CN113426135A - User information processing method and device in game and electronic equipment - Google Patents
User information processing method and device in game and electronic equipment Download PDFInfo
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
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Abstract
The application discloses a method and a device for processing user information in a game and electronic equipment, wherein the method comprises the following steps: acquiring user information sent by a game client; extracting a plurality of behavior information associated with the user information according to the user information, wherein the plurality of behavior information at least comprises chat behavior information and game behavior information; weighting the initial scores of the behavior information to obtain the feature score of the user information; and after determining the credit risk level of the user information according to the feature score, detecting the credit risk level, and when the credit risk level is a preset prohibition level, performing prohibition processing on the user information.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing user information in a game, and an electronic device.
Background
The user can exchange information by issuing information in the game process. However, in a large amount of distributed information, pull information distributed by a malicious user inevitably appears, and the pull information lures the user to pull together by using baits such as high welfare. On one hand, the malicious users swipe the screen for a long time in the game, so that the game experience of the normal users is seriously influenced; on the other hand, if the user is pulled to other game platforms, the user's churn rate will be increased, and the website traffic will be reduced. Therefore, in the prior art, the chat information of the user is acquired, then the chat information of the user is identified, the keyword is extracted from the chat information and then the chat information is compared, so that the user needing to perform information blocking processing is selected according to the comparison result.
However, the related art depends on the matching degree of the chat information, and many malicious chat information are avoided by using the font distortion currently, so that the judgment of whether to forbid the user information through the chat information has a large rate of missed judgment and a large rate of false judgment.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the prior art, and provides a method, an apparatus and an electronic device for processing user information in a game, so as to reduce the rate of missing judgment and the rate of erroneous judgment of user information to be prohibited, so as to improve the accuracy of selecting user information to be prohibited.
In a first aspect, an embodiment of the present application provides a method for processing user information in a game, including:
acquiring user information sent by a game client;
extracting a plurality of behavior information associated with the user information according to the user information, wherein the behavior information at least comprises chat behavior information and game behavior information;
weighting the initial scores of the behavior information to obtain the feature score of the user information;
and after determining the credit risk level of the user information according to the feature score, detecting the credit risk level, and when the credit risk level is a preset prohibition level, performing prohibition processing on the user information.
In the game, the normal user and the abnormal user have larger difference in user behaviors, so that the user information is processed by acquiring the chat behavior information and the game behavior information of the user, the malicious user can be effectively prohibited, the selection efficiency of the malicious user is improved, the false prohibition of the normal user is reduced, and the bad game experience is prevented from being brought to the user.
Further, according to the user information, extracting a plurality of behavior information associated with the user information, including:
acquiring a plurality of target clients related to the user information according to the user information, wherein the target clients comprise the game clients;
and extracting a plurality of behavior information associated with the user information in each target client.
Further, the behavior information is extracted from a game log according to the user information.
Further, before weighting the initial scores of the plurality of chat messages, the method further includes:
matching the behavior information with a plurality of preset box-separating intervals, and acquiring a target box-separating interval corresponding to the behavior information from the plurality of preset box-separating intervals;
and determining the initial score of the behavior information according to the target box-dividing interval.
Further, when the credit risk level is a preset prohibition level, performing prohibition processing on the user information, including:
when the credit risk level is a preset prohibition level, obtaining chat information corresponding to the user information;
determining the chat risk level of the user information according to the chat score of the chat information;
and detecting the chat risk level, and carrying out forbidden processing on the user information when the chat risk level is a preset level.
Further, the chat score is determined according to the sensitive words in the chat message.
Further, the chat score is determined according to the occurrence frequency of the same sentence in the chat message.
Further, the method also comprises the following steps:
and when the chat risk level is not a preset level, adding the user information into a white list.
In a second aspect, in an embodiment of the present application, there is also provided a user information processing apparatus in a game, including:
the user information acquisition module is used for acquiring user information sent by the game client;
the behavior information acquisition module is used for extracting a plurality of behavior information related to the user information according to the user information, wherein the behavior information at least comprises chat behavior information and game behavior information;
the characteristic score acquisition module is used for weighting the initial scores of the behavior information to acquire the characteristic scores of the user information;
and the user information processing module is used for detecting the credit risk level after determining the credit risk level of the user information according to the feature score, and carrying out the block processing on the user information when the credit risk level is a preset block level.
