Focus team member screening method, device, terminal and storage medium
阅读说明：本技术 一种焦点小组成员筛选方法、装置、终端及存储介质 (Focus team member screening method, device, terminal and storage medium ) 是由 赖信君 黄桂涛 于 2021-08-25 设计创作，主要内容包括：本申请公开了一种焦点小组成员筛选方法、装置、终端及存储介质,本申请提供的一种基于网络社交媒体数据挖掘的焦点小组成员筛选方法,通过爬取用户在社交平台发表的与产品信息相关的会话数据,并根据回复用户对针对该会话数据的回复观点,确定发帖用户的用户影响力系数,根据该用户影响力系数,再加上根据利用GraphSage模型得到的发帖用户的用户特征表示向量,通过聚类和特征相似度计算得到的用户区分度系数,从社交平台的用户中自动找到符合条件的焦点小组候选人,而不会因为人的主观因素造成偏差,还可以节省出大量的人力挑选时间,解决了目前产品调研工作存在投入高、成效低的技术问题。(The application discloses a focus team member screening method based on network social media data mining, which comprises the steps of crawling conversation data relevant to product information published by a user on a social platform, determining a user influence coefficient of a posting user according to a reply viewpoint of the replying user to the conversation data, automatically finding focus team candidates meeting conditions from users of the social platform according to the user influence coefficient and a user feature expression vector of the posting user obtained by utilizing a GraphSage model through a user discrimination coefficient obtained by clustering and feature similarity calculation, avoiding deviation caused by human subjective factors, saving a large amount of manpower selection time, and solving the problems that the investment is high, and the like in the current product research work, Low effect.)
1. A method for screening a focus team member, comprising:
according to product information of a product to be researched and developed, crawling data published on a social platform by a user in a web crawler mode to obtain user session data associated with the product information, wherein the user comprises: a posting user, and a replying user associated with the posting user;
inputting the user session data into a session opinion mining model so as to extract viewpoint characteristics of the user session data through the session opinion mining model;
determining an influence factor coefficient of the posting user according to a comparison result of a first viewpoint feature and each second viewpoint feature, and calculating a user influence coefficient of the posting user according to the influence factor coefficient, wherein the first viewpoint feature is a viewpoint feature extracted according to user session data of the posting user, and the second viewpoint feature is a viewpoint feature extracted according to user session data of the replying user;
inputting the user characteristics of the posting user into a preset GraphSage model to obtain a user characteristic expression vector, and clustering the posting user based on the user characteristic expression vector to obtain a plurality of clustering clusters;
calculating the feature similarity of posting users among different clustering clusters in a feature similarity calculation mode so as to obtain a user discrimination coefficient of the posting users according to the feature similarity conversion;
and determining a member screening list of the focus group according to the user influence coefficient and the user distinguishing coefficient.
2. The method as claimed in claim 1, wherein the product information specifically comprises: product name information and product field information.
3. The method according to claim 2, wherein crawling data published by a user on a social platform in a web crawler manner according to product information of a product to be researched to obtain user session data associated with the product information specifically comprises:
and according to the product field information, crawling data published on a social platform by a user in a web crawler mode to obtain user session data associated with the product field information.
4. The method of claim 1, wherein the opinion characteristics specifically comprise: viewpoint object features, viewpoint attribute features, and emotion polarity features.
5. The method as claimed in claim 4, wherein the determining the influence factor coefficient of the posting user according to the comparison result of the first viewpoint features and the second viewpoint features specifically comprises:
when the comparison result of the first viewpoint feature and the current second viewpoint feature is that the viewpoint object feature, the viewpoint attribute feature and the emotion polarity feature are the same, performing a self-increment operation on the influence factor coefficient of the posting user;
when the comparison result of the first viewpoint feature and the current second viewpoint feature is that the viewpoint object feature and the viewpoint attribute feature are the same, but the emotion polarity features are different, performing self-subtraction operation on the influence factor coefficient of the posting user;
and determining the influence factor coefficient of the posting user according to each comparison result.
6. The method as claimed in claim 1, wherein said calculating the user influence coefficient of the posting user according to the influence factor coefficient specifically comprises:
and calculating the user influence coefficient of the posting user through a PageRank algorithm according to the influence factor coefficient.
