CN111163137A - User identity identification method and device of application program - Google Patents
User identity identification method and device of application program Download PDFInfo
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- CN111163137A CN111163137A CN201911312118.7A CN201911312118A CN111163137A CN 111163137 A CN111163137 A CN 111163137A CN 201911312118 A CN201911312118 A CN 201911312118A CN 111163137 A CN111163137 A CN 111163137A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
The invention discloses a method and a device for identifying user identity of an application program, wherein the method comprises the following steps: acquiring a user track uploaded by an APP user in a preset time window, wherein the user track is position data uploaded by an APP background when the user uses the APP; for each APP user, identifying whether the identity of the APP user is a truck driver according to the user track of the APP user; if yes, then add truck driver identity label for this APP user, and then can utilize identity label screening truck driver to carry out effectual relevant information propelling movement.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a user identity identification method and device of an application program.
Background
With the rapid development of software technology, various APP applications emerge in the market, each APP is generally developed for some specific users, but when the APP is applied, users with other identities actually use the APP. In order to realize effective information push, information push is usually performed after the user identity of the APP is identified as a specific user.
Use truck driver identity as an example, to possessing truck driver user's peculiar APP, if need to the APP user propelling movement relevant information who has truck driver identity, just need discern APP user's identity.
However, at present, no technical solution is available specifically for recognizing that the APP user has the identity of the truck driver.
Disclosure of Invention
The present invention provides a method and an apparatus for identifying a user identity of an application program, which are provided to overcome the above-mentioned deficiencies in the prior art, and the object is achieved by the following technical solutions.
The first aspect of the present invention provides a method for identifying a user identity of an application, where the method includes:
acquiring a user track uploaded by an APP user in a preset time window, wherein the user track is position data uploaded by an APP background when the user uses the APP;
for each APP user, identifying whether the identity of the APP user is a truck driver according to the user track of the APP user;
and if so, adding a truck driver identity label for the APP user.
A second aspect of the present invention provides an apparatus for identifying a user identity of an application, the apparatus comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a user track uploaded by an APP user in a preset time window, and the user track is position data uploaded by an APP background when the user uses the APP;
the identification module is used for identifying whether the identity of each APP user is a truck driver or not according to the user track of the APP user;
and the label module is used for adding a truck driver identity label to the APP user when the identification result is yes.
In the embodiment of the invention, the user track uploaded by the APP user in the preset time window is obtained, the user track is position data uploaded by the APP user in the background when the user uses the APP, whether the identity of the APP user is a truck driver is identified according to the user track of the APP user, if so, a truck driver identity tag can be added to the APP user, and then the truck driver can be screened by using the identity tag to carry out effective related information pushing.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart illustrating an embodiment of a method for identifying a user identity of an application according to an exemplary embodiment of the present invention;
FIG. 2 is a diagram illustrating a hardware configuration of an electronic device in accordance with an exemplary embodiment of the present invention;
fig. 3 is a flowchart illustrating an embodiment of a user identification apparatus for an application according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The method fully considers the relative characteristics of low APP frequency used by a truck driver in the driving process, high APP frequency used after the truck driver stops driving and long-distance movement of the truck driver, and analyzes whether the APP user has the identity of the truck driver.
The following describes in detail a technical scheme for user identification of an application program according to a specific embodiment.
Fig. 1 is a flowchart illustrating an embodiment of a method for identifying a user identity of an application according to an exemplary embodiment of the present invention, where the method for identifying a user identity of an application can be applied to an electronic device (e.g., a PC, a server, a terminal, etc.). As shown in fig. 1, the method for identifying the user identity of the application program includes the following steps:
step 101: the method comprises the steps of obtaining a user track uploaded by an APP user in a preset time window, wherein the user track is position data uploaded by an APP background when the user uses the APP.