In a third aspect, an embodiment of the present application provides an electronic device, including: the game system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the user information processing method in the game according to the embodiment.
In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the method for processing user information in a game according to the foregoing embodiment.
Drawings
The present application is further described with reference to the following figures and examples;
FIG. 1 is a diagram showing an application environment of a user information processing method in a game in one embodiment;
FIG. 2 is a flow chart illustrating a method for processing user information in a game according to an embodiment;
FIG. 3 is a flow diagram that illustrates the determination of an initial score for behavior information, according to one embodiment;
FIG. 4 is a flowchart illustrating a process of performing a blocking process on user information in one embodiment;
FIG. 5 is a block diagram showing the configuration of a user information processing device in a game in one embodiment;
FIG. 6 is a block diagram of a computer device in one embodiment.
Detailed Description
Reference will now be made in detail to the present embodiments of the present application, preferred embodiments of which are illustrated in the accompanying drawings, which are for the purpose of visually supplementing the description with figures and detailed description, so as to enable a person skilled in the art to visually and visually understand each and every feature and technical solution of the present application, but not to limit the scope of the present application.
The user can exchange information by issuing information in the game process. However, in a large amount of distributed information, pull information distributed by a malicious user inevitably appears, and the pull information lures the user to pull together by using baits such as high welfare. On one hand, the malicious users swipe the screen for a long time in the game, so that the game experience of the normal users is seriously influenced; on the other hand, if the user is pulled to other game platforms, the user's churn rate will be increased, and the website traffic will be reduced. Therefore, in the prior art, the chat information of the user is acquired, then the chat information of the user is identified, and the keyword is extracted from the chat information and then compared, so that the user needing to be prohibited is selected according to the comparison result.
However, the method of determining whether to perform user information prohibition is easy to avoid by the matching degree of the simple chat information, and the prohibition accuracy is not high. For example, when the advertisement usually contains the word "video chat" and the chat message contains the word "video chat" as a condition for blocking the user information, the advertisement sender may change the word to "video chat" to avoid blocking. For example, when wishing to require other people to perform normal video chat, the chat information sent in the game may also contain a word of "video chat", and at this time, if it is determined by the matching degree of the chat information whether to perform user information blocking, misjudgment is easily caused, and user experience is affected.
In order to solve the above technical problem, in an embodiment, a method for processing user information in a game is provided, and the embodiment is exemplified by applying the method to a server in a user information processing system in a game. Fig. 1 is a diagram showing an application environment of a user information processing method in a game in one embodiment. Referring to fig. 1, the system includes a terminal 110 and a server 120. The terminal 110 and the local server 120 are connected through a network. The terminal 110 may be specifically a desktop terminal or a mobile terminal, and the mobile terminal may be one of a mobile phone, a tablet computer, a notebook computer, a wearable device, and the like. The server 120 may be implemented by an independent server or a server cluster composed of a plurality of servers, and may also be a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, middleware service, a domain name service, a security service, a CDN, and a big data and artificial intelligence platform. The terminal 110 runs a game client, and the game client and the server correspond to the application program of the same network game. The game client is used for sending user information for logging in the game client to the server. The server is used for extracting a plurality of behavior information associated with the user information according to the user information of the user information, then determining the credit risk level of the user information according to the plurality of behavior information, detecting the credit risk level, and carrying out the block processing on the user information when the credit risk level is detected to be a preset block level. Wherein the plurality of behavior information at least comprises chat behavior information and game behavior information.
In the game, the normal user and the abnormal user have larger difference in user behaviors, so that the user information is processed by acquiring the chat behavior information and the game behavior information of the user, the malicious user can be effectively prohibited, the selection efficiency of the malicious user is improved, the false prohibition of the normal user is reduced, and the bad game experience is prevented from being brought to the user.
Hereinafter, the user information processing method in the game provided by the embodiment of the present application will be described and explained in detail through several specific embodiments.
As shown in fig. 2, in one embodiment, a method of processing user information in a game is provided. The embodiment is mainly illustrated by applying the method to computer equipment. The computer device may specifically be the server 120 in fig. 1 described above.
