Method, device and equipment for identifying heterogeneous broadband users and storage medium

Abstract

The application provides a method, device and equipment for identifying a broadband user in a different network and a storage medium. The method obtains mobile data traffic and broadband traffic of a terminal device of a target user in a preset time period to obtain a first data set. The method performs data filtering processing on the first data set according to a preset unit time mobile data traffic threshold and a preset unit time broadband traffic threshold to obtain a second data set. The method performs normalization processing on the second data set, and judges the normalized second data set according to a pattern recognition criterion to obtain a broadband user in a different network. The broadband user in the different network can be simply and quickly screened out, the accuracy of identification is improved through the pattern recognition criterion, the identification step is simplified compared with a method of training a model in the prior art, and the implementation is simple.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method, apparatus, device, and storage medium for identifying broadband users on different networks. Background Technology

[0002] With the development of communication technology, broadband services have become a standard feature in almost every household. As operators continue to build their networks, broadband speeds are gradually increasing. To facilitate the widespread development of broadband services, it is necessary to identify the potential broadband needs of users on mobile terminals, i.e., to find broadband users on other networks.

[0003] Currently, existing technologies can collect user information from a single user on the network, use a random forest classification algorithm to train and determine a broadband user identification model, and then use this broadband user identification model to identify suspected broadband users.

[0004] However, existing methods for identifying broadband users from other networks are complex to implement and have low accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for identifying broadband users on different networks, thereby solving the technical problems of complex implementation and low accuracy of existing methods for identifying broadband users on different networks.

[0006] Firstly, this application provides a method for identifying broadband users on different networks, including:

[0007] The first dataset is obtained by acquiring the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period.

[0008] Based on the preset unit time mobile data traffic threshold and the preset unit time broadband traffic threshold, the first dataset is filtered to obtain the second dataset.

[0009] The second dataset is normalized to reflect the target users, and the normalized second dataset is judged according to the pattern recognition criteria to obtain the broadband users of the different networks.

[0010] In order to identify broadband users on different networks, this application first obtains the mobile data traffic and broadband traffic of the corresponding terminal device within a preset time period for the target user. By filtering and normalizing the mobile data traffic and broadband traffic within the preset time period, broadband users on different networks can be easily and quickly identified. Furthermore, by using pattern recognition criteria, the accuracy of identification is improved. Compared with existing technologies that require model training, this application simplifies the identification steps and is easy to implement.

[0011] Optionally, the step of filtering the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain a second dataset includes:

[0012] Based on the first dataset, determine the target user's mobile data traffic and broadband traffic per unit time within a preset time period;

[0013] If the target user's mobile data traffic per unit time is greater than a preset mobile data traffic threshold per unit time, and the broadband traffic per unit time is less than a preset broadband traffic threshold per unit time, then the target user's mobile data traffic and broadband traffic within a preset time period are added to the second dataset.

[0014] This application uses preset mobile data traffic thresholds and preset broadband traffic thresholds per unit time to determine whether to add the user's data to the second dataset of the corresponding cross-network broadband user. If the target user's mobile data traffic per unit time is greater than the preset mobile data traffic threshold and the broadband traffic per unit time is less than the preset broadband traffic threshold, it can be determined that the target user mostly accesses the Internet via mobile data and uses broadband less, thus identifying the user as a cross-network broadband user. The comparison of the two datasets further improves the accuracy of cross-network broadband user identification.

[0015] Optionally, before performing data filtering processing on the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain the second dataset, the method further includes:

[0016] The applications in the target user's terminal device are classified to obtain at least one application type; a preset category unit time mobile data traffic threshold and a preset category unit time broadband traffic threshold are determined for each application type.

[0017] Accordingly, the step of filtering the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain a second dataset includes:

[0018] The data in the first dataset is classified by application type to obtain at least one application classification dataset; based on the application type corresponding to the data in the application classification dataset, a preset category unit time mobile data traffic threshold and a preset category unit time broadband traffic threshold are determined; based on the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold, the classification dataset is filtered to obtain a second dataset.

[0019] Optionally, determining the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold for each application type includes:

[0020] Obtain the historical traffic data of the target user; determine the target user's preference ranking for each application type based on the historical traffic data; determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type based on the preference ranking.

