Social network management method and system based on address book
By receiving the known information of the newly created contact and inferring the estimated value of the label of its empty space, the problem of incomplete information in the address book is solved, a multi-dimensional understanding of the contact is achieved, and the fluency and user experience of the social network are improved.
Patent Information
- Application Number
- CN202510847727.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-24
- Publication Date
- 2025-10-03
AI Technical Summary
The incomplete information of contacts in the address book prevents users from understanding contacts in multiple dimensions, affecting the smooth operation of social networks.
By receiving the known information of the new contact entered by the user, it is determined whether there are duplicate contacts in the address book, and the estimated value of the label of the new contact in the empty label is inferred. Then, the missing information is filled in through the label value inference model and correction value algorithm to improve the integrity of the contact information.
The information integrity of contacts in the address book is improved, users can understand contacts in multiple dimensions, promote the smoothness of social networks, and enhance user experience.
Smart Images
Figure CN120751052A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of social network technology, and in particular to a social network management method and system based on an address book. Background Art
[0002] In today's society, people's lives and careers depend not only on the individual but also on the social environment, including the social networks surrounding them. Whether in life or work, people are increasingly dependent on others. As interpersonal relationships in modern society become increasingly complex, the methods of communication between individuals are also constantly changing. In addition to traditional letters, phone calls, and mobile phones, there are also emails, instant messaging, and other technologies unique to the internet society. The innovation of communication tools and the accelerated pace of life have increased the difficulty of managing interpersonal relationships. The continuous expansion and transformation of each individual's relationship network has made the address book a crucial tool for maintaining and sustaining connections between people.
[0003] The address book is a collection of people's addresses, contact information, identity information, etc. Through the relevant information in the address book, we can understand the relevant information of each person and establish social communication with the relevant contacts in the address book when necessary.
[0004] However, when most of us create an address book, we only know one or two or a few items of the contact information, and the other related information and content are not clear, which makes the relevant information of the contacts in the address book incomplete and unable to understand the relevant information of the contacts in multiple dimensions. This can easily lead to a social situation where the user does not have sufficient knowledge of the contact information. Summary of the Invention
[0005] To address at least one of the aforementioned technical problems, the present invention proposes a method and system for managing a social network based on an address book. The method analyzes and processes known information about newly created contacts to infer tag information for vacant tags for the newly created contacts, thereby filling in the missing information for the newly created contacts and improving the completeness of the relevant information about the contacts in the address book. This allows users to gain multi-dimensional understanding of the relevant information about the contacts, further facilitating a smooth social network based on complete address book information and enhancing the user experience.
[0006] A first aspect of the present invention provides a social network management method based on an address book, the method comprising:
[0007] Receive the known information of the new contact edited by the user in the corresponding label field;
[0008] Based on the known information of the newly created contact, determine whether there is a duplicate contact in the address book;
[0009] If there is a duplicate, the information of the duplicate contact in the address book will be completed based on the known information of the new contact. If there is no duplicate, proceed to the next step;
[0010] According to the label column of the address book, determine the empty label for the new contact;
[0011] Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred;
[0012] Edit and write the inferred tag estimate value into the corresponding empty tag.
[0013] In this solution, based on the known information of the new contact, the estimated value of the tag for the new contact in the empty tag is inferred, specifically including:
[0014] Build a label value inference model;
[0015] Optimize the label value inference model through sample data to obtain an optimized label value inference model;
[0016] Input the known information of the newly created contact into the label value inference model;
[0017] The tag value inference model analyzes and processes the known information of the new contact and infers the tag estimated value of the new contact in the empty tag.
[0018] In this solution, after the tag value inference model analyzes and processes the known information of the newly created contact and infers the tag estimated value of the vacant tag of the newly created contact, the method further includes:
[0019] Get tag information of other contacts from the address book;
[0020] and determining whether other contacts have a tag edit value in the tag field corresponding to the empty tag of the newly created contact; if so, adding the tag information of the other contacts to the first database; otherwise, not adding the tag information to the first database;
[0021] Based on the tag information of all other contacts in the first database, a tag correction value of the vacant tag is calculated using a preset correction value algorithm;
[0022] The estimated tag value of the newly created contact in the empty tag is added to the tag correction value to obtain a corrected tag estimated value.
[0023] In this solution, based on the tag information of all other contacts in the first database, the tag correction value of the vacant tag is calculated using a preset correction value algorithm, specifically including:
[0024] Based on all other contacts in the first database, extracting the tag edit value on the tag field corresponding to the empty tag of the newly created contact and the remaining tag edit values from the tag information of each other contact;
[0025] Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact;
[0026] Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact;
[0027] Calculate the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two;
[0028] Selecting tag information of other contacts whose difference is less than a first preset threshold and adding it to the second database;
[0029] Based on all other contacts in the second database, the tag value inference model analyzes and processes the remaining tag edit values of each other contact to infer the estimated tag value of each other contact in the tag field corresponding to the empty tag of the newly created contact;
[0030] Based on all other contacts in the second database, the difference between the edited label value and the estimated label value of each other contact in the label field corresponding to the empty label of the newly created contact is calculated to obtain the label difference value of each other contact;
[0031] The label differences of all other contacts in the second database are added together to obtain a label difference sum, and the label difference sum is divided by the number of other contacts in the second database to obtain an average label difference value as the label correction value of the vacant label.
