Disclosure of Invention
The embodiment of the invention provides a content delivery method, a content delivery device and a terminal, which are used for solving one or more technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a content delivery method, including:
Clustering is carried out according to the portrait information of the user, and a first category to which the user belongs is obtained;
clustering is carried out on the evaluation information of the first content set according to the user, so that a second category to which the user belongs is obtained;
Determining a target delivery page corresponding to the user belonging to the first category according to the access information of the user belonging to the first category to each page;
Selecting target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the second category on the second content set;
and throwing the target throwing content to the target throwing page.
In one embodiment, determining a target delivery page corresponding to the user belonging to the first category according to the access information of the user belonging to the first category to each page includes:
According to the access information of the users belonging to the first category to each page, calculating the access weight value of the users belonging to the first category to each page;
And determining the target delivery page corresponding to the user belonging to the first category according to the ordering result of the access weight value of the user belonging to the first category to each page.
In one embodiment, selecting the target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the second category on the second content set includes:
Predicting the evaluation information of the user belonging to the first category on the second content set according to the evaluation information of the user belonging to the second category on the second content set, the evaluation information of the user belonging to the second category on the first content set and the evaluation information of the user belonging to the first category on the first content set;
and selecting target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the first category on the second content set.
In one embodiment, predicting the rating information of the user belonging to the first category for the second content set based on the rating information of the user belonging to the second category for the second content set, the rating information of the user belonging to the second category for the first content set, includes:
The method for obtaining the evaluation information of the user i belonging to the first category on the first content set comprises the following steps: the predictive score of user i belonging to the first category for any content c1 in the first set of content is referred to as a first predictive score Ri,c1, and the average predictive score of user i belonging to the first category for all content in the first set of content is referred to as a first predictive average score
The method for obtaining the evaluation information of the user j belonging to the second category on the first content set comprises the following steps: the predictive score of user j belonging to the second category for any content c1 in the first set of content is referred to as the second predictive score Rj,c1, and the average predictive score of user j belonging to the second category for all content in the first set of content is referred to as the second predictive average score
And acquiring the evaluation information of the user j belonging to the second category on the second content set, wherein the evaluation information comprises the following steps: a predictive score for any content c2 in the second set of content by user j belonging to the second category, referred to as a third predictive score Rj,c2;
the method for obtaining the evaluation information of the user j belonging to the second category on all the contents in the first content set and the second content set comprises the following steps: the average predictive score of user j belonging to the second category over all of the first and second sets of content, referred to as the third predictive average score
According to the first predictive score Ri,c1, the second predictive score Rj,c1 and the first predictive average scoreAverage of the second predictionCalculating content click prediction score similarity sim (i, j);
average score according to the content click prediction score similarity sim (i, j)The third predictive score Rj,c2, the third predictive average scoreA predictive score Ri,c2 for the second set of content is calculated for the user belonging to the first category, and the rating information for the second set of content for the user belonging to the first category includes Ri,c2.
In a second aspect, an embodiment of the present invention provides a content delivery apparatus, including:
The first class clustering module is used for clustering according to the portrait information of the user to obtain a first class to which the user belongs;
The second category clustering module is used for clustering the evaluation information of the first content set according to the user to obtain a second category to which the user belongs;
the target delivery page determining module is used for determining a target delivery page corresponding to the user belonging to the first category according to the access information of the user belonging to the first category to each page;
The target delivery content selection module is used for selecting target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the second category on the second content set;
and the delivery operation module is used for delivering the target delivery content to the target delivery page.
In one embodiment, the targeting page determination module includes:
the access weight value calculation unit is used for calculating the access weight value of the user belonging to the first category to each page according to the access information of the user belonging to the first category to each page;
and the target delivery page determining unit is used for determining the target delivery page corresponding to the user belonging to the first category according to the ordering result of the access weight value of the user belonging to the first category to each page.
