CN113486055B - Data processing system for determining public wifi category - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/10—Small scale networks; Flat hierarchical networks
- H04W84/12—WLAN [Wireless Local Area Networks]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to a data processing system for determining a public wifi category, which is used for obtaining a public wifi list in a preset time period from a database in a step S1, wherein the public wifi list is obtained by the step S1 m Represents the mth public wifi; step S2, obtaining wifi m Corresponding target app candidate set, app mn Representing an nth target candidate app corresponding to an mth public wifi; step S3, acquiring the app from the database in the preset time period, and installing the app in the total data mn Is the total number of devices C mn And wifi m Connected installation app mn D of the total number of devices of (a) mn Determining app mn Relative to wifi m First characteristic ratio A mn ，A mn ＝D mn /C mn Determination of A mn App corresponding to the maximum value of (a); s4, obtaining wifi from the database in a preset time period, and connecting wifi in the total data m Is the total number of equipment E m Determining app mn Relative to wifi m Second characteristic ratio G of (2) mn ＝D mn /E m Determining G mn Corresponds to app; step S5, judging A based on app category mapping table mn And G mn If the maximum value of the corresponding app belongs to the same category, determining the category as wifi m Is a category of (2). The method and the device can accurately determine the category of the public wifi.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a data processing system for determining public wifi categories.
Background
With the rapid development of information technology, public wifi coverage is wider and wider, and in many data analysis scenes, data analysis needs to be performed based on public wifi categories, and then the categories of the public wifi, such as hospital wifi, market wifi, high-speed rail wifi and the like, need to be determined first. The prior art establishes classification model based on characteristics such as public wifi's name usually, discerns public wifi class, because some public wifi probably not names according to other public wifi's naming rule, can lead to unable accurate discernment public wifi class, in addition, if public wifi renames, perhaps along with the joining of new public wifi, can make the accuracy of classification model reduce. Therefore, how to improve the accuracy of determining the public wifi is a technical problem to be solved.
Disclosure of Invention
The invention aims to provide a data processing system for determining the category of public wifi, which can accurately determine the category of public wifi.
According to an aspect of the present invention, there is provided a data processing system for determining a public wifi category, including a database, an app category mapping table, a processor, and a memory storing a computer program, the database being configured to store a device report record, the device report record including a device id, app feature information, wifi information, and time information, the app feature information including app installation information and/or app activity information; the app category mapping table is used for storing a mapping relation between app ids and app categories, and when the processor executes the computer program, the following steps are implemented:
step S1, obtaining a public wifi list { wifi ] within a preset time period from the database 1 ,wifi 2 ,...wifi M (Wifi) m Represents the M-th public wifi, and the value of M is 1 to M;
step S2, obtaining wifi m Corresponding target app candidate set { app m1 ,app m2 ,...app mN }, wherein app mn The method comprises the steps of representing an nth target candidate app corresponding to an mth public wifi, wherein the value of N is 1 to N, and the target candidate app refers to a non-head app with app features meeting the requirement of preset saliency for the public wifi;
step S3, acquiring the app installed in the total data in the preset time period from the database mn Is the total number of devices C mn and wifim Connected installation app mn D of the total number of devices of (a) mn Based on C mn and Dmn Determining apps mn Relative to wifi m First characteristic ratio A mn ，A mn ＝D mn /C mn All A are mn Sequencing, determining A mn App corresponding to the maximum value of (a);
s4, obtaining wifi from the database in the preset time period, wherein wifi is connected to the total data m Is the total number of equipment E m Based on E m and Dmn Determining apps mn Relative to wifi m Second characteristic ratio G of (2) mn ＝D mn /E m All G' s mn Sequencing, determining G mn Corresponds to app;
step S5, judging the A based on the app category mapping table mn App and G corresponding to maximum values of (2) mn If the maximum value of the corresponding app belongs to the same category, determining the category as wifi m Is a category of (2).
Compared with the prior art, the invention has obvious advantages and beneficial effects. By means of the technical scheme, the data processing system for determining the public wifi category provided by the invention can achieve quite technical progress and practicality, has wide industrial utilization value, and has at least the following advantages:
according to the method and the device for determining the class of the public wifi, based on the app features of the equipment connected with the wifi, the first feature occupation ratio and the second feature occupation ratio of the app relative to the wifi can be established and collided, the class of the public wifi is determined, and the accuracy of determining the class of the public wifi is improved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention, as well as the preferred embodiments thereof, together with the following detailed description of the invention, given by way of illustration only, together with the accompanying drawings.
