CN112395301A - Mobile device association degree determination method, electronic device and medium - Google Patents
Mobile device association degree determination method, electronic device and medium Download PDFInfo
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Abstract
The invention relates to a method for determining the relevancy of mobile equipment, electronic equipment and a medium, wherein the method comprises the step S1 of respectively obtaining a first wifi list corresponding to a first equipment id and a second wifi list corresponding to a second equipment id from a preset database within a preset first time period, wherein the first wifi list comprises a wifi name, a wifi type and connection time corresponding to the first equipment id, and the second wifi list comprises a wifi name, a wifi type and connection time corresponding to the second equipment id; step S2, generating a relevancy list based on the first wifi list and the second wifi list; and step S3, determining the association degree of the first device id and the second device id based on the association degree list. The method and the device improve the efficiency and accuracy of determining the association degree of the equipment.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an electronic device, and a medium for determining a relevancy of a mobile device.
Background
With the advent of the big data era, various data are explosively increased, when business processing is performed, the relevance of a user needs to be analyzed and determined based on the big data, and with the popularization of mobile devices, the relevance of the user can be determined through the relevance of user equipment. Specifically, it may be necessary to analyze user equipment data dispersed in each system, and perform association processing on massive user equipment data in each system to determine the association degree between user equipments.
However, the data of the user equipment of different systems have differences, and the analysis difficulty for determining the association degree of the user equipment by acquiring the data from different systems is large, the time is long, and the accuracy cannot be ensured, so that the existing method for determining the association degree of the mobile equipment is low in efficiency and poor in accuracy.
Disclosure of Invention
The invention aims to provide a method, electronic equipment and a medium for determining the association degree of mobile equipment, so that the efficiency and the accuracy of determining the association degree of the equipment are improved.
According to a first aspect of the present invention, there is provided a method for determining a mobile device association degree, including the following steps:
step S1, respectively acquiring a first wifi list corresponding to a first device id and a second wifi list corresponding to a second device id from a preset database within a preset first time period, wherein the first wifi list comprises a wifi name, a wifi type and connection time corresponding to the first device id, and the second wifi list comprises a wifi name, a wifi type and connection time corresponding to the second device id;
step S2, generating a relevancy list based on the first wifi list and the second wifi list;
and step S3, determining the association degree of the first device id and the second device id based on the association degree list.
According to a second aspect of the present invention, there is provided an electronic apparatus comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of the first aspect of the invention.
According to a third aspect of the invention, there is provided a computer readable storage medium, the computer instructions being for performing the method of the first aspect of the invention.
Compared with the prior art, the invention has obvious advantages and beneficial effects. By the technical scheme, the method for determining the association degree of the mobile equipment, the electronic equipment and the medium provided by the invention can achieve considerable technical progress and practicability, have wide industrial utilization value and at least have the following advantages:
the method and the device can quickly and accurately determine the association degree of the device based on wifi data corresponding to the device.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a method for determining a relevancy of a mobile device according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for determining relevancy of a mobile device based on a wifi type according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments and effects of a method, an electronic device and a medium for determining a mobile device association degree according to the present invention will be made with reference to the accompanying drawings and preferred embodiments.
An embodiment of the present invention provides a method for determining a relevance of a mobile device, as shown in fig. 1, including the following steps:
step S1, respectively acquiring a first wifi list corresponding to a first device id and a second wifi list corresponding to a second device id from a preset database within a preset first time period, wherein the first wifi list comprises a wifi name, a wifi type and connection time corresponding to the first device id, and the second wifi list comprises a wifi name, a wifi type and connection time corresponding to the second device id;
step S2, generating a relevancy list based on the first wifi list and the second wifi list;
wherein an intersection of the first wifi list and the second wifi list may be obtained, and an association list is generated based on the intersection of the first wifi list and the second wifi list, as an example,
and step S3, determining the association degree of the first device id and the second device id based on the association degree list.
According to the present invention, a device refers to a mobile terminal, which may be physically implemented as a smart phone, PAD, or other mobile device capable of installing an application (e.g., APP). Those skilled in the art will appreciate that parameters such as the model and specification of the mobile terminal do not affect the scope of the present invention. It should be noted that some exemplary embodiments of the present invention are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. Moreover, the order of steps is merely set forth for convenience of reference and does not imply a required order of execution or steps to be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
In the step S1, the preset database includes a plurality of records of device connection wifi, and the fields include a device id, a wifi name of device connection, a wifi type, and a connection time, where the device id, the wifi name of device connection, and the connection time can be directly obtained by the device through information reported by a Software Development Kit (SDK), and by classifying wifi, a type of each wifi is determined, and a tag can be tagged to each wifi. Specifically, the following operations may be performed to classify wifi before step S1:
step S11, acquiring wifi attribute information;
step S12, determining a corresponding wifi type based on the wifi attribute information;
step S13, constructing a preset database based on the wifi type, the device id reported by the device, the wifi name connected with the device and the connection time;
then, continuing to execute the steps S1-S3 as the next steps S14-S15, further determination of the device association degree based on the wifi classification can be realized, as shown in FIG. 2.
