CN113283483B - Device type determining method based on wifi, electronic device and storage medium - Google Patents
Device type determining method based on wifi, electronic device and storage medium Download PDFInfo
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- CN113283483B CN113283483B CN202110529368.7A CN202110529368A CN113283483B CN 113283483 B CN113283483 B CN 113283483B CN 202110529368 A CN202110529368 A CN 202110529368A CN 113283483 B CN113283483 B CN 113283483B
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- G06F18/00—Pattern recognition
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- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
Abstract
The invention discloses a device type determining method based on wifi, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring a device list of target wifi; performing feature processing on the Ai to obtain a first feature vector; obtaining a first probability value according to the first feature vector and a first target weight vector corresponding to the first feature vector; comparing the first probability value with a preset first probability threshold; when the first probability value is not less than the first probability threshold, determining that the target device is a first type of device; when the target probability value is smaller than the probability threshold, acquiring application information of the target equipment to re-determine the type of the target equipment; according to the method, the device can be screened by determining wifi, on one hand, the accuracy of determining the type and the characteristic of the user corresponding to the interference data is low, and on the other hand, the type and the characteristic of the same type of user can be determined.
Description
Technical Field
The invention relates to the technical field of device processing, in particular to a wifi-based device type determining method, an electronic device and a storage medium.
Background
With the popularization of intelligent devices, users are required to have terminal devices, and the terminal devices can reflect characteristics of some users, such as types and characteristics of the users, life styles corresponding to the users, and the like.
At present, the type and the characteristics of the user are directly obtained through a database, and the data volume is large in the case, so that the accuracy of determining the type and the characteristics of the user is low due to the fact that the interference data are large, and the type and the characteristics of the same type of user are influenced.
Disclosure of Invention
In order to solve the problems in the prior art, a device list of target wifi is obtained; obtaining a first probability value according to the first feature vector and a first target weight vector corresponding to the first feature vector; comparing the first probability value with a preset first probability threshold; when the first probability value is not less than the first probability threshold, determining that the target device is a first type of device; when the target probability value is smaller than the probability threshold, acquiring application information of the target equipment to re-determine the type of the target equipment; the method has the advantages that the device can be screened by determining wifi firstly, so that on one hand, the accuracy of determining the type and the feature of the user is low due to too much interference data, and on the other hand, the type and the feature of the same type of user can be determined; the embodiment of the invention provides a device type determining method based on wifi, an electronic device and a readable storage medium. The technical scheme is as follows:
on one hand, the method for determining the type of the device based on the wifi comprises the following steps:
s101, acquiring a first device set A (A1, A2, A3, … … and Am) corresponding to the target wifi, wherein m is the number of the target devices; wherein, the step of S101 further comprises:
s1011, acquiring a designated device set B ═ (B1, B2, B3, … …, Bp), where p is the number of devices;
s1012, determining a second device set according to the B
q is the number of the second type of equipment;
s1013, according to the
Determining a target wifi:
s102, performing feature processing on any Ai in the A to obtain a first feature vector C (C1, C2, C3, … … and Cn), wherein n is the number of features;
s103, obtaining a first probability value K according to the C and a first target weight vector W corresponding to C (W1, W2, W3, … …, Wn);
s104, comparing the K with a preset first probability threshold;
s105, when the K is not smaller than the first probability threshold, determining that the target device is a first type of device;
s106, when the K is smaller than the first probability threshold, acquiring the application information of the target device to redetermine the type of the target device.
In another aspect, an electronic device includes a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to implement the wifi-based device type determining method as described in any one of the above.
In another aspect, a computer-readable storage medium has at least one instruction or at least one program stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the wifi-based device type determining method as in any one of the above.