In one embodiment, the targeted delivery content selection module includes:
The evaluation information prediction unit is used for predicting the evaluation information of the user belonging to the first category on the second content set according to the evaluation information of the user belonging to the second category on the second content set, the evaluation information of the user belonging to the second category on the first content set and the evaluation information of the user belonging to the first category on the first content set;
And the target delivery content selection unit is used for selecting target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the first category on the second content set.
In one embodiment, the evaluation information prediction unit includes:
A first prediction information obtaining subunit, configured to obtain the evaluation information of the user i belonging to the first category on the first content set, where the evaluation information includes: the predictive score of user i belonging to the first category for any content c1 in the first set of content is referred to as a first predictive score Ri,c1, and the average predictive score of user i belonging to the first category for all content in the first set of content is referred to as a first predictive average score
A second prediction information obtaining subunit, configured to obtain the evaluation information of the user j belonging to the second category on the first content set, where the second prediction information obtaining subunit includes: the predictive score of user j belonging to the second category for any content c1 in the first set of content is referred to as the second predictive score Rj,c1, and the average predictive score of user j belonging to the second category for all content in the first set of content is referred to as the second predictive average score
A third prediction information obtaining subunit, configured to obtain the evaluation information of the user j belonging to the second category on the second content set, where the third prediction information obtaining subunit includes: a predictive score for any content c2 in the second set of content by user j belonging to the second category, referred to as a third predictive score Rj,c2;
a fourth prediction information obtaining subunit, configured to obtain evaluation information of all contents in the first content set and the second content set by the user j belonging to the second category, where the evaluation information includes: the average predictive score of user j belonging to the second category over all of the first and second sets of content, referred to as the third predictive average score
A similarity calculation subunit for calculating average score according to the first prediction score Ri,c1, the second prediction score Rj,c1, and the first prediction scoreAverage of the second predictionCalculating content click prediction score similarity sim (i, j)
An evaluation information calculating subunit for calculating average score according to the content click prediction score similarity sim (i, j)The third predictive score Rj,c2, the third predictive average scoreA predictive score Ri,c2 for the second set of content is calculated for the user belonging to the first category, and the rating information for the second set of content for the user belonging to the first category includes Ri,c2.
In a third aspect, an embodiment of the present invention provides a content delivery terminal, where a function of the content delivery terminal may be implemented by hardware, or may be implemented by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above.
In one possible design, the structure of the content delivery terminal includes a processor and a memory, where the memory is used for storing a program for supporting the content delivery terminal to execute the content delivery method described above, and the processor is configured to execute the program stored in the memory. The content delivery terminal may also include a communication interface for communicating with other devices or communication networks.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer software instructions for a content delivery terminal, which includes a program for executing the content delivery method described above.
In a fifth aspect, embodiments of the present invention provide a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
One of the above technical solutions has the following advantages or beneficial effects: the content delivery method can deliver the content on the most frequently used page or application program function and path of the user, and also enables the favorite content types of different user types to be delivered. Not only improves the accuracy of content delivery, but also improves the pertinence of content delivery for different types of users.
The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will become apparent by reference to the drawings and the following detailed description.
Detailed Description
Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in various different ways without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
Example 1
In a specific embodiment, a method for delivering content is provided, where content delivery may include delivering advertisements, videos, pictures, and the like in an internet page. As shown in fig. 1, the method includes:
Step S10: and clustering according to the portrait information of the user to obtain a first category to which the user belongs.
In one example, the portrait information of the user may be data information describing attributes of the user. For example, the user's age, region, academic, occupation, gender, etc. When clustering is performed according to portrait information of a user, clustering can be performed according to occupation of the user. For example, the portrait information of the user a includes: age 18, regional Beijing, academic high school and professional students, etc. The portrait information of the user B includes: age 32, beijing area, academic doctor, professional teacher. The portrait information of the user C includes: age 22, regional Shanghai, academic family, and professional actors. According to the job classification, user A belongs to the student class, user B belongs to the teacher class, and user C belongs to the actor class. Of course, it may also include: class of students, teachers, scientists, actors, doctors, and composes. It should be noted that when the corresponding content needs to be put into the user a, the user a may be first categorized into a first category, i.e., a student category.
