User information identification method, device, equipment and storage medium

Abstract

This application provides a user information identification method, apparatus, device, and storage medium, relating to the field of computer technology, which can effectively improve the accuracy of identifying abnormal user information corresponding to Fiber to the Room (FTTR) services. The method includes: acquiring service information corresponding to the FTTR service and usage information of the FTTR service; filtering user information based on the service information to obtain a pre-selected user information set, wherein the pre-selected user information set refers to a collection of user information corresponding to the FTTR service with potential abnormal risks; and identifying abnormal user information in the pre-selected user information set based on the usage information of the FTTR service.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a user information identification method, apparatus, device and storage medium. Background Technology

[0002] Fiber to the Room (FTTR) is a next-generation home broadband access technology that uses fiber optic cables to replace traditional network cables, transmitting network signals directly from a central node in a home or office to every room or terminal device to achieve whole-house network coverage.

[0003] Due to the large volume of FTTR (Fixed Time to Trade) transactions, it is necessary to conduct risk assessments on user-submitted FTTR transactions to identify abnormal user information and thus achieve effective management of FTTR transactions. Related technologies employ simple rules for risk assessment of FTTR transactions. For example, Rule 1: If a user's information is used to conduct more than three FTTR transactions within a single day, that user's information will be marked as abnormal. Rule 2: If a user's information is used to conduct FTTR transactions for multiple consecutive days, the system will automatically mark that user's information as abnormal.

[0004] However, while rule-based methods are easy to understand, the amount of abnormal user information data that conforms to the rules is too large, making it impossible to accurately locate the real abnormal user information, and the evaluation efficiency is low. Summary of the Invention

[0005] This application provides a user information identification method, apparatus, device, and storage medium, which can accurately identify abnormal user information when handling FTTR business.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a user information identification method, the method comprising:

[0008] Obtain service information and usage information corresponding to the FTTR service. Based on the service information, filter user information to obtain a pre-selected user information set, which refers to the collection of user information with potential anomaly risks related to the FTTR service. Based on the FTTR service usage information, identify abnormal user information within the pre-selected user information set.

[0009] The solution provided in this application first filters user information based on business information to obtain a pre-selected user information set, thus narrowing the scope of user information filtering. Then, it identifies abnormal user information from the pre-selected user information set and further narrows down the user information in the pre-selected user information set. This progressive user information identification process involves less data computation, reduces the consumption of computing resources, and improves the efficiency of user information identification. At the same time, the pre-selected user information set is a collection of user information with potential abnormal risks that has been pre-screened. Further identification based on the pre-selected user information set is less likely to miss or misjudge user information with abnormal risks. The abnormal user information obtained is more accurate, effectively protecting user experience and the interests of operators.

[0010] One possible implementation involves using service information including at least one of broadband bearer information, account change information, or broadband account information associated with FTTR services. User information is filtered based on this service information to obtain a pre-selected user information set. This can be specifically implemented as follows: a first filtering operation is performed on user information based on broadband bearer information to obtain a first dataset; a second filtering operation is performed on user information based on account change information to obtain a second dataset; a third filtering operation is performed on user information based on broadband account information associated with FTTR services to obtain a third dataset; and the pre-selected user information set is obtained based on the first, second, and third datasets. Different filtering operations are performed on user information based on broadband bearer information, account change information, or broadband account information associated with FTTR services to obtain the pre-selected user information set. By employing filtering operations from different perspectives, the filtering rules of each operation do not interfere with each other, resulting in higher accuracy in user information identification. Simultaneously, filtering based on different service information yields different types of abnormal user information, and each type of abnormal user information can be effectively identified, resulting in more comprehensive user information identification.

[0011] Among them, the broadband bearer information is used to describe the usage status of the broadband where the FTTR service is located.

[0012] Another possible implementation involves performing a first screening operation on user information based on broadband bearer information to obtain a first dataset. Specifically, this can be achieved by: assigning user information corresponding to broadband accounts that meet the first condition to an abnormal broadband user set; further assigning user information corresponding to broadband accounts that have subscribed to FTTR services to the first dataset within the abnormal broadband user set; identifying abnormal broadband accounts by determining whether they meet the first condition; and then extracting user data for users who subscribed to FTTR services on these abnormal broadband accounts, effectively filtering out pre-selected abnormal user information for those who subscribed to FTTR services on abnormal broadband.

[0013] Another possible implementation involves a first condition that includes at least one of the following: more than n broadband accounts at the same address; the broadband account was registered within the target billing period; and the broadband account has not been used for m consecutive months. Here, the target billing period refers to the specified billing cycle corresponding to the broadband account, n is a positive integer greater than 1, and m is a positive integer. By setting different conditions to filter broadband accounts, users can configure settings according to their specific circumstances, thereby achieving the pre-identification effect of abnormal broadband accounts and improving the accuracy of identification.

[0014] Another possible implementation involves performing a second filtering operation on user information based on account change information to obtain a second dataset. Specifically, this can be achieved by: among FTTR users who have subscribed to the FTTR service, classifying the user information of those whose accounts have changed within a preset period into an abnormal change user set; and further classifying the user information of FTTR users whose account changes are greater than or equal to a certain threshold into the second dataset. By determining the number of times a user's FTTR service has involved account changes, users who frequently change their accounts can be quickly identified, thus identifying potential abnormal users in advance.

[0015] Another possible implementation involves grouping FTTR users who have subscribed to FTTR services into an abnormal change user set based on their account changes within a preset period. This can be achieved by: identifying a first identifier for each FTTR user in the target billing period, and obtaining a second identifier for FTTR users whose account changes occurred compared to the previous billing period. Based on the second identifier, all FTTR users corresponding to that second identifier are retrieved. Among all FTTR users corresponding to the second identifier, the user information for those whose account changes occurred within the preset period is then grouped into the abnormal change user set. The first identifier is used to filter out user information with account changes related to FTTR services, and the second identifier is used to aggregate all FTTR services with account changes under that user information within the preset period, thereby statistically analyzing the account change information for FTTR services under that user information.

[0016] Another possible implementation involves performing a third filtering operation on user information based on the broadband account information associated with FTTR services, resulting in a third dataset. Specifically, this can be achieved by matching the first identifier corresponding to the FTTR user with the broadband identifier corresponding to the broadband account within the target billing period. User information corresponding to broadband accounts for multiple FTTR users is then allocated to the third dataset. By matching the identifiers of FTTR users and broadband account services, user information corresponding to multiple FTTR services under the same broadband account is obtained. This effectively pre-identifies abnormal user information involving multiple FTTR services under the same broadband account, improving the user experience.

