CN106034277B - A kind of information processing method and device - Google Patents

Abstract

The invention discloses an information processing method. The method includes: identifying key attributes of the first machine from an attribute set of the first machine according to a preset first matching condition, where the first machine is registered the machine; identify the key operation of the first machine from the operation set of the first machine according to the preset second matching condition; judge the key attribute of the first machine and/or the key operation of the first machine Whether the preset attention strategy is satisfied, a first judgment result is obtained; when the first judgment result indicates that the key attribute of the first machine and/or the key operation of the first machine satisfies the attention strategy, all The first machine is recommended to the registered second machine or the owner of the second machine, so as to realize the attention between the machines. The invention also discloses an electronic device.

Description

An information processing method and device

technical field

The present invention relates to the Internet of Things technology, in particular to an information processing method and device.

Background technique

The Internet of Things (IoT, Internet of things) is an important part of the new generation of information processing technology, and it is also an important development stage in the information age. As the name suggests, the Internet of Things is the Internet of things connected. This has two meanings: First, the core and foundation of the Internet of Things is still the Internet, which is an extended and expanded network based on the Internet; exchange and communication. It can be seen that the Internet of Things is to use communication technologies such as local networks or the Internet to connect sensors, controllers, machines, people and objects in a new way to form the connection between people and things, things and things, and realize informatization and remote management. Control and intelligent network. The Internet of Things is an extension of the Internet. It includes the Internet and all resources on the Internet, and is compatible with all applications on the Internet.

Social network (SNS, Social Network Site) such as Weibo, WeChat, Facebook refers to some Internet application services designed to help people build social networks. In order to strengthen social connections between people, usually There will be some recommended features. Taking Weibo as an example, in the existing human Weibo system, users usually follow friends by actively searching for friends or recommending friends by the system. Among them, the active search for friends can be regarded as some attention strategies set by the user, and the system recommendation can be regarded as some pre-set attention strategies. In addition to friend recommendation, Weibo can also perform information recommendation, which usually focuses on information recommendation in the same field. For example, according to the user's movie browsing record, recommend movie works related to the movie browsing record to the user. The recommended movie work can be a movie with the same starring role as in the movie browsing record, or a movie with the same director as in the movie browsing record, or is the same type of movie as in the movie browsing history.

With the rise of the Internet of Things, many of the user's machines can be connected to the network, thus becoming part of the user in the social network, or the machine becoming the user of the social network. Existing social networks generally provide social services for people or human behavior records, and there is no social service provided for machine behavior records.

SUMMARY OF THE INVENTION

In view of this, the embodiments of the present invention provide an information processing method and apparatus to solve at least one problem in the prior art, which can provide social services between machines, thereby improving human experience as a user.

The technical solution of the embodiment of the present invention is realized as follows:

In a first aspect, an embodiment of the present invention provides an information processing method, the method includes:

Identify the key attributes of the first machine from the attribute set of the first machine according to the preset first matching condition, and the first machine is a registered machine;

Identify the key operation of the first machine from the operation set of the first machine according to the preset second matching condition;

Judging whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result;

When the first judgment result indicates that the key attribute of the first machine and/or the key operation of the first machine satisfy the attention policy, recommending the first machine to the registered second machine or the first machine Two machine masters to achieve attention between machines.

In a second aspect, an embodiment of the present invention provides an information processing method, the method includes:

constructing a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, where the first machine circle includes one or more machines that have a social relationship with each other;

According to the key attributes and key operations of each machine in the first machine circle, a key behavior for identifying the first machine circle is constructed, and the key behavior is used to reflect the key between the machines in the first machine circle. The logical relationship between operations;

Get the second machine circle;

The key behaviors of the first machine circle are recommended to the second machine circle, the attributes and operations of the machines in the second machine circle are the same as the attributes and operations of the machines in the first machine circle.

In a third aspect, an embodiment of the present invention provides an information processing device, the device includes a first identification unit, a second identification unit, a first judgment unit, and a first recommendation unit, wherein:

The first identification unit is configured to identify the key attributes of the first machine from the attribute set of the first machine according to a preset first matching condition, and the first machine is a registered machine;

the second identification unit, configured to identify the key operation of the first machine from the operation set of the first machine according to a preset second matching condition;

the first judgment unit, configured to judge whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result;

The first recommending unit, configured to recommend the first machine when the first judgment result indicates that the key attributes of the first machine and/or the key operations of the first machine satisfy the attention strategy To the registered second machine or the owner of the second machine to realize the attention between the machines.

In a fourth aspect, an embodiment of the present invention provides an information processing apparatus, the apparatus includes a third constructing unit, a determining unit, an obtaining unit, and a second recommending unit, wherein:

The third construction unit is configured to construct a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, where the first machine circle includes a social relationship with each other of more than one machine;

The determining unit is configured to construct, according to key attributes and key operations of each machine in the first machine circle, a key behavior for identifying the first machine circle, and the key behavior is used to embody the first machine The mutual logical relationship of key operations among the machines in the circle;

the obtaining unit, used to obtain the second machine circle;

The second recommendation unit is used to recommend the key behaviors of the first machine circle to the second machine circle, and the attributes and operations of the machines in the second machine circle are the same as those of the machines in the first machine circle. Properties and operations are the same.

In the information processing method and device provided by the embodiments of the present invention, the key attributes of the first machine are identified from the attribute set of the first machine according to a preset first matching condition, and the first machine is registered machine; identify the key operation of the first machine from the operation set of the first machine according to the preset second matching condition; determine whether the key attribute of the first machine and/or the key operation of the first machine Satisfy the preset attention strategy, and obtain a first judgment result; when the first judgment result indicates that the key attribute of the first machine and/or the key operation of the first machine satisfies the attention strategy, the The first machine is recommended to the registered second machine or the owner of the second machine, so as to realize the attention between the machines. In this way, a social service between the machines can be provided, thereby improving the experience of a person as a user.

