CN112785069A - Prediction method and device for terminal equipment changing machine, storage medium and electronic equipment - Google Patents

Prediction method and device for terminal equipment changing machine, storage medium and electronic equipment Download PDF

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CN112785069A
CN112785069A CN202110125577.5A CN202110125577A CN112785069A CN 112785069 A CN112785069 A CN 112785069A CN 202110125577 A CN202110125577 A CN 202110125577A CN 112785069 A CN112785069 A CN 112785069A
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阳宇翔
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Beijing Minglue Zhaohui Technology Co Ltd
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Abstract

The application provides a prediction method and a prediction device for terminal equipment changing, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring target operation data of a target object, wherein the target operation data is operation data generated by a plurality of selection operations executed by the target object on resource information on a target page within target time; acquiring a target time sequence and target object characteristics according to the target operation data, wherein the target time sequence is used for recording a plurality of selection operations according to the execution time sequence, and the target object characteristics are used for describing a target object; and performing change prediction according to the target device data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target device data is the device data of the target terminal device held by the target object, and the first prediction result is used for indicating whether the target object changes the target terminal device. The method and the device can objectively reflect whether the target object is replaced by the target terminal equipment, and improve the accuracy of replacing the machine prediction.

Description

Prediction method and device for terminal equipment changing machine, storage medium and electronic equipment
Technical Field
The present application relates to the field of data processing, and in particular, to a method and an apparatus for predicting a terminal device replacement, a storage medium, and an electronic device.
Background
The sale of the mobile terminal and the matched products becomes the strategic core of each large mobile phone manufacturer, and the accurate prediction of the user change is beneficial to the mobile phone manufacturer to increase the sales volume of the mobile terminal and enlarge the market scale. However, the user switching behavior is influenced by various factors, such as the income condition of the user, the consumption habit, the dependence degree of the mobile phone, the call behavior, the traffic use behavior and the like, and great challenges are brought to terminal switching prediction. In a big data advertisement monitoring scene, in order to realize accurate recommendation of mobile phone manufacturers, the change behavior of people before and after changing the mobile phone can be accurately predicted by deeply mining behavior data before and after changing the mobile phone.
In the related technology, a large amount of user information is firstly obtained, feature coding is carried out on the information, then complex feature extraction is carried out by using a composite parameter quantum particle swarm optimization algorithm, and finally a decision tree model is trained for prediction. However, the feature extraction project is large, more labor cost is needed, and the prediction accuracy of the training decision tree model is low.
Therefore, the problem that the accuracy of prediction of the mobile phone changing group is low exists in the related technology.
Disclosure of Invention
The application provides a prediction method and device for a terminal equipment switching machine, a storage medium and electronic equipment, so as to at least solve the problem of low accuracy of prediction of mobile phone switching machine people in the related art.
According to an aspect of an embodiment of the present application, a method for predicting a terminal device change is provided, including: acquiring target operation data of a target object, wherein the target operation data is operation data generated by a plurality of selection operations executed by the target object on resource information on a target page within target time; acquiring a target time sequence and target object characteristics according to the target operation data, wherein the target time sequence is used for recording the multiple selection operations according to the execution time sequence, and the target object characteristics are used for describing the target object; and performing change prediction according to the target device data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target device data is the device data of the target terminal device held by the target object, and the first prediction result is used for indicating whether the target object changes the target terminal device.
Optionally, obtaining the target time sequence and the target object feature according to the target operation data includes: extracting the execution time of the multiple selection operations and the resource information operated by each selection operation in the multiple selection operations from the target operation data; generating the target time sequence according to the sequence of the execution time of the plurality of selection operations; and determining the target object characteristics according to the execution time of the plurality of selection operations and the resource information operated by each selection operation, wherein the target object characteristics are used for describing the preference of the target object and the consumption level of the target object.
Optionally, the method further comprises: before performing switch prediction according to target device data, the target time sequence and the target object characteristics to obtain a first prediction result, obtaining a target field in a device log of the target terminal device, wherein the target field is used for indicating the device model of the target terminal device; matching the target field with the equipment models in an equipment database, wherein the equipment database stores equipment data corresponding to different equipment models; and determining the device data of the target device model as the target device data when the target device model matched with the target field exists in the device database.
Optionally, the performing a switch prediction according to the target device data, the target time sequence, and the target object feature, and obtaining a first prediction result includes: extracting context information of the target time sequence by using a target network model to obtain target time sequence characteristics; and performing switch prediction according to the target equipment data, the target time sequence characteristics and the target object characteristics to obtain a first prediction result.