The device type determining method based on wifi, the electronic device and the readable storage medium provided by the invention have the following technical effects:
the method includes the steps that a device list of target wifi is obtained; performing feature processing on the Ai to obtain a first feature vector; obtaining a first probability value according to the first feature vector and a first target weight vector corresponding to the first feature vector; comparing the first probability value with a preset first probability threshold; when the first probability value is not less than the first probability threshold, determining that the target device is a first type of device; when the target probability value is smaller than the probability threshold value, acquiring application information of the target equipment to re-determine the type of the target equipment, and screening equipment through wifi, wherein on one hand, the accuracy of determining the type and the feature of a user is low due to too much interfered data, and on the other hand, the type and the feature of the same type of user can be determined; in addition, the method and the device determine the second type of equipment based on the determined wifi type, so that the target wifi is determined according to the second type of equipment, the type of the target equipment is further determined, and meanwhile, the accuracy can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a wifi-based device type determining method according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of determining target wifi according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of another second type of device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
With reference to fig. 1 and fig. 2, a first embodiment provides a wifi-based device type determining method, where the method includes the following steps:
s101, obtaining a first device set a (a1, a2, A3, … …, Am) corresponding to the target wifi, where m is the number of target devices, where the step S101 further includes the following steps of determining the target wifi:
s1011, acquiring a specified device set B ═(B1, B2, B3, … …, Bp), where p is the number of devices;
s1012, determining a second device set according to the B
q is the number of the second type of equipment;
s1013, according to the
Determining a target wifi;
s102, performing feature processing on any Ai in the A to obtain a first feature vector C (C1, C2, C3, … … and Cn), wherein n is the number of features in C;
s103, obtaining a first probability value K according to the C and a first target weight vector W corresponding to C (W1, W2, W3, … …, Wn);
s104, comparing the K with a preset first probability threshold;
s105, when the K is not smaller than the first probability threshold, determining that the target device is a first type of device;
s106, when the K is smaller than the first probability threshold, acquiring the application information of the target device to redetermine the type of the target device.
Specifically, the A refers to a set formed by all devices connected to the target wifi.
Specifically, B refers to a set of devices in the sample database.
Specifically, the
Refers to a collection of devices of the second type.
Specifically, the first class of devices refers to devices with low priority, and the second class of devices refers to devices with high priority, that is, the priority corresponding to the second class of devices is higher than the priority corresponding to the first class of devices.
Specifically, the step S1012 further includes:
acquiring any Bj according to the B;
performing feature processing on the Bj to obtain a second feature vector
r is as
The number of middle features;
according to the above
And said
Corresponding second target weight vector
Obtaining a second probability value
Will be described in
Comparing with a preset second probability threshold;
when said
If the second probability threshold is larger than the second probability threshold, determining the designated equipment as second equipment, and generating the equipment
Specifically, said W and said
The method comprises the steps of obtaining the weight data through a weight database, wherein the weight database is a preset database.
Further, the
Wherein the content of the first and second substances,
further, the second probability threshold is in the range of 0-0.5, and preferably, the second probability threshold is 0.5.
Further, Bj refers to attribute information corresponding to the j-th specified device, j is 1 … … p, and Bj includes: one or more combinations of APP installation quantity, equipment charging times and equipment intervals are specified.
Preferentially, the Bj comprises the appointed APP installation number, the equipment charging times and the equipment distance, and the characteristic processing of the Bj refers to the judgment of different characteristics in the Bj and conditions corresponding to the characteristics of the characteristics; for example, when the specified APP installation number is not more than the preset APP number, the
Is marked as 1, otherwise is marked as 0; when the charging times of the equipment are less than the preset charging times, the equipment is charged for a while
Mark as 1, otherwise mark as 0; when the equipment distance meets a second preset spacing distance
The value of winning is marked as 1, otherwise, the value is marked as 0.
Further, the designated APP is a life APP, wherein the number range of the preset APP is 1-3; preferably, the preset number of APPs is 2.
Further, the device charging number refers to an average charging number of devices in a first time period, where the preset charging number range is 250-350, and preferentially, the preset charging number is 300.
