A mobile WiFi identification method, electronic device and storage medium

Abstract

This invention relates to the field of wireless communication technology, and in particular to a mobile WiFi identification method, electronic device, and storage medium. The method includes: acquiring several location information of each mobile WiFi sample within an initial time period, acquiring several first moving distances, filtering out several second moving distances and normalizing them to obtain several third moving distances, calculating the moving distance fluctuation degree based on the several third moving distances, identifying mobile WiFi samples with moving distance fluctuation degrees less than a preset fluctuation degree threshold as key mobile WiFi, determining a preset time period based on the proportion of key mobile WiFi, collecting several location information of each target WiFi within the preset time period and obtaining the maximum distance, and identifying whether the target WiFi is a mobile WiFi based on the maximum distance. This invention can reasonably evaluate target WiFi and improve the accuracy of mobile WiFi identification.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a mobile WiFi identification method, electronic device, and storage medium. Background Technology

[0002] With the development of WiFi technology, in addition to fixed indoor wireless access points, wireless access points with dynamically changeable deployment locations are provided in some mobile scenarios. These mobile scenarios include users driving, riding high-speed trains or buses, renting portable WiFi while traveling, and sharing WiFi hotspots using user terminals. We call these mobile wireless access points mobile WiFi devices. In these scenarios, when a user terminal communicates with a server located on the Internet, the server cannot accurately identify whether the WiFi the user terminal is connected to is fixed or mobile WiFi. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a mobile WiFi identification method, electronic device, and storage medium, which can reasonably evaluate target WiFi and improve the accuracy of mobile WiFi identification.

[0004] According to a first aspect of the present invention, a mobile WiFi identification method is provided, comprising the following steps:

[0005] S100, based on a given mobile WiFi sample set D={D1, D2, ..., D...} e , ..., D f}, obtain D e Several mobile WiFi location information entries are reported within a preset initial time period T1, and D is obtained based on these mobile WiFi location information entries. e The corresponding first movement distances, where D e Let e ​​be the e-th mobile WiFi sample, e = 1, 2, ..., f, where f is the number of mobile WiFi samples. The first moving distance refers to the distance between any two adjacent mobile WiFi location information.

[0006] S200, from D e Several second movement distances are selected from the corresponding first movement distances, and all the second movement distances are normalized to between 0 and 10 to obtain several third movement distances; the second movement distance refers to any first movement distance other than the maximum first movement distance and the minimum first movement distance.

[0007] S300, based on several third movement distances, calculates the degree of movement distance fluctuation, and when the degree of movement distance fluctuation is less than a preset fluctuation threshold, D...e It has been identified as a critical mobile WiFi network.

[0008] S400, when the ratio of the number of key mobile WiFi devices to f is greater than a preset ratio threshold, the initial time period T1 is determined as the preset time period T; otherwise, T1-ΔT is replaced with a new T1, and the process returns to steps S100-S400 until the preset time period T is determined, or when T1 < T0, T0 is determined as the preset time period T, where ΔT is the preset first time threshold and T0 is the preset second time threshold.

[0009] S500, based on a preset target WiFi set A={A1, A2, ..., A...} i , ..., A m}, obtain A i A contains several WiFi location information corresponding to a preset time period T. i It refers to the i-th target WiFi, where i = 1, 2, ..., m, and m is the number of target WiFi.

[0010] S600, targeting A i Within a preset time period T, for several WiFi location information points, obtain the target distance between every two WiFi location information points to obtain A. i The corresponding distances to several targets.

[0011] S700, A is determined i The maximum distance among several corresponding target distances, and when the maximum distance is greater than a preset distance threshold δ, A will be... i It was identified as a mobile WiFi network.

[0012] According to a second aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the mobile WiFi identification method described above.

[0013] According to a third aspect of the present invention, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0014] The present invention has at least the following beneficial effects:

[0015] This invention provides a mobile WiFi identification method. First, it acquires several location information points for each mobile WiFi sample within a preset initial time period and obtains several first movement distances. From these first movement distances, several second movement distances are selected and normalized to obtain several third movement distances. The movement distance fluctuation level is calculated based on these third movement distances. Normalization ensures that all movement distances are within a uniform range, making the calculated fluctuation level more reasonable. Mobile WiFi samples with movement distance fluctuation levels less than a preset fluctuation threshold are identified as key mobile WiFi samples. A preset time period is determined based on the proportion of key mobile WiFi samples, making the determined preset time period more reasonable. This reduces the preset time period length while ensuring sufficient location information data is collected, facilitating the accurate identification of more mobile WiFi samples. Finally, several location information points for each target WiFi sample within the preset time period are collected, and the maximum distance is obtained. The maximum distance is used to identify whether the target WiFi is a mobile WiFi sample. By using the determined preset time period, several movement scenarios of the target WiFi can be collected relatively comprehensively, obtaining the movement range of the target WiFi within the preset time period. This facilitates a reasonable and reliable evaluation of whether a WiFi sample is a mobile WiFi sample, and helps in the accurate identification of mobile WiFi samples. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of a mobile WiFi identification method provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides a mobile WiFi identification method, such as... Figure 1 As shown, the method includes the following steps:

[0020] S100, based on a given mobile WiFi sample set D={D1, D2, ..., D...}e , ..., D f}, obtain D e Several mobile WiFi location information entries are reported within a preset initial time period T1, and D is obtained based on these mobile WiFi location information entries. e The corresponding first movement distances, where D e Let f be the e-th mobile WiFi sample, where e = 1, 2, ..., f, and f is the number of mobile WiFi samples.

[0021] Furthermore, the first moving distance refers to the distance between any two adjacent mobile WiFi location information.

[0022] Specifically, D e The location information of several mobile WiFi devices reported within the preset initial time period T1 is D. e Several geohash values ​​are reported within a preset initial time period T1.

[0023] Preferably, the preset initial time period T1 is not less than 20 days.

[0024] S200, from D e Several second movement distances are selected from the corresponding first movement distances, and all the second movement distances are normalized to between 0 and 10 to obtain several third movement distances; the second movement distance refers to any first movement distance other than the maximum first movement distance and the minimum first movement distance.

[0025] As mentioned above, since the moving distance of mobile WiFi devices is generally similar between adjacent time points, but considering the problem of unreasonable moving distance caused by location drift or other circumstances, the maximum and minimum moving distances are deleted. By normalizing the remaining moving distances, the moving distances can be normalized to a uniform range, making the fluctuation of the calculated moving distance more reasonable, which is conducive to determining a reasonable number of mobile WiFi devices.

[0026] S300, based on several third movement distances, calculates the degree of movement distance fluctuation, and when the degree of movement distance fluctuation is less than a preset fluctuation threshold, D... e It has been identified as a critical mobile WiFi network.

[0027] Specifically, the degree of fluctuation in travel distance S meets the following conditions:

[0028] , where x k For the k-th new movement distance, It is the average distance of several new movement distances.

[0029] As mentioned above, when the fluctuation of the moving distance is less than the preset fluctuation threshold, it indicates that the moving distance of the mobile WiFi sample is similar between each adjacent time point. For example, the moving distance of in-vehicle WiFi is roughly the same at the same time point. In this way, some unstable WiFi or devices that have location drift can be screened out, or users of the devices can choose other modes of transportation. This makes the identified key mobile WiFi a reasonable mobile WiFi, which is helpful for determining whether the preset time period setting is reasonable based on the number of reasonable mobile WiFi.

[0030] S400, when the ratio of the number of key mobile WiFi devices to f is greater than a preset ratio threshold, the initial time period T1 is determined as the preset time period T; otherwise, T1-ΔT is replaced with a new T1, and the process returns to steps S100-S400 until the preset time period T is determined, or when T1 < T0, T0 is determined as the preset time period T, where ΔT is the preset first time threshold and T0 is the preset second time threshold.

[0031] As mentioned above, when the number of reasonably determined mobile WiFi networks is small, it indicates that the initial time period is not reasonable enough. Since the longer the time period, the greater the difference in the location of mobile WiFi networks, the duration of the time period should be shortened. When shortening the time period, it is necessary to ensure that enough location information data is collected so that the determined preset time period is more reasonable and can accurately identify more mobile WiFi networks.

[0032] S500, based on a preset target WiFi set A={A1, A2, ..., A...} i , ..., A m}, obtain A i A contains several WiFi location information corresponding to a preset time period T. i It refers to the i-th target WiFi, where i = 1, 2, ..., m, and m is the number of target WiFi.

[0033] In another implementation, the preset time period T is set by those skilled in the art according to actual needs. For example, the preset time period T is a number of consecutive days prior to the current time point; in practical use cases, it can be set to 16 days to conform to the number of WiFi collection days analyzed by the beacon library.

