User value evaluation method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a user value evaluation method and device, electronic equipment and storage medium, and relates to the technical field of computers. The method comprises: when a target user applies for network access, obtaining pre-network information of the target user; determining a first user value evaluation result of the target user according to the pre-network information of the target user; when the first user value evaluation result meets a preset value threshold, allowing the target user to access the network; obtaining current network environment data of the target user, determining a current environment data matrix according to the current network environment data; obtaining current Internet behavior data of the target user, determining a current Internet behavior matrix according to the current Internet behavior data; and evaluating the user value of the target user according to the current environment data matrix and the current Internet behavior matrix to obtain a second user value evaluation result. Therefore, the present disclosure can accurately evaluate the user value of the user, thereby improving the quality of service.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to a user value evaluation method and device, electronic equipment and storage medium. BACKGROUND

[0002] With the continuous development of computer technology, in order to improve the income of network service and optimize the service quality of network, the user value evaluation method needs to be actively explored, so as to provide differentiated products and services.

[0003] In the related art, the method for determining user value often focuses on the instant state of the user, and is seriously disturbed by subjective experience, and needs a large amount of manual supervision and correction. In the face of explosive data on the Internet, it is impossible to quickly and effectively update the model and upgrade the strategy, resulting in inaccurate user value evaluation.

[0004] Therefore, there is an urgent need for a method that can accurately evaluate the user value of a user, so as to provide differentiated products and services for different users and improve the service quality.

[0005] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The present disclosure provides a user value evaluation method and device, electronic equipment and storage medium, which at least partially solves the problem of inaccurate user value evaluation in the related art.

[0007] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0008] According to one aspect of the embodiments of the present disclosure, a user value evaluation method is provided, comprising: when a target user applies for network access, obtaining pre-network information of the target user; determining a first user value evaluation result of the target user according to the pre-network information of the target user; when the first user value evaluation result meets a preset value threshold, allowing the target user to access the network; obtaining current network environment data of the target user, and determining a current environment data matrix according to the current network environment data; obtaining current Internet behavior data of the target user, and determining a current Internet behavior matrix according to the current Internet behavior data; evaluating the user value of the target user according to the current environment data matrix and the current Internet behavior matrix, and obtaining a second user value evaluation result.

[0009] In some embodiments of the present disclosure, the method provided by the embodiments of the present disclosure further includes: classifying the current state of the target user according to the current environment data matrix and the current Internet behavior matrix; when the current state of the target user does not belong to any one of the preset state categories, re-determining the current environment data matrix and the current Internet behavior matrix; obtaining a re-determined second user value evaluation result according to the re-determined current environment data matrix and the re-determined current Internet behavior matrix.

[0010] In some embodiments of the present disclosure, the current network environment data is determined by at least one of data flow duration data, region network level data, roaming in and out frequency data, 5G network duration data and 4G network duration data; and the current Internet behavior data is determined by access information in user data exchange packets.

[0011] In some embodiments of the present disclosure, the pre-network information of the user includes network access channel information, and the method provided by the embodiments of the present disclosure further includes: judging whether the network access channel of the target user is valid.

[0012] According to the pre-network information of the target user, determining the first user value evaluation result of the target user includes: when the network access channel is valid, determining the first user value evaluation result of the target user according to the pre-network information of the target user.

[0013] In some embodiments of the present disclosure, the pre-network information of the target user includes individual identity information, and determining the first user value evaluation result of the target user according to the pre-network information of the target user includes: obtaining user information of a plurality of other users belonging to the same individual as the target user according to the individual identity information, wherein the plurality of other users include at least one of all online users and all historical users of the individual; determining initial state data of the target user according to the user information of each of the other users; and obtaining the first user value evaluation result of the target user according to the initial state data of the target user.

[0014] In some embodiments of the present disclosure, determining the initial state data of the target user according to the user information of each of the other users includes: obtaining a plurality of feature index vectors according to the user information of each of the other users, wherein any feature index vector is used to describe one user information; obtaining a pre-life cycle feature vector set of the target user according to the plurality of feature index vectors, wherein any pre-life cycle feature vector in the pre-life cycle feature vector set corresponds to at least one feature index vector; and determining the initial state data of the target user according to the pre-life cycle feature vector set of the target user.

[0015] In some embodiments of the present disclosure, the pre-life cycle feature vector set of the target user is obtained according to the plurality of feature indicator vectors, including: determining a plurality of feature indicator vectors corresponding to each pre-life cycle feature respectively according to each pre-life cycle feature; assigning a corresponding weight information to each pre-life cycle feature respectively corresponding feature indicator vector to obtain each pre-life cycle feature vector; and merging each pre-life cycle feature vector to obtain the pre-life cycle feature vector set of the target user.

[0016] In some embodiments of the present disclosure, the first user value evaluation result of the target user is obtained according to the initial state data of the target user, including: when the element value of any feature indicator vector in the pre-life cycle feature vector set of the target user does not satisfy the corresponding preset feature condition, setting the first user value evaluation result of the target user to zero, wherein the element value of any feature indicator vector corresponds to a preset feature threshold; and when the element value of any feature indicator vector in the pre-life cycle feature vector set of the target user satisfies the corresponding preset feature condition, taking the initial state data of the target user as the first user value evaluation result of the target user.

[0017] According to another aspect of the present disclosure, a user value evaluation device is provided, including:

[0018] The pre-network information acquisition module is configured to acquire pre-network information of the target user when the target user applies for network access;

[0019] The first user value evaluation result determination module is configured to determine the first user value evaluation result of the target user according to the pre-network information of the target user;

[0020] The network access judgment module is configured to allow the target user to access the network when the first user value evaluation result satisfies a preset value threshold.

[0021] The matrix determination module is configured to acquire current network environment data of the target user, determine a current environment data matrix according to the current network environment data; acquire current Internet behavior data of the target user, and determine a current Internet behavior matrix according to the current Internet behavior data.

