CN112257924A - Position prediction method, position prediction device, electronic equipment and storage medium - Google Patents
Position prediction method, position prediction device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure provides a position prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: determining the user intention of a target user according to the region attribute information of a target region where the target user is located; acquiring a historical travel track and a historical travel speed of the target user within a preset time period from the current time; determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention; and determining the target position of the target user at the future specified time according to the predicted travel track and the historical travel speed. The embodiment of the disclosure can improve the accuracy of position prediction.
Description
Technical Field
Embodiments of the present disclosure relate to the field of location prediction technologies, and in particular, to a location prediction method and apparatus, an electronic device, and a storage medium.
Background
LBS (location based service) based recommendations are very sensitive to the user's location, and both the user's current location and future likely location are critical to the accuracy of the recommendation, and therefore, there is a need to predict the user's future likely consumption location.
In the prior art, the action track sequence of the user is generally considered, the existing position track of the user is obtained and the inertial prediction is carried out, and in a closed environment, an outlet is not linear with the existing track, so that the prediction directly according to the existing track has larger deviation.
Disclosure of Invention
Embodiments of the present disclosure provide a position prediction method, an apparatus, an electronic device, and a storage medium, so as to improve accuracy of position prediction.
According to a first aspect of embodiments of the present disclosure, there is provided a location prediction method, including:
determining the user intention of a target user according to the region attribute information of a target region where the target user is located;
acquiring a historical travel track and a historical travel speed of the target user within a preset time period from the current time;
determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention;
and determining the target position of the target user at the future specified time according to the predicted travel track and the historical travel speed.
Optionally, the determining the user intention of the target user according to the area attribute information of the target area where the target user is located includes:
determining the area attribute type of the target area according to the entrance and exit position of the target area and the distribution information of entity service parties around the target area;
determining regional property information of the target region according to historical behavior data of a plurality of users after leaving the target region;
and determining the user intention of the target user according to the region attribute type and the region property information.
Optionally, the determining, according to historical behavior data of a plurality of users after leaving the target area, regional property information of the target area includes:
acquiring the probability that the plurality of users reach a set position after leaving the target area;
generating a probability displacement matrix according to a plurality of probabilities;
and determining the region property information corresponding to the target region based on the probability displacement matrix.
Optionally, the determining a target position at which the target user is located at a specified time in the future according to the predicted travel track and the historical travel speed includes:
inputting the predicted travel track and the historical travel speed to a preset position prediction model;
outputting, by the predictive model, a target location of the target user at a specified time in the future.
Optionally, before the inputting the predicted travel trajectory and the historical travel speed into a preset position prediction model, the method further includes:
acquiring a preset number of initial advancing tracks after leaving the target area and initial advancing speeds corresponding to the initial advancing tracks;
and training an initial position prediction model according to the initial travel track and the initial travel speed to obtain the preset position prediction model.
According to a second aspect of embodiments of the present disclosure, there is provided a position prediction apparatus including:
the user intention determining module is used for determining the user intention of the target user according to the region attribute information of the target region where the target user is located;
the historical travel track acquisition module is used for acquiring the historical travel track and the historical travel speed of the target user within a preset time period from the current time;
the predicted travelling track determining module is used for determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention;
and the target position determining module is used for determining the target position of the target user at a specified time in the future according to the predicted travel track and the historical travel speed.
Optionally, the user intent determination module comprises:
the area attribute type determining unit is used for determining the area attribute type of the target area according to the entrance and exit position of the target area and the distribution information of the entity service party around the target area;
the regional property information determining unit is used for determining regional property information of the target region according to historical behavior data of a plurality of users after leaving the target region;
and the user intention determining unit is used for determining the user intention of the target user according to the region attribute type and the region property information.
Optionally, the region property information determining unit includes:
a probability obtaining subunit, configured to obtain probabilities that the plurality of users reach a set position after leaving the target area;
a displacement matrix generation subunit, configured to generate a probability displacement matrix according to the plurality of probabilities;
and the region property determining subunit is used for determining the region property information corresponding to the target region based on the probability displacement matrix.
