CN112463856A - Character feature analysis method and device based on real-time virtual interaction and terminal equipment - Google Patents
Character feature analysis method and device based on real-time virtual interaction and terminal equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a character feature analysis method and device based on real-time virtual interaction and terminal equipment. The method comprises the following steps: after the virtual task of the virtual role corresponding to the target user is finished, obtaining evaluation information of other users of the same group aiming at the target user, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished; obtaining current character evaluation data of the target user according to all the evaluation information; and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user. The character feature data of the user can be comprehensively and accurately obtained without influencing the user experience in the user immersive product experience process. Therefore, a data basis is provided for the accurate recommendation of subsequent friends, contents, products, services and the like.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a character feature analysis method and device based on real-time virtual interaction and terminal equipment.
Background
In the field of internet application, it is necessary to accurately analyze the personality characteristics of a user. More accurate recommendations are based on more accurate analysis of user personality traits. There are two main types of current user character analysis, one is to analyze user preferences, such as counting user click-through rates for a certain type of content. The method only counts the preference of the user, does not depict the character features of the user, and is incomplete. If the user is recommended with such one-sided preference, the user is likely to be given cognitive bias. Another approach is to use a psychometric paper. Although the method can comprehensively analyze the character characteristics of the user theoretically, the user is required to complete a boring psychological evaluation paper violently, the user is resistant, the evaluation can be completed at will, and the accuracy of the evaluation result cannot be guaranteed. Meanwhile, when a user fills in a psychological evaluation paper, the user psychologically processes the state of vigilance and prevention, so that the real idea of the user is disguised, and the evaluation result is also distorted.
Therefore, in the existing user character feature analysis method, the analysis result is one-sided, and the accuracy of the analysis result cannot be guaranteed. The user character feature data can not be obtained comprehensively and accurately without influencing the product experience of the user.
Disclosure of Invention
The embodiment of the invention aims to provide a character feature analysis method and device based on real-time virtual interaction, terminal equipment and a processor.
In order to achieve the above object, a first aspect of the present invention provides a method for analyzing character features based on real-time virtual interaction, including:
after the virtual task of the virtual role corresponding to the target user is finished, obtaining evaluation information of other users of the same group aiming at the target user, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished;
obtaining current character evaluation data of the target user according to all the evaluation information;
and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
In an embodiment of the invention, the personality assessment data comprises a plurality of personality dimensions; updating the current personality assessment data to historical personality assessment data for the target user, comprising: acquiring a characteristic value of each character dimension in current character evaluation data; and merging the characteristic value of each character dimension in the current character evaluation data with the historical character evaluation data to obtain the latest characteristic value of each character dimension.
In an embodiment of the invention, the method further comprises obtaining the latest eigenvalue for each of the term lattice dimensions according to the following formula:
wherein Z isnIs the characteristic value corresponding to character dimension n, M is the numerical value accumulated by the full score of the user, YnThe accumulated value of the character dimension n, Q is a weighting factor, and m is the number of people in the group of the target user.
In the embodiment of the invention, the value M of the user full score accumulation is calculated according to the following formula:
m ═ M' + (M-1) × Q; wherein M' is the numerical value accumulated by the full score of the user before the current time.
In an embodiment of the present invention, the weighting factor Q is calculated according to the following formula:
wherein S is the score of the task group of the local site, and L is the proficiency level of the target user in the task of the virtual role.
In an embodiment of the present invention, the task cumulative total score for the target user is calculated according to the following formula: t' + S × Q; wherein T is the task accumulation total score of the target user; t' is the accumulated score of the target user participating in the real-time virtual interaction task before the current time, S is the score of the local task group, and Q is a weight factor.
In an embodiment of the invention, the method further comprises: and when the task accumulated total score T reaches a preset score, determining that the proficiency level of the task of the virtual role of the target user reaches an upgrading standard, and improving the proficiency level of the target user.