The method is characterized in that the APP is used frequently in the driving process of the truck, uploaded user track points are few, after the truck stops driving, the user opens and uses the APP possibly, the uploaded user track points are more, the longer the preset time window is, the more the acquired user track points are, the higher the identification accuracy is, and the preset time window can comprehensively consider the setting of the operational performance of equipment and the number of the track points, such as time windows of half a year, one year or two years.
Illustratively, a user track uploaded by an APP user contains user track points, and each user track point comprises data such as time point information and position information.
Step 102: and for each APP user, identifying whether the identity of the APP user is a truck driver according to the user track of the APP user, if so, executing a step 103, otherwise, continuing to execute the step 102 until all the APP users finish identification.
In an embodiment, the moving distance of the APP user may be calculated by using a user trajectory, the trajectories of trucks uploaded by all trucks in the preset time window are obtained at the same time, for each truck, the trajectory matching similarity between the truck trajectory of the truck and the user trajectory is calculated, and then whether the identity of the APP user is a truck driver is identified according to the calculated trajectory matching similarity and the moving distance.
Illustratively, the truck track uploaded by the truck comprises truck track points, and each truck track point comprises time point information, position information, vehicle speed and other data.
Firstly, aiming at the process of calculating the moving distance of the APP user, the user track points in the user track can be sequenced from small to large according to the time points, the third distance between every two adjacent user track points in the sequencing is calculated, and then the sum of the calculated third distances is used as the moving distance of the APP user.
Secondly, aiming at the process of calculating the track matching similarity between the track of the truck and the track of the user, the track of the truck can be divided into a track section in driving and a track section in stopping according to a preset dividing principle, then aiming at each track section in driving, a track section of the user belonging to the track section in driving in the corresponding time section of the track section in driving is obtained from the track of the user, a first distance between each track point of the user in the track section and the track point in driving corresponding to the time point in the track section in driving is calculated, the number of the first distance which is smaller than a first distance threshold value is counted, the sum of the counted number of the track sections in driving is taken as the number N of the matched track points, meanwhile aiming at each track section in stopping, a track section of the user belonging to the track section in corresponding time section in stopping is obtained from the track, a second distance between each track point of the user in the track section and the track point in stopping corresponding to the time point in the track section in stopping is calculated, and if a second distance smaller than a second distance threshold exists in the calculated second distances, adding 1 to the N, finally obtaining the total number m1 of the user track points in the user track segment in the time period corresponding to each driving track segment, counting the number m2 of the stop track segments with the user track points, and determining the track matching similarity by using the sum of the N and (m1+ m 2).
The formula of the track matching similarity can be N/(m1+ m 2).
For each stopping track segment of the truck, the movement range of a driver is usually very small, and no matter the track points of the truck or the track points of a user are almost unchanged, so that the number N of the matched track points is increased by 1 as long as a second distance smaller than a second distance threshold exists. Correspondingly, when the m2 is counted, the number of the user track points in each stopping process is also counted as 1, that is, the number of the stopping track segments with the user track points is counted as the number m2 of the user track points when the truck stops, so that the consistency of the numerator and the denominator in the track matching similarity formula is ensured.
For example, for the preset division principle, the following may be used: after the truck track points contained in the truck track are sequenced from small to large according to the time points, the truck track points with the speed variation smaller than the speed threshold and the moving distance smaller than the third distance threshold in the preset time range are obtained as the stop track points, the stop track points with continuous time points are used as a stop track section, and then the track sections except the stop track section in the truck track are used as the running track sections, so that the running track sections and the stop track sections are divided.
It should be noted that, because the user trajectory is the data uploaded by the positioning module on the APP, and the truck trajectory is the data uploaded by the vehicle-mounted terminal, the two trajectories with different sources generally have a large difference in positioning accuracy, and can perform unified standard conversion in order to improve the recognition accuracy. That is, the user trajectory and the truck trajectory may be data converted to a unified map standard before calculating the trajectory matching similarity.