Optionally, the performing a switch prediction according to the target device data, the target time series characteristic, and the target object characteristic, and obtaining a first prediction result includes: inputting the target device data, the target time series characteristics and the target object characteristics into a first classifier to obtain a first classification result, wherein the first classification result is used for indicating the probability of changing the target object; and determining the first prediction result according to the probability of changing the target object.
Optionally, the determining the first prediction result according to the probability of the target object changing the machine includes: comparing the probability of changing the machine of the target object with a preset threshold value; and determining the first prediction result when the probability of changing the target object is greater than or equal to the preset threshold, wherein the first prediction result is used for indicating the target object to change the target terminal device.
Optionally, the method further comprises: after performing switch prediction according to target device data, the target time sequence and the target object characteristics to obtain a first prediction result, and under the condition that the first prediction result indicates that the target object changes the target terminal device, inputting the target device data, the target time sequence characteristics and the target object characteristics into a second classifier to obtain a second classification result, wherein the second classification result is used for indicating the probability that the target terminal device is changed into each preset terminal device; and determining a second prediction result according to the second classification result, wherein the second prediction result is used for indicating the terminal equipment to which the target terminal equipment is replaced.
According to another aspect of the embodiments of the present application, there is also provided a device for predicting a terminal device change, including: the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring target operation data of a target object, and the target operation data is operation data generated by a plurality of selection operations executed by the target object on resource information on a target page within a target time; a second obtaining unit, configured to obtain a target time sequence and a target object feature according to the target operation data, where the target time sequence is used to record the multiple selection operations according to an execution time sequence, and the target object feature is used to describe the target object; and the predicting unit is used for performing switch prediction according to target equipment data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target equipment data is the equipment data of target terminal equipment held by the target object, and the first prediction result is used for indicating whether the target object is changed by the target terminal equipment.
Optionally, the first obtaining unit includes: the first extraction module is used for extracting the execution time of the multiple selection operations and the resource information operated by each selection operation in the multiple selection operations from the target operation data; a generating module, configured to generate the target time sequence according to an execution time sequence of the multiple selection operations; and the determining module is used for determining the target object characteristics according to the execution time of the plurality of selection operations and the resource information operated by each selection operation, wherein the target object characteristics are used for describing the preference of the target object and the consumption level of the target object.
Optionally, the apparatus further comprises: a third obtaining unit, configured to obtain a target field in an equipment log of the target terminal equipment before performing a machine change prediction according to target equipment data, the target time sequence, and the target object feature to obtain a first prediction result, where the target field is used to indicate an equipment model of the target terminal equipment; the matching unit is used for matching the target field with the equipment models in the equipment database, wherein the equipment database stores equipment data corresponding to different equipment models; a first determining unit, configured to determine, when a target device model matching the target field exists in the device database, device data of the target device model as the target device data.
Optionally, the prediction unit comprises: the extraction module is used for extracting the context information of the target time sequence by using a target network model to obtain the characteristics of the target time sequence; and the prediction module is used for carrying out switch prediction according to the target equipment data, the target time sequence characteristics and the target object characteristics to obtain a first prediction result.
Optionally, the prediction module comprises: the input submodule is used for inputting the target equipment data, the target time series characteristics and the target object characteristics into a first classifier to obtain a first classification result, wherein the first classification result is used for indicating the probability of changing the target object; and the determining submodule is used for determining the first prediction result according to the probability of changing the machine of the target object.
Optionally, the determining sub-module comprises: the comparison subunit is used for comparing the probability of changing the machine of the target object with a preset threshold value; a determining subunit, configured to determine the first prediction result when the probability that the target object changes the machine is greater than or equal to the preset threshold, where the first prediction result is used to indicate that the target object changes the target terminal device.
Optionally, the apparatus further comprises: an input unit, configured to input the target device data, the target time series characteristic, and the target object characteristic into a second classifier to obtain a second classification result when the first prediction result indicates that the target object changes the target terminal device, where the second classification result is used to indicate a probability that the target terminal device is changed into each preset terminal device; and a second determining unit, configured to determine a second prediction result according to the second classification result, where the second prediction result is used to indicate the terminal device to which the target terminal device is replaced.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein the memory is used for storing the computer program; a processor for executing the steps of the prediction method for terminal device switch in any of the above embodiments by running the computer program stored in the memory.