Further, the first period of time ranges from 7 to 15 days, preferably, the first period of time is 7 days.
Further, the device distance refers to a separation distance between a current position of the device and a set fixed position, for example, the fixed position may be a residential address or the like; wherein the second preset separation distance range is 150-250m, and preferably, the second preset separation distance is 200 m.
In a specific embodiment, when the number of designated APPs installed in the Bj is 1, the number of times of charging the device is 280, and the device interval is 500m, and the preset number of APPs is 2, the preset number of times of charging is 300, and the second preset interval distance is 200m, performing feature processing on the Bj to obtain the result
According to the
Corresponding thereto said
Obtaining a second probability value
When said
Greater than the second probability threshold, determining that
The corresponding device is a second type of device.
Specifically, the step S1013 further includes:
according to the
Generating a first wifi set, D ═(D1, D2, D3, … …, Dp), p referring to the number of wifi lists of the first type, wherein Dp referring to the p-th wifi list of the first type;
acquiring the frequency of any first type of wifi in the D;
comparing the times of the first type of wifi with a preset time threshold;
when the times of the first type of wifi are not smaller than a preset time threshold, determining the first type of wifi as a target wifi;
and in the same way, traversing all the first wifi in the D.
Specifically, the first type of wifi means
Wifi to which it is connectable.
Furthermore, the first wifi type list is a list formed by the first wifi type;
further, the D refers to a set formed by wifi lists of the first type.
Specifically, the preset number threshold range is 3 to 5 times, and preferably, the preset number threshold is 3 times.
Specifically, the frequency of the first type of wifi is the frequency of the first type of wifi when the first type of wifi has the same frequency in different first type of wifi lists.
In a particular embodiment, in said
The generated D (D1, D2, D3, D4), D1 (alpha-wifi, beta-wifi, gamma-wifi, beta 1-wifi), D2 (beta 4-wifi, beta 2-wifi, lambda-wifi), D3 (beta 9-wifi, theta-wifi, beta 6-wifi, mu-wifi), D4 (delta 0-wifi, phi-wifi, theta-wifi, beta 3-wifi, psi-wifi), wherein alpha-wifi, beta 0-wifi, gamma-wifi, theta-wifi, delta-wifi, beta 7-wifi, lambda-wifi, beta 8-wif, mu-wifi and phi-wifi all represent a relatively independent wifi, wherein the number of alpha-wifi is 4, beta-wifi, and the number of gamma-wifi is 1, delta-wifi is 2 times, epsilon-wifi is 1 time, lambda-wifi is 1 time, theta-wifi is 2 times, mu-wifi is 1 time, phi-wifi is 1 time, eta-wifi is 1 time, psi-wifi is 1 time; and determining that the alpha-wifi is a target wifi when the times of the alpha-wifi are larger than the preset time threshold value 3.
Specifically, the K ═ C1 × W1+ C2 × W2+ C3 × W3+ … + Cn × Wn, where W1+ W2+ W3+ … + Wn is 1.
Specifically, the Ai refers to attribute information corresponding to the ith target device, where i is 1 … … m, and the Ai includes: one or more combinations of the installation number of appointed APP, the type of equipment, the number of wifi appointed by connection and the equipment interval are specified.
Preferentially, the Ai comprises the steps of appointing APP installation quantity, equipment type, wifi connection quantity and equipment distance; the step of carrying out feature processing on the Ai refers to the step of judging different features in the Ai and conditions corresponding to the features of the Ai; for example, when the number of the designated APPs installed is greater than the preset number of APPs, the C is marked as 1, otherwise, the C is marked as 0; when the number of the connected wifi is larger than the preset number of wifi, marking the C as 1, otherwise, marking the C as 0; when the equipment type meets a preset equipment type, marking the C as 1, otherwise, marking the C as 0; and when the equipment distance meets a second preset spacing distance, marking the equipment distance as 1 in the C, otherwise, marking the equipment distance as 0.