Step S20: and clustering the evaluation information of the first content set according to the user to obtain a second category to which the user belongs.
In one example, evaluation information for various types of content in a page may be acquired by recording access of the user to various types of content in the page when the user accesses various types of content in the page, such as advertising, video, etc., through respective access page burial points in the mobile terminal. The first set of content may include various different types of advertisements in the advertisement type content, such as travel type advertisements, house type advertisements, food type advertisements, book type advertisements, and the like. Building material advertisements, etc. Of course, the first content set may also include a video set, a picture set, and the like, which are all within the protection scope of the present embodiment.
The user's rating information for the first set of content may include a user's click-through rate prediction score for each content in the first set of content, e.g., a click-through rate prediction score for travel-type advertisements, a click-through rate prediction score for property-type advertisements, etc. Clustering is carried out according to the evaluation information of the user on various advertisements in the first content set, and the clustering basis can be the similarity of the evaluation information of the user on various advertisements. For example, the predicted click rate score of the student-class user for the food-class advertisement is compared with the predicted click rate score of the teacher-class user for the food-class advertisement, the predicted click rate score of the scientist-class user for the food-class advertisement, and the predicted click rate score of the actor-class user for the food-class advertisement, respectively. If the click rate prediction scores of the student class users, the teacher class user, the scientist class user and the composer class user on the food class advertisement are closer, the student class user, the teacher class user, the scientist class user and the composer class user are considered to be classified into a second class.
Of course, the users in the second category may also be ranked according to the similarity of the click rate prediction scores of the users in the second category. For example, if the student users are currently going to put in the third category, the similarity ranking is performed from high to low according to the click rate prediction scores of the student users and other various users on the food advertisements, so as to obtain a set { teacher, composer and scientist } after the similarity ranking.
Step S30: and determining a target delivery page corresponding to the user belonging to the first category according to the access information of the user belonging to the first category to each page.
In one example, each page accessed by the user may be an interface in a mobile terminal, such as a mobile phone, where the interface may include a plurality of controls, or may be each web page browsed, etc. The access information of the user belonging to the first category to each page may include information as follows: a device unique identifier, e.g., cuid (Called User IDentification number ), that is assigned to the first category of user access. The ID (Identification) of each page in the page chain visited by the user belonging to the first category includes the ID of the destination page, the type of the destination page, the ID of each jump page, and the like. When a user belonging to the first category operates each control (such as an application program) on the access page, functions and functional paths thereof which are used more frequently are used. For example, the control ID of the control where the operation occurs in each page, the path sequence uniquely identifies Pid (proportion, integration, differentiation, proportional, integral, derivative), the operation occurrence time, the operation parameters, and so on. The user belonging to the first category accesses the page, and the weight value of the page accessed by the user belonging to the first category can be calculated. And determining the target delivery page according to the ordering of the weight values. For example, the determination that the weight value is the largest is the target impression page.
Step S40: and selecting target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the second category on the second content set.
In one example, users belonging to the second category include: student class users, teacher class users, scientist class users, and composer class users. The evaluation information of the teacher user, the scientist user and the composer user on the advertisements such as the property, the automobile and the body building can be obtained, and the student user does not have the evaluation information of the advertisements such as the property, the automobile and the body building. Therefore, the second set of content includes only teacher-based users, scientist-based users, and composer-based users advertising on properties, automobiles, fitness, etc.
Since the set { teacher, composer, scientist }, in which the similarity is sorted, is obtained in step S20, the evaluation information of the teacher user, the scientist user, and the composer user on the advertisements such as the property, the car, and the exercise can be used as the reference of the evaluation information of the student user on the advertisements such as the property, the car, and the exercise, and further the target delivery content corresponding to the first class, that is, the student user can be selected.
Step S50: and delivering the target delivery content to the target delivery page.