Finally, when the identity of the APP user is identified according to the track matching similarity and the moving distance obtained through calculation, the APP user can be identified in a grading mode according to different use scenes:
1) for scenes meeting requirements and having higher accuracy
And if the track matching similarity which is greater than or equal to the similarity threshold exists in the track matching similarities obtained through calculation and the moving distance is greater than a first distance threshold, determining that the APP user identity is a truck driver.
That is, both conditions are satisfied before a truck driver is identified.
2) For scenes with low accuracy meeting requirements
And if the track matching similarity which is greater than or equal to the similarity threshold exists in the track matching similarity obtained through calculation or the moving distance is greater than a first distance threshold, determining that the identity of the APP user is a truck driver.
That is, a truck driver is identified when either condition is satisfied.
Assuming that the accuracy grade satisfied by both conditions is defined as a, the accuracy grade satisfied by either condition is defined as B, and one condition is that the similarity threshold is p, and the first distance threshold is k kilometers, for the following two cases:
the trajectory matching similarity which is greater than p exists in the trajectory matching similarities calculated by the APP user A, and the moving distance k1 of the user A is greater than k, so that the APP user A meets the use scene of the accuracy level A;
the trajectory matching similarity calculated by the APP user B does not have the trajectory matching similarity larger than p, but the moving distance k2 of the user B is larger than k, so that the APP user B meets the use scene of the accuracy level B.
Step 103: adding a truck driver identity tag to the APP user.
In this embodiment, through obtaining the user's orbit of APP user upload in the time window of predetermineeing, the user's orbit is the position data that APP backstage uploaded when the user uses APP to whether the identity of APP user is the truck driver according to APP user's user orbit discernment APP user, if yes, alright add truck driver identity label for the APP user, and then can utilize identity label screening truck driver to carry out effectual relevant information propelling movement.
Fig. 2 is a hardware block diagram of an electronic device according to an exemplary embodiment of the present invention, the electronic device including: a communication interface 201, a processor 202, a machine-readable storage medium 203, and a bus 204; wherein the communication interface 201, the processor 202 and the machine-readable storage medium 203 communicate with each other via a bus 204. The processor 202 may execute the above-described user identification method of the application program by reading and executing machine executable instructions corresponding to the control logic of the user identification method of the application program in the machine readable storage medium 203, and the specific content of the method is referred to the above-described embodiments and will not be described again here.
The machine-readable storage medium 203 referred to in this disclosure may be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: volatile memory, non-volatile memory, or similar storage media. In particular, the machine-readable storage medium 203 may be a RAM (random Access Memory), a flash Memory, a storage drive (e.g., a hard drive), any type of storage disk (e.g., an optical disk, a DVD, etc.), or similar storage medium, or a combination thereof.
Corresponding to the embodiment of the user identity identification method of the application program, the invention also provides an embodiment of a user identity identification device of the application program.
Fig. 3 is a flowchart illustrating an embodiment of a user identification apparatus for an application, which may be applied to an electronic device, according to an exemplary embodiment of the present invention. As shown in fig. 3, the user identification apparatus of the application includes:
an obtaining module 310, configured to obtain a user trajectory uploaded by an APP user within a preset time window, where the user trajectory is position data uploaded by an APP background when the user uses an APP;
the identification module 320 is used for identifying whether the identity of each APP user is a truck driver according to the user track of the APP user;
and the tag module 330 is configured to add a truck driver identity tag to the APP user when the identification result is yes.
In an optional implementation manner, the identifying module 320 is specifically configured to calculate a moving distance of the APP user by using the user trajectory; acquiring truck tracks uploaded by all trucks in the preset time window, and calculating track matching similarity between the truck track of the truck and the user track aiming at each truck; and identifying whether the identity of the APP user is a truck driver according to the track matching similarity obtained through calculation and the moving distance.