[0032] In this solution, based on all other contacts in the first database, feature calculation is performed on the remaining tag edit values of each other contact to obtain the feature value of each other contact; feature calculation is performed on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact; specifically, the following steps are included:
[0033] The preset other tag fields have different degrees of association with the tag field corresponding to the empty tag;
[0034] Counting the number of remaining tag edit values of each other contact in the first database, and recording it as the feature component A of each other contact in the first database;
[0035] Extracting, based on the first database, a correlation degree between the tag field corresponding to each remaining tag edit value and the tag field corresponding to the vacant tag for each other contact, and summing the correlation degrees between the tag fields corresponding to all remaining tag edit values and the tag fields corresponding to the vacant tag for each other contact to obtain a sum of the first correlation degrees, which is recorded as a feature component B for each other contact in the first database;
[0036] Feature component A and feature component B together constitute the feature value of each other contact in the first database;
[0037] Count the number of newly created contacts edited and written into the corresponding tag field, and record it as the feature component C of the newly created contact;
[0038] Extract the degree of association between each tag field written in the newly created contact and the tag field corresponding to the empty tag, and add the degree of association between each tag field written in the newly created contact and the tag field corresponding to the empty tag to obtain the sum of the second degree of association, which is recorded as the feature component D of the newly created contact;
[0039] Feature component C and feature component D together constitute the feature value of the newly created contact.
[0040] In this solution, the difference between the feature value of each other contact and the feature value of the newly created contact is calculated to obtain the difference between the two, specifically including:
[0041] The number of preset tag fields and the relevance of the tag fields have different influence weights on the calculated eigenvalue difference, and are influence weight W1 and influence weight W2 respectively;
[0042] Subtract the characteristic component C from the characteristic component A to obtain a first characteristic component difference value, and divide the first characteristic component difference value by the characteristic component C to obtain a first characteristic difference value;
[0043] Subtract the characteristic component D from the characteristic component B to obtain a second characteristic component difference value, and divide the second characteristic component difference value by the characteristic component D to obtain a second characteristic difference value;
[0044] Multiply the first feature difference value by the influence weight W1 to obtain the first feature weight difference value;
[0045] Multiply the second feature difference value by the influence weight W2 to obtain the second feature weight difference value;
[0046] The first feature weight difference value and the second feature weight difference value are added to obtain the feature difference between the other contacts and the newly created contact.
[0047] A second aspect of the present invention further provides a social network management system based on an address book, comprising a memory and a processor. The memory includes a social network management method program based on an address book, and when the social network management method program based on an address book is executed by the processor, the following steps are implemented:
[0048] Receive the known information of the new contact edited by the user in the corresponding label field;
[0049] Based on the known information of the newly created contact, determine whether there is a duplicate contact in the address book;
[0050] If there is a duplicate, the information of the duplicate contact in the address book will be completed based on the known information of the new contact. If there is no duplicate, proceed to the next step;
[0051] According to the label column of the address book, determine the empty label for the new contact;
[0052] Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred;
[0053] Edit and write the inferred tag estimate value into the corresponding empty tag.
[0054] In this solution, based on the known information of the new contact, the estimated value of the tag for the new contact in the empty tag is inferred, specifically including:
[0055] Build a label value inference model;
[0056] Optimize the label value inference model through sample data to obtain an optimized label value inference model;
[0057] Input the known information of the newly created contact into the label value inference model;
[0058] The tag value inference model analyzes and processes the known information of the new contact and infers the tag estimated value of the new contact in the empty tag.
[0059] In this solution, after the tag value inference model analyzes and processes the known information of the newly created contact and infers the tag estimated value of the vacant tag of the newly created contact, the address book-based social network management method program, when executed by the processor, further implements the following steps:
[0060] Get tag information of other contacts from the address book;
[0061] and determining whether other contacts have a tag edit value in the tag field corresponding to the empty tag of the newly created contact; if so, adding the tag information of the other contacts to the first database; otherwise, not adding the tag information to the first database;
[0062] Based on the tag information of all other contacts in the first database, a tag correction value of the vacant tag is calculated using a preset correction value algorithm;
[0063] The estimated tag value of the newly created contact in the empty tag is added to the tag correction value to obtain a corrected tag estimated value.
[0064] In this solution, based on the tag information of all other contacts in the first database, the tag correction value of the vacant tag is calculated using a preset correction value algorithm, specifically including:
[0065] Based on all other contacts in the first database, extracting the tag edit value on the tag field corresponding to the empty tag of the newly created contact and the remaining tag edit values from the tag information of each other contact;
[0066] Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact;
[0067] Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact;
[0068] Calculate the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two;
[0069] Selecting tag information of other contacts whose difference is less than a first preset threshold and adding it to the second database;
[0070] Based on all other contacts in the second database, the tag value inference model analyzes and processes the remaining tag edit values of each other contact to infer the estimated tag value of each other contact in the tag field corresponding to the empty tag of the newly created contact;
[0071] Based on all other contacts in the second database, the difference between the edited label value and the estimated label value of each other contact in the label field corresponding to the empty label of the newly created contact is calculated to obtain the label difference value of each other contact;
[0072] The label differences of all other contacts in the second database are added together to obtain a label difference sum, and the label difference sum is divided by the number of other contacts in the second database to obtain an average label difference value as the label correction value of the vacant label.