In an embodiment of the invention, the method further comprises: displaying virtual tasks of virtual roles corresponding to the target user on terminal interfaces of the target user and other users of the target user belonging to the same group; acquiring control operations of a target user and other users for respective corresponding virtual roles; and determining the task score of the group where the target user is located according to the control operation.
In the embodiment of the present invention, determining the task score of the group where the target user is located according to the control operation includes: determining whether the group where the target user is located completes the virtual task or not according to the control operation; and determining the task score of the group where the target user is located according to the completion condition of the virtual task.
In an embodiment of the invention, the method further comprises: acquiring registration information of a user for a virtual task; adding the user with the determined entry into an entry user set; and randomly selecting a preset number of users from the registration user set to form a group so that the users in the same group can jointly complete the virtual task.
A second aspect of the present invention provides a processor configured to execute the above method for real-time virtual interaction-based character analysis.
The third aspect of the present invention provides a character feature analysis device based on real-time virtual interaction, including:
the user evaluation module is used for acquiring evaluation information of other users of the same group aiming at the target user after the virtual task of the virtual role corresponding to the target user is finished, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished;
the character data merging module is used for obtaining the current character evaluation data of the target user according to all the evaluation information; and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
A fourth aspect of the invention provides a machine-readable storage medium having stored thereon instructions which, when executed by a processor, cause the processor to be configured to perform the above-described method of real-time virtual interaction based personality traits analysis.
A fifth aspect of the present invention provides a terminal device, including: memory, transceiver, processor and bus system:
wherein, the memory is used for storing programs;
the processor is used for executing the program in the memory and executing the character feature analysis method based on the real-time virtual interaction according to the instructions in the program codes;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
Through the technical scheme, the character feature data of the user can be comprehensively and accurately obtained without influencing the user experience in the user immersive product experience process. Therefore, a data basis is provided for the accurate recommendation of subsequent friends, contents, products, services and the like. Meanwhile, the character feature data can also be displayed as a part of the user data, so that other users can see the external image of the user through the Internet and can also know the internal characteristics of the user.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a diagram schematically illustrating an application environment of a real-time virtual interaction-based personality trait analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for real-time virtual interaction based personality trait analysis according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a method for real-time virtual interaction based personality trait analysis in accordance with another embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating an interface of virtual tasks of the same team according to an embodiment of the invention;
FIG. 5 is a schematic illustration of an interface diagram two of the same group of virtual tasks, according to an embodiment of the invention;
FIG. 6 is a schematic illustration of a third interface diagram for virtual tasks of the same team according to an embodiment of the invention;
FIG. 7 schematically illustrates an evaluation interface diagram according to an embodiment of the invention;
FIG. 8 is a schematic diagram illustrating an interface for a user to complete a virtual task score, according to an embodiment of the invention;
FIG. 9 is a schematic diagram illustrating an interface of the user's latest character evaluation data according to an embodiment of the invention;
FIG. 10 is a block diagram schematically illustrating a personality characteristics analysis device based on real-time virtual interaction according to an embodiment of the present invention;
fig. 11 schematically shows an internal configuration diagram of a computer apparatus according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
The character feature analysis method based on real-time virtual interaction can be applied to the application environment shown in fig. 1. There may be a plurality of terminals, each user may have a corresponding terminal, fig. 1 schematically shows 2 terminals, which are terminal 101 and terminal 102, respectively, and the terminals may communicate with server 103 through a network. The terminal may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, smart televisions, and portable wearable devices, and the server 103 may be implemented by an independent server or a server cluster formed by a plurality of servers.
Fig. 2 is a schematic flow chart illustrating a character feature analysis method based on real-time virtual interaction according to an embodiment of the present invention. As shown in fig. 2, in an embodiment of the present invention, a method for analyzing character features based on real-time virtual interaction is provided, which includes the following steps:
And step 202, obtaining the current character evaluation data of the target user according to all the evaluation information.