Further, the specified APP installation number and the preset APP number are consistent with the specified APP installation number and the preset APP number corresponding to the Bj, and are not described herein again.
Further, the number of connected wifi means the number of connected wifi with high priority, wherein the preset wifi number range is 1-3, and preferentially, the preset wifi number is 2.
Further, the preset device type refers to a device type stored in a preset device sample database.
Further, the first preset spacing distance is greater than the second preset spacing distance, preferably, the range of the first preset spacing distance is 30-50km, and preferably, the first preset spacing distance is 50 km.
In a specific embodiment, when the number of APP installations specified in Ai is 3, the number of wifi connections is 3, the device type is a specified type, and the device distance is 53km, and the preset number of APP is 2, the preset number of wifi connections is 2, and the first preset separation distance is 50km, performing feature processing by using Aj to obtain (1, 0, 1, 1); and obtaining a first probability value K of 0.6 according to the C-value (1, 1, 1, 1) and the corresponding W-value (0.4, 0.1, 0.1), and determining that the device corresponding to the K is the first type device when the K is greater than the first probability threshold.
Specifically, the step S106 obtains the application information of the target device to redetermine the type of the target device, and those skilled in the art can implement the method based on any known method, which is not described herein again.
As shown in fig. 3, the method further includes another method of determining a second type of device:
s201, obtaining a second wifi set F ═(F1, F2, F3, … …, Fs), where S is the wifi number;
s202, performing aggregation processing on Fy in F, and determining that a third wifi set V is (V1, V2, V3 … …, Vt), and t is the number of wifi, where Vg is a device list corresponding to the g wifi, and g is 1 … … t;
and S203, screening out second-class equipment according to the t equipment lists.
Specifically, the second wifi set is a set stored in a wifi sample database, and the device sample database is a set wifi database.
Specifically, the third wifi set is a set formed by wifi screened out after aggregation processing, namely the V belongs to the F.
Specifically, the step S202 may be implemented by any method known to those skilled in the art, and will not be described herein again.
Specifically, in the step S203, the sample database is configured according to the t device lists, and the second type of devices are screened out by referring to the steps S1011 to S1012, which is not described herein again.
Specifically, Fy refers to attribute information corresponding to the y wifi, where y is 1 … … s, and Fy includes: one or more of connected device information, connection mode information, device attribute information, and wifi location information.
Preferably, the Fy includes: connected device information, connection mode information, device attribute information, and wifi location information.
In a specific embodiment, the Fy includes: connecting equipment information, connection mode information, equipment attribute information and wifi positional information, right connecting equipment information the connection mode information the equipment attribute information with wifi positional information carries out polymerization, polymerization can adopt arbitrary data polymerization processing method among the prior art to realize, obtains wifi aggregate value, according to wifi aggregate value compares with preset aggregate threshold value, if wifi aggregate value is greater than preset aggregate threshold value, then according to wifi that wifi aggregate value corresponds constructs V.
Further, the connection device information includes: the number of devices scanned by wifi in the second time period and the number of devices connected with wifi in the second time period, wherein the range in the second time period is 7-14 days, preferably, the second time period is 7 days.
Further, the connection device information further includes: according to the number of the devices scanned by the wifi in the second time period, the number of the devices scanned by the wifi in the second time period and a proportion value of the devices scanned by the wifi in the second time period are generated, the number of the devices scanned by the wifi in the working day and the proportion value of the devices scanned by the wifi in the non-working day are generated, the number of the devices scanned by the wifi in the third time period and the proportion value of the devices scanned by the wifi in the third time period are generated, the number of the devices scanned by the wifi in the fourth time period and the proportion value of the devices scanned by the wifi in the fourth time period are generated, and the number of the devices scanned by the wifi in the fifth time period and the proportion value of the wifi in the fifth time period are generated.
