Social group processing method, device and electronic equipment

Abstract

The application discloses a kind of processing method, device and electronic equipment of social group.It relates to the field of financial technology or other fields, and the method comprises: receiving the request instruction triggered by target object, wherein the request instruction is used to request to establish target group;Based on the request instruction, the historical chat information of the current social group to which the target object belongs is obtained;The first processing is carried out on the historical chat information, the association degree of the target object and other objects in the current social group is determined, and a plurality of association degree values are obtained;The second processing is carried out on the historical chat information, and the credibility value corresponding to the target object is obtained;According to a plurality of association degree values, credibility value and current social group, target group is established.The application solves the technical problem of low processing efficiency in the process of processing social group in the prior art.

Description

Technical Field

[0001] This invention relates to the field of financial technology or other fields, and more specifically, to a method, apparatus, and electronic device for processing social groups. Background Technology

[0002] As people's work and life paces become increasingly fast, traditional communication methods such as telephone calls and text messages can no longer meet users' needs. More and more users are starting to use social groups to chat (including sending text messages in groups, initiating audio and video conferences in groups, etc.) to communicate.

[0003] In existing technologies, creating a social group usually requires searching for and adding people one by one, which leads to low processing efficiency and a poor user experience.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for processing social groups, which at least solves the technical problem of low processing efficiency in the prior art when processing social groups.

[0006] According to one aspect of the present invention, a method for processing social groups is provided, comprising: receiving a request instruction triggered by a target object, wherein the request instruction is used to request the establishment of a target group; obtaining historical chat information of the current social group to which the target object belongs based on the request instruction; performing a first processing on the historical chat information to determine the degree of association between the target object and other objects in the current social group, thereby obtaining multiple association values; performing a second processing on the historical chat information to obtain a credibility value corresponding to the target object; and establishing the target group based on the multiple association values, the credibility value, and the current social group.

[0007] Furthermore, the historical chat information undergoes a first processing step to determine the degree of association between the target object and other objects in the current social group, resulting in multiple association values. This includes: processing the historical chat information to obtain first target information, wherein the first target information includes at least first information, multiple second information, and multiple third information. The first information consists of all chat information of the target object, each second information consists of interaction information between the target object and another object, and each third information consists of chat information replied to by another object. Based on the number of first information, the number of multiple second information, and the number of multiple third information, the degree of association between the target object and other objects in the current social group is determined, resulting in multiple association values.

[0008] Furthermore, based on the number of first pieces of information, the number of multiple second pieces of information, and the number of multiple third pieces of information, the degree of association between the target object and other objects in the current social group is determined, resulting in multiple association values. This includes: calculating the ratio of the number of each second piece of information to the number of first pieces of information to obtain multiple first values, where each first value is used to characterize the degree of interaction between the target object and another object; calculating the ratio of the number of each third piece of information to the number of first pieces of information to obtain multiple second values, where each second value is used to characterize the degree of information response from another object to the target object; and based on the first and second values, determining the degree of association between the target object and other objects in the current social group, resulting in multiple association values.

[0009] Further, the historical chat information is processed a second time to obtain the credibility value corresponding to the target object. This includes: processing the historical chat information a second time to obtain the second target information, wherein the second target information includes at least the fourth, fifth, and sixth information. The fourth information is the chat information published by the target object containing product promotion information, the fifth information is the interaction information between the target object and all other objects, and the sixth information is the chat information of all other objects replying to the target object. The credibility value is obtained based on the number of fourth, fifth, and sixth information.

[0010] Furthermore, based on the quantity of the fourth, fifth, and sixth pieces of information, a credibility value is obtained, including: calculating the ratio of the quantity of the fourth piece of information to the quantity of the first piece of information to obtain a third value, wherein the third value is used to characterize the target object's sales performance; calculating the ratio of the quantity of the fifth piece of information to the quantity of the first piece of information to obtain a fourth value, wherein the fourth value is used to characterize the degree of interaction between the target object and all other objects; calculating the ratio of the quantity of the sixth piece of information to the quantity of the first piece of information to obtain a fifth value, wherein the fifth value is used to characterize the degree of response of all other objects to the target object's information; and obtaining a credibility value based on the third, fourth, and fifth values.

