An electronic business card associated data analysis system and method based on artificial intelligence

Abstract

The application discloses an electronic business card associated data analysis system and method based on artificial intelligence, and relates to the technical field of information mining. The application standardizes the analysis of all different types of collected electronic business cards, normalizes the user information in each electronic business card, and constructs an electronic business card database. The social relationships of all users are integrated to generate a social relationship network of the users. The emotional characteristics of each user in history and when communicating with different users are utilized to generate mining elements of each user in combination with the user information in different electronic business cards. Potential customers matched with real-time users are discovered and recommended to the real-time users with electronic business cards of the potential customers. The reasons for recommending each potential customer are visually displayed, and the mining elements of the real-time users are optimized by using the electronic business card information corresponding to the key features.

Description

Technical Field

[0001] The present invention relates to the technical field of information mining, and in particular to an electronic business card associated data analysis system and method based on artificial intelligence. Background Art

[0002] With the development of computer and network technology, early business card management software emerged, primarily focused on information storage and simple classification. Compared to traditional paper business cards, electronic business cards are more convenient and have a wider reach, leading to their widespread adoption. The advancement of various modern computer technologies has also played a key role in driving research on electronic business cards. The maturity of data mining technology has led companies to explore how to extract value from customer data, with social network analysis becoming a research hotspot. The rise of social media platforms has furthered the use of electronic business cards, with the expansion of user information and network relationships becoming a key research direction. The development of big data technologies has enabled the processing of massive amounts of electronic business card data, and the application of cloud computing has made data storage and analysis more flexible and efficient. However, due to the large volume of data, data mining for electronic business cards often relies solely on user-specific data, failing to consider user preferences. Consequently, the mined customer may not be interested in the user's preferences, even though the big data matches the criteria. Furthermore, the key to mining potential customers lies in accurately constructing social relationship networks. Existing methods, which rely on electronic business card big data, often fail to reflect the user's subjective consciousness and are therefore incomplete. This significantly impacts the user experience, making the development of personalized mining methods crucial. Summary of the Invention

[0003] The purpose of the present invention is to provide an electronic business card associated data analysis system and method based on artificial intelligence to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An artificial intelligence-based electronic business card associated data analysis method, the method comprising the following steps:

[0006] S100: The user fills in their own information in the software to form their own electronic business card and transmits it to the cloud for storage. The cloud collects the electronic business cards of all users, analyzes and standardizes all different types of collected electronic business cards, normalizes the user information in each electronic business card, and constructs an electronic business card database;

[0007] Furthermore, the specific steps for constructing the electronic business card database are:

[0008] S101. Collect all users' electronic business cards in the cloud. Suppose the format of all collected user electronic business cards is , represents the 1st, 2nd, 3rd,..., pth format of all the collected user electronic cards, p is a positive integer; the number of each electronic card in the collected cloud is calculated, and the one with the largest number is selected as the standard format Gb of the electronic card; the user information in the electronic card of different formats is extracted, and the user information contained in the collected electronic card is denoted as , represents the 1st, 2nd, 3rd,..., n-th user information contained in the user electronic card, n is a positive integer; the extracted user information of different formats is rewritten according to the standard format, and all the user electronic cards in the collected cloud are standardized;

[0009] S102, after standardizing all the user electronic cards, each user information in the electronic card is converted into vector data Xi using an encoder, and the vector data of each user information in the electronic card is normalized, and the formula is:

[0010] ;

[0011] In the formula, represents the normalized vector data, Xi represents the vector data of user information, and m represents the dimension of the converted user information vector data; the normalized vector data of user information is used to construct an electronic card database.

[0012] Standardizing and normalizing all the electronic cards in the collected cloud makes the information format in the electronic card uniform. When integrating electronic card data from multiple sources, this consistency can enable the data to be quickly and accurately merged and stored, reducing data errors and repeated entry problems caused by format differences; and ensuring the accurate transmission of important information in the electronic card.

[0013] S200, collect the communication data between all users in the history, and use the communication data and the user information in the electronic card to construct the social relationship of each user, and integrate the social relationship of all users to generate a social relationship network of the user;

[0014] Further, the specific steps of generating the user social relationship network are:

[0015] S201, collect the communication data between all users in the history, and extract the communication end in the collected communication data as , represents the communication end in the 1st, 2nd, 3rd,..., mth communication data collected in the history, and a relationship edge is formed using the communication end in each communication data, finally forming m relationship edges, and generating an explicit social relationship network using the user node and the relationship edge;

[0016] S202, extract each user information in the electronic business card of all users, take each user as a node, compare the extracted user information in the electronic business card, compare the electronic business cards of two users, traverse and compare all user information contained in the two electronic business cards, when there is, it is judged that the corresponding two users have contact, and a relationship edge is generated; when all user information is traversed, there is no, it is judged that the corresponding two users have no contact; X and X' represent the same kind of user information contained in the electronic business cards of two users; all electronic business cards of users are compared by using the traversal comparison method, and the relationship edge matrix of all users is constructed by setting 1 for the relationship edge that exists and 0 for the relationship edge that does not exist.