Optionally, the target position determination module includes:
a predicted travel trajectory input unit for inputting the predicted travel trajectory and the historical travel speed to a preset position prediction model;
and the target position output unit is used for outputting the target position of the target user at a specified time in the future through the prediction model.
Optionally, the method further comprises:
the initial advancing track acquiring module is used for acquiring a preset number of initial advancing tracks after leaving the target area and initial advancing speeds corresponding to the initial advancing tracks;
and the position prediction model acquisition module is used for training an initial position prediction model according to the initial travel track and the initial travel speed to obtain the preset position prediction model.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor, a memory, and a computer program stored on the memory and executable on the processor, the processor implementing the position prediction method of any of the above when executing the program.
According to a fourth aspect of embodiments of the present disclosure, there is provided a readable storage medium having instructions that, when executed by a processor of an electronic device, enable the electronic device to perform any one of the location prediction methods described above.
According to the position prediction scheme provided by the embodiment of the disclosure, the user intention of the target user is determined according to the region attribute information of the target region where the target user is located, the historical travel track and the historical travel speed of the target user within a preset time period from the current time are obtained, the predicted travel track corresponding to the target user is determined according to the historical travel track, the historical travel track and the user intention, and the target position where the target user is located at the future designated time is determined according to the predicted travel track and the historical travel speed. According to the embodiment of the disclosure, the intention of the user is acquired by combining the attribute information of the area where the user is located, and the future position is predicted according to the intention of the user and the historical travel information of the user in the preset time period, so that the problem of deviation of position prediction caused by inertial prediction is avoided, and the accuracy of position prediction is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments of the present disclosure will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart illustrating steps of a method for location prediction according to an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating steps of another method for location prediction according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a position prediction apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of another position prediction apparatus according to an embodiment of the present disclosure.
Detailed Description
Technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.
Example one
Referring to fig. 1, a flowchart illustrating steps of a position prediction method provided in an embodiment of the present disclosure is shown, and as shown in fig. 1, the position prediction method may specifically include the following steps:
step 101: and determining the user intention of the target user according to the region attribute information of the target region where the target user is located.
The embodiment of the disclosure can be applied to a scene for predicting the position of the user at the future time.
The target user refers to a user who needs to make a location prediction of a future time. For example, the users in the target area include user a, user B, and user C, and user a may be a target user, user a and user B may be target users, and user a, user B, and user C may be target users at the same time.
The target area refers to an area where the target user is currently located.
The area attribute information refers to attribute information associated with the target area, and in this embodiment, the area attribute information may include an area attribute type and area property information, where the area attribute type refers to physical attributes of a parcel where the target area is located, such as attributes of a closed scene, a building such as a highway/subway, a house/office building, a shopping place such as a mall/pedestrian street, and an exit position/entrance position of the target area. The property information of the area refers to the property of the area determined according to the subsequent behavior of the user who has historically appeared in the target area, such as shopping, tourism, etc. in this embodiment, the property of the area can be determined according to the probability transition matrix stored in the subsequent consumption position of the user who has historically appeared in the target area.
The user intention is an intention of the target user determined from the area attribute information of the target area, and for example, when the area attribute information of the target area is a shopping place such as a mall, it can be determined that the intention of the target user is shopping or the like.
It should be understood that the above examples are only examples for better understanding of the technical solutions of the embodiments of the present application, and are not to be taken as the only limitation to the embodiments.
After the target area where the target user is located is obtained, area attribute information corresponding to the target area may be obtained, and further, the user intention of the target user may be determined according to the area attribute information.
After determining the user intention of the target user according to the area attribute information of the target area where the target user is located, step 102 is executed.
Step 102: and acquiring the historical travel track and the historical travel speed of the target user within a preset time period from the current time.
The preset time period refers to a time period having a certain length from the current time, for example, a time period having a distance of 30min from the current time (i.e., a time period from 30min before to the current time), or a time period having a distance of 15min from the current time (i.e., a time period from 15min before to the current time), and the like.
The historical travel track refers to a travel track of the target user within a preset time period. For example, in the preset period of time, 7/2020, 11/8: 11/7/11/00-2020: when 00, the historical travel track is 7, 11, and 8 in 2020: 00-11: travel trajectory within 00.