[0021] In order to achieve accurate identification of broadband users on different networks, this application first classifies the applications on the user's terminal device. Based on the different categories of applications, it determines the different preset mobile data traffic thresholds and broadband traffic thresholds per unit time for each category. This allows for a more accurate determination of the user's mobile data and broadband usage based on their usage preferences, fully combining user behavior characteristics to achieve accurate identification of broadband users on different networks.

[0022] Optionally, the step of obtaining the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period to obtain the first dataset includes:

[0023] By bypassing and mirroring traffic on the operator's backbone network, and performing deep packet inspection analysis on the traffic on the terminal devices of the mirrored target users, the first dataset is obtained.

[0024] This application obtains the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period by bypassing and mirroring traffic on the operator's backbone network and performing deep packet inspection analysis on the traffic on the mirrored target user's terminal device. This method accurately and comprehensively obtains the user's network data, is simple to use, improves the accuracy and efficiency of identifying broadband users on different networks, and is easy to operate.

[0025] Optionally, after normalizing the second dataset for target users and determining the cross-network broadband users based on pattern recognition criteria, the method further includes:

[0026] Send information to the terminal device of the broadband user on the other network.

[0027] In this application, after determining that the target user is a broadband user from another network, information can be sent to the user's terminal device so that the user can understand the broadband information, provide the user with a reasonable and feasible broadband solution, and further improve the user experience.

[0028] Secondly, this application provides a cross-network broadband user identification device, comprising:

[0029] The acquisition module is used to acquire the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period to obtain the first dataset.

[0030] The first processing module is used to perform data filtering processing on the first dataset according to a preset unit time mobile data traffic threshold and a preset unit time broadband traffic threshold to obtain a second dataset.

[0031] The second processing module is used to normalize the second dataset for target users, and to judge the normalized second dataset according to the pattern recognition criteria to obtain the broadband users of the different networks.

[0032] Optionally, the first processing module is specifically used for:

[0033] Based on the first dataset, determine the target user's mobile data traffic and broadband traffic per unit time within a preset time period;

[0034] If the target user's mobile data traffic per unit time is greater than a preset mobile data traffic threshold per unit time, and the broadband traffic per unit time is less than a preset broadband traffic threshold per unit time, then the target user's mobile data traffic and broadband traffic within a preset time period are added to the second dataset.

[0035] Optionally, before the first processing module performs data filtering on the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain the second dataset, the above-mentioned device further includes:

[0036] An application classification module is used to classify the applications in the target user's terminal device to obtain at least one application type;

[0037] The threshold determination module is used to determine the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold for each of the application types.

[0038] Accordingly, the first processing module is specifically used for:

[0039] The data in the first dataset is classified by application type to obtain at least one application classification dataset; based on the application type corresponding to the data in the application classification dataset, a preset category unit time mobile data traffic threshold and a preset category unit time broadband traffic threshold are determined; based on the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold, the classification dataset is filtered to obtain a second dataset.

[0040] Optionally, the threshold determination module is specifically used for:

[0041] Obtain the historical traffic data of the target user;

[0042] Based on the historical traffic data, determine the target user's preference ranking for each application type;

[0043] Based on the preference sorting, determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

[0044] Optionally, the acquisition module is specifically used for:

[0045] By bypassing and mirroring traffic on the operator's backbone network, and performing deep packet inspection analysis on the traffic on the terminal devices of the mirrored target users, the first dataset is obtained.

[0046] Optionally, after the second processing module performs target user normalization processing on the second dataset and judges the normalized second dataset according to pattern recognition criteria to obtain the inter-network broadband users, the above device further includes:

[0047] The sending module is used to send information to the terminal devices of the broadband users on the other network.

[0048] Thirdly, this application provides a cross-network broadband user identification device, comprising: at least one processor and a memory;

[0049] The memory stores computer-executed instructions;

[0050] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the cross-network broadband user identification method as described in the first aspect and various possible designs of the first aspect.

[0051] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the cross-network broadband user identification method described in the first aspect and various possible designs of the first aspect.

[0052] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the cross-network broadband user identification method as described in the first aspect and various possible designs of the first aspect.