[0017] ;

[0018] In the matrix, each row and each column represents a user node, and 1 / 0 represents the judgment result of whether two user nodes generate a relationship edge; the relationship edge matrix output by all users is generated after judgment; each user node and the relationship edge matrix are used to construct an implicit social relationship network; the explicit social relationship network and the implicit social relationship network are combined to construct a social relationship network of all users.

[0019] The explicit social relationship network and the implicit social relationship network are constructed by using all communication data and electronic business cards of users in history, and the two social relationship networks are combined to generate a comprehensive social relationship network, which simultaneously connects the relationship between the customers found by the user and the customers matched by the electronic business card, ensuring the comprehensiveness of the social relationship;

[0020] S300, using natural language processing algorithm to analyze the language of each user in history and different electronic business card users when communicating, obtaining the emotional characteristics of each user in history and different users when communicating, and combining the user information in different electronic business cards to generate the mining elements of each user;

[0021] Further, the specific steps of combining the user information in different electronic business cards to generate the mining elements of each user are as follows:

[0022] S301, collect the communication data of each user in history when communicating with the rest of different users, extract all language data in each communication data, extract all words appearing in the language data, and calculate the importance of each word respectively, the formula is:

[0023] ;

[0024] In the formula, represents the importance of each word, represents the number of the ith word in the kth language data, represents the total number of all words appearing in the kth language data; N represents the total number of language data, represents the number of language data containing the ith word; the importance of all words is calculated as represents the importance of the 1st, 2nd, 3rd,..., Qth word in the communication data, Q is a positive integer;

[0025] S302, after calculating the importance of all words in the communication data, the sentiment of each word is valued according to the sentiment dictionary, and the importance and sentiment value of each word are comprehensively analyzed to obtain the total sentiment value of each word, the formula is: , in the formula, E represents the total sentiment value of each word, represents the importance of each word, represents the sentiment value of each word; after calculating the total sentiment value of all words in a communication data, the one with the largest total sentiment value is selected as the sentiment feature of the communication data;

[0026] S303, using the sentiment dictionary, the sentiment features of the communication data in all sub-communications are divided into positive and negative; the user information associated between all communication users of the electronic business card of the positive and negative sentiment features is extracted, and the extracted associated user information is compared to screen out the associated user information existing alone in the positive and negative sentiment features; the associated user information existing alone is marked using the sentiment feature, the associated user information marked by the positive sentiment feature is used as the mining element of each user, and the associated user information marked by the negative sentiment feature is used as the shielding element of each user.

[0027] According to the analysis of the communication data of the user in the history, the corresponding sentiment features are extracted, the user information in the user electronic business card is marked using the sentiment features, and the mining element of each user is generated; the mining element is used to find potential customers to realize personalized intelligent recommendation, which not only recommends according to the user's own conditions, but also contains the user's preferences in communication, so that the recommendation of potential customers is more humanized and meets the user's sense.

[0028] S400, when a real-time user logs in the system and uses an electronic business card, the mining element generated by the real-time user is used to mine the electronic business card of each user in the user social relationship network, find potential customers matched with the real-time user, and recommend the electronic business card of the potential customers to the real-time user;

[0029] Further, the specific steps of recommending the electronic business card of the potential customer to the real-time user are:​

[0030] S401, when the real-time user logs in the system using the electronic card, the electronic card of each user is mined in the implicit social relationship network by using the mining elements generated by the real-time user, the mining elements and each electronic card in the implicit social relationship network are compared, the mining weight is configured by using the emotional total value of each mining element in S300 when the comparison is performed, when the mining element exists in the electronic card and the shielding element does not exist, it is judged that the user corresponding to the electronic card is a potential customer; when the shielding element exists, the electronic card is shielded;

[0031] The weight of each mining element in the mining of the potential customer is calculated as In the formula, R represents the weight of each mining element, E represents the emotional total value of each mining element, and f represents the number of mining elements.

[0032] S402, the mining elements generated by the real-time user are mined in the explicit social relationship network, the user nodes associated with the real-time user are extracted in the explicit social relationship network, the potential customers of the associated user nodes are found according to the method in S401 by indirectly using the mining elements of the associated user nodes, the potential customers of the associated user nodes are taken as the potential customers of the real-time user; all the potential customers in the implicit and explicit social relationship networks are combined to recommend to the real-time user.