[0022] The second user value evaluation result module is configured to evaluate the user value of the target user according to the current environment data matrix and the current Internet behavior matrix to obtain a second user value evaluation result.

[0023] In some example embodiments, the second user value evaluation result module is further configured to classify the current state of the target user according to the current environment data matrix and the current Internet behavior matrix; when the current state of the target user does not belong to any one of the preset state categories, re-determine the current environment data matrix and the current Internet behavior matrix; and obtain a re-determined second user value evaluation result according to the re-determined current environment data matrix and the re-determined current Internet behavior matrix.

[0024] In some example embodiments, the current network environment data is determined by at least one of data flow duration data, region network level data, roaming in and out frequency data, 5G network duration data, and 4G network duration data; and the current Internet behavior data is determined by access information in user data exchange packets.

[0025] In some example embodiments, the pre-network information of the user includes network access channel information, and the pre-network information acquisition module is further configured to determine whether the network access channel of the target user is valid.

[0026] The first user value evaluation result determination module is configured to, when the network access channel is valid, determine a first user value evaluation result of the target user according to the pre-network information of the target user.

[0027] In some example embodiments, the pre-network information of the target user includes individual identity information, and the first user value evaluation result determination module is configured to obtain user information of a plurality of other users belonging to the same individual as the target user according to the individual identity information, wherein the plurality of other users include at least one of all online users and all historical users of the individual; determine initial state data of the target user according to the user information of each of the other users; and obtain the first user value evaluation result of the target user according to the initial state data of the target user.

[0028] In some example embodiments, the first user value evaluation result determination module is configured to obtain a plurality of feature index vectors according to the user information of each of the other users, wherein any feature index vector is used to describe one user information; obtain a pre-life cycle feature vector set of the target user according to the plurality of feature index vectors, wherein any pre-life cycle feature vector in the pre-life cycle feature vector set corresponds to at least one feature index vector; and determine the initial state data of the target user according to the pre-life cycle feature vector set of the target user.

[0029] In some exemplary embodiments, the first user value assessment result determination module is used to determine multiple corresponding feature index vectors based on each pre-lifecycle feature; assign corresponding weight information to the feature index vectors corresponding to each pre-lifecycle feature to obtain each pre-lifecycle feature vector; and merge the various pre-lifecycle feature vectors to obtain the pre-lifecycle feature vector set of the target user.

[0030] In some exemplary embodiments, the first user value assessment result determination module is used to set the first user value assessment result of the target user to zero when the element value of any feature index vector corresponding to the pre-lifecycle feature vector set of the target user does not meet the corresponding preset feature condition, wherein the element value of any feature index vector corresponds to a preset feature threshold.

[0031] When the element values ​​of any feature index vector in the target user's pre-lifecycle feature vector set all satisfy the corresponding preset feature conditions, the target user's initial state data is used as the target user's first user value assessment result.

[0032] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the aforementioned user value assessment method by executing the executable instructions.

[0033] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the above-described user value assessment method.

[0034] According to another aspect of this disclosure, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the user value assessment method provided in various alternative embodiments of this disclosure.

[0035] The technical solution provided in this disclosure can determine the first user value assessment result of the target user based on the target user's pre-network information. Therefore, this disclosure can consider the target user's pre-network information when conducting user value assessment. Furthermore, this disclosure periodically performs user value assessment after the target user joins the network using the target user's current network environment data and current internet behavior data. Therefore, this disclosure provides a method for accurately assessing user value, thereby ensuring that differentiated products and services are subsequently provided to different users, improving service quality.

[0036] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0038] Figure 1 A schematic diagram illustrating the implementation environment of a user value assessment method according to an embodiment of this disclosure is shown.

[0039] Figure 2 This diagram illustrates a user value assessment method according to an embodiment of the present disclosure.

[0040] Figure 3 This diagram illustrates a process of a user value assessment method according to an embodiment of the present disclosure.

[0041] Figure 4 This illustration shows a process diagram of another user value assessment method in an embodiment of this disclosure;

[0042] Figure 5 This illustration shows a process diagram of a method for determining a first user value assessment result according to an embodiment of the present disclosure;

[0043] Figure 6 This diagram illustrates a process for obtaining a second user value assessment result in an embodiment of the present disclosure.

[0044] Figure 7 This diagram illustrates a user value assessment device according to an embodiment of the present disclosure.

[0045] Figure 8 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0046] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0047] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0048] Figure 1 A schematic diagram illustrating the implementation environment of a possible user value assessment method provided in an embodiment of this disclosure is shown.

[0049] like Figure 1 As shown, the implementation environment of the user value assessment method provided in this embodiment may include terminal device 101, network 102 and server 103.

[0050] For example, terminal device 101 can obtain the target user's pre-network information when the target user applies for network access. It can also send this pre-network information to server 103. Server 103 can obtain this pre-network information and, based on it, determine a first user value assessment result for the target user; if the first user value assessment result meets a preset value threshold, allow the target user to access the network; obtain the target user's current network environment data and, based on this data, determine a current environment data matrix; obtain the target user's current internet behavior data and, based on this, determine a current internet behavior matrix; and, based on the current environment data matrix and the current internet behavior matrix, assess the target user's user value to obtain a second user value assessment result.

[0051] For example, network 102 is a medium used to provide a communication link between terminal device 101 and server 103, and can be a wired network or a wireless network.

[0052] Optionally, the aforementioned wireless or wired network uses standard communication technologies and / or protocols. Network 102 is typically the Internet, but can also be any network, including but not limited to Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), mobile, wired or wireless network, private network or virtual private network (VPN). In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0053] Terminal device 101 can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

[0054] Optionally, the client of the application installed on different terminal devices 101 may be the same, or the client of the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client may also be different; for example, the application client may be a mobile client, a PC client, etc.

[0055] Server 103 can be a server that provides various services, such as a backend management server that supports the device operated by the user using terminal device 101. The backend management server can analyze and process received requests and other data, and feed the processing results back to terminal device 101.