[0017] Another possible implementation, the user information identification method provided in this application, may further include: deduplicating the user information in the third dataset with the user information in the historical billing period to obtain an updated third dataset. By combining the user information in the historical billing period and deduplicating duplicate user information, duplicate information is avoided, making the filtered user information more concise and clear, and improving identification efficiency.

[0018] Another possible implementation involves obtaining a pre-selected user information set based on the first, second, and third datasets. Specifically, this can be achieved by merging and deduplicating the first, second, and third datasets to obtain the pre-selected user information set. By merging and deduplicating datasets obtained from different perspectives, user information can be filtered more comprehensively, and the user information between datasets can be complementary. Furthermore, during the merging process, the data in the datasets can be cleaned simultaneously, removing invalid or erroneous data, saving significant time and improving work efficiency for subsequent dataset processing.

[0019] Another possible implementation involves identifying anomalous user information within a pre-selected user information set based on FTTR service usage information. Specifically, this can be achieved by identifying users whose FTTR service usage frequency is less than a threshold as anomalous users. Furthermore, the pre-selected user information set can be further optimized and narrowed down to include users with inactive FTTR service activity as anomalous users, thus achieving the desired identification of anomalous user information.

[0020] Secondly, a user information identification device is provided, the device comprising:

[0021] The acquisition module is used to acquire service information corresponding to FTTR services, as well as usage information of FTTR services.

[0022] The processing module is used to filter user information based on business information to obtain a pre-selected user information set. The pre-selected user information set refers to the collection of user information with potential abnormal risks corresponding to the FTTR service. The processing module is also used to identify abnormal user information in the pre-selected user information set based on the usage information of the FTTR service.

[0023] One possible implementation involves the service information including at least one of: broadband bearer information, account change information, or broadband account information associated with the FTTR service. The broadband bearer information describes the usage status of the broadband where the FTTR service is located. The processing module is further configured to: perform a first filtering operation on user information based on the broadband bearer information to obtain a first dataset; perform a second filtering operation on user information based on the account change information to obtain a second dataset; perform a third filtering operation on user information based on the broadband account information associated with the FTTR service to obtain a third dataset; and obtain a pre-selected user information set based on the first, second, and third datasets.

[0024] In another possible implementation, the processing module is further configured to: allocate user information corresponding to broadband accounts that meet the first condition to an abnormal broadband user set. Within the abnormal broadband user set, the user information corresponding to broadband accounts that have subscribed to FTTR services is then allocated to a first dataset.

[0025] Another possible implementation includes at least one of the following conditions as the first condition: having more than n broadband accounts at the same address; the broadband accounts being registered within the target billing period; and the broadband accounts not being used for m consecutive months. Here, the target billing period refers to the specified billing cycle corresponding to the broadband account, n is a positive integer greater than 1, and m is a positive integer.

[0026] In another possible implementation, the processing module is further configured to: among FTTR users who have subscribed to the FTTR service, group the user information of FTTR users whose account changes occur within a preset period into an abnormal change user set. Within the abnormal change user set, group the user information of FTTR users whose account change count is greater than or equal to a threshold into a second dataset.

[0027] In another possible implementation, the processing module is further configured to: identify a first identifier corresponding to an FTTR user in the target billing period, and obtain a second identifier corresponding to an FTTR user whose account has changed compared to the previous billing period. Based on the second identifier, obtain all FTTR users corresponding to the second identifier. Among all FTTR users corresponding to the second identifier, the user information corresponding to FTTR users whose account has changed within a preset period is grouped into an abnormal change user set.

[0028] In another possible implementation, the processing module is also used to: match the first identifier corresponding to the FTTR user and the broadband identifier corresponding to the broadband account in the target billing period, and divide the user information corresponding to the broadband accounts of multiple FTTR users into a third dataset.

[0029] In another possible implementation, the processing module is also used to: deduplicate the user information in the third dataset with the user information in the historical billing period to obtain the updated third dataset.

[0030] In another possible implementation, the processing module is also used to merge and deduplicate the first, second, and third datasets to obtain a pre-selected user information set.

[0031] In another possible implementation, the processing module is also used to: identify users whose FTTR service usage count is less than the usage count threshold as abnormal user information in the pre-selected user information set.

[0032] The technical effects of any implementation method in the second aspect can be found in the technical effects of any implementation method in the first aspect mentioned above, and will not be repeated here.

[0033] Thirdly, a computer device is provided, comprising: a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the user information identification method described above.

[0034] Fourthly, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the user information identification method of the above.

[0035] Fifthly, a computer program product is provided, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the user information identification method described above is implemented.

[0036] The solutions provided in aspects three through five above are used to implement the method provided in aspect one above, and their specific implementations will not be described in detail here. The technical effects corresponding to any implementation method of the solutions provided in aspects three through five above can be found in the technical effects corresponding to any implementation method in aspect one above, and will not be described in detail here.

[0037] It should be noted that any of the possible implementations of any of the above aspects can be combined, provided that the solutions do not contradict each other. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A schematic diagram of the architecture of the FTTR service provided in the embodiments of this application;

[0040] Figure 2 A schematic diagram of the computer system architecture provided in the embodiments of this application;

[0041] Figure 3 A flowchart illustrating a user information identification method provided in an embodiment of this application;

[0042] Figure 4 A flowchart illustrating a user information identification method provided in an embodiment of this application;

[0043] Figure 5 A flowchart illustrating a user information identification method provided in an embodiment of this application;

[0044] Figure 6 This is a schematic diagram of the structure of a user information identification device provided in an embodiment of this application;

[0045] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0046] In the embodiments of this application, in order to clearly describe the technical solutions of the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different. There is no sequential or major order among the technical features described by "first" and "second".

[0047] In the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0048] In the embodiments of this application, at least one can also be described as one or more, and multiple can be two, three, four or more, and this application does not impose any restrictions.

[0049] Furthermore, the network architecture and scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0050] To facilitate understanding, the terms used in the embodiments of this application will be explained first.

[0051] Broadband is a high-speed data transmission method, specifically referring to internet access technology that utilizes different channels for multiple transmissions over the same transmission medium, with transmission speeds exceeding 1.5 Mbps. Compared to traditional narrowband, broadband offers significantly higher data transmission speeds and greater bandwidth, typically tens of times faster or more than ordinary dial-up internet access. Furthermore, broadband supports simultaneous transmission of multiple services such as voice, data, and video, meeting the needs of modern homes and businesses for high-speed, stable network connections, and supporting various application scenarios such as online gaming, high-definition video streaming, and video conferencing.