[0033] S500, when the potential customers are recommended to the real-time user, the recommendation features of each potential customer are generated according to the tracing of the mining process of the potential customers; the recommended potential customers are marked by using the recommendation features, the recommendation reasons of each recommended potential customer are visually displayed, and the real-time user can select and communicate with the potential customers according to the recommendation features;

[0034] Further, the specific steps of making the real-time user select and communicate with the potential customers according to the recommendation features are as follows:

[0035] S501, when the potential customers are recommended to the real-time user, the types of the mining elements existing when each potential customer is mined are extracted according to the tracing of the mining process of the potential customers, and the mining element with the largest weight in the types of the existing mining elements is taken as the recommendation feature of each potential customer;

[0036] S502, the recommended potential customers are marked by using the recommendation features, the recommendation reasons of each recommended potential customer are visually displayed, the real-time user can select and communicate with the potential customers according to the recommendation features, and the personalized potential customer recommendation scheme is provided.

[0037] The visual display of the recommendation features of the potential customers can clearly display a variety of potential customers, facilitate the user to understand the recommendation reasons, and enhance the transparency of the system;

[0038] S600, collecting and analyzing real-time user communication data when the real-time user selects a potential customer for communication, obtaining key features of the real-time user and the potential customer during communication and corresponding to the electronic business card of the potential customer, and optimizing the mining elements of the real-time user by using the electronic business card information corresponding to the key features.

[0039] Further, the specific steps for optimizing the mining elements of the real-time user by using the electronic business card information corresponding to the key features are as follows:

[0040] S601, collecting and analyzing real-time user communication data when the real-time user selects a potential customer for communication, calculating the total value of the user information emotion of the real-time communication data in the method of S300, and then comparing the total value of the real-time user information emotion and the weight difference of each mining element in the mining elements; the maximum value of the difference value corresponds to the important feature of the user information;

[0041] S602, adjusting and optimizing the weight of the mining element corresponding to the important feature in the mining element by using the difference value of the calculated important feature.

[0042] When mining the potential customer, the system continuously learns the user behavior and feedback, so that the recommendation and analysis results are more in line with the user's needs, improving the accuracy and personalization of AI, and solving the problem that the system recommendation does not meet the user's expectations.

[0043] An electronic business card associated data analysis system based on artificial intelligence, the electronic business card associated data analysis system comprising a data collection module, a database construction module, a social relationship network generation module, a mining element module, a mining module and an optimization module;

[0044] The data collection module is used to collect the communication data and electronic business cards of all users in history;

[0045] The database construction module is used to analyze and process the collected all different types of electronic business cards by using all user electronic business cards collected in the cloud, normalize the user information in each electronic business card, and construct an electronic business card database;

[0046] The social relationship network generation module is used to generate an explicit social relationship network by using the communication data in history, generate an implicit social relationship network by using the user information in the electronic business card, and combine the explicit social relationship network and the implicit social relationship network to construct a social relationship network of all users;

[0047] The mining element module is used to analyze the emotion of each word in the user communication data in history, obtain the emotional features in each communication data, and generate mining elements combined with user information;

[0048] The mining module is used for mining potential customers of the user in the social relationship network according to the generated mining elements of each user and visually displaying;

[0049] The optimization module is used for analyzing real-time communication data of the user and the potential customers, calculating important features, and adjusting and optimizing the mining elements by using the important features.

[0050] The social relationship network generation module comprises an explicit social relationship network unit and an implicit social relationship network unit;

[0051] The explicit social relationship network unit is used for generating relationship edges from the communication data of each user in the history, and combining the user nodes to build an explicit social relationship network;

[0052] The implicit social relationship network unit is used for generating relationship edges from the user information in all electronic cards, and combining the user nodes to build an implicit social relationship network.

[0053] The mining module comprises a mining unit and a visual display unit;

[0054] The mining unit is used for mining potential customers of the user in the social relationship network according to the generated mining elements of each user;

[0055] The visual display unit is used for marking the recommended potential customers by using the recommendation features, and visually displaying the recommendation reasons of each recommended potential customer.

[0056] Compared with the prior art, the beneficial effects of the present application are:

[0057] 1. The present application marks the user information in the user electronic card by using the emotional features, generates the mining elements of each user, and realizes personalized intelligent recommendation by using the mining elements to find potential customers, which not only recommends according to the conditions of the user itself, but also contains the preferences of the user in communication, so that the recommended potential customers are more personalized and meet the user's sense of perception.