7. The method for screening focus panelists according to claim 1, wherein said calculating feature similarities of the posting users among different clustering clusters by a feature similarity calculation method, and obtaining a user discrimination coefficient of the posting users through conversion according to the feature similarities specifically comprises:
and calculating cosine similarity between the posting user and posting users of other clustering clusters in a cosine similarity calculation mode according to the user characteristics of the posting user, and converting according to the average value of the cosine similarity to obtain a user discrimination coefficient of the posting user.
8. A focus team member screening apparatus, comprising:
the session data crawling unit is used for crawling data published on a social platform by a user in a web crawler mode according to product information of a product to be researched and researched so as to obtain user session data associated with the product information, and the user comprises: a posting user, and a replying user associated with the posting user;
a viewpoint feature extraction unit, configured to input the user session data to a session opinion mining model, so as to extract a viewpoint feature of the user session data through the session opinion mining model;
a user influence coefficient calculation unit, configured to determine an influence factor coefficient of the posting user according to a comparison result between a first viewpoint feature and each second viewpoint feature, and calculate a user influence coefficient of the posting user according to the influence factor coefficient, where the first viewpoint feature is a viewpoint feature extracted from user session data of the posting user, and the second viewpoint feature is a viewpoint feature extracted from user session data of the replying user;
the clustering unit is used for inputting the user characteristics of the posting users into a preset GraphSage model to obtain user characteristic expression vectors, and clustering the posting users based on the user characteristic expression vectors to obtain a plurality of clustering clusters;
the user distinguishing degree coefficient calculating unit is used for calculating the feature similarity of the posting users among different clustering clusters through a feature similarity calculating mode so as to obtain the user distinguishing degree coefficient of the posting users through conversion according to the feature similarity;
and the member list determining unit is used for determining a member screening list of the focus group according to the user influence coefficient and the user distinguishing degree coefficient.
9. A focus team member screening terminal, comprising: a memory and a processor;
the memory for storing program code corresponding to the focus panelist screening method of any one of claims 1 to 7;
the processor is configured to execute the program code.
10. A computer-readable storage medium having stored therein program code corresponding to the focus panelist screening method of any one of claims 1 to 7.
Technical Field
The present application relates to the field of big data technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for screening focus team members.
Background
Before a new product is released to the market, enterprises typically organize a focal group to try out the product and collect user opinions after the trial to better improve product design. The focus team members are generally the target group to which the new product is directed. In particular, interviews take time, labor and cost, and therefore the focus group is generally not large (about ten people), so the individual attributes of the constituent group members should be as different as possible to maximize the inclusion of various user groups.
In reality, to construct a focal group by artificial organization, the following common problems often exist: (1) for some products, it may be difficult to find a suitable target population or candidate that is familiar with such products. (2) Some of the tare-filled candidates may be mixed in. In order to cheat the investigation cost, the crowd searches for a gunner help answer question to enter a focus group in the preliminary screening stage, but deep opinion feedback is difficult to provide in the formal investigation stage, so that the interview effect is poor. (3) It is difficult to find panelists whose backgrounds or user attributes are different. Many investigation companies have fixed groups of visited personnel, so that the visited personnel are highly homogeneous, the improvement of products is misled, and the technical problems of high investment and low effect of the conventional product investigation work are caused.
Disclosure of Invention
The application provides a focus team member screening method, a focus team member screening device, a focus team member screening terminal and a storage medium, which are used for solving the technical problems of high investment and low effect of the conventional product research work.
The application provides a focus team member screening method in a first aspect, which comprises the following steps:
according to product information of a product to be researched and developed, crawling data published on a social platform by a user in a web crawler mode to obtain user session data associated with the product information, wherein the user comprises: a posting user, and a replying user associated with the posting user;
inputting the user session data into a session opinion mining model so as to extract viewpoint characteristics of the user session data through the session opinion mining model;
determining an influence factor coefficient of the posting user according to a comparison result of a first viewpoint feature and each second viewpoint feature, and calculating a user influence coefficient of the posting user according to the influence factor coefficient, wherein the first viewpoint feature is a viewpoint feature extracted according to user session data of the posting user, and the second viewpoint feature is a viewpoint feature extracted according to user session data of the replying user;
calculating feature similarity among the posting users in a feature similarity calculation mode according to the user features of the posting users, and converting according to the feature similarity to obtain a user discrimination coefficient of the posting users;
and determining a member screening list of the focus group according to the user influence coefficient and the user distinguishing coefficient.