Description of drawings

1-1 is a schematic diagram of the relationship between users in a social network according to an embodiment of the present invention;

1-2 are schematic diagrams of migration between scenarios in a social network according to an embodiment of the present invention;

1-3 are schematic diagrams of implementation flowcharts of an information processing method according to Embodiment 1 of the present invention;

FIG. 2 is a schematic flowchart of the implementation of the information processing method according to the second embodiment of the present invention;

FIG. 3 is a schematic diagram of the implementation flow of the information processing method according to Embodiment 3 of the present invention;

FIG. 4 is a schematic diagram of the implementation flow of the information processing method according to the fourth embodiment of the present invention;

FIG. 5 is a schematic flowchart of the implementation of the information processing method according to Embodiment 5 of the present invention;

6 is a schematic diagram of the composition and structure of an information processing apparatus according to Embodiment 6 of the present invention;

7 is a schematic diagram of the composition and structure of an information processing apparatus according to Embodiment 7 of the present invention;

8 is a schematic diagram of the composition and structure of an information processing apparatus according to Embodiment 8 of the present invention;

9 is a schematic diagram of the composition and structure of an intelligent attention engine according to Embodiment 9 of the present invention;

FIG. 10 is a schematic diagram of the composition and structure of an information recommendation engine according to Embodiment 10 of the present invention.

Detailed ways

The following examples illustrate social networks in the age of the Internet of Things. In the era of the Internet of Things, users of social networks can be divided into two or three categories according to the user's attributes. Among them, the user's attribute mainly refers to whether the user is a person, a physical object or a virtual object, and a virtual object can be written in a computer language. s application. When the users of the social network are divided into two categories, the users of the social network include people and objects (physical objects), and when divided into three categories, the users of the social network are divided into people, objects (physical objects) and applications (virtual objects) ); in the embodiments of the present invention, the users of the social network are mainly divided into two categories, namely people and objects (physical objects). In the absence of special instructions, the objects referred to in the embodiments of the present invention are all An object that can be connected to the Internet and is a user registered on a social network. In addition, in the embodiment of the present invention, a machine and an object have the same meaning.

When an object is a user of a social network, the owner of the object is also a user of the social network, where the owner of the object refers to all the relationships between the object and people. In the existing social network, whether an object actively searches for another object or a social platform recommends another object to the object, it cannot satisfy the attention between objects in the social circle, because the current social network is limited to the behavior of people. The recorded recommendation does not use the object as a user or an important part of a person's social activities. Therefore, the object cannot actively initiate a search, nor can it determine whether to pay attention to another object. In addition, for information recommendation, the existing technology is not suitable for information recommendation between machines, because the information recommendation between machines is not limited to the recommendation based on the content information of people's browsing records. Recommendations may be recommendations for how to use machines, or even recommendations for collaborative behavior among multiple machines.

The following takes human users and machine users as examples to illustrate the users involved in the embodiment of the present invention. Figure 1-1 is a schematic diagram of the relationship between users in the social network according to the embodiment of the present invention. As shown in Figure 1-1, in the social network The users include character users and machine users. Among them, the character users include three children, namely Taotao 10, Yueyue 20 and Lele 30. Taotao 10, Yueyue 20 and Lele 30 form a circle of friends. Among them, Taotao 10, Yueyue 20 and Lele 30 Tao, Yueyue and Lele are the names of three children respectively; while machine users include TV users and toy users, among which TV users include TV 11, TV 21 and TV 31, and TV 11, TV 21 and TV 31 form a TV circle. Toy users include toy 12, toy 22 and toy 32, and toy 12, toy 22 and toy 32 form a toy circle; among them, TV 11 and toy 12 belong to Taotao 10, that is, the owner of TV 11 and toy 12 is Taotao 10; 21 and toy 22 belong to Yueyue 20, that is, the owner of TV 21 and toy 22 is Yueyue 20; TV 31 and toy 32 belong to Lele 30, that is, the owner of TV 31 and toy 32 is Lele 30. For Taotao 10, TV 11 and Toys 12 can be used as the assets of Taotao 10, and TV 11 and Toys 12 form the asset circle of Taotao 10. For the month 20, TV 21 and Toy 22 can be used as the assets of the month 20, and the TV 21 and the toy 22 form the asset circle of the month 20. For Lele 30, TV 31 and toys 32 can be used as assets of Lele 30, and TV 31 and toys 32 constitute Lele 30's asset circle.

Information recommendation is a common function in social networks, but the social network with users as shown in Figure 1-1 can perform more forms of information recommendation than the existing social network with only people as users. You can recommend various information about people's social activities among the circles of friends, recommend TV programs among the TV circles; recommend how to use toys, and recommend songs and stories among the toy circles ( It depends on the function of the toy itself); between the asset circles, the user's TV browsing records can be used to recommend corresponding stories, songs, etc. to the toy.

In addition to the above-mentioned information recommendation, the embodiment of the present invention also involves another concept, that is, behavior recommendation. In order to better introduce the behavior recommendation, the following describes the behavior recommendation scenario. A schematic diagram of inter-migration, as shown in Figure 1-2, Taotao Dad 40 and Yueyue Dad 50 are colleagues, Taotao Dad 40 and Yueyue Dad 50 are friends who follow each other, in other words, Taotao Dad 40 and Yueyue Dad 50 are friends. Month Dad 50 to form a circle of friends.

Taotao Dad 40's home has a dust meter 411, a window brake 412 and an air conditioner 413. The dust meter is used to detect the dust content in the air, the window brake is used to open or close the window, and the home dust meter 411 and window brake 412 and the air conditioner 413 to form a home circle 410; the car surface of Taotao Dad 40 has a dust meter 421, a window brake 422 and an air conditioner 423, and the dust meter 421, window brake 422 and air conditioner 423 form a car home circle 420; The dust meter 431 , the window brake 432 and the air conditioner 433 constitute the office circle 430 . And Yueyue Dad 50 also has a car, including a dust meter 511, a window brake 512 and an air conditioner 513, of which the dust meter 511, the window brake 512 and the air conditioner 513 form a car residence circle 510.