[0056] Optionally, server 103 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0057] Those skilled in the art will know that Figure 1 The number of terminal devices 101, networks 102, and servers 103 shown is merely illustrative. Any number of terminal devices 101, networks 102, and servers 103 can be used as needed. This disclosure does not limit the number of such devices.

[0058] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0059] First, this disclosure provides a user value assessment method, which can be executed by any electronic device with computing power.

[0060] Figure 2 This invention discloses a flowchart of a user value assessment method according to an embodiment of the present invention, as shown below. Figure 2 As shown, the user value assessment method provided in this embodiment includes the following steps S202 to S210.

[0061] S202: When a target user applies for network access, obtain the target user's pre-network information.

[0062] This disclosure does not limit the application scenarios of the user value assessment method. For example, this disclosure can be applied to e-commerce platforms to assess users' purchasing power and product preferences, etc., to evaluate user value. As another example, this disclosure can be applied to video platforms to assess users' video viewing preferences, etc., to evaluate user value.

[0063] For example, taking an e-commerce platform as an application scenario, when a target user applies to join the network, the target user can submit a registration application on the e-commerce platform. Afterwards, the e-commerce platform can obtain the target user's pre-network information.

[0064] This disclosure does not limit the target user's pre-network information. For example, the target user's pre-network information may include at least one of the following: network access channel information, application time information, individual identification information, application information, and activity location information.

[0065] The network access channel information, also known as the channel through which the user submitted the registration application, can be, for example, the name and type of a mobile application, a mobile webpage, a computer webpage, an offline form summary, or a service center. The individual identification information can be the user's personal ID. This application information may include, for example, at least one of the following: the user's real name, mailing address, and mobile phone number.

[0066] It should be noted that the content of the pre-network information considered in this embodiment includes the multiple types mentioned above. Obtaining information from multiple sources can improve the accuracy of user value assessment.

[0067] This disclosure does not limit the method of obtaining the target user's pre-network information. For example, the pre-network information can be actively filled in by the target user when applying for network access, or it can be obtained by the terminal side when the user submits a registration application on the terminal side.

[0068] S204. Based on the target user's pre-network information, determine the first user value assessment result for the target user.

[0069] In some embodiments, the target user's pre-network information may include individual identification information. In this case, determining the target user's first user value assessment result based on the target user's pre-network information may include: obtaining user information of multiple other users belonging to the same individual as the target user based on the individual identification information, wherein the multiple other users include at least one of the individual's all current users and all historical users; determining the target user's initial state data based on the user information of each other user; and obtaining the target user's first user value assessment result based on the target user's initial state data.

[0070] In an exemplary embodiment, the target user had already registered under the name of one or more other users prior to this network access application. In this case, the individual identification information corresponding to the target user is consistent with that of the other users. Therefore, user information of multiple other users belonging to the same individual as the target user can be obtained based on this individual identification information.

[0071] For example, the aforementioned "current online user" refers to a user who has not yet cancelled their account, while "historical user" refers to a user who previously joined the network but has since cancelled their account. Furthermore, this embodiment of the disclosure does not limit the user information of other users. For example, the user information of other users may include their pre-network information and various network data generated after joining the network. For instance, it may include a list of network service sales information, network entry channel information, financial consumption information, reasons and times for leaving the network, network usage habits, etc.

[0072] In some embodiments, determining the initial state data of the target user based on the user information of other users may include: obtaining multiple feature index vectors based on the user information of other users, wherein any feature index vector is used to describe a user information; obtaining a pre-lifecycle feature vector set of the target user based on the multiple feature index vectors, wherein any pre-lifecycle feature vector in the pre-lifecycle feature vector set corresponds to at least one feature index vector; and determining the initial state data of the target user based on the pre-lifecycle feature vector set of the target user.

[0073] For example, taking an e-commerce platform, the user information may include age information. It can be assumed that all current online users involve m types of age information. For example, these m age information pieces can be denoted as [p1, p2, ..., p...]. m Additionally, it can be assumed that all currently active users involve k delivery location information. For example, the k delivery location information can be denoted as [q1, q2, ..., q]. k Here, m and k can both be integers greater than 0. Therefore, the age information and delivery location information mentioned above can each correspond to a feature index vector.

[0074] For example, the pre-lifecycle data of a target user can be all network-related data of multiple other users belonging to the same entity as the target user from the time they are granted network access until they leave the network. Therefore, the pre-lifecycle feature vector set of a target user can include user information of various other users belonging to the same entity as the target user.

[0075] For example, taking age information as an example, the age range can be divided, and clustering can be performed based on this. For instance, the age range can be divided into several categories: under 16 years old, 16 to 18 years old, ..., over 60 years old.

[0076]

[0077] For example, the age feature index corresponding to age1 can be set to 1, the age feature index corresponding to age2 to 2, ..., and the age feature index corresponding to age3 to 3. Correspondingly, other feature indices can be quantified into numbers for representation. This embodiment of the disclosure does not limit the representation method after quantification of the feature index. For example, if the age feature index of any other user is 2, the gender feature index is 1, and the occupation feature index is 6, then the feature index vector corresponding to the age, gender, and occupation of that other user can be [2, 1, 6].

[0078] For example, if another user applies for network access in City A, District B, Street C, then the feature index vector of the location where the other user applies for network access can be [3, 3, 2]. This embodiment of the disclosure does not limit the method of quantifying the location of the network access application to obtain the corresponding feature index vector.

[0079] In some embodiments, obtaining a pre-lifecycle feature vector set of a target user based on multiple feature index vectors includes: determining multiple feature index vectors corresponding to each pre-lifecycle feature; assigning corresponding weight information to the feature index vectors corresponding to each pre-lifecycle feature to obtain each pre-lifecycle feature vector; and merging the various pre-lifecycle feature vectors to obtain the pre-lifecycle feature vector set of the target user.

[0080] This disclosure does not limit the pre-lifecycle features included in the pre-lifecycle feature vector set. For example, the pre-lifecycle features may include fraud-related features, user loyalty features, etc. The pre-lifecycle features included in the pre-lifecycle feature vector set can be determined based on experience or application scenarios.