According to another aspect of the embodiments of the present application, there is further provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the steps of the prediction method for terminal device switch in any of the above embodiments when the computer program is executed.
In the embodiment of the application, a mode of performing switch prediction on target equipment data, a target time sequence and target object characteristics is adopted, target operation data executed by a target object on resource information on a target page within target time is obtained, the target time sequence and the target object characteristics are determined according to the target operation data, and then switch prediction is performed on target terminal equipment of the target object by using the target operation data, the target time sequence and the target object characteristics; the target device data is the device data of the target terminal device held by the target object, the target time sequence is the sequence of a plurality of selection operations executed by the target object on the resource information on the target page, and the target object characteristics can be used for describing the target object, so that the three data, namely the target operation data, the target time sequence and the target object characteristics, can objectively reflect whether the target object changes the target terminal device, the technical effect of improving the accuracy of the switch prediction is achieved, and the problem that the accuracy of the prediction of the mobile phone switch group is low in the related technology is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, 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 schematic diagram of an alternative prediction method for terminal device switch according to an embodiment of the present invention, which is a hardware environment;
fig. 2 is a schematic flowchart of a prediction method for an optional terminal device switch provided in an embodiment of the present invention;
fig. 3 is a schematic flowchart of an overall prediction method for an optional terminal device switch according to an embodiment of the present invention;
fig. 4 is a block diagram of an alternative prediction apparatus for a terminal device switch according to an embodiment of the present invention;
fig. 5 is a block diagram of an alternative electronic device according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application 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 application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, 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 apparatus 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.
According to one aspect of the embodiment of the application, a prediction method for terminal equipment change is provided. Alternatively, in this embodiment, the prediction method for terminal device change may be applied to the hardware environment shown in fig. 1. As shown in fig. 1, the terminal 102 may include a memory 104, a processor 106, and a display 108 (optional components). The terminal 102 may be communicatively coupled to a server 112 via a network 110, the server 112 may be configured to provide services (e.g., gaming services, application services, etc.) to the terminal or to clients installed on the terminal, and a database 114 may be provided on the server 112 or separate from the server 112 to provide data storage services to the server 112. Additionally, a processing engine 116 may be run in the server 112, and the processing engine 116 may be used to perform the steps performed by the server 112.
Alternatively, the terminal 102 may be, but is not limited to, a terminal capable of calculating data, such as a mobile terminal (e.g., a mobile phone, a tablet Computer), a notebook Computer, a PC (Personal Computer) Computer, and the like, and the network may include, but is not limited to, a wireless network or a wired network. Wherein, this wireless network includes: bluetooth, WIFI (Wireless Fidelity), and other networks that enable Wireless communication. Such wired networks may include, but are not limited to: wide area networks, metropolitan area networks, and local area networks. The server 112 may include, but is not limited to, any hardware device capable of performing computations.
Alternatively, in this embodiment, the prediction method for changing the terminal device may be executed by the server 112, the terminal 102, or both the server 112 and the terminal 102. The terminal 102 may execute the prediction method for changing the terminal device according to the embodiment of the present application, or may be executed by a client installed thereon.
Taking an operation in a server as an example, fig. 2 is a schematic flowchart of a prediction method for an optional terminal device switch according to an embodiment of the present application, and as shown in fig. 2, the flow of the method may include the following steps:
step S201, obtaining target operation data of a target object, where the target operation data is operation data generated by a plurality of selection operations performed by the target object on resource information on a target page within a target time.
Alternatively, the target object may be a certain user in the group of machine changing people; the target page can be a page currently browsed by the target object; the resource information may be advertisement information displayed in a target page, such as beauty makeup, mother and baby, fitness, mobile phone, and the like, wherein the advertisement information may include: a certain product, type of product, etc.; the target time may be a custom preset time, such as 2 minutes.
The server obtains multiple selection operations of the target object on the resource information on the target page within a target time, for example, the server obtains that the user A performs multiple click operations on advertisement information on a currently browsed page within 2 minutes.
Illustratively, all big data advertisement monitoring data of the machine change crowd is acquired by using the A monitoring system, and the monitoring data comprises but is not limited to at least one of the following data: the advertisement clicked by the target object on the target page, the advertisement type, the product type, the mobile device model, the click log time, the geographic position of the target object and the like.
Step S202, according to the target operation data, a target time sequence and target object characteristics are obtained, wherein the target time sequence is used for recording a plurality of selection operations according to the execution time sequence, and the target object characteristics are used for describing a target object.