[0052] Fiber to the Room (FTTR) is a next-generation home broadband access technology. Broadband access refers to using broadband technologies such as fiber optic cables, Digital Subscriber Line (DSL), and cable modems to connect user devices (such as computers, mobile phones, and smart TVs) to the network of an Internet Service Provider (ISP), enabling users to enjoy high-speed and stable internet services. Specifically, FTTR replaces traditional network cables with fiber optic cables, transmitting network signals directly from a central node in the home or office (such as an optical modem or router) to every room or terminal device, achieving whole-house network coverage and solving problems such as uneven wireless signal coverage and low signal speeds. Figure 1 The diagram shows the architecture of the FTTR service. FTTR uses one main optical modem 100 and multiple secondary optical modems 110 for indoor signal coverage. The main optical modem 100 is responsible for receiving network signals from the operator, and the secondary optical modems 110 are responsible for extending the network signal to every room in the home or office. Specifically, the downlink optical port of the main optical modem 100 is connected to a splitter, and then the splitter connects to each secondary optical modem 110. The main optical modem 100 and the secondary optical modems 110 are usually connected by a butterfly optical cable or a concealed optical cable, which is easy to deploy. Figure 1 The maximum network speed near each optical modem can reach 1000Mbps and the network speed is stable. When switching between different optical modems, terminals such as mobile phones, telephones, and computers can also achieve smooth switching, which improves the user experience.

[0053] Abnormal business development, also known as "false business development," refers to the act of a company creating abnormal business data and operating results by fabricating transactions, forging documents, or exaggerating performance during its business operations. This misleads investors, consumers, or monitoring agencies in order to obtain illicit gains. In the telecommunications field, it mainly refers to fabricating non-existent telecommunications services or exaggerating the scale and benefits of existing services to attract the attention of investors or consumers.

[0054] Abnormal broadband: Also known as "fake broadband," this refers to actual bandwidth that is far lower than the advertised broadband service. This phenomenon is mainly caused by outdated network architecture, insufficient network equipment performance, and technical and operational problems of service providers. It typically manifests as users experiencing internet speeds far lower than advertised by the operator, frequent buffering, latency, and unstable network connections.

[0055] It should be noted that all information (including but not limited to device information, personal information of the target, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the target or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. For example, broadband bearer information, account change information, broadband account information associated with FTTR service, FTTR service usage information, user information, etc., involved in this application, were all obtained with full authorization.

[0056] For example, the industry-standard methods for identifying abnormal users often involve risk assessment using simple rules, which will be briefly explained below.

[0057] The system acquires or sets relevant rules in the communications field regarding broadband and FTTR services, such as bandwidth definitions, measurement standards, and service commitments. It then determines the characteristics or rules of FTTR service data under abnormal development conditions. A large amount of FTTR service data is extracted and compared with the determined data characteristics or rules. Data that conforms to the rules is used as risky service data. This allows the system to identify user information with the risk of abnormal FTTR development, i.e., abnormal user information corresponding to FTTR services.

[0058] Furthermore, the above process can also be implemented based on a pre-trained model. Specifically, the identification model is trained using a large amount of normal and abnormal FTTR service data, enabling the trained model to directly output risk prediction results based on FTTR service data. Normal FTTR service data refers to FTTR service data that conforms to regulations or promotional standards, such as network speed, device connection status and configuration data, as well as IP address, transport layer protocol, uplink bytes, downlink bytes, uplink flow rate, and downlink flow rate data for broadband services subscribed to FTTR services. Abnormal FTTR service data refers to FTTR service data that does not conform to regulations or promotional standards. The identification model is a model capable of identifying data that deviates significantly from normal patterns or expected standards, such as outlier detection models and risk identification models. The FTTR service data is input into the trained identification model, directly outputting risk prediction results. FTTR service data includes user name, user type, network number, broadband quantity, network speed, etc., and the output risk prediction result is the abnormal development risk level or risk score of the FTTR service.

[0059] However, the above-mentioned technical solutions involve a large amount of data for judgment and analysis during the identification or reasoning process, consume a lot of computing resources, and have low identification efficiency. Moreover, when the types of abnormal data are more complex, the data characteristics of FTTR business data are also more complex, making it impossible to accurately locate risky behaviors through simple rule evaluation and model reasoning. A lot of manpower is still needed to verify the suspected risks identified, and it is impossible to discover and deal with users with abnormal development risks of FTTR business in a timely manner, resulting in poor risk control.

[0060] Based on this, this application provides a user information identification method. It performs preliminary screening of user information based on FTTR service information to obtain a pre-selected user information set with potentially abnormal user information. Then, it identifies abnormal user information within the pre-selected set. The scope of user information screening is narrowed beforehand, reducing the amount of data computation involved in the screening process, lowering computational resource consumption, and improving the efficiency of user information identification. Furthermore, since all user information in the pre-selected set carries potential abnormal risks, further identification based on this pre-selected set is less likely to miss or misjudge user information with abnormal risks. The resulting abnormal user information is more accurate, effectively protecting user experience and the interests of operators.

[0061] The solutions provided by the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0062] The solution provided in this application can be applied to Figure 2 In the computer system shown, such as Figure 2 The diagram shows the architecture of the computer system.

[0063] For example, Figure 2 The illustrated computer system includes computer device 200, which can be a device capable of collecting and analyzing FTTR service information and usage information. This computer device 200 can connect to and manage an operator's network, directly collecting and analyzing FTTR service information and usage information to obtain a dataset of user information related to abnormal FTTR service development. Alternatively, it can obtain FTTR service information and usage information from a service management system, network management platform, log server, etc., identify a dataset of risky user information, and store / send the output dataset of risky user information to its own display module or an external human-computer interaction platform. The term "acquisition" in this application's computer device 200 includes any term with acquisition function such as querying, discovering, and extracting, and this application does not limit this terminology.

[0064] Optionally, the computer device 200 can be a network management server. The network management server can automatically discover and identify various devices connected to the network, such as switches, routers, and firewalls, by scanning the network, and automatically configure devices such as IP addresses, ports, and access permissions. It can also monitor the performance parameters of network devices, such as bandwidth utilization, CPU utilization, and memory utilization, display the performance status of devices in real time, and provide historical data analysis to help operators or management personnel understand the network's operating status and performance trends.

[0065] Figure 2 An exemplary computer device 200 is shown. Optionally, the computer device 200 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services such as cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data, etc. This application embodiment does not limit the implementation method or application scenario of the computer device 200.

[0066] Optionally, the computer system may also include service / broadband management equipment and control equipment, etc., which are not limited in this application embodiment.

[0067] Figure 3 This is a flowchart illustrating a user information identification method provided in an embodiment of this application. The method can be executed by a computer device.