[0081] In some possible implementations, age can be used as a feature metric, influencing one or more pre-lifecycle feature vectors. Multiple feature metric vectors corresponding to a pre-lifecycle feature vector can form a feature metric vector set. Therefore, the age-related feature metric vector can be applied to one or more feature metric vector sets. For example, feature metric vectors corresponding to age, gender, and occupation may influence the pre-lifecycle feature vector corresponding to fraud-related features, or they may influence the pre-lifecycle feature vector corresponding to user loyalty features. Taking age as an example, users under 16 or over 60 may have a higher probability of being involved in fraud than users in other age ranges. Therefore, the likelihood of a target user being involved in fraud can be determined using the age-related feature metric vector.

[0082] For example, when determining multiple feature index vectors corresponding to each pre-lifecycle feature, consider multiple feature index vectors affecting a particular pre-lifecycle feature vector. These multiple feature index vectors can form a feature index vector set. This feature index vector set can then be represented as [c 1j c 2j c nj ], where n and j can be integers greater than 0.

[0083] In an exemplary embodiment, after obtaining the feature index vector set [c 1j c 2j c njAfter that, corresponding weight information can be assigned to the feature index vectors corresponding to each pre-lifecycle feature, resulting in each pre-lifecycle feature vector. For example, this weight information can be represented as [w 1j w 2j ,…,w nj ], where n can be an integer greater than 0. Then, the pre-lifecycle feature vector V can be obtained based on the following formula (1). j :

[0084]

[0085] Here, the subscript j can be used to distinguish different pre-lifecycle feature vectors, and the subscript i can be used to distinguish the various feature indicators in the corresponding pre-lifecycle feature vector.

[0086] It should be noted that the other pre-lifecycle feature vectors can be determined based on the above method, thus obtaining all the pre-lifecycle feature vectors. Then, these pre-lifecycle feature vectors can be merged to obtain the target user's pre-lifecycle feature vector set, which can be represented as [V1, V2, ..., V...]. m ]. Where m is a positive integer, and 1≤j≤m.

[0087] For example, for a certain other user, there may be 9 pre-lifecycle feature vectors. Therefore, the pre-lifecycle feature vector set corresponding to this other user is [V1, V2, ..., V9]. Where V1 can correspond to [c 11 c 21 c n1 V2 can correspond to [c] 12 c 22 c n2 ], ..., V9 can correspond to [c 19 c 29 c n9 ].

[0088] In some embodiments, obtaining the first user value assessment result of the target user based on the initial state data of the target user includes: when the element value of any feature index vector corresponding to the pre-lifecycle feature vector set of the target user does not meet the corresponding preset feature condition, setting the first user value assessment result of the target user to zero, wherein each element value in any feature index vector corresponds to a preset feature threshold; when the element values ​​of any feature index vector in the pre-lifecycle feature vector set of the target user all meet the corresponding preset feature condition, using the initial state data of the target user as the first user value assessment result of the target user.

[0089] For example, it can be done through f(c) ij The element value of any feature index vector can be represented by a preset feature threshold. For example, when the element value of a feature index vector is greater than or less than its corresponding preset feature threshold, the element value of that feature index vector is considered to satisfy the corresponding preset feature condition. This disclosure does not limit the preset feature condition.

[0090] Furthermore, the preset feature threshold can be determined based on experience or application scenarios, and the present disclosure does not limit the method for determining the preset feature threshold.

[0091] For example, the first user value assessment result V of the target user can be determined by the following formula (2):

[0092]

[0093] Wherein, σ j This can be the weight information of the corresponding pre-lifecycle feature vector, σ j The value of σ can be determined based on experience or application scenarios; this disclosure does not specify the value of σ. j The value of δ is limited. Additionally, this δ ii This can represent the corresponding preset feature threshold. The subscript j can be used to distinguish different pre-lifecycle feature vectors, and the subscript i can be used to distinguish the various feature indicators in the corresponding pre-lifecycle feature vector.

[0094] In an exemplary embodiment, the feature index vector may include feature indices related to power outage and activation records. Therefore, each element value of the feature index related to power outage and activation records in the feature index vector may have a corresponding preset feature threshold, for example, the preset feature threshold may be 3. Thus, when the feature value corresponding to the power outage and activation records in the feature index vector is greater than 3, the target user's first user value assessment result can be directly set to zero. For example, if the target user's first user value assessment result is too low at this time, the target user may be refused network access.

[0095] In an exemplary embodiment, when the element values ​​of any feature index vector in the pre-lifecycle feature vector set of the target user all satisfy the corresponding preset feature conditions, the initial state data of the target user is used as the first user value assessment result of the target user.

[0096] For example, the initial state data of the target user can be calculated as shown in formula (2). The elements of the target user's pre-lifecycle feature vector set are summed and multiplied by the corresponding σ. j This means that the initial state data of the target user can be obtained.

[0097] In some embodiments, the user's pre-network information includes network access channel information, and the method further includes: determining whether the target user's network access channel is valid.

[0098] In some embodiments, the network access channel information includes the name and type of the mobile application, mobile webpage, computer webpage, offline form summary, and service hall, etc. After obtaining the target user's pre-network information, the network access channel information can be used to determine whether it is a valid network access channel. This disclosure does not limit the method for determining the validity of the network access channel; it can be limited based on experience or application scenarios.

[0099] In this case, the first user value assessment result of the target user is determined based on the target user's pre-network information, including: when the network access channel is effective, the first user value assessment result of the target user is determined based on the target user's pre-network information.

[0100] For example, if the network access channel is invalid, feedback can be given to the target user and the target user's network access can be rejected.

[0101] S206: When the first user value assessment result meets the preset value threshold, the target user is allowed to join the network.

[0102] S208, Obtain the target user's current network environment data, and determine the current environment data matrix based on the current network environment data; Obtain the target user's current internet behavior data, and determine the current internet behavior matrix based on the current internet behavior data.