Preferably, the product information specifically includes: product name information and product field information.
Preferably, the crawling, according to the product information of the product to be researched and developed, the data published on the social platform by the user in a web crawler manner, so as to obtain the user session data associated with the product information specifically includes:
and according to the product field information, crawling data published on a social platform by a user in a web crawler mode to obtain user session data associated with the product field information.
Preferably, said point of view features include in particular: viewpoint object features, viewpoint attribute features, and emotion polarity features.
Preferably, the determining, according to the comparison result between the first perspective feature and each second perspective feature, the influence factor coefficient of the posting user specifically includes:
when the comparison result of the first viewpoint feature and the current second viewpoint feature is that the viewpoint object feature, the viewpoint attribute feature and the emotion polarity feature are the same, performing a self-increment operation on the influence factor coefficient of the posting user;
when the comparison result of the first viewpoint feature and the current second viewpoint feature is that the viewpoint object feature and the viewpoint attribute feature are the same, but the emotion polarity features are different, performing self-subtraction operation on the influence factor coefficient of the posting user;
and determining the influence factor coefficient of the posting user according to each comparison result.
Preferably, the calculating the user influence coefficient of the posting user according to the influence factor coefficient specifically includes:
and calculating the user influence coefficient of the posting user through a PageRank algorithm according to the influence factor coefficient.
Preferably, the calculating the feature similarity between the posting user and other posting users according to the user features of the posting user in a feature similarity calculation manner, and the obtaining the user discrimination coefficient of the posting user according to the feature similarity conversion specifically includes:
and calculating the cosine similarity between the posting user and other posting users in a cosine similarity calculation mode according to the user characteristics of the posting user, and converting according to the average value of the cosine similarity to obtain a user discrimination coefficient of the posting user.
A second aspect of the present application provides a focus team member screening apparatus, comprising:
the session data crawling unit is used for crawling data published on a social platform by a user in a web crawler mode according to product information of a product to be researched and researched so as to obtain user session data associated with the product information, and the user comprises: a posting user, and a replying user associated with the posting user;
a viewpoint feature extraction unit, configured to input the user session data to a session opinion mining model, so as to extract a viewpoint feature of the user session data through the session opinion mining model;
a user influence coefficient calculation unit, configured to determine an influence factor coefficient of the posting user according to a comparison result between a first viewpoint feature and each second viewpoint feature, and calculate a user influence coefficient of the posting user according to the influence factor coefficient, where the first viewpoint feature is a viewpoint feature extracted from user session data of the posting user, and the second viewpoint feature is a viewpoint feature extracted from user session data of the replying user;
the clustering unit is used for inputting the user characteristics of the posting users into a preset GraphSage model to obtain user characteristic expression vectors, and clustering the posting users based on the user characteristic expression vectors to obtain a plurality of clustering clusters;
the user distinguishing degree coefficient calculating unit is used for calculating the feature similarity of the posting users among different clustering clusters through a feature similarity calculating mode so as to obtain the user distinguishing degree coefficient of the posting users through conversion according to the feature similarity;
and the member list determining unit is used for determining a member screening list of the focus group according to the user influence coefficient and the user distinguishing degree coefficient.
The third aspect of the present application provides a focus team member screening terminal, including: a memory and a processor;
the memory is for storing program code corresponding to the focus panelist screening method as provided in the first aspect of the present application;
the processor is configured to execute the program code.
A fourth aspect of the present application provides a computer-readable storage medium, in which program codes corresponding to the focus panelist screening method provided in the first aspect of the present application are stored.