[0068] like Figure 3 As shown, the user information identification method provided in this application embodiment may include:

[0069] Step S301: The computer equipment obtains the service information corresponding to the Fiber to the Room (FTTR) service, as well as the usage information of the FTTR service.

[0070] Among them, business information refers to data associated with FTTR services, which is used to indicate the usage status of FTTR services and can clearly convey the technical characteristics, application scenarios, processing procedures, fee descriptions and other information of FTTR services.

[0071] FTTR service usage information is used to indicate the activity level of the FTTR service.

[0072] Optionally, the usage information of the FTTR service includes at least one of the following: FTTR service usage time, number of uses, access time, outgoing time, connection status, or cost information, but is not limited thereto, and this application embodiment does not specifically limit this. For example, the usage information of the FTTR service may be the number of logins to the FTTR main gateway.

[0073] The FTTR master gateway is used to connect fiber optic signals from the operator's network to the user's home or office, and connects multiple slave gateways via fiber optic cables. By monitoring the login count of the FTTR master gateway, abnormal login behaviors such as frequent logins and unauthorized access can be detected, allowing for timely measures to protect network security.

[0074] In some embodiments, service information corresponding to the aforementioned FTTR service and usage information of the FTTR service are collected from the FTTR main gateway, optical modem, network management platform, service management system, user management system, etc., and these data are preprocessed and integrated to facilitate the subsequent screening and identification process of user information.

[0075] For example, data preprocessing includes data cleaning, data format unification, and data standardization of business information and FTTR business usage information collected through different channels. For instance, it involves unifying data formats such as date formats and text encoding formats from different platforms.

[0076] Data integration includes data merging, deduplication, summarization, and aggregation of preprocessed data. For example, it may involve aggregating all user information for each user based on their ID number and merging and deduplicating data for the same user across different platforms.

[0077] Step S302: The computer equipment filters user information based on business information to obtain a pre-selected user information set.

[0078] The pre-selected user information set refers to the collection of user information with potential abnormal risks corresponding to the FTTR service.

[0079] User information with potential abnormal risks refers to user information that may pose a risk of abnormal development of FTTR business.

[0080] Optionally, abnormal development of FTTR services includes at least one of the following behaviors: handling FTTR services on abnormal broadband, abnormal transfer operations, and handling multiple FTTR packages with a single broadband account, but is not limited thereto, and the embodiments of this application do not specifically limit this.

[0081] User information is primarily used to identify users, record the services they conduct, and track their device usage, in order to provide targeted, high-quality services to customers.

[0082] Optionally, user information includes user name, ID number, billing period, development affiliation, package, and whether it is integrated, etc., but this application embodiment does not specifically limit this.

[0083] Optionally, user names, ID numbers, etc., can be represented by different strings or fields to distinguish different broadband users or FTTR users.

[0084] The billing period refers to the complete billing cycle stipulated by the operator, during which the user's broadband or service usage fees will be accumulated and calculated.

[0085] Development attribution refers to the source of users, growth trends, geographical distribution, and the service providers to which users belong. It is of great significance for understanding user needs and market dynamics, and for developing effective marketing strategies and service plans.

[0086] A package is a bundled product offered by a communication service provider (such as a telecom operator or internet service provider) that includes a range of communication services and features. For example, a data plan.

[0087] Whether or not information is integrated refers to whether user information is integrated across different platforms, or whether user service packages are integrated.

[0088] Step S303: The computer equipment identifies abnormal user information in the pre-selected user information set based on the usage information of the FTTR service.

[0089] Abnormal user information refers to user information that poses a risk of abnormal development in FTTR business. Alternatively, abnormal user information refers to user information corresponding to users who abnormally processed FTTR business.

[0090] In some embodiments, the computer device identifies inactive users corresponding to FTTR services in a pre-selected user information set and uses the user information of inactive users corresponding to FTTR services as abnormal user information.

[0091] In this context, inactive users of the FTTR service refer to users whose FTTR service usage frequency is less than the usage frequency threshold; or, inactive users of the FTTR service refer to users whose FTTR service login frequency is less than the login frequency threshold; or, inactive users of the FTTR service refer to users who have not logged in or used the FTTR service for a period of time.

[0092] For example, users who haven't logged in for half a month can be filtered out from the pre-selected user information and identified as inactive users for the FTTR service.

[0093] In some embodiments, the computer device identifies active users corresponding to FTTR services in a pre-selected user information set, and treats user information corresponding to users other than active users in the pre-selected user information set as abnormal user information.

[0094] For example, users whose login frequency exceeds a threshold within half a month are selected from the pre-selected user information and identified as active users for the FTTR service. Users not included in the active user group are identified as abnormal users.

[0095] Optionally, the time period and login frequency threshold can be system default values ​​or human preset values, but are not limited thereto, and the embodiments of this application do not make specific limitations.

[0096] In summary, the solution provided in this embodiment proposes a user information identification method. Based on service information, a preliminary screening of user information is performed to obtain a pre-selected user information set with potential anomalies. Then, based on FTTR service usage information, abnormal user information within the pre-selected user information set is identified, yielding abnormal user information corresponding to the FTTR service. The preliminary screening process narrows the scope of user information selection beforehand, reducing the amount of data computation involved, lowering computational resource consumption, and improving the efficiency of user information identification. Furthermore, further identification based on the pre-selected user information set makes it less likely to miss or misjudge user information with anomalies, resulting in more accurate abnormal user information. This enables timely detection and handling of abnormal developments related to the FTTR service, providing strong support for risk management of abnormal developments by telecom operators and effectively protecting user experience and operator interests.

[0097] Figure 4 This is a flowchart illustrating a user information identification method provided in an embodiment of this application. The method can be executed by a computer device.

[0098] Step S401: The computer equipment obtains the service information corresponding to the Fiber to the Room (FTTR) service, as well as the usage information of the FTTR service.

[0099] For example, the service information includes at least one of the following: broadband bearer information, account change information, or broadband account information associated with FTTR service, but is not limited thereto, and the embodiments of this application do not specifically limit it.

[0100] The broadband bearer information describes the usage status of the FTTR service. FTTR is an upgrade service provided on top of existing broadband; that is, users need to subscribe to broadband first before they can subscribe to the FTTR service.

[0101] For example, the broadband bearer information includes at least one of the following: broadband installation address information, broadband account application time, broadband account usage information, and broadband account information for applying for FTTR service, but is not limited thereto, and the embodiments of this application do not specifically limit it.

[0102] The broadband account usage information is used to indicate the activity level of the broadband account. For example, the number of times the broadband account has logged in within three consecutive months starting from the month following subscription.