[0034] As mentioned above, since mobile WiFi devices travel at different speeds in different scenarios, it is necessary to select a reasonable time period. By determining the preset time period, it is possible to collect more comprehensive information about the movement of the target WiFi and obtain its movement range within that preset time period. This is beneficial for making a reasonable and reliable assessment of whether WiFi is mobile WiFi and for accurately identifying mobile WiFi.

[0035] S600, targeting A i Within a preset time period T, for several WiFi location information points, obtain the target distance between every two WiFi location information points to obtain A. i The corresponding target distances can be understood as: calculating the target distance for any two WiFi location information.

[0036] As described above, by calculating the distance between any two WiFi location information, the farthest distance that each target WiFi can move within its preset time period can be obtained, providing a basis for determining whether each target WiFi is a mobile WiFi.

[0037] S700, A is determined i The maximum distance among several corresponding target distances, and when the maximum distance is greater than a preset distance threshold δ, A will be... i It was identified as a mobile WiFi network.

[0038] In another specific implementation, the following steps are included after S600:

[0039] S001, according to A i The maximum distance among several corresponding target distances, starting from the preset initial WiFi cluster B={B1, B2, ..., B j , ..., B n The target WiFi cluster was identified in} and A was set up. i Insert into the target WiFi cluster; where B j Let j be the preset initial WiFi cluster, where j = 1, 2, ..., n, and n is the number of initial WiFi clusters. This can be understood as: after inserting the target WiFi into an empty initial WiFi cluster, it is updated to the target WiFi cluster.

[0040] Specifically, B1-B n The initial states are all empty and B1-B n The corresponding distance intervals are set continuously from smallest to largest; this can be understood as: B j The upper limit of the corresponding distance interval and B j+1 The lower limits of the corresponding distance intervals are connected.

[0041] Specifically, step S001 also includes the following steps:

[0042] S0011, according to A i The maximum distance among several corresponding target distances, from B1-B n The distance interval in which the maximum distance is located is determined from the corresponding distance interval.

[0043] S0012, the initial WiFi cluster corresponding to the distance interval where the maximum distance is located is determined as the target WiFi cluster. For example, A i The corresponding maximum distance is 4km, while the distance range for B2 is (3km, 6km]. Therefore, A... i Place it in B2.

[0044] Furthermore, B j The corresponding distance interval is ((j-1)×δ, j×δ).

[0045] Preferably, δ=3km. In specific implementations, since the distance between base stations is mostly concentrated around 3 kilometers, δ is set to 3km.

[0046] S002, based on the number of characters corresponding to each keyword in the given first mobile WiFi keyword library, extract the characters of the SSID name of each target WiFi in each target WiFi cluster according to the number of characters, and obtain several target strings corresponding to each target WiFi.

[0047] In one specific embodiment, step S002 further includes the following step:

[0048] S0021. Based on the number of characters corresponding to each keyword in the first mobile WiFi keyword database, obtain several character counts. For example, by iterating through the character counts of each keyword in the first mobile WiFi keyword database, the total number of characters counted is five, namely 2, 3, 4, 5, and 6.

[0049] S0022, for any number of characters, extract consecutive characters from the SSID name of each target WiFi according to the number of characters to obtain several target strings corresponding to each target WiFi. For example, when the number of characters is 3, extract all 3 consecutive characters from the SSID name of the target WiFi. That is, when the SSID name has a total of 8 characters, a total of 6 target strings are extracted, and each string includes 3 consecutive characters.

[0050] S0023. Based on the number of keywords corresponding to each character count, sort the character counts in descending order of keyword count. Then, sort the target strings corresponding to each target WiFi according to the character count sorting result, resulting in a string sorting result. For example, if the character count sorting result is 5-4-6-3-2, for any target WiFi, place the target string with 5 characters at the beginning of the string sorting result, and the target string with 2 characters at the end. It should be noted that for target strings with the same number of characters, sort them according to the order in which they appear in the SSID name.

[0051] As described above, by extracting the SSID name string based on the number of characters in the keyword, the more keywords corresponding to the same number of characters, the greater the probability of a successful string match. Therefore, sorting the extracted target strings based on the number of characters and matching the target strings with the keywords in sequence according to the sorting results can improve the probability of a successful match at any time and also improve the matching accuracy.

