CN112528166A - User relationship analysis method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a user relationship analysis method and device, computer equipment and a storage medium, and belongs to the field of big data. The user relationship analysis method can acquire the basic information and the behavior information of each user accessing the target port, track the behavior information of each user to obtain the relationship between the users to construct a candidate relationship network, determine the target relationship network by analyzing and screening the candidate relationship network, analyze the basic information and the behavior information of each user based on the target relationship network to generate the user relationship information associated with the target port, and accordingly, effective user relationship information related to the target port can be mined out from complex internet data in a targeted and rapid and effective manner, and the intimacy degree between the users can be measured visually.
Description
Technical Field
The invention relates to the field of big data, in particular to a user relationship analysis method and device, computer equipment and a storage medium.
Background
The social relationship is an interpersonal interest relationship network scattered to the periphery by taking the user as a central unit to form a good social relationship circle, and plays a vital role in development of work and study of people. At present, in order to facilitate people to develop social activities, more and more intelligent terminals and social APPs (applications) appear in people's daily life and work. However, it is very difficult to rapidly mine social relationships in cluttered and complex internet data. Currently, only basic information, posting information, friend information, hometown, residence, living, education experience, work experience and other information of users can be searched based on network forums such as Facebook, linkedIn and the like, but the searched personal information of the users has no direct relationship, and the contents and modes of each person shown in the forums are different.
In summary, effective social information of a user cannot be effectively and quickly mined out based on complex internet data in a targeted manner at present.
Disclosure of Invention
Aiming at the problem that effective social information of a user cannot be effectively and quickly mined based on complex internet data, a user relationship analysis method, a device, computer equipment and a storage medium aiming at pointedly, quickly and effectively mining the effective social information of the user based on the complex internet data are provided.
In order to achieve the above object, the present invention provides a user relationship analysis method based on a social platform, including:
acquiring basic information and behavior information of each user accessing a target port;
tracking the behavior information of each user respectively to obtain a candidate relationship network;
analyzing and screening the candidate relationship network according to a preset rule to obtain a target relationship network;
matching the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generating user relationship information corresponding to the target port based on the user matched with the target relationship network and the first preset condition.
Optionally, the target port is a link of a target page or a two-dimensional code corresponding to the target page, the basic information includes a user ID, browsing data, and a user account, and the behavior information includes: triggering a buried point event, a sharing event, a forwarding event and an order event, wherein each event corresponds to an identifier;
the acquiring of the basic information and the behavior information of each user accessing the target port includes:
acquiring browsing data of each user through a timer of the target page, and acquiring a user ID and a user account of each user through a progressive frame of the target page;
and respectively identifying the behaviors of each user to obtain a triggering buried point event, a sharing event, a forwarding event and an order event of each user, and classifying and storing corresponding events according to different event identifications.
Optionally, the tracking the behavior information of each user to obtain a candidate relationship network includes:
analyzing the sharing event of each user through full link tracking to obtain a user sharing path;
and generating the candidate relationship network based on the user sharing path.
Optionally, the generating the candidate relationship network based on the user sharing path includes:
and generating the candidate relationship network represented by the user nodes by adopting an echart control according to the user sharing path.
Optionally, the analyzing and screening the candidate relationship network according to the preset rule to obtain the target relationship network includes:
obtaining the relation degree of each user sharing path in the candidate relation network;
taking a plurality of user sharing paths with the same degree of relation and the same head user node and tail user node in the user sharing paths as a candidate path set;
extracting the user sharing path meeting the preset rule from the candidate path set to serve as a relation path;
and taking the relationship path and the user sharing path except the candidate path set as the target relationship network, and recording the target relationship network.
Optionally, the user node in the user sharing path is associated with a timestamp shared by the user;
the preset rule is that when one user node corresponds to a plurality of upstream nodes in the candidate path set, the user sharing path corresponding to the upstream node with the earliest timestamp is selected as a relationship path.
