Similar user identification method, device, equipment and medium based on knowledge graph
阅读说明：本技术 基于知识图谱的相似用户识别方法、装置、设备及介质 (Similar user identification method, device, equipment and medium based on knowledge graph ) 是由 谭庆丰 陈小龙 谭润楠 于 2020-11-11 设计创作，主要内容包括：本发明公开了一种基于知识图谱的相似用户识别方法、装置、设备及介质。方法包括：计算不同用户之间共同关注的目标群组数量或者目标账号数量；将不同用户之间的兴趣爱好衡量度大于第一阈值的不同用户确定为相似用户；对兴趣爱好衡量度小于或等于第一阈值的不同用户进行关键词打标；根据不同用户之间的共同标签数量,计算不同用户之间的标签重合度,将标签重合度大于第一阈值的不同用户确定为相似用户；对标签重合度小于或等于第一阈值的不同用户进行用户知识图谱构建；根据计算得到用户知识图谱,计算不同用户之间的欧氏距离,将欧氏距离大于第二阈值的不同用户确定为相似用户。本发明能够准确且快速高效地找到相似用户,可广泛应用于互联网技术领域。(The invention discloses a method, a device, equipment and a medium for identifying similar users based on a knowledge graph. The method comprises the following steps: calculating the number of target groups or target account numbers which are concerned commonly among different users; determining different users with the interest and taste measurement larger than a first threshold value among the different users as similar users; marking keywords for different users with the interest and hobby measurement degrees smaller than or equal to a first threshold value; calculating the label contact ratio between different users according to the number of common labels among the different users, and determining the different users with the label contact ratio larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to a first threshold value; and calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users. The method and the device can accurately, quickly and efficiently find similar users, and can be widely applied to the technical field of the Internet.)
1. The similar user identification method based on the knowledge graph is characterized by comprising the following steps:
counting a target group and a target account number concerned by a user;
calculating the number of target groups or target account numbers which are concerned commonly among different users;
calculating the interest and hobby measurement among different users according to the number of the target groups or the number of the target accounts which are concerned together, and determining the different users with the interest and hobby measurement degree larger than a first threshold value as similar users; performing keyword marking on different users of which the interest balance measurement is less than or equal to the first threshold;
calculating the number of common labels among different users according to the marking result of the keyword;
according to the number of the common tags, calculating tag contact ratios among different users, and determining the different users with the tag contact ratios larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to the first threshold value;
calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users; determining different users whose Euclidean distance is less than or equal to the second threshold value as dissimilar users.
2. The method for identifying similar users based on knowledge graph according to claim 1, wherein the counting target groups and target accounts concerned by users comprises:
determining importance ranking of the groups and importance ranking of the accounts according to the speaking times of the users in different groups and the speaking times of different accounts;
and according to the importance ranking result, selecting a group of the ranking topN as a target group, and selecting an account of the ranking topN as a target account.
3. The method for identifying similar users based on knowledge-graph according to claim 1, wherein the calculating the interest and taste balance measure between different users according to the number of the target groups or the number of the target accounts concerned together comprises:
the same hobby among different users is converted into a first interval through a jaccard coefficient to obtain a hobby balance measurement;
the formula for the conversion is:
wherein hobby (a, b) represents the first interest and hobby coincidence of a user and a user; a represents a target group or a set of target accounts concerned by a user, and B represents a target group or a set of target accounts concerned by B user.
4. The method of claim 1, wherein the keyword spotting for different users whose interestingness metric is less than or equal to the first threshold comprises:
acquiring a message set sent by a user;
extracting the keywords in the message set to obtain target keywords, and taking the target keywords as user tags.
5. The method for identifying similar users based on knowledge-graph according to claim 4, wherein the extracting keywords in the message set to obtain target keywords comprises:
mining the keywords in the message set by a regular matching method, a tf-idf algorithm or an LDA algorithm;
and performing word segmentation processing on the mined keywords according to preset keyword tags to obtain target keywords.
6. The method for identifying similar users based on knowledge-graph according to claim 1, wherein in the step of calculating the label overlap ratio between different users according to the number of the common labels, the calculation formula of the label overlap ratio is as follows:
tag (a, B) represents the label coincidence degree of a user and a user, A 'represents a set of a user labels, and B' represents a set of B user labels.
