Game role behavior control method and device, storage medium and electronic equipment

Abstract

The invention discloses a game role behavior control method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining game behavior data of a plurality of users in a parkour game when a preset moment arrives, carrying out the statistical analysis of the game behavior data, and determining an analysis result. The analysis result comprises the preference obstacle avoidance action of the user. And when the target user starts an automatic breakthrough running mode, controlling a game role of the target user to execute an automatic breakthrough running task according to the analysis result. According to the method, the game behavior preferred by the user is determined through the game behavior data of the user and serves as the behavior of the game role of the user during automatic breakthrough breaking, so that the breakthrough breaking behavior of the game role of the user is closer to the game behavior of the real user. And the game role can be replaced according to the period, so that the behavior of the game role of the user in the automatic breakthrough mode is flexibly changed and is more intelligent, and the game experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of computers, and more particularly to a method, device, storage medium and electronic device for controlling the behavior of a game character. Background Art

[0002] In parkour games, in order to save users' time and improve user experience, an automatic level-breaking mode is usually designed during game design. However, in this mode, the level-breaking behavior of the user's game character is usually fixed, resulting in a monotonous gaming experience for the user. For example, when encountering an obstacle, the user can choose to control the game character to break through the level by jumping, sliding, and other actions, but in the automatic level-breaking mode, the game character's level-breaking behavior will be fixed to jumping, or jumping three times and then sliding once, and other fixed behaviors.

[0003] So, how to make the game character's level-breaking behavior intelligent in the automatic level-breaking mode is an urgent problem to be solved.

[0004] Based on this, this specification provides a game character behavior control method. Summary of the invention

[0005] This specification provides a game character behavior control method, device, storage medium and electronic device to partially solve the above problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a game character behavior control method, the method comprising:

[0008] When the preset time arrives, obtain the game behavior data of several users in the parkour game;

[0009] Performing statistical analysis on the game behavior data to determine analysis results, wherein the analysis results include the user's preferred obstacle avoidance actions;

[0010] When the target user turns on the automatic level-breaking mode, the game character of the target user is controlled to perform the automatic level-breaking task according to the analysis result.

[0011] Optionally, the game behavior data includes at least one of a level, a parkour path, an obstacle avoidance action, a level-clearing time, a number of failures, and a number of parkour path repetitions.

[0012] Optionally, statistical analysis is performed on the game behavior data to determine analysis results, specifically including:

[0013] Performing statistical analysis on the game behavior data of several users other than the target user to determine a first analysis result;

[0014] Performing statistical analysis on the game behavior data of the target user to determine a second analysis result;

[0015] A final analysis result is determined according to the first analysis result and the second analysis result.

[0016] Optionally, determining a final analysis result according to the first analysis result and the second analysis result specifically includes:

[0017] Determine a first level-clearing pass rate of the target user; and determine a second level-clearing pass rate of a plurality of users, and determine an average level-clearing pass rate based on the second level-clearing pass rates of the plurality of users;

[0018] When the first level-clearing rate of the target user is not greater than the average level-clearing rate, determining the first analysis result as the final analysis result;

[0019] When the first level-clearing rate of the target user is greater than the average level-clearing rate, the second analysis result is determined as the final analysis result.