[0011] Furthermore, based on multiple relevance values, credibility values, and the current social group, a target group is established, including: when the number of first relevance values ​​among the multiple relevance values ​​is greater than or equal to a first preset threshold, and the credibility value is greater than or equal to a second preset threshold, a target group is established based on all objects in the current social group, wherein the first relevance value is greater than or equal to a third preset threshold.

[0012] Furthermore, when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold, and the credibility value is greater than or equal to a second preset threshold, the social group processing method further includes: determining other objects corresponding to each first correlation value to obtain multiple first objects; and establishing a target group based on the multiple first objects.

[0013] According to another aspect of the present invention, a social group processing apparatus is also provided, comprising: a receiving module for receiving a request instruction triggered by a target object, wherein the request instruction is used to request the establishment of a target group; an obtaining module for obtaining historical chat information of the current social group to which the target object belongs based on the request instruction; a first processing module for performing a first processing on the historical chat information to determine the degree of association between the target object and other objects in the current social group, thereby obtaining multiple association values; a second processing module for performing a second processing on the historical chat information to obtain a credibility value corresponding to the target object; and a group creation module for establishing the target group based on the multiple association values, credibility values, and the current social group.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described social group processing method when it is run.

[0015] According to another aspect of the present invention, an electronic device is also provided, the electronic device including one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are configured to run the programs, wherein the programs are configured to execute the above-described social group processing method during runtime.

[0016] In this embodiment of the invention, a request instruction triggered by the target object is first received, wherein the request instruction is used to request the establishment of a target group; then, based on the request instruction, historical chat information of the current social group to which the target object belongs is obtained; the historical chat information is processed in the first way to determine the degree of association between the target object and other objects in the current social group, and multiple association values ​​are obtained; then, the historical chat information is processed in the second way to obtain the credibility value corresponding to the target object; finally, the target group is established based on the multiple association values, credibility values ​​and the current social group.

[0017] In the above process, the relevance value is the degree of association between the target object and other objects in the current social group, and the credibility value is the degree of trustworthiness of the target object. The target group is established by using the relevance value, credibility value, and the current social group, eliminating the need to search and add people one by one. This achieves intelligent establishment of target groups based on relevance and credibility values, improving the user experience and thus improving the processing efficiency of social groups. This solves the technical problem of low processing efficiency in the processing of social groups in existing technologies. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of an optional social group processing method according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of an optional social group processing flow according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of an optional social group processing device according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of an optional electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] It should be noted that the social group processing methods, devices, and electronic devices disclosed herein can be used in the fintech field, or in any field other than fintech. The application areas of the social group processing methods, devices, and electronic devices disclosed herein are not limited.

[0026] It should be noted that all relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this invention are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with the relevant user or organization. Before obtaining relevant information, it needs to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent from the aforementioned user or organization.

[0027] Example 1

[0028] According to an embodiment of the present invention, an embodiment of a method for processing social groups is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a social group processing method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S101: Receive the request instruction triggered by the target object.

[0031] In an optional embodiment, a social group processing system can serve as the execution entity of the social group processing method in this application embodiment. For ease of description, the social group processing system will be simply referred to as the system below. For example, such as Figure 2As shown, the social group processing system includes a module for applying to create a duplicate group, a module for storing chat information, a module for evaluating user relevance, a module for evaluating user credibility, and a module for creating a duplicate group.

[0032] In step S101, the request instruction is used to request the creation of a target group. Specifically, the system's request to copy the group creation module receives the group creation request sent by the target object, generates a request instruction, and instructs the system to create the target group. For example, when the target object sends a group creation request to copy other objects in the target object's current social group to create a target group, the system's request to copy the group creation module receives the group creation request sent by the target object, generates a request instruction, and instructs the system to create the target group.

[0033] Step S102: Obtain the historical chat information of the current social group to which the target object belongs based on the request instruction.

[0034] In step S102, as Figure 2 The chat information storage module shown stores the historical chat information of the current social group to which the target object belongs. The system can retrieve the historical chat information from the chat information storage module through a request command. The historical chat information is the chat history of all objects in the current social group, which includes at least the chat information with product promotion information posted by the target object, the interaction information between the target object and other objects in the current social group, and the reply information of other objects in the current social group to the target object.