And step 203, updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
The character feature analysis method based on real-time virtual interaction in the embodiment is mainly used for acquiring relatively comprehensive and accurate user character feature data in the real-time virtual interaction process of the user immersive experience product. In particular, real-time virtual interactions are designed as a link in the user's online daily social activities, in which the user actively participates and completes contextual tasks with other users. The behavior of the user in the virtual interaction process can be displayed to other users in real time, and the other users can sense the behavior intention in real time. Therefore, after the virtual interaction process is finished, other users can evaluate the personality characteristics of the user according to the behaviors of the user in the virtual interaction process, namely, other users in the same group can evaluate the target user based on the performance of the target user when finishing the virtual task, so that evaluation information of the users in the same group on the target user can be obtained.
After the evaluation information of all the users to the target user is acquired, the current character evaluation data of the target user can be acquired according to all the evaluation information. And then, the character evaluation of other users on the user is further comprehensively analyzed, so that the character characteristics of the user can be portrayed. Because the user actively participates in the real-time virtual interaction activity and the virtual interaction process is fed back in real time, the behavior of the user in the virtual interaction process is spontaneous and subconscious reaction to the situation. Therefore, the character characteristics of the user can be truly reflected in the virtual interaction process. Other users participating in the real-time virtual interaction together are randomly assigned, and when the character features of the users are evaluated, no motivation for deliberately giving a good comment or a bad comment exists, so that the evaluation is real. As the number of times that the user participates in the real-time virtual interaction is increased, other users who evaluate the character features of the user are increased, and then the character features of the user can be accurately and comprehensively depicted by hundreds of percent through statistical analysis of the historical evaluation data.
In one embodiment, the personality assessment data comprises a plurality of personality dimensions; updating the current personality assessment data to historical personality assessment data for the target user, comprising: acquiring a characteristic value of each character dimension in current character evaluation data; and merging the characteristic value of each character dimension in the current character evaluation data with the historical character evaluation data to obtain the latest characteristic value of each character dimension.
Specifically, the latest eigenvalue for each term character dimension can be found according to the following formula:
wherein Z isnIs the characteristic value corresponding to character dimension n, M is the numerical value accumulated by the full score of the user, YnThe accumulated value of the character dimension n, Q is a weighting factor, and m is the number of people in the group of the target user.
In one embodiment, the value M of the user's full score accumulation may be calculated according to the following formula:
M＝M′+(m-1)*Q；
wherein M' is the numerical value accumulated by the full score of the user before the current time.
In one embodiment, the weighting factor Q may be calculated according to the following formula:
wherein S is the score of the task group of the local site, and L is the proficiency level of the target user in the task of the virtual role.
In one embodiment, the task cumulative total score for the target user may be calculated according to the following formula:
T＝T′+S*Q；
wherein T is the task accumulation total score of the target user; t' is the accumulated score of the target user participating in the real-time virtual interaction task before the current time, S is the score of the local task group, and Q is a weight factor.
In one embodiment, the method further comprises: acquiring registration information of a user for a virtual task; adding the user with the determined entry into an entry user set; and randomly selecting a preset number of users from the registration user set to form a group so that the users in the same group can jointly complete the virtual task.
In one embodiment, the method further comprises: displaying virtual tasks of virtual roles corresponding to the target user on terminal interfaces of the target user and other users of the target user belonging to the same group; acquiring control operations of a target user and other users for respective corresponding virtual roles; and determining the task score of the group where the target user is located according to the control operation.
In one embodiment, determining the task score of the group in which the target user is located according to the control operation comprises: determining whether the group where the target user is located completes the virtual task or not according to the control operation; and determining the task score of the group where the target user is located according to the completion condition of the virtual task.
As shown in fig. 3, a schematic flow chart of a character feature analysis method based on real-time virtual interaction is also provided, and the specific process is as follows:
1. the user operates the terminal device, logs in the client and keeps network connection with the server, so that bidirectional network communication is established between the user terminal and the server.
