CN113098987A - Home-wide target user identification method and device based on big data and electronic equipment - Google Patents
Home-wide target user identification method and device based on big data and electronic equipment Download PDFInfo
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- CN113098987A CN113098987A CN201911335172.3A CN201911335172A CN113098987A CN 113098987 A CN113098987 A CN 113098987A CN 201911335172 A CN201911335172 A CN 201911335172A CN 113098987 A CN113098987 A CN 113098987A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2101/00—Indexing scheme associated with group H04L61/00
- H04L2101/60—Types of network addresses
- H04L2101/69—Types of network addresses using geographic information, e.g. room number
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2101/00—Indexing scheme associated with group H04L61/00
- H04L2101/60—Types of network addresses
- H04L2101/618—Details of network addresses
- H04L2101/622—Layer-2 addresses, e.g. medium access control [MAC] addresses
Abstract
The invention discloses a home-wide target user identification method and device based on big data and electronic equipment, wherein the method comprises the following steps: determining residential area users according to the acquired OTT data and MDT data of the users; and judging whether the residential area user is a target user or not according to the flow use condition of the residential area user. The embodiment of the invention can accurately position the position of the user and the home-wide MAC address by using the OTT data and the MDT data of the user, and determines the target user according to the traffic use condition and the home-wide MAC address. Compared with the traditional technologies such as triangulation positioning, simulation and the like in the prior art, the method is higher in positioning accuracy, more comprehensive in judgment than the method only counting data in the S1-MME signaling aspect, and more accurate in determined target user.
Description
Technical Field
The invention relates to the field of big data, in particular to a family wide target user identification method and device based on big data and electronic equipment.
Background
With the development of new technologies such as internet of things, cloud computing, internet +, and the like, the Ministry of industry and belief increases the strategic implementation strength of broadband China, and promotes the telecommunication industry to accelerate the broadband acceleration process. Under the promotion, the household broadband market is rapidly increased, the broadband rate is continuously improved, and how to distinguish potential household broadband users and locate a target customer group is a problem to be solved urgently at present.
The current positioning method is to use MRO (Measurement Report) data to perform positioning. According to the 3GPP (3rd generation Partnership Project) specification, the association of MROs with actual communities uses conventional triangulation, simulation, etc. techniques. Because the positioning accuracy is influenced by factors such as the accuracy of working parameters and the change of a wireless environment, and is also influenced by characteristics such as refraction, diffraction and reflection of radio transmission, the positioning accuracy is not high. The existing potential client judgment method only counts data in S1-MME signaling, and has a single judgment process.
Disclosure of Invention
The embodiment of the invention provides a home broadband target user identification method and device based on big data and electronic equipment, and aims to solve the problems that potential home broadband users are inaccurate in positioning and single in judgment process in the prior art.
In order to solve the technical problem, the invention is realized as follows:
in a first aspect, a method for identifying a home-wide target user based on big data is provided, and the method includes:
determining residential area users according to the acquired OTT data and MDT data of the users;
and judging whether the residential area user is a target user or not according to the flow use condition of the residential area user.
In a second aspect, a big data-based home-wide target user identification device is provided, the device comprising:
the determining module is used for determining residential area users according to the acquired OTT data and MDT data of the users;
and the judging module is used for judging whether the residential area user is a target user according to the flow use condition of the residential area user.
In a third aspect, an electronic device is provided, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the method according to the first aspect.
In the embodiment of the invention, residential area users are determined according to the acquired OTT data and MDT data of the users, and whether the residential area users are target users is judged according to the flow use condition of the residential area users. The embodiment of the invention accurately judges the position information of the user through the acquired user data so as to determine the residential area user, and then determines whether the residential area user is a target user according to the flow use condition of the residential area user. The OTT data and the MDT data of the user can be used for accurately positioning the position of the user and the home-wide MAC address, and the target user is determined according to the traffic use condition and the home-wide MAC address. Compared with the traditional technologies such as triangulation positioning, simulation and the like in the prior art, the method is higher in positioning accuracy, more comprehensive in judgment than the method only counting data in the S1-MME signaling aspect, and more accurate in determined target user.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a home wide target user identification method based on big data according to an embodiment of the present invention;
fig. 2 is a flow chart illustrating a method for determining users in a residential area according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a method for determining whether a residential user is a target user according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a big data architecture for determining a user representation and a user orientation according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating the determination of a location tag representation for an entire user in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of a big data-based home-wide target user identification apparatus according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a big data-based home wide target user identification method and device and electronic equipment. The method is perfect in mining the judgment rule of the home wide potential user, and comprehensively analyzes whether the user is the home wide potential user or not from data such as user distribution, user service activity, user 4G wireless network perception, Media Access Control (MAC) address matching, resource management data matching and the like.