[0058] 2. The present application builds an explicit social relationship network and an implicit social relationship network by using all communication data of the user in the history and electronic cards, combines the two social relationship networks to generate a comprehensive social relationship network, and connects the relationships between the customers found by the user subjectively and the customers matched by the electronic cards, so as to ensure the comprehensiveness of the social relationship. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is a module distribution diagram of the electronic card associated data analysis system based on artificial intelligence.

[0060] Figure 2 A flowchart of a method for analyzing associated data of an electronic business card based on artificial intelligence according to the present application is shown in FIG. 1.

[0061] Figure 3 A social relationship network diagram of a method for analyzing associated data of an electronic business card based on artificial intelligence according to the present application is shown in FIG. 2. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0063] Embodiment: As shown in FIG. 1, the present application provides a technical solution, Figures 1-3

[0064] A method for analyzing associated data of an electronic business card based on artificial intelligence, the method comprising the following steps:

[0065] S100, a user fills in information of himself in software to form an electronic business card belonging to himself and transmits the electronic business card to a cloud for storage, collects all electronic business cards of users in the cloud, analyzes all collected electronic business cards of different types and performs standardization processing, normalizes user information in each electronic business card, and constructs an electronic business card database.

[0066] The specific steps for constructing the electronic business card database are as follows:

[0067] S101, all electronic business cards of users are collected in the cloud, and the format of all collected electronic business cards of users is set as , wherein p is a positive integer, the number of each type of electronic business card collected in the cloud is calculated, the type with the largest number is selected as the standard format Gb of the electronic business card, user information in electronic business cards of different formats is extracted, and the user information contained in the collected electronic business cards is set as , wherein n is a positive integer, the extracted user information of different formats is rewritten according to the standard format, and all electronic business cards of users in the cloud are standardized processed.

[0068] ​S102, after standardizing the electronic business cards of all users, each kind of user information in the electronic business card is converted into vector data Xi by using an encoder, and the vector data of each kind of user information in the electronic business card is normalized, and the formula is:

[0069] ;

[0070] In the formula, Xi represents the vector data of user information, and m represents the dimension of the vector data of user information after conversion; the normalized vector data of user information is used to construct an electronic business card database.

[0071] Standardizing and normalizing all the electronic business cards collected in the cloud makes the information format in the electronic business cards uniform. When integrating electronic business card data from multiple sources, such consistency can enable the data to be quickly and accurately merged and stored, reducing data errors and repeated entry problems caused by format differences; and ensuring accurate transmission of important information in the electronic business card.

[0072] S200, collecting communication data between all users in history, using the communication data and user information in the electronic business card to construct the social relationship of each user, integrating the social relationship of all users to generate a social relationship network of the user;

[0073] The specific steps for generating the social relationship network of the user are as follows:

[0074] S201, collecting communication data between all users in history, taking each user as a node, and extracting the communication end in the collected communication data as , Xi represents the vector data of user information, and m represents the dimension of the vector data of user information after conversion; the normalized vector data of user information is used to construct an electronic business card database.

[0075] S202, extracting each kind of user information in the electronic business card of all users, taking each user as a node, and using each kind of user information extracted from the electronic business card for comparison, comparing the electronic business cards of two users, and traversing and comparing all the user information contained in the two electronic business cards, when a relationship edge is generated when there is a contact between the two users; after traversing all the user information, If there is no X, it is determined that there is no contact between the two users; X and X' represent the same kind of user information contained in the electronic business card of the two users; all the electronic business cards of the users are compared by using the traversal comparison method, and the relationship edges that can be generated are set to 1, and the relationship edges that cannot be generated are set to 0, and the relationship edge matrix of all users is constructed as follows:

[0076] ;

[0077] In the matrix, each row and each column represents a user node, and 1 / 0 represents the judgment result of whether the two user nodes generate relationship edges; the relationship edge matrix output by all users is generated after judgment; the implicit social relationship network is constructed by using each user node and the relationship edge matrix; the explicit social relationship network and the implicit social relationship network are combined to construct the social relationship network of all users.

[0078] The communication data and the electronic business card of all users in the history are used to construct the explicit social relationship network and the implicit social relationship network, respectively, and the two social relationship networks are combined to generate a comprehensive social relationship network, which simultaneously connects the relationship between the customers found by the user and the customers matched by the electronic business card, ensuring the comprehensiveness of the social relationship.