[0073] The present invention proposes a social network management method and system based on an address book. The method and system first receive the known information of a newly created contact edited and written by the user in the corresponding tag field; then, based on the known information of the newly created contact, determine whether there are duplicate contacts in the address book; if there are duplicates, complete the information of the duplicate contacts in the address book based on the known information of the newly created contact; if there are no duplicates, determine the empty tag of the newly created contact based on the tag field of the address book; then, based on the known information of the newly created contact, infer the tag estimate value of the newly created contact in the empty tag; finally, edit and write the inferred tag estimate value into the corresponding empty tag. The present invention analyzes and processes the known information of the newly created contact, thereby inferring the tag information of the newly created contact in the empty tag, thereby filling in the missing information of the newly created contact and improving the completeness of the relevant information of the contact in the address book, so that the user can understand the relevant information of the contact in multiple dimensions, further promoting the user to achieve a smooth social network based on the complete address book information, and improving the user experience.
[0074] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0075] Figure 1 A flowchart of a social network management method based on an address book of the present invention is shown;
[0076] Figure 2 A flow chart showing a method for estimating a tag estimate value according to the present invention is shown;
[0077] Figure 3 A block diagram of a social network management system based on an address book according to the present invention is shown. DETAILED DESCRIPTION
[0078] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.
[0079] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.
[0080] Figure 1 A flow chart of a social network management method based on an address book of the present invention is shown.
[0081] like Figure 1As shown, the first aspect of the present invention provides a social network management method based on an address book, the method comprising:
[0082] S102, receiving the known information of the new contact edited and written by the user in the corresponding tag field;
[0083] S104, judging whether a duplicate contact already exists in the address book based on the known information of the newly created contact;
[0084] S106: If there is a duplicate, then the duplicate contact in the address book is supplemented with information based on the known information of the new contact. If there is no duplicate, proceed to the next step.
[0085] S108, determining an empty tag for a new contact based on the tag column of the address book;
[0086] S110, inferring an estimated tag value of the new contact in the empty tag based on known information of the new contact;
[0087] S112, editing and writing the inferred tag estimation value into the corresponding empty tag.
[0088] It is understood that the tag fields in the address book of the present invention may include age, birthday, constellation, zodiac sign, gender, relationship category, etc., and of course, the user may also customize the tag fields, such as campus (including elementary school, middle school, high school, university, etc.), entertainment (including game A, game B, game C, etc.), rice group (including hot pot rice group, barbecue rice group, etc.), introducer, etc., but the present invention is not limited to this.
[0089] According to a specific embodiment of the present invention, determining whether there are duplicate contacts in the address book specifically includes:
[0090] Extracting the name and contact information of the new contact from the known information of the new contact;
[0091] The name and contact information of the newly created contact are compared with other contacts in the address book. If any of the name and contact information are repeated;
[0092] It is determined that there are duplicate contacts in the address book and a duplicate prompt is given.
[0093] The present invention first receives the known information of the newly created contact edited and written by the user in the corresponding label field; then, based on the known information of the newly created contact, determines whether there are duplicate contacts in the address book; if there are duplicates, the information of the duplicate contacts already existing in the address book is completed based on the known information of the newly created contact; if there are no duplicates, the empty label of the newly created contact is determined based on the label field of the address book; then, based on the known information of the newly created contact, the label estimation value of the newly created contact in the empty label is inferred; finally, the inferred label estimation value is edited and written into the corresponding empty label. The present invention analyzes and processes the known information of the newly created contact, thereby inferring the label information of the newly created contact in the empty label, thereby filling in the missing information of the newly created contact, improving the integrity of the relevant information of the contact in the address book, so that the user can understand the relevant information of the contact in multiple dimensions, further promoting the user to achieve a smooth social network based on the complete address book information, and improving the user experience.
[0094] like Figure 2 As shown, based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred, specifically including:
[0095] S202, building a label value inference model;
[0096] S204, optimizing the label value inference model using sample data to obtain an optimized label value inference model;
[0097] S206, inputting the known information of the newly created contact into the tag value inference model;
[0098] S208: The tag value inference model analyzes and processes the known information of the newly created contact to infer the tag estimated value of the vacant tag of the newly created contact.
[0099] It should be noted that the label value inference model of the present invention is a BP neural network model. The BP neural network model is a multi-layer network that trains the weights of nonlinear differentiable functions. Using only sample data, without establishing a mathematical model of the system, it can implement a highly nonlinear mapping from the pm space composed of the pattern vectors p of m input neurons to the yn space n (the number of output nodes).
[0100] According to an embodiment of the present invention, after the tag value inference model analyzes and processes the known information of the newly created contact and infers the tag estimated value of the vacant tag of the newly created contact, the method further includes:
[0101] Get tag information of other contacts from the address book;
[0102] and determining whether other contacts have a tag edit value in the tag field corresponding to the empty tag of the newly created contact; if so, adding the tag information of the other contacts to the first database; otherwise, not adding the tag information to the first database;
[0103] Based on the tag information of all other contacts in the first database, a tag correction value of the vacant tag is calculated using a preset correction value algorithm;
[0104] The estimated tag value of the newly created contact in the empty tag is added to the tag correction value to obtain a corrected tag estimated value.