Now suppose that the objects (machines) in the car-residential circle, home circle, and office circle in Figure 1-2 are all objects of the Internet of Things, and they have all been registered on the social network platform. As the owner of the car, Taotao Dad 40 is also the owner of the machines in the car-residential circle. Assume that the logic of Taotao-dad 40 setting the machines in the car-residential circle is as follows: when the dust meter 421 detects dust (such as PM2.5 ) is greater than 200, the window brake 422 automatically closes the window; when the window is closed, if the temperature sensor in the air conditioner detects that the home temperature is higher than 28 degrees, the air conditioner starts to work, and the parameters during operation are temperature 25 degrees, cooling mode, up and down Sweep, etc. Since Taotao Dad's 40's home and office also have the same machines as those in the car, Taotao's father's logic between the machines set in the car-residential circle is also applicable to Taotao's father's office circle and home circle. Therefore, the social platform can recommend the behavior logic to Taotao Dad's car and office circle by recording the behavior logic between machines in Taotao Dad's car home circle. Since Yueyue Dad's car home circle is similar to Taotao Dad's car home circle, the behavioral logic between machines in Taotao Dad's car home circle is also applicable to Yueyue Dad's car home circle. Therefore, social platforms By recording the behavioral logic between the machines in Taotao Dad's car home circle, the behavior logic can be recommended to Yueyue Dad's car home circle.

The technical solutions of the present invention will be further elaborated below with reference to the accompanying drawings and specific embodiments.

Example 1

An embodiment of the present invention provides an information processing method, which is applied to a server side, where the server refers to a server that provides a social network. Taking microblog as an example, the server refers to a server that provides a microblog function. The functions implemented by the information processing method can be implemented by calling a program code by a processor in the server, and of course the program code can be stored in a computer storage medium.

Figure 1-3 is a schematic diagram of the implementation flow of an information processing method according to Embodiment 1 of the present invention. As shown in Figure 1-3, the method includes:

Step 101, identifying the key attribute of the first machine from the attribute set of the first machine according to the preset first matching condition;

Here, the first machine may be one machine or more than one machine.

Here, the first machine is a registered machine; the following takes the toy representing the first machine as an example to illustrate the attributes of the machine; the possible attributes of the toy are: toy identification (ID) information, toy name, brand, model , color, material, type, description, location, owner, price, applicable age, fan list, watch list, etc.; these attributes of the toy together form the attribute set of the toy.

Here, the first matching condition is some conditions related to the attribute set of the first machine. During the specific implementation process, those skilled in the art can set the first matching condition according to the attribute of the machine. The matching condition includes the setting of a single attribute or the setting of a combined attribute, wherein the setting of a single attribute means that when the first machine is a toy, the first matching condition may be the name of the toy; or, the first matching condition may be the toy's name. Model; the setting of the combination attribute means that when the first machine is a toy, the first matching condition can be the model and name of the toy.

Step 102, identifying the key operation of the first machine from the operation set of the first machine according to the preset second matching condition;

Here, continue to take a toy as an example to illustrate the operation set of the first machine. The operations of the toy may include: following a friend, removing a friend, downloading content, playing music, playing a story, and so on. These operations of the toy together constitute the toy's operation set.

Here, the second matching condition is some conditions related to the operation set of the first machine. During the specific implementation process, those skilled in the art can set the second matching condition according to the operation of the machine, wherein the second matching condition is set Including the setting of a single operation or the setting of a combined operation, wherein, the setting of a single operation means that when the first machine is a toy, the second matching condition can be content download; or, the second matching condition can be story playback; combined operation The setting of , means that when the first machine is a toy, the first matching condition can be content download and story playback, etc.

Step 103, judging whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result;

Here, the attention policy refers to some conditions set according to key attributes of the first machine and/or key operations of the first machine.

Step 104, when the first judgment result indicates that the key attributes of the first machine and/or the key operations of the first machine satisfy the attention policy, recommend the first machine to the registered second machine. The master of a machine or a second machine to achieve attention between machines.

Here, the second machine includes one machine, and may also include more than one machine.

The technical method provided by the embodiment of the present invention can be used in the following scenarios. Continuing to take the scenario shown in Figure 1-1 as an example, Taotao and Lele are good friends in the circle of friends, and Taotao 10 owns a toy 12 and a toy TV 11, this toy 12 and TV 11 are users of social networks; while Lele 30 has only one TV 31; however, Lele 30 has recently added toy 32, assuming that the attributes of toy 32 are the same as those of toy 12, the operation of toy 32 The operation is also the same as that of the toy 12 . Then Lele 30 registers the toy 32 as a user of the social network. After the registration is completed, the social network will acquire the attributes and operations of the toy 32 . Based on the foregoing pre-operations, the technical solution provided by the embodiment of the present invention can be implemented. The server of the social platform identifies the key attributes of the first machine from the attribute set of the first machine according to the preset first matching condition, and the social The server of the platform identifies the key operation of the first machine from the operation set of the first machine according to the preset second matching condition; then, the server determines the key attribute of the first machine and/or the first machine The key operation of satisfies the preset attention strategy, and recommends the first machine to the registered second machine or the owner of the second machine, so as to realize the attention between the machines. In the embodiment of the present invention, the first machine may be the toy 12 , then the second machine may be the toy 32 ; of course, the first machine may be the toy 32 , then the second machine may be the toy 12 .

Embodiment 2

Based on the foregoing first embodiment, an embodiment of the present invention provides an information processing method. FIG. 2 is a schematic diagram of an implementation flowchart of the information processing method according to the second embodiment of the present invention. As shown in FIG. 2 , the method includes:

Step 201, build an attribute library, and find out the attribute set of the first machine from the attribute library according to the identification information of the first machine;

Here, the attribute library includes the attributes of the registered machines, and the attribute library can be designed in the following form:

Field 1: machine type;

Field two: attribute 1;

Field three: attribute 2;

Field four: ...;

The constructed attribute library can be a simple data table or a data table obtained through massive data analysis.

Step 202, identifying the key attribute of the first machine from the attribute set of the first machine according to the preset first matching condition;

Here, the first machine is a registered machine. It should be noted that this embodiment of the present invention may further include a key attribute library, and the key attribute library may also include only the key attributes of all registered machines. Correspondingly, the method further includes: constructing a key attribute library, which is about The key attributes identified in step 202 are stored in a key attribute library. The key attribute library can be designed in the following form:

Field 1: Object Type

Field 2: key attribute 1;

Field three: key attribute 2;

Field four: ...;

The constructed key attribute library can be a simple data table or a data table including the key attributes of various items extracted through massive data analysis.