2. And clicking the registration on the terminal equipment by the user to send a virtual interaction joining instruction to the server. And after receiving the instruction, the server adds the UID corresponding to the user into the registered set.
3. And after the entry time is expired, the server randomly divides the UID in the entry set into a group of m persons. The process of randomly dividing the groups can modify the details as required, for example, a male user can randomly select 1, a female user can randomly select 1, and thus the 2 users form a group.
4. And the server sends a local interaction starting instruction to the user terminal, wherein the instruction comprises a virtual scene ID, prop configuration information and other user information.
5. And after receiving the local interaction starting instruction, the user terminals of the same group display the virtual scene picture on the screen of the user terminal. The pictures show various interaction props arranged in the virtual scene space according to configuration and virtual images of the same group of users, so that the users in the same group enter the same virtual scene.
6. And the user clicks the terminal equipment, and the client running at the terminal sends a behavior instruction to the server according to the operation of the user. The action instructions include moving in a scene, operating props, and the like. And after receiving the instruction, the server broadcasts the behavior instruction to other users in the scene. After other user terminals receive the behavior instruction, the client side of the other user terminals can express the corresponding behavior of the user in the scene, so that the other users can see the behavior action of the user from the terminal screen.
7. And the server randomly selects scene tasks according to the time sequence to the group of users and issues task instructions. And the user terminal prompts the current task in the virtual scene after receiving the task instruction.
8. After the group of users see the task prompt on the terminal screen, each member clicks the terminal device according to own will, the client is operated to continuously send out own behavior instructions, and properties in the scene are changed, so that the live situation in the scene is changed step by step. Eventually the contextual task either completes or fails, thereby affecting the team score of the local task.
9. And after the local interaction ending time is reached, the server sends a local interaction ending instruction. And after receiving the ending instruction, the user terminal displays an evaluation interface for other users in the same group.
10. And the user evaluates other users one by one according to the feeling in the interaction process. And according to the n dimensions divided by the character features, the evaluation interface displays multiple options for embodying the n dimensions. The user clicks on which personality dimension represents the most prominent option.
11. And after the user finishes evaluation, the terminal sends the evaluation data to the server. The format of the evaluation data is { UID: [1,...., n ] }, wherein UID is the UID of the user and n is the dimension number of the character.
12. And after receiving the m evaluation data of all the users in the same group, the server integrates the evaluation data. The integration process is to accumulate each personality dimension of each user of the group m-1 times, thereby forming a local personality evaluation data for each user of the group. The field personality evaluation data format is { UID: [ Y1.,. Yn ], score: S }, wherein UID is the UID of the user, n is the dimension number of the personality, Yn is the accumulated value of the dimension personality, score represents the final score of the field task team, and S is the score of the field task team.
13. And finally, the server merges the local character evaluation data of the user into historical evaluation data, and updates the character characteristic data of the user in real time. The historical evaluation data format of the user is { UID: [ Z1., Zn ], level: L, total: T, Max: M }, wherein UID is UID of the user, n is the dimension number of the character, Zn is the characteristic value of the character dimension, the characteristic value is expressed by percentage, level represents the familiarity of the user to the real-time virtual, L is the proficiency level of the user participating in the real-time virtual interaction, total represents the historical accumulated score of the user participating in the real-time virtual interaction task, T is the accumulated score of the user participating in the real-time virtual interaction task, Max represents the historical accumulation of the full score of the user character evaluation and is used as an evaluation reference, and M is the numerical value of the full score accumulation of the user. The steps of integrating the local character evaluation data into the historical evaluation data are as follows:
in a first step, a weighting factor, Q ═ S/100 × L2, for the local personality assessment is calculated, the weighting factor being related to the local task score S and the proficiency level L of the user, for correcting the contingent results of the personality assessment.
And secondly, calculating and updating the character characteristic value of each dimension, wherein Zn (Zn M + Yn Q)/(M + (M-1) Q), and M is the number of the local group.