7. The method of knowledge-graph based similar user identification as claimed in claim 1, wherein the user knowledge-graph construction comprises:
acquiring attribute information of different users;
performing node connection on different users according to the same attribute commonly owned by the different users;
establishing a relation space for the relation between different users through a TransR model;
mapping the attribute information of each user to a corresponding relationship space to obtain a representation vector of the attribute information in the current relationship space;
and mapping the representation vectors of the head and tail entities of each user to a relation space through a transformation matrix to obtain vectorized representation of each user.
8. The similar user identification device based on knowledge graph is characterized by comprising:
the statistical module is used for counting a target group and a target account number concerned by a user;
the computing module is used for computing the number of target groups or the number of target accounts which are concerned commonly among different users;
the interest and taste measurement determining module is used for calculating interest and taste measurement among different users according to the number of the commonly concerned target groups or the number of the target accounts, and determining the different users with the interest and taste measurement degree larger than a first threshold value as similar users; performing keyword marking on different users of which the interest balance measurement is less than or equal to the first threshold;
the label quantity calculating module is used for calculating the quantity of common labels among different users according to the marking result of the keywords;
the label contact ratio determining module is used for calculating the label contact ratio among different users according to the number of the common labels, and determining the different users with the label contact ratio larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to the first threshold value;
the Euclidean distance calculation module is used for calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users; determining different users whose Euclidean distance is less than or equal to the second threshold value as dissimilar users.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-7.
Technical Field
The invention relates to the technical field of internet, in particular to a method, a device, equipment and a medium for identifying similar users based on a knowledge graph.
Background
At present, the process of discovering similar users on the internet generally includes firstly obtaining information of users on certain social applications, including basic information, behavior information, sent content and the like of the users, in an automatic, manual, semi-automatic or third-party cooperative manner; then extracting the information, including user name, mobile phone or mailbox number, address, sex, concerned friends or group, sending message, etc.; then, similarity calculation, clustering calculation and the like of character strings are carried out on the contents; and finally, finding out users with similar viewpoints or interests to support subsequent applications.
Due to the fact that social network applications are diverse, users have different behaviors and emotions in the network, and the data volume of the users is huge and numerous, user information on the network has the characteristics of high data dimension, sparseness and noise, although the prior art can analyze the users to a certain extent, the methods are more suitable for scenes with simple and standard structures and lower data dimension, and various types of information are not comprehensively considered, so that the problems that the calculation result is not accurate enough and the calculation efficiency is low exist, and similar users cannot be found quickly and effectively in the face of various types of user information which are increasing and change quickly.
Disclosure of Invention
In view of this, embodiments of the present invention provide an accurate and efficient method, apparatus, device and medium for identifying similar users based on a knowledge graph.
The invention provides a knowledge graph-based similar user identification method, which comprises the following steps:
counting a target group and a target account number concerned by a user;
calculating the number of target groups or target account numbers which are concerned commonly among different users;
calculating the interest and hobby measurement among different users according to the number of the target groups or the number of the target accounts which are concerned together, and determining the different users with the interest and hobby measurement degree larger than a first threshold value as similar users; performing keyword marking on different users of which the interest balance measurement is less than or equal to the first threshold;
calculating the number of common labels among different users according to the marking result of the keyword;
according to the number of the common tags, calculating tag contact ratios among different users, and determining the different users with the tag contact ratios larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to the first threshold value;
calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users; determining different users whose Euclidean distance is less than or equal to the second threshold value as dissimilar users.
In some embodiments, the counting target groups and target accounts of interest to the user includes:
determining importance ranking of the groups and importance ranking of the accounts according to the speaking times of the users in different groups and the speaking times of different accounts;
and according to the importance ranking result, selecting a group of the ranking topN as a target group, and selecting an account of the ranking topN as a target account.
In some embodiments, the calculating the interest-balance measure between different users according to the number of the target groups or the number of the target accounts of common interest includes:
the same hobby among different users is converted into a first interval through a jaccard coefficient to obtain a hobby balance measurement;
the formula for the conversion is:
wherein the content of the first and second substances,representing the first interest and hobby coincidence of a user and a user; a represents a target group or a set of target accounts concerned by a user, and B represents a target group or a set of target accounts concerned by B user.
In some embodiments, the keyword tagging of different users whose interestingness metric is less than or equal to the first threshold comprises:
acquiring a message set sent by a user;
extracting the keywords in the message set to obtain target keywords, and taking the target keywords as user tags.
In some embodiments, the extracting the keywords in the message set to obtain the target keyword includes:
mining the keywords in the message set by a regular matching method, a tf-idf algorithm or an LDA algorithm;
and performing word segmentation processing on the mined keywords according to preset keyword tags to obtain target keywords.