Optionally, the first preset condition includes a second preset condition and a third preset condition;
the matching the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generating user relationship information corresponding to the target port based on the user matched with the target relationship network and the first preset condition includes:
acquiring the basic information and behavior information of a user corresponding to each user node in the target relational network, matching the basic information with a second preset condition, and matching the behavior information with a third preset condition;
marking the user node corresponding to the user matched with the second preset condition, and marking the user node corresponding to the user matched with the third preset condition;
generating user relationship information corresponding to the target port based on the user node carrying the mark;
the user relationship information includes the sharing path, the basic information, and the behavior information of the user.
In order to achieve the above object, the present invention further provides a user relationship analysis device based on a social platform, including:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring basic information and behavior information of each user accessing a target port;
the tracking unit is used for tracking the behavior information of each user to obtain a candidate relationship network;
the screening unit is used for analyzing and screening the candidate relationship network according to a preset rule to obtain a target relationship network;
the generating unit is configured to match the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generate user relationship information corresponding to the target port based on the user matched with the first preset condition in the target relationship network.
To achieve the above object, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above method.
The user relationship analysis method, the user relationship analysis device, the computer equipment and the storage medium can acquire the basic information and the behavior information of each user accessing the target port, track the behavior information of each user to obtain the relationship between the users to construct a candidate relationship network, analyze and screen the candidate relationship network to determine the target relationship network, analyze the basic information and the behavior information of each user based on the target relationship network to generate the user relationship information associated with the target port, and accordingly, effective user relationship information related to the target port can be extracted from complex internet data in a targeted and rapid and effective mode, and the intimacy degree between the users can be measured visually.
Drawings
FIG. 1 is a flowchart of a method according to an embodiment of a social platform based user relationship analysis method of the present invention;
FIG. 2 is a flow diagram of one embodiment of tracking behavior information of a user to obtain candidate relationship networks;
FIG. 3 is a flowchart of an embodiment of analyzing and screening candidate relationship networks to obtain a target relationship network;
FIG. 4 is a block diagram of an embodiment of a social platform based user relationship analysis apparatus according to the present invention;
fig. 5 is a hardware architecture diagram of one embodiment of the computer apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The user relationship analysis method, the user relationship analysis device, the computer equipment and the storage medium are suitable for the field of insurance business. The user relationship analysis method can acquire the basic information and the behavior information of each user accessing the target port, track the behavior information of each user to obtain the relationship between the users to construct a candidate relationship network, determine the target relationship network by analyzing and screening the candidate relationship network, analyze the basic information and the behavior information of each user based on the target relationship network to generate the user relationship information associated with the target port, and accordingly, effective user relationship information related to the target port can be mined out from complex internet data in a targeted and rapid and effective manner, and the intimacy degree between the users can be measured visually.
Example one
Referring to fig. 1, a method for analyzing a user relationship based on a social platform according to the embodiment includes the following steps:
s1, acquiring basic information and behavior information of each user accessing a target port.
In this embodiment, the target port is a link of a target page or a two-dimensional code corresponding to the target page, the basic information includes a user ID, browsing data, and a user account, and the behavior information includes: triggering a buried point event, sharing an event, forwarding an event and an order event, wherein each event corresponds to an identifier.
By way of example and not limitation, the target port may be a link or two-dimensional code of HTML5 describing a product (e.g., applet), a consultation, or a soft text. The browsing data is browsing duration and browsing times, and the basic information may also include information such as a nickname and an image of the user account.
Further, step S1 may obtain browsing data of each user through a timer of the target page, and obtain a user ID and a user account of each user through a progressive frame (vue.
Specifically, the timer of the target page is a timer in javascript (JS for short, a high-level programming language of just-in-time compilation type), and the timer may be a countdown timer or a cycle timer. The countdown timer is set to execute the function () function after a certain period of time, and the loop timer is set to execute the function () function once every other period of time. Vue may be applied layer by layer from bottom to top. Vue, the core library focuses only on the viewing layer for easy integration with third party libraries or existing projects. Vue is fully capable of providing a driver for complex single page applications when used in conjunction with modern tool chains and various supporting class libraries.