Step 203: Build an operation library, and search out the operation set of the first machine from the operation library according to the identification information of the first machine.

Here, the operation library includes the operations of the registered machines; the operation library can be designed in the following form:

Field 1: machine type;

Field 2: Operation 1;

Field 3: Operation 2;

Field four: ...;

The constructed operation library can be a simple data table or a data table obtained through massive data analysis.

Step 204, identifying the key operation of the first machine from the operation set of the first machine according to the preset second matching condition;

Here, the embodiment of the present invention may further include a key operation library, and the key operation library may also include only the key operations of all registered machines. Correspondingly, the method further includes: constructing a key operation library, that is, step 204 The identified critical operations are stored in the critical operations library. The key operation library can be designed in the following form:

Field 1: Object Type

Field 2: key operation 1;

Field three: key operation 2;

Field four: ...;

The key operation library constructed can be a simple data table or a data table including key operations of various items extracted through massive data analysis.

Step 205, judging whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result;

Step 206, when the first judgment result indicates that the key attributes of the first machine and/or the key operations of the first machine satisfy the attention policy, recommend the first machine to the registered second machine. The master of a machine or a second machine to achieve attention between machines.

Embodiment 3

Based on the foregoing Embodiment 1 and Embodiment 2, an embodiment of the present invention provides an information processing method. FIG. 3 is a schematic flowchart of the implementation of the information processing method according to Embodiment 3 of the present invention. As shown in FIG. 3 , the method includes:

Step 301, according to the key attributes of the first machine and the key operation of the first machine, determine the first machine circle to which the first machine belongs;

Here, the first machine circle includes one or more machines that have a social relationship with each other;

Here, the key attributes of the first machine can be obtained by the method in Embodiment 2. Specifically, obtaining the key attributes of the first machine includes: building an attribute library, and the attribute library includes the attributes of the registered machines, Find the attribute set of the first machine from the attribute library according to the identification information of the first machine, and then identify the first machine from the attribute set of the first machine according to a preset first matching condition key attributes.

Here, the operation attribute of the first machine can be obtained by the method in Embodiment 2. Specifically, obtaining the operation attribute of the first machine includes: constructing an operation library, and the operation library includes the operations of the registered machine, Find out the operation set of the first machine from the operation library according to the identification information of the first machine, and identify the operation set of the first machine from the operation set of the first machine according to the preset second matching condition key operations.

Step 302, constructing a key behavior for identifying the first machine circle according to the key attributes and key operations of each machine in the first machine circle;

Here, the key behavior is used to reflect the mutual logical relationship between the key operations among the machines in the first machine circle;

Step 303, obtaining the second machine circle;

Here, in step 303, the acquiring the second machine circle includes:

Step 3031, analyze the key attributes and key operations of each machine in the first machine circle;

Step 3032, acquiring each machine with the same key attributes and key operations as each machine in the first machine circle;

In step 3033, each machine that meets the preset condition is formed into a second machine circle.

Here, the preset condition may be at least any one of the following conditions: the same owner, the owners are friends, having a geographic location within a preset distance threshold range, and so on. Taking Fig. 1-2 as an example, it is assumed that the home circle 410 formed by the dust meter 411, the window brake 412 and the air conditioner 413 is the first machine circle. When the preset condition is the same owner, the identified second machine circle can be dust. The vehicle home circle 420 formed by the instrument 421 , the window brake 422 and the air conditioner 423 , or the identified second machine circle may be the office circle 430 formed by the dust detector 431 , the window brake 432 and the air conditioner 433 . When the preset condition is friends between the owners, the second machine circle may further include a car living circle 510 composed of a dust meter 511 , a window brake 512 and an air conditioner 513 .

Step 304, recommending the key behavior of the first machine circle to the second machine circle, the attributes and operations of each machine in the second machine circle are the same as the attributes and operations of each machine in the first machine circle.

Embodiment 4

Based on the foregoing third embodiment, an embodiment of the present invention provides an information processing method. FIG. 4 is a schematic diagram of the implementation flow of the information processing method according to the fourth embodiment of the present invention. As shown in FIG. 4 , the method includes:

Step 401, according to the key attributes of the first machine and the key operation of the first machine, determine the first machine circle to which the first machine belongs;

Step 402, constructing a key behavior for identifying the first machine circle according to the key attributes and key operations of each machine in the first machine circle;

Step 403, obtaining the second machine circle;

Here, in step 403, the acquiring the second machine circle includes:

Step 4031, analyze the key attributes and key operations of each machine in the first machine circle;

Step 4032, acquiring each machine with the same key attributes and key operations as each machine in the first machine circle;

In step 4033, each machine that meets the preset condition is formed into a second machine circle.

Step 404, judging whether the key behavior of the first machine circle satisfies a preset machine behavior strategy, and obtaining a second judgment result;

Step 405, when the second judgment result indicates that the key behavior of the first machine circle satisfies the preset machine behavior policy, recommend the key behavior of the first machine circle to the second machine circle, and the second machine circle The properties and operations of the machines in the machine circle are the same as the properties and operations of the machines in the first machine circle.

Embodiment 5

Based on the foregoing Embodiment 1, an embodiment of the present invention provides an information processing method. FIG. 5 is a schematic diagram of an implementation flow of the information processing method according to Embodiment 5 of the present invention. As shown in FIG. 5 , the method includes:

Step 501, perform feature analysis on the key operations of the first machine to obtain key operations related to the use method;

Step 502, identifying a third machine according to key attributes of the first machine and/or key operations of the first machine;

Here, the key attributes of the first machine can be obtained by the method in Embodiment 2. Specifically, obtaining the key attributes of the first machine includes: building an attribute library, and the attribute library includes the attributes of the registered machines, Find the attribute set of the first machine from the attribute library according to the identification information of the first machine, and then identify the first machine from the attribute set of the first machine according to a preset first matching condition key attributes.