[0053] The present application provides a method, apparatus, device, and storage medium for identifying broadband users on different networks. This method, in order to identify broadband users on different networks, first acquires the mobile data traffic and broadband traffic of the corresponding terminal device within a preset time period for the target user. By filtering and normalizing the mobile data traffic and broadband traffic within the preset time period, broadband users on different networks can be easily and quickly identified. Furthermore, by using pattern recognition criteria, the accuracy of identification is improved. Compared with existing methods that require model training, this method simplifies the identification steps and is easy to implement. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0055] Figure 1 A schematic diagram of a cross-network broadband user identification system architecture is provided for an embodiment of this application;

[0056] Figure 2 A flowchart illustrating a method for identifying broadband users on different networks provided in this application embodiment;

[0057] Figure 3 A flowchart illustrating another method for identifying broadband users on different networks provided in this application embodiment;

[0058] Figure 4 This is a schematic diagram of the structure of a cross-network broadband user identification device provided in an embodiment of this application;

[0059] Figure 5 This is a schematic diagram of the structure of a cross-network broadband user identification device provided in an embodiment of this application.

[0060] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0062] The terms “first,” “second,” “third,” and “fourth,” etc. (if present), in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0063] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0064] Broadband users can be categorized into those subscribing to their own network and those subscribing to other operators' broadband services. Identifying these users helps operators gain a more detailed understanding of their user base and provide tailored solutions for different user needs. Currently, existing technologies collect user information from individual users within the network, train a broadband user identification model using a random forest classification algorithm, and then use this model to identify potential users. However, this method requires a large amount of data for model training, is complex to implement, involves a wide range of data and numerous indicators, and suffers from significant differences in data acquisition time and data volume across different dimensions. Forcing a model based on data from different dimensions leads to numerous null values, impacting the model's accuracy and efficiency. In conclusion, existing methods for identifying users on different networks are complex to implement and have low accuracy.

[0065] To address the aforementioned issues, embodiments of this application provide a method, apparatus, device, and storage medium for identifying broadband users on different networks. This method targets specific users by first acquiring the mobile data traffic and broadband traffic of the corresponding terminal device within a preset time period. By filtering and normalizing the mobile data traffic and broadband traffic within the preset time period, broadband users on different networks can be easily and quickly identified.

[0066] Optional, Figure 1 This is a schematic diagram of a cross-network broadband user identification system architecture provided in an embodiment of this application. Figure 1 In the above architecture, at least one of data acquisition device 101, processing device 102 and display device 103 is included.

[0067] It is understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the architecture of the inter-network broadband user identification system. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0068] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface, and the data acquisition device 101 can be connected to the terminal device through the input / output interface or the communication interface.

[0069] The processing device 102 can acquire the mobile data traffic and broadband traffic of the target user's terminal device collected by the data acquisition device 101 within a preset time period to obtain a first dataset; according to the preset mobile data traffic threshold and the preset broadband traffic threshold per unit time, the first dataset is filtered to obtain a second dataset; the second dataset is normalized for the target user, and the normalized second dataset is judged according to the pattern recognition criteria to obtain the cross-network broadband user.

[0070] The display device 103 can be used to display the above results, and can also interact with the user through the display device. The display device 103 can be a user terminal / terminal device, a staff member's user terminal / terminal device, or a server, etc.

[0071] The display device 103 can also be a touch screen, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0072] It should be understood that the aforementioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0073] It is understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the architecture of the inter-network broadband user identification system. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0074] It should be understood that the aforementioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0075] Furthermore, the network architecture and business 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.

[0076] The technical solution of this application will be described in detail below with reference to specific embodiments:

[0077] Optionally, Figure 2 This is a flowchart illustrating a method for identifying broadband users on different networks, provided in an embodiment of this application. The execution entity of this embodiment can be... Figure 1 The specific execution entity of the processing device 102 can be determined according to the actual application scenario. For example... Figure 2 As shown, the method includes the following steps:

[0078] S201: Obtain the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period to obtain the first dataset.