[0105] It should be noted that the label value inference model is limited by the amount of training sample data and the limitations of its own model parameters, which may lead to errors in the label estimation value inferred by the label value inference model. In order to further compensate for the errors in model inference, the present invention selects some contacts with label edit values in the label fields corresponding to the vacant labels of the newly created contacts from other contacts, and adds them to the first database. Then, based on the label information of the contacts in the first database, the label correction value of the vacant label is calculated through a preset correction value algorithm, so that the label estimation value inferred by the model is corrected according to the label correction value, further improving the accuracy of the label estimation value.
[0106] In a specific embodiment, if the age tag of a newly created contact is an empty tag, the estimated value of the age tag of the newly created contact can be inferred based on known information of the newly created contact, such as the classmate or relative relationship with the user, the time of acquaintance, etc. However, the present invention is not limited thereto.
[0107] According to an embodiment of the present invention, based on the tag information of all other contacts in the first database and using a preset correction value algorithm, the tag correction value of the vacant tag is calculated, specifically including:
[0108] Based on all other contacts in the first database, extracting the tag edit value on the tag field corresponding to the empty tag of the newly created contact and the remaining tag edit values from the tag information of each other contact;
[0109] Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact;
[0110] Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact;
[0111] Calculate the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two;
[0112] Selecting tag information of other contacts whose difference is less than a first preset threshold and adding it to the second database;
[0113] Based on all other contacts in the second database, the tag value inference model analyzes and processes the remaining tag edit values of each other contact to infer the estimated tag value of each other contact in the tag field corresponding to the empty tag of the newly created contact;
[0114] Based on all other contacts in the second database, the difference between the edited label value and the estimated label value of each other contact in the label field corresponding to the empty label of the newly created contact is calculated to obtain the label difference value of each other contact;
[0115] The label differences of all other contacts in the second database are added together to obtain a label difference sum, and the label difference sum is divided by the number of other contacts in the second database to obtain an average label difference value as the label correction value of the vacant label.
[0116] It can be understood that since the other contacts in the first database are initially screened based on whether there is a label edit value in the label field corresponding to the empty label of the newly created contact, some other contacts in the first database are quite different from the newly created contact. If the other contacts in the first database are used as reference objects for calculating the correction value, it may result in the obtained label correction value not accurately fitting the newly created contact. In order to further make the label correction value more suitable for the newly created contact and to further improve the accuracy of the final label estimate, the present invention calculates the difference between the feature value of the newly created contact and each other contact in the first database, and filters the other contacts whose difference is less than the first preset threshold to the second database. The second database finally obtained has other contacts with a high similarity to the features of the newly created contact. Then, based on the other contacts in the second database, a label correction value that is more suitable for the newly created contact can be calculated.
[0117] According to an embodiment of the present invention, based on all other contacts in the first database, feature calculation is performed on the remaining tag edit values of each other contact to obtain the feature value of each other contact; feature calculation is performed on the known information edited into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact; specifically, the feature calculation includes:
[0118] The preset other tag fields have different degrees of association with the tag field corresponding to the empty tag;
[0119] Counting the number of remaining tag edit values of each other contact in the first database, and recording it as the feature component A of each other contact in the first database;
[0120] Extracting, based on the first database, a correlation degree between the tag field corresponding to each remaining tag edit value and the tag field corresponding to the vacant tag for each other contact, and summing the correlation degrees between the tag fields corresponding to all remaining tag edit values and the tag fields corresponding to the vacant tag for each other contact to obtain a sum of the first correlation degrees, which is recorded as a feature component B for each other contact in the first database;
[0121] Feature component A and feature component B together constitute the feature value of each other contact in the first database;
[0122] Count the number of newly created contacts edited and written into the corresponding tag field, and record it as the feature component C of the newly created contact;
[0123] Extract the degree of association between each tag field written in the newly created contact and the tag field corresponding to the empty tag, and add the degree of association between each tag field written in the newly created contact and the tag field corresponding to the empty tag to obtain the sum of the second degree of association, which is recorded as the feature component D of the newly created contact;
[0124] Feature component C and feature component D together constitute the feature value of the newly created contact.
[0125] According to an embodiment of the present invention, extracting the degree of association between the tag field corresponding to each remaining tag edit value of each other contact and the tag field corresponding to the empty tag specifically includes:
[0126] Pre-generate a correlation table between tag fields, where the correlation table pre-stores the correlation degree between every two tag fields;
[0127] Pair the label fields corresponding to each remaining label edit value of each other contact with the label fields corresponding to the empty label;
[0128] Based on each pair of label fields, the corresponding correlation degree is extracted from the correlation degree table.
[0129] Preferably, the correlation degree may be a value between 0% and 100%, but is not limited thereto.
[0130] It should be noted that the number of remaining tag edit values and the degree of association between the tag fields corresponding to the empty tags are important features for evaluating whether two contacts are referenceable. The present invention calculates these two feature components separately to facilitate fine-grained difference calculation.