[0079] S300, the language of each user in the history and different electronic business card users is analyzed by using a natural language processing algorithm, and the emotional characteristics of each user in the history and different users are obtained, and the mining elements of each user are generated by combining the user information in different electronic business cards;

[0080] The specific steps of generating the mining elements of each user by combining the user information in different electronic business cards are as follows:

[0081] S301, collect the communication data of each user in the history when communicating with the rest of the different users, extract all the language data in each communication data, extract all the words in the language data, and calculate the importance of each word, the formula is as follows:

[0082] ;

[0083] In the formula, represents the importance of each word, represents the number of the i-th word in the k-th language data, represents the total number of all words appearing in the k-th language data; N represents the total number of language data, represents the number of language data containing the i-th word; after calculation, the importance of all words is , representing the importance of the first, second, third, Qth,... Qth word in the exchange data, Q being a positive integer;

[0084] S302, after calculating the importance of all words in the exchange data, the sentiment of each word is valued according to the sentiment dictionary, and the importance and sentiment value of each word are comprehensively analyzed to obtain the total sentiment value of each word, the formula is: , in the formula, E represents the total sentiment value of each word, represents the importance of each word, represents the sentiment value of each word; after calculating the total sentiment value of all words in an exchange data, the one with the largest total sentiment value is selected as the sentiment feature of the exchange data;

[0085] S303, using the sentiment dictionary, the exchange data of all sub-exchanges is divided into positive and negative; the user information associated between all exchange users of the electronic business card of the positive and negative sentiment features is extracted, and the extracted associated user information is compared to screen out the associated user information that exists alone in the positive and negative sentiment features; the associated user information that exists alone is marked using the sentiment feature, the associated user information marked by the positive sentiment feature is used as the mining element of each user, and the associated user information marked by the negative sentiment feature is used as the shielding element of each user.

[0086] According to the analysis of the exchange data of the user in the history, the corresponding sentiment features are extracted, the user information in the user electronic business card is marked using the sentiment features, and the mining elements of each user are generated; the mining elements are used to find potential customers to realize personalized intelligent recommendation, which not only recommends according to the user's own conditions, but also contains the user's preferences in communication, making the recommendation of potential customers more humanized and meeting the user's sense of perception.

[0087] S400, when a real-time user logs in the system and uses an electronic business card, the mining elements generated by the real-time user are used to mine the electronic business card of each user in the user social relationship network, find potential customers matched with the real-time user, and recommend the electronic business card of the potential customers to the real-time user;

[0088] The specific steps of recommending the electronic business card of the potential customers to the real-time user are:

[0089] S401, when the real-time user logs in the system using the electronic card, the electronic card of each user is mined in the implicit social relationship network by using the mining elements generated by the real-time user, the mining elements and each electronic card in the implicit social relationship network are compared, the mining weight is configured by using the emotional total value of each mining element in S300 when the comparison is performed, when the mining element exists in the electronic card and the shielding element does not exist, it is judged that the user corresponding to the electronic card is a potential customer; when the shielding element exists, the electronic card is shielded;

[0090] The weight of each mining element in the mining of the potential customer is calculated as In the formula, R represents the weight of each mining element, E represents the emotional total value of each mining element, and f represents the number of mining elements.

[0091] S402, the mining elements generated by the real-time user are mined in the explicit social relationship network, the user nodes associated with the real-time user are extracted in the explicit social relationship network, the potential customers of the associated user nodes are found according to the method in S401 by indirectly using the mining elements of the associated user nodes, the potential customers of the associated user nodes are taken as the potential customers of the real-time user; all the potential customers in the implicit and explicit social relationship networks are combined to recommend to the real-time user.

[0092] S500, when the potential customers are recommended to the real-time user, the recommendation characteristics of each potential customer are generated according to the tracing of the mining process of the potential customers; the recommended potential customers are marked by using the recommendation characteristics, the recommendation reasons of each recommended potential customer are visually displayed, and the real-time user selects and communicates with the potential customers according to the recommendation characteristics;

[0093] The specific steps for the real-time user to select and communicate with the potential customers according to the recommendation characteristics are as follows:

[0094] S501, when the potential customers are recommended to the real-time user, the types of the mining elements existing when each potential customer is mined are extracted according to the tracing of the mining process of the potential customers, and the mining element with the largest weight in the types of the existing mining elements is taken as the recommendation characteristic of each potential customer;

[0095] S502, the recommended potential customers are marked by using the recommendation characteristics, the recommendation reasons of each recommended potential customer are visually displayed, the real-time user selects and communicates with the potential customers according to the recommendation characteristics, and a personalized potential customer recommendation scheme is provided.

[0096] The visual display of the recommendation characteristics of the potential customers can clearly display a plurality of potential customers, facilitate the user to understand the recommendation reasons, and enhance the transparency of the system;

[0097] S600, collecting and analyzing real-time user communication data when the real-time user selects a potential customer for communication, obtaining key features of the real-time user and the potential customer during communication and corresponding to the electronic business card of the potential customer, and optimizing the mining elements of the real-time user by using the electronic business card information corresponding to the key features.