According to the technical scheme, the method has the following advantages:
the utility model provides a focus team member screening method based on network social media data mining, through crawling the conversation data that the user published at social platform and relevant with the product information, and according to replying the user to the view to this conversation data, confirm posting user's user influence coefficient, according to this user influence coefficient, and according to the user characteristic expression vector who posts the user who utilizes GraphSage model to obtain, user discrimination coefficient through clustering and characteristic similarity calculation, automatically find the focus team candidate who accords with the condition from the user of social platform, and can not cause the deviation because of the subjective factor of people, can also save a large amount of manpower and select the time, the technical problem that there is high input, the result is low in the work of product investigation at present is solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, 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 flowchart of an embodiment of a method for screening a focus team member according to the present application.
Fig. 2 is a schematic structural diagram of an embodiment of a focus team member screening apparatus provided in the present application.
Detailed Description
The embodiment of the application provides a method, a device, a terminal and a storage medium for screening focus group members, which are used for solving the technical problems of high investment and low effect in the current product research work.
At present, massive user big data on the internet social media provide a chance for solving the problems: (1) the social network has the characteristics of large user quantity, spontaneous propagation of users and large personal information quantity. The information of the user can be obtained in the social network (2) by collecting the personal information of the user to perform condition screening on the candidate, so that the cost for constructing a focus group can be greatly reduced. (3) The faithful users of a large number of products can publish their own opinions on the products by means of the social platform, and good candidates can be selected by collecting the speaking records of the social platforms of the users. (4) The user can spontaneously establish a community on the social network platform, and the property of the social network user can help people to narrow the range of selecting candidates and reduce the cost of screening the candidates. (5) By analyzing the user big data, the user portrait for the product can be generated for the candidate personnel, so that the research personnel can conveniently screen or put forward targeted problems in interviews, and the preparation and research efficiency is improved. With the increasing development of mobile internet and communication tools and the cultural popularization of network conferences in epidemic situations and post-epidemic situations, a foundation is laid for the application to screen focus group members through the network and even for interviews.
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only a part of the embodiments of the present application, and not all of the 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.
Referring to fig. 1, a first embodiment of the present application provides a method for screening focus panelists, including:
step 101, crawling data published on a social platform by a user in a web crawler mode according to product information of a product to be researched to obtain user session data associated with the product information, wherein the user comprises: a posting user, and a replying user associated with the posting user.
It should be noted that, to implement the method provided by the present application, first, through a web crawler technology, user session data associated with product information published by a user on a social platform is crawled, and the users mentioned in the present application include: the replying method comprises a posting user and a replying user associated with the posting user, wherein the replying user specifically refers to a user who replies to session data initiated by the posting user, and assuming that a user B replies to a message post posted by the user A, the user A can be regarded as the posting user at this moment, and the user B can be regarded as the replying user associated with the user A.
Step 102, inputting the user session data into a session opinion mining model so as to extract viewpoint characteristics of the user session data through the session opinion mining model.
And 103, determining an influence factor coefficient of the posting user according to a comparison result of the first viewpoint features and each second viewpoint feature, and calculating a user influence coefficient of the posting user according to the influence factor coefficient, wherein the first viewpoint features are viewpoint features extracted according to user session data of the posting user, and the second viewpoint features are viewpoint features extracted according to user session data of the replying user.
Step 102 and step 103 of this embodiment are processes of determining a user influence coefficient of a posting user, specifically, by inputting user session data into a pre-trained session opinion mining model, extracting viewpoint features in the user session data by using the session opinion mining model, and according to different user types, the extracted viewpoint features are specifically divided into two types, including: the first viewpoint features extracted from the user session data of the posting user and the second viewpoint features extracted from the user session data of the replying user.
By comparing the first viewpoint features with the second viewpoint features, the influence factor coefficient of the posting user is larger as the number of the second viewpoint features identical to the first viewpoint features is larger, the final influence factor coefficient of the posting user can be determined, and the user influence coefficient of the posting user is calculated based on the influence factor coefficients according to the comparison result.
Step 104, inputting the user characteristics of the posting user into a preset GraphSage model to obtain a user characteristic expression vector, and clustering the posting user based on the user characteristic expression vector to obtain a plurality of clustering clusters;
and 105, calculating the feature similarity of the posting users among different clustering clusters through a feature similarity calculation mode, and converting according to the feature similarity to obtain a user discrimination coefficient of the posting users.
