section, to obtain first user and the second user interpersonal relation metric in each time section.

16. interpersonal relation analytical approachs as claimed in claim 15, it is characterized in that, distance B between described first user and the second user is determined according to following methods: if be separated by n people between first user and the second user, determine that the distance between first user and the second user is n+1.

17. interpersonal relation analytical approachs as claimed in claim 15, is characterized in that, described ralation method is E=1/(U*D).

18. interpersonal relation analytical approachs as claimed in claim 11, it is characterized in that, described interpersonal relation trend map also comprises a movably time grid, in the time that this time, grid moved, according to predetermined order, the photo identifying in each time section of this time grid is presented to the below of interpersonal relation trend map.

19. interpersonal relation analytical approachs as claimed in claim 18, is characterized in that, the width of described time grid can expand or dwindle.

20. interpersonal relation analytical approachs as claimed in claim 11, is characterized in that, the method also comprises:

Interpersonal relation analytical procedure two, according to the number of pictures that comprises first user and the second user identifying in each time section, determines first user and the second user interpersonal relation metric in each time section.

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