[0103] In some embodiments, the current network environment data is determined by at least one of data traffic duration data, local network level data, roaming-in / roaming-out frequency data, 5G (5th Generation Mobile Communication Technology) network duration data, and 4G network duration data; the current internet behavior data is determined by access information in user data exchange packets.

[0104] In one possible implementation, the data traffic duration data can be used to describe the target user's online time after joining the network, for example, the data traffic duration data can be 45 hours or 112 hours and 30 minutes. The network level data of the location can be used to describe the network security level of the target user's current location, for example, the network level data of the location can be level A or level B. The roaming-in / roaming-out frequency data can be used to describe the frequency of the target user's roaming-in and roaming-out. The 5G network duration data can be used to describe the duration of the target user's access to the 5G network, and the 4G network duration data can be used to describe the duration of the target user's access to the 4G network.

[0105] In some embodiments, at least one of the above-mentioned data traffic duration data, local network level data, roaming-in and roaming-out frequency data, 5G network duration data, and 4G network duration data can be periodically acquired and used as the current network environment data.

[0106] This disclosure does not limit the period for acquiring at least one of the following: data traffic duration data, network level data of the location, roaming in and roaming out frequency data, 5G network duration data, and 4G network duration data. The period can be limited based on experience or application scenarios. For example, the period can be 24 hours or 48 hours.

[0107] In some embodiments, the current network environment data may consist of data traffic duration data within a period, network level data of the location, roaming in and roaming out frequency data, 5G network duration data, and 4G network duration data. Then, the current network environment data can be mapped using a predetermined strategy to obtain the current network environment data value vector [k]. 1i x 1i k 2i x 2i , ..., k ni x ni ].

[0108] Where, x 1i x 2i ... x ni For the value of data in each current network environment, k 1i k 2i ... k ni This represents the weight information corresponding to the data value of each current network environment. Both n and i can be integers greater than zero. This disclosure does not specify the weight of k. 1i k 2i ... k ni The value of k is limited. 1i k 2i ... k ni The value can be preset manually and adjusted through a neural network model.

[0109] Then, based on the current network environment data value vector, the current environment data matrix can be determined. In some embodiments, the current environment data matrix can be represented as:

[0110]

[0111] Where n and m can both be positive integers. m can be used to distinguish the acquisition time of different current network environment data. For example, if current network environment data is acquired every 4 days, the value of m can be 1, 2, 3, or 4. Or, if current network environment data is acquired every 24 hours, the value of m can be 1, 2, ..., 24. Additionally, n can be used to distinguish different types of current network environment data. For example, if the current network environment data is determined by data traffic duration data, regional network level data, and roaming frequency data, then n can be 1, 2, or 3 to distinguish the current network environment data corresponding to the data traffic duration data, regional network level data, and roaming frequency data, respectively.

[0112] In some embodiments, current internet behavior data can be determined from access information in a user data exchange packet. For example, the access information in the user data exchange packet may include data generated when a target user browses web pages, watches videos, and watches news.

[0113] For example, the period for obtaining access information in the user data exchange packet can be consistent with the period for obtaining at least one of the following: data traffic duration data, network level data of the location, roaming in and roaming out frequency data, 5G network duration data, and 4G network duration data.

[0114] In some possible implementations, after obtaining the current internet behavior data, the current internet behavior data can be mapped using a predetermined strategy to obtain the current internet behavior data value vector.

[0115] For example, the current internet behavior data value vector can be represented as [b 1p y 1p b 2p y 2p , ..., b qp y qp ]. Among them, y 1p y 2p ... y qp For the value of various current internet behavior data, b 1p b 2p ... b qp This represents the weight information corresponding to the value of each current internet behavior data point. Both q and p can be integers greater than zero.

[0116] This disclosure does not address the issue of b. 1p b 2p ... b qp The value of b is limited. 1p b 2p ... b qpThe value can be preset manually and adjusted through a neural network model.

[0117] Then, based on the current internet behavior data value vector, the current internet behavior matrix can be determined. In some embodiments, the current internet behavior matrix can be represented as:

[0118]

[0119] Where q and p can both be positive integers. p can be used to distinguish the acquisition time of each current internet behavior data. n can be used to distinguish current internet behavior data determined based on access information in different user data exchange packets.

[0120] It should be noted that in determining the second user value assessment result, the embodiments of this disclosure refer to the current Internet behavior data and the current network environment data, fully consider the influence factors of the objective environment, and can set different periods according to the application scenario. The time sequence division can be more flexible, and the subsequent correction of the second user value assessment result can be more timely, thereby achieving a more accurate user value assessment.

[0121] S210. Based on the current environmental data matrix and the current internet behavior matrix, evaluate the user value of the target user to obtain the second user value evaluation result.

[0122] In an exemplary embodiment, the current environmental data matrix and the current internet behavior matrix can be compared with a matrix library of user trajectories, and the user value of the target user can be evaluated based on the state transition of the similarity matrix to obtain a second user value evaluation result.

[0123] This disclosure does not limit the method for obtaining the matrix library of user trajectories. For example, the matrix library of user trajectories can be constructed manually or obtained based on a neural network model.

[0124] In some embodiments, the user value assessment method provided in this disclosure may further include: classifying the current state of a target user according to a current environmental data matrix and a current internet behavior matrix; when the current state of the target user does not belong to any preset state category, redetermining the current environmental data matrix and the current internet behavior matrix; and obtaining a redetermined second user value assessment result based on the redetermined current environmental data matrix and the redetermined current internet behavior matrix.

[0125] This disclosure does not limit the method for classifying the current state of the target user. For example, the current environment data matrix and the current internet behavior matrix can be clustered using multiple preset state categories to determine whether the target user's current state belongs to any preset state category. For instance, if the clustering error is greater than a preset error threshold, the current environment data matrix and the current internet behavior matrix can be redefined. If the clustering error is less than the preset error threshold, the target user's current state corresponding to the clustering result can be used as the second user value assessment result. This disclosure does not limit the size of the preset error threshold.