And thirdly, calculating and updating the full score accumulation, wherein M is M + (M-1) Q.
And fourthly, calculating and updating the accumulated score, wherein T is T + S Q.
And fifthly, judging whether T reaches an upgrading threshold value according to the accumulated score T calculated in the previous step, and if T reaches the upgrading threshold value, upgrading the proficiency level L of the user.
In one embodiment, the method further comprises: and when the task accumulated total score T reaches a preset score, determining that the proficiency level of the task of the virtual role of the target user reaches an upgrading standard, and improving the proficiency level of the target user.
For example, user A (UID: U-5f4B4bc6-cc348ab9ba482ab6-3504) and user B (UID: U-5f4B40f1-d274bf10B6ba70d4-8881) register for online interaction between strangers. As shown in fig. 4, the activity is divided into two people and one group, and they are randomized to just the same group. As shown in fig. 5, they entered the same virtual kitchen scenario. Kitchen ware, cooking utensils, food materials and the like in the scene are props, and scene tasks are arranged above the screen. According to the scene task prompt, the users collaborate to cook dishes according to the menu. As shown in fig. 6, the user a operates the virtual character in the scene at the client, and completes actions such as walking, collecting food materials, sorting food materials, cooking food materials, serving food, and the like. These actions are also synchronized in real time to user B's client and vice versa, with the client screen displays of both being consistent. As shown in FIG. 7, after the activity is completed, the client displays an interface for evaluation by other team members. Since the group has only two users, user a rates user B, and user B rates user a. In this example, the character features are divided into 6 dimensions of breach, service and solicitation, cooperation, agility, concentration, wisdom, etc., and the numbers are 1,2,3,4,5,6 respectively. The user A feels that the personality performances of the user B in three aspects of the breach, the cooperation and the concentration are more prominent in the process of completing the cooking task together with the user B, and then the options of the breach, the cooperation and the concentration are selected. The evaluation data received by the server at this time is { U-5f4b40f1-d274bf10b6ba70d4-8881: [1,3,5] }. After the evaluation is completed, the server forms cost field character evaluation data for each user in the group. The local field character evaluation data is { U-5f4B40f1-d274bf10B6ba70d4-8881: [1,0,1,0,1,0], U-5f4B4bc6-cc348ab9ba482ab6-3504: [0,0,1,0,1,0], score:16}, wherein the local field evaluation result of the user B is shown in FIG. 8. The local character evaluation data is merged into the historical evaluation data of the user, so that the latest character feature data of the user is obtained. FIG. 9 shows the latest personality trait data { U-5f4B40f1-d274bf10B6ba70d4-8881: [0.88,0.69,0.9,0.81,0.65,0.8], level:2, total:999, Max:100} of user B, wherein the radar map shows personality trait values for 6 dimensions, proficiency level is level 2, and cumulative score is 999 points.
According to the character feature analysis method based on real-time virtual interaction, character feature data of a user can be comprehensively and accurately obtained without influencing user experience in the user immersive product experience process. Therefore, a data basis is provided for the accurate recommendation of subsequent friends, contents, products, services and the like. Meanwhile, the character feature data can also be displayed as a part of the user data, so that other users can see the external image of the user through the Internet and can also know the internal characteristics of the user.
The embodiment of the invention provides a processor, which is used for running a program, wherein the method for analyzing the character characteristics based on real-time virtual interaction is executed when the program runs.
In one embodiment, a character feature analysis device based on real-time virtual interaction is provided, which includes the above processor, and the processor is configured to execute the above character feature analysis method based on real-time virtual interaction.
In one embodiment, as shown in fig. 10, there is also provided a character feature analysis apparatus based on real-time virtual interaction, including:
the user evaluation module 1001 is configured to, after the virtual task of the virtual role corresponding to the target user is completed, obtain evaluation information of other users of the same group for the target user, where the evaluation information is obtained by evaluating other users of the same group based on performance of the target user when the virtual task is completed.