In an optional implementation manner, the identifying module 320 is specifically configured to determine that the identity of the APP user is a truck driver if the calculated trajectory matching similarity is greater than or equal to a similarity threshold and the moving distance is greater than a first distance threshold in the process of identifying whether the identity of the APP user is a truck driver according to the calculated trajectory matching similarity and the moving distance; alternatively, the first and second electrodes may be,
and if the track matching similarity which is greater than or equal to the similarity threshold exists in the track matching similarity obtained through calculation or the moving distance is greater than a first distance threshold, determining that the identity of the APP user is a truck driver.
In an optional implementation manner, the identification module 320 is specifically configured to, in the process of calculating the track matching similarity between the truck track of the truck and the user track, divide the truck track into a driving track segment and a stopping track segment according to a preset division principle; aiming at each track segment in driving, acquiring a user track segment belonging to a time period corresponding to the track segment in driving from the user track, calculating a first distance between each user track point in the user track segment and a track point in driving corresponding to a time point in the track segment in driving, and counting the number of the first distances smaller than a first distance threshold value; taking the sum of the number obtained by counting aiming at each track segment in driving as the number N of matched track points; for each stopping track segment, acquiring a user track segment belonging to the stopping track segment in a corresponding time period from the user track, calculating a second distance between each user track point in the user track segment and a stopping track point corresponding to a time point in the stopping track segment, and if a second distance smaller than a second distance threshold exists in the calculated second distance, adding 1 to N; acquiring the total number m1 of user track points in the user track segment in the time period corresponding to each track segment in driving, and counting the number m2 of the stop track segments with the user track points; and determining the track matching similarity by using the N sum (m1+ m 2).
In an optional implementation manner, the preset partitioning rule includes:
after the truck track points contained in the truck track are sequenced from small to large according to the time points, the truck track points of which the speed change is smaller than a speed threshold and the moving distance in a preset time range is smaller than a third distance threshold are obtained as the stopping track points, and the stopping track points of which the time points are continuous are used as a stopping track section; and taking track sections except the stopping track section in the truck track as driving track sections.
In an optional implementation manner, the identifying module 320 is specifically configured to, in the process of calculating the moving distance of the APP user by using the user trajectory, sort the user trajectory points in the user trajectory in an order from small to large according to time points; calculating a third distance between every two adjacent user track points in the sequence; and taking the sum of the calculated third distances as the moving distance of the APP user.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
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 made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for identifying a user of an application, the method comprising:
acquiring a user track uploaded by an APP user in a preset time window, wherein the user track is position data uploaded by an APP background when the user uses the APP;
for each APP user, identifying whether the identity of the APP user is a truck driver according to the user track of the APP user;
and if so, adding a truck driver identity label for the APP user.
2. The method of claim 1, wherein identifying whether the identity of the APP user is a truck driver based on the user trajectory of the APP user comprises:
calculating the moving distance of the APP user by using the user track;
acquiring truck tracks uploaded by all trucks in the preset time window, and calculating track matching similarity between the truck track of the truck and the user track aiming at each truck;
and identifying whether the identity of the APP user is a truck driver according to the track matching similarity obtained through calculation and the moving distance.
3. The method of claim 2, wherein identifying whether the APP user's identity is a truck driver based on the computed track matching similarity and the travel distance comprises:
if the track matching similarity which is greater than or equal to the similarity threshold exists in the track matching similarities obtained through calculation and the moving distance is greater than a first distance threshold, determining that the identity of the APP user is a truck driver; alternatively, the first and second electrodes may be,
and if the track matching similarity which is greater than or equal to the similarity threshold exists in the track matching similarity obtained through calculation or the moving distance is greater than a first distance threshold, determining that the identity of the APP user is a truck driver.