Referring to fig. 2, the method for processing user information in the game specifically includes the following steps:
and S11, acquiring the user information sent by the game client.
In one embodiment, the server receives the user information sent by the game client through the terminal running the game client. Wherein, the game client can be a client of the network game. The user information is used to identify the user, and may be generally composed of at least one character of a number, a letter, a symbol, and a letter, and may be a user account or a game ID.
And S12, extracting a plurality of behavior information related to the user information according to the user information of the user information, wherein the behavior information at least comprises chat behavior information and game behavior information.
In an embodiment, the chat behavior information includes at least one of chat time information, private chat number information, and chat preference information, and the chat time information may be the number of times that the user corresponding to the user information performs chat text transmission within a first preset time period, for example, the number of times that the user performs chat text transmission within one hour. The specific value of the first preset time period can be preset according to actual requirements. The private chat number information may be the number of people that the user corresponding to the user information performs private chat in a second preset time period, for example, the number of people that the user performs private chat in one hour. The specific value of the second preset time period may be preset according to actual requirements, and may be specifically the same as or different from the first preset time period. The chat preference information can be the ratio of the chat time length of the user corresponding to the user information to the online time length, or the chat object selection and the chat channel selection of the user, wherein the chat object selection comprises cross-level or same-level chat and the like, and the chat channel selection comprises public channel chat or other exclusive channel chat and the like. The chat preference information of the user may be determined by a game level of a chat channel or a private chat object of the user corresponding to the user information and biased in a third preset time period, wherein a specific numerical value of the third preset time period may be preset according to actual requirements, specifically may be the same as the first preset time period and/or the second preset time period, and may also be different from the first preset time period and/or the second preset time period. For example, if the biased chat channel is the chat channel that the user consumes the most time in the certain chat channel, the biased chat channel is determined as the biased chat channel of the user, and the level of the biased private chat object can be judged by all objects that the user performs private chat; if the level of the objects is mostly higher than the game level of the user information, for example, the game level of the private chat objects is over fifty percent, the chat preference information is judged to be cross-level chat, and otherwise, the chat is judged to be same-level chat. The game behavior information may include at least one of historical online time, game level, battle force, etc. of the user information. The plurality of behavior information may include at least one of recharge behavior information, social behavior information, and login behavior information, in addition to the chat behavior information and the game behavior information.
And S13, weighting the initial scores of the behavior information to obtain the feature score of the user information.
In an embodiment, the server presets a regular relationship table or a mapping relationship table of the behavior information and the initial score, for example, the chat time information is taken as an example, and the corresponding score of the chat time information being 5 times is 5 points. Meanwhile, the regular relation table or the mapping relation table also records the characteristic weight corresponding to each behavior information. The regular relationship or the mapping relationship between the behavior information and the initial score can be preset according to actual requirements, and only the chat times information and the private chat number information are required to be guaranteed to be in direct proportion to the corresponding initial score, namely the more the chat times are, the larger the corresponding initial score is, the more the private chat number information is, and the larger the corresponding initial score is. The feature weight corresponding to each behavior information can be preset according to actual requirements. After acquiring the plurality of behavior information, acquiring an initial score corresponding to each behavior information from a regular relation table or a mapping relation table according to the acquired behavior information, weighting according to the initial scores of the behavior information and the corresponding feature weights, and acquiring the feature score of the user information.
And S14, after determining the credit risk level of the user information according to the feature score, detecting the credit risk level, and when the credit risk level is a preset prohibition level, performing prohibition processing on the user information.
In an embodiment, a preset relationship between the feature score interval and the credit risk level is preset, and if the feature score is less than 12 points, the credit risk level is 1 level, the feature score is greater than 12 points, and the credit risk level is 2 level. The number of the feature scoring intervals and the specific interval numerical values can be preset according to actual requirements, and the preset relation between the feature scoring intervals and the credit risk level can also be preset according to the actual requirements. The preset relationship between the feature scoring interval and the credit risk level can be a one-to-one corresponding relationship, that is, one feature scoring interval can correspond to the same credit risk level; or a many-to-one correspondence relationship, that is, a plurality of feature scoring intervals may correspond to the same credit risk level.