In some embodiments, in calculating the degree of label overlap between different users according to the number of common labels, the calculation formula of the degree of label overlap is:
tag (a, B) represents the label coincidence degree of a user and a user, A 'represents a set of a user labels, and B' represents a set of B user labels.
In some embodiments, the user knowledge graph is constructed, comprising:
acquiring attribute information of different users;
performing node connection on different users according to the same attribute commonly owned by the different users;
establishing a relation space for the relation between different users through a TransR model;
mapping the attribute information of each user to a corresponding relationship space to obtain a representation vector of the attribute information in the current relationship space;
and mapping the representation vectors of the head and tail entities of each user to a relation space through a transformation matrix to obtain vectorized representation of each user.
Another aspect of the present invention provides a similar users identification apparatus based on knowledge-graph, comprising:
the statistical module is used for counting a target group and a target account number concerned by a user;
the computing module is used for computing the number of target groups or the number of target accounts which are concerned commonly among different users;
the interest and taste measurement determining module is used for calculating interest and taste measurement among different users according to the number of the commonly concerned target groups or the number of the target accounts, and determining the different users with the interest and taste measurement degree larger than a first threshold value as similar users; performing keyword marking on different users of which the interest balance measurement is less than or equal to the first threshold;
the label quantity calculating module is used for calculating the quantity of common labels among different users according to the marking result of the keywords;
the label contact ratio determining module is used for calculating the label contact ratio among different users according to the number of the common labels, and determining the different users with the label contact ratio larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to the first threshold value;
the Euclidean distance calculation module is used for calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users; determining different users whose Euclidean distance is less than or equal to the second threshold value as dissimilar users.
Another aspect of the invention provides an electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Another aspect of the invention provides a computer readable storage medium storing a program for execution by a processor to implement a method as described above.
According to the embodiment of the invention, the similar users are identified by calculating the interest and taste balance measurement, the label contact ratio or the knowledge graph among different users, and the similar users can be accurately, quickly and efficiently found.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are 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 to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating the overall steps of a similar user identification method according to an embodiment of the present invention;
fig. 2 is a diagram illustrating a user map according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Aiming at the problems in the prior art, the embodiment of the invention provides a similar user identification method based on a knowledge graph, which comprises the following steps:
counting a target group and a target account number concerned by a user;
calculating the number of target groups or target account numbers which are concerned commonly among different users;
calculating the interest and hobby measurement among different users according to the number of the target groups or the number of the target accounts which are concerned together, and determining the different users with the interest and hobby measurement degree larger than a first threshold value as similar users; performing keyword marking on different users of which the interest balance measurement is less than or equal to the first threshold;
calculating the number of common labels among different users according to the marking result of the keyword;
according to the number of the common tags, calculating tag contact ratios among different users, and determining the different users with the tag contact ratios larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to the first threshold value;
calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users; determining different users whose Euclidean distance is less than or equal to the second threshold value as dissimilar users.
It can be understood that similar users refer to users with the same interests, similar attributes, and similar viewpoints, and these information can be embodied from topics commonly followed by users, groups or communities commonly joined, and common friends, which can represent the group characteristics of users to a certain extent; it is also possible to extract from the text spoken. By means of the information, the character image of the user can be constructed in a mode of a map, further embedding analysis is conducted, and the similar user or user group is found. The primary step is therefore the extraction of relevant information.
In some embodiments, the counting target groups and target accounts of interest to the user includes:
determining importance ranking of the groups and importance ranking of the accounts according to the speaking times of the users in different groups and the speaking times of different accounts;
and according to the importance ranking result, selecting a group of the ranking topN as a target group, and selecting an account of the ranking topN as a target account.
It should be noted that the group and account numbers concerned by the users together mean that all groups and account numbers concerned by the users are counted, wherein the group and the account numbers focus on the group which has the most speech and the account number which has the most communication, 1 is added to the activity of the corresponding group or account number every speech or every communication, the group and the account number of topN before the activity are taken as one of preference characteristics of the users, the same number in the group and the account number of topN before the activity between the users is compared, and the fact that the two users have the same interest and preference with higher probability is proved if the number exceeds a certain threshold value. In order to measure the probability of the same interest and hobby more conveniently, the measure of the same interest and hobby is converted into a [0,1] interval by a jaccard coefficient, and the specific formula is as follows:
wherein the content of the first and second substances,representing the degree of coincidence of interests and hobbies of users a and B, A representing a group or a set of accounts concerned by the user a, B representing a group or a set of accounts concerned by the user B, if hobby (a, B)>Mu and mu are threshold value over parameters, two users can be directly judged to be similar users, and the purpose of doing so is to reduce the calculation time as much as possible without losing the accuracy.