Here, the operation attribute of the first machine can be obtained by the method in Embodiment 2. Specifically, obtaining the operation attribute of the first machine includes: constructing an operation library, and the operation library includes the operations of the registered machine, Find out the operation set of the first machine from the operation library according to the identification information of the first machine, and identify the operation set of the first machine from the operation set of the first machine according to the preset second matching condition key operations.

Step 503, recommending the key operations related to the usage method to the third machine.

The technical solutions provided by the embodiments of the present invention can be used in the following scenarios. Continuing to take the scenario shown in Figure 1-1 as an example, Taotao and Lele are good friends in the circle of friends, Taotao 10 owns a toy 12; 30 also has a toy 32. Assuming that toy 32 has the same attributes as toy 12, the operation of toy 32 is also the same as the operation of toy 12. Assuming that the key operations of the toy 12 include: content download + music playback + story playback, after analyzing the key operations of the toy 12, it is found that the content download is a key operation related to the usage of the toy 12; Key attributes and/or key operations of the toy, identify the toy 32, and then recommend the content download to the toy 32; I would recommend toy 12 downloading "Little Apple" to Lele's Toy 32.

Embodiment 6

Based on the above method embodiments, the embodiments of the present invention provide an information processing device, each unit in the device and each module included in each unit can be implemented by a server in a social platform; of course, it can also be implemented by The specific logic circuit is implemented; in the process of the specific embodiment, the processor may be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA) or the like.

FIG. 6 is a schematic structural diagram of an information processing apparatus according to Embodiment 6 of the present invention. As shown in FIG. 6 , the information processing apparatus 600 includes a first identification unit 601 , a second identification unit 602 , a first judgment unit 603 and a first recommendation unit 604, where:

The first identification unit 601 is configured to identify the key attributes of the first machine from the attribute set of the first machine according to a preset first matching condition, and the first machine is a registered machine;

The second identification unit 602 is configured to identify the key operation of the first machine from the operation set of the first machine according to a preset second matching condition;

The first judgment unit 603 is configured to judge whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result;

The first recommending unit 604 is configured to, when the first judgment result indicates that the key attributes of the first machine and/or the key operations of the first machine satisfy the attention strategy, recommend the first machine Recommended for registered second machine or second machine owner to achieve attention between machines.

In the embodiment of the present invention, the apparatus further includes a third construction unit, a determination unit, an acquisition unit, and a second recommendation unit, wherein:

The third construction unit is configured to construct a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, and the first machine circle includes mutual more than one machine in a social relationship;

The determining unit is configured to determine, according to key attributes and key operations of each machine in the first machine circle, a key behavior used to identify the first machine circle, and the key behavior is used to embody the first machine The mutual logical relationship of key operations among the machines in the circle;

the obtaining unit, used to obtain the second machine circle;

Here, the acquisition unit includes an analysis module, an acquisition module and a determination module, wherein:

the analysis module for analyzing key attributes and key operations of each machine in the first machine circle;

The acquisition module is used to acquire each machine with the same key attributes and key operations as each machine in the first machine circle;

The determining module is configured to determine each machine that meets the preset condition as the second machine circle.

The second recommendation unit is used to recommend the key behaviors of the first machine circle to the second machine circle, and the attributes and operations of the machines in the second machine circle are the same as those of the machines in the first machine circle. Properties and operations are the same.

In the embodiment of the present invention, the device further includes a second judgment unit, configured to judge whether the key behavior of the first machine circle satisfies the preset machine shutdown behavior strategy, and obtain a second judgment result; when the second judgment When the result shows that the key behavior of the first machine circle satisfies the preset machine behavior policy, the second recommendation unit is triggered.

In the embodiment of the present invention, the device further includes a feature analysis unit, a third identification unit, and a third recommendation unit, wherein:

The feature analysis unit is configured to perform feature analysis on key operations of the first machine to obtain key operations related to the use method;

the third identification unit, configured to identify a third machine according to key attributes of the first machine and/or key operations of the first machine;

The third recommending unit is configured to recommend the key operation related to the usage method to the third machine.

Embodiment 7

Based on the above-mentioned embodiments, the embodiments of the present invention provide an information processing device. Each unit in the device and each module included in each unit can be implemented by a server in a social platform; In the process of the specific embodiment, the processor may be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or the like.

FIG. 7 is a schematic structural diagram of an information processing apparatus according to Embodiment 7 of the present invention. As shown in FIG. 7 , the information processing apparatus 700 includes a first construction unit 701, a first search unit 702, a first identification unit 703, and a second construction unit 704, the second search unit 705, the second identification unit 706, the first judgment unit 707 and the first recommendation unit 708, wherein:

The first construction unit 701 is used to construct an attribute library, the attribute library includes the attributes of the registered machines;

The first search unit 702 is configured to search for the attribute set of the first machine from the attribute library according to the identification information of the first machine;

The first identification unit 703 is configured to identify the key attributes of the first machine from the attribute set of the first machine according to the preset first matching condition, and the first machine is a registered machine;

The second construction unit 704 is used to construct an operation library, and the operation library includes the operations of the registered machines;

The second search unit 705 is configured to search for the operation set of the first machine from the operation library according to the identification information of the first machine.

The second identification unit 706 is configured to identify the key operation of the first machine from the operation set of the first machine according to a preset second matching condition;

The first judgment unit 707 is configured to judge whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result;

The first recommendation list 708 is used for, when the first judgment result indicates that the key attributes of the first machine and/or the key operations of the first machine satisfy the attention strategy, the first machine Recommended for registered second machine or second machine owner to achieve attention between machines.

Embodiment 8

Based on the above-mentioned embodiments, the embodiments of the present invention provide an information processing device. Each unit in the device and each module included in each unit can be implemented by a server in a social platform; In the process of the specific embodiment, the processor may be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or the like.