Further, step S1 may identify the behavior of each user to obtain a trigger embedding event, a sharing event, a forwarding event, and an order event of each user, and store the corresponding events according to different event identifications in a classified manner.
In this embodiment, when a user accesses a target port, a behavior event of the user can be marked by monitoring the behavior of the user, and the trigger buried point event is an event that the user clicks a button or a link of a page corresponding to the target port, or an event that the user inputs information on the page; the sharing event is a behavior event of a user sharing target port; the forwarding event is a behavior event of a user forwarding target port; the order event is a behavioral event of a transaction (including payment and pending payment) that occurs by the user based on the target port. Different identifications can be adopted to identify different types of behavior events so as to distinguish different types of behavior events, and corresponding behavior events are stored in a yellowbrick database according to the different identifications so as to facilitate subsequent data analysis. The yellowbrick database is a machine learning visualization library, and can provide visualization of various machine learning algorithms, such as feature visualization, classification visualization, regression visualization, cluster visualization, model selection visualization, target visualization, character visualization and the like.
And S2, tracking the behavior information of each user respectively to obtain a candidate relationship network.
Further, the step S2 shown in fig. 2 may include the following steps:
and S21, analyzing the sharing event of each user through full link tracking to obtain a user sharing path.
In this embodiment, when a user accesses a target port, a server associated with the target port receives an access request, and then generates a traced (full link tracking), and tracks a sharing event log of the user through the traced, so as to determine a sharing path of the user according to the recorded log.
And S22, generating the candidate relationship network based on the user sharing path.
Specifically, step S22 may include:
and generating the candidate relationship network represented by the user nodes by adopting an echart control according to the user sharing path.
In this embodiment, since there may be a plurality of users accessing the target port, and each user may be related to each other or may be independent from each other, the shared paths of each user need to be analyzed and sorted respectively, repeated paths are merged, and mutually independent paths are distinguished, so that a candidate relationship network composed of a plurality of paths is generated by using the echart control.
And S3, analyzing and screening the candidate relationship network according to a preset rule to obtain a target relationship network.
In this embodiment, considering that the user nodes in the candidate relationship network are all nodes accessing the target port, which nodes are users interested in the target port and which nodes are users not interested in the target port cannot be highlighted, the nodes in the candidate relationship network need to be screened through step S3, so that the analysis accuracy of the user relationship data is improved.
Further, referring to step S3 shown in fig. 3, the method may include:
and S31, obtaining the relation degree of each user sharing path in the candidate relation network.
And the user nodes in the user sharing path are associated with the timestamp shared by the user.
In this embodiment, the number of relation degrees is the number of users participating in accessing the target port in the user sharing path.
And S32, taking a plurality of user sharing paths with the same degree of relation and the same head user node and tail user node in the user sharing paths as candidate path sets.
It should be noted that: the user sharing path includes a sharing direction.
By way of example and not limitation, the user sharing path is: target port → user a → user B → user C → user D. The user sharing path is that the user A shares the target port to the user B after accessing the target port, the user B shares the target port to the user C after accessing the target port, and the user C shares the target port to the user D after accessing the target port. The user A is a head user node in the user sharing path, and the user D is a tail user node in the user sharing path. And when the relation degrees of the multiple user sharing paths are all 4, the first user node is the user A, and the last user node is the user D, the user sharing paths are put into the candidate path set.
And S33, extracting the user sharing path meeting the preset rule from the candidate path set to serve as a relation path.
And when one user node corresponds to a plurality of upstream nodes in the candidate path set, selecting the user sharing path corresponding to the upstream node with the earliest timestamp as a relationship path according to the preset rule. And the upper level node of the user node.