[0103] Network access refers to connecting to the Internet via broadband access.

[0104] Broadband access refers to the method of establishing a connection between a user terminal and a communication network to achieve high-speed data transmission. Examples include wired access methods such as Ethernet and fiber optic broadband, and wireless access methods such as satellite broadband and local area networks.

[0105] Account change information is used to describe the transfer of ownership of FTTR users.

[0106] Optionally, the account change information includes at least one of account ownership change, account location change, and account name change, but is not limited thereto, and the embodiments of this application do not specifically limit this.

[0107] Account ownership change refers to the transfer of ownership and usage rights of the FTTR service to another user.

[0108] Account location change means that the account's location changes from area A to area B.

[0109] An account name change refers to a change in the identifying name of an account on a particular platform, system, or application. For example, the account name changes from Aa to aa.

[0110] The FTTR service associated broadband account information is used to describe the association between the FTTR service and the broadband account.

[0111] For example, the broadband account information associated with FTTR services can be the number of FTTR services bound to the broadband account. For instance, the same broadband account can be bound to 3 FTTR services.

[0112] For further details on this step, please refer to step S301; it will not be elaborated upon here.

[0113] Step S402: The computer device performs a first filtering operation on user information based on the broadband bearer information to obtain the first dataset.

[0114] The first filtering operation refers to the process of filtering user information with potential anomalies based on broadband bearer information. The first dataset refers to the set of user information with potential anomalies obtained after the first filtering operation.

[0115] Specifically, the first filtering operation is to classify the user information corresponding to broadband accounts that meet the first condition into the abnormal broadband user set; then, in the abnormal broadband user set, the user information corresponding to broadband accounts that have subscribed to FTTR service is classified into the first dataset.

[0116] Among them, the abnormal broadband user set refers to the set of user information with abnormal broadband.

[0117] For example, the first condition includes at least one of the following: having more than n broadband accounts at the same address, the broadband account being processed during the target billing period, or the broadband account not being used for m consecutive months, but is not limited thereto, and the embodiments of this application do not specifically limit this.

[0118] Having more than n broadband accounts at the same address means having two or more broadband accounts at the same physical address (such as a home address or company address). For example, having more than 5 broadband accounts at the same address.

[0119] The target billing period refers to the specified billing cycle corresponding to a broadband account. For example, when the target billing period is T-3, it means that the target billing period is 3 months before or after time T.

[0120] Optionally, the billing cycle may be based on a calendar month or on the date the broadband account is first used; however, this embodiment does not specifically limit this.

[0121] A broadband account that has not been used for m consecutive months means that the broadband account has not made any active data connection or login operations for m months.

[0122] Optionally, "unused" situations include not logging in, logging in for less than a time threshold, or logging in without any data traffic. For example, a broadband account may not have been logged in for three consecutive months.

[0123] Optionally, n, m, target payment period, and time threshold are all default values ​​or preset values, and this application embodiment does not specifically limit them.

[0124] For example, the user information in the first dataset is represented by different strings or fields. The user information corresponding to broadband accounts that meet the first condition is divided into an abnormal broadband user set, which includes the following user data: User ID: 001, User ID: 002, User ID: 003, User ID: 004, User ID: 004. Here, User ID is used to distinguish different users. If the users corresponding to User ID: 001, User ID: 002, and User ID: 004 have subscribed to FTTR services, then the first dataset includes: User ID: 001, User ID: 002, User ID: 004.

[0125] Step S403: The computer device performs a second filtering operation on user information based on account change information to obtain a second dataset.

[0126] The second screening operation refers to the operation of screening user information with potential abnormal risks based on account change information.

[0127] The second dataset refers to the set of user information with potential anomaly risks obtained after the second screening operation.

[0128] Specifically, the second filtering operation involves grouping the user information of FTTR users who have subscribed to FTTR services into an abnormal change user set based on their account changes within a preset period. Within this abnormal change user set, the user information of FTTR users whose account changes are greater than or equal to a certain threshold is then grouped into a second dataset.

[0129] Among them, the abnormal change user set refers to the set of user information that has undergone account changes within a preset period.

[0130] Optionally, the preset period can be a period that includes the target payment period, a period independent of the target payment period, a period that intersects with the target payment period, or a period included in the target payment period, but it is not limited thereto, and the embodiments of this application do not specifically limit it. For example, when the target payment period is April to June, the preset period can be March to June, January to March, February to May, or April to May.

[0131] Optionally, the preset period and frequency thresholds can be default values ​​or manually preset values, and this application embodiment does not specifically limit them. For example, the preset period can be manually set to 6 months and the frequency threshold to 2 times.

[0132] In some embodiments, the computer device identifies a first identifier corresponding to an FTTR user in the target billing period, and obtains a second identifier corresponding to an FTTR user whose account has changed compared to the previous billing period. Based on the second identifier, all FTTR users corresponding to the second identifier are retrieved. Among all FTTR users corresponding to the second identifier, the user information corresponding to FTTR users whose account has changed within a preset period is grouped into an abnormal change user set.

[0133] The previous payment period refers to the payment period preceding the target payment period.

[0134] The first identifier is used to identify at least one of the following: account ownership, account location, or account name corresponding to the FTTR service. When the account changes, the first identifier also changes.

[0135] For example, the first identifier can be a customer identifier. For instance, the first identifier can be at least one of customer number, customer name, customer phone number, and customer tag, but is not limited thereto, and the embodiments of this application do not specifically limit it.

[0136] The second identifier refers to an identifier that can aggregate all FTTR services for the same user; or, the second identifier refers to a unique identifier corresponding to the user. For example, the second identifier can be a user's ID card number, passport number, or other document number.

[0137] Optionally, the target payment period and the previous payment period are default values ​​or preset values. This application embodiment does not specifically limit these values.

[0138] For example, the target payment period is the current payment period. The target payment period and the previous payment period may be different or the same; this application embodiment does not specifically limit this. For example, the target payment period is 1 month, and the previous payment period is 3 months.

[0139] For example, the user information from the previous billing period is as follows:

[0140] FTTR user: F01, first identifier: C001; FTTR user: F02, first identifier: C002;

[0141] FTTR user: F03, first identifier: C003; FTTR user: F04, first identifier: C004;

[0142] FTTR user: F05, first identifier: C005; FTTR user: F06, first identifier: C006.

[0143] The user information for the target billing period is as follows:

[0144] FTTR user: F01, first identifier: C001; FTTR user: F02, first identifier: C007;

[0145] FTTR user: F03, first identifier: C003; FTTR user: F04, first identifier: C009;

[0146] FTTR user: F05, first identifier: C010; FTTR user: F06, first identifier: C006.