Drawings
Fig. 1 is a schematic diagram of a data processing system for determining a public wifi category according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purposes, the following detailed description refers to a specific implementation of a data processing system for determining a public wifi class and its effects according to the present invention with reference to the accompanying drawings and preferred embodiments.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The embodiment of the invention provides a data processing system for determining a public wifi category, which is shown in fig. 1 and comprises a database, an app (Application) category mapping table, a processor and a memory storing a computer program, wherein the database is used for storing a device report record, the device report record comprises a device id, app characteristic information, wifi information and time information, and the app characteristic information comprises app installation information and/or app activity information. The app category mapping table is used for storing a mapping relation between app ids and app categories, and when the processor executes the computer program, the following steps are implemented:
step S1, obtaining a public wifi list { wifi ] within a preset time period from the database 1 ,wifi 2 ,...wifi M (Wifi) m Representing the M-th public wifi, wherein the value of M is 1 to M, and M is a positive integer;
as an embodiment, the step S1 may include:
step S11, traversing the database, obtaining one or more of the number of each wifi connection device, the frequency of connection devices and the frequency of connection of a single device in the preset time period, weighting and summing to obtain a first reference value, determining wifi with the first reference value larger than a threshold value from a reference value threshold value as candidate wifi, and constructing a wifi candidate list;
it can be understood that other parameters capable of identifying public wifi can be added to judge according to specific application requirements.
Step S12, wifi in the wifi candidate list can be de-duplicated, invalid wifi and wifi with the number of connected devices exceeding a preset connection number threshold in the preset time period are removed, and the public wifi list { wifi ] is obtained 1 ,wifi 2 ,...wifi M }。
It should be noted that, how to determine whether wifi is invalid wifi directly adopts the existing algorithm, and description is not repeated here. Noise wifi can be removed through step S12, interference of noise wifi is avoided, calculated amount is reduced, and accuracy and efficiency of public wifi recognition are improved.
As a preferred embodiment, the preset time period is one week or one month, but it is understood that the length of the preset time period may be adjusted according to the specific application requirements.
Step S2, obtaining wifi m Corresponding target app candidate set { app m1 ,app m2 ,...app mN }, wherein app mn The method comprises the steps of representing an nth target candidate app corresponding to an mth public wifi, wherein the value of N is 1 to N, and N is a positive integer, and the target candidate app refers to a non-head app with app features meeting the preset significance requirement for the public wifi significance;
if steps S3 and S4 are performed directly based on global data in the database, since the amount of data is huge and there are many apps with little impact on different wifi, and some head apps increase a large amount of useless calculation and affect the accuracy of the judgment result, before steps S3 and S4 are performed, the target app candidate set is determined, so as to improve the calculation efficiency and accuracy, as an embodiment, the step S2 may include:
step S21, obtaining from the databaseWifi is taken m All non-header app lists { app } 'corresponding to the connected devices within the preset time period' m1 ,app' m2 ,...app' mX }, wherein app' mx Representing wifi m The corresponding X-th non-header, wherein the value range of X is 1 to X, and the non-header app refers to an app with an installation amount smaller than a preset installation threshold value in the preset time period;
as an example, the preset installation threshold may take a value of 300 ten thousand, and it may be understood that the preset installation threshold may be adjusted according to specific application requirements.