In some embodiments, the calculating the interest-balance measure between different users according to the number of the target groups or the number of the target accounts of common interest includes:
the same hobby among different users is converted into a first interval through a jaccard coefficient to obtain a hobby balance measurement;
the formula for the conversion is:
wherein the content of the first and second substances,representing the first interest and hobby coincidence of a user and a user; a represents a target group or a set of target accounts concerned by a user, and B represents a target group or a set of target accounts concerned by B user.
In some embodiments, the keyword tagging of different users whose interestingness metric is less than or equal to the first threshold comprises:
acquiring a message set sent by a user;
extracting the keywords in the message set to obtain target keywords, and taking the target keywords as user tags.
It should be noted that ifFurther determination with the user tag is required. The user label generation is extracted through information sent by a user, the main method is to splice all messages sent by the user, then use a keyword extraction algorithm such as regular matching, tf-idf, LDA and the like to mine high-frequency and important keywords in the messages, take 5 to 10 words as labels of the user, and filter out words with little meaning by using a common word dictionary and a stop word dictionary in the process. For some keyword labels in specific fields, such as topics in different fields of crime, politics, black and grey products, economy, livelihood and the like, some collected linguistic data in the early stage are utilized, then corresponding categories are marked for the linguistic data through manual marking, and the linguistic data with the labeled categories are used for word segmentation, so that label mining of users is facilitated.
Counting the number of the same labels among the users by adopting the same method as the common group through the mined labels as to judge whether the users are similar in interest:
wherein tag (a, B) represents the tag contact ratio of a user and B user, A ' represents the set of a user tag, B ' represents the set of B user tag, if tag (a, B) > mu ' (mu ' is a threshold value over-parameter), two users can be directly judged to be similar users, if tag (a, B) ≦ mu ', a user knowledge graph needs to be constructed, and the discovery of similar users is realized through the embedding alignment of the knowledge graph.
In some embodiments, the extracting the keywords in the message set to obtain the target keyword includes:
mining the keywords in the message set by a regular matching method, a tf-idf algorithm or an LDA algorithm;
and performing word segmentation processing on the mined keywords according to preset keyword tags to obtain target keywords.
In some embodiments, in calculating the degree of label overlap between different users according to the number of common labels, the calculation formula of the degree of label overlap is:
tag (a, B) represents the label coincidence degree of a user and a user, A 'represents a set of a user labels, and B' represents a set of B user labels.
In some embodiments, the user knowledge graph is constructed, comprising:
acquiring attribute information of different users;
performing node connection on different users according to the same attribute commonly owned by the different users;
establishing a relation space for the relation between different users through a TransR model;
mapping the attribute information of each user to a corresponding relationship space to obtain a representation vector of the attribute information in the current relationship space;
and mapping the representation vectors of the head and tail entities of each user to a relation space through a transformation matrix to obtain vectorized representation of each user.
It should be noted that, when the group or account concerned by the user and the user tag cannot determine whether the users are similar, a user knowledge graph is constructed by integrating a plurality of information of the users, where the information includes a user name, a user gender, an address, an occupation, a personal description, and a calculated account number or group concerned by the user and a user interest tag.
Through the user information, all users from different social applications are integrated to form a user knowledge graph consisting of all user related information, a central node of the knowledge graph is each user node, the user related information is used as attribute information of the user related information, if the users have the same user name, address and gender, the users are connected through the same attribute, each label, concerned group or account of the user is also used as a single attribute node, and the users are connected through the same attribute node, as shown in fig. 2:
after the knowledge graph is constructed, embedded calculation needs to be carried out on the knowledge graph, and the network representation model used by the method is a TransR model. The TransR model considers that one entity is a complex of multiple attributes, different relationships concern different attributes of the entity, and different relationships have different semantic spaces, so that the relationship between a user and the attributes in the user knowledge graph can be well processed, and meanwhile, the conditions of one-to-many, many-to-one and many-to-many can be processed. When the method is used, the TransR model establishes respective relationship spaces for different relationships, the entities are mapped to the relationship spaces for calculation during calculation, each relationship has a transformation matrix Mr and a representation vector r in the relationship space, and the representation vectors of head and tail entities are mapped to the relationship spaces through the transformation matrix:
tr＝tMr
by training in different relationship spacesThereby obtaining a sectionVectorized representation of points. For example, a is taken as a head entity, each attribute value of a is taken as a tail entity, the attribute name of a is taken as a relationship, and the vectorization representation of a is obtained through such triple training as (a, name, Li Ming) and the like. The user knowledge graph is subjected to embedded calculation through TransR, unified representation of each user in a low-dimensional vector space can be obtained, the similarity between the users can be judged by calculating the distance represented by different user vectors, the distance can be measured by adopting the Euclidean distance, and the two users can be considered to be similar if the obtained distance is less than a certain threshold value.