[0098] The specific steps of optimizing the mining elements of the real-time user by using the electronic business card information corresponding to the key features are as follows:

[0099] S601, collecting and analyzing real-time user communication data when the real-time user selects a potential customer for communication, calculating the total value of the user's information emotion of the real-time communication data in the method of S300, and then comparing the total value of the real-time user information emotion and the weight difference of each mining element in the mining elements; the maximum value of the difference value corresponds to the important feature of the user information;

[0100] S602, adjusting and optimizing the weight of the mining element corresponding to the important feature in the mining element by using the difference value of the calculated important feature.

[0101] When mining the potential customer, the user behavior and feedback are continuously learned, so that the recommendation and analysis results are more in line with the user's needs, the accuracy and personalization degree of AI are improved, and the problem that the system recommendation does not meet the user's expectations is solved.

[0102] An electronic business card associated data analysis system based on artificial intelligence, the electronic business card associated data analysis system comprising a data collection module, a database construction module, a social relationship network generation module, a mining element module, a mining module and an optimization module;

[0103] The data collection module is used for collecting the communication data and electronic business cards of all users in history;

[0104] The database construction module is used for analyzing and standardizing all collected different types of electronic business cards by using all user electronic business cards collected in the cloud, normalizing the user information in each electronic business card, and constructing an electronic business card database;

[0105] The social relationship network generation module is used for generating an explicit social relationship network by using the communication data in history, generating an implicit social relationship network by using the user information in the electronic business card, and combining the explicit social relationship network and the implicit social relationship network to construct a social relationship network of all users;

[0106] The mining element module is used for analyzing the emotion of each word in the user communication data in history, obtaining the emotional features in each communication data, and generating mining elements in combination with the user information;

[0107] The mining module is configured to mine potential customers of the user in the social relationship network according to the generated mining elements of each user and visually display the potential customers.

[0108] The optimization module is configured to analyze real-time communication data of the user and the potential customers, calculate important features, and adjust and optimize the mining elements by using the important features.

[0109] The social relationship network generation module comprises an explicit social relationship network unit and an implicit social relationship network unit.

[0110] The explicit social relationship network unit is configured to generate relationship edges from the communication data of each user in the history, and combine the user nodes to build an explicit social relationship network.

[0111] The implicit social relationship network unit is configured to generate relationship edges from the user information in all electronic cards, and combine the user nodes to build an implicit social relationship network.

[0112] The mining module comprises a mining unit and a visual display unit.

[0113] The mining unit is configured to mine potential customers of the user in the social relationship network according to the generated mining elements of each user.

[0114] The visual display unit is configured to mark the recommended potential customers by using the recommendation features, and visually display the recommendation reasons of each recommended potential customer.

[0115] Embodiment: In a certain APP, the electronic cards and the historical communication data of all users are analyzed to obtain a social relationship network, and the total number of users is 5. First, the explicit social relationship network is obtained according to the historical communication data, as shown in Figure 3 The relationship edge matrix obtained by analyzing the electronic cards and the user information is: The implicit social relationship network formed by the relationship edge matrix is as shown in Figure 3 The comprehensive social relationship network formed by combining the two kinds of social relationship networks is as shown in Figure 3

[0116] The mining elements of user 1 according to the historical communication data are young, 25-35 years old, and the position is above the minister.

[0117] The potential customers are found in the generated social network by using the mining elements to make recommendations.

[0118] ​It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the present application can be implemented in other particular forms without departing from the spirit or essential characteristics of the present application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims should be considered as limiting the scope of the claims with respect to the figures of the patent document.

Claims

1. An artificial intelligence-based electronic business card associated data analysis method, characterized by: The method comprises the following steps: S100: The user fills in their own information in the software to form their own electronic business card and transmits it to the cloud for storage. The cloud collects the electronic business cards of all users, analyzes and standardizes all different types of collected electronic business cards, normalizes the user information in each electronic business card, and constructs an electronic business card database; S200, collecting historical communication data between all users, using the communication data and user information in the electronic business card to build the social relationship of each user, and integrating the social relationships of all users to generate a social relationship network of users; S300, using a natural language processing algorithm to analyze the language used by each user in historical communications with different electronic business card users, obtaining emotional characteristics of each user in historical communications with different users, and generating mining factors for each user by combining user information in different electronic business cards; S400: When a real-time user logs into the system and uses an electronic business card, mining elements generated by the real-time user in the user social relationship network is used to mine each user's electronic business card to find potential customers that match the real-time user, and recommend the potential customer's electronic business card to the real-time user; S500: When recommending potential customers to real-time users, trace the potential customer mining process to generate recommendation features for each potential customer; use the recommendation features to tag the recommended potential customers, and visually display the recommendation reasons for each potential customer, so that the real-time user can select and communicate with potential customers based on the recommendation features; S600. When a real-time user selects a potential customer for communication, the real-time user communication data is collected and analyzed to obtain key features of the communication between the real-time user and the potential customer and match them with the potential customer's electronic business card. The electronic business card information corresponding to the key features is used to optimize the mining factors of the real-time user.