FIG. 8 is a schematic structural diagram of an information processing apparatus according to Embodiment 8 of the present invention. As shown in FIG. 8 , the information processing apparatus 800 includes a third construction unit 801, a determination unit 802, an acquisition unit 803 and a second recommendation unit 804, wherein:

The third construction unit 801 is configured to construct a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, and the first machine circle includes mutual more than one machine with a social relationship between them;

The determining unit 802 is configured to determine, according to the key attributes and key operations of each machine in the first machine circle, key behaviors used to identify the first machine circle, and the key behaviors are used to embody the first machine circle. The mutual logical relationship between the key operations among the machines in the machine circle;

The obtaining unit 803 is used to obtain the second machine circle;

Here, the acquisition unit includes an analysis module, an acquisition module and a determination module, wherein:

the analysis module for analyzing key attributes and key operations of each machine in the first machine circle;

The acquisition module is used to acquire each machine with the same key attributes and key operations as each machine in the first machine circle;

The determining module is configured to determine each machine that meets the preset condition as the second machine circle.

The second recommendation unit 804 is configured to recommend the key behaviors of the first machine circle to the second machine circle, and the attributes and operations of each machine in the second machine circle are the same as those of each machine in the first machine circle. The properties and operations are the same.

In the embodiment of the present invention, the device further includes a second judgment unit, configured to judge whether the key behavior of the first machine circle satisfies a preset machine behavior strategy, and obtain a second judgment result; when the second judgment result When it is indicated that the key behavior of the first machine circle satisfies the preset machine behavior policy, the second recommendation unit is triggered.

In the embodiment of the present invention, the device further includes a feature analysis unit, a third identification unit, and a third recommendation unit, wherein:

The feature analysis unit is configured to perform feature analysis on key operations of the first machine to obtain key operations related to the use method;

the third identification unit, configured to identify a third machine according to key attributes of the first machine and/or key operations of the first machine;

The third recommending unit is configured to recommend the key operation related to the usage method to the third machine.

It should be pointed out here that the descriptions of the above apparatus embodiments are similar to the descriptions of the above method embodiments, and have similar beneficial effects to those of the method embodiments, so they will not be repeated. For the technical details that are not disclosed in the apparatus embodiments of the present invention, please refer to the description of the method embodiments of the present invention for understanding, and to save space, therefore, no further descriptions will be given.

Embodiment 9

The friend following function is an important function in social networks. Taking the scene shown in Figure 1-1 as an example, how to realize the automatic construction of TV circle, toy circle, and asset circle? For the human microblogging system, users can follow friends through active search, or through the system's associated friend recommendation. As for the social network of machines, the social platform needs to calculate the correlation between the machines through certain intelligent algorithms, and analyze the needs of their potential social circles, so as to realize the construction of the machine social circle. For example, the construction of the TV circle is due to the social platform analysis of the relationship between its owners and the circle of friends, and the three owners are of similar age, all children, and may have common TV viewing hobbies, so the three TVs are formed into a TV circle. The construction of the toy circle is similar. Through a certain intelligent analysis, the platform believes that the three children may have the same toy preferences, and these three toys are the same product, and can share some music and stories, so the toy circle is established. In the same way, the construction of the asset circle, because the platform analyzes that there may be a certain potential connection between the toys and the TV under the same owner, such as the theme music, the recommendation of the story, so the asset circle is established.

In order to realize the above-mentioned friend attention function, an intelligent attention engine can be designed. FIG. 9 is a schematic diagram of the composition structure of the intelligent attention engine according to the ninth embodiment of the present invention. As shown in FIG. 9 , the intelligent attention engine includes two main modules: a machine feature analysis module And the machine intelligence attention module. in:

The machine feature analysis module aims to identify machine features that can be used for intelligent attention from all the attributes and operations of the machine, that is, the key attributes of the machine and the key operations of the machine. The machine feature analysis module can be further divided into two sub-modules: a machine attribute analysis sub-module and a machine behavior analysis sub-module. Among them, the machine attribute analysis module searches for the matching attributes from the key attribute library as key attributes according to all attributes defined by the machine. The machine behavior analysis sub-module is similar to the machine attribute analysis sub-module, and the machine behavior analysis sub-module searches for the matching operation as the key operation from the key operation library.

For example, the attributes possessed by a toy may include: toy ID, name, brand, model, color, material, type, description, location, owner, price, applicable age, fan list, watch list, etc. The operations that the toy has can include: following friends, removing friends, downloading content, playing music, playing stories, etc. The machine feature analysis module, according to the key attribute library and key operation library, realizes the identification of the key attributes and operations related to the recommended function of the usage method from the many attributes and operations of the machine, such as: the name of the toy + model + content download.

The machine intelligence attention module, according to the machine attention strategy set by the application, different application scenarios can set different attention strategies. The attention strategy depends on the attributes and behavior operations of the machine, and can be set as a weighted average of multiple attributes/operations, and Combined with the results of the machine feature analysis module, intelligent attention between machines is realized. It should be noted that the potential social relationship between machines can be reflected by the correlation between machines. The correlation between machines can be calculated by setting the machine attention strategy. The machine pays attention to the strategy, which can be set by simple human experience or optimized by techniques such as machine learning.

Embodiment ten

Information recommendation is another important function of social networks. In the specific implementation process, in order to realize the information recommendation function, an information recommendation engine can be designed. Figure 10 is a schematic diagram of the composition and structure of an information recommendation engine according to Embodiment 10 of the present invention. As shown in Figure 10, the information recommendation engine mainly includes three main functional modules: a content information recommendation module, a usage method recommendation module and a machine behavior recommendation module, wherein:

1) The content information recommendation module can adopt the information recommendation function in the prior art, so it will not be repeated here.

2) The usage method recommendation module is used to implement usage method recommendation. Unlike human users, machines, as users, may have operations that can be performed in addition to their own attributes. For example, a toy can download songs, stories, etc. through a network configuration. After toy 1 successfully completes the configuration and downloads songs and stories, the platform can recommend the configuration method and download method of toy 1 to other toys in the toy circle.