[0079] Optionally, the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period are obtained to obtain a first dataset, including: bypass mirrored traffic on the operator's backbone network, and deep packet inspection analysis is performed on the traffic on the mirrored target user's terminal device to obtain the first dataset.

[0080] In one possible implementation, traffic is bypassed and mirrored on the operator's backbone network. Deep Packet Inspection (DPI) analysis is then performed on the mirrored Hypertext Transfer Protocol (HTTP) traffic of mobile internet users. This extracts unique identifiers (MobileSubscriber International ISDN / PSTN number, MSISDN), Uniform Resource Locator (URL), Internet Protocol (IP) port, data stored on the user's local terminal (cookie), User-Agent (UA), timestamps, and other information from the original DPI log data. This information is then further correlated with the MSISDN, application (APP) name, APP uplink and downlink traffic, APP uplink and downlink radio transmission technology (RTT) latency, Transmission Control Protocol (TCP) two-way handshake latency, TCP three-way handshake latency, etc., and stored in memory.

[0081] Among them, the bypass mirroring traffic of the operator's backbone network includes 234G wireless network and core network interfaces of the mobile network, including at least Gb, Iu-PS, S1-MME, S1-U, S5 / S8, and SGs interfaces.

[0082] Specifically, traffic is bypassed and mirrored on the operator's backbone network. DPI analysis is performed on the HTTP traffic of mirrored fixed-line internet users, including detailed multi-interface records (XDR), network address translation (NAT) call detail records (CDRs), and remote authentication dial-in user service (RADIUS) CDRs. This extracts fields such as APP Type, APP Sub-type, UL / DL Data, TCP connection establishment response latency (ms), TCP connection establishment confirmation latency (ms), first HTTP response packet latency (ms), last HTTP content packet latency (ms), last ACK confirmation packet latency (ms), and online / offline authentication information, including time, user account, activated bandwidth, private network IP address, user start / end time, public / private network converted IP address, port number, and destination address port number. This information is then stored in memory.

[0083] Among them, the bypass mirroring traffic of the operator's backbone network includes the interconnection exit, metropolitan area network exit, inter-provincial exit, metropolitan area backbone network exit and Internet Data Center (IDC) exit for fixed network.

[0084] This application embodiment obtains the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period by bypassing and mirroring traffic on the operator's backbone network and performing deep packet inspection analysis on the traffic on the mirrored target user's terminal device. It accurately and comprehensively obtains the user's network data, and the acquisition method is simple, improving the accuracy and efficiency of identifying broadband users on different networks. The operation is also simple.

[0085] S202: Based on the preset mobile data traffic threshold per unit time and the preset broadband traffic threshold per unit time, perform data filtering processing on the first dataset to obtain the second dataset.

[0086] Optionally, based on preset mobile data traffic thresholds and preset broadband traffic thresholds per unit time, the first dataset is filtered to obtain a second dataset, including:

[0087] Based on the first dataset, determine the target user's mobile data traffic and broadband traffic per unit time within a preset time period. If the target user's mobile data traffic per unit time is greater than a preset threshold for mobile data traffic per unit time, and the broadband traffic per unit time is less than a preset threshold for broadband traffic per unit time, then add the target user's mobile data traffic and broadband traffic within the preset time period to the second dataset.

[0088] It is understood that the mobile data traffic threshold per unit time and the preset broadband traffic threshold per unit time can be determined according to the actual situation, and this application embodiment does not impose specific restrictions on them.

[0089] In this embodiment, a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold are used to determine whether to add the user's data to the second dataset of the corresponding cross-network broadband user. If the target user's unit-time mobile data traffic is greater than the preset unit-time mobile data traffic threshold and the unit-time broadband traffic is less than the preset unit-time broadband traffic threshold, it can be determined that the target user mostly accesses the Internet via mobile data and uses broadband less, thus identifying the user as a cross-network broadband user. The comparison of the two datasets further improves the accuracy of cross-network broadband user identification.

[0090] S203: Normalize the second dataset for target users, and judge the normalized second dataset according to the pattern recognition criteria to obtain the broadband users of different networks.

[0091] Optionally, for the second dataset, the target user, mobile data traffic and broadband traffic within a preset time period are normalized to meet the periodic statistical P / N decision, and a list of home broadband users on different networks is output and stored in memory.