Optionally, the target time sequence is obtained according to a sequence of multiple selection operations executed by the target object on the resource information on the target page, illustratively, aggregation statistics is performed according to the advertisement clicked by the target object, the number of times of clicking the advertisement by the target object each day in a period of time is counted, and the target time sequence is generated according to the number of clicks and the time sequence. For example, when the target object clicks a cosmetic advertisement on the target page at 10:00, and when the target object clicks a mobile phone advertisement on the target page at 10:01, the information recorded in the target time series includes: beauty makeup advertisement and mobile phone advertisement.
Target object characteristics can also be obtained according to target operation data of the target object, wherein the target object characteristics can be used for describing the target object.
Step S203, performing a switch prediction according to the target device data, the target time sequence, and the target object characteristics to obtain a first prediction result, where the target device data is device data of a target terminal device held by the target object, and the first prediction result is used to indicate whether the target object changes the target terminal device.
Optionally, device data of a target terminal device held by the target object is acquired as target device data, and switch prediction is performed according to the target device data, the target time sequence, and the target object characteristics to obtain a first prediction result, where the first prediction result is used to indicate whether the held target terminal device is to be changed by the current target object.
In the embodiment of the application, a mode of performing switch prediction on target equipment data, the target time sequence and the target object characteristics is adopted, the target time sequence and the target object characteristics are determined according to target operation data executed by a target object on resource information on a target page within target time, and then the switch prediction is performed on target terminal equipment of the target object by using the target operation data, the target time sequence and the target object characteristics; the target device data is the device data of the target terminal device held by the target object, the target time sequence is the sequence of a plurality of selection operations executed by the target object on the resource information on the target page, and the target object characteristics can be used for describing the target object, so that the technical effects of objectively reflecting whether the target object is changed by the target terminal device or not and improving the accuracy of the switch prediction are achieved according to the target operation data, the target time sequence and the target object characteristics, and the problem that the accuracy of the prediction of the mobile phone switch group is low in the related technology is solved.
As an alternative embodiment, the acquiring the target time series and the target object characteristics according to the target operation data includes:
extracting the execution time of a plurality of selection operations and the resource information operated by each selection operation in the plurality of selection operations from the target operation data;
generating a target time sequence according to the sequence of the execution time of the plurality of selection operations;
and determining the characteristics of the target object according to the execution time of the plurality of selection operations and the resource information operated by each selection operation, wherein the characteristics of the target object are used for describing the preference of the target object and the consumption level of the target object.
Optionally, the time of multiple selection operations performed by the target object on the resource information on the target page and the resource information selected by each selection operation are extracted from the target operation data, and a target time sequence is obtained according to the sequence of the selection operation time, where for example, the target operation data of the target object is: at 10:00, the beauty advertisement is clicked on the target page, and at 10:01, the mobile phone advertisement is clicked on the target page.
At this time, the resource information of the selecting operation executed by the target object is sequentially acquired from the target operation data according to the time sequence, and the resource information is recorded in the target time sequence, at this time, the information recorded in the target time sequence is: beauty makeup advertisement and mobile phone advertisement.
And obtaining the target object characteristics of the target object according to the execution time of the plurality of selection operations and the resource information operated by each selection operation. For example, within the execution time of the plurality of selection operations, if the acquired selected resource information of the target object is concentrated on the advertisements such as beauty cosmetics and ladies, it can be inferred that the gender of the current target object is female, and shopping is preferred, and meanwhile, the consumption capacity of the target object can be inferred according to the price of the resource information selected by the target object. Here, female, preference, and consumption ability are target object characteristics.
According to the method and the device, the sparse characteristics of the resource information are collected according to the time sequence of the selection operation of the target object on the resource information, the behavior habit of the target object can be further dug, and the target object characteristics describing the target object can be further obtained, so that the probability of replacing the target object can be accurately obtained according to the carved target object characteristics.
As an optional embodiment, before performing the switch machine prediction according to the target device data, the target time series, and the target object characteristics to obtain the first prediction result, the method further includes:
acquiring a target field in a device log of a target terminal device, wherein the target field is used for indicating the device model of the target terminal device;
matching the target field with the equipment models in the equipment database, wherein the equipment database stores equipment data corresponding to different equipment models;
and determining the device data of the target device model as the target device data when the target device model matched with the target field exists in the device database.