A character data merging module 1002, configured to obtain current character evaluation data of the target user according to all the evaluation information; and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
In one embodiment, the personality assessment data comprises a plurality of personality dimensions. The character data merging module 1002 is further configured to obtain a feature value of each character dimension in the current character evaluation data; and merging the characteristic value of each character dimension in the current character evaluation data with the historical character evaluation data to obtain the latest characteristic value of each character dimension.
In one embodiment, the latest eigenvalue for each term's personality dimension is obtained according to the following formula:
wherein Z isnIs the characteristic value corresponding to character dimension n, M is the numerical value accumulated by the full score of the user, YnThe accumulated value of the character dimension n, Q is a weighting factor, and m is the number of people in the group of the target user.
In one embodiment, the value M for the user's full score accumulation is calculated according to the following formula: m ═ M' + (M-1) × Q; wherein M' is the numerical value accumulated by the full score of the user before the current time.
In one embodiment, the weighting factor Q is calculated according to the following formula:
wherein S is the score of the task group of the local site, and L is the proficiency level of the target user in the task of the virtual role.
In one embodiment, the task cumulative total score for the target user is calculated according to the following formula: t' + S × Q; wherein T is the task accumulation total score of the target user; t' is the accumulated score of the target user participating in the real-time virtual interaction task before the current time, S is the score of the local task group, and Q is a weight factor.
In one embodiment, the apparatus further includes a virtual task module (not shown in the figure) configured to display a virtual task of a virtual role corresponding to the target user on terminal interfaces of the target user and other users belonging to the same group; acquiring control operations of a target user and other users for respective corresponding virtual roles; determining whether the group where the target user is located completes the virtual task or not according to the control operation; and determining the task score of the group where the target user is located according to the completion condition of the virtual task.
In one embodiment, the virtual task module is further configured to obtain entry information of the user for the virtual task; adding the user with the determined entry into an entry user set; and randomly selecting a preset number of users from the registration user set to form a group so that the users in the same group can jointly complete the virtual task.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the character feature analysis method based on real-time virtual interaction is realized by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the invention provides a storage medium, wherein a program is stored on the storage medium, and the program realizes the character feature analysis method based on real-time virtual interaction when being executed by a processor.
In one embodiment, there is provided a terminal device including: memory, transceiver, processor and bus system: wherein, the memory is used for storing programs; the processor is used for executing the program in the memory and executing the character feature analysis method based on the real-time virtual interaction according to the instructions in the program codes; the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 11. The computer device includes a processor a01, a network interface a02, a memory (not shown), and a database (not shown) connected by a system bus. Wherein processor a01 of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises an internal memory a03 and a non-volatile storage medium a 04. The non-volatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a 04. The database of the computer device is used for storing relevant data such as evaluation information of the user. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02 is adapted to be executed by the processor a01 to carry out a method of personality trait analysis based on real-time virtual interaction.
Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps: after the virtual task of the virtual role corresponding to the target user is finished, obtaining evaluation information of other users of the same group aiming at the target user, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished; obtaining current character evaluation data of the target user according to all the evaluation information; and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
In one embodiment, the personality assessment data comprises a plurality of personality dimensions; updating the current personality assessment data to historical personality assessment data for the target user, comprising: acquiring a characteristic value of each character dimension in current character evaluation data; and merging the characteristic value of each character dimension in the current character evaluation data with the historical character evaluation data to obtain the latest characteristic value of each character dimension.
In one embodiment, the method further comprises obtaining the latest feature value for each term dimension according to the following formula:
wherein Z isnIs the characteristic value corresponding to character dimension n, M is the numerical value accumulated by the full score of the user, YnThe accumulated value of the character dimension n, Q is a weighting factor, and m is the number of people in the group of the target user.
In one embodiment, in an embodiment of the present invention, the value M of the user full score accumulation is calculated according to the following formula:
m ═ M' + (M-1) × Q; wherein M' is the numerical value accumulated by the full score of the user before the current time.