The usage method recommendation module implements the usage method recommendation that matches the machine time. The usage recommendation module is divided into three sub-modules: machine feature analysis sub-module, usage matching analysis sub-module, usage recommendation sub-module, among which:

The machine feature analysis sub-module realizes the feature analysis of the key attributes and key operations of the machine. For example, a toy may have attributes: toy ID, name, brand, model, color, material, type, description, location, owner, price, applicable age, fan list, watch list, etc. Possible operations include: following friends, removing friends, downloading content, playing music, playing stories, etc. The machine feature analysis sub-module, according to the key attribute library and key operation library, realizes the identification of the key attributes and operations related to the recommended function of the usage method from the many attributes and operations of the machine, such as: the name of the toy + model + content download.

Use method matching analysis sub-module, according to the output results of the machine characteristic behavior analysis module, such as key attributes + key operations, through certain intelligent algorithms, identify relevant machines in the machine circle, such as toy circles, that are suitable for this method of use. The intelligent algorithm can be the most simple and direct algorithm as follows, that is, the key attributes + key operations of the two machines are matched. If they match completely, it is considered that the applicable method of machine 1 can be recommended to machine 2. But the algorithm is one-sided. For example, 1 xiyangyang music doll and 1 little white rabbit music doll, they have different names and models, but they can play the same music and story, so the music 1 downloaded by xiyangyang music doll is also applicable to the little white rabbit music doll. Similarly, if you only judge from key operations, there is also one-sidedness. Therefore, to use the method matching analysis module, it is necessary to analyze the output results (key attributes + key operations) of the machine characteristic behavior analysis module, and analyze it through a certain intelligent algorithm, and even use the machine learning algorithm for continuous improvement, and finally output the recommended method of use. the target machine. The aforementioned pseudo-algorithm of the machine intelligence attention module can be used as one of the algorithm implementation methods of the module, so it is not repeated here.

The usage method recommendation sub-module matches the output result of the analysis module according to the usage method, and recommends the usage method of machine 1 to other machines. For example, toy 1 has downloaded the music Smurfs through the network configuration, and can recommend its network configuration and music download method to other toys in the circle. If it is the exact same machine, the recommendation engine can even directly write the relevant configuration information to other toys to be recommended, so as to realize the automatic configuration download without manual participation.

3) The machine behavior recommendation module is used to implement machine behavior recommendation, that is, to recommend the internal operation logic of the machine (which may be the linkage between multiple machines, refer to the scenario shown in Figure 1-2) to other machines. The machine behavior recommendation module includes three submodules: machine behavior analysis submodule, machine behavior matching analysis submodule, and machine behavior recommendation submodule.

The machine behavior analysis module mainly realizes the logic behavior analysis of the machine and extracts its features into the machine behavior strategy. For example, in the behavior recommendation scene in the scenario, the logical behavior of each machine in the home circle is as follows: when the dust meter detects that the concentration of dust (such as PM2.5) is greater than 200, the window brake will automatically close the window; when the window is closed, If the temperature sensor on the air conditioner detects that the home temperature is higher than 28 degrees, the air conditioner will be turned on. The parameters of the air conditioner when it is working are parameters such as temperature 25 degrees, cooling mode, and upper and lower sweeping. The machine behavior analysis module analyzes the machine behavior in the home circle, and extracts the machine behavior policy (Policy), as follows:

Policy1:

Actor:Window1;

Action:Close;

Condition: PM2.5>200.

Policy2:

Actor:Air_conditioner1

Action: Set temprature=25; Set model=cold; Set wind=up-down;

Condition:Window.status=close&&Home.tempreture>28

The machine behavior matching analysis sub-module reads the machine behavior policy file, extracts the behavior subject that executes the machine behavior, performs matching analysis in the association circle, and finally identifies the target machine (circle) recommended by the machine behavior.

The machine behavior recommendation sub-module recommends the machine behavior strategy to be recommended to the target machine (circle) identified by the machine behavior matching analysis module, that is, the strategy of adding the target machine (circle) to the machine behavior strategy.

It should be noted that although the modules in the ninth embodiment of the present invention and the modules in the tenth embodiment are different from the components in the foregoing information processing device embodiments in terms of names, those skilled in the art should understand that However, there is no difference between the functions implemented in the ninth and tenth embodiments and the fourth embodiment of the device. For example, the machine attribute analysis sub-module in the machine feature analysis module can The machine behavior analysis sub-module can be realized by the second construction unit, the second search unit and the second identification unit.

It is to be understood that reference throughout the specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic associated with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present invention, the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, rather than the embodiments of the present invention. implementation constitutes any limitation.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined, or Can be integrated into another system, or some features can be ignored, or not implemented. In addition, the coupling, or direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical or other forms. of.

The unit described above as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit; it may be located in one place or distributed to multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may all be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integration The unit can be implemented either in the form of hardware or in the form of hardware plus software functional units.

Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by program instructions related to hardware, the aforementioned program can be stored in a computer-readable storage medium, and when the program is executed, the execution includes: The steps of the above method embodiments; and the aforementioned storage medium includes: a removable storage device, a read only memory (Read Only Memory, ROM), a magnetic disk or an optical disk and other media that can store program codes.

Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products are stored in a storage medium and include several instructions for A computer device (which may be a personal computer, a server, or a network device, etc.) is caused to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.

The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (16)