[0035] Step S103: Perform first processing on historical chat information to determine the degree of association between the target object and other objects in the current social group, and obtain multiple association values.

[0036] In step S103, the system can, as follows: Figure 2 The user relevance assessment module shown performs a first processing on historical chat information to obtain first information, multiple second information, and multiple third information. The first information is all chat information of the target object, each second information is the interaction information between the target object and another object, and each third information is the chat information of another object replying to the target object. Then, the system can obtain multiple relevance values ​​based on the number of first information, multiple second information, and multiple third information.

[0037] Step S104: Perform a second processing on the historical chat information to obtain the credibility value corresponding to the target object.

[0038] In step S104, the system can, as follows: Figure 2The user credibility assessment module shown performs a second processing on historical chat information to obtain fourth, fifth, and sixth information. The fourth information is the chat information posted by the target object that contains product promotion information, the fifth information is the interaction information between the target object and all other objects, and the sixth information is the chat information of all other objects replying to the target object. The system obtains the credibility value based on the number of fourth, fifth, and sixth information.

[0039] Step S105: Establish the target group based on multiple relevance values, credibility values, and the current social group.

[0040] In step S105, the system can, as follows: Figure 2 The copy group creation module shown above creates a target group based on objects in the current social group when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold and the credibility value is greater than or equal to a second preset threshold. The first correlation value is greater than or equal to a third preset threshold.

[0041] Based on the scheme defined in steps S101 to S105 above, it can be understood that in this embodiment of the invention, firstly, a request instruction triggered by the target object is received, wherein the request instruction is used to request the establishment of a target group; then, based on the request instruction, historical chat information of the current social group to which the target object belongs is obtained; the historical chat information is processed in the first way to determine the degree of association between the target object and other objects in the current social group, and multiple association values ​​are obtained; then, the historical chat information is processed in the second way to obtain the credibility value corresponding to the target object; finally, the target group is established based on the multiple association values, credibility values ​​and the current social group.

[0042] It is noteworthy that in the above process, the relevance value is the degree of association between the target object and other objects in the current social group, and the credibility value is the degree of trustworthiness of the target object. By using the relevance value, credibility value, and the current social group, the target group is established without having to search and add people one by one. This achieves intelligent establishment of the target group based on the relevance value and credibility value, improves the user experience, and thus achieves the technical effect of improving the processing efficiency of social groups. This solves the technical problem of low processing efficiency in the processing of social groups in existing technologies.

[0043] Optionally, in the social group processing method provided in this embodiment of the invention, the system performs a first processing on historical chat information to determine the degree of association between the target object and other objects in the current social group, and obtains multiple association values. This includes: obtaining first target information by performing a first processing on historical chat information, wherein the first target information includes at least first information, multiple second information, and multiple third information. The first information is all chat information of the target object, each second information is the interaction information between the target object and another object, and each third information is the chat information of another object replying to the target object; then, based on the number of first information, the number of multiple second information, and the number of multiple third information, the degree of association between the target object and other objects in the current social group is determined, and multiple association values ​​are obtained.

[0044] For example, the system can first process historical chat information through the user relevance assessment module, and filter out the first information, which includes all chat information of the target object, multiple second information, which includes the interaction information between the target object and each other object, and multiple third information, which includes the chat information of each other object replying to the target object. The system can then count the number of chat records of the first information, the multiple second information, and the multiple third information. Based on the number of chat records of the first information, the multiple second information, and the multiple third information, the system can determine multiple relevance values.

[0045] The system can use a user association assessment module to filter chat information between a target object and another object from the first information. It then removes chat information containing product promotion messages sent by the target object to that other object, obtaining a second piece of information, and so on, until it obtains the interaction information between the target object and all other objects. For example, by filtering all chat information of target object A, the system obtains chat information between A and object B. Then, it removes chat information containing product promotion messages sent by A to B, obtaining the interaction information between A and B. Similarly, by filtering all chat information of target object A, the system obtains chat information between A and object C. Then, it removes chat information containing product promotion messages sent by A to C, obtaining the interaction information between A and C, and so on, until it obtains the interaction information between A and all other objects, thus obtaining multiple pieces of second information.