The historical travel speed refers to the travel speed of the target user within a preset time period, for example, within the preset time period of 2020, 7, 11, 8: 11/7/11/00-2020: when 00, the historical travel speed is 7, month, 11, day 8 in 2020: 00-11: average speed within 00.
When it is acquired that the target user is in the target area and the position of the target user at the future time point needs to be predicted, the historical travel track and the historical travel speed of the target user within a preset time period from the current time may be acquired, and step 103 is further performed.
Step 103: and determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention.
The predicted travel track refers to a predicted travel track of the target user in a certain future period, namely a predicted travel route.
After the area attribute information of the target area is acquired, the predicted travel track of the target user can be predicted according to the area attribute information, the historical travel speed and the historical travel track of the target user in a preset time period.
After the predicted travel track corresponding to the target user is determined, step 104 is performed.
Step 104: and determining the target position of the target user at the future specified time according to the predicted travel track and the historical travel speed.
The future designated time is a point in time in the future at which the location of the target user is predicted.
The target position refers to a predicted position to be reached by the target user at a specified time in the future.
After the predicted travel track of the target user is determined, the position which can be reached by the target user at the future designated time can be predicted by combining the predicted travel track and the historical travel speed so as to obtain the target position of the target user at the future designated time.
According to the position prediction method provided by the embodiment of the disclosure, the user intention of the target user is determined according to the area attribute information of the target area where the target user is located, the historical travel track and the historical travel speed of the target user within the preset time period from the current time are obtained, the predicted travel track corresponding to the target user is determined according to the historical travel track, the historical travel track and the user intention, and the target position where the target user is located at the future designated time is determined according to the predicted travel track and the historical travel speed. According to the embodiment of the disclosure, the intention of the user is acquired by combining the attribute information of the area where the user is located, and the future position is predicted according to the intention of the user and the historical travel information of the user in the preset time period, so that the problem of deviation of position prediction caused by inertial prediction is avoided, and the accuracy of position prediction is improved.
Referring to fig. 2, a flowchart illustrating steps of another location prediction method provided in the embodiment of the present disclosure is shown, and as shown in fig. 2, the location prediction method may specifically include the following steps:
step 201: and determining the area attribute type of the target area according to the entrance and exit position of the target area and the distribution information of the entity service party around the target area.
The embodiment of the disclosure can be applied to a scene for predicting the position of the user at the future time.
The target area refers to an area where a target user is currently located, and the target user refers to a user who needs to perform position prediction at a future time. For example, users in the target area include: the user a, the user B and the user C may use the user a as a target user, may use the user a and the user B as target users, and may use the user a, the user B and the user C as target users at the same time.
The area attribute type refers to a physical attribute type of a land parcel where the target area is located, such as attribute types of closed scenes, buildings such as highways/subways and houses/office buildings, shopping places such as shopping malls/pedestrian streets and the like.
The entity business party refers to business parties such as an entity store, such as a restaurant, a gas station and the like.
In this embodiment, the exit position and the entrance position of the target area may be searched on the map, and according to the exit position and the entrance position of the target area, the area attribute type of the target area is determined by combining the distribution situation of the entity service parties around the target area, for example, whether the target area is an expressway, a subway, or the like is determined by the exit position, the entrance position, and the distribution situation of the entity service parties around the target area.
After the area attribute type of the target area is determined according to the entrance and exit position of the target area and the distribution information of the entity service party around the target area, step 202 is executed.
Step 202: and determining the regional property information of the target region according to the historical behavior data of a plurality of users after leaving the target region.
The historical behavior data refers to the behavior of the entity business party going to the periphery of the target area after the historical appearance of the target area.
The region property information refers to a property of the region determined according to a subsequent behavior of the user that has historically appeared in the target region, such as shopping, tourism, and in this embodiment, the property of the region may be determined according to a probability displacement matrix stored in a subsequent consumption location of the user that has historically appeared in the target region.
When the target user is in the target area, historical behavior data of a plurality of users on the target area can be acquired, and the regional property information of the target area can be determined by combining the historical behavior data. In particular, the detailed description may be combined with the following specific implementations.