Optionally, the server obtains a target field recorded in a device log of the target terminal device, where the target field is used to indicate a device model of the target terminal device, such as a NOTE model, and then matches a terminal device that is the same as the target field from different device models stored in the device database, and if the terminal device can be matched, the device data of the matched terminal device is used as the device data of the target terminal device.
For example, a target device model identical to the target field (NOTE model) is matched in the device database, and device data corresponding to the target device model, including but not limited to terminal device data such as a terminal brand, a terminal model, a terminal price, a time to market, and an operating system type, is used as the target device data.
According to the embodiment, the device model stored in the device database is matched with the target field of the target terminal device, and the device data of the successfully matched terminal device is used as the target device data under the condition of successful matching, so that the target device data can be accurately and quickly obtained.
As an optional embodiment, performing a switch machine prediction according to the target device data, the target time series, and the target object characteristics, and obtaining the first prediction result includes:
extracting context information of a target time sequence by using a target network model to obtain target time sequence characteristics;
and performing change machine prediction according to the target equipment data, the target time sequence characteristics and the target object characteristics to obtain a first prediction result.
Optionally, in the embodiment of the present application, the target time sequence may be input into a target network model, and then the target network model extracts context information of the target time sequence to obtain a target time sequence feature, where the target time sequence feature may represent the context information of the time sequence of the resource information selected by the target object, the target network model may be an LSTM (Long Short-Term Memory, time-cycle neural network) network model, and the like, and the target time sequence feature is output by a last layer of the LSTM network model.
And then, performing switch prediction according to the target equipment data, the target time sequence characteristics and the target object characteristics to obtain a first prediction result.
According to the method and the device, the context information of the target time sequence is extracted by using the target network model to obtain the target time sequence characteristics, and the switch prediction result is obtained according to the target device data, the target time sequence characteristics and the target object characteristics, so that the labor cost for extracting the target time sequence characteristics can be reduced, and meanwhile, the accurate switch prediction result is obtained.
As an optional embodiment, performing a switch machine prediction according to the target device data, the target time series characteristic, and the target object characteristic, and obtaining the first prediction result includes:
inputting the target equipment data, the target time sequence characteristics and the target object characteristics into a first classifier to obtain a first classification result, wherein the first classification result is used for indicating the probability of changing the target object;
and determining a first prediction result according to the probability of changing the machine of the target object.
Optionally, the server inputs the obtained target device data, the target time series feature, and the target object feature into a first classifier to obtain a first classification result, where the first classifier may be a second classifier, such as a logistic regression classifier, and the like.
And obtaining the probability of the target object changing machine according to the first classification result, wherein the obtained probability of the target object changing machine is generally a decimal value, and determining a first prediction result according to the decimal value.
As an alternative embodiment, determining the first prediction result according to the probability of the target object changing machine includes:
comparing the probability of changing the target object with a preset threshold value;
and under the condition that the probability of replacing the target object is greater than or equal to a preset threshold value, determining a first prediction result, wherein the first prediction result is used for indicating the target object to replace the target terminal device.
Optionally, the obtained probability of replacing the target object is compared with a preset threshold, for example, the threshold is set to 0.8, and when the obtained probability of replacing the target object is greater than or equal to the preset threshold (0.8), it indicates that the target object is more likely to replace the target terminal device, and a first prediction result is determined, where the first prediction result is used to indicate that the target object will replace the target terminal device.
In addition, under the condition that the obtained probability of replacing the target object is smaller than a preset threshold value, the probability that the target object replaces the target terminal device is low, and a first prediction result is determined, wherein the first prediction result is used for indicating that the target object does not replace the target terminal device.
According to the embodiment, the probability of replacing the target object is compared with the preset threshold, the first prediction result is determined according to the comparison result, a mobile terminal manufacturer can conveniently and accurately find the people who replace the target object according to the first prediction result, and the sales success rate of the terminal equipment is improved.
As an optional embodiment, after performing the switch machine prediction according to the target device data, the target time series, and the target object characteristics to obtain a first prediction result, the method further includes:
under the condition that the first prediction result indicates that the target object is replaced by the target terminal device, inputting target device data, target time sequence characteristics and target object characteristics into a second classifier to obtain a second classification result, wherein the second classification result is used for indicating the probability that the target terminal device is replaced by each preset terminal device;
and determining a second prediction result according to the second classification result, wherein the second prediction result is used for indicating the terminal equipment to which the target terminal equipment is replaced.