Step S22, installing app 'in the database total data' mx Is determined as a first target sample, randomly extracts the same number of device ids as the first target sample from the database full data as a first natural sample, and determines app 'based on the first target sample and the first natural sample' mx Is of the first significance H' mx ：
wherein ,representing app 'installed in the first target sample' mx The ratio of the number to the number of all apps installed in the first target sample, +.>Representing app 'installed in the first natural sample' mx The ratio of the number to the number of all apps installed in the first natural sample will be H' mx Greater than a preset first saliency threshold app' mx Determining as a first app candidate set;
step S23, connecting wifi with the database m Selecting and installing app 'in the device of (1)' mx Is determined as a second target sample, and wifi is connected to the database m Randomly extracting the same device id as the second target sample number as the device of (a)A second natural sample, app 'being determined based on the second target sample and the second natural sample' mx Is of the second significance F m ' x ：
wherein ,representing an app 'installed in the second target sample' mx The ratio of the number to the number of all apps installed in the second target sample, +.>Representing app 'installed in the second natural sample' mx The ratio of the number to the number of all apps installed in the second natural sample, F m ' x Greater than a preset first saliency threshold app' mx Determining as a second app candidate set;
step S23, determining the intersection or union of the first app candidate set and the second app candidate set as the target app candidate set { app } m1 ,app m2 ,...app mN }。
It should be noted that, if the intersection of the first app candidate set and the second app candidate set is determined as the target app candidate set { app } m1 ,app m2 ,...app mN And the method can accurately calculate, the selected dimensions are apps with high-frequency similarity between the global sample and the target sample, but the saturation of the features is low, and candidate apps may not exist between partial wifi, and the apps have no similarity if there is no intersection. However, taking the union may quantify this lack more finely, the saturation may be higher, but the accuracy relative to taking the intersection may be lower, so the intersection or union of the first app candidate set and the second app candidate set may be determined as the target app candidate set { app }, depending on the specific application requirements m1 ,app m2 ,...app mN }。
S3, acquiring the preset time from the databaseInstalling apps in full data within a segment mn Is the total number of devices C mn and wifim Connected installation app mn D of the total number of devices of (a) mn Based on C mn and Dmn Determining apps mn Relative to wifi m First characteristic ratio A mn ，A mn ＝D mn /C mn All A are mn Sequencing, determining A mn App corresponding to the maximum value of (a);
s4, obtaining wifi from the database in the preset time period, wherein wifi is connected to the total data m Is the total number of equipment E m Based on E m and Dmn Determining apps mn Relative to wifi m Second characteristic ratio G of (2) mn ＝D mn /E m All G' s mn Sequencing, determining G mn Corresponds to app;
step S5, judging the A based on the app category mapping table mn App and G corresponding to maximum values of (2) mn If the maximum value of the corresponding app belongs to the same category, determining the category as wifi m Is a category of (2).
According to the embodiment of the invention, based on the app features of the wifi-connected device, the first feature occupation ratio and the second feature occupation ratio of the app relative to the wifi can be established and collided, the category of the public wifi is determined, and the accuracy of determining the category of the public wifi is improved.
According to the invention, the system may be physically implemented as one server, or may be implemented as a server group including a plurality of servers; the device may be physically implemented as a mobile device capable of installing an application (e.g., app) such as a smart phone, PAD, or the like. Those skilled in the art will recognize that the parameters such as the model number, specification, etc. of the server and the mobile terminal do not affect the protection scope of the present invention.
It can be understood that, according to the importance degree of the app installation information and the app activity information on the association relation with the wifi category and the difficulty degree of the app installation information and the app activity information in a specific application scenario, the app installation information or the app activity information is comprehensively determined and selected, or the app installation information and the app activity information are comprehensively considered to determine the public wifi category, and according to different set app features, the following description is provided by several specific embodiments respectively:
embodiment 1,
The app feature information includes app installation information or app activity information, and in the step S5, if the a mn App and G corresponding to maximum values of (2) mn If the maximum value of the corresponding apps does not belong to the same category, the following steps are executed:
step S6, apps mn The corresponding first characteristic occupation ratio and second characteristic occupation ratio are weighted and summed to obtain a third characteristic occupation ratio;
step S7, wifi is carried out m All apps corresponding to mn Sorting the third characteristic occupation ratio, and sorting apps corresponding to the maximum value of the third characteristic occupation ratio mn Is determined as wifi m Is a category of (2).
The system further comprises a wifi address mapping table and a POI (Point of interesting, interest point) information mapping table, wherein the wifi address mapping table is used for storing the mapping relation between wifi and addresses corresponding to wifi, the POI information mapping table is used for storing the mapping relation between address information and POI information, and if the wifi determined in the step S7 is m More than two categories, the following steps are executed:
step S01, acquiring wifi based on the wifi address mapping table m Wifi address of (a);
step S02, determining wifi based on the mapping relation of the POI information m POI information corresponding to the wifi address;
step S03, based on wifi m POI information corresponding to wifi address and wifi m Is matched with the category of the POI information, and the category matched with the POI information is determined as wifi m Is a category of (2).
It can be appreciated that wifi is determined m When more than two categories exist, inaccurate wifi categories may exist, and the POI information can be introduced to further judge, so that the inaccurate wifi categories are removed, and the accuracy of public wifi determination is further improved.