[0131] According to an embodiment of the present invention, calculating the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two specifically includes:
[0132] The number of preset tag fields and the relevance of the tag fields have different influence weights on the calculated eigenvalue difference, and are influence weight W1 and influence weight W2 respectively;
[0133] Subtract the characteristic component C from the characteristic component A to obtain a first characteristic component difference value, and divide the first characteristic component difference value by the characteristic component C to obtain a first characteristic difference value;
[0134] Subtract the characteristic component D from the characteristic component B to obtain a second characteristic component difference value, and divide the second characteristic component difference value by the characteristic component D to obtain a second characteristic difference value;
[0135] Multiply the first feature difference value by the influence weight W1 to obtain the first feature weight difference value;
[0136] Multiply the second feature difference value by the influence weight W2 to obtain the second feature weight difference value;
[0137] The first feature weight difference value and the second feature weight difference value are added to obtain the feature difference between the other contacts and the newly created contact.
[0138] It should be noted that the present invention further considers the different influence weights of the number of label fields and the correlation of label fields on the calculation of characteristic value differences, and continues to introduce their respective influence weights, and combines their respective influence weights to finally obtain the characteristic differences corresponding to other contacts and newly created contacts, and this characteristic difference is more suitable for newly created contacts to screen other contacts, further improving the fit and accuracy of subsequent label correction values.
[0139] According to a specific embodiment of the present invention, after editing and writing the inferred tag estimation value into the corresponding vacant tag, the method further includes:
[0140] Count all tag information edited and written into the corresponding tag field of the newly created contact, including receiving the known information of the newly created contact edited and written by the user in the corresponding tag field, and the tag estimated value information edited and written into the corresponding empty tag field;
[0141] Compare each tag information of the newly created contact with the remaining tag information to determine whether there is any information conflict between the two;
[0142] If there is information conflict between the former and the latter, the former tag information will be recorded as an exception once;
[0143] After all tag information of the newly created contact is compared and analyzed pairwise, the total number of times each tag information is recorded abnormally is counted;
[0144] The tag information for which the total number of times of recorded abnormalities is greater than a second preset threshold is marked as abnormal tag information.
[0145] It is understood that there may be more than one empty tag. After all empty tags are edited and the tag estimated values are written, it is necessary to self-check the information of each tag field of the newly created contact to detect abnormal tag information and prompt the user to make corrections or additions.
[0146] During the pairwise comparison analysis of each tag of a newly created contact against the remaining tags, for example, if one tag of a newly created contact indicates age 40, but another tag indicates elementary school classmate, and the user's age is 30, then if the newly created contact is an elementary school classmate, the new contact and the user should be of similar age, not 10 years apart. Therefore, the two tags are considered to conflict, and an exception is recorded for the age tag.
[0147] In the case where it is unknown whether which tag information is true or not, the present invention compares and analyzes all tag information pairwise, and accumulates the total number of times each tag information is recorded abnormally. The tag information with a total number of recorded abnormalities greater than a second preset threshold is marked as abnormal tag information, thereby achieving self-inspection of the tag information of a newly created contact without any judgment criteria, helping users to identify and update abnormal tag information in a timely manner.
[0148] According to a specific embodiment of the present invention, after editing and writing the inferred tag estimation value into the corresponding vacant tag, the method further includes:
[0149] Based on all contacts in the address book, take each contact as the base contact in turn;
[0150] Compare and analyze the tag field information of the baseline contact with the tag field information of each remaining contact to determine whether there is any information conflict between the two;
[0151] If there is conflicting information between the two, an exception will be recorded for the baseline contact;
[0152] After completing the pairwise information comparison analysis for all contacts in the address book, count the total number of abnormalities recorded for each contact;
[0153] A contact whose total number of recorded exceptions is greater than a third preset threshold is marked as an abnormal contact.
[0154] It is understood that there may be information association or duplication between contacts. If the information of one contact conflicts with that of another, for example, if the phone numbers are the same, an exception will be recorded for the former contact. Without knowing which contact's information is accurate, the present invention performs a pairwise comparison analysis on all contacts and accumulates the total number of times an exception has been recorded for each contact. Contacts with a total number of recorded exceptions exceeding a third preset threshold are marked as abnormal contacts. This allows for self-checking of abnormal contacts in the address book without any judgment criteria, helping users to promptly identify and update or delete abnormal contacts.
[0155] According to a specific embodiment of the present invention, after editing and writing the inferred tag estimation value into the corresponding vacant tag, the method further includes:
[0156] The user's local communication terminal generates a video call request for the newly created contact;
[0157] Send the video call request to the local communication terminal of the newly created contact;
[0158] After receiving the video call consent response from the local communication terminal of the newly created contact, the video call is established.