In one example, selected targeted delivery content, such as various types of advertisements, videos, pictures, and the like, is delivered to a targeted delivery page. As shown in fig. 2, the advertisement of "super member free collar" placed in a certain function page of the application program, and the advertisement of "special live in ease and comfort free collar of gift bag" in the backup page of the mobile phone are placed as examples. A plurality of selectable drop positions are arranged in each page. The target delivery position can be selected from a plurality of selectable delivery positions in the target delivery page, so that the delivered content can be dynamically configured and freely displayed.
According to the content delivery method provided by the embodiment, the content is delivered on the function and path of the page or the application program which are most frequently used by the user, and the favorite content types of different user types are delivered. Not only improves the accuracy of content delivery, but also improves the pertinence of content delivery for different types of users.
In one embodiment, as shown in fig. 3, step S30 includes:
step S310: according to the access information of the users belonging to the first category to each page, calculating the access weight value of the users belonging to the first category to each page;
step S320: and determining the target delivery page corresponding to the user belonging to the first category according to the ordering result of the access weight value of the user belonging to the first category to each page.
In one example, the portrait information of the user and the access information to each page are input to a BP (back propagation) neural network, and a training result is output. And comparing the training result with the existing user access information, and obtaining the neural network model through modifying the network parameters and continuously carrying out iterative training. And inputting the portrait information of the user belonging to the first category and the access information of each page into the neural network model to obtain the access weight value of the user belonging to the first category to each page.
When the user belonging to the first category accesses each page, acquiring an accessed page chain according to the access time and the access address. And (5) corresponding the weight value to each page in the page chain, and endowing each page with an access weight value. For example, as shown in fig. 4 and 5, the page chain 1 to which the a user belonging to the first category accesses includes page 1, page 2, page 3. The multiple weight values of page chain 1 accessed by user a include: weight A1 for page 1, weight A2 for page 2, weight a3 for page 3. The page chain 2 visited by the B user includes page 1, page 2, page 4. The plurality of weight values for page chain 2 accessed by the B user include: weight B1 for page 1, weight B2 for page 2, weight B4. for page 4.
In one embodiment, as shown in fig. 3, step S40 includes:
Step S410: predicting the evaluation information of the user belonging to the first category on the second content set according to the evaluation information of the user belonging to the second category on the second content set, the evaluation information of the user belonging to the second category on the first content set and the evaluation information of the user belonging to the first category on the first content set;
step S420: and selecting target delivery content corresponding to the user belonging to the first category according to the evaluation information of the user belonging to the first category on the second content set.
In one example, the rating information for the second set of content attributed to the user of the second category may include: and according to the predictive scoring of advertisements such as the property, the automobile, the body building and the like by teacher users, scientist users and composer users belonging to the second category. And calculating according to the predictive scores to obtain click predictive scores of students on advertisements such as real estate, automobiles, fitness and the like, wherein the click predictive scores are respectively 20, 40 and 60. And selecting the ordering of the target put-in content to be body-building advertisements, automobile advertisements and property advertisements in sequence according to the predictive score. Of course, the above embodiments are included but not limited to, and are within the scope of the present embodiment.
In one embodiment, as shown in fig. 6, step S40 includes:
Step S4101: acquiring evaluation information of the user i belonging to the first category on the first content set, wherein the evaluation information comprises the following steps: the predictive score of user i belonging to the first category for any content c1 in the first set of content is referred to as a first predictive score Ri,c1, and the average predictive score of user i belonging to the first category for all content in the first set of content is referred to as a first predictive average score
Step S4102: acquiring evaluation information of the user j belonging to the second category on the first content set, wherein the evaluation information comprises the following steps: the predictive score of user j belonging to the second category for any content c1 in the first set of content is referred to as the second predictive score Rj,c1, and the average predictive score of user j belonging to the second category for all content in the first set of content is referred to as the second predictive average score
Step S4103: obtaining the evaluation information of the user j belonging to the second category on the second content set, wherein the evaluation information comprises the following steps: a predictive score for any content c2 in the second set of content by user j belonging to the second category, referred to as a third predictive score Rj,c2;
Step S4104: the method for acquiring the evaluation information of the user j belonging to the second category on all contents in the first content set and the second content set comprises the following steps: the average predictive score of user j belonging to the second category over all of the first and second sets of content, referred to as the third predictive average score
Step S4105: based on the first predictive score Ri,c1, the second predictive score Rj,c1, the first predictive average scoreSecond prediction average scoreCalculating content click prediction score similarity sim (i, j);
Step S4106: average score according to content click prediction score similarity sim (i, j), first predictionThird prediction score Rj,c2, third prediction average scoreThe predictive score Ri,c2 of the user belonging to the first category for the second set of content is calculated, and the rating information of the user belonging to the first category for the second set of content includes Ri,c2.