As shown in fig. 1, a schematic flow chart of a big data based home wide target user identification method according to an embodiment of the present invention is provided. As shown in the figure, the big data based home wide target user identification method may include: the contents of step S101 and step S102.
In step S101, residential area users are determined from the acquired user OTT data and MDT data.
In The embodiment of The present invention, according to The obtained OTT (Over The Top, various application services are provided to The user through The internet) data and MDT (Minimization of drive tests) data of The user, The MRO data can collect latitude and longitude information of a Global Positioning System (GPS) terminal, and The accuracy of an MR (Measurement Report, LTE Measurement Report data) Positioning fingerprint library can reach within 50m, so that The address of The user can be accurately positioned, and The residential area user therein can be screened.
In step S102, it is determined whether the residential users are target users according to the traffic usage of the residential users.
In the embodiment of the invention, the behavior of the residential area user is analyzed, so that the probability that the residential area user goes to work in the daytime or surfs the Internet outdoors is higher, and the residential area user habitually surfs the Internet by using WI-FI if a broadband exists at home after going home at night, therefore, whether the residential area user is a target user can be judged according to the traffic use condition of the residential area user.
According to the embodiment of the invention, the residential area users are determined according to the acquired OTT data and MDT data of the users, and then whether the residential area users are target users or not is judged according to the flow use condition of the residential area users. The embodiment of the invention accurately judges the position information of the user through the acquired user data so as to determine the residential area user, and then determines whether the residential area user is a target user according to the flow use condition of the residential area user. The OTT data and the MDT data of the user can be used for accurately positioning the position of the user and the home-wide MAC address, and the target user is determined according to the traffic use condition and the home-wide MAC address. Compared with the traditional technologies such as triangulation positioning, simulation and the like in the prior art, the method is higher in positioning accuracy, more comprehensive in judgment than the method only counting data in the S1-MME signaling aspect, and more accurate in determined target user.
In one possible implementation of the present invention, as shown in fig. 2, a flow chart of a method for determining users in residential areas according to an embodiment of the present invention is shown. As shown in the figure, determining residential area users according to the obtained OTT data and MDT data of the users specifically includes: step S201 to step S204.
In step S201, the obtained OTT data and MDT data of the user are filtered based on a first preset time.
In the embodiment of the invention, the positioning data of the OTT data, the MDT data and the like of the discrete users are filtered from time, generally, the work and rest time of the users from Monday to Friday is fixed, and the users return to home at night during working in the daytime, so that the MR positioning data of 19-23 o' clock at night are filtered.
The first preset time may be 19 to 23 points described in this embodiment, or may be other time periods, and the embodiment of the present invention is not specifically limited.
The data are filtered in time, so that the data are more accurate, and the obtained residential area users are more accurate.
In step S202, the filtered OTT data and MDT data of the user are rasterized to obtain GIS data of the user grid.
In the embodiment of the invention, the filtered user positioning data is subjected to rasterization processing, the user positioning data is gathered and counted according to grid longitude of 50 x 50, and the positioning longitude and latitude are unified into grid center longitude and latitude to obtain user grid GIS (Geographic Information System) data.
The rasterization process may be performed according to the grid longitude of 50 × 50, or may be performed by using another grid, which is not specifically limited in the embodiment of the present invention.
In step S203, intersection operation is performed on the user grid GIS data and the residential area frame GIS data to obtain user position data.
In the embodiment of the invention, intersection operation is carried out on the obtained user grid GIS data and the residential area frame GIS data crawled on the internet in GIS software to obtain the user position data.
In step S204, the residential users are determined based on the user position data.
In the embodiment of the invention, which users are residential area users are determined according to the obtained user position data.