In the user sharing path, destination port → user a → user B → user C → user D, where user a is an upstream node of user B, user B is an upstream node of user C, and user C is an upstream node of user D.
In this embodiment, when the relationship degrees of the plurality of paths are the same and the head user node and the tail user node of the path are the same, it indicates that the sharing paths are similar, and the earlier shared path can be used as the relationship path according to the priority sharing principle, so as to filter out the similar path.
And S34, taking the relationship path and the user sharing path except the candidate path set as the target relationship network, and recording the target relationship network.
It is emphasized that, to further ensure the privacy and security of the target relationship network, the target relationship network may also be stored in a node of a block chain.
In this embodiment, the user sharing paths with the same degree of relationship and different head user nodes and tail user nodes, the user sharing paths with the different degree of relationship and the same head user nodes and tail user nodes, and the relationship path are used as the target relationship network.
And S4, matching the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generating user relationship information corresponding to the target port based on the user matched with the first preset condition in the target relationship network.
Further, the first preset condition includes a second preset condition and a third preset condition. Step S4 may include: acquiring the basic information and behavior information of a user corresponding to each user node in the target relational network, matching the basic information with a second preset condition, and matching the behavior information with a third preset condition; marking the user node corresponding to the user matched with the second preset condition, and marking the user node corresponding to the user matched with the third preset condition; and generating user relationship information corresponding to the target port based on the user node carrying the mark.
The second preset condition is that the browsing time in the browsing data reaches a time threshold (such as 1 minute) and/or the browsing times reach a time threshold (such as 5 times). The third preset condition is that the sharing times reach the sharing threshold (for example, 10 times), and/or the forwarding times reach the forwarding threshold (for example, 10 times), and/or the order times reach the order threshold (for example, 1 piece, 2 pieces, etc.).
It should be noted that: the user relationship information includes the sharing path, the basic information, and the behavior information of the user.
In the embodiment, the attention of each user to the page corresponding to the target port can be visually displayed according to the marked user node, and the user portrait corresponding to the page can be constructed based on the user in the user relationship information, so that the purpose of data analysis is achieved.
In a preferred embodiment, the total browsing duration, the total sharing times, the total forwarding times and the total number of order events of all the users may be respectively counted according to the target relationship network, and the analysis data of the target port is generated based on the total browsing duration, the total sharing times, the total forwarding times and the total number of order events.
In this embodiment, the user relationship analysis method based on the social platform may obtain basic information and behavior information of each user accessing the target port, track the behavior information of each user to obtain relationships between the users to construct a candidate relationship network, determine the target relationship network by analyzing and screening the candidate relationship network, analyze the basic information and the behavior information of each user based on the target relationship network to generate user relationship information associated with the target port, and thereby achieve targeted, fast and effective mining of effective user relationship information associated with the target port from complex internet data, so as to intuitively measure intimacy degree between the users.
Example two
Referring to fig. 4, a user relationship analysis apparatus 1 based on a social platform of the present embodiment includes: an acquisition unit 11, a tracking unit 12, a screening unit 13 and a generation unit 14.
An obtaining unit 11, configured to obtain basic information and behavior information of each user accessing the target port.
In this embodiment, the target port is a link of a target page or a two-dimensional code corresponding to the target page, the basic information includes a user ID, browsing data, and a user account, and the behavior information includes: triggering a buried point event, sharing an event, forwarding an event and an order event, wherein each event corresponds to an identifier.
By way of example and not limitation, the target port may be a link or two-dimensional code of HTML5 describing a product (e.g., applet), a consultation, or a soft text. The browsing data is browsing duration and browsing times, and the basic information may also include information such as a nickname and an image of the user account.
Further, the obtaining unit 11 may obtain browsing data of each user through a timer of the target page, and obtain a user ID and a user account of each user through a progressive frame (vue.