[0046] It should be noted that by obtaining multiple correlation values ​​based on the number of first information, multiple second information, and multiple third information, it is possible to determine the degree of correlation between the target object and each other object, thus preparing for the subsequent establishment of target groups based on the correlation values.

[0047] Optionally, in the social group processing method provided in this embodiment of the invention, the system determines the degree of association between the target object and other objects in the current social group based on the number of first information, the number of multiple second information, and the number of multiple third information, and obtains multiple association values. This includes: obtaining multiple first values ​​by calculating the ratio of the number of each second information to the number of first information, wherein each first value is used to characterize the degree of interaction between the target object and another object; then calculating the ratio of the number of each third information to the number of first information, and obtaining multiple second values, wherein each second value is used to characterize the degree of information response from another object to the target object; finally, based on the first and second values, determining the degree of association between the target object and other objects in the current social group, and obtaining multiple association values.

[0048] For example, when the total number of chat messages for target A is 500 (i.e., the number of first messages is 500) and the number of interaction messages between A and other target B is 100 (i.e., the number of one of the multiple second messages is 100), the system can calculate the first value as the number of second messages / the number of first messages = 100 / 500 = 0.2 through the user association evaluation module; when the number of interaction messages between A and other target C is 200, the system can calculate the first value as the number of second messages / the number of first messages = 200 / 500 = 0.4 through the user association evaluation module, until all the first values ​​are calculated. When the total number of chat messages from target A is 500 (i.e., the number of first messages is 500) and other target B replies to A with 300 chat messages (i.e., the number of third messages), the system can calculate the second value using the user relevance evaluation module as the number of third messages / the number of first messages = 300 / 500 = 0.6. When other target C replies to A with 50 chat messages (i.e., the number of third messages), the system can calculate the second value using the user relevance evaluation module as the number of third messages / the number of first messages = 50 / 500 = 0.1, until all the second values ​​are calculated.

[0049] For example, the system can calculate multiple relevance values ​​by multiplying a first value and a second value through the user relevance evaluation module. Specifically, if the first value between target object A and other object B is 0.2 and the second value is 0.6, then the relevance value between target object A and other object B is 0.2 * 0.6 = 0.12; if the first value between target object A and other object C is 0.2 and the second value is 0.6, then the relevance value between target object A and other object B is 0.4 * 0.1 = 0.4.

[0050] It should be noted that by calculating the degree of interaction and response between the target object and other objects, a correlation value is obtained based on the degree of interaction and response between the target object and other objects. This allows us to determine the degree of correlation between the target object and other objects based on their chat behavior in the current social group, which prepares us for establishing target groups based on the correlation value.

[0051] Optionally, in the social group processing method provided in this embodiment of the invention, the system performs a second processing on historical chat information to obtain a credibility value corresponding to the target object, including: obtaining second target information by performing a second processing on historical chat information, wherein the second target information includes at least fourth information, fifth information and sixth information, the fourth information is chat information published by the target object with product promotion information, the fifth information is used to characterize the interaction information between the target object and all other objects, and the sixth information is chat information of all other objects replying to the target object; then, the credibility value is obtained based on the number of fourth information, the number of fifth information and the number of sixth information.

[0052] For example, the system can perform a second processing on historical chat records through the user credibility assessment module. It can filter out the fourth information from the historical chat information, which includes chat information with product promotion information posted by the target object, the fifth information, which includes the interaction information between the target object and all other objects, and the chat information including the replies from all other objects to the target object. Then, it can count the number of chat records for the fourth information, the fifth information, and the sixth information respectively. Based on the number of chat records for the fourth information, the fifth information, and the sixth information, the system can determine the credibility value of the target object.

[0053] The system can use the user credibility assessment module to remove the fourth information, including chat messages containing product promotion information posted by the target, from all chat messages of the target, and obtain the fifth information.

[0054] It should be noted that by determining the number of chat records in the fourth, fifth, and sixth information sections, the credibility value of the target is established. This allows for determining the credibility value of the target based on the number of product promotion messages sent by the target in the current social group chat, preparing for the subsequent creation of target groups based on credibility values. Specifically, the more product promotion messages the target sends in the current social group chat, the lower their credibility value; conversely, the fewer product promotion messages the target sends, the higher their credibility value.