In this embodiment, step 104 and step 105 are processes of determining a user distinction coefficient of a posting user, and according to user features of the posting user, including but not limited to features formed by personal information of the user's age, region, social platform age, gender, interest, and the like, feature similarity between the posting users is calculated in a feature similarity calculation manner to obtain the user distinction coefficient of the posting user through conversion according to the feature similarity, in general, the more similar the user feature of a certain user is to the user features of other users, the lower the user distinction coefficient is, and vice versa, the higher the user distinction coefficient is.
And step 106, determining a member screening list of the focus group according to the user influence coefficient and the user distinguishing coefficient.
And then, determining a member screening list of the focus group according to the user influence coefficient and the user discrimination coefficient obtained in the above steps as screening standards, wherein in general, the higher the user influence coefficient/user discrimination coefficient of one user is, the more the ranking is, the more easily the user is determined to be a candidate member of the focus group, and finally, the research company can send an invitation for joining the focus group to the users in the list according to the obtained member screening list by referring to the following table 1, and establish the focus group on the line and/or off the line, thereby carrying out product research work.
Table 1 focus group final membership list partial data
The embodiment of the application provides a focus team member screening method based on network social media data mining, conversation data which are published by a user on a social platform and are related to product information are crawled, a user influence coefficient of a posting user is determined according to a reply viewpoint of the posting user to the conversation data, and according to the user influence coefficient and a user discrimination coefficient obtained according to user characteristics of the posting user, focus team candidates meeting conditions are automatically found from users of the social platform without deviation caused by human subjective factors, a large amount of manpower selection time can be saved, and the technical problems of high investment and low effect of the existing product research work are solved.
The above is a detailed description of an embodiment of a method for screening a focus panelist provided herein, and the following is a detailed description of a second embodiment of a method for screening a focus panelist provided herein.
On the basis of the first embodiment, a second embodiment of the present application provides a focus team member screening method, including:
further, the product information specifically includes: product name information and product field information.
Further, according to the product information of the product to be researched and developed, crawling data published on the social platform by the user in a web crawler manner to obtain user session data associated with the product information specifically includes:
and according to the product field information, crawling data published on the social platform by the user in a web crawler mode to obtain user session data associated with the product field information.
It should be noted that, a social platform related to product research may be entered, a product posting of a product in the same product field as the product field to be researched is extracted, for example, a mobile phone, a garment, and the like, and postings of replying users about the product field information are collected to obtain user session data of the users about product research.
Further, the viewpoint features specifically include: viewpoint object features, viewpoint attribute features, and emotion polarity features.
Wherein, the viewpoint object feature can be understood as: the evaluated characteristics of product name, type and the like, such as millet 10, Huaqi P40, millet plate and the like; the viewpoint attribute features can be understood as: evaluating attributes of the evaluated product, such as workmanship, material quality, use fluency and the like; the emotional polarity characteristics can be understood as: the satisfaction degree of the user for evaluating the product reflects the dissatisfaction of the user for the product if the evaluation of the product by the user shows negative emotion. Otherwise, it is satisfactory.
Further, determining the influence factor coefficient of the posting user according to the comparison result of the first viewpoint features and each of the second viewpoint features specifically includes:
when the comparison result of the first viewpoint feature and the current second viewpoint feature is that the viewpoint object feature, the viewpoint attribute feature and the emotion polarity feature are the same, performing self-increment operation on the influence factor coefficient of the posting user;
when the comparison result of the first viewpoint characteristic and the current second viewpoint characteristic is that the viewpoint object characteristic and the viewpoint attribute characteristic are the same, but the emotion polarity characteristics are different, performing self-subtraction operation on the influence factor coefficient of the posting user;
and determining the influence factor coefficient of the posting user according to each comparison result.
Analyzing the part of speech of the viewpoint words through an emotion dictionary, finally calculating the influence factor coefficient of the user by comparing whether the viewpoints of the two users in the conversation are consistent, for example, a posting user criticizes the heat dissipation problem of the product a through posting, taking this posting as an example, it can be determined that the perspective object of the first perspective feature is characterized as product a, the perspective attribute feature is heat dissipation, the emotional polarity feature is negative, then, by comparing the first viewpoint feature with the second viewpoint feature of each replying user, if the viewpoint object feature, the viewpoint attribute feature and the emotion polarity feature of the replying user are the same as those of the posting user, the perspective of the posting user for the heat dissipation problem of the product A is agreed by the replying user, if the perspective object characteristic and the perspective attribute characteristic are the same, but the emotional polarity characteristic is different, it means that the posting user's view of the heat dissipation problem of the product a is not agreed by the replying user.