Optionally, in a case that it is determined that the target terminal device is to be replaced by the target object, the target device data, the target time series characteristic, and the target object characteristic are input into a second classifier, so as to obtain a second classification result, where the second classifier may be a multi-classifier, such as a Support Vector Machine (SVM) classifier, a cascade classifier, or the like, and the second classification result is a probability that the target terminal device is replaced by each preset terminal device.
And then, the terminal equipment with the maximum probability value in the second classification result is taken as the terminal equipment to be replaced by the target terminal equipment.
Through the embodiment, the terminal equipment to be replaced of the target object is obtained through the second classification result, so that a mobile terminal seller can be helped to accurately put in mobile phone advertisements when putting the mobile phone advertisements in the target object, the cost is reduced, and the income is improved.
As an alternative embodiment, as shown in fig. 3, fig. 3 is a schematic flowchart of an overall prediction method for an alternative terminal device switch provided in the embodiment of the present invention, and the specific flow is as follows:
step S301, acquiring advertisement monitoring data of a target object;
step S302, calculating a time sequence of the click resource information of the target object according to the advertisement monitoring data; executing the step S304;
step S303, acquiring target object characteristics and target terminal equipment data according to the advertisement monitoring data;
step S304, inputting the time sequence into an LSTM network model, and outputting time sequence characteristics;
step S305, inputting the time series characteristics, the target object characteristics and the target terminal equipment data into a logistic regression model to obtain the probability of changing the target object.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, an optical disk) and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the methods of the embodiments of the present application.
According to another aspect of the embodiment of the present application, there is also provided a terminal device switch prediction apparatus for implementing the terminal device switch prediction method. Fig. 4 is a schematic diagram of an alternative prediction apparatus for terminal device change according to an embodiment of the present application, and as shown in fig. 4, the prediction apparatus may include:
a first obtaining unit 401, configured to obtain target operation data of a target object, where the target operation data is operation data generated by multiple selection operations performed by the target object on resource information on a target page within a target time;
a second obtaining unit 402, configured to obtain a target time sequence and a target object feature according to the target operation data, where the target time sequence is used to record multiple selection operations according to an execution time sequence, and the target object feature is used to describe a target object;
the predicting unit 403 is configured to perform switch prediction according to the target device data, the target time series, and the target object feature, to obtain a first prediction result, where the target device data is device data of a target terminal device held by the target object, and the first prediction result is used to indicate whether the target object changes the target terminal device.
In the embodiment of the application, a mode of performing switch prediction on target equipment data, a target time sequence and target object characteristics is adopted, the target time sequence and the target object characteristics are determined according to target operation data executed by a target object on resource information on a target page within target time, and then switch prediction is performed on target terminal equipment of the target object by using the target operation data, the target time sequence and the target object characteristics; the target device data is the device data of the target terminal device held by the target object, the target time sequence is the sequence of a plurality of selection operations executed by the target object on the resource information on the target page, and the target object characteristics can be used for describing the target object, so that the technical effects of objectively reflecting whether the target object is changed by the target terminal device or not and improving the accuracy of the switch prediction are achieved according to the target operation data, the target time sequence and the target object characteristics, and the problem that the accuracy of the prediction of the mobile phone switch group is low in the related technology is solved.
As an alternative embodiment, the first obtaining unit includes: the first extraction module is used for extracting the execution time of a plurality of selection operations and the resource information operated by each selection operation in the plurality of selection operations from the target operation data; the generating module is used for generating a target time sequence according to the sequence of the execution time of the plurality of selection operations; and the determining module is used for determining the characteristics of the target object according to the execution time of the plurality of selection operations and the resource information operated by each selection operation, wherein the characteristics of the target object are used for describing the preference of the target object and the consumption level of the target object.
As an alternative embodiment, the apparatus further comprises: a third obtaining unit, configured to obtain a target field in an equipment log of the target terminal equipment before performing a machine change prediction according to the target equipment data, the target time sequence, and the target object characteristic to obtain a first prediction result, where the target field is used to indicate an equipment model of the target terminal equipment; the matching unit is used for matching the target field with the equipment models in the equipment database, wherein the equipment database stores equipment data corresponding to different equipment models; and a first determining unit, configured to determine, when a target device model matching the target field exists in the device database, device data of the target device model as the target device data.
As an alternative embodiment, the prediction unit comprises: the extraction module is used for extracting the context information of the target time sequence by using the target network model to obtain the characteristics of the target time sequence; and the prediction module is used for carrying out switch prediction according to the target equipment data, the target time sequence characteristics and the target object characteristics to obtain a first prediction result.