1. an information processing method, it is characterised in that the method comprises: Identify the key attributes of the first machine from the attribute set of the first machine according to the preset first matching condition, and the first machine is a registered machine; Identify the key operation of the first machine from the operation set of the first machine according to the preset second matching condition; Judging whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result; When the first judgment result indicates that the key attribute of the first machine and/or the key operation of the first machine satisfy the attention policy, recommending the first machine to the registered second machine or the first machine Two machine masters to achieve attention between machines. 2. The method of claim 1, wherein the first machine comprises more than one machine and the second machine comprises more than one machine. 3. The method according to claim 1, wherein before identifying the key attributes of the first machine from the attribute set of the first machine according to a preset first matching condition, the method further comprises: constructing an attribute library, which includes the attributes of the registered machines, and finds out the attribute set of the first machine from the attribute library according to the identification information of the first machine; An operation library is constructed, the operation library includes the operations of the registered machines, and the operation set of the first machine is searched out from the operation library according to the identification information of the first machine. 4. The method according to claim 1, wherein the method further comprises: A first machine circle to which the first machine belongs is determined according to key attributes of the first machine and key operations of the first machine, where the first machine circle includes one or more machines that have a social relationship with each other ; According to the key attributes and key operations of each machine in the first machine circle, a key behavior for identifying the first machine circle is constructed, and the key behavior is used to reflect the key between the machines in the first machine circle. The logical relationship between operations; Get the second machine circle; The key behaviors of the first machine circle are recommended to the second machine circle, the attributes and operations of the machines in the second machine circle are the same as the attributes and operations of the machines in the first machine circle. 5. The method of claim 4, wherein before the recommending the key behavior of the first machine lap to the second machine lap, the method further comprises: Judging whether the key behavior of the first machine circle satisfies the preset machine behavior strategy, and obtaining a second judgment result; When the second judgment result indicates that the key behavior of the first machine circle satisfies the preset machine behavior policy, the key behavior of the first machine circle is recommended to the second machine circle. 6. The method according to claim 4, wherein the acquiring the second machine circle comprises: Analyzing key attributes and key operations of each machine in the first machine circle; Obtain each machine with the same key attributes and key operations as each machine in the first machine circle; The machines that meet the preset conditions are formed into a second machine circle. 7. The method according to any one of claims 1 to 6, wherein the method further comprises: Perform characteristic analysis on the key operations of the first machine to obtain key operations related to the use method; Identifying a third machine based on key attributes of the first machine and/or key operations of the first machine; The key operations related to the usage method are recommended to the third machine. 8. An information processing method, wherein the method comprises: constructing a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, where the first machine circle includes one or more machines that have a social relationship with each other; According to the key attributes and key operations of each machine in the first machine circle, a key behavior for identifying the first machine circle is constructed, and the key behavior is used to reflect the key between the machines in the first machine circle. The logical relationship between operations; Get the second machine circle; The key behaviors of the first machine circle are recommended to the second machine circle, the attributes and operations of the machines in the second machine circle are the same as the attributes and operations of the machines in the first machine circle. 9. The method of claim 8, wherein before the recommending the key behavior of the first machine lap to the second machine lap, the method further comprises: Judging whether the key behavior of the first machine circle satisfies the preset machine behavior strategy, and obtaining a second judgment result; When the second judgment result indicates that the key behavior of the first machine circle satisfies the preset machine behavior policy, the key behavior of the first machine circle is recommended to the second machine circle. 10. An information processing device, characterized in that the device comprises a first identification unit, a second identification unit, a first judgment unit and a first recommendation unit, wherein: The first identification unit is configured to identify the key attributes of the first machine from the attribute set of the first machine according to a preset first matching condition, and the first machine is a registered machine; the second identification unit, configured to identify the key operation of the first machine from the operation set of the first machine according to a preset second matching condition; the first judgment unit, configured to judge whether the key attributes of the first machine and/or the key operations of the first machine satisfy a preset attention strategy, and obtain a first judgment result; The first recommending unit, configured to recommend the first machine when the first judgment result indicates that the key attributes of the first machine and/or the key operations of the first machine satisfy the attention strategy To the registered second machine or the owner of the second machine to realize the attention between the machines. 11. The device according to claim 10, wherein the device further comprises a first building unit, a first searching unit, a second building unit and a second searching unit, wherein: The first construction unit is used to construct an attribute library, the attribute library includes the attributes of the registered machines; the first search unit, configured to search out the attribute set of the first machine from the attribute library according to the identification information of the first machine; The second construction unit is used to construct an operation library, and the operation library includes the operations of the registered machines; The second search unit is configured to search out the operation set of the first machine from the operation library according to the identification information of the first machine. 12. The device according to claim 10, wherein the device further comprises a third constructing unit, a determining unit, an acquiring unit and a second recommending unit, wherein: The third construction unit is configured to construct a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, and the first machine circle includes mutual more than one machine in a social relationship; The determining unit is configured to determine, according to key attributes and key operations of each machine in the first machine circle, a key behavior used to identify the first machine circle, and the key behavior is used to embody the first machine The mutual logical relationship of key operations among the machines in the circle; the obtaining unit, used to obtain the second machine circle; The second recommendation unit is used to recommend the key behaviors of the first machine circle to the second machine circle, and the attributes and operations of the machines in the second machine circle are the same as those of the machines in the first machine circle. Properties and operations are the same. 13 . The device according to claim 12 , wherein the device further comprises a second judgment unit for judging whether the key behavior of the first machine circle satisfies a preset machine behavior strategy, and obtaining a second judgment. 14 . Result: when the second judgment result indicates that the key behavior of the first machine circle satisfies the preset machine behavior policy, the second recommendation unit is triggered. 14. The device according to claim 12, wherein the acquisition unit comprises an analysis module, an acquisition module and a determination module, wherein: the analysis module for analyzing key attributes and key operations of each machine in the first machine circle; The acquisition module is used to acquire each machine with the same key attributes and key operations as each machine in the first machine circle; The determining module is configured to determine each machine that meets the preset condition as the second machine circle. 15. The device according to any one of claims 10 to 14, wherein the device further comprises a feature analysis unit, a third identification unit and a third recommendation unit, wherein: The feature analysis unit is configured to perform feature analysis on key operations of the first machine to obtain key operations related to the use method; the third identification unit, configured to identify a third machine according to key attributes of the first machine and/or key operations of the first machine; The third recommending unit is configured to recommend the key operation related to the usage method to the third machine. 16. An information processing device, characterized in that the device comprises a third construction unit, a determination unit, an acquisition unit and a second recommendation unit, wherein: The third construction unit is configured to construct a first machine circle to which the first machine belongs according to key attributes of the first machine and key operations of the first machine, where the first machine circle includes a social relationship with each other of more than one machine; The determining unit is configured to construct, according to key attributes and key operations of each machine in the first machine circle, a key behavior for identifying the first machine circle, and the key behavior is used to embody the first machine The mutual logical relationship of key operations among the machines in the circle; the obtaining unit, used to obtain the second machine circle; The second recommendation unit is used to recommend the key behaviors of the first machine circle to the second machine circle, and the attributes and operations of the machines in the second machine circle are the same as those of the machines in the first machine circle. Properties and operations are the same.