[0159] According to a specific embodiment of the present invention, after establishing the video call, the method further includes:
[0160] The user's local communication terminal pre-stores the video stream images of the recent preset time period, and the local communication terminal of the newly created contact also synchronously stores the video stream images of the recent preset time period;
[0161] The user's local communication terminal collects and obtains the video image at the current moment;
[0162] Compare the current video image with the video stream images stored in the user's local communication terminal for the past preset time period one by one;
[0163] Find the video image with the smallest difference from the current video image from the video stream images pre-stored in the user's local communication terminal for the past preset time period, and use it as the reference video image, and extract the time corresponding to the reference video image;
[0164] Compare and analyze the current video image with the reference video image to obtain difference image data;
[0165] Sending the difference image data and the time corresponding to the reference video image to the local communication terminal of the newly created contact;
[0166] The local communication terminal of the newly created contact extracts the corresponding reference video image from the video stream images pre-stored locally in the near preset time period according to the time corresponding to the reference video image;
[0167] The difference image data is integrated with the extracted corresponding reference video image to obtain the video image at the current moment.
[0168] It is understood that the video information is a video stream image, that is, a plurality of video images arranged in chronological order, which may include video images of multiple moments arranged in chronological order. The near preset time period refers to the preset time period before the current moment.
[0169] As can be appreciated, since the differences between video images within a preset time period are minimal, during a video call, to further reduce network transmission data, the present invention selects a video image from the preset time period that is closest to the current video image and uses it as a reference video image. The user's local communication terminal then compares the reference video image with the current video image to identify the difference image data, specifically the difference pixel data, including the difference pixel RGB values and location information. The user's local communication terminal then sends the difference image data and the time corresponding to the reference video image to the local communication terminal of the newly created contact. The local communication terminal of the newly created contact then extracts the corresponding reference video image from the locally stored video stream images from the nearly preset time period based on the time corresponding to the reference video image. This image is then combined with the received difference image data to restore the current video image on the local communication terminal of the newly created contact. Furthermore, during the entire video image transmission process, only the smaller difference video image and the time corresponding to the reference video image are transmitted, significantly reducing network communication pressure and facilitating smooth video call performance.
[0170] It can be understood that during the image integration process, the local communication end of the newly created contact finds the corresponding difference pixel point based on the position information of each difference pixel point in the difference image data, and then adds the RGB value of the difference pixel point to the RGB value of the pixel point corresponding to the reference video image to obtain the integrated RGB value of each difference pixel point; the reference video image is updated according to the integrated RGB value of each difference pixel point to obtain the video image at the current moment.
[0171] According to a specific embodiment of the present invention, sending the difference image data and the time corresponding to the reference video image to the local communication terminal of the newly created contact specifically includes:
[0172] The user's local communication terminal obtains multiple encoding forwarding nodes by calculating from the directory node server and forms an encoding forwarding network. Each node in the encoding forwarding network only knows its predecessor node and successor node.
[0173] Encapsulate the difference image data and the corresponding moments of the reference video image into a network data packet through a protocol;
[0174] The user's local communication terminal divides the network data packet into multiple coding information fragments of the same length using a splitting function. At the same time, the user's local communication terminal generates a coding coefficient matrix, and the coding coefficients are divided into coding coefficient fragments by column.
[0175] Redundant encoding is performed on the coding information slices and the coding coefficient slices by using a data redundancy algorithm to obtain redundant data;
[0176] Transmitting coded information fragments, coding coefficient fragments and redundant data through a multi-path coding network;
[0177] When the coding forwarding node receives the coding information fragment and the coding coefficient fragment, it fills, rearranges and delays the data before forwarding it to the next node.
[0178] After all the coded information fragments, coding coefficient fragments and redundant data arrive at the local communication terminal of the newly created contact, the original coded information fragments and coding coefficient fragments are decoded according to the inverse operation of the data redundancy algorithm to obtain the network data packet.
[0179] The present invention transmits the encoded information fragments and the coding coefficient fragments along different paths respectively. Since the intermediate nodes can only obtain part of the coding coefficients and cannot decode the transmitted information fragments, the security of communication messages transmitted in the forwarding network is effectively protected.
[0180] Figure 3 A block diagram of a social network management system based on an address book according to the present invention is shown.
[0181] like Figure 3 As shown, the second aspect of the present invention further provides a social network management system 3 based on an address book, comprising a memory 31 and a processor 32. The memory includes a social network management method program based on an address book, and when the social network management method program based on an address book is executed by the processor, the following steps are implemented:
[0182] Receive the known information of the new contact edited by the user in the corresponding label field;
[0183] Based on the known information of the newly created contact, determine whether there is a duplicate contact in the address book;
[0184] If there is a duplicate, the information of the duplicate contact in the address book will be completed based on the known information of the new contact. If there is no duplicate, proceed to the next step;
[0185] According to the label column of the address book, determine the empty label for the new contact;
[0186] Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred;
[0187] Edit and write the inferred tag estimate value into the corresponding empty tag.
[0188] According to an embodiment of the present invention, based on the known information of the newly created contact, the estimated tag value of the newly created contact in the empty tag is inferred, specifically including:
[0189] Build a label value inference model;
[0190] Optimize the label value inference model through sample data to obtain an optimized label value inference model;
[0191] Input the known information of the newly created contact into the label value inference model;
[0192] The tag value inference model analyzes and processes the known information of the new contact and infers the tag estimated value of the new contact in the empty tag.