In a specific implementation manner of the present disclosure, the step 202 may include:
substep S1: and acquiring the probability that the plurality of users reach the set position after the target area when leaving.
In the embodiment of the present disclosure, the set position refers to a position that a plurality of users arrive after leaving the target area.
By combining historical behavior data of a plurality of users, positions (namely set positions) reached by the plurality of users after leaving the target area can be obtained, and furthermore, the probability of reaching the set positions after leaving the target area can be counted according to the number of the users of the plurality of users and the number of the users reaching the set positions respectively, for example, the number of the users is 1000, the number of the 1000 users after leaving the target area reaches the position a is 300, the number of the users reaching the position B is 200, and the number of the users reaching the position C is 500, so that the probability of reaching the position a is 30%, the probability of reaching the position B is 20%, and the probability of reaching the position C is 50%.
It should be understood that the above examples are only examples for better understanding of the technical solutions of the embodiments of the present disclosure, and are not to be taken as the only limitation to the embodiments.
After obtaining the probabilities that the plurality of users reach the set positions after leaving the time target area, sub-step S2 is performed.
Substep S2: and generating a probability displacement matrix according to a plurality of the probabilities.
After obtaining probabilities that a plurality of users reach the set position after leaving the target area, a probability displacement matrix may be generated in combination with the plurality of probabilities, and then, sub-step S3 is performed.
Substep S3: and determining the region property information corresponding to the target region based on the probability displacement matrix.
After the probability displacement matrix is generated by combining the plurality of probabilities, the regional property information of the target region can be determined according to the probability displacement matrix, that is, the subsequent behaviors of the user, which historically appear in the target region, of the target region are determined according to the probability displacement matrix, and according to the probability of the subsequent behaviors, a maximum probability value is counted, so that the regional property information of the target region is determined according to the subsequent arrival position with the maximum probability value and the behavior of the user at the position.
Step 203: and determining the user intention of the target user according to the region attribute type and the region property information.
The user intention means an intention of the target user determined based on the area attribute type and the area property information of the target area, and for example, when the area attribute type of the target area is a highway and the area property information is shopping property information, it may be determined that the user intention of the target user is a shopping intention according to the situation.
After the area attribute type and the area property information corresponding to the target area are obtained, the user intention of the target user can be determined according to the area attribute type and the area property information.
After determining the user intent of the target user based on the region attribute type and the region property information, step 204 is performed.
Step 204: and acquiring the historical travel track and the historical travel speed of the target user within a preset time period from the current time.
The preset time period refers to a time period having a certain length from the current time, for example, a time period having a distance of 30min from the current time (i.e., a time period from 30min before to the current time), or a time period having a distance of 15min from the current time (i.e., a time period from 15min before to the current time), and the like.
The historical travel track refers to a travel track of the target user within a preset time period, namely a travel route of the target user.
The historical travel speed refers to an average speed at which the target user travels within a preset period of time.
After the preset time period is determined, the historical travel track and the historical travel speed of the target user within the preset time period may be acquired, and then step 205 is performed.
Step 205: and determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention.
The preset travel track refers to a predicted travel track of the target user in a certain future period, namely a predicted travel route.
After the area attribute type of the target area and the area property information of the target area are obtained, the user intention of the target user is determined, and the historical travel track and the historical travel speed of the target user in a preset time period are obtained, the predicted travel track corresponding to the target user can be determined by combining the historical travel track, the historical travel speed and the user intention.
After the predicted travel trajectory corresponding to the target user is determined, step 206 is performed.
Step 206: and inputting the predicted travel track and the historical travel speed into a preset position prediction model.
The preset position prediction model is a model for predicting the position of the user at a future time according to the travel track.
After the predicted travel trajectory of the target user is determined, the predicted travel trajectory and the historical travel speed may be input to a preset position prediction model to predict a target position of the target user at a specified time in the future through the preset position prediction model.
The training process for the preset position prediction model can be described in detail in conjunction with the following specific implementation.