Further, the connection device information further includes: according to the number of devices connected with the wifi in the second time period, the number of devices connected with the wifi and a proportional value in a working day, the number of devices connected with the wifi and a proportional value in a non-working day, the number of devices connected with the wifi and a proportional value in the third time period, the number of devices connected with the wifi and a proportional value in the fourth time period, and the number of devices connected with the wifi and a proportional value in the fifth time period are obtained in the blacklist.
Further, the third time period is less than the fourth time period, which is less than the fifth time period, i.e., the third time period < the fourth time period < the fifth time period; wherein the third time period ranges from 2 to 3 days, the fourth time period ranges from 4 to 5 days, and the fifth time period ranges from 6 to 7 days.
Preferably, the third period of time is 3 days, the fourth period of time is 5 days and the fifth period of time is 7 days.
Further, the connection device information further includes first type information, second type information, and third type information; the first type of information refers to user basic information corresponding to the device, where the user basic information includes gender, age, family members, marital, residence address, etc., and a proportional value corresponding to the first type of information, for example, when the age in the basic information is 20-30 years old; the second type of information refers to user attention information corresponding to the device, and the user attention information includes: education, automobiles, reading, photography, games and the like, and the corresponding proportional value of the second type of information; the third type of information refers to device attribute information, and the device attribute information includes: the manufacturer of the device, the type of the device, etc., and a proportional value corresponding to the third type of information.
Specifically, the connection mode information includes: the APP activity in the sixth time period, the number of designated devices connected with wifi in the sixth time period and the number of non-designated devices connected with wifi in the sixth time period.
Further, the sixth time period ranges from 4 to 6 hours, preferably, the sixth time period is 4 hours, and 4 hours are from 0 to 4 o' clock per day.
Preferably, the connection mode information further includes: and the variance value corresponding to the APP activity degree is generated through the APP activity degree in the sixth time period.
Further, the designated device refers to a virtual device used by no real user.
Further, the non-specific device refers to a virtual device used by a real user.
Specifically, the device attribute information includes: the number of devices connected with the wifi in different time periods and the corresponding proportional value thereof, the maximum difference value between the number of the devices connected with the wifi in different time periods and the number of the devices connected with the wifi in a preset time period; wherein, different time periods refer to different time areas divided in any day.
Further, the preset time period ranges from 3 hours to 5 hours, and preferably, the preset time period ranges from 3 hours.
Specifically, the wifi location information includes: the method comprises the steps of generating a geohash corresponding to wifi in a second time period, and generating the change times of the geohash, the type of the geohash and the number of devices logged into a blacklist in the geohash according to the geohash.
In the method provided by the embodiment, a device list of target wifi is obtained; performing feature processing on the Ai to obtain a first feature vector; obtaining a first probability value according to the first feature vector and a first target weight vector corresponding to the first feature vector; comparing the first probability value with a preset first probability threshold; when the first probability value is not less than the first probability threshold, determining that the target device is a first type of device; when the target probability value is smaller than the probability threshold value, acquiring application information of the target equipment to re-determine the type of the target equipment, and screening the target equipment through wifi, wherein on one hand, the accuracy of determining the type and the feature of a user is low due to too much interfered data, and on the other hand, the type and the feature of the same type of user can be determined;
in addition, another method for determining the second type of device is provided in this embodiment, the second type of device is determined based on the wifi type determination, so that the target wifi is determined according to the second type of device, the type of the target device is further determined, and meanwhile, the accuracy can be improved.
An embodiment of the present invention further provides an electronic device, which includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the wifi-based device type determining method as described above.
The computer apparatus of the embodiments of the present invention exists in many forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., tpphone), multimedia phones, functional phones, and low-end phones, etc.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has mobile internet access characteristics. Such terminals include: PDA, MtD, and UMPC devices, etc., such as tPad.