Wherein dist (a, b) represents the distance between users a and b in the embedding space, if dist (a, b) < then a and b are similar, α represents the similarity threshold based on the distance, otherwise, a and b are considered dissimilar.
In summary, the invention mainly divides the similar user discovery problems into three parts, namely the contact degree of the group or account number which is commonly concerned by the users, the contact degree of the user labels and the similarity of the user knowledge map embedded vector, and the similarity between the users is measured through the three indexes.
Specifically, as shown in fig. 1, the method for identifying similar users in the embodiment of the present invention specifically includes:
1) counting a top TopN group or an account number concerned by a user;
2) calculating the number of common concern groups or accounts among users;
3) calculating a jaccard value and judging whether the jaccard value is greater than a threshold value;
4) if the jaccard value is larger than the threshold value, the two users are similar, otherwise, the step (5) is reached;
5) marking by user keywords or viewpoints;
6) calculating the number of common labels among users;
7) calculating a jaccard value and judging whether the jaccard value is greater than a threshold value;
8) if the jaccard value is larger than the threshold value, the two users are similar, otherwise, the step (9) is reached;
9) constructing a user knowledge graph;
10) embedding and calculating the Euclidean distance dist between users by using a knowledge graph;
11) if dist is larger than the threshold value, the two users are similar, otherwise, the users are not similar
Compared with the prior art, the method has the advantages that the user information is extracted and marked, the label and the group or account concerned by the user are utilized to help the similar user to find, the characteristics of large user data volume and high complexity are effectively reduced, the comparison and calculation speed can be greatly improved, meanwhile, various information of the user, including user names, addresses, user behaviors and the like, is comprehensively utilized to construct the knowledge graph, the weakly associated user data is more intuitively and conveniently linked, the dimensionality of the data is reduced by an embedding method, the direct comparison of the user similarity is facilitated, and the higher calculation accuracy is realized. Compared with the prior art, the method can more efficiently and accurately find out the similar users among the users, and supports various downstream applications.
Aiming at the conditions that various user information is complex and diverse, the user behavior characteristics are rapidly changed and have large differences, and data are diverse and sparse in social network application, the method generates the user tags by using a keyword and topic extraction method, calculates the similarity degree of the users according to the contact degree of the user tags and the contents of groups, friends and the like which are commonly concerned by the users, and can effectively shorten the comparison and calculation time of user similarity judgment; if the similarity between users cannot be judged through the user tags, common group friends and the like, a knowledge graph between the users needs to be constructed by combining other information of the users, such as position, gender, age, occupation and the like, the information of user nodes in the knowledge graph is expressed in a vectorization mode by using a graph embedding algorithm, and users with similar vector positions can be similar users.
The embodiment of the invention also provides a similar user identification device based on the knowledge graph, which comprises the following steps:
the statistical module is used for counting a target group and a target account number concerned by a user;
the computing module is used for computing the number of target groups or the number of target accounts which are concerned commonly among different users;
the interest and taste measurement determining module is used for calculating interest and taste measurement among different users according to the number of the commonly concerned target groups or the number of the target accounts, and determining the different users with the interest and taste measurement degree larger than a first threshold value as similar users; performing keyword marking on different users of which the interest balance measurement is less than or equal to the first threshold;
the label quantity calculating module is used for calculating the quantity of common labels among different users according to the marking result of the keywords;
the label contact ratio determining module is used for calculating the label contact ratio among different users according to the number of the common labels, and determining the different users with the label contact ratio larger than a first threshold value as similar users; constructing a user knowledge graph for different users with the label contact ratio smaller than or equal to the first threshold value;
the Euclidean distance calculation module is used for calculating Euclidean distances among different users according to the user knowledge graph obtained by calculation, and determining the different users with the Euclidean distances larger than a second threshold value as similar users; determining different users whose Euclidean distance is less than or equal to the second threshold value as dissimilar users.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units 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 logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.