In a specific implementation manner of the present disclosure, before the step 206, the method may further include:
step M1: and acquiring a preset number of initial traveling tracks after leaving the target area and initial traveling speeds corresponding to the initial traveling tracks.
In the embodiment of the present disclosure, the preset number refers to the number of samples used for obtaining the training initial position prediction model.
In this example, the initial position prediction model may be trained by using a travel trajectory after the user leaves the target area and a travel speed corresponding to the travel trajectory as training samples.
When the initial position prediction model needs to be trained, a preset number of initial travel tracks after leaving the target area and initial travel speeds corresponding to the initial travel tracks may be obtained, and then step M2 is executed.
Step M2: and training an initial position prediction model according to the initial travel track and the initial travel speed to obtain the preset position prediction model.
After the initial travel tracks and the initial travel speeds of the preset number are obtained, the initial position prediction model can be trained according to the initial travel tracks and the initial travel speeds of the preset number to obtain the trained preset position prediction model, specifically, the preset number of training samples can be sequentially input into the initial position prediction model to train the initial position prediction model, and the preset position prediction model can be obtained when all the training samples are trained.
Of course, after the preset position prediction model is trained, the trained preset position prediction model can be tested by combining with the test sample, so as to determine the accuracy of the predicted position of the trained preset position prediction model. The test samples are the travelling track and the travelling speed of the user which historically appears in the target area after the user leaves the target area, and therefore the preset position prediction model is tested, and the accuracy of the predicted position of the preset position prediction model is verified.
After the predicted travel locus and the historical travel speed are input to the preset position prediction model, step 207 is performed.
Step 207: outputting, by the predictive model, a target location of the target user at a specified time in the future.
The future designated time is a point in time in the future at which the location of the target user is predicted.
The target location refers to a predicted location to be reached by the target user at a specified time in the future.
After the predicted travel trajectory and the historical travel speed are input to the preset position prediction model, the target position of the target user at a specified time in the future may be output through the preset position prediction model.
According to the position prediction method provided by the embodiment of the disclosure, the user intention of the target user is determined according to the area attribute information of the target area where the target user is located, the historical travel track and the historical travel speed of the target user within the preset time period from the current time are obtained, the predicted travel track corresponding to the target user is determined according to the historical travel track, the historical travel track and the user intention, and the target position where the target user is located at the future designated time is determined according to the predicted travel track and the historical travel speed. According to the embodiment of the disclosure, the intention of the user is acquired by combining the attribute information of the area where the user is located, and the future position is predicted according to the intention of the user and the historical travel information of the user in the preset time period, so that the problem of deviation of position prediction caused by inertial prediction is avoided, and the accuracy of position prediction is improved.
Referring to fig. 3, a schematic structural diagram of a position prediction apparatus provided in the embodiment of the present disclosure is shown, and as shown in fig. 3, the position prediction apparatus may specifically include the following modules:
a user intention determining module 310, configured to determine a user intention of a target user according to area attribute information of a target area where the target user is located;
a historical travel track obtaining module 320, configured to obtain a historical travel track and a historical travel speed of the target user within a preset time period from a current time;
a predicted travel track determining module 330, configured to determine, according to the historical travel track, the historical travel speed, and the user intention, a predicted travel track corresponding to the target user;
a target position determining module 340, configured to determine a target position at which the target user is located at a future specified time according to the predicted travel track and the historical travel speed.
The position prediction device provided by the embodiment of the disclosure determines the user intention of the target user according to the region attribute information of the target region where the target user is located, acquires the historical travel track and the historical travel speed of the target user within a preset time period from the current time, determines the predicted travel track corresponding to the target user according to the historical travel track, the historical travel track and the user intention, and determines the target position where the target user is located at the future designated time according to the predicted travel track and the historical travel speed. According to the embodiment of the disclosure, the intention of the user is acquired by combining the attribute information of the area where the user is located, and the future position is predicted according to the intention of the user and the historical travel information of the user in the preset time period, so that the problem of deviation of position prediction caused by inertial prediction is avoided, and the accuracy of position prediction is improved.