[0126] In an exemplary embodiment, the current environment data matrix and the current internet behavior matrix can be redefined by adjusting the weight information corresponding to the value of each current network environment data and the weight information corresponding to the value of each current internet behavior data. Furthermore, the method for redetermining the second user value assessment result can be the same as the method for determining the second user value assessment result initially. Alternatively, the current environment data matrix and the current internet behavior matrix can also be redefined by adjusting the weight information corresponding to each feature index vector.

[0127] It should be noted that this disclosure introduces a first user value assessment result determined based on the pre-lifecycle characteristics of the target user, and innovatively establishes a strategy and method for determining the first user value assessment result, creatively realizing a hot start for user value assessment, establishing a data-based description of the user value status at the beginning of the network access lifecycle, thereby enabling the rapid and accurate formation of a user value model.

[0128] Furthermore, this disclosure innovatively proposes a strategy to determine the current environment data matrix based on current network environment data and the current internet behavior matrix based on current internet behavior data. It introduces a time dimension to describe the life trajectory and can dynamically adjust the calculation strategy in a timely manner to more accurately assess the current user value, thereby enabling faster prediction of the user's future value.

[0129] In some embodiments, a possible user value assessment method provided by this disclosure can be as follows: Figure 3 As shown.

[0130] First, information on network access channels can be collected and processed. Then, individual authentication can be performed. Individual authentication can include comparing characteristics of the same individual, determining the user value of the same individual, and assessing the risk of the same individual. For example, comparing characteristics of the same individual can identify other users belonging to the same individual as the target user through individual identification information. Determining the user value of the same individual can determine the user value of the target user based on the user information of other users. Assessing the risk of the same individual can evaluate the network access risk of the target user based on the user information of other users. This disclosure does not limit the steps of the risk assessment for the same individual.

[0131] Then, the validity of the first user value assessment result for the target user can be determined. Furthermore, the current network environment data and current internet behavior data of the target user can be collected and analyzed. Finally, the second user value assessment result for the target user can be determined.

[0132] For example, such as Figure 3 As shown, the second user value assessment result can be used to predict the churn trend of target users, generate target user profiles, and estimate the value of target users. It can also periodically collect and analyze the current network environment data and current internet behavior data of target users to determine the second user value assessment result of the target users. This allows for the periodic prediction of churn trends, generation of target user profiles, and estimation of target user value.

[0133] This disclosure does not limit the methods for predicting the churn trend of target users, generating target user profiles, and estimating the value of target users.

[0134] In some embodiments, a possible user value assessment method provided by this disclosure can be as follows: Figure 4 As shown.

[0135] First, target users can apply for network access on the terminal. For example, users can fill in the network access application information and submit it for review.

[0136] Then, the terminal can obtain network access application information and network access channel information, and determine the validity of the network access channel based on the network access channel information. Both the network access application information and the network access channel information are pre-network data, and the network access application information may contain individual identification information.

[0137] When the access channel is invalid, the target user can be denied access to the network. When the access channel is valid, the user information of all other users belonging to the same individual can be determined by comparing the individual's identification information with the network service provider's user database.

[0138] Subsequently, based on the user information of all other users, including current and historical users, an initial state data model of the target user's identity can be constructed to obtain the first user value assessment result for the target user. When the first user value assessment result is valid, the target user can be allowed to join the network, and a network access permission notification can be sent. For example, a threshold for the first user value assessment result can be set; when the first user value assessment result is higher than this threshold, the first user value assessment result is deemed valid. Furthermore, when the first user value assessment result is invalid, feedback can be provided to the terminal, and the feedback information may include, for example, the reason for invalidity and a solution.

[0139] Finally, a network access permission notification can be sent to the target user, user identity can be established, and user activation can be actively initiated, allowing the target user to access the network via their terminal device. For example, after accessing the network, the target user can browse web pages, watch videos, and read news. Therefore, the target user's current internet behavior data can be obtained.

[0140] Additionally, the user value of target users can be periodically evaluated to obtain a second user value evaluation result. For example, clustering can be performed using features such as pre-lifecycle characteristics, behavioral preferences, interest preferences, and time periods, and the second user value evaluation result can be calculated based on the clustering results.

[0141] Next, it can be determined whether the second user value assessment result matches the actual situation, that is, whether the target user's current state belongs to any preset state category. If it does not, the second user value assessment result can be redefined. For example, at this time, the actual correction strategy can be modified, and a time-series behavior assessment strategy can be calculated to obtain the redefined second user value assessment result.

[0142] In an exemplary embodiment, a method for determining the validity of a network access channel may first obtain network access application information and network access channel information. For example, the network access application information may include individual identification information, the name of the person applying for network access, and the communication address information for receiving the network access certificate. Additionally, the network access channel information may include, for example, application medium information, application date and time, temporary identification code, application page identification code, and the sales product ID of the selected network service. The application medium information may, for example, include information such as an app or webpage, and the device type.

[0143] Afterwards, the network access application information and network access channel information can be identified. If the application is in the whitelist, the terminal that submitted the application will be given approval feedback and the network access application information will be recorded. If the application is not in the whitelist, the terminal that submitted the application will be given rejection feedback.

[0144] In some embodiments, a possible method for determining the first user value assessment result of a target user, provided by this disclosure, can be as follows: Figure 5 As shown.

[0145] First, target users can apply for network access through an app or website. The application can be submitted via mobile phone, computer, offline data entry, or at a service center. Next, the application channel information is retrieved and entered into the database, and its validity is determined. If invalid, feedback is provided; if valid, user information for multiple other users belonging to the same individual as the target user can be obtained based on individual identification information.

[0146] For example, feature extraction can be performed first based on individual identification information to determine whether the individual identification information matches the name of an already registered user. If there is no match, an invalidation feedback is issued; if there is a match, feature index vectors can be determined based on the user information of multiple other users, and it can be determined whether any element value in each feature index vector does not meet the corresponding preset feature conditions. If such a situation exists, an invalidation feedback is issued; if not, the target user is granted access. At this point, an access notification or an access certificate can be sent to the target user.