(3) A portable entertainment device: such devices may display and play multimedia content. This kind of equipment includes: audio, video players (e.g., tPod), handheld game players, electronic books, and smart toys and portable car navigation devices.
(4) A server: the device for providing the computing service comprises a processor, a hard disk, a memory, a system bus and the like, and the server is similar to a general computer architecture, but has higher requirements on processing capacity, stability, reliability, safety, expandability, manageability and the like because of the need of providing high-reliability service.
(5) And other electronic devices with data interaction functions.
The embodiment of the present invention further provides a computer-readable storage medium, which may be disposed in an electronic device to store at least one instruction or at least one program for implementing a virus detection method in the method embodiment, where the at least one instruction or the at least one program is loaded and executed by the processor to implement the wifi-based device type determination method provided in the method embodiment.
Optionally, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A wifi-based device type determination method is characterized by comprising the following steps:
s101, acquiring a first device set A (A1, A2, A3, … … and Am) corresponding to the target wifi, wherein m is the number of the target devices; the step S101 further comprises the following steps of determining a target wifi:
s1011, acquiring a designated device set B ═ B1 (B2, B3, … …, Bp), p is the number of devices, Bj refers to attribute information corresponding to the jth designated device, j ═ 1 … … p, and Bj includes: one or more combinations of APP installation quantity, equipment charging times and equipment intervals are specified;
s1012, determining a second device set according to the B
q is the number of the second type devices, wherein the step S1012 further includes:
acquiring any Bj according to the B;
performing feature processing on the Bj to obtain a second feature vector
r is a characteristic number;
according to the above
And said
Corresponding second target weight vector
Obtaining a second probability value
Will be described in
Comparing with a preset second probability threshold;
when said
If the probability is larger than the second probability threshold, determining the designated equipment as second equipment, and generating the target equipment
S1013, according to the
Determining a target wifi, wherein the step S1013 further includes:
according to the
Generating a first wifi set, D ═ (D1, D2, D3, … …, Dp), p refers to the number of wifi lists of the first type, wherein the Dp refers to the p wifi list of the first type;
acquiring the frequency of any first type of wifi in the D;
comparing the times of the first type of wifi with a preset time threshold;
when the times of the first type of wifi are not smaller than a preset time threshold, determining the first type of wifi as a target wifi;
sequentially analogizing, and traversing all the first wifi types in the D;
s102, performing feature processing on any Ai in a to obtain a first feature vector C ═ (C1, C2, C3, … …, Cn), n is a feature number, where Ai refers to attribute information corresponding to the ith target device, i ═ 1 … … m, and the Ai includes: one or more combinations of the installation number of the APP, the equipment type, the number of the wifi in the connection specification and the equipment interval are specified;
s103, obtaining a first probability value K according to C and a first target weight vector W corresponding to C (W1, W2, W3, … …, Wn);
s104, comparing the K with a preset first probability threshold;
s105, when the K is not smaller than the first probability threshold, determining that the target device is a first type of device;
s106, when the K is smaller than the first probability threshold, acquiring the application information of the target device to redetermine the type of the target device.
2. The wifi-based device type determining method of claim 1, wherein said wifi is said device type determining method
The following conditions are met:
3. the method as claimed in claim 1, wherein the first type of wifi refers to the wifi
Wifi to which it can connect.
4. The wifi-based device type determining method according to claim 1, wherein the K meets the following condition:
k ═ C1 × W1+ C2 × W2+ C3 × W3+ … + Cn × Wn, where W1+ W2+ W3+ … + Wn is 1.
5. An electronic device, comprising a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executed to implement the wifi-based device type determining method as claimed in any one of claims 1-4.
6. A computer readable storage medium having stored therein at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the wifi-based device type determining method as claimed in any one of claims 1-4.
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| CN202110529368.7A CN113283483B (en) | 2021-05-14 | 2021-05-14 | Device type determining method based on wifi, electronic device and storage medium |
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