Specifically, the timer of the target page is a timer in javascript (JS for short, a high-level programming language of just-in-time compilation type), and the timer may be a countdown timer or a cycle timer. The countdown timer is set to execute the function () function after a certain period of time, and the loop timer is set to execute the function () function once every other period of time. Vue may be applied layer by layer from bottom to top. Vue, the core library focuses only on the viewing layer for easy integration with third party libraries or existing projects. Vue is fully capable of providing a driver for complex single page applications when used in conjunction with modern tool chains and various supporting class libraries.
Further, the obtaining unit 11 may respectively identify the behavior of each user, obtain a triggering buried point event, a sharing event, a forwarding event, and an order event of each user, and store the corresponding events according to different event identifications in a classified manner.
In this embodiment, when a user accesses a target port, a behavior event of the user can be marked by monitoring the behavior of the user, and the trigger buried point event is an event that the user clicks a button or a link of a page corresponding to the target port, or an event that the user inputs information on the page; the sharing event is a behavior event of a user sharing target port; the forwarding event is a behavior event of a user forwarding target port; the order event is a behavioral event of a transaction (including payment and pending payment) that occurs by the user based on the target port. Different identifications can be adopted to identify different types of behavior events so as to distinguish different types of behavior events, and corresponding behavior events are stored in a yellowbrick database according to the different identifications so as to facilitate subsequent data analysis. The yellowbrick database is a machine learning visualization library, and can provide visualization of various machine learning algorithms, such as feature visualization, classification visualization, regression visualization, cluster visualization, model selection visualization, target visualization, character visualization and the like.
A tracking unit 12, configured to track the behavior information of each user respectively to obtain a candidate relationship network.
Further, the tracking unit 12 may analyze the sharing event of each user through full link tracking to obtain a user sharing path; and generating the candidate relationship network based on the user sharing path.
In this embodiment, when a user accesses a target port, a server associated with the target port receives an access request, and then generates a traced (full link tracking), and tracks a sharing event log of the user through the traced, so as to determine a sharing path of the user according to the recorded log. The tracking unit 12 may generate the candidate relationship network represented by the user node according to the user sharing path by using an echart control.
In this embodiment, since there may be a plurality of users accessing the target port, and each user may be related to each other or may be independent from each other, the shared paths of each user need to be analyzed and sorted respectively, repeated paths are merged, and mutually independent paths are distinguished, so that a candidate relationship network composed of a plurality of paths is generated by using the echart control.
And the screening unit 13 is configured to analyze and screen the candidate relationship network according to a preset rule to obtain a target relationship network.
In this embodiment, considering that the user nodes in the candidate relationship network are all nodes accessing the target port, which nodes are users interested in the target port and which nodes are users not interested in the target port cannot be highlighted, the nodes in the candidate relationship network need to be screened by the screening unit 13, so that the analysis accuracy of the user relationship data is improved.
Further, the screening unit 13 obtains the degree of relationship of each user sharing path in the candidate relationship network, and uses a plurality of user sharing paths in which the degree of relationship is the same and a head user node and a tail user node in the user sharing paths are the same as a candidate path set; extracting the user sharing path meeting a preset rule from the candidate path set to serve as a relation path; and taking the relationship path and the user sharing path except the candidate path set as the target relationship network, and recording the target relationship network.
And when one user node corresponds to a plurality of upstream nodes in the candidate path set, selecting the user sharing path corresponding to the upstream node with the earliest timestamp as a relationship path according to the preset rule. And the upper level node of the user node.
It is emphasized that, to further ensure the privacy and security of the target relationship network, the target relationship network may also be stored in a node of a block chain.
In this embodiment, when the relationship degrees of the plurality of paths are the same and the head user node and the tail user node of the path are the same, it indicates that the sharing paths are similar, and the earlier shared path can be used as the relationship path according to the priority sharing principle, so as to filter out the similar path. And taking the user sharing paths with the same relation degrees and different head user nodes and tail user nodes, the user sharing paths with the different relation degrees and the same head user nodes and tail user nodes, and the relation paths as a target relation network.