In one embodiment, the weighting factor Q is calculated according to the following formula:
wherein S is the score of the task group of the local site, and L is the proficiency level of the target user in the task of the virtual role.
In one embodiment, the task cumulative total score for the target user is calculated according to the following formula: t' + S × Q; wherein T is the task accumulation total score of the target user; t' is the accumulated score of the target user participating in the real-time virtual interaction task before the current time, S is the score of the local task group, and Q is a weight factor.
In one embodiment, the method further comprises: and when the task accumulated total score T reaches a preset score, determining that the proficiency level of the task of the virtual role of the target user reaches an upgrading standard, and improving the proficiency level of the target user.
In one embodiment, the method further comprises: displaying virtual tasks of virtual roles corresponding to the target user on terminal interfaces of the target user and other users of the target user belonging to the same group; acquiring control operations of a target user and other users for respective corresponding virtual roles; and determining the task score of the group where the target user is located according to the control operation.
In one embodiment, determining the task score of the group in which the target user is located according to the control operation comprises: determining whether the group where the target user is located completes the virtual task or not according to the control operation; and determining the task score of the group where the target user is located according to the completion condition of the virtual task.
In one embodiment, the method further comprises: acquiring registration information of a user for a virtual task; adding the user with the determined entry into an entry user set; and randomly selecting a preset number of users from the registration user set to form a group so that the users in the same group can jointly complete the virtual task.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: after the virtual task of the virtual role corresponding to the target user is finished, obtaining evaluation information of other users of the same group aiming at the target user, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished; obtaining current character evaluation data of the target user according to all the evaluation information; and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
In one embodiment, the personality assessment data comprises a plurality of personality dimensions; updating the current personality assessment data to historical personality assessment data for the target user, comprising: acquiring a characteristic value of each character dimension in current character evaluation data; and merging the characteristic value of each character dimension in the current character evaluation data with the historical character evaluation data to obtain the latest characteristic value of each character dimension.
In one embodiment, the method further comprises obtaining the latest feature value for each term dimension according to the following formula:
wherein Z isnIs the characteristic value corresponding to character dimension n, M is the numerical value accumulated by the full score of the user, YnThe accumulated value of the character dimension n, Q is a weighting factor, and m is the number of people in the group of the target user.
In one embodiment, in an embodiment of the present invention, the value M of the user full score accumulation is calculated according to the following formula:
m ═ M' + (M-1) × Q; wherein M' is the numerical value accumulated by the full score of the user before the current time.
In one embodiment, the weighting factor Q is calculated according to the following formula:
wherein S is the score of the task group of the local site, and L is the proficiency level of the target user in the task of the virtual role.
In one embodiment, the task cumulative total score for the target user is calculated according to the following formula: t' + S × Q; wherein T is the task accumulation total score of the target user; t' is the accumulated score of the target user participating in the real-time virtual interaction task before the current time, S is the score of the local task group, and Q is a weight factor.
In one embodiment, the method further comprises: and when the task accumulated total score T reaches a preset score, determining that the proficiency level of the task of the virtual role of the target user reaches an upgrading standard, and improving the proficiency level of the target user.
In one embodiment, the method further comprises: displaying virtual tasks of virtual roles corresponding to the target user on terminal interfaces of the target user and other users of the target user belonging to the same group; acquiring control operations of a target user and other users for respective corresponding virtual roles; and determining the task score of the group where the target user is located according to the control operation.
In one embodiment, determining the task score of the group in which the target user is located according to the control operation comprises: determining whether the group where the target user is located completes the virtual task or not according to the control operation; and determining the task score of the group where the target user is located according to the completion condition of the virtual task.