[0052] S003, match several target strings corresponding to each target WiFi with keywords in the first mobile WiFi keyword database. The target WiFi corresponding to a successfully matched target string is identified as a mobile WiFi of the first preset type. Otherwise, match several target strings corresponding to each target WiFi with keywords in the second mobile WiFi keyword database. The target WiFi corresponding to a successfully matched target string is identified as a mobile WiFi of the second preset type. This can be understood as: the matching priority of mobile WiFi of the first preset type is higher than that of mobile WiFi of the second preset type. It should be noted that since the number of characters in the keywords obtained from the first mobile WiFi keyword database basically includes the number of characters in the keywords of the second mobile WiFi keyword database, it is sufficient to directly match the obtained target strings with the keywords in the second mobile WiFi keyword database. In specific implementations, the number of characters in the keywords of the second mobile WiFi keyword database can also be counted, and the SSID name of each target WiFi in each target WiFi cluster can be extracted again. Subsequent keyword matching operations can then be performed based on the extracted strings.

[0053] Specifically, the first preset type of mobile WiFi is in-vehicle WiFi. For example, the SSID name of in-vehicle WiFi includes the car's brand, model, and dashcam information.

[0054] Furthermore, the second preset type of mobile WiFi is a mobile hotspot. For example, the SSID name of the mobile hotspot includes the mobile phone model - Android, etc. In specific implementations, a third mobile WiFi keyword library can also be set according to actual needs, and the corresponding third preset type of mobile WiFi is portable WiFi.

[0055] As mentioned above, considering that in actual use cases, some mobile WiFi SSID names include keywords corresponding to both in-vehicle WiFi and mobile hotspots, in this case, all mobile WiFi belong to in-vehicle WiFi. Therefore, when matching keywords, the matching priority of the first mobile WiFi keyword with the target string is higher than that of the second mobile WiFi keyword with the target string, which is beneficial for quickly and accurately determining the type of mobile WiFi.

[0056] In one specific embodiment, step S003 further includes the following step:

[0057] S0031, for any target WiFi corresponding to the string sorting result, match the target string with the keywords in the first mobile WiFi keyword library in the order of the target strings in the string sorting result.

[0058] S0032, when any target string successfully matches a keyword in the first mobile WiFi keyword database, the matching of the target string is stopped, and the target WiFi corresponding to the successfully matched target string is determined as the first preset type of mobile WiFi.

[0059] As described above, by matching the target strings with keywords in the order they appear, and stopping the matching process once a match is found, the entire target string is not matched, thus improving matching efficiency. Furthermore, the order of the target strings corresponds to the order of the number of characters. The more keywords corresponding to a certain number of characters, the higher the probability of a successful match. Therefore, matching in the order of the target strings can improve the initial success rate. Moreover, by matching target strings with different numbers of characters with keywords, the mobile WiFi can be quickly and accurately identified.

[0060] Furthermore, the method also includes the following steps:

[0061] S10, obtain the initial WiFi cluster corresponding to each target mobile WiFi; wherein, the target mobile WiFi refers to any mobile WiFi of the first preset type and the second preset type.

[0062] S20, obtain the preset WiFi cluster priority corresponding to each initial WiFi cluster in the initial WiFi cluster set B; where B1-B n The priority of the corresponding preset WiFi clusters increases sequentially; this can be understood as: the larger the distance range corresponding to the initial WiFi cluster, the higher the priority of the preset WiFi cluster corresponding to the initial WiFi cluster.

[0063] S30: Based on the initial WiFi cluster corresponding to each target mobile WiFi and the preset WiFi cluster priority corresponding to each initial WiFi cluster, sort the several target mobile WiFis and obtain the accuracy priority sorting result of the target mobile WiFis. For example, the higher the preset WiFi cluster priority corresponding to the target mobile WiFi, the higher the accuracy priority corresponding to the target mobile WiFi.

[0064] As mentioned above, since the farther the WiFi location is, the greater the probability that it belongs to mobile WiFi, a higher priority is set for the initial WiFi cluster with a larger corresponding distance range. By setting it in this way, the accuracy of each identified mobile WiFi can be sorted. That is, the higher the priority of the corresponding initial WiFi cluster, the greater the accuracy of the target WiFi belonging to mobile WiFi, which can provide a basis for measuring the accuracy of the identified mobile WiFi.