As an example, the step S12 includes:
step S121, a first configuration table is constructed in advance, and fields of the first configuration packet comprise position types, keyword information, quantity characteristics of connected equipment in a preset time period, a wifi quantity threshold, model characteristics of the connected equipment and the like;
and S122, determining the type of the wifi to be classified based on the first configuration table and the attribute information of the wifi to be classified.
The wifi attribute information comprises a wifi name, wifi information, geographical location information, connection time information and connection equipment information, and the geographical location information can be geohash location information.
As an embodiment, the step S122 further includes:
step S1221, determining a corresponding position type according to the geographical position information of the wifi to be classified;
the location types include an airport type, a station type, a mall type, a home type, a company type, and the like.
Step S1222, obtaining the keyword information corresponding to the location type from the first configuration table, extracting keywords from the wifi name to be classified, matching the keywords with the keyword information corresponding to the location type, if the matching is successful, executing step S123, otherwise, returning to step S1211, and re-determining the corresponding location type from other location types except the location type;
the method comprises the steps of classifying the names of the wifi to be classified, and extracting the corresponding keywords by operations such as word segmentation and word stop.
Step S1223, obtaining the quantity characteristics of the connected devices in the preset time period corresponding to the position types from the first configuration table, determining the quantity characteristics of the connected devices in the preset time period based on the connection time information and the connected device information of the wifi to be classified, executing step S124 if the quantity characteristics of the connected devices in the preset time period corresponding to the position types are met, otherwise, returning to step S1211, and re-determining the corresponding position types from other position types except the position types;
the number characteristics of the connected devices in the preset time period include the number of the connected devices in the working time period and the variation characteristics of the number, the number of the connected devices in the non-working time period and the variation characteristics of the number, and the like.
Step S1224, obtaining a wifi number threshold and/or a connection device model feature corresponding to the location type from the first configuration table, determining the wifi number based on the wifi information to be classified, determining the connection device feature based on the connection device information to be classified, judging whether the wifi number and/or the connection device feature corresponding to the wifi to be classified meet the wifi number threshold and/or the connection device model feature corresponding to the location type, if so, determining the wifi type as the location type, otherwise, returning to step S1211, and re-determining the corresponding location type from other location types except the location type.
For example, a family type wifi generally corresponds to a smaller number of wifi macs, typically within 5, while a mall type wifi corresponds to a larger number of wifi macs, typically above 5. The model of the equipment that airport wifi connects is the proportion of high-end model more, and the high-end and low-end distribution of the model of the equipment that station wifi connects can be relatively even.
When returning to step S1211, except for temporarily excluding the current location type, if the number of times of returning to step S211 exceeds a certain threshold, for example, 5 times, the accuracy of geographic location matching may be increased by a preset step length, so as to improve the accuracy of obtaining the keyword information corresponding to the location type and avoid increasing useless calculation amount.
The wifi type may be temporarily classified within a certain range through step S1221, for example, the location type corresponding to the geographical location information of the wifi to be classified is an airport type, but the wifi type is determined from only one dimension of the location information, and the accuracy is low, so it may be continuously confirmed through step S1222 whether the wifi to be classified is an airport type wifi. In step S1222, through further analysis of the wifi name keywords, the wifi to be classified has no keywords corresponding to the airport type, and obviously it may be excluded that the wifi to be classified is not an airport type, therefore, step S1221 may be returned to re-match the geographic location information of the wifi to be classified from other types except the airport type to determine the corresponding location type, and if the matching in step S1222 is successful, step S1223 may be further performed to determine whether the wifi type is an airport type wifi. Since there may be other wifi in the airport location range besides wifi really belonging to the airport, for example, there may also be keywords corresponding to the airport type in the name of wifi in the shopping mall near the airport, a small amount of deviation may occur after the determination in step S1223, because the terminal connection number characteristics of wifi in the shopping mall may be similar to that of the airport, and then further determination is performed in step S1224. For example, step S1224 determines the model characteristics of the device connected by wifi to be classified, the high-end model ratio of the device model appearing in the airport is high, and the high-end, medium-end and low-end device models connected by wifi in the mall are relatively uniform, so that the type of wifi to be classified can be determined as the airport type wifi after the determination is passed through step S1224. The wifi type is determined through the step S1221 to the step S1224 from multiple dimension analysis, accuracy of wifi classification is greatly improved, the selection range of the position type is continuously adjusted and reduced based on the analysis result, repeated calculation is avoided, and classification efficiency is improved.