After the feature score of the user information is obtained, the corresponding feature score interval is matched according to the feature score, namely the credit risk level corresponding to the feature score can be determined based on the matched feature score interval, so that the credit risk level of the user information is obtained. And when the credit risk level is a preset sealing level, sealing the user information. Illustratively, the credit risk level with the score less than 12 is set as level 1, the credit risk level with the score greater than 12 is set as level 2, and the preset barring level is set as level 2. And if the credit risk level of the user information is level 2, selecting the user information to carry out the blocking processing.
In the game, the normal user and the abnormal user have larger difference in user behaviors, so that the user information is processed by acquiring the chat behavior information and the game behavior information of the user, the malicious user can be effectively prohibited, the selection efficiency of the malicious user is improved, the false prohibition of the normal user is reduced, and the bad game experience is prevented from being brought to the user.
Considering that most of the user information to be prohibited is newly registered user information, when the newly registered user information is processed, because the data samples are fewer, the data samples need to be acquired for judgment for a long time, which takes a long time, and at this time, a large amount of malicious information has been sent by the user information to be prohibited. To this end, in one embodiment, for S12, comprising: according to the user information, a plurality of target clients related to the user information are obtained, wherein the target clients comprise game clients; and extracting a plurality of behavior information of the associated user information in each target client.
A server is associated with a plurality of clients, and these clients are typically clients issued by the same issuer. For example, the server is associated with a Daisy sword, a song on the city of the cloud, a world war and a dark Daisy. After acquiring the user information, the server searches the client terminal with the user information, and takes the client terminal with the user information as a target client terminal. After obtaining each target client, extracting a plurality of chat information of the associated user information in each target client. Illustratively, when the number of chats for acquiring the corresponding user information from the first target client is 3, and the number of chats for acquiring the corresponding user information from the second target client is 8, the number of chats corresponding to the user information is determined to be 11, so that the initial score of the user information in the game client is determined according to the number of chats finally determined by each target client, which is 11.
The initial scoring is determined by acquiring the behavior information of the users at the target clients, so that the reliability of the final selection result is improved, the users needing to be forbidden in the game clients can be screened out more quickly, and the selection efficiency is improved.
Considering that the game log generated by the server usually contains behavior information when the client runs, the plurality of behavior information of the user is acquired, and the plurality of behavior information can be directly extracted from the game log generated by the server without data monitoring again.
In determining the initial score, the initial score of the behavior information may be determined by performing a difference matching using a single value. Taking the chat time information as an example, when the preset chat time is 6 times, the corresponding score is 6; when the preset number of chats is 10, the corresponding score is 10. At this time, when the obtained chat time information is 7 times, the obtained chat time information can be differentiated from the preset chat times, the difference value between the obtained chat time information and each preset chat time is determined, and then the preset chat times corresponding to the chat time information are determined according to the minimum difference value, so that the initial score of the chat time information is determined.
However, the use of a single value for the difference matching to determine the initial score may not allow the determination of the best single value when the behavior information is the same as the difference between the plurality of single values. To this end, in an embodiment, before weighting the initial scores of the plurality of chat messages, as shown in fig. 3, the method further includes:
and S21, matching the behavior information with a plurality of preset box-separating intervals, and acquiring a target box-separating interval corresponding to the behavior information from the plurality of preset box-separating intervals.
In one embodiment, after acquiring each behavior information, the server performs binning on each behavior information through best-KS. For each behavior information, a binning interval that maximizes the difference between suspicious users and non-suspicious users is obtained through best-KS. Taking the chat times as an example, the category of the chat times is divided into three preset binning intervals of [1,10], [11,20], > 20 according to the number of the chat times, and the obtained chat time information is matched with the three preset binning intervals so as to obtain a target binning interval corresponding to the chat time information from the three preset binning intervals.
And S22, determining the initial score of the behavior information according to the target box-dividing interval.
The preset binning intervals corresponding to the chat time information are [1,10], [11,20], > 20, and the corresponding initial scores are 0 score, 1 score, 2 score and 3 score. After a target binning interval is obtained from a plurality of preset binning intervals, initial scores of the chat time information can be determined according to the target binning interval.