[0193] According to an embodiment of the present invention, after the tag value inference model analyzes and processes known information of the newly created contact and infers an estimated tag value for the vacant tag of the newly created contact, the address book-based social network management method program, when executed by the processor, further implements the following steps:
[0194] Get tag information of other contacts from the address book;
[0195] and determining whether other contacts have a tag edit value in the tag field corresponding to the empty tag of the newly created contact; if so, adding the tag information of the other contacts to the first database; otherwise, not adding the tag information to the first database;
[0196] Based on the tag information of all other contacts in the first database, a tag correction value of the vacant tag is calculated using a preset correction value algorithm;
[0197] The estimated tag value of the newly created contact in the empty tag is added to the tag correction value to obtain a corrected tag estimated value.
[0198] According to an embodiment of the present invention, based on the tag information of all other contacts in the first database and using a preset correction value algorithm, the tag correction value of the vacant tag is calculated, specifically including:
[0199] Based on all other contacts in the first database, extracting the tag edit value on the tag field corresponding to the empty tag of the newly created contact and the remaining tag edit values from the tag information of each other contact;
[0200] Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact;
[0201] Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact;
[0202] Calculate the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two;
[0203] Selecting tag information of other contacts whose difference is less than a first preset threshold and adding it to the second database;
[0204] Based on all other contacts in the second database, the tag value inference model analyzes and processes the remaining tag edit values of each other contact to infer the estimated tag value of each other contact in the tag field corresponding to the empty tag of the newly created contact;
[0205] Based on all other contacts in the second database, the difference between the edited label value and the estimated label value of each other contact in the label field corresponding to the empty label of the newly created contact is calculated to obtain the label difference value of each other contact;
[0206] The label differences of all other contacts in the second database are added together to obtain a label difference sum, and the label difference sum is divided by the number of other contacts in the second database to obtain an average label difference value as the label correction value of the vacant label.
[0207] The present invention proposes a social network management method and system based on an address book. The method and system first receive the known information of a newly created contact edited and written by the user in the corresponding tag field; then, based on the known information of the newly created contact, determine whether there are duplicate contacts in the address book; if there are duplicates, complete the information of the duplicate contacts in the address book based on the known information of the newly created contact; if there are no duplicates, determine the empty tag of the newly created contact based on the tag field of the address book; then, based on the known information of the newly created contact, infer the tag estimate value of the newly created contact in the empty tag; finally, edit and write the inferred tag estimate value into the corresponding empty tag. The present invention analyzes and processes the known information of the newly created contact, thereby inferring the tag information of the newly created contact in the empty tag, thereby filling in the missing information of the newly created contact and improving the completeness of the relevant information of the contact in the address book, so that the user can understand the relevant information of the contact in multiple dimensions, further promoting the user to achieve a smooth social network based on the complete address book information, and improving the user experience.
[0208] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.
[0209] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.
[0210] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.
[0211] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.
[0212] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.
[0213] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.
Claims
1. A social network management method based on an address book, characterized in that: The method comprises: Receive the known information of the new contact edited by the user in the corresponding label field; Based on the known information of the newly created contact, determine whether there is a duplicate contact in the address book; If there is a duplicate, the information of the duplicate contact in the address book will be completed based on the known information of the new contact. If there is no duplicate, proceed to the next step; According to the label column of the address book, determine the empty label for the new contact; Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred; Edit and write the inferred tag estimate value into the corresponding empty tag.
2. The method for managing a social network based on an address book according to claim 1, wherein: Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred, including: Build a label value inference model; Optimize the label value inference model through sample data to obtain an optimized label value inference model; Input the known information of the newly created contact into the label value inference model; The tag value inference model analyzes and processes the known information of the new contact and infers the tag estimated value of the new contact in the empty tag.
3. The method for managing a social network based on an address book according to claim 2, wherein: After the tag value inference model analyzes and processes the known information of the newly created contact to infer the tag estimate value of the vacant tag of the newly created contact, the method further includes: Get tag information of other contacts from the address book; and determining whether other contacts have a tag edit value in the tag field corresponding to the empty tag of the newly created contact; if so, adding the tag information of the other contacts to the first database; otherwise, not adding the tag information to the first database; Based on the tag information of all other contacts in the first database, a tag correction value of the vacant tag is calculated using a preset correction value algorithm; The estimated tag value of the newly created contact in the empty tag is added to the tag correction value to obtain a corrected tag estimated value.
4. The method for managing a social network based on an address book according to claim 3, wherein: Based on the tag information of all other contacts in the first database, and using a preset correction value algorithm, a tag correction value of the vacant tag is calculated, specifically including: Based on all other contacts in the first database, extracting the tag edit value on the tag field corresponding to the empty tag of the newly created contact and the remaining tag edit values from the tag information of each other contact; Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact; Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact; Calculate the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two; Selecting tag information of other contacts whose difference is less than a first preset threshold and adding it to the second database; Based on all other contacts in the second database, the tag value inference model analyzes and processes the remaining tag edit values of each other contact to infer the estimated tag value of each other contact in the tag field corresponding to the empty tag of the newly created contact; Based on all other contacts in the second database, the difference between the edited label value and the estimated label value of each other contact in the label field corresponding to the empty label of the newly created contact is calculated to obtain the label difference value of each other contact; The label differences of all other contacts in the second database are added together to obtain a label difference sum, and the label difference sum is divided by the number of other contacts in the second database to obtain an average label difference value as the label correction value of the vacant label.