In one example, clustering is performed according to portrait information of a user to obtain a first category to which the user belongs. For example, U is a data set of m j-dimensional users to be clustered. U= { Ui|ui＝(ui1,ui2,......uij), i=1, 2..m }, wherein, Uik is the kth attribute value of user i. Such as: i: zhang san, lifour, wang Wu …; preference, occupation, age …. The users are clustered by calculating the Euclidean distance between two users, and the Euclidean distance formula is: The smaller the Euclidean distance, the more similar the two users. After calculating the Euclidean distance, the output result is a two-dimensional matrix of user classifications { uik }, i: zhang san, lifour, wang Wu …; preference, occupation, age …. The two-dimensional matrix of output results indicates that the users are clustered into a first category according to the respective image information. In the two-dimensional matrix, a matrix u= |u1,U2,U3.....Un | of a first category obtained by classifying according to a certain user attribute is extracted, and n is a user category, for example, the first category set includes a student category, an actor category, a scientist category, a teacher category, a dancer category and the like according to a professional classification category. Clustering the content according to the type of the content to obtain a content classification matrix I= |I1,I2,I3.....Ie |. Such as food, books, real estate, automotive, fitness, etc. Multiplying the matrix of the first category by the content classification matrix to obtain R= |U|×|I| which is the click ratio matrix of the user on each content. According to R= |U|×|I|, a first prediction score Ri,c1, a second prediction score Rj,c1 and a third prediction score Rj,c2 can be obtained, and the first prediction is equally dividedSecond prediction average scoreThird prediction average score
The content click prediction scoring similarity between every two users belonging to the first category, such as students, actors, scientists, teachers, chorea, etc., can be calculated by a similarity calculation formula. There are many choices for the similarity calculation formula, such as cosine similarity, euclidean distance, spearman level correlation coefficient, and the like.
In this embodiment, the pearson correlation formula is used:
And according to the calculation result, the similarity close to the student class belongs to a second class, and the second class is excluded with larger difference. In the second category, the resulting set in high-low order may be m= { teacher, composer, scientist }.
Finally, a predictive score Ri,c2 for the second set of content is calculated for the user belonging to the first category,
Note that c1 is any content in the first content set, and in the second category, the student class, teacher class, composer class, and scientist class have evaluation information on a common content set, which is the first content set. c2 is any content in the second content set, in the second category, the content set shared by the teacher, the composer and the scientist has evaluation information, the student does not have the evaluation information of the content set, and the content set is the second content set.
Example two
In another embodiment, as shown in fig. 7, there is provided a content delivery apparatus, including:
a first class clustering module 10, configured to cluster according to the portrait information of the user, to obtain a first class to which the user belongs;
The second category clustering module 20 is configured to cluster the evaluation information of the first content set according to the user, so as to obtain a second category to which the user belongs;
The target delivery page determining module 30 is configured to determine a target delivery page corresponding to a user belonging to the first category according to access information of the user belonging to the first category to each page;
a target delivery content selection module 40, configured to select target delivery content corresponding to a user belonging to the first category according to evaluation information of the user belonging to the second category on the second content set;
And the delivery operation module 50 is used for delivering the target delivery content to the target delivery page.