The initial score is determined through the box-dividing interval, and the initial score is determined without adopting a single numerical value to perform difference matching, so that the condition that the best single numerical value cannot be determined when the behavior information is the same as the difference values of a plurality of single numerical values is avoided, the accuracy of matching the initial score according to the chat information is improved, and the follow-up accurate selection of the user needing to be prohibited is better ensured.
To improve the accuracy of the user blocking process, as shown in fig. 4, in an embodiment, when the credit risk level is a preset blocking level, the blocking process is performed on the user information, which includes:
and S31, when the credit risk level is a preset forbidden level, obtaining the chat information corresponding to the user information.
The chat message of the user information may be a text message composed of at least one character of a number, a letter, a symbol and a character, or may be a voice message. The chat message may be historical chat messages of the user in the game client in a period of time, such as all historical chat messages in a week before the current time, all historical chat messages of the user in the game client since the user registered the user message, or N pieces of historical chat messages of the user in the game client before the current time. In order to reduce the operation pressure of the server, for example, all the historical chat information from the registration of the user information may be acquired, and if the number of all the historical chat information is greater than the preset number, the historical chat information within a certain period of time is acquired from all the historical chat information, or N pieces of historical chat information before the current time are acquired.
In an embodiment, when it is detected that the credit risk level is not the preset barring level, the user information is added into a white list, that is, the user information is determined to be normal user information, and meanwhile, the chat information is not acquired.
And S32, determining the chat risk level of the user information according to the chat score of the chat information.
The server is preset with a plurality of chat scoring intervals and chat risk levels corresponding to the chat scoring intervals. After the chat scores of the chat information are obtained, the server can perform box separation on the chat scores through best-KS to obtain the chat score interval corresponding to the chat information, and therefore the chat risk level of the user information is determined according to the chat score interval corresponding to the chat information. The number of the chat scoring intervals and the specific interval numerical value can be preset according to actual requirements, and the preset relation between the chat scoring intervals and the chat risk level can also be preset according to the actual requirements. The preset relationship between the chat scoring interval and the chat risk level can be a one-to-one corresponding relationship, namely, one chat scoring interval can correspond to the same chat risk level; or a many-to-one correspondence relationship, that is, a plurality of chat scoring intervals can correspond to the same chat risk level. For example, a chat risk level with a chat score less than 12 points may be set to level 1, and a chat risk level with a score greater than 12 points may be set to level 2.
And S33, detecting the chat risk level, and carrying out forbidden processing on the user information when the chat risk level is a preset level.
When it is detected that the chat risk level is not the preset level,
after the suspicious user information is judged through the behavior information, the suspicious user information is judged through the chat information, and therefore the accuracy of the blocking processing can be improved. And the direct judgment is not directly carried out according to the chat risk level of the chat information, but the behavior information judgment is carried out firstly, and when the judgment is abnormal, the judgment of the chat risk level is carried out, so that the doubtful degree of the normal user is reduced, the doubtful degree of the abnormal user is improved, and the aims of not influencing the chat of the normal user and filtering the doubtful chat of the abnormal user are fulfilled.
In one embodiment, the chat score is determined based on sensitive words in the chat message. Considering that the sensitive words cannot be exhausted, the method of only matching the sensitive word library does not play a good role in the occurrence of variant sensitive words, which is also a defect of many existing methods and can result in higher misjudgment rate and missed judgment rate, so that in order to further reduce the misjudgment rate and the missed judgment rate, the chat information can be matched with each sensitive word in the sensitive word library to judge whether the chat information has the sensitive words; if yes, obtaining a score corresponding to the sensitive word as a chat score; otherwise, obtaining the sound-shape codes of the chat information, carrying out similarity matching on the sound-shape codes and the sensitive words, and obtaining the chat scores according to the similarity matching result.
If the sensitive words exist in the chat information through the matching of the sensitive word bank, direct scoring is carried out, wherein the sensitive words correspond to corresponding scores, and the corresponding scores of different sensitive words can be the same or different. If the sensitive words exist in the chat information which is not matched through the sensitive word stock, the sound-shape codes of the variant words can be extracted from the chat information through the regular expression or the variant word stock, then the sound-shape codes are subjected to similarity matching with the sensitive words in the sensitive word stock, and the scores of the chat information are determined according to the similarity matching result. For example, if the similarity between the phonetic-configurational code and the sensitive word is 80%, 10 points are taken, and if the similarity is 100%, 15 points are taken. The specific correspondence between the similarity and the score may be preset according to actual requirements, and is not limited herein.