[0092] In this embodiment of the application, in order to identify broadband users on different networks, the mobile data traffic and broadband traffic of the corresponding terminal device within a preset time period are first obtained for the target user. By filtering and normalizing the mobile data traffic and broadband traffic within the preset time period, broadband users on different networks can be easily and quickly screened out. Furthermore, the accuracy of identification is improved by using pattern recognition criteria. Compared with the existing technology that requires model training, the identification steps are simplified and the implementation method is simple.

[0093] Optionally, after normalizing the second dataset for the target user and judging the normalized second dataset according to the pattern recognition criteria to obtain the broadband users of the other network, the method further includes: sending information to the terminal devices of the broadband users of the other network.

[0094] Alternatively, after obtaining the list of broadband users from other networks, the list can be sent to the terminal equipment of the operator's staff.

[0095] In this embodiment of the application, after determining that the target user is a broadband user from another network, information can be sent to the user's terminal device so that the user can understand the broadband information, provide the user with a reasonable and feasible broadband solution, and further improve the user experience.

[0096] Optionally, embodiments of this application can also accurately identify users' preferences for different types of apps, and accordingly, Figure 3 This is a flowchart illustrating another method for identifying broadband users across different networks provided in an embodiment of this application, as shown below. Figure 3 As shown, the method includes:

[0097] S301: Obtain the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period to obtain the first dataset.

[0098] S302: Classify the applications in the target user's terminal device to obtain at least one application type.

[0099] S303: Determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

[0100] Optionally, a preset category unit-time mobile data traffic threshold and a preset category unit-time broadband traffic threshold are determined for each application type, including:

[0101] Obtain historical traffic data of the target user; based on the historical traffic data, determine the target user's preference ranking for each application type; based on the preference ranking, determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

[0102] S304: Perform application type classification processing on the data in the first dataset to obtain at least one application classification dataset.

[0103] S305: Based on the application type corresponding to the data in the application classification dataset, determine the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold.

[0104] S306: Based on the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold, perform data filtering processing on the classification dataset to obtain the second dataset.

[0105] S307: Normalize the second dataset for target users, and judge the normalized second dataset according to the pattern recognition criteria to obtain the broadband users of the different networks.

[0106] In one possible implementation, the APP category could include three main types of entertainment and leisure apps: web apps, short video apps, and mobile games. Optionally, instant messaging apps, due to their dual nature as both work and leisure apps, are excluded from the APP statistics.

[0107] Among them, the relevant indicators for the APP category include MSISDN, APP category identifier, APP name, uplink and downlink traffic per unit time, business start time, and duration.

[0108] The duration of each app is calculated by converting the total uplink and downlink traffic of the app within a unit of time. The duration of each app category is calculated by converting the total uplink and downlink traffic of each app within that app category within a unit of time.

[0109] Specifically, taking web-based software A as an example, excluding pages with a size of 50KB, the average text page size is 1MB, and each page is viewed for 5 minutes. Assuming that a user views a page for a total of 30 minutes within an hour, the total upload and download traffic of software A for that user is 6MB.

[0110] Specifically, taking short video app B as an example, if the app's bitrate is mostly 480P, the average download speed is 5MB, and each short video is watched for an average of 3 minutes, including 1 minute of full-speed download, and assuming a user watches app B for a cumulative 20 minutes per hour, the total upload and download traffic would be approximately 50MB.

[0111] Specifically, taking mobile game software C as an example, the average game of this app lasts 0.5 hours. There are 20 transactions in the first dataset. Assuming that a user accumulates 50 transactions in one hour, then the user's software C lasts for 1 hour.

[0112] The threshold is reached if the preset value meets one of the following criteria: 6MB for web-related content, 50 times for mobile games, and 50MB for short videos per hour.

[0113] The APP category, corresponding APP name, and preset values ​​are dynamically adjusted based on the user penetration rate and DPI discrimination capability obtained from DPI analysis.

[0114] The preset time period is dynamically adjusted based on user behavior. For example, the preset time period for mobile internet users is 08:00-22:00, and the preset time period for broadband internet users is 23:00-06:00.