[0147] The comparison shows that the first identifier of FTTR users F02, F04, and F05 in the target billing period has changed compared to the previous billing period, indicating that FTTR users F02, F04, and F05 have undergone account changes.

[0148] Furthermore, the second identifiers corresponding to FTTR users F02, F04, and F05 are obtained, and the results are as follows:

[0149] FTTR User: F02, Second Identifier: 1485692;

[0150] FTTR User: F04, Second Identifier: 1485652;

[0151] FTTR User: F05, Second Identifier: 1485673.

[0152] Furthermore, all FTTR users corresponding to the second identifier mentioned above are obtained, and the results are as follows:

[0153] Second identifier: 1485692, FTTR users: F02, F24, F58;

[0154] Second identifier: 1485652, FTTR users: F04, F31;

[0155] Second identifier: 1485673, FTTR users: F05, F76, F52, F63.

[0156] For example, assuming a preset period of 6 months, the user information of all FTTR users corresponding to the second identifier mentioned above who have undergone account changes within 6 months is classified into the abnormal change user set. The user information in the abnormal change user set is as follows:

[0157] Second identifier: 1485692, FTTR users: F02, F24;

[0158] Second identifier: 1485652, FTTR user: F04;

[0159] Second identifier: 1485673, FTTR users: F05, F76, F63.

[0160] Optionally, whether an FTTR user's account has changed within a preset period is also determined based on the first identifier, which will not be elaborated further here.

[0161] For example, assuming the threshold for the number of changes is 2, if the number of FTTR user changes for the second identifier 1485692 and the second identifier 1485673 is greater than or equal to 2, then the user information corresponding to the FTTR users for the second identifier 1485692 and the second identifier 1485673 is assigned to the second dataset. After identifying the FTTR user with the account change through the first identifier, the second identifier corresponding to the FTTR user with the account change is obtained again, and all FTTR users corresponding to the second identifier are obtained based on the second identifier. The purpose is to expand the pre-screening scope and filter other FTTR users associated with the potentially risky account change FTTR user, thereby avoiding omissions and improving the screening accuracy.

[0162] Step S404: The computer device performs a third filtering operation on user information based on the broadband account information associated with the FTTR service to obtain a third dataset.

[0163] The third screening operation refers to the operation of screening user information with potential abnormal risks based on broadband account information associated with FTTR services.

[0164] The third dataset refers to a set of user information with potential anomalies obtained after a third screening operation.

[0165] Specifically, the third filtering operation is to match the first identifier corresponding to the FTTR user and the broadband identifier corresponding to the broadband account within the target billing period, and divide the user information corresponding to the broadband accounts of multiple FTTR users into the third dataset.

[0166] The broadband identifier is used to identify the broadband account owner, account location, or account name. When the broadband account changes, the first identifier also changes.

[0167] When the broadband identifier and the first identifier are identical or have a relationship, it indicates that the broadband account corresponding to the broadband identifier is associated with the FTTR user corresponding to the first identifier.

[0168] The broadband account and FTTR user correspond to the broadband account having FTTR service.

[0169] Optionally, the number of multiple FTTR users is a default value or a preset value, and this application embodiment does not specifically limit this. For example, one broadband account may correspond to more than two FTTR services.

[0170] For example, the first identifier of an FTTR user and the broadband identifier of a broadband account are as follows:

[0171] FTTR user: F01, first identifier: 000; FTTR user: F02, first identifier: 001;

[0172] FTTR user: F03, first identifier: 002; FTTR user: F04, first identifier: 000;

[0173] FTTR user: F05, first identifier: 000; FTTR user: F06, first identifier: 001.

[0174] Broadband account: B001, Broadband ID: 000; Broadband account: B002, Broadband ID: 001;

[0175] Broadband account: B003, Broadband ID: 002; Broadband account: B004, Broadband ID: 003.

[0176] Matching the first identifier and the broadband identifier reveals that broadband account B001 corresponds to three FTTR users: FTTR user F01, FTTR user F04, and FTTR user F05; broadband account B002 corresponds to two FTTR users: FTTR user F02 and FTTR user F06; broadband account B003 corresponds to one FTTR user: FTTR user F03; and broadband account B004 does not correspond to any FTTR user.

[0177] For example, assuming the number of multiple FTTR users is set to more than two, the user information corresponding to broadband account B001 and broadband account B002 is divided into a third dataset.

[0178] In some embodiments, the user information in the third dataset is deduplicated from the user information in the historical billing period to obtain an updated third dataset.

[0179] Among them, the historical billing period refers to the billing period prior to the target billing period, and the user information in the historical billing period refers to the abnormal user information of FTTR services identified within the historical billing period.

[0180] In some embodiments, the user information in the first dataset and the second dataset may also be deduplicated from the user information in the historical billing period.

[0181] Step S405: The computer device obtains a pre-selected user information set based on the first dataset, the second dataset, and the third dataset.

[0182] In some embodiments, the first dataset, the second dataset, and the third dataset are merged and deduplicated to obtain a pre-selected user information set.

[0183] Step S406: The computer device identifies users whose FTTR service usage count is less than the usage count threshold from the pre-selected user information set as abnormal user information.

[0184] The number of times the FTTR service is used refers to the frequency with which the FFTR service is used by users within a preset time period.

[0185] Optionally, the usage frequency threshold and preset time can be default values ​​of the device or preset values ​​by the user. This application embodiment does not specifically limit these settings.

[0186] For example, the number of times the FTTR service is used can be the number of times the FTTR main gateway logs in within a certain period of time.

[0187] For example, in the pre-selected user information set, the user information corresponding to users whose cumulative login count to the FTTR main gateway is less than 3 times in 3 consecutive months is identified as abnormal user information.

[0188] In summary, the solution provided in this embodiment proposes a user information identification method. First, user information is filtered from different angles based on service information such as broadband bearer information, account change information, or broadband account information associated with FTTR services to obtain a pre-selected user information set. Then, abnormal user information is identified from the pre-selected user information set. The filtering scope is small, the amount of data computation involved is low, and the user information identification process is more efficient. Furthermore, different filtering operations are performed from different angles to identify abnormal user information, resulting in more comprehensive and accurate abnormal user information. The separate execution of different filtering operations further increases the efficiency of the user information identification process, effectively protecting user experience and operator interests.

[0189] For example, Figure 5 This is a flowchart illustrating a user information identification method provided in an embodiment of this application. The method is executed by a computer device.

[0190] Step S500: Begin.