In order to make the final processing result more accurate, if the chat information has sensitive words, the chat score of the chat information can be determined according to the number of the sensitive words in the chat information. If the matching relationship between the number of the sensitive words and the chat scores is preset in the server, the following table shows that:
| number of sensitive words | Score value |
| 1 | 5 |
| 2 | 15 |
| 3 | 25 |
| …… | …… |
The chat scores corresponding to the number of the sensitive words can be preset according to actual requirements, and only the condition that the number of the sensitive words is in direct proportion to the corresponding text scores is needed, namely the more the number of the sensitive words is, the higher the corresponding chat scores are, is the mapping relation. Taking the above table as an example, when the number of the sensitive words obtained from the chat message by the server is 3, traversing the message mapping table, and determining that the chat score is 25 points through the message mapping table. The chat score is limited through the number of the sensitive words in the chat information, so that the association degree of the chat score and the chat information is higher, and the accuracy of the processing result of the chat information is improved.
In addition to determining the score of the chat message by the sensitive words, in one embodiment, the score of the chat message may be determined according to the frequency of occurrence of the same sentence in the chat message. That is, the chat score can be determined according to the sensitive words in the chat message or according to the occurrence frequency of the same sentence in the chat message.
When the chat score is determined according to the occurrence frequency of the same sentence in the chat information, the server presets the matching relationship between the occurrence frequency of the same sentence and the chat score, as shown in the following table:
| frequency of the same sentence | Score value |
| 2 | 5 |
| 3 | 15 |
| 4 | 25 |
| …… | …… |
The chat scores corresponding to the frequency of occurrence of the same sentence can be preset according to actual requirements, and the condition that the frequency of occurrence of the same sentence is in direct proportion to the corresponding text score is only required to be met, namely the more the frequency of occurrence of the same sentence is, the higher the corresponding chat score is, and the mapping relation is. Taking the above table as an example, when the frequency of occurrence of the same sentence obtained by the server from the chat message is 3, traversing the information mapping table, and determining that the chat score is 25 points through the information mapping table. The chat scoring is limited by the occurrence frequency of the same sentence in the chat information, so that the association degree of the chat scoring and the chat information is higher, and the accuracy of the processing result of the chat information is improved.
In one embodiment, as shown in fig. 5, there is provided an in-game user information processing apparatus including:
and the user information acquisition module 101 is configured to acquire user information sent by the game client.
The behavior information obtaining module 102 is configured to extract a plurality of behavior information related to the user information according to the user information, where the plurality of behavior information at least includes chat behavior information and game behavior information.
And the feature score obtaining module 103 is configured to weight the initial scores of the plurality of behavior information, and obtain a feature score of the user information.
And the user information processing module 104 is configured to detect a credit risk level after determining the credit risk level of the user information according to the feature score, and perform the barring processing on the user information when the credit risk level is a preset barring level.
In an embodiment, the behavior information obtaining module 102 is specifically configured to: according to the user information, a plurality of target clients related to the user information are obtained, wherein the target clients comprise game clients; and extracting a plurality of behavior information of the associated user information in each target client.
In one embodiment, the behavior information is extracted from the game log based on the user information.
In an embodiment, the feature score obtaining module 103 is further configured to: matching the behavior information with a plurality of preset box-separating intervals, and acquiring a target box-separating interval corresponding to the behavior information from the plurality of preset box-separating intervals; and determining the initial score of the behavior information according to the target box-dividing interval.
In an embodiment, the user information processing module 104 is specifically configured to: when the credit risk level is a preset forbidden level, chat information corresponding to the user information is obtained; determining the chat risk level of the user information according to the chat score of the chat information; and detecting the chat risk level, and carrying out sealing processing on the user information when the chat risk level is a preset level.
In one embodiment, the chat score is determined based on sensitive words in the chat message.