Embodiment II,
Before executing step S5, executing step S3 and step S4 based on the app installation information and the app activity information to obtain a first feature ratio maximum value and a second feature ratio maximum value corresponding to the app installation feature information, and a first feature ratio maximum value and a second feature ratio maximum value corresponding to the app activity information, where step S5 includes:
step S51, judging whether all apps corresponding to the first feature occupation value maximum value and the second feature occupation value corresponding to the app installation feature information and the first feature occupation value maximum value and the second feature occupation value maximum value corresponding to app activity information belong to the same category based on the app category mapping table, and if so, determining the category as wifi m Is a category of (2).
If in the step S51, the first feature ratio maximum value and the second feature ratio corresponding to the app installation feature information and the apps corresponding to the first feature ratio maximum value and the second feature ratio maximum value corresponding to the app activity information do not all belong to the same category, the following steps are executed:
step S52, app mn The method comprises the steps of obtaining a first characteristic occupation ratio and a second characteristic occupation ratio corresponding to corresponding app installation characteristic information and obtaining a fourth characteristic occupation ratio by corresponding weighted summation of app active information;
step S53, wifi is performed m All apps corresponding to mn Sorting the fourth characteristic occupation ratio, and sorting apps corresponding to the maximum value of the fourth characteristic occupation ratio mn Is determined as wifi m Is a category of (2).
If the wifi determined in step S53 m If the number of categories is two or more, step S01-step S03 of the embodiment is also executed, and will not be described here again.
The present invention is not limited to the above-mentioned embodiments, but is intended to be limited to the following embodiments, and any modifications, equivalents and modifications can be made to the above-mentioned embodiments without departing from the scope of the invention.
Claims (8)
1. A data processing system for determining a common wifi category, characterized in that,
the system comprises a database, an app category mapping table, a processor and a memory storing a computer program, wherein the database is used for storing a device report record, the device report record comprises a device id, app characteristic information, wifi information and time information, and the app characteristic information comprises app installation information and/or app activity information; the app category mapping table is used for storing a mapping relation between app ids and app categories, and when the processor executes the computer program, the following steps are implemented:
step S1, obtaining a public wifi list { wifi ] within a preset time period from the database 1 ,wifi 2 ,...wifi M (Wifi) m Represents the M-th public wifi, and the value of M is 1 to M;
step S2, obtaining wifi m Corresponding target app candidate set { app m1 ,app m2 ,...app mN }, wherein app mn The method comprises the steps of representing an nth target candidate app corresponding to an mth public wifi, wherein the value of N is 1 to N, and the target candidate app refers to a non-head app with app features meeting the requirement of preset saliency for the public wifi;
step S3, acquiring the app installed in the total data in the preset time period from the database mn Is the total number of devices C mn and wifim Connected installation app mn D of the total number of devices of (a) mn Based on C mn and Dmn Determining apps mn Relative to wifi m First characteristic ratio A mn ，A mn ＝D mn /C mn All A are mn Sequencing, determining A mn App corresponding to the maximum value of (a);
s4, obtaining wifi from the database in the preset time period, wherein wifi is connected to the total data m Is the total number of equipment E m Based on E m and Dmn Determining apps mn Relative to wifi m Second characteristic ratio G of (2) mn ＝D mn /E m All G' s mn Sequencing, determining G mn Corresponds to app;
step S5, judging the A based on the app category mapping table mn App and G corresponding to maximum values of (2) mn If the maximum value of the corresponding app belongs to the same category, determining the category as wifi m Is a category of (2).