[0055] Optionally, in the social group processing method provided in this embodiment of the invention, the system obtains a credibility value based on the quantity of fourth information, the quantity of fifth information, and the quantity of sixth information, including: obtaining a third value by calculating the ratio of the quantity of fourth information to the quantity of first information, wherein the third value is used to characterize the sales performance of the target object; then calculating the ratio of the quantity of fifth information to the quantity of first information to obtain a fourth value, wherein the fourth value is used to characterize the degree of interaction between the target object and all other objects; then calculating the ratio of the quantity of sixth information to the quantity of first information to obtain a fifth value, wherein the fifth value is used to characterize the degree of information response from all other objects to the target object; finally, the credibility value is obtained based on the third value, the fourth value, and the fifth value.

[0056] For example, when the total number of chat messages from the target is 500 (i.e., the number of first messages is 500), the number of chat messages from the target containing product promotion information (i.e., the fourth messages) is 40, the number of interaction messages between the target and all other targets (i.e., the fifth messages) is 200, and the number of chat messages from all other targets replying to the target (i.e., the sixth messages) is 100, the system can calculate the following using the user credibility assessment module: Third value = number of fourth messages / number of first messages = 40 / 500 = 0.08, Fourth value = number of fifth messages / number of first messages = 200 / 500 = 0.4, Fifth value = number of sixth messages / number of first messages = 100 / 500 = 0.2.

[0057] For example, the system can calculate the credibility value through the user credibility assessment module as (fourth value * fifth value) / third value = (0.4 * 0.2) / 0.08 = 1.

[0058] It should be noted that the system obtains the relevance value between the target object and all other objects based on the degree of interaction and response between the target object and all other objects. This allows the system to determine the credibility value of the target object based on the relevance value and the degree of product promotion information sent by the target object in the current social group chat, thus preparing for the subsequent establishment of target groups based on the credibility value.

[0059] Optionally, in the social group processing method provided in the embodiments of the present invention, the system establishes a target group based on multiple correlation values, credibility values ​​and the current social group, including: establishing a target group based on all objects in the current social group when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold and the credibility value is greater than or equal to a second preset threshold, wherein the first correlation value is greater than or equal to a third preset threshold.

[0060] For example, in this embodiment, the first correlation value is greater than or equal to the third preset threshold, meaning that the correlation between other objects corresponding to the first correlation value and the target object is relatively high. The number of first correlation values ​​among multiple correlation values ​​is greater than or equal to the first preset threshold, meaning that the number of other objects with a high correlation to the target object reaches the first preset threshold. For instance, when 95% of the other objects have a high correlation to the target object, and the target object's credibility value is greater than 1, the system can create a target group based on all objects in the current social group using the copy group creation module.

[0061] Optional, such as Figure 2 As shown, after the target group is created through the copy group creation module, the system can send a message to the target object that the group has been successfully created.

[0062] It should be noted that by establishing a target group based on all objects in the current social group when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold and the credibility value is greater than or equal to a second preset threshold, intelligent group building based on correlation and credibility values ​​is achieved, eliminating the need to create groups by searching for objects one by one, thus improving the user experience and increasing the processing efficiency of social groups. This solves the technical problem of low processing efficiency in the processing of social groups in existing technologies.

[0063] In one optional embodiment, when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold, and the confidence value is greater than or equal to a second preset threshold, the system obtains multiple first objects by determining other objects corresponding to each first correlation value; and then establishes a target group based on the multiple first objects.

[0064] In this embodiment, the system can identify multiple first objects with high correlation to the target object through the replication and group building module, and establish a target group based on the multiple first objects with high correlation to the target object.

[0065] It should be noted that by establishing target groups based on multiple primary objects with high relevance to the target object, the system achieves the goal of establishing groups only based on objects with high relevance to the target object, thereby improving the processing efficiency of social groups and enhancing the user experience.

[0066] Therefore, the technical solution of this invention enables an intelligent group creation method. By evaluating user relevance and credibility based on data such as the degree of interaction, response, and promotional content between the user (i.e., the target user) and other users, the invention intelligently replicates group creation, eliminating the need to search and add people one by one to establish target groups. This achieves intelligent creation of target groups based on relevance and credibility values, improving user experience and thus increasing the processing efficiency of social groups. This solves the technical problem of low processing efficiency in existing technologies for social groups.