In the embodiment of the invention, the user position data is filtered in time to obtain more accurate user data, then the filtered data is subjected to grid processing to determine the grid GIS data of the user, and intersection operation is performed on the grid GIS data and the frame GIS data of the residential area to determine which users are residential area users. The grid processing is carried out after the filtering, so that a large amount of processed data is reduced, the operand is further reduced, the intersection operation is carried out, the operand is further reduced, and the calculation accuracy is improved.
In a possible implementation manner of the present invention, as shown in fig. 3, a flowchart of a method for determining whether a residential user is a target user is provided according to an embodiment of the present invention. As shown in the figure, according to the flow usage of the residential area users, whether the residential area users are target users is judged, which specifically includes: step S301 to step S313.
In step 301, the flow usage of the residential area users within a first preset time is counted.
In the embodiment of the invention, the probability that the user uses the mobile phone traffic to surf the internet in the daytime or outdoors is higher according to the user behavior analysis, and the user generally uses the Wi-Fi to surf the internet habitually if the broadband exists at home after returning home at night, so that the traffic using condition of 19-23 pm of the user in a residential area is counted during data processing.
The first preset time may be the time period, or may be other time periods, and may be set according to specific situations, which is not specifically limited herein.
In step S302, it is determined whether the flow usage within the first preset time is greater than a preset flow value.
In the embodiment of the present invention, the counted flow value in the first preset time is compared with a preset flow value, and whether the flow usage in the first preset time is greater than the preset flow value is determined, if so, the steps from step S303 to step S306 are performed, and if not, the steps from step S307 to step S313 are performed.
In step S303, it is determined whether the residential users are high-traffic users.
In the embodiment of the invention, if the flow usage of the residential area user in the first preset time is judged to be larger than the preset flow value, whether the residential area user is a high-flow user is further judged. If the residential user is determined to be a high-traffic user, step S304 is executed. Otherwise, judging the residential area user as a non-target user.
In step S304, it is determined whether the residential user is handling the home broadband.
In the embodiment of the invention, if the flow usage of the residential area user in the time period is higher than the preset flow value and the residential area user is a high-flow user, the residential area user may not handle the household broadband. Furthermore, through the analysis of the OTT data, the Wi-Fi information carried in the APP software with the joint positioning can be extracted, in order to accurately judge whether the Wi-Fi broadband exists in the home of the user, the MAC address and the level information list of the Wi-Fi need to be extracted from the OTT analysis data and are independently used as a user Wi-Fi information table for storage, and whether the home broadband is handled by the user in the residential area is further judged. If it is determined that the residential user is not transacted, step S305 is executed. If the transaction of the broadband is determined, step S309 is executed.
In step S305, it is determined whether the residential area user is a no-restriction package user.
In the embodiment of the invention, if the residential area user is a high-flow user and does not handle the household broadband, whether the user handles the unlimited flow package needs to be further judged. If the user has not transacted the unlimited flow package, go to step S306. Otherwise, judging the residential area user as a non-target user.
In step S306, it is determined that the residential user is the target user.
In the embodiment of the invention, if the residential area user is a high-flow user, and a household broadband is not installed, or the current-limiting package user is not installed, the residential area user can be judged as the target user. Because the user has more traffic usage and is a residential area user, the user can be recommended to handle the household broadband, and the residential area user can be used as a target user.
In step S307, it is determined whether the OTT data includes the MAC address of the residential area user WIFI.
In the embodiment of the present invention, if it is determined that the flow usage amount of the residential user within the first preset time is smaller than the preset flow value, it is determined whether the residential user installs the home broadband or has a small flow usage amount. Therefore, it is necessary to determine whether the OTT data includes the MAC address of the WIFI of the residential area user. That is, it is determined whether the user installs the home broadband. If the OTT data includes the MAC address of the residential area user WIFI, step S308 to step S313 are executed. If the OTT data does not include the MAC address of the residential area user WIFI, step S312 to step S313 are executed.
In step S308, it is determined whether the MAC address of the residential user WIFI is in the MAC addresses registered for the mobile broadband.
In the embodiment of the invention, if the OTT data contains the MAC address of the residential area user WIFI, whether the MAC address of the residential area user WIFI is in the MAC address registered by the mobile broadband is further judged. If the MAC address is registered in the mobile broadband, the residential area user is indicated to have the home broadband installed. Step S309 is performed.
In step S309, it is determined whether the residential user is an own customer.