Referring to fig. 4, a schematic structural diagram of another position prediction apparatus provided in the embodiment of the present disclosure is shown, and as shown in fig. 4, the position prediction apparatus may specifically include the following modules:
a user intention determining module 410, configured to determine a user intention of a target user according to area attribute information of a target area where the target user is located;
a historical travel track obtaining module 420, configured to obtain a historical travel track and a historical travel speed of the target user within a preset time period from a current time;
a predicted travel track determining module 430, configured to determine, according to the historical travel track, the historical travel speed, and the user intention, a predicted travel track corresponding to the target user;
a target position determining module 440, configured to determine a target position at which the target user is located at a future specified time according to the predicted travel track and the historical travel speed.
Optionally, the user intent determination module 410 comprises:
an area attribute type determining unit 411, configured to determine an area attribute type of the target area according to an entrance/exit position of the target area and distribution information of entity service parties around the target area;
a region property information determining unit 412, configured to determine region property information of the target region according to historical behavior data of a plurality of users after leaving the target region;
a user intention determining unit 413, configured to determine the user intention of the target user according to the region attribute type and the region property information.
Optionally, the region property information determining unit includes:
a probability obtaining subunit, configured to obtain probabilities that the plurality of users reach a set position after leaving the target area;
a displacement matrix generation subunit, configured to generate a probability displacement matrix according to the plurality of probabilities;
and the region property determining subunit is used for determining the region property information corresponding to the target region based on the probability displacement matrix.
Optionally, the target position determining module 440 includes:
a predicted travel trajectory input unit for inputting the predicted travel trajectory and the historical travel speed to a preset position prediction model;
and the target position output unit is used for outputting the target position of the target user at a specified time in the future through the prediction model.
Optionally, the apparatus further comprises:
the initial advancing track acquiring module is used for acquiring a preset number of initial advancing tracks after leaving the target area and initial advancing speeds corresponding to the initial advancing tracks;
and the position prediction model acquisition module is used for training an initial position prediction model according to the initial travel track and the initial travel speed to obtain the preset position prediction model.
The position prediction device provided by the embodiment of the disclosure determines the user intention of the target user according to the region attribute information of the target region where the target user is located, acquires the historical travel track and the historical travel speed of the target user within a preset time period from the current time, determines the predicted travel track corresponding to the target user according to the historical travel track, the historical travel track and the user intention, and determines the target position where the target user is located at the future designated time according to the predicted travel track and the historical travel speed. According to the embodiment of the disclosure, the intention of the user is acquired by combining the attribute information of the area where the user is located, and the future position is predicted according to the intention of the user and the historical travel information of the user in the preset time period, so that the problem of deviation of position prediction caused by inertial prediction is avoided, and the accuracy of position prediction is improved.
An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a computer program stored on the memory and executable on the processor, the processor implementing the location prediction method of the foregoing embodiments when executing the program.
Embodiments of the present disclosure also provide a readable storage medium, in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform the location prediction method of the foregoing embodiments.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present disclosure are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the embodiments of the present disclosure as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the embodiments of the present disclosure.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the embodiments of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, claimed embodiments of the disclosure require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this disclosure.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
The various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be understood by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a motion picture generating device according to an embodiment of the present disclosure. Embodiments of the present disclosure may also be implemented as an apparatus or device program for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present disclosure may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit embodiments of the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present disclosure and is not to be construed as limiting the embodiments of the present disclosure, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the embodiments of the present disclosure are intended to be included within the scope of the embodiments of the present disclosure.
The above description is only a specific implementation of the embodiments of the present disclosure, but the scope of the embodiments of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present disclosure, and all the changes or substitutions should be covered by the scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure shall be subject to the protection scope of the claims.
Claims (12)
1. A method of location prediction, comprising:
determining the user intention of a target user according to the region attribute information of a target region where the target user is located;
acquiring a historical travel track and a historical travel speed of the target user within a preset time period from the current time;
determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention;
and determining the target position of the target user at the future specified time according to the predicted travel track and the historical travel speed.
2. The method according to claim 1, wherein the determining the user intention of the target user according to the area attribute information of the target area where the target user is located comprises:
determining the area attribute type of the target area according to the entrance and exit position of the target area and the distribution information of entity service parties around the target area;
determining regional property information of the target region according to historical behavior data of a plurality of users after leaving the target region;
and determining the user intention of the target user according to the region attribute type and the region property information.