[0067] Example 2

[0068] According to an embodiment of the present invention, an embodiment of a social group processing device is provided, wherein, Figure 3 A schematic diagram of an optional social group processing device according to Embodiment 1 of the present invention is shown below. Figure 3 As shown, the device includes:

[0069] The receiving module 301 is used to receive a request instruction triggered by the target object, wherein the request instruction is used to request the establishment of a target group;

[0070] The acquisition module 302 is used to acquire historical chat information of the current social group to which the target object belongs based on the request command;

[0071] The first processing module 303 is used to perform first processing on historical chat information, determine the degree of association between the target object and other objects in the current social group, and obtain multiple association values;

[0072] The second processing module 304 is used to perform a second processing on the historical chat information to obtain the credibility value corresponding to the target object.

[0073] The group creation module 305 is used to create target groups based on multiple relevance values, credibility values, and current social groups.

[0074] It should be noted that the above-mentioned receiving module 301, obtaining module 302, first processing module 303, second processing module 304 and group building module 305 correspond to steps S101 to S105 in the above embodiment 1. The five modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0075] Optionally, the first processing module includes a first processing unit and a first determining unit. The first processing unit is used to perform a first processing on historical chat information to obtain first target information, wherein the first target information includes at least first information, multiple second information, and multiple third information. The first information is all chat information of the target object, each second information is the interaction information between the target object and another object, and each third information is the chat information of another object replying to the target object. The first determining unit is used to determine the degree of association between the target object and other objects in the current social group based on the number of first information, the number of multiple second information, and the number of multiple third information, and obtain multiple association values.

[0076] Optionally, the first determining unit includes: a first calculation subunit, a second calculation subunit, and a first determining subunit. The first calculation subunit is used to calculate the ratio of the quantity of each second piece of information to the quantity of the first piece of information, obtaining multiple first values, wherein each first value is used to characterize the degree of interaction between the target object and another object; the second calculation subunit is used to calculate the ratio of the quantity of each third piece of information to the quantity of the first piece of information, obtaining multiple second values, wherein each second value is used to characterize the degree of information response from another object to the target object; the third determining subunit is used to determine the degree of association between the target object and other objects in the current social group based on the first and second values, obtaining multiple association values.

[0077] Optionally, the second processing module includes a second processing unit and a second determining unit. The second processing unit is used to perform a second processing on the historical chat information to obtain second target information, wherein the second target information includes at least a fourth message, a fifth message, and a sixth message. The fourth message is chat information posted by the target object containing product promotion information, the fifth message is interaction information between the target object and all other objects, and the sixth message is chat information from all other objects replying to the target object. The second determining unit is used to obtain a credibility value based on the number of fourth messages, the number of fifth messages, and the number of sixth messages.

[0078] Optionally, the second determining unit includes: a third calculation subunit, a fourth calculation subunit, a fifth calculation subunit, and a second determining subunit. The third calculation subunit calculates the ratio of the quantity of fourth information to the quantity of first information to obtain a third value, wherein the third value characterizes the target object's sales performance. The fourth calculation subunit calculates the ratio of the quantity of fifth information to the quantity of first information to obtain a fourth value, wherein the fourth value characterizes the degree of interaction between the target object and all other objects. The fifth calculation subunit calculates the ratio of the quantity of sixth information to the quantity of first information to obtain a fifth value, wherein the fifth value characterizes the degree of information response from all other objects to the target object. The second determining subunit obtains a credibility value based on the third, fourth, and fifth values.

[0079] Optionally, the group building module includes: a first group building unit, used to build a target group based on all objects in the current social group when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold and the credibility value is greater than or equal to a second preset threshold, wherein the first correlation value is greater than or equal to a third preset threshold.

[0080] Optionally, the social group processing device further includes: a first determining module and a first group building module. The first determining module is used to determine other objects corresponding to each first correlation value when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold, and the credibility value is greater than or equal to a second preset threshold, thereby obtaining multiple first objects; the first group building module is used to establish a target group based on the multiple first objects.

[0081] Example 3

[0082] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program is configured to execute the above-described social group processing method when it is run.