4. The method of claim 2, wherein calculating a track match similarity between the truck track of the truck and the user track comprises:
dividing the track of the truck into a track section in driving and a track section in stopping according to a preset dividing principle;
aiming at each track segment in driving, acquiring a user track segment belonging to a time period corresponding to the track segment in driving from the user track, calculating a first distance between each user track point in the user track segment and a track point in driving corresponding to a time point in the track segment in driving, and counting the number of the first distances smaller than a first distance threshold value;
taking the sum of the number obtained by counting aiming at each track segment in driving as the number N of matched track points;
for each stopping track segment, acquiring a user track segment belonging to the stopping track segment in a corresponding time period from the user track, calculating a second distance between each user track point in the user track segment and a stopping track point corresponding to a time point in the stopping track segment, and if a second distance smaller than a second distance threshold exists in the calculated second distance, adding 1 to N;
acquiring the total number m1 of user track points in the user track segment in the time period corresponding to each track segment in driving, and counting the number m2 of the stop track segments with the user track points;
and determining the track matching similarity by using the N sum (m1+ m 2).
5. The method of claim 4, wherein the preset partition rule comprises:
after the truck track points contained in the truck track are sequenced from small to large according to the time points, the truck track points of which the speed change is smaller than a speed threshold and the moving distance in a preset time range is smaller than a third distance threshold are obtained as the stopping track points, and the stopping track points of which the time points are continuous are used as a stopping track section;
and taking track sections except the stopping track section in the truck track as driving track sections.
6. The method of claim 2, wherein calculating the moving distance of the APP user using the user trajectory comprises:
sequencing the user track points in the user track from small to large according to the time points;
calculating a third distance between every two adjacent user track points in the sequence;
and taking the sum of the calculated third distances as the moving distance of the APP user.
7. An apparatus for identifying a user of an application, the apparatus comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a user track uploaded by an APP user in a preset time window, and the user track is position data uploaded by an APP background when the user uses the APP;
the identification module is used for identifying whether the identity of each APP user is a truck driver or not according to the user track of the APP user;
and the label module is used for adding a truck driver identity label to the APP user when the identification result is yes.
8. The apparatus of claim 7, wherein the identifying module is specifically configured to calculate a moving distance of the APP user using the user trajectory; acquiring truck tracks uploaded by all trucks in the preset time window, and calculating track matching similarity between the truck track of the truck and the user track aiming at each truck;
and identifying whether the identity of the APP user is a truck driver according to the track matching similarity obtained through calculation and the moving distance.
9. The apparatus according to claim 8, wherein the identification module is specifically configured to, in the process of identifying whether the APP user identity is a truck driver according to the calculated trajectory matching similarity and the movement distance, determine that the APP user identity is a truck driver if the calculated trajectory matching similarity includes a trajectory matching similarity greater than or equal to a similarity threshold and the movement distance is greater than a first distance threshold; alternatively, the first and second electrodes may be,
and if the track matching similarity which is greater than or equal to the similarity threshold exists in the track matching similarity obtained through calculation or the moving distance is greater than a first distance threshold, determining that the identity of the APP user is a truck driver.
10. The device according to claim 8, wherein the recognition module is specifically configured to, in the process of calculating the track matching similarity between the truck track of the truck and the user track, divide the truck track into a driving track segment and a stopping track segment according to a preset division rule; aiming at each track segment in driving, acquiring a user track segment belonging to a time period corresponding to the track segment in driving from the user track, calculating a first distance between each user track point in the user track segment and a track point in driving corresponding to a time point in the track segment in driving, and counting the number of the first distances smaller than a first distance threshold value; taking the sum of the number obtained by counting aiming at each track segment in driving as the number N of matched track points; for each stopping track segment, acquiring a user track segment belonging to the stopping track segment in a corresponding time period from the user track, calculating a second distance between each user track point in the user track segment and a stopping track point corresponding to a time point in the stopping track segment, and if a second distance smaller than a second distance threshold exists in the calculated second distance, adding 1 to N; acquiring the total number m1 of user track points in the user track segment in the time period corresponding to each track segment in driving, and counting the number m2 of the stop track segments with the user track points; and determining the track matching similarity by using the N sum (m1+ m 2).
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