A generating unit 14, configured to match the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generate user relationship information corresponding to the target port based on the user whose target relationship network is matched with the first preset condition.
Further, the first preset condition includes a second preset condition and a third preset condition. Acquiring the basic information and behavior information of a user corresponding to each user node in the target relationship network through a generating unit 14, matching the basic information with a second preset condition, and matching the behavior information with a third preset condition; marking the user node corresponding to the user matched with the second preset condition, and marking the user node corresponding to the user matched with the third preset condition; and generating user relationship information corresponding to the target port based on the user node carrying the mark.
The second preset condition is that the browsing time in the browsing data reaches a time threshold (such as 1 minute) and/or the browsing times reach a time threshold (such as 5 times). The third preset condition is that the sharing times reach the sharing threshold (for example, 10 times), and/or the forwarding times reach the forwarding threshold (for example, 10 times), and/or the order times reach the order threshold (for example, 1 piece, 2 pieces, etc.).
It should be noted that: the user relationship information includes the sharing path, the basic information, and the behavior information of the user.
In the embodiment, the attention of each user to the page corresponding to the target port can be visually displayed according to the marked user node, and the user portrait corresponding to the page can be constructed based on the user in the user relationship information, so that the purpose of data analysis is achieved.
In a preferred embodiment, the generating unit 14 may further count the total browsing duration, the total sharing number, the total forwarding number, and the total number of order events of all users according to the target relationship network, and generate the analysis data of the target port based on the total browsing duration, the total sharing number, the total forwarding number, and the total number of order events.
In this embodiment, the user relationship analysis apparatus 1 based on the social platform may obtain basic information and behavior information of each user accessing the target port through the obtaining unit 11, track the behavior information of each user by using the tracking unit 12 to obtain a relationship between users to construct a candidate relationship network, analyze and screen the candidate relationship network by using the screening unit 13 to determine the target relationship network, analyze the basic information and the behavior information of each user by using the generating unit 14 based on the target relationship network to generate user relationship information associated with the target port, thereby achieving that effective user relationship information associated with the target port can be purposefully, quickly and effectively mined from complex internet data, so as to intuitively measure the intimacy degree between users.
EXAMPLE III
In order to achieve the above object, the present invention further provides a computer device 2, where the computer device 2 includes a plurality of computer devices 2, components of the social platform based user relationship analysis apparatus 1 according to the second embodiment may be distributed in different computer devices 2, and the computer device 2 may be a smartphone, a tablet computer, a laptop computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster formed by a plurality of servers) that executes a program, and the like. The computer device 2 of the present embodiment includes at least, but is not limited to: the social platform-based user relationship analysis device 1 includes a memory 21, a processor 23, a network interface 22, and a social platform-based user relationship analysis device 1 (see fig. 5) that are communicatively connected to each other via a system bus. It is noted that fig. 5 only shows the computer device 2 with components, but it is to be understood that not all of the shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the memory 21 includes at least one type of computer-readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both an internal storage unit of the computer device 2 and an external storage device thereof. In this embodiment, the memory 21 is generally configured to store an operating system and various application software installed on the computer device 2, for example, a program code of the social platform-based user relationship analysis method in the first embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 23 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data Processing chip in some embodiments. The processor 23 is typically used for controlling the overall operation of the computer device 2, such as performing control and processing related to data interaction or communication with the computer device 2. In this embodiment, the processor 23 is configured to execute the program code stored in the memory 21 or process data, for example, execute the social platform based user relationship analysis apparatus 1.
The network interface 22 may comprise a wireless network interface or a wired network interface, and the network interface 22 is typically used to establish a communication connection between the computer device 2 and other computer devices 2. For example, the network interface 22 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 5 only shows the computer device 2 with components 21-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the social platform based user relationship analysis apparatus 1 stored in the memory 21 may be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 23) to complete the present invention.