3. The method of claim 2, wherein determining the regional property information of the target region according to historical behavior data of a plurality of users after leaving the target region comprises:
acquiring the probability that the plurality of users reach a set position after leaving the target area;
generating a probability displacement matrix according to a plurality of probabilities;
and determining the region property information corresponding to the target region based on the probability displacement matrix.
4. The method of claim 1, wherein determining the target location at which the target user is located at a specified time in the future based on the predicted travel trajectory and the historical travel speed comprises:
inputting the predicted travel track and the historical travel speed to a preset position prediction model;
outputting, by the predictive model, a target location of the target user at a specified time in the future.
5. The method of claim 4, further comprising, prior to said inputting said predicted travel trajectory and said historical travel speed to a preset position prediction model:
acquiring a preset number of initial advancing tracks after leaving the target area and initial advancing speeds corresponding to the initial advancing tracks;
and training an initial position prediction model according to the initial travel track and the initial travel speed to obtain the preset position prediction model.
6. A position prediction apparatus, comprising:
the user intention determining module is used for determining the user intention of the target user according to the region attribute information of the target region where the target user is located;
the historical travel track acquisition module is used for acquiring the historical travel track and the historical travel speed of the target user within a preset time period from the current time;
the predicted travelling track determining module is used for determining a predicted travelling track corresponding to the target user according to the historical travelling track, the historical travelling speed and the user intention;
and the target position determining module is used for determining the target position of the target user at a specified time in the future according to the predicted travel track and the historical travel speed.
7. The apparatus of claim 6, wherein the user intent determination module comprises:
the area attribute type determining unit is used for determining the area attribute type of the target area according to the entrance and exit position of the target area and the distribution information of the entity service party around the target area;
the regional property information determining unit is used for determining regional property information of the target region according to historical behavior data of a plurality of users after leaving the target region;
and the user intention determining unit is used for determining the user intention of the target user according to the region attribute type and the region property information.
8. The apparatus according to claim 7, wherein the region property information determining unit includes:
a probability obtaining subunit, configured to obtain probabilities that the plurality of users reach a set position after leaving the target area;
a displacement matrix generation subunit, configured to generate a probability displacement matrix according to the plurality of probabilities;
and the region property determining subunit is used for determining the region property information corresponding to the target region based on the probability displacement matrix.
9. The method of claim 6, wherein the target location determination module comprises:
a predicted travel trajectory input unit for inputting the predicted travel trajectory and the historical travel speed to a preset position prediction model;
and the target position output unit is used for outputting the target position of the target user at a specified time in the future through the prediction model.
10. The apparatus of claim 9, further comprising:
the initial advancing track acquiring module is used for acquiring a preset number of initial advancing tracks after leaving the target area and initial advancing speeds corresponding to the initial advancing tracks;
and the position prediction model acquisition module is used for training an initial position prediction model according to the initial travel track and the initial travel speed to obtain the preset position prediction model.
11. An electronic device, comprising:
a processor, a memory and a computer program stored on the memory and executable on the processor, the processor implementing the position prediction method of any one of claims 1 to 5 when executing the program.
12. A readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the location prediction method of any of claims 1 to 5.
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| CN202011134106.2A CN112257924A (en) | 2020-10-21 | 2020-10-21 | Position prediction method, position prediction device, electronic equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114237239A (en) * | 2021-12-09 | 2022-03-25 | 珠海格力电器股份有限公司 | Device control method, device, electronic device and computer-readable storage medium |
| CN115038043A (en) * | 2021-11-08 | 2022-09-09 | 荣耀终端有限公司 | Information code popup method, medium and electronic equipment |
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2020
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115038043A (en) * | 2021-11-08 | 2022-09-09 | 荣耀终端有限公司 | Information code popup method, medium and electronic equipment |
| CN114237239A (en) * | 2021-12-09 | 2022-03-25 | 珠海格力电器股份有限公司 | Device control method, device, electronic device and computer-readable storage medium |
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