In the embodiment of the present invention, if it is determined that the user has installed the home broadband, it is further determined whether the residential user is a customer of the residential user. Specifically, hardware device MAC address libraries such as optical modems and wireless WIFI issued by an operator are cross-compared through MAC addresses reported in user OTT data, and whether a user is a customer of the user is judged. If it is determined that the residential user is not a customer, step S310 is performed. Otherwise, judging the residential area user as a non-target user.
In step S310, a level value in the OTT data is extracted.
In step S311, it is determined whether the level value exceeds a preset level value.
In the embodiment of the present invention, when it is determined that the residential area user is not a client of the residential area user, in order to further determine whether the experience of the residential area user using the home broadband is good, a level value of the home broadband may be extracted from OTT data, and the level value is compared with a preset level value, where the preset level value is a level value when the user experience is good, and is not limited herein. If the level value does not exceed the preset level value, it indicates that the home broadband does not provide a good user experience, then step S312 is executed. Otherwise, judging the residential area user as a non-target user.
In step S312, it is determined whether the duration for using the preset application program in the residential area exceeds a preset duration.
In the embodiment of the invention, when the user experience is not good, whether the duration of the residential area user using the preset application program exceeds the preset duration is further judged. That is, it is further determined whether the residential users often use the application program with a large amount of consumed traffic. If yes, go to step S313. Otherwise, judging the residential area user as a non-target user.
In step S313, it is determined that the residential user is a target user.
According to the embodiment of the application, the residential area user is determined to be a large-flow user by judging the flow, then whether a household broadband is installed or whether a non-flow-limiting package user is handled is judged, and if not, the residential area user is judged to be a target user. If the residential area user installs the household broadband, whether the residential area user is a client of the residential area user is further judged, if not, the level value of the household broadband is compared with a preset level value, if the level value of the household broadband does not exceed the preset level value, the experience of the residential area user is not good, whether the user is a user who often uses high-flow application programs is judged, and if yes, the residential area user is judged to be a target user. And judging from multiple dimensions, determining whether the residential area user is a traffic active user, whether the home broadband is installed, which operator's home broadband is installed and other information. Whether the residential area user is the target user or not can be judged according to the information, so that the finally judged user is more accurate, and the accuracy is improved.
In one possible embodiment of the present invention, the big-data-based home-wide target user identification method may further include the following steps:
judging whether the data download rate of residential area users is lower than a preset download rate or whether the HTTP delay of the residential area users is larger than a preset delay value;
and if the data download rate of the residential area user is lower than the preset download rate and/or the HTTP delay is larger than the preset delay value, judging that the residential area user is the target user.
In the embodiment of the invention, by judging the data download rate and the HTTP (hyper text transfer protocol) delay of the residential area user, if the data download rate is lower than the preset download rate and/or the HTTP delay is larger than the preset delay rate, the network speed of the residential area user is slow, and the residential area user can be used as a target user.
In an embodiment of the present invention, the home-wide target user identification method based on big data may further include the following steps:
counting the application programs of which the use duration exceeds the preset duration within the second preset time of the residential area users;
and taking the application program with the use time length exceeding the preset time length as the preset application program.
In the embodiment of the present invention, the common application programs of the residential users from 7 am to 18 pm are counted, and these application programs may be used as the preset application programs described in the above embodiment.
That is, an application frequently used by a residential user other than at home can be used as an application frequently used by the residential user at home.
In one embodiment of the present invention, as shown in FIG. 4, a schematic diagram of a big data architecture for determining a user representation and a user location is provided according to an embodiment of the present invention. The user portrait refers to the fact that various internet access behaviors, positioning information, traffic use habits, wireless network perception, home-wide MAC information and the like of the user are subjected to data tagging processing, and all concerned users can be screened out in a targeted mode through tagged data. The following is a representation implementation process for a potential home-wide user.
User location tag portrait:
at present, the OTT and MR based accurate positioning fingerprint algorithm technology is very mature, particularly the application of the MDT technology enables the terminal GPS longitude and latitude information to be collected in MRO data, the accuracy of an MR positioning fingerprint library reaches within 50m, and accurate position label portrait can be realized for a single user. Since the residence position of the user needs to be labeled to realize accurate marketing of the home-wide user, the discrete positioning data of the user needs to be processed, and finally the residence area of the user is identified instead of the office or consumer entertainment place where the user resides.