5. The method for managing a social network based on an address book according to claim 4, wherein: Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact; Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact; Specifically include: The preset other tag fields have different degrees of association with the tag field corresponding to the empty tag; Counting the number of remaining tag edit values of each other contact in the first database, and recording it as the feature component A of each other contact in the first database; Extracting, based on the first database, a correlation degree between the tag field corresponding to each remaining tag edit value and the tag field corresponding to the vacant tag for each other contact, and summing the correlation degrees between the tag fields corresponding to all remaining tag edit values and the tag fields corresponding to the vacant tag for each other contact to obtain a sum of the first correlation degrees, which is recorded as a feature component B for each other contact in the first database; Feature component A and feature component B together constitute the feature value of each other contact in the first database; Count the number of newly created contacts edited and written into the corresponding tag field, and record it as the feature component C of the newly created contact; Extract the degree of association between each tag field written in the newly created contact and the tag field corresponding to the empty tag, and add the degree of association between each tag field written in the newly created contact and the tag field corresponding to the empty tag to obtain the sum of the second degree of association, which is recorded as the feature component D of the newly created contact; Feature component C and feature component D together constitute the feature value of the newly created contact.
6. The method for managing a social network based on an address book according to claim 5, wherein: Calculate the difference between the feature value of each other contact and the feature value of the new contact to obtain the difference between the two, specifically including: The number of preset tag fields and the relevance of the tag fields have different influence weights on the calculated eigenvalue difference, and are influence weight W1 and influence weight W2 respectively; Subtract the characteristic component C from the characteristic component A to obtain a first characteristic component difference value, and divide the first characteristic component difference value by the characteristic component C to obtain a first characteristic difference value; Subtract the characteristic component D from the characteristic component B to obtain a second characteristic component difference value, and divide the second characteristic component difference value by the characteristic component D to obtain a second characteristic difference value; Multiply the first feature difference value by the influence weight W1 to obtain the first feature weight difference value; Multiply the second feature difference value by the influence weight W2 to obtain the second feature weight difference value; The first feature weight difference value and the second feature weight difference value are added to obtain the feature difference between the other contacts and the newly created contact.
7. A social network management system based on address book, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a social network management method program based on an address book, and when the social network management method program based on an address book is executed by the processor, the following steps are implemented: Receive the known information of the new contact edited by the user in the corresponding label field; Based on the known information of the newly created contact, determine whether there is a duplicate contact in the address book; If there is a duplicate, the information of the duplicate contact in the address book will be completed based on the known information of the new contact. If there is no duplicate, proceed to the next step; According to the label column of the address book, determine the empty label for the new contact; Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred; Edit and write the inferred tag estimate value into the corresponding empty tag.
8. The social network management system based on address book according to claim 7, characterized in that: Based on the known information of the new contact, the estimated value of the label of the new contact in the empty label is inferred, including: Build a label value inference model; Optimize the label value inference model through sample data to obtain an optimized label value inference model; Input the known information of the newly created contact into the label value inference model; The tag value inference model analyzes and processes the known information of the new contact and infers the tag estimated value of the new contact in the empty tag.
9. The social network management system based on address book according to claim 8, characterized in that: After the tag value inference model analyzes and processes the known information of the newly created contact and infers the tag estimate value of the vacant tag of the newly created contact, the address book-based social network management method program, when executed by the processor, further implements the following steps: Get tag information of other contacts from the address book; and determining whether other contacts have a tag edit value in the tag field corresponding to the empty tag of the newly created contact; if so, adding the tag information of the other contacts to the first database; otherwise, not adding the tag information to the first database; Based on the tag information of all other contacts in the first database, a tag correction value of the vacant tag is calculated using a preset correction value algorithm; The estimated tag value of the newly created contact in the empty tag is added to the tag correction value to obtain a corrected tag estimated value.
10. The social network management system based on address book according to claim 9, characterized in that: Based on the tag information of all other contacts in the first database, and using a preset correction value algorithm, a tag correction value of the vacant tag is calculated, specifically including: Based on all other contacts in the first database, extracting the tag edit value on the tag field corresponding to the empty tag of the newly created contact and the remaining tag edit values from the tag information of each other contact; Based on all other contacts in the first database, perform feature calculation on the remaining tag edit value of each other contact to obtain a feature value of each other contact; Perform feature calculation on the known information written into the corresponding tag field of the newly created contact to obtain the feature value of the newly created contact; Calculate the difference between the feature value of each other contact and the feature value of the newly created contact to obtain the difference between the two; Selecting tag information of other contacts whose difference is less than a first preset threshold and adding it to the second database; Based on all other contacts in the second database, the tag value inference model analyzes and processes the remaining tag edit values of each other contact to infer the estimated tag value of each other contact in the tag field corresponding to the empty tag of the newly created contact; Based on all other contacts in the second database, the difference between the edited label value and the estimated label value of each other contact in the label field corresponding to the empty label of the newly created contact is calculated to obtain the label difference value of each other contact; The label differences of all other contacts in the second database are added together to obtain a label difference sum, and the label difference sum is divided by the number of other contacts in the second database to obtain an average label difference value as the label correction value of the vacant label.