Example four
To achieve the above objects, the present invention also provides a computer-readable storage medium including a plurality of storage media such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by the processor 23, implements corresponding functions. The computer-readable storage medium of the present embodiment is used for storing the social platform based user relationship analysis apparatus 1, and when executed by the processor 23, the social platform based user relationship analysis method of the first embodiment is implemented.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A user relationship analysis method based on a social platform is characterized by comprising the following steps:
acquiring basic information and behavior information of each user accessing a target port;
tracking the behavior information of each user respectively to obtain a candidate relationship network;
analyzing and screening the candidate relationship network according to a preset rule to obtain a target relationship network;
matching the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generating user relationship information corresponding to the target port based on the user matched with the target relationship network and the first preset condition.
2. The social platform-based user relationship analysis method according to claim 1, wherein the target port is a link of a target page or a two-dimensional code corresponding to the target page, the basic information includes a user ID, browsing data, and a user account, and the behavior information includes: triggering a buried point event, a sharing event, a forwarding event and an order event, wherein each event corresponds to an identifier;
the acquiring of the basic information and the behavior information of each user accessing the target port includes:
acquiring browsing data of each user through a timer of the target page, and acquiring a user ID and a user account of each user through a progressive frame of the target page;
and respectively identifying the behaviors of each user to obtain a triggering buried point event, a sharing event, a forwarding event and an order event of each user, and classifying and storing corresponding events according to different event identifications.
3. The social platform based user relationship analysis method according to claim 2, wherein the tracking the behavior information of each user to obtain a candidate relationship network comprises:
analyzing the sharing event of each user through full link tracking to obtain a user sharing path;
and generating the candidate relationship network based on the user sharing path.
4. The social platform based user relationship analysis method of claim 3, wherein the generating the candidate relationship network based on the user sharing path comprises:
and generating the candidate relationship network represented by the user nodes by adopting an echart control according to the user sharing path.
5. The social platform based user relationship analysis method according to claim 4, wherein the analyzing and screening the candidate relationship network according to the preset rule to obtain the target relationship network comprises:
obtaining the relation degree of each user sharing path in the candidate relation network;
taking a plurality of user sharing paths with the same degree of relation and the same head user node and tail user node in the user sharing paths as a candidate path set;
extracting the user sharing path meeting the preset rule from the candidate path set to serve as a relation path;
and taking the relationship path and the user sharing path except the candidate path set as the target relationship network, and recording the target relationship network.
6. The social platform based user relationship analysis method according to claim 5, wherein the user node in the user sharing path is associated with a timestamp of user sharing;
the preset rule is that when one user node in the candidate path set corresponds to a plurality of upstream nodes, the user sharing path corresponding to the upstream node with the earliest timestamp is selected as a relationship path.
7. The social platform based user relationship analysis method according to claim 4, wherein the first preset condition comprises a second preset condition and a third preset condition;
the matching the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generating user relationship information corresponding to the target port based on the user matched with the target relationship network and the first preset condition includes:
acquiring the basic information and behavior information of a user corresponding to each user node in the target relational network, matching the basic information with a second preset condition, and matching the behavior information with a third preset condition;
marking the user node corresponding to the user matched with the second preset condition, and marking the user node corresponding to the user matched with the third preset condition;
generating user relationship information corresponding to the target port based on the user node carrying the mark;
the user relationship information includes the sharing path, the basic information, and the behavior information of the user.
8. A social platform-based user relationship analysis device, comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring basic information and behavior information of each user accessing a target port;
the tracking unit is used for tracking the behavior information of each user to obtain a candidate relationship network;
the screening unit is used for analyzing and screening the candidate relationship network according to a preset rule to obtain a target relationship network;
the generating unit is configured to match the basic information and the behavior information corresponding to each user in the target relationship network with a first preset condition, and generate user relationship information corresponding to the target port based on the user matched with the first preset condition in the target relationship network.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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