In one embodiment, the chat score is determined based on the frequency of occurrence of the same sentence in the chat message.
In an embodiment, the user information processing module 104 is further configured to: and when the chat risk level is not the preset level, adding the user information into a white list.
In one embodiment, a computer apparatus is provided, as shown in fig. 6, which includes a processor, a memory, a network interface, an input device, and a display screen connected by a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement a user information processing method in a game. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to execute a method of processing user information in a game. Those skilled in the art will appreciate that the architecture shown in fig. 6 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.
In one embodiment, the user information processing apparatus in the game provided by the present application may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in fig. 6. The memory of the computer device may store therein various program modules constituting the user information processing means in the game. The computer program constituted by the respective program modules causes the processor to execute the steps in the user information processing method in the game of the respective embodiments of the present application described in this specification.
In one embodiment, there is provided an electronic device including: the game system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to execute the steps of the user information processing method in the game. The steps of the in-game user information processing method herein may be the steps of the in-game user information processing methods of the respective embodiments described above.
In one embodiment, a computer-readable storage medium is provided, which stores computer-executable instructions for causing a computer to perform the steps of the user information processing method in the above game. The steps of the in-game user information processing method herein may be the steps of the in-game user information processing methods of the respective embodiments described above.
The foregoing is a preferred embodiment of the present application, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations are also regarded as the protection scope of the present application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (10)
1. A method for processing user information in a game, comprising:
acquiring user information sent by a game client;
extracting a plurality of behavior information associated with the user information according to the user information, wherein the behavior information at least comprises chat behavior information and game behavior information;
weighting the initial scores of the behavior information to obtain the feature score of the user information;
and after determining the credit risk level of the user information according to the feature score, detecting the credit risk level, and when the credit risk level is a preset prohibition level, performing prohibition processing on the user information.
2. The in-game user information processing method according to claim 1, wherein extracting, based on the user information, a plurality of pieces of behavior information associated with the user information includes:
acquiring a plurality of target clients related to the user information according to the user information, wherein the target clients comprise the game clients;
and extracting a plurality of behavior information associated with the user information in each target client.
3. The in-game user information processing method according to claim 1 or 2, wherein the behavior information is extracted from a game log based on the user information.
4. The in-game user information processing method according to claim 1, further comprising, before weighting the initial scores of the plurality of pieces of chat information:
matching the behavior information with a plurality of preset box-separating intervals, and acquiring a target box-separating interval corresponding to the behavior information from the plurality of preset box-separating intervals;
and determining the initial score of the behavior information according to the target box-dividing interval.
5. The method for processing the user information in the game according to claim 1, wherein when the credit risk level is a preset prohibition level, performing prohibition processing on the user information includes:
when the credit risk level is a preset prohibition level, obtaining chat information corresponding to the user information;
determining the chat risk level of the user information according to the chat score of the chat information;
and detecting the chat risk level, and carrying out forbidden processing on the user information when the chat risk level is a preset level.
6. The method of claim 5, wherein the chat score is determined according to a sensitive word in the chat message, or wherein the chat score is determined according to the frequency of occurrence of the same sentence in the chat message.
7. The in-game user information processing method according to claim 5, further comprising:
and when the chat risk level is not a preset level, adding the user information into a white list.
8. An in-game user information processing apparatus, comprising:
the user information acquisition module is used for acquiring user information sent by the game client;
the behavior information acquisition module is used for extracting a plurality of behavior information related to the user information according to the user information, wherein the behavior information at least comprises chat behavior information and game behavior information;
the characteristic score acquisition module is used for weighting the initial scores of the behavior information to acquire the characteristic scores of the user information;
and the user information processing module is used for detecting the credit risk level after determining the credit risk level of the user information according to the feature score, and carrying out the block processing on the user information when the credit risk level is a preset block level.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of processing user information in a game according to any one of claims 1 to 7 when executing the program.
10. A computer-readable storage medium, in which a computer program is stored which is adapted to be loaded and executed by a processor to cause a computer device having said processor to carry out the method of any one of claims 1 to 7.
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| CN202110606264.1A CN113426135A (en) | 2021-05-31 | 2021-05-31 | User information processing method and device in game and electronic equipment |
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