2. The method for analyzing electronic business card related data based on artificial intelligence according to claim 1, characterized in that: The specific steps of constructing the electronic business card database in S100 are: S101. Collect all users' electronic business cards in the cloud. Suppose the format of all collected user electronic business cards is , Represents the 1st, 2nd, 3rd, ..., pth format of all collected user electronic business cards, where p is a positive integer; calculate the number of each electronic business card in the cloud, and select the one with the largest number as the standard format of electronic business cards Gb; extract user information from electronic business cards of different formats, and let the user information contained in the collected electronic business cards be , Indicates the first, second, third, ..., nth type of user information contained in the user's electronic business card, where n is a positive integer; the extracted user information in different formats is re-filled according to the standard format, and the electronic business cards of all users collected in the cloud are standardized; S102. After standardizing the electronic business cards of all users, convert each user information in the electronic business card into vector data Xi using an encoder. Normalize the vector data of each user information in the electronic business card using the formula: ； In the formula, Represents the normalized vector data, Xi represents the vector data of user information, and m represents the dimension of the transformed vector data of user information; the normalized vector data of user information is used to construct an electronic business card database.

3. The method for analyzing electronic business card related data based on artificial intelligence according to claim 1, characterized in that: The specific steps of generating the user social relationship network in S200 are: S201, collect the communication data between all users in history, take each user as a node, extract the communication end in the collected communication data , Indicates the communication ends in the 1st, 2nd, 3rd, ..., mth communication data in the collected history. Use the communication ends in each communication data to form a relationship edge, and finally form m relationship edges. Use user nodes and relationship edges to generate an explicit social relationship network; A202, extract each user information in the electronic business cards of all users, take each user as a node, compare each user information in the extracted electronic business cards, compare the electronic business cards of two users, and traverse and compare all user information contained in the two electronic business cards. If it exists, it is determined that there is a connection between the two users and a relationship edge is generated; after traversing all user information, If it does not exist, it is determined that there is no connection between the two users; X and X' represent the same type of user information contained in the electronic business cards of the two users respectively; the electronic business cards of all users are compared using the traversal comparison method, and the ones that have a connection and can generate a relationship edge are set to 1, and the ones that do not have a connection and cannot generate a relationship edge are set to 0. The relationship edge matrix of all users is constructed as follows: ； In the matrix, each row and column represents a user node, and 1 / 0 represents the judgment result of whether two user nodes generate a relationship edge. After judging the relationship edges generated by all users, the relationship edge matrix is ​​output. The implicit social relationship network is constructed using each user node and the relationship edge matrix. Combine explicit social relationship networks and implicit social relationship networks to construct the social relationship networks of all users.

4. The method for analyzing electronic business card related data based on artificial intelligence according to claim 1, characterized in that: The specific steps of generating mining elements for each user by combining user information in different electronic business cards in S300 are: S301. Collect the communication data of each user in the history when communicating with other different users, extract all the language data of each communication data, extract all the words that appear in the language data, and calculate the importance of each word respectively. The formula is: ； In the formula, Indicates the importance of each word. Indicates the number of the i-th word in the k-th language data, Indicates the total number of words that appear in the k-th language data; N indicates the total number of language data, Indicates the number of language data containing the i-th word; After calculation, the importance of all words is , Indicates the importance of the 1st, 2nd, 3rd, ..., Qth words in the communication data, where Q is a positive integer; S302: After calculating the importance of all words in the communication data, assign a sentiment value to each word according to the sentiment dictionary, and comprehensively analyze the importance and sentiment value of each word to obtain the total sentiment value of each word. The formula is: ,In the formula, E represents the total sentiment value of each word, Indicates the importance of each word. Indicates the sentiment value of each word; After calculating the total sentiment value of all words in a communication data, the word with the largest total sentiment value is selected as the sentiment feature of the communication data; S303, using a sentiment dictionary to classify the sentiment characteristics of the communication data from all exchanges into positive and negative; extracting user information associated with all users of the electronic business cards with positive and negative sentiment characteristics, and comparing the extracted associated user information to screen out associated user information that exists solely in the positive and negative sentiment characteristics; The emotional features are used to mark the associated user information that exists alone, and the associated user information marked with positive emotional features is used as the mining factor of each user, and the associated user information marked with negative emotional features is used as the shielding factor of each user.