[0191] Step S511: Obtain broadband bearer information, account change information, broadband account information associated with fiber-to-the-room (FTTR) service, and FTTR service usage information.

[0192] The broadband bearer information describes the usage status of the FTTR service. FTTR is an upgrade service provided on top of existing broadband; that is, users need to subscribe to broadband first before they can subscribe to the FTTR service. For example, the number of logins to the broadband account within three consecutive months starting from the month following activation.

[0193] Account change information is used to describe the transfer of ownership of FTTR users.

[0194] For example, account change information includes at least one of account ownership change, account location change, and account name change, but is not limited thereto, and the embodiments of this application do not specifically limit it.

[0195] Account ownership change refers to transferring the ownership and right to use the FTTR service to another user.

[0196] Account location change means that the account's location changes from area A to area B.

[0197] An account name change refers to a change in the identifying name of an account on a particular platform, system, or application. For example, the account name changes from Aa to aa.

[0198] The FTTR service associated broadband account information is used to describe the association between the FTTR service and the broadband account.

[0199] For example, the broadband account information associated with FTTR services can be the number of FTTR services bound to the broadband account. For instance, the same broadband account can be bound to 3 FTTR services.

[0200] Step S521: Extract user information with more than n broadband accounts at the same address to obtain result set A1.

[0201] Having more than n broadband accounts at the same address means having two or more broadband accounts at the same physical address (such as a home address or company address). For example, having more than 5 broadband accounts at the same address.

[0202] User information mainly consists of user identity information and service information. Identity information is used to distinguish different users, while service information describes the user's service usage. For example, user information includes personal identification information such as the user's name, gender, and age, as well as service information such as broadband services, FTTR services, and telephone packages subscribed to by the user.

[0203] Step S522: Filter user information for the Tn billing period from result set A1 to obtain result set B1.

[0204] The billing period refers to the complete billing cycle agreed upon by the user and the operator.

[0205] User information for the Tn billing period refers to user information calculated from time T, either forward or backward for n months.

[0206] For example, result set B1 is obtained by filtering user information for the T-3 payment period from result set A1.

[0207] Step S523: Filter user information from result set B1 whose broadband accounts have not been used for m consecutive months to obtain result set C1.

[0208] A broadband account that has not been used for m consecutive months means that the broadband account has not made any active data connection or login operations for m months.

[0209] Optionally, "unused" situations include not logging in, logging in for less than a time threshold, or logging in without any data traffic. For example, a broadband account may not have been logged in for three consecutive months.

[0210] For example, result set C1 is obtained by filtering user information of broadband accounts that have not been used for three months starting from the month following network access from result set B1.

[0211] Step S524: Filter the user information that has applied for FTTR service from result set C1 to obtain result set D1.

[0212] Step S531: Extract FTTR users whose first identifier is inconsistent between the target billing period and the previous billing period to obtain result set A2.

[0213] The target billing period refers to the specified billing cycle corresponding to the broadband account.

[0214] For example, the target payment period can be the current payment period.

[0215] The previous payment period refers to the payment period preceding the target payment period.

[0216] The first identifier is used to identify at least one of the following: account ownership, account location, or account name corresponding to the FTTR service. When the account changes, the first identifier also changes.

[0217] For example, the first identifier can be a customer identifier. For instance, the first identifier can be at least one of customer number, customer name, customer phone number, and customer tag, but is not limited thereto, and the embodiments of this application do not specifically limit it.

[0218] Step S532: Extract the second identifier of the FTTR user from result set A2 as result set B2.

[0219] The second identifier refers to an identifier that can aggregate all FTTR services for the same user; or, the second identifier refers to a unique identifier corresponding to the user. For example, the second identifier can be a user's ID card number, passport number, or other document number.

[0220] Step S533: Take the user information of all FTTR users corresponding to the second identifier in result set B2 as result set C2.

[0221] Step S534: Take the user information corresponding to the FTTR users whose accounts have changed within Q months from result set C2 as result set D2.

[0222] Here, Q months is the preset period. For example, FTTR users in result set C2 who have changed their accounts within 6 months are used as result set D2.

[0223] Step S535: Summarize the FTTR users who have changed their accounts in result set D2 using the second identifier, and extract the information of users who have changed their accounts more than N1 times under the same second identifier as result set E2.

[0224] For example, extract the information of FTTR users with more than 2 account changes under the same ID number as the result set E2.

[0225] Step S541: Extract all FTTR users for the current billing period as result set A3.

[0226] Among them, the current billing period full FTTR users refers to all FTTR users in the current billing period.

[0227] Step S542: Extract all broadband accounts for the current billing period as result set A4.

[0228] Among them, the current billing period full broadband account refers to all broadband accounts in the current billing period.

[0229] Step S543: Associate and summarize result set A3 and result set B3, and extract users corresponding to N2 or more FTTR users of the same broadband account as result set C3.

[0230] For example, result set A3 and result set B3 can be associated and summarized by the first identifier and the broadband identifier, and data of two or more FTTR users corresponding to the same broadband account can be extracted as result set C3.

[0231] The broadband identifier is used to identify the broadband account's ownership, location, or name.

[0232] When the broadband identifier and the first identifier are identical or have a relationship, it indicates that the broadband account corresponding to the broadband identifier is associated with the FTTR user corresponding to the first identifier.

[0233] Step S544: Associate the data in result set C3 with user information, and remove duplicates of the user information and historical billing period user information to obtain result set D3.

[0234] Among them, the historical billing period refers to the billing period prior to the target billing period, and the user information in the historical billing period refers to the abnormal user information of FTTR services identified within the historical billing period.

[0235] Linking user information ensures that the types of user information included in result set D3 are consistent with those in result sets D1 and E2. For example, if result sets D1 and E2 both include the user's name, phone number, and transaction information, result set D3 should also include the user's name, phone number, and transaction information.

[0236] Step S551: Integrate and remove duplicates from result sets D1, E2, and D3 to obtain result set F.

[0237] Step S552: Extract the user information of F whose cumulative login count to the FTTR main gateway is less than S times in consecutive months from the result set F as the result set G.

[0238] The FTTR master gateway is used to connect fiber optic signals from the operator's network to the user's home or office, and connects multiple slave gateways via fiber optic cables.

[0239] For example, user information with fewer than 3 cumulative logins to the FTTR main gateway over 3 consecutive months is extracted from result set F and used as result set G.

[0240] Step S553: ​​End.

[0241] The embodiments of this application do not restrict the order of the steps for filtering result set D1, filtering result set E2, and filtering result set D3.

[0242] The foregoing mainly describes the solution provided in this application. Accordingly, this application also provides a user information identification device for implementing the above-described method embodiments.