As an alternative embodiment, the prediction module comprises: the input submodule is used for inputting the target equipment data, the target time sequence characteristics and the target object characteristics into the first classifier to obtain a first classification result, wherein the first classification result is used for indicating the probability of changing the target object; and the determining submodule is used for determining a first prediction result according to the probability of changing the machine of the target object.
As an alternative embodiment, the determining sub-module comprises: the comparison subunit is used for comparing the probability of changing the machine of the target object with a preset threshold value; the determining subunit is configured to determine a first prediction result when the probability that the target object changes the machine is greater than or equal to a preset threshold, where the first prediction result is used to indicate that the target object changes the target terminal device.
As an alternative embodiment, the apparatus further comprises: the input unit is used for inputting the data of the target equipment, the target time series characteristics and the target object characteristics into the second classifier to obtain a second classification result under the condition that the first prediction result indicates that the target object is replaced by the target terminal equipment, wherein the second classification result is used for indicating the probability that the target terminal equipment is replaced by each preset terminal equipment; and a second determining unit, configured to determine a second prediction result according to the second classification result, where the second prediction result is used to indicate the terminal device to which the target terminal device is replaced.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, which may be a server, a terminal, or a combination thereof, for implementing the method for predicting the terminal device to change the machine.
Fig. 5 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 5, including a processor 501, a communication interface 502, a memory 503, and a communication bus 504, where the processor 501, the communication interface 502, and the memory 503 are communicated with each other through the communication bus 504, where,
a memory 503 for storing a computer program;
the processor 501, when executing the computer program stored in the memory 503, implements the following steps:
s1, acquiring target operation data of the target object, wherein the target operation data is operation data generated by a plurality of selection operations executed by the target object on the resource information on the target page within the target time;
s2, acquiring a target time sequence and target object characteristics according to the target operation data, wherein the target time sequence is used for recording a plurality of selection operations according to the execution time sequence, and the target object characteristics are used for describing a target object;
and S3, performing switch prediction according to the target device data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target device data is the device data of the target terminal device held by the target object, and the first prediction result is used for indicating whether the target object is changed by the target terminal device.
Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include RAM, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
As an example, as shown in fig. 5, the memory 503 may include, but is not limited to, a first obtaining module 401, a second obtaining module 402, and a predicting module 403 in the predicting apparatus of the terminal device switch. In addition, the prediction apparatus may further include, but is not limited to, other module units in the prediction apparatus for changing the terminal device, which is not described in detail in this example.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
In addition, the electronic device further includes: and the display is used for displaying the predicted result of the terminal equipment changing machine.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 5 is only an illustration, and the device implementing the prediction method for terminal device changing may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 5 is a diagram illustrating a structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in this embodiment, the storage medium may be a program code for executing a prediction method for a terminal device to change a machine.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
s1, acquiring target operation data of the target object, wherein the target operation data is operation data generated by a plurality of selection operations executed by the target object on the resource information on the target page within the target time;
s2, acquiring a target time sequence and target object characteristics according to the target operation data, wherein the target time sequence is used for recording a plurality of selection operations according to the execution time sequence, and the target object characteristics are used for describing a target object;
and S3, performing switch prediction according to the target device data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target device data is the device data of the target terminal device held by the target object, and the first prediction result is used for indicating whether the target object is changed by the target terminal device.
Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.
According to yet another aspect of an embodiment of the present application, there is also provided a computer program product or a computer program comprising computer instructions stored in a computer readable storage medium; the processor of the computer device reads the computer instruction from the computer readable storage medium, and the processor executes the computer instruction, so that the computer device executes the steps of the prediction method for changing the terminal device in any of the embodiments.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the prediction method for terminal device switching according to the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed consumer terminal may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
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, and may also be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims (10)

1. A prediction method for terminal equipment changing is characterized by comprising the following steps:
acquiring target operation data of a target object, wherein the target operation data is operation data generated by a plurality of selection operations executed by the target object on resource information on a target page within target time;
acquiring a target time sequence and target object characteristics according to the target operation data, wherein the target time sequence is used for recording the multiple selection operations according to the execution time sequence, and the target object characteristics are used for describing the target object;
and performing change prediction according to the target device data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target device data is the device data of the target terminal device held by the target object, and the first prediction result is used for indicating whether the target object changes the target terminal device.