The determination of the whole user position label portrait is shown in fig. 5, firstly discrete user positioning data is filtered from time, generally, the work and rest time of the user from week 1 to week 5 is fixed, the user gets back to home at night during working in daytime, so that MR positioning data of 19-23 points of night of the user is filtered out, the positioning data of the user is subjected to rasterization processing, the user positioning data is converged and counted according to grid longitude of 50 x 50, the positioning longitude and latitude are unified into grid center longitude and latitude, then intersection operation is carried out on residential area frame data crawled from the internet in GIS software, the name of the residential area after operation is updated to the position label of the user, and the position label portrait process of the user is completed.
User traffic usage habit profiles:
according to the analysis of user behaviors, the probability that a user uses mobile phone traffic to surf the internet in the daytime or outdoors is high, and if a broadband exists at home after the user goes home at night, the user can habitually surf the internet by using Wi-Fi, so that traffic statistics and APP (Application) preference, internet surfing rate and HTTP (hyper text transport protocol) delay information extraction are respectively carried out according to two time periods of daytime and night in a data preprocessing stage, and preparation of basic data is made for the next potential user to mine.
The daytime flow counting time interval is from 7 am to 18 pm, and the evening flow counting time interval is from 19 pm to 23 pm and from 0 to 6 pm. And respectively adding label data of daytime flow and night flow by taking the user as statistical granularity.
In addition, the APP preference tag is also used as a tag for potential evaluation of the broadband user, and the APP preference user of video, music and games has higher requirements on the network download rate, so that the APP preference tag is also a condition for evaluating whether the user has broadband requirements.
User wireless network perception portrait:
the user wireless network perception is to perform labeling processing on wireless indexes such as level, quality, interference and the like according to users, time intervals and grids aiming at massive MROs.
Original MRO data does not have user mobile phone number information, user number correlation backfill processing is firstly needed to be carried out on S1-MME data, then MR positioning and rasterization processing are carried out, wireless network perception KPI data labeling statistical processing such as MR coverage rate, average RSRP and the like is finally carried out according to users, time periods and grids, and reference data are provided for next-step user mining.
Extracting WI-FI information of a user:
through the analysis of OTT data, Wi-Fi information carried in APP software with combined positioning can be extracted, in order to accurately judge whether a Wi-Fi broadband exists in a user home, an MAC address and a level information list of Wi-Fi need to be extracted from OTT analysis data and independently used as a Wi-Fi information list of the user to be stored for cleaning the existing broadband user in the next step. Table 1 shows json format data samples analyzed from OTT data, and the last action is list data of the MAC address and level signal strength of Wi-Fi.
TABLE 1
The process of mining the latent broad band users is a process of screening and cleaning label data after user portrait, and the label data after user portrait is used for gradually screening and cleaning users in the whole network, and finally whether the user broad band is popularized is judged.
The non-local users are cleaned firstly in the whole cleaning process, then the users with internet surfing habits and with flow reaching a certain threshold are screened out, on the basis, the users in the non-residential area are cleaned according to the user position tags, and the reserved users are all high-flow users located in the residential area.
And then, the possibility of whether the home width exists in the user is distinguished according to whether the user generates flow or has high flow at night, the user in the divided data is used for further cleaning and filtering according to whether the user transacts the broadband and unlimited package label information, and the reserved user group is the user group which preferentially carries out home width service promotion.
In addition, considering that the user traffic is suppressed due to the fact that wireless environment problems possibly exist in the user home, video, music and game APP prone users are selected, the wireless network environment, the downloading rate, the time delay and the like of the users at night are judged and analyzed, and the user group with poor wireless environment or suppressed traffic is used as a general home wide service promotion user group.
The scheme is used for mining the perfect judgment rule of the home-wide potential user, and comprehensively analyzing whether the user is the home-wide potential user or not from data such as user distribution, user service activity, user 4G wireless network perception, MAC address matching, resource management data matching and the like.
The embodiment of the invention also provides a home wide target user identification device based on the big data. Fig. 6 is a schematic diagram of a home-wide target user identification device based on big data according to an embodiment of the present invention. The device includes: a determination module 601 and a judgment module 602.
The determination module 601 is configured to determine residential users based on the obtained OTT data and MDT data of the users.