2. The system of claim 1, wherein the system further comprises a controller configured to control the controller,
preferably, the step S1 includes:
step S11, traversing the database, obtaining one or more of the number of each wifi connection device, the frequency of connection devices and the frequency of connection of a single device in the preset time period, weighting and summing to obtain a first reference value, determining wifi with the first reference value larger than a threshold value from a reference value threshold value as candidate wifi, and constructing a wifi candidate list;
step S12, wifi in the wifi candidate list can be de-duplicated, invalid wifi and wifi with the number of connected devices exceeding a preset connection number threshold in the preset time period are removed, and the public wifi list { wifi ] is obtained 1 ,wifi 2 ,...wifi M }。
3. The system of claim 1, wherein the system further comprises a controller configured to control the controller,
preferably, the step S2 includes:
step S21, obtaining wifi from the database m All non-header app lists { app } 'corresponding to the connected devices within the preset time period' m1 ,app' m2 ,...app' mX }, wherein app' mx Representing wifi m The corresponding x-th non-head, x fetchThe value range is 1 to X, and the non-header app refers to an app with an installation amount smaller than a preset installation threshold value in the preset time period;
step S22, installing app 'in the database total data' mx Is determined as a first target sample, randomly extracts the same number of device ids as the first target sample from the database full data as a first natural sample, and determines app 'based on the first target sample and the first natural sample' mx Is of the first significance H' mx ：
wherein ,representing app 'installed in the first target sample' mx The ratio of the number to the number of all apps installed in the first target sample, +.>Representing app 'installed in the first natural sample' mx The ratio of the number to the number of all apps installed in the first natural sample will be H' mx Greater than a preset first saliency threshold app' mx Determining as a first app candidate set;
step S23, connecting wifi with the database m Selecting and installing app 'in the device of (1)' mx Is determined as a second target sample, and wifi is connected to the database m Randomly extracting the same device id as the second target sample number as a second natural sample, determining app 'based on the second target sample and the second natural sample' mx Is of the second significance F m ' x ：
wherein ,representing an app 'installed in the second target sample' mx The ratio of the number to the number of all apps installed in the second target sample, +.>Representing app 'installed in the second natural sample' mx The ratio of the number to the number of all apps installed in the second natural sample, F m ' x Greater than a preset first saliency threshold app' mx Determining as a second app candidate set;
step S23, determining the intersection or union of the first app candidate set and the second app candidate set as the target app candidate set { app } m1 ,app m2 ,...app mN }。
4. The system of claim 1, wherein the system further comprises a controller configured to control the controller,
the app feature information includes app installation information or app activity information, and in the step S5, if the a mn App and G corresponding to maximum values of (2) mn If the maximum value of the corresponding apps does not belong to the same category, the following steps are executed:
step S6, apps mn The corresponding first characteristic occupation ratio and second characteristic occupation ratio are weighted and summed to obtain a third characteristic occupation ratio;
step S7, wifi is carried out m All apps corresponding to mn Sorting the third characteristic occupation ratio, and sorting apps corresponding to the maximum value of the third characteristic occupation ratio mn Is determined as wifi m Is a category of (2).
5. The system of claim 1, wherein the system further comprises a controller configured to control the controller,
before executing step S5, executing step S3 and step S4 based on the app installation information and the app activity information to obtain a first feature ratio maximum value and a second feature ratio maximum value corresponding to the app installation feature information, and a first feature ratio maximum value and a second feature ratio maximum value corresponding to the app activity information, where step S5 includes:
step S51, judging whether all apps corresponding to the first feature occupation value maximum value and the second feature occupation value corresponding to the app installation feature information and the first feature occupation value maximum value and the second feature occupation value maximum value corresponding to app activity information belong to the same category based on the app category mapping table, and if so, determining the category as wifi m Is a category of (2).
6. The system of claim 5, wherein the system further comprises a controller configured to control the controller,
if in the step S51, the first feature ratio maximum value and the second feature ratio corresponding to the app installation feature information and the apps corresponding to the first feature ratio maximum value and the second feature ratio maximum value corresponding to the app activity information do not all belong to the same category, the following steps are executed:
step S52, app mn The method comprises the steps of obtaining a first characteristic occupation ratio and a second characteristic occupation ratio corresponding to corresponding app installation characteristic information and obtaining a fourth characteristic occupation ratio by corresponding weighted summation of app active information;
step S53, wifi is performed m All apps corresponding to mn Sorting the fourth characteristic occupation ratio, and sorting apps corresponding to the maximum value of the fourth characteristic occupation ratio mn Is determined as wifi m Is a category of (2).
7. The system of claim 4 or 6, wherein the system comprises a plurality of sensors,
the system further comprises a wifi address mapping table and a POI information mapping table, wherein the wifi address mapping table is used for storing the mapping relation between the wifi and the address corresponding to the wifi, the POI information mapping table is used for storing the mapping relation between the address information and the POI information, and if the wifi is determined in the step S7 or the step S53 m More than two categories, the following steps are executed:
step S01, acquiring wifi based on the wifi address mapping table m Wifi of (a)An address;
step S02, determining wifi based on the mapping relation of the POI information m POI information corresponding to the wifi address;
step S03, based on wifi m POI information corresponding to wifi address and wifi m Is matched with the category of the POI information, and the category matched with the POI information is determined as wifi m Is a category of (2).
8. The system of claim 1, wherein the system further comprises a controller configured to control the controller,
the preset time period is one week or one month.
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