2. The method of claim 1, wherein obtaining a target time series and a target object feature from the target operational data comprises:
extracting the execution time of the multiple selection operations and the resource information operated by each selection operation in the multiple selection operations from the target operation data;
generating the target time sequence according to the sequence of the execution time of the plurality of selection operations;
and determining the target object characteristics according to the execution time of the plurality of selection operations and the resource information operated by each selection operation, wherein the target object characteristics are used for describing the preference of the target object and the consumption level of the target object.
3. The method of claim 1, wherein before performing a switch prediction based on the target device data, the target time series, and the target object characteristics to obtain a first prediction result, the method further comprises:
acquiring a target field in a device log of the target terminal device, wherein the target field is used for indicating the device model of the target terminal device;
matching the target field with the equipment models in an equipment database, wherein the equipment database stores equipment data corresponding to different equipment models;
and determining the device data of the target device model as the target device data when the target device model matched with the target field exists in the device database.
4. The method according to any one of claims 1 to 3, wherein the performing a switch machine prediction according to the target device data, the target time series, and the target object feature, and obtaining a first prediction result comprises:
extracting context information of the target time sequence by using a target network model to obtain target time sequence characteristics;
and performing switch prediction according to the target equipment data, the target time sequence characteristics and the target object characteristics to obtain a first prediction result.
5. The method according to claim 4, wherein the performing a switch prediction according to the target device data, the target time series characteristic, and the target object characteristic to obtain a first prediction result comprises:
inputting the target device data, the target time series characteristics and the target object characteristics into a first classifier to obtain a first classification result, wherein the first classification result is used for indicating the probability of changing the target object;
and determining the first prediction result according to the probability of changing the target object.
6. The method of claim 5, wherein determining the first prediction result based on the probability of the target object changing machine comprises:
comparing the probability of changing the machine of the target object with a preset threshold value;
and determining the first prediction result when the probability of changing the target object is greater than or equal to the preset threshold, wherein the first prediction result is used for indicating the target object to change the target terminal device.
7. The method of claim 4, wherein after the performing a switch prediction based on the target device data, the target time series, and the target object characteristics to obtain a first prediction result, the method further comprises:
under the condition that the first prediction result indicates that the target object changes the target terminal device, inputting the target device data, the target time series characteristics and the target object characteristics into a second classifier to obtain a second classification result, wherein the second classification result is used for indicating the probability that the target terminal device is changed into each preset terminal device;
and determining a second prediction result according to the second classification result, wherein the second prediction result is used for indicating the terminal equipment to which the target terminal equipment is replaced.
8. A prediction device for terminal equipment changing is characterized by comprising:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring target operation data of a target object, and the target operation data is operation data generated by a plurality of selection operations executed by the target object on resource information on a target page within a target time;
a second obtaining unit, configured to obtain a target time sequence and a target object feature according to the target operation data, where the target time sequence is used to record the multiple selection operations according to an execution time sequence, and the target object feature is used to describe the target object;
and the predicting unit is used for performing switch prediction according to target equipment data, the target time sequence and the target object characteristics to obtain a first prediction result, wherein the target equipment data is the equipment data of target terminal equipment held by the target object, and the first prediction result is used for indicating whether the target object is changed by the target terminal equipment.
9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,
the memory for storing a computer program;
the processor configured to execute the terminal device change prediction method steps of any one of claims 1 to 7 by executing the computer program stored on the memory.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of the method for predicting a terminal device change, as claimed in any one of claims 1 to 7, when executed.
CN202110125577.5A 2021-01-29 2021-01-29 Prediction method and device for terminal equipment changing machine, storage medium and electronic equipment Pending CN112785069A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113905078A (en) * 2021-09-27 2022-01-07 中国联合网络通信集团有限公司 Information pushing method, device, equipment and readable storage medium
CN115022872A (en) * 2021-11-05 2022-09-06 荣耀终端有限公司 Data transmission method and electronic equipment

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113905078A (en) * 2021-09-27 2022-01-07 中国联合网络通信集团有限公司 Information pushing method, device, equipment and readable storage medium
CN113905078B (en) * 2021-09-27 2023-07-04 中国联合网络通信集团有限公司 Information pushing method, device, equipment and readable storage medium
CN115022872A (en) * 2021-11-05 2022-09-06 荣耀终端有限公司 Data transmission method and electronic equipment
CN115022872B (en) * 2021-11-05 2023-04-07 荣耀终端有限公司 Data transmission method, electronic equipment and readable storage medium

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