[0147] Once a target user joins the network, their initial status can be determined, which means determining the target user's first user value assessment result.

[0148] For example, after establishing a user identity for the target user and actively activating network access via a terminal device, the user value and potential value of the target user can be periodically evaluated to obtain a second user value evaluation result. The process of obtaining the second user value evaluation result can be as follows: Figure 6 As shown.

[0149] First, a uniform time interval can be set for the period in which the second user value assessment result is obtained. Current network environment data, current internet behavior data, and financial consumption data generated by the mobile internet network environment of the target user can be collected periodically to construct the user value status. This user value status can be composed of a current environment data matrix and a current internet behavior matrix. In one possible implementation, the user value status can also be determined based on financial consumption data generated by the internet network environment, which may include ARUP (Average Revenue Per User), balance, and service plan tier.

[0150] Next, based on the current environmental data matrix and the current internet behavior matrix, the current state of the target user is classified. It is then determined whether the user belongs to an existing category. If the error exceeds a predetermined threshold, the calculation strategy is corrected, and the calculation is re-performed to obtain a newly determined second user value assessment result.

[0151] Furthermore, the error between the currently calculated second user value assessment result and the second user value assessment result determined in the previous period can be compared and measured. If the error exceeds a predetermined threshold, the calculation strategy is corrected, and the second user value assessment result is recalculated. For example, the method for correcting the calculation strategy may include modifying the corresponding weight information.

[0152] Repeat the process of obtaining the second user value assessment result to determine the second user value assessment result for the next period.

[0153] The method provided in this disclosure can determine a first user value assessment result for a target user based on the target user's pre-network information. Therefore, this disclosure can consider the target user's pre-network information when performing user value assessment. Furthermore, this disclosure periodically performs user value assessment after the target user joins the network using the target user's current network environment data and current internet behavior data. Therefore, this disclosure provides a method for accurately assessing user value, thereby ensuring the provision of differentiated products and services to different users and improving service quality.

[0154] Based on the same inventive concept, this disclosure also provides a user value assessment device, as shown in the following embodiment. Since the principle by which this device embodiment solves the problem is similar to that of the above-described method embodiment, the implementation of this device embodiment can refer to the implementation of the above-described method embodiment, and repeated details will not be elaborated further.

[0155] Figure 7 This diagram illustrates a user value assessment device according to an embodiment of the present disclosure, such as... Figure 7 As shown, the device includes:

[0156] The pre-network information acquisition module 701 is used to acquire the pre-network information of a target user when the target user applies for network access.

[0157] The first user value assessment result determination module 702 is used to determine the first user value assessment result of the target user based on the target user's pre-network information;

[0158] The network access judgment module 703 is used to allow the target user to join the network when the first user value assessment result meets the preset value threshold.

[0159] The matrix determination module 704 is used to acquire the target user's current network environment data, determine the current environment data matrix based on the current network environment data, acquire the target user's current internet behavior data, and determine the current internet behavior matrix based on the current internet behavior data.

[0160] The second user value assessment result module 705 is used to assess the user value of the target user based on the current environmental data matrix and the current Internet behavior matrix, and obtain the second user value assessment result.

[0161] In some exemplary embodiments, the second user value assessment result module 705 is further configured to classify the current state of the target user according to the current environmental data matrix and the current internet behavior matrix; when the current state of the target user does not belong to any preset state category, the current environmental data matrix and the current internet behavior matrix are redefined; and a redefined second user value assessment result is obtained based on the redefined current environmental data matrix and the redefined current internet behavior matrix.

[0162] In some exemplary embodiments, the current network environment data is determined by at least one of data traffic duration data, network level data of the location, roaming-in and roaming-out frequency data, 5G network duration data, and 4G network duration data; the current Internet behavior data is determined by access information in the user data exchange packet.

[0163] In some exemplary embodiments, the user's pre-network information includes network access channel information, and the pre-network information acquisition module 701 is also used to determine whether the target user's network access channel is valid;

[0164] The first user value assessment result determination module 702 is used to determine the first user value assessment result of the target user based on the target user's pre-network information when the network access channel is effective.

[0165] In some exemplary embodiments, the target user's pre-network information includes individual identification information. The first user value assessment result determination module 702 is used to obtain user information of multiple other users who belong to the same individual as the target user based on the individual identification information, wherein the multiple other users include at least one of the individual's all online users and all historical users; determine the target user's initial state data based on the user information of each other user; and obtain the target user's first user value assessment result based on the target user's initial state data.

[0166] In some exemplary embodiments, the first user value assessment result determination module 702 is used to obtain multiple feature index vectors based on the user information of each other user, wherein any feature index vector is used to describe a user information; to obtain a pre-lifecycle feature vector set of the target user based on the multiple feature index vectors, wherein any pre-lifecycle feature vector in the pre-lifecycle feature vector set corresponds to at least one feature index vector; and to determine the initial state data of the target user based on the pre-lifecycle feature vector set of the target user.

[0167] In some exemplary embodiments, the first user value assessment result determination module 702 is used to determine multiple corresponding feature index vectors based on each pre-lifecycle feature; assign corresponding weight information to the feature index vectors corresponding to each pre-lifecycle feature to obtain each pre-lifecycle feature vector; and merge the various pre-lifecycle feature vectors to obtain the pre-lifecycle feature vector set of the target user.

[0168] In some exemplary embodiments, the first user value assessment result determination module 702 is used to set the first user value assessment result of the target user to zero when the element value of any feature index vector corresponding to the pre-lifecycle feature vector set of the target user does not meet the corresponding preset feature condition, wherein the element value of any feature index vector corresponds to a preset feature threshold.

[0169] When the element values ​​of any feature index vector in the target user's pre-lifecycle feature vector set all satisfy the corresponding preset feature conditions, the target user's initial state data is used as the target user's first user value assessment result.