The determination module 602 is configured to determine whether a residential user is a target user according to the traffic usage of the residential user.
In the embodiment of the present invention, the determining module 601 first determines residential area users according to the obtained OTT data and MDT data of the users, and then the determining module 602 determines whether the residential area users are target users according to the traffic usage of the residential area users. The embodiment of the invention accurately judges the position information of the user through the acquired user data so as to determine the residential area user, and then determines whether the residential area user is a target user according to the flow use condition of the residential area user. The OTT data and the MDT data of the user can be used for accurately positioning the position of the user and the home-wide MAC address, and the target user is determined according to the traffic use condition and the home-wide MAC address. Compared with the traditional technologies such as triangulation positioning, simulation and the like in the prior art, the method is higher in positioning accuracy, more comprehensive in judgment than the method only counting data in the S1-MME signaling aspect, and more accurate in determined target user.
Preferably, the determining module 601 is specifically configured to:
filtering the obtained OTT data and MDT data of the user based on first preset time;
rasterizing the filtered OTT data and MDT data of the user to obtain grid GIS data of the user;
performing intersection operation on the user grid GIS data and the residential area frame GIS data to obtain user position data;
and determining users in residential areas according to the user position data.
Preferably, the determining module 602 is specifically configured to:
counting the flow usage amount of the residential area users within a first preset time;
judging whether the flow usage in the first preset time is larger than a preset flow value or not;
if the flow usage in the first preset time is larger than a preset flow value, judging whether the residential area user is a high-flow user;
if the residential area user is a high-flow user, judging whether the residential area user handles the household broadband or not;
if the residential area user does not handle the household broadband, judging whether the residential area user is a current-quantity-unlimited package user or not;
and if the residential area user is not the current-limiting quantity package user, judging that the residential area user is the target user.
Preferably, the determining module 602 may further be configured to:
if the traffic usage amount in the first preset time is smaller than a preset traffic value, judging whether the OTT data contains the MAC address of the residential area user WIFI;
if the OTT data contains the MAC address of the residential area user WIFI, judging whether the MAC address of the residential area user WIFI is in the MAC address registered by the mobile broadband;
if the MAC address of the residential area user WIFI is in the MAC address registered by the mobile broadband, judging whether the residential area user is a client of the residential area user;
if the residential area user is not the client of the residential area user, extracting a level value in the OTT data;
judging whether the level value exceeds a preset level value or not;
if the level value does not exceed the preset level value, judging whether the duration of the residential area user using a preset application program exceeds a preset duration;
and if the duration of the residential area user using the preset application program exceeds the preset duration, judging the residential area user to be a target user.
Preferably, the determining module 602 may further be configured to:
if the OTT data does not contain the MAC address of the WIFI of the residential area user, judging whether the duration of the residential area user using a preset application program exceeds a preset duration or not;
and if the time length of the user using the preset application program exceeds the preset time length, judging that the residential area user is the target user.
Preferably, the determining module 602 may further be configured to:
judging whether the data download rate of the residential area users is lower than a preset download rate or whether the HTTP delay of the residential area users is larger than a preset delay value;
and if the data download rate of the residential area user is lower than a preset download rate and/or the HTTP delay is larger than a preset delay value, judging that the residential area user is a target user.
Preferably, the determining module 602 may further be configured to:
counting the application programs of which the use duration exceeds the preset duration within the second preset time of the residential area users;
and taking the application program with the use time length exceeding the preset time length as a preset application program.
The functions of the device for implementing home wide target user identification based on big data according to the present invention have been described in detail in the method embodiments shown in fig. 1 to 5, so that the description of this embodiment is not detailed, and reference may be made to the related description in the foregoing embodiments, and further description is omitted here.
Fig. 7 is a schematic diagram of a hardware structure of a terminal device for implementing various embodiments of the present invention.
The terminal device 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, a processor 710, a power supply 711, and the like. Those skilled in the art will appreciate that the terminal device configuration shown in fig. 7 does not constitute a limitation of the terminal device, and that the terminal device may include more or fewer components than shown, or combine certain components, or a different arrangement of components. In the embodiment of the present invention, the terminal device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable device, a pedometer, and the like.
Wherein, the processor 710 may be configured to:
determining residential area users according to the acquired OTT data and MDT data of the users;
and judging whether the residential area user is a target user or not according to the flow use condition of the residential area user.