As an example, the step S2 includes:
s21, based on the first wifi list and the second wifi list, obtaining wifi names, corresponding wifi types, connection time and target connection times of target wifi connected by the first device id and the second device id together in a preset first time period, and generating a relevancy list.
The wifi comprises M types, the wifi of the M types is divided into a first wifi subset and a second wifi subset, M is a positive integer greater than or equal to 2, the fixing performance of the wifi type connecting equipment in the first wifi subset is greater than or equal to a preset fixing threshold value, and the fixing performance of the wifi type connecting equipment in the second wifi subset is smaller than the preset fixing threshold value. For example, the first wifi subset includes a family type wifi and a company type wifi; the second wifi subset type comprises an entertainment place type wifi, a station type wifi, an airport type wifi and a market type wifi. The step S21 may further include:
step S211, acquiring the names of the common wifi connected by the first device id and the second device id in a preset first time period and corresponding wifi types and connection time based on the first wifi list and the second wifi list;
step S212, a wifi subset to which the shared wifi type belongs is judged, if the shared wifi type belongs to the first wifi subset, the step S213 is executed, and if the shared wifi type belongs to the second wifi subset, the step S214 is executed;
step S213, determining the shared wifi as the target wifi, and taking the connection times of the first device id and the second device id respectively connected with the target wifi in the preset first time period as the target connection times;
the wifi in the first wifi subset has high fixity, for example, family wifi is basically the device id corresponding to family members to be connected, or the device id of family members to be connected, and company wifi is basically the employee device id to be connected, so that the common connection of the wifi is realized only by respectively acquiring the wifi times in a time interval without considering the simultaneity.
Step S214, a first time period set of the first device id connected with the shared wifi and a second time period set of the second device id connected with the shared wifi are respectively obtained, whether a time period intersection exists between the first time period set and the second time period set is judged, if yes, the shared wifi is determined to be a target wifi, and the number of the time periods in the time period intersection is determined to be the target connection times of the target wifi.
Wherein the wifi in the second wifi subset has a low fixity, for example wifi in an entertainment place or wifi in a shopping mall, and devices of persons having an association relationship are usually simultaneously present in these places, so that for the common connection of such wifi, the simultaneity needs to be considered, but many factors such as signal stability and device leaving midway are considered, and the simultaneous connection is considered when there is an intersection in a continuous connection period instead of the same fixed time, and as an embodiment, the method further comprises:
step S201, acquiring a wifi name, a corresponding wifi type and connection time of a first wifi connected by a device id in a preset first time period, and arranging the wifi name, the corresponding wifi type and the connection time according to a time sequence;
step S202, determining whether a time interval between every two consecutive time points is smaller than a preset second time period, and if so, determining that the two consecutive time points belong to the same time period.
Wherein the value range of the first time period is from one month to three months, and the value range of the second time period is from 25 minutes to 45 minutes. Taking the example that the first time period is set to three months, and the second time period is set to 30 minutes, it is assumed that the time for connecting the first wifi reported by the first device id in the first day is 9:30, 10:15,10:28, 10: 50,11:50, 12:30, the continuous time period for the first device-id to connect to the first wifi is 10:15-11: 50. Suppose the time for connecting the first wifi reported by the second device in the first day is 8:00,8:31,8:55,9:05,9:28, 10:15,10: 30,11:05, the continuous time period for the second device id to connect to the first wifi is 8:30-10: 30. And if the intersection of the time periods of the first device id and the second device id is 10:15-10:30, the first device id and the second device id are considered to be connected with the first wifi together.