5. The method for analyzing electronic business card related data based on artificial intelligence according to claim 1, characterized in that: The specific steps of recommending the potential customer's electronic business card to the real-time user in S400 are: S401. When a real-time user logs into the system and uses an electronic business card, mining each user's electronic business card in the implicit social relationship network using mining elements generated by the real-time user. The mining elements are compared with each electronic business card in the implicit social relationship network. During the comparison, the mining weight is configured using the total sentiment value of each mining element in S300. When the mining element exists in the electronic business card and the blocking element does not exist, the user corresponding to the electronic business card is determined to be a potential customer. When the blocking element exists, the electronic business card is blocked. S402. Utilize the mining elements generated by the real-time user to conduct mining in the explicit social relationship network, extract the user nodes associated with the real-time user in the explicit social relationship network, indirectly utilize the mining elements of the associated user nodes to find potential customers of the associated user nodes according to the method in S401, and use the potential customers of the associated user nodes as the potential customers of the real-time user; combine all the potential customers in the implicit and explicit social relationship networks and recommend them to the real-time user.

6. The method for analyzing electronic business card related data based on artificial intelligence according to claim 1, characterized in that: The specific steps of enabling the real-time user to select and communicate with potential customers based on the recommendation features in S500 are: S501. When recommending potential customers to real-time users, trace the mining process of potential customers, extract the types of mining factors that were determined to exist when each potential customer was mined, and use the mining factor with the largest weight among the existing mining factors as the recommendation feature for each potential customer; S502. Use recommendation features to mark recommended potential customers, visualize the recommendation reasons for each potential customer, and enable real-time users to select and communicate with potential customers based on the recommendation features; provide personalized potential customer recommendation solutions.

7. The method for analyzing electronic business card related data based on artificial intelligence according to claim 1, characterized in that: The specific steps of optimizing the mining factors of real-time users by using the electronic business card information corresponding to the key features in S600 are as follows: S601. When a real-time user selects a potential customer for communication, real-time user communication data is collected and analyzed. The method in S300 is used to calculate the total sentiment value of the user toward each user information of the potential customer in the real-time communication data. Then, the difference between the total sentiment value of the real-time user information and the weight of each mining factor in the mining factors is compared and calculated; the user information corresponding to the maximum difference value is used as the important feature; S602: Using the calculated difference values ​​of the important features, adjust and optimize the mining element category weights corresponding to the important features in the mining elements.

8. An artificial intelligence-based electronic business card related data analysis system, characterized by: The electronic business card association data analysis system includes a data collection module, a database construction module, a social relationship network generation module, a mining element module, a mining module and an optimization module; The data collection module is used to collect communication data and electronic business cards of all users in history; The database construction module is used to utilize all user electronic business cards collected in the cloud, analyze and standardize all different types of collected electronic business cards, normalize the user information in each electronic business card, and construct an electronic business card database; The social relationship network generation module is used to generate an explicit social relationship network using historical communication data and to generate an implicit social relationship network using user information in electronic business cards; Combine explicit social relationship networks and implicit social relationship networks to construct the social relationship networks of all users; The mining element module is used to analyze the sentiment of each word in the historical user communication data, obtain the sentiment characteristics in each communication data, and generate mining elements in combination with user information; The mining module is used to mine potential customers of each user in the social relationship network according to the generated mining elements of each user and visualize the results; The optimization module is used to analyze the real-time communication data between users and potential customers, calculate important features, and use the important features to adjust and optimize the mining factors.

9. The artificial intelligence-based electronic business card related data analysis system according to claim 8, characterized in that: The social relationship network generation module includes an explicit social relationship network unit and an implicit social relationship network unit; The explicit social relationship network unit is used to generate relationship edges based on the communication data of each user in history, and to build an explicit social relationship network in combination with user nodes; The implicit social relationship network is used to generate relationship edges according to user information in all electronic business cards, and to construct the implicit social relationship network in combination with user nodes.

10. The artificial intelligence-based electronic business card related data analysis system according to claim 8, characterized in that: The mining module includes a mining unit and a visual display unit; The mining unit is used to mine potential customers of the user in the social relationship network according to the generated mining elements of each user; The visual display unit is used to mark the recommended potential customers using the recommendation features and visually display the recommendation reason for each recommended potential customer.

Images