[0170] The apparatus provided in this disclosure can determine a first user value assessment result for the target user based on the target user's pre-network information. Therefore, this disclosure can consider the target user's pre-network information when performing user value assessment. Furthermore, this disclosure periodically performs user value assessment after the target user joins the network using the target user's current network environment data and current internet behavior data. Therefore, this disclosure provides a method for accurately assessing user value, thereby ensuring the provision of differentiated products and services to different users and improving service quality.

[0171] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0172] The following reference Figure 8 To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0173] like Figure 8 As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0174] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Detailed Description" section of this specification according to various exemplary embodiments of this disclosure.

[0175] Storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include a read-only memory (ROM) 8203.

[0176] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0177] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0178] Electronic device 800 can also communicate with one or more external devices 840 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0179] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0180] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Detailed Description" section of this specification.

[0181] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0182] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0183] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0184] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0185] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0186] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0187] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0188] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope of this disclosure is indicated by the appended claims.

Claims

1. A user value assessment method, characterized in that, include: When a target user applies to join the network, the target user's pre-network information is obtained, including individual identification information; Based on the target user's pre-network information, determine the target user's first user value assessment result, including obtaining user information of multiple other users belonging to the same individual as the target user based on the individual identification information; and obtaining the target user's first user value assessment result based on the user information of each other user, wherein the user information of the other users includes the other users' pre-network information and various network data generated after the user joins the network. When the first user value assessment result meets the preset value threshold, the target user is allowed to join the network; Obtain the current network environment data of the target user, and determine the current environment data matrix based on the current network environment data; Obtain the target user's current internet behavior data, and determine the current internet behavior matrix based on the current internet behavior data; Based on the current environment data matrix and the current internet behavior matrix, the user value of the target user is evaluated to obtain a second user value evaluation result.

2. The user value assessment method according to claim 1, characterized in that, The method further includes: Based on the current environment data matrix and the current internet behavior matrix, the current state of the target user is classified. When the current state of the target user does not belong to any preset state category, the current environment data matrix and the current Internet behavior matrix are redefined. Based on the redefined current environmental data matrix and the redefined current internet behavior matrix, a redefined second user value assessment result is obtained.

3. The user value assessment method according to any one of claims 1 or 2, characterized in that, The current network environment data is determined by at least one of the following: data traffic duration data, network level data of the region, roaming in and roaming out frequency data, 5G network duration data, and 4G network duration data. The current internet behavior data is determined through access information in the user data exchange packet.

4. The user value assessment method according to any one of claims 1 or 2, characterized in that, The user's pre-network information includes network access channel information, and the method further includes: Determine whether the target user's network access channel is valid; The step of determining the first user value assessment result of the target user based on the target user's pre-network information includes: When the network access channel is effective, the first user value assessment result of the target user is determined based on the target user's pre-network information.

5. The user value assessment method according to claim 1 or 2, characterized in that, The target user's pre-network information includes individual identification information. Determining the target user's first user value assessment result based on the target user's pre-network information includes: Based on the individual identification information, obtain user information of multiple other users who belong to the same individual as the target user, wherein the multiple other users include at least one of all current users and all historical users of the individual; The initial state data of the target user is determined based on the user information of each other user; Based on the initial state data of the target user, obtain the first user value assessment result of the target user.

6. The user value assessment method according to claim 5, characterized in that, The step of determining the initial state data of the target user based on the user information of each other user includes: Based on the user information of other users, multiple feature index vectors are obtained, where each feature index vector is used to describe a user's information; Based on multiple feature index vectors, a pre-lifecycle feature vector set of the target user is obtained, wherein any pre-lifecycle feature vector in the pre-lifecycle feature vector set corresponds to at least one feature index vector. The initial state data of the target user is determined based on the target user's pre-lifecycle feature vector set.

7. The user value assessment method according to claim 6, characterized in that, The step of obtaining the pre-lifecycle feature vector set of the target user based on multiple feature index vectors includes: Based on each pre-lifecycle characteristic, determine the corresponding multiple feature index vectors; Assign corresponding weight information to the feature index vectors corresponding to each pre-lifecycle feature to obtain each pre-lifecycle feature vector; The various pre-lifecycle feature vectors are merged to obtain the pre-lifecycle feature vector set of the target user.

8. The user value assessment method according to any one of claims 6 or 7, characterized in that, The step of obtaining the first user value assessment result of the target user based on the initial state data of the target user includes: When the element value of any feature index vector in the pre-lifecycle feature vector set of the target user does not meet the corresponding preset feature condition, the first user value evaluation result of the target user is set to zero, wherein the element value of any feature index vector corresponds to a preset feature threshold. When the element values ​​of any feature index vector in the pre-lifecycle feature vector set of the target user all satisfy the corresponding preset feature conditions, the initial state data of the target user is used as the first user value evaluation result of the target user.

9. A user value assessment device, characterized in that, include: The pre-network information acquisition module is used to acquire the pre-network information of a target user when the target user applies for network access. The pre-network information includes individual identification information. The first user value assessment result determination module is used to determine the first user value assessment result of the target user based on the target user's pre-network information, including obtaining user information of multiple other users belonging to the same individual as the target user based on the individual identification information; and obtaining the first user value assessment result of the target user based on the user information of each other user, wherein the user information of the other users includes the other users' pre-network information and various network data generated after the user joins the network. The network access judgment module is used to allow the target user to join the network when the first user value assessment result meets the preset value threshold. The matrix determination module is used to acquire the current network environment data of the target user and determine the current environment data matrix based on the current network environment data. Obtain the target user's current internet behavior data, and determine the current internet behavior matrix based on the current internet behavior data; The second user value assessment result module is used to assess the user value of the target user based on the current environment data matrix and the current internet behavior matrix, and obtain the second user value assessment result.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the user value assessment method of any one of claims 1 to 8 by executing the executable instructions.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the user value assessment method according to any one of claims 1 to 8.

Images