[0243] like Figure 6 The schematic diagram shown illustrates the structure of a user information identification device. The user information identification device may include an acquisition module 601 and a processing module 602. The acquisition module 601 is used to perform… Figure 3 The illustrated method includes the operation of step S301 and Figure 4 The illustrated method includes step S401; the processing module 602 is used to execute... Figure 3 The operations of steps S302 and S303 in the middle and Figure 4 The operations in steps S402, S403, S404, S405, and S406.

[0244] In some embodiments, the user information identification device includes hardware structures and / or software modules corresponding to the execution of each function in order to achieve the above-described functions. Those skilled in the art will readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0245] This application embodiment can divide the user information identification device into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0246] like Figure 7 As shown, the computer device provided in this application embodiment may include a processor 701, a bus 702, a communication interface 703, and a memory 704. The processor 701, memory 704, and communication interface 703 communicate with each other via the bus 702. It should be understood that this application does not limit the number of processors and memories in the computer device.

[0247] The 702 bus can be a PCI bus, an Extended Industry Standard Architecture (EISA) bus, or a UB bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus 702 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 702 may include a path for transmitting information between various components of a computer device (e.g., memory 704, processor 701, communication interface 703).

[0248] Processor 701 may include any one or more processors such as CPU, graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0249] The memory 704 may include volatile memory, such as random access memory (RAM). The processor 701 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0250] The communication interface 703 uses transceiver modules, such as, but not limited to, network interface cards and transceivers, to enable communication between computer devices and other devices or communication networks.

[0251] The memory 704 stores executable program code, and the processor 701 executes the executable program code to implement the functions of the aforementioned method embodiments. That is, the memory 704 stores instructions for executing the aforementioned user information identification method.

[0252] In another aspect, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor to implement the user information identification method provided in the above-described method embodiments.

[0253] On the other hand, a computer program product is provided, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the user information identification method described above is implemented.

[0254] Through the above description of the implementation methods, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the module can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, modules, and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0255] Since the user information identification device, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above methods, the technical effects they can achieve can also be referred to the above method embodiments. The embodiments of the present invention will not be described again here.

[0256] The method steps in this embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. One exemplary embodiment couples a storage medium to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a computer device. Of course, the processor and storage medium can also exist as discrete components in a computer device.

[0257] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer programs or instructions. When a computer program or instruction is loaded and executed on a computer, the processes or functions of the embodiments of this application are performed, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable module. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, a computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD). The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A user information identification method, characterized in that, The method includes: Obtain service information corresponding to the Fiber to the Room (FTTR) service, as well as the usage information of the FTTR service; wherein, the service information includes at least one of: broadband bearer information, account change information, or broadband account information associated with the FTTR service, and the broadband bearer information is used to describe the usage status of the broadband where the FTTR service is located; Based on the broadband bearer information, a first filtering operation on user information is performed to obtain a first dataset; A second filtering operation on user information is performed based on the account change information to obtain a second dataset; A third filtering operation is performed on the user information based on the broadband account information associated with the FTTR service to obtain a third dataset. The first dataset, the second dataset, and the third dataset are merged and deduplicated to obtain a pre-selected user information set; the pre-selected user information set refers to the collection of user information with potential abnormal risks corresponding to the FTTR service. In the pre-selected user information set, the user information corresponding to users whose FTTR service usage count is less than the usage count threshold is identified as abnormal user information.

2. The method according to claim 1, characterized in that, The first filtering operation on user information based on the broadband bearer information, to obtain a first dataset, includes: The user information corresponding to broadband accounts that meet the first condition is classified into the abnormal broadband user set; In the abnormal broadband user set, the user information corresponding to the broadband accounts that have subscribed to the FTTR service is divided into the first dataset.

3. The method according to claim 2, characterized in that, The first condition includes at least one of the following: having more than n broadband accounts at the same address, the broadband accounts being processed during the target billing period, and the broadband accounts not being used for m consecutive months; Wherein, the target billing period refers to the specified billing cycle corresponding to the broadband account, n is a positive integer greater than 1, and m is a positive integer.

4. The method according to claim 1, characterized in that, The second filtering operation on user information based on the account change information yields a second dataset, including: Among FTTR users who have applied for the aforementioned FTTR service, the user information of FTTR users whose accounts have changed within a preset period will be classified into the abnormal change user set. In the abnormal change user set, the user information corresponding to FTTR users whose account change count is greater than or equal to the count threshold is assigned to the second dataset.

5. The method according to claim 4, characterized in that, Among the FTTR users who have applied for the FTTR service, the user information corresponding to FTTR users whose account changes occur within a preset period will be classified into an abnormal change user set, including: Identify the first identifier corresponding to the FTTR user in the target billing period, and obtain the second identifier corresponding to the FTTR user whose account has changed compared to the previous billing period; Based on the second identifier, obtain all FTTR users corresponding to the second identifier; Among all FTTR users corresponding to the second identifier, the user information of FTTR users whose accounts have changed within a preset period will be classified into the abnormal change user set.

6. The method according to claim 1, characterized in that, The third filtering operation on user information based on the broadband account information associated with the FTTR service yields a third dataset, including: During the target billing period, the first identifier corresponding to the FTTR user and the broadband identifier corresponding to the broadband account are matched, and the user information corresponding to the broadband accounts of multiple FTTR users is divided into the third dataset.

7. The method according to claim 6, characterized in that, The method further includes: deduplicating the user information in the third dataset with the user information in the historical billing period to obtain an updated third dataset.

8. A user information identification device, characterized in that, The device includes: The acquisition module is used to acquire service information corresponding to the FTTR service, as well as the usage information of the FTTR service; wherein, the service information includes at least one of: broadband bearer information, account change information, or broadband account information associated with the FTTR service, and the broadband bearer information is used to describe the usage status of the broadband where the FTTR service is located; The processing module is used to perform a first filtering operation on user information based on the broadband bearer information to obtain a first dataset; perform a second filtering operation on user information based on the account change information to obtain a second dataset; perform a third filtering operation on user information based on the broadband account information associated with the FTTR service to obtain a third dataset; and merge and deduplicate the first dataset, the second dataset, and the third dataset to obtain a pre-selected user information set; the pre-selected user information set refers to the collection of user information with potential abnormal risks corresponding to the FTTR service. The processing module is further configured to identify, within the preselected user information set, user information corresponding to users whose FTTR service usage count is less than the usage count threshold as abnormal user information.

9. A computer device, characterized in that, The computer device includes a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the user information identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to implement the user information identification method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program or instructions that, when executed by a processor, implement the user information identification method as described in any one of claims 1 to 7.

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