[0083] Example 4

[0084] According to another aspect of the present invention, an electronic device is also provided, wherein, Figure 4 This is a schematic diagram of an optional electronic device according to an embodiment of the present invention, such as... Figure 4 As shown, the electronic device includes one or more processors; and a memory for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to run the programs, wherein the programs are configured to execute the aforementioned social group processing method during runtime.

[0085] like Figure 4As shown, this application embodiment provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps:

[0086] The system receives a request instruction triggered by the target object, wherein the request instruction is used to request the establishment of a target group; based on the request instruction, it obtains the historical chat information of the current social group to which the target object belongs; it performs a first processing on the historical chat information to determine the degree of association between the target object and other objects in the current social group, and obtains multiple association values; it performs a second processing on the historical chat information to obtain the credibility value corresponding to the target object; and it establishes the target group based on the multiple association values, credibility values, and the current social group.

[0087] Optionally, when the processor executes the program, it also performs the following steps: performing a first processing on historical chat information to obtain first target information, wherein the first target information includes at least first information, multiple second information, and multiple third information, the first information being all chat information of the target object, each second information being interaction information between the target object and another object, and each third information being chat information of another object replying to the target object; determining the degree of association between the target object and other objects in the current social group based on the number of first information, the number of multiple second information, and the number of multiple third information, and obtaining multiple association values.

[0088] Optionally, when the processor executes the program, it also performs the following steps: calculating the ratio of the number of each second piece of information to the number of first pieces of information to obtain multiple first values, wherein each first value is used to characterize the degree of interaction between the target object and another object; calculating the ratio of the number of each third piece of information to the number of first pieces of information to obtain multiple second values, wherein each second value is used to characterize the degree of information response from another object to the target object; and determining the degree of association between the target object and other objects in the current social group based on the first and second values ​​to obtain multiple association values.

[0089] Optionally, when the processor executes the program, it also performs the following steps: performing a second processing on the historical chat information to obtain second target information, wherein the second target information includes at least a fourth message, a fifth message, and a sixth message, the fourth message being chat information with product promotion information posted by the target object, the fifth message being interaction information between the target object and all other objects, and the sixth message being chat information in response to the target object by all other objects; and obtaining a credibility value based on the number of fourth messages, the number of fifth messages, and the number of sixth messages.

[0090] Optionally, the processor, when executing the program, also performs the following steps: calculating the ratio of the quantity of fourth information to the quantity of first information to obtain a third value, wherein the third value is used to characterize the sales performance of the target object in promoting its products; calculating the ratio of the quantity of fifth information to the quantity of first information to obtain a fourth value, wherein the fourth value is used to characterize the degree of interaction between the target object and all other objects; calculating the ratio of the quantity of sixth information to the quantity of first information to obtain a fifth value, wherein the fifth value is used to characterize the degree of information response from all other objects to the target object; and obtaining a credibility value based on the third, fourth, and fifth values.

[0091] Optionally, when the processor executes the program, it also implements the following steps: when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold, and the credibility value is greater than or equal to a second preset threshold, a target group is established based on all objects in the current social group, wherein the first correlation value is greater than or equal to a third preset threshold.

[0092] Optionally, when the processor executes the program, it also performs the following steps: when the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold, and the confidence value is greater than or equal to a second preset threshold, it determines other objects corresponding to each first correlation value to obtain multiple first objects; and establishes a target group based on the multiple first objects.

[0093] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0094] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0096] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0099] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing social groups, characterized in that, include: Receive a request instruction triggered by a target object, wherein the request instruction is used to request the establishment of a target group; Based on the request instruction, retrieve the historical chat information of the current social group to which the target object belongs; The historical chat information is first processed to determine the degree of association between the target object and other objects in the current social group, and multiple association values ​​are obtained. The historical chat information is processed a second time to obtain the credibility value corresponding to the target object; The target group is established based on multiple correlation values, the credibility values, and the current social group; The second processing of the historical chat information to obtain the credibility value corresponding to the target object includes: The historical chat information is processed in a second way to obtain second target information, wherein the second target information includes at least a fourth information, a fifth information and a sixth information, wherein the fourth information is chat information with product promotion information posted by the target object, the fifth information is interaction information between the target object and all the other objects, and the sixth information is chat information in which all the other objects reply to the target object; The confidence value is obtained based on the number of the fourth piece of information, the number of the fifth piece of information, and the number of the sixth piece of information.