[0115] In this embodiment of the application, in order to achieve accurate identification of broadband users on different networks, the applications on the user's terminal device are first classified. Based on the different categories of applications, different preset thresholds for mobile data traffic per unit time and broadband traffic per unit time are determined for each category. This allows for a more accurate determination of the user's mobile data and broadband usage based on the user's usage preferences, fully combining user behavior characteristics to achieve accurate identification of broadband users on different networks.

[0116] Figure 4 This is a schematic diagram of the structure of a cross-network broadband user identification device provided in an embodiment of this application, as shown below. Figure 4 As shown, the apparatus in this embodiment includes: an acquisition module 401, a first processing module 402, and a second processing module 403. The inter-network broadband user identification device described above can be... Figure 1 The processing device 102 itself, or the chip or integrated circuit that implements the functions of the processing device 102, or a server. It should be noted that the division of the acquisition module 401, the first processing module 402 and the second processing module 403 is only a logical functional division. Physically, the two can be integrated or independent.

[0117] The acquisition module is used to acquire the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period to obtain the first dataset.

[0118] The first processing module is used to perform data filtering on the first dataset according to the preset mobile data traffic threshold and the preset broadband traffic threshold per unit time to obtain the second dataset.

[0119] The second processing module is used to normalize the second dataset for target users, and to judge the normalized second dataset according to the pattern recognition criteria to obtain the broadband users of different networks.

[0120] Optionally, the first processing module is specifically used for:

[0121] Based on the first dataset, determine the target user's mobile data traffic and broadband traffic per unit time within a preset time period.

[0122] If the target user's mobile data traffic per unit time is greater than the preset mobile data traffic threshold per unit time, and the broadband traffic per unit time is less than the preset broadband traffic threshold per unit time, then the target user's mobile data traffic and broadband traffic within the preset time period will be added to the second dataset.

[0123] Optionally, before the first processing module performs data filtering on the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain the second dataset, the above-mentioned device further includes:

[0124] The application classification module is used to classify the applications on the target user's terminal device to obtain at least one application type;

[0125] The threshold determination module is used to determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

[0126] Accordingly, the first processing module is specifically used for:

[0127] The data in the first dataset is classified by application type to obtain at least one application classification dataset. Based on the application type corresponding to the data in the application classification dataset, a preset category unit time mobile data traffic threshold and a preset category unit time broadband traffic threshold are determined. Based on the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold, the classification dataset is filtered to obtain the second dataset.

[0128] Optionally, the threshold determination module is specifically used for:

[0129] Obtain historical traffic data of the target user;

[0130] Based on historical traffic data, determine the target users' preference ranking for each application type;

[0131] Based on preference sorting, determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

[0132] Optionally, the acquisition module is specifically used for:

[0133] By bypassing and mirroring traffic on the operator's backbone network, and performing deep packet inspection analysis on the traffic on the terminal devices of the mirrored target users, the first dataset is obtained.

[0134] Optionally, after the second processing module normalizes the second dataset for target users and judges the normalized second dataset according to pattern recognition criteria to obtain the inter-network broadband users, the above device further includes:

[0135] The sending module is used to send information to the terminal devices of broadband users on different networks.

[0136] refer to Figure 5 This document illustrates a structural schematic diagram of a cross-network broadband subscriber identification device 500 suitable for implementing embodiments of the present disclosure. The cross-network broadband subscriber identification device 500 can be a terminal device or a server. The terminal device can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The illustrated cross-network broadband user identification device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments disclosed herein.

[0137] like Figure 5 As shown, the inter-network broadband subscriber identification device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the inter-network broadband subscriber identification device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0138] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows the cross-network broadband user identification device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 A cross-network broadband subscriber identification device 500 with various devices is shown; however, it should be understood that implementation or possession of all the devices shown is not required. More or fewer devices may be implemented alternatively.

[0139] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0140] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0141] The aforementioned computer-readable medium may be included in the aforementioned inter-network broadband subscriber identification device; or it may exist independently and not assembled into the inter-network broadband subscriber identification device.

[0142] The aforementioned computer-readable medium carries one or more programs, which, when executed by the inter-network broadband user identification device, cause the inter-network broadband user identification device to perform the method shown in the above embodiments.

[0143] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0145] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0146] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0147] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on 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.

[0148] The cross-network broadband user identification device in this application can be used to execute the technical solutions in the above-described method embodiments of this application. Its implementation principle and technical effect are similar, and will not be described again here.

[0149] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement any of the above-described data processing methods.

[0150] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement any of the above-described data processing methods.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0152] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0153] 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 application 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 and spirit of this disclosure are indicated by the following claims.

[0154] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for identifying broadband users on different networks, characterized in that, include: The first dataset is obtained by acquiring the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period. Based on the preset unit time mobile data traffic threshold and the preset unit time broadband traffic threshold, the first dataset is filtered to obtain the second dataset. The second dataset is normalized for target users, and the normalized second dataset is judged according to the pattern recognition criteria to obtain the broadband users of the different networks. The pattern recognition criteria include: periodic statistical P / N decision. Before performing data filtering processing on the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain the second dataset, the method further includes: The applications in the target user's terminal device are classified to obtain at least one application type; Determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type; Accordingly, the step of filtering the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain a second dataset includes: The data in the first dataset are classified by application type to obtain at least one application classification dataset. Based on the application type corresponding to the data in the application classification dataset, determine the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold. The second dataset is obtained by filtering the classified dataset based on the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold. The step of determining the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type includes: Obtain the historical traffic data of the target user; Based on the historical traffic data, determine the target user's preference ranking for each application type; Based on the preference sorting, determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

2. The method according to claim 1, characterized in that, The process of filtering the first dataset according to a preset unit-time mobile data traffic threshold and a preset unit-time broadband traffic threshold to obtain a second dataset includes: Based on the first dataset, determine the target user's mobile data traffic and broadband traffic per unit time within a preset time period; If the target user's mobile data traffic per unit time is greater than a preset mobile data traffic threshold per unit time, and the broadband traffic per unit time is less than a preset broadband traffic threshold per unit time, then the target user's mobile data traffic and broadband traffic within a preset time period are added to the second dataset.

3. The method according to claim 1 or 2, characterized in that, The first dataset is obtained by acquiring the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period, including: By bypassing and mirroring traffic on the operator's backbone network, and performing deep packet inspection analysis on the traffic on the terminal devices of the mirrored target users, the first dataset is obtained.

4. The method according to claim 3, characterized in that, After normalizing the second dataset for target users and judging the normalized second dataset according to pattern recognition criteria to obtain cross-network broadband users, the process further includes: Send information to the terminal device of the broadband user on the other network.

5. A cross-network broadband user identification device, characterized in that, include: The acquisition module is used to acquire the mobile data traffic and broadband traffic of the target user's terminal device within a preset time period to obtain the first dataset. The first processing module is used to perform data filtering processing on the first dataset according to a preset unit time mobile data traffic threshold and a preset unit time broadband traffic threshold to obtain a second dataset. The second processing module is used to normalize the second dataset for target users, and to judge the normalized second dataset according to the pattern recognition criteria to obtain the broadband users of the different networks. The pattern recognition criteria include: periodic statistical P / N decision. The application classification module is used to classify the applications in the target user's terminal device to obtain at least one application type before the first processing module performs data filtering processing on the first dataset according to the preset unit time mobile data traffic threshold and the preset unit time broadband traffic threshold to obtain the second dataset. The threshold determination module is used to determine the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold for each of the application types. Accordingly, the first processing module is specifically used for: The data in the first dataset are classified by application type to obtain at least one application classification dataset. Based on the application type corresponding to the data in the application classification dataset, determine the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold. The second dataset is obtained by filtering the classified dataset based on the preset category unit time mobile data traffic threshold and the preset category unit time broadband traffic threshold. The threshold determination module is specifically used for: Obtain the historical traffic data of the target user; Based on the historical traffic data, determine the target user's preference ranking for each application type; Based on the preference sorting, determine the preset category unit time mobile data traffic threshold and preset category unit time broadband traffic threshold for each application type.

6. A cross-network broadband user identification device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the cross-network broadband user identification method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the cross-network broadband user identification method as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the cross-network broadband user identification method according to any one of claims 1 to 4.

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