In one embodiment, the method further comprises: acquiring registration information of a user for a virtual task; adding the user with the determined entry into an entry user set; and randomly selecting a preset number of users from the registration user set to form a group so that the users in the same group can jointly complete the virtual task.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A character feature analysis method based on real-time virtual interaction is characterized by comprising the following steps:
after a virtual task of a virtual role corresponding to a target user is finished, obtaining evaluation information of other users of the same group aiming at the target user, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished;
obtaining current character evaluation data of the target user according to all the evaluation information;
and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
2. The method of claim 1, wherein the personality assessment data comprises a plurality of personality dimensions;
the updating the current personality evaluation data to the historical personality evaluation data of the target user includes:
acquiring a characteristic value of each character dimension in current character evaluation data;
and merging the characteristic value of each character dimension in the current character evaluation data with the historical character evaluation data to obtain the latest characteristic value of each character dimension.
3. The method of claim 2, further comprising obtaining a latest eigenvalue for each term dimension according to the following formula:
wherein, Z isnThe characteristic value corresponding to the character dimension n, M is the numerical value accumulated by the full score of the user, and Y isnThe accumulated value of the character dimension n is obtained, Q is a weighting factor, and m is the number of people in the group where the target user is located.
4. The method of claim 3, wherein the value M of the user's full score accumulation is calculated according to the following formula:
M＝M′+(m-1)*Q；
wherein M' is the numerical value accumulated by the full score of the user before the current time.
5. A method according to claim 3, characterized in that the weighting factor Q is calculated according to the following formula:
and S is the score of the local task group, and L is the proficiency level of the target user in participating in the task of the virtual role.
6. The method of claim 1, wherein the target user's cumulative task total score is calculated according to the following formula:
T＝T′+S*Q；
wherein T is a task cumulative total score of the target user; the T' is the accumulated score of the target user participating in the real-time virtual interaction task before the current time, the S is the score of the local task group, and the Q is a weight factor.
7. The method of claim 1, further comprising:
displaying virtual tasks of virtual roles corresponding to the users on terminal interfaces of the target user and other users of the target user belonging to the same group;
acquiring control operations of the target user and other users for respective corresponding virtual roles;
determining whether the group where the target user is located completes the virtual task according to the control operation;
and determining the task score of the group where the target user is located according to the completion condition of the virtual task.
8. The method of claim 1, further comprising:
acquiring registration information of a user for a virtual task;
adding the user with the determined entry into an entry user set;
and randomly selecting a preset number of users from the entry user set to form a group so that the users in the same group complete the virtual task together.
9. A personality trait analysis device based on real-time virtual interaction, the device comprising:
the user evaluation module is used for acquiring evaluation information of other users of the same group aiming at a target user after a virtual task of a virtual role corresponding to the target user is finished, wherein the evaluation information is obtained by evaluating the other users of the same group based on the performance of the target user when the virtual task is finished;
the character data merging module is used for obtaining the current character evaluation data of the target user according to all the evaluation information; and updating the current character evaluation data to the historical character evaluation data of the target user so as to update the character evaluation data of the target user.
10. A terminal device, comprising: memory, transceiver, processor and bus system:
wherein the memory is used for storing programs;
the processor for executing a program in the memory, the processor for performing the method of any one of claims 1 to 8 according to instructions in the program code;
the bus system is used for connecting the memory and the processor so as to enable the memory and the processor to communicate.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113704634A (en) * | 2021-07-29 | 2021-11-26 | 深圳市镜玩科技有限公司 | Marriage and love pairing method, device, system and medium based on multidimensional character data |
| CN116050939A (en) * | 2023-03-07 | 2023-05-02 | 深圳市人马互动科技有限公司 | User evaluation method based on interaction novel and related device |
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2020
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113704634A (en) * | 2021-07-29 | 2021-11-26 | 深圳市镜玩科技有限公司 | Marriage and love pairing method, device, system and medium based on multidimensional character data |
| CN116050939A (en) * | 2023-03-07 | 2023-05-02 | 深圳市人马互动科技有限公司 | User evaluation method based on interaction novel and related device |
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