2. The method according to claim 1, characterized in that, The historical chat information is first processed to determine the degree of association between the target object and other objects in the current social group, resulting in multiple association values, including: The historical chat information is first processed to obtain first target information, wherein the first target information includes at least first information, a plurality of second information and a plurality of third information, the first information is all chat information of the target object, each second information is interaction information between the target object and one of the other objects, and each third information is chat information of the other object replying to the target object; Based on the quantity of the first information, the quantity of multiple second information, and the quantity of multiple third information, the degree of association between the target object and other objects in the current social group is determined, and multiple association values ​​are obtained.

3. The method according to claim 2, characterized in that, Based on the quantity of the first information, the quantity of multiple pieces of the second information, and the quantity of multiple pieces of the third information, the degree of association between the target object and other objects in the current social group is determined, resulting in multiple association values, including: Calculate the ratio of the quantity of each second piece of information to the quantity of the first piece of information to obtain multiple first values, wherein each first value is used to characterize the degree of interaction between the target object and one of the other objects; Calculate the ratio of the quantity of each third piece of information to the quantity of the first piece of information to obtain multiple second values, wherein each second value is used to characterize the degree of information response from one of the other objects to the target object; Based on the first value and the second value, the degree of association between the target object and other objects in the current social group is determined, and multiple association values ​​are obtained.

4. The method according to claim 1, characterized in that, The confidence value is obtained based on the number of the fourth piece of information, the number of the fifth piece of information, and the number of the sixth piece of information, including: The ratio of the quantity of the fourth information to the quantity of the first information is calculated to obtain a third value, wherein the third value is used to characterize the sales performance of the target object in promoting the product, and the first information is all the chat information of the target object; The ratio of the quantity of the fifth information to the quantity of the first information is calculated to obtain a fourth value, wherein the fourth value is used to characterize the degree of interaction between the target object and all the other objects; The ratio of the quantity of the sixth information to the quantity of the first information is calculated to obtain a fifth value, wherein the fifth value is used to characterize the degree of information response from all the other objects to the target object; The confidence value is obtained based on the third, fourth, and fifth values.

5. The method according to claim 1, characterized in that, Based on multiple correlation values, the credibility value, and the current social group, the target group is established, including: When the number of first correlation values ​​among multiple correlation values ​​is greater than or equal to a first preset threshold, and the credibility value is greater than or equal to a second preset threshold, the target group is established based on all objects in the current social group, wherein the first correlation value is greater than or equal to a third preset threshold.

6. The method according to claim 5, characterized in that, When the number of first correlation values ​​among the plurality of correlation values ​​is greater than or equal to a first preset threshold, and the confidence value is greater than or equal to a second preset threshold, the method further includes: Identify the other objects corresponding to each of the first correlation values ​​to obtain multiple first objects; The target group is established based on multiple of the first objects.

7. A social group processing device, characterized in that, include: A receiving module is used to receive a request instruction triggered by a target object, wherein the request instruction is used to request the establishment of a target group; The acquisition module is used to acquire historical chat information of the current social group to which the target object belongs, based on the request instruction; The first processing module is used to perform a first processing on the historical chat information, determine the degree of association between the target object and other objects in the current social group, and obtain multiple association values; The second processing module is used to perform a second processing on the historical chat information to obtain the credibility value corresponding to the target object; The group creation module is used to create the target group based on multiple correlation values, the credibility values, and the current social group; The second processing module includes: a second processing unit for performing a second processing on historical chat information to obtain second target information, wherein the second target information includes at least a fourth message, a fifth message, and a sixth message, wherein the fourth message is chat information with product promotion information posted by the target object, the fifth message is interaction information between the target object and all other objects, and the sixth message is chat information in which all other objects reply to the target object; and a second determining unit for obtaining a credibility value based on the number of fourth messages, the number of fifth messages, and the number of sixth messages.

8. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the social group processing method according to any one of claims 1 to 6 when it is run.

9. An electronic device, characterized in that, It includes one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the social group processing method according to any one of claims 1 to 6.

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