And finally, according to each comparison result, counting the difference between the identified viewpoint and the different identified viewpoint, and obtaining the influence factor coefficient of the posting user.
Further, calculating the user influence coefficient of the posting user according to the influence factor coefficient specifically includes:
and calculating the user influence coefficient of the posting user through a PageRank algorithm according to the influence factor coefficient.
The influence factor coefficient between the users is used as the weight between the two users, the user influence coefficients of different users are calculated by utilizing a PageRank algorithm,
further, calculating the feature similarity of the posting users among different clustering clusters by a feature similarity calculation mode, and obtaining the user discrimination coefficient of the posting users according to the feature similarity conversion specifically comprises:
and calculating cosine similarity between the posting user and posting users of other clustering clusters in a cosine similarity calculation mode according to the user characteristics of the posting user, and converting according to the average value of the cosine similarity to obtain a user discrimination coefficient of the posting user.
All information of the users including but not limited to age, region, social platform age, gender, interests and other information are used as basic characteristics of the users as user characteristics, graph embedding is carried out by using a graph sage model to add dialogue information between the users, and finally user clustering is carried out by using a kmeans algorithm. And determining the number of the clustered clusters by using an elbow rule, and solving cosine similarity between the user characteristic vector of a target user of a certain cluster and the characteristic vectors of users of other clusters to serve as user discrimination. And finally, averaging the user discrimination of the target user and other cluster users to obtain a user discrimination coefficient.
The above is a detailed description of a second embodiment of a focus team member screening method provided by the present application, and the following is a detailed description of a first embodiment of a focus team member screening apparatus provided by the present application.
Referring to fig. 2, a third embodiment of the present application provides a screening apparatus for focus panelists, which corresponds to the screening method for focus panelists provided in the first embodiment of the present application, and includes:
the session data crawling unit 201 is configured to crawl, according to product information of a product to be researched and developed, data published on a social platform by a user in a web crawler manner to obtain user session data associated with the product information, where the user includes: a posting user, and a replying user associated with the posting user;
a viewpoint feature extraction unit 202 for inputting the user session data to the session opinion mining model to extract a viewpoint feature of the user session data through the session opinion mining model;
a user influence coefficient calculation unit 203, configured to determine an influence factor coefficient of the posting user according to a comparison result between a first viewpoint feature and each second viewpoint feature, and calculate a user influence coefficient of the posting user according to the influence factor coefficient, where the first viewpoint feature is a viewpoint feature extracted from user session data of the posting user, and the second viewpoint feature is a viewpoint feature extracted from user session data of the replying user;
a clustering unit 204, configured to input the user characteristics of the posting user into a preset GraphSage model to obtain a user characteristic representation vector, and perform clustering on the posting user based on the user characteristic representation vector to obtain a plurality of clustering clusters;
a user discrimination coefficient calculation unit 205, configured to calculate feature similarities of posting users between different clustering clusters through a feature similarity calculation manner, so as to obtain a user discrimination coefficient of the posting user through conversion according to the feature similarities;
and a member list determining unit 206, configured to determine a member screening list of the focal group according to the user influence coefficient and the user distinguishing coefficient.
In addition, a fourth embodiment of the present application provides a focus team member screening terminal, including: a memory and a processor;
the memory is used for storing program codes, and the program codes correspond to the focus team member screening method provided by the first embodiment or the second embodiment of the application;
the processor is configured to execute the program code to implement the focus team member screening method according to the first embodiment or the second embodiment of the present application.
A fifth embodiment of the present application provides a computer-readable storage medium, in which program codes corresponding to the focus team member screening method provided in the first embodiment or the second embodiment of the present application are stored.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the terminal, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, 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, devices or units, and may be in an electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, 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, for example, capable of operation in sequences other than those illustrated or otherwise 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.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention 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 integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.