As an embodiment, the method further includes step S4, outputting and displaying the association degree list on the information interaction interface. As an embodiment, the step S16 (i.e., step S3) may further include the steps of determining the association degree of the analysis device according to the parameters presented in the association degree list, and further determining the association degree through a preset determination algorithm:
step S161, setting a first connection quantity threshold value connecting the quantity of wifi in the first wifi subset and a second connection quantity threshold value connecting the quantity of wifi in the second wifi subset;
step S162, determining a first number of wifi in a first wifi subset and a second number of wifi in a second wifi subset which are connected together by a first device id and a second device id in a first time period based on wifi names, corresponding wifi types and target connection times of target wifi which are connected together by the first device id and the second device id in a preset first time period;
step S163, comparing the first number with a first number threshold, and comparing the second number with a second number threshold:
determining the first device id and the second device id association degree as a first association degree if the first number is greater than a first number threshold and the second number is greater than a second number threshold,
if the first number is larger than a first number threshold and the second number is smaller than or equal to a second number threshold, determining the first device id and the second device id association degree as a second association degree,
if the first number is less than or equal to a first number threshold and the second number is greater than a second number threshold, determining the first device id and the second device id association degree as a third association degree,
and if the first quantity is less than or equal to a first quantity threshold value and the second quantity is less than or equal to a second quantity threshold value, determining the association degree of the first device id and the second device id as a fourth association degree, wherein the first association degree > the second association degree > the third association degree > the fourth association degree.
For example, if the number of the first device id and the second device id that are connected to wifi together exceeds the first number threshold in one month, and the number of the connected shopping malls and the number of the entertainment venues both exceed the second number threshold, it may be indicated that the association degree between the first device id and the second device id is extremely high, corresponding to the first association degree.
An embodiment of the present invention further provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform a method according to an embodiment of the invention.
The embodiment of the invention also provides a computer-readable storage medium, and the computer instructions are used for executing the method of the embodiment of the invention.
The embodiment of the invention can quickly and accurately determine the wifi type based on the wifi attribute information and can quickly and accurately determine the device association degree based on the wifi type connected with the device.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for determining the association degree of a mobile device is characterized by comprising the following steps:
step S1, respectively acquiring a first wifi list corresponding to a first device id and a second wifi list corresponding to a second device id from a preset database within a preset first time period, wherein the first wifi list comprises a wifi name, a wifi type and connection time corresponding to the first device id, and the second wifi list comprises a wifi name, a wifi type and connection time corresponding to the second device id;
step S2, generating a relevancy list based on the first wifi list and the second wifi list;
and step S3, determining the association degree of the first device id and the second device id based on the association degree list.
2. The method of claim 1,
the preset database comprises records of connecting wifi of a plurality of devices, and the fields comprise device id, wifi name of device connection, wifi types and connection time.
3. The method of claim 2,
the step S2 includes:
s21, based on the first wifi list and the second wifi list, obtaining wifi names, corresponding wifi types, connection time and target connection times of target wifi connected by the first device id and the second device id together in a preset first time period, and generating a relevancy list.
4. The method of claim 3,
wifi includes M types, the wifi of M types divides into first wifi subset and second wifi subset again, M is the positive integer more than or equal to 2, the fixity more than or equal to of wifi type connecting device in the first wifi subset predetermined fixity threshold value, the fixity of wifi type connecting device in the second wifi subset is less than predetermined fixity threshold value, step S21 includes:
step S211, acquiring the names of the common wifi connected by the first device id and the second device id in a preset first time period and corresponding wifi types and connection time based on the first wifi list and the second wifi list;
step S212, a wifi subset to which the shared wifi type belongs is judged, if the shared wifi type belongs to the first wifi subset, the step S213 is executed, and if the shared wifi type belongs to the second wifi subset, the step S214 is executed;
step S213, determining the shared wifi as the target wifi, and taking the connection times of the first device id and the second device id respectively connected with the target wifi in the preset first time period as the target connection times;
step S214, a first time period set of the first device id connected with the shared wifi and a second time period set of the second device id connected with the shared wifi are respectively obtained, whether a time period intersection exists between the first time period set and the second time period set is judged, if yes, the shared wifi is determined to be a target wifi, and the number of the time periods in the time period intersection is determined to be the target connection times of the target wifi.
5. The method of claim 4,
the method further comprises the following steps:
step S201, acquiring a wifi name, a corresponding wifi type and connection time of a first wifi connected by a device id in a preset first time period, and arranging the wifi name, the corresponding wifi type and the connection time according to a time sequence;
step S202, determining whether a time interval between every two consecutive time points is smaller than a preset second time period, and if so, determining that the two consecutive time points belong to the same time period.
6. The method of claim 5,
the value range of the first time period is from one month to three months, and the value range of the second time period is from 25 minutes to 45 minutes.
7. The method of claim 4,
the first wifi subset comprises a family type wifi and a company type wifi; the second wifi subset type comprises an entertainment place type wifi, a station type wifi, an airport type wifi and a market type wifi.
8. The method according to any one of claims 1 to 7,
the method further comprises a step S4 of outputting and displaying the association degree list on an information interaction interface.
9. An electronic device, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-8.
10. A computer-readable storage medium having stored thereon computer-executable instructions for performing the method of any of the preceding claims 1-8.
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