In the embodiment of the invention, the residential area users are determined according to the acquired OTT data and MDT data of the users, and then whether the residential area users are target users or not is judged according to the flow use condition of the residential area users. The embodiment of the invention accurately judges the position information of the user through the acquired user data so as to determine the residential area user, and then determines whether the residential area user is a target user according to the flow use condition of the residential area user. The OTT data and the MDT data of the user can be used for accurately positioning the position of the user and the home-wide MAC address, and the target user is determined according to the traffic use condition and the home-wide MAC address. Compared with the traditional technologies such as triangulation positioning, simulation and the like in the prior art, the method is higher in positioning accuracy, more comprehensive in judgment than the method only counting data in the S1-MME signaling aspect, and more accurate in determined target user.
It should be understood that, in the embodiment of the present invention, the radio frequency unit 701 may be used for receiving and sending signals during a message transmission and reception process or a call process, and specifically, receives downlink data from a base station and then processes the received downlink data to the processor 710; in addition, the uplink data is transmitted to the base station. In general, radio frequency unit 701 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 701 may also communicate with a network and other devices through a wireless communication system.
The terminal device provides the user with wireless broadband internet access through the network module 702, such as helping the user send and receive e-mails, browse webpages, access streaming media, and the like.
The audio output unit 703 may convert audio data received by the radio frequency unit 701 or the network module 702 or stored in the memory 709 into an audio signal and output as sound. Also, the audio output unit 703 may also provide audio output related to a specific function performed by the terminal device 700 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 703 includes a speaker, a buzzer, a receiver, and the like.
The input unit 704 is used to receive audio or video signals. The input Unit 704 may include a Graphics Processing Unit (GPU) 7041 and a microphone 7042, and the Graphics processor 7041 processes image data of a still picture or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 706. The image frames processed by the graphic processor 7041 may be stored in the memory 709 (or other storage medium) or transmitted via the radio unit 701 or the network module 702. The microphone 7042 may receive sounds and may be capable of processing such sounds into audio data. The processed audio data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 701 in case of a phone call mode.
The terminal device 700 further comprises at least one sensor 705, such as light sensors, motion sensors and other sensors. Specifically, the light sensor includes an ambient light sensor that adjusts the luminance of the display panel 7061 according to the brightness of ambient light, and a proximity sensor that turns off the display panel 7061 and/or a backlight when the terminal device 700 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used to identify the terminal device posture (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration identification related functions (such as pedometer, tapping), and the like; the sensors 705 may also include fingerprint sensors, pressure sensors, iris sensors, molecular sensors, gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which are not described in detail herein.
The display unit 706 is used to display information input by the user or information provided to the user. The Display unit 706 may include a Display panel 7061, and the Display panel 7061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 707 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the terminal device. Specifically, the user input unit 707 includes a touch panel 7071 and other input devices 7072. The touch panel 7071, also referred to as a touch screen, may collect touch operations by a user on or near the touch panel 7071 (e.g., operations by a user on or near the touch panel 7071 using a finger, a stylus, or any other suitable object or attachment). The touch panel 7071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 710, receives a command from the processor 710, and executes the command. In addition, the touch panel 7071 can be implemented by various types such as resistive, capacitive, infrared, and surface acoustic wave. The user input unit 707 may include other input devices 7072 in addition to the touch panel 7071. In particular, the other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described herein again.
Further, the touch panel 7071 may be overlaid on the display panel 7061, and when the touch panel 7071 detects a touch operation on or near the touch panel 7071, the touch operation is transmitted to the processor 710 to determine the type of the touch event, and then the processor 710 provides a corresponding visual output on the display panel 7061 according to the type of the touch event. Although in fig. 7, the touch panel 7071 and the display panel 7061 are implemented as two independent components to implement the input and output functions of the terminal device, in some embodiments, the touch panel 7071 and the display panel 7061 may be integrated to implement the input and output functions of the terminal device, which is not limited herein.
The interface unit 708 is an interface for connecting an external device to the terminal apparatus 700. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 708 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the terminal apparatus 700 or may be used to transmit data between the terminal apparatus 700 and the external device.