[0065] Embodiments of the present invention also provide a non-transitory computer-readable storage medium that can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a method in the method embodiments, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0066] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0067] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A mobile WiFi identification method, characterized in that, The method includes the following steps: S100, based on a given mobile WiFi sample set D={D1, D2, ..., D...} e , ..., D f }, obtain D e Several mobile WiFi location information entries are reported within a preset initial time period T1, and D is obtained based on these mobile WiFi location information entries. e The corresponding first movement distances, where D e Let e ​​be the e-th mobile WiFi sample, e = 1, 2, ..., f, where f is the number of mobile WiFi samples. The first moving distance refers to the distance between any two adjacent mobile WiFi location information. S200, from D e Several second movement distances are selected from the corresponding first movement distances, and all the second movement distances are normalized to between 0 and 10 to obtain several third movement distances; the second movement distance refers to any first movement distance other than the maximum first movement distance and the minimum first movement distance; S300, based on several third movement distances, calculates the degree of movement distance fluctuation, and when the degree of movement distance fluctuation is less than a preset fluctuation threshold, D... e Identified as a critical mobile WiFi network; S400, when the ratio of the number of key mobile WiFi devices to f is greater than a preset ratio threshold, the initial time period T1 is determined as the preset time period T; otherwise, T1-ΔT is replaced with a new T1, and the process returns to steps S100-S400 until the preset time period T is determined, or when T1 < T0, T0 is determined as the preset time period T, where ΔT is the preset first time threshold and T0 is the preset second time threshold. S500, based on a preset target WiFi set A={A1, A2, ..., A...} i , ..., A m }, obtain A i A contains several WiFi location information corresponding to a preset time period T. i This refers to the i-th target WiFi, where i = 1, 2, ..., m, and m is the number of target WiFi networks. S600, targeting A i Within a preset time period T, for several WiFi location information points, obtain the target distance between every two WiFi location information points to obtain A. i The corresponding distances to several targets; S700, A is determined i The maximum distance among several corresponding target distances, and when the maximum distance is greater than a preset distance threshold δ, A will be... i It was identified as a mobile WiFi network.

2. The mobile WiFi identification method according to claim 1, characterized in that, The following steps are included after step S600: S001, according to A i The maximum distance among several corresponding target distances, starting from the preset initial WiFi cluster B={B1, B2, ..., B j , ..., B n The target WiFi cluster was identified in} and A was set up. i Insert into the target WiFi cluster; where B j Let B1-B be the preset initial WiFi cluster, where j = 1, 2, ..., n, and n is the number of initial WiFi clusters. n The initial states are all empty and B1-B n The corresponding distance intervals are set continuously from smallest to largest; S002, based on the number of characters corresponding to each keyword in the given first mobile WiFi keyword library, extract the characters of the SSID name of each target WiFi in each target WiFi cluster according to the number of characters, and obtain several target strings corresponding to each target WiFi; S003, match several target strings corresponding to each target WiFi with keywords in the first mobile WiFi keyword library, and determine the target WiFi corresponding to the successfully matched target string as a first preset type of mobile WiFi; otherwise, match several target strings corresponding to each target WiFi with keywords in the second mobile WiFi keyword library, and determine the target WiFi corresponding to the successfully matched target string as a second preset type of mobile WiFi.

3. The mobile WiFi identification method according to claim 2, characterized in that, Step S001 also includes the following steps: S0011, according to A i The maximum distance among several corresponding target distances, from B1-B n The distance interval in which the maximum distance is located is determined from the corresponding distance interval; S0012, the initial WiFi cluster corresponding to the distance interval where the maximum distance is located is determined as the target WiFi cluster.

4. The mobile WiFi identification method according to claim 2, characterized in that, B j The corresponding distance interval is ((j-1)×δ, j×δ).

5. The mobile WiFi identification method according to claim 1, characterized in that, Step S002 also includes the following steps: S0021, Based on the number of characters corresponding to each keyword in the first mobile WiFi keyword database, obtain a number of character counts; S0022, For any number of characters, extract consecutive characters from the SSID name of each target WiFi according to the number of characters to obtain several target strings corresponding to each target WiFi; S0023, based on the number of keywords corresponding to each character, sort the character counts in descending order of the number of corresponding keywords, and sort the target strings corresponding to each target WiFi according to the character count sorting result to obtain the string sorting result.

6. The mobile WiFi identification method according to claim 5, characterized in that, Step S003 also includes the following steps: S0031, for any target WiFi corresponding to the string sorting result, according to the order of the target strings in the string sorting result, match the target strings with the keywords in the first mobile WiFi keyword library in turn; S0032, when any target string successfully matches a keyword in the first mobile WiFi keyword database, the matching of the target string is stopped, and the target WiFi corresponding to the successfully matched target string is determined as the first preset type of mobile WiFi.

7. A non-transitory computer-readable storage medium, wherein the storage medium stores at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the mobile WiFi identification method as described in any one of claims 1-6.

8. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 7.

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