In one embodiment, as shown in fig. 8, the target delivery page determining module 30 includes:
an access weight value calculating unit 301, configured to calculate an access weight value of each page of a user belonging to a first category according to access information of each page of the user belonging to the first category;
the target delivery page determining unit 302 is configured to determine, according to a result of ordering access weight values of the users belonging to the first category to the pages, a target delivery page corresponding to the users belonging to the first category.
In one embodiment, as shown in fig. 8, the target delivery content selection module 40 includes:
An evaluation information prediction unit 401, configured to predict, according to the evaluation information of the second content set by the user belonging to the second category, the evaluation information of the first content set by the user belonging to the second category, and the evaluation information of the first content set by the user belonging to the first category, the evaluation information of the second content set by the user belonging to the first category;
the target delivery content selection unit 402 is configured to select target delivery content corresponding to a user belonging to the first category according to evaluation information of the user belonging to the first category on the second content set.
In one embodiment, as shown in fig. 9, the evaluation information prediction unit 401 includes:
the first prediction information acquisition subunit 4011 is configured to acquire the evaluation information of the user i belonging to the first category on the first content set, and includes: the predictive score of user i belonging to the first category for any content c1 in the first set of content is referred to as a first predictive score Ri,c1, and the average predictive score of user i belonging to the first category for all content in the first set of content is referred to as a first predictive average score
The second prediction information acquisition subunit 4012 is configured to acquire the evaluation information of the user j belonging to the second category on the first content set, and includes: the predictive score of user j belonging to the second category for any content c1 in the first set of content is referred to as the second predictive score Rj,c1, and the average predictive score of user j belonging to the second category for all content in the first set of content is referred to as the second predictive average score
The third prediction information acquisition subunit 4013 is configured to acquire the evaluation information of the user j belonging to the second category on the second content set, and includes: a predictive score for any content c2 in the second set of content by user j belonging to the second category, referred to as a third predictive score Rj,c2;
The fourth prediction information acquisition subunit 4014 is configured to acquire the evaluation information of the user j belonging to the second category on all the contents in the first content set and the second content set, and includes: the average predictive score of user j belonging to the second category over all of the first and second sets of content, referred to as the third predictive average score
The similarity calculation subunit 4015 calculates, based on the first prediction score Ri,c1, the second prediction score Rj,c1, and the first prediction average scoreAverage of the second predictionCalculating content click prediction score similarity sim (i, j);
an evaluation information calculation subunit 4016 for calculating a first predicted average score according to the content click prediction score similarity sim (i, j)The third predictive score Rj,c2, the third predictive average scoreA predictive score Ri,c2 for the second set of content is calculated for the user belonging to the first category, and the rating information for the second set of content for the user belonging to the first category includes Ri,c2.
The functions of each module in each device of the embodiments of the present invention may be referred to the corresponding descriptions in the above methods, and are not described herein again.
Example III
Fig. 10 shows a block diagram of the structure of a content delivery terminal according to an embodiment of the present invention. As shown in fig. 10, the terminal includes: memory 910 and processor 920, memory 910 stores a computer program executable on processor 920. The processor 920 implements the content delivery method in the above embodiment when executing the computer program. The number of the memories 910 and the processors 920 may be one or more.
The terminal further includes:
And the communication interface 930 is used for communicating with external equipment and carrying out data interaction transmission.
The memory 910 may include high-speed RAM memory or may further include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 910, the processor 920, and the communication interface 930 are implemented independently, the memory 910, the processor 920, and the communication interface 930 may be connected to each other and perform communication with each other through buses. The bus may be an industry standard architecture (ISA, industry Standard Architecture) bus, a peripheral component interconnect (PCI, PERIPHERAL COMPONENT INTERCONNECT) bus, or an extended industry standard architecture (EISA, extended Industry Standard Architecture) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 10, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 910, the processor 920, and the communication interface 930 are integrated on a chip, the memory 910, the processor 920, and the communication interface 930 may communicate with each other through internal interfaces.
An embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements a method as in any of the above embodiments.
An embodiment of the invention provides a computer program product comprising a computer program which, when executed by a processor, implements a method as in any of the embodiments described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing 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 read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may 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 is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.