The memory 709 may be used to store software programs as well as various data. The memory 709 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 709 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 710 is a control center of the terminal device, connects various parts of the entire terminal device by using various interfaces and lines, and performs various functions of the terminal device and processes data by running or executing software programs and/or modules stored in the memory 709 and calling data stored in the memory 709, thereby performing overall monitoring of the terminal device. Processor 710 may include one or more processing units; preferably, the processor 710 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 710.
The terminal device 700 may further include a power supply 711 (e.g., a battery) for supplying power to various components, and preferably, the power supply 711 may be logically connected to the processor 710 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system.
In addition, the terminal device 700 includes some functional modules that are not shown, and are not described in detail herein.
Preferably, an embodiment of the present invention further provides a terminal device, including a processor 710, a memory 709, and a computer program stored in the memory 709 and capable of running on the processor 710, where the computer program is executed by the processor 710 to implement each process of the implementation method embodiment for home wide target user identification based on big data, and can achieve the same technical effect, and in order to avoid repetition, the detailed description is omitted here.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the foregoing home wide target user identification method embodiment based on big data, and can achieve the same technical effect, and is not described herein again to avoid repetition. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A home wide target user identification method based on big data is characterized by comprising the following steps:
determining residential area users according to the acquired OTT data and MDT data of the users;
and judging whether the residential area user is a target user or not according to the flow use condition of the residential area user.
2. The method according to claim 1, wherein the determining residential subscribers based on the obtained OTT data and MDT data of the subscribers comprises:
filtering the obtained OTT data and MDT data of the user based on first preset time;
rasterizing the filtered OTT data and MDT data of the user to obtain grid GIS data of the user;
performing intersection operation on the user grid GIS data and the residential area frame GIS data to obtain user position data;
and determining users in residential areas according to the user position data.
3. The method according to claim 1, wherein said determining whether a residential user is a target user based on traffic usage of the residential user comprises:
counting the flow usage amount of the residential area users within a first preset time;
judging whether the flow usage in the first preset time is larger than a preset flow value or not;
if the flow usage in the first preset time is larger than a preset flow value, judging whether the residential area user is a high-flow user;
if the residential area user is a high-flow user, judging whether the residential area user handles the household broadband or not;
if the residential area user does not handle the household broadband, judging whether the residential area user is a current-quantity-unlimited package user or not;
and if the residential area user is not the current-limiting quantity package user, judging that the residential area user is the target user.
4. The method of claim 3, further comprising:
if the traffic usage amount in the first preset time is smaller than a preset traffic value, judging whether the OTT data contains the MAC address of the residential area user WIFI;
if the OTT data contains the MAC address of the residential area user WIFI, judging whether the MAC address of the residential area user WIFI is in the MAC address registered by the mobile broadband;
if the MAC address of the residential area user WIFI is in the MAC address registered by the mobile broadband, judging whether the residential area user is a client of the residential area user;
if the residential area user is not the client of the residential area user, extracting a level value in the OTT data;
judging whether the level value exceeds a preset level value or not;
if the level value does not exceed the preset level value, judging whether the duration of the residential area user using a preset application program exceeds a preset duration;
and if the duration of the residential area user using the preset application program exceeds the preset duration, judging the residential area user to be a target user.
5. The method of claim 4, further comprising:
if the OTT data does not contain the MAC address of the WIFI of the residential area user, judging whether the duration of the residential area user using a preset application program exceeds a preset duration or not;
and if the time length of the user using the preset application program exceeds the preset time length, judging that the residential area user is the target user.
6. The method according to claim 4 or 5, characterized in that the method further comprises:
judging whether the data download rate of the residential area users is lower than a preset download rate or whether the HTTP delay of the residential area users is larger than a preset delay value;
and if the data download rate of the residential area user is lower than a preset download rate and/or the HTTP delay is larger than a preset delay value, judging that the residential area user is a target user.
7. The method according to claim 4 or 5, wherein before determining whether the duration of use of a preset application by the residential user exceeds a preset duration, the method further comprises:
counting the application programs of which the use duration exceeds the preset duration within the second preset time of the residential area users;
and taking the application program with the use time length exceeding the preset time length as a preset application program.
8. A big data-based home-wide target user identification device, comprising:
the determining module is used for determining residential area users according to the acquired OTT data and MDT data of the users;
and the judging module is used for judging whether the residential area user is a target user according to the flow use condition of the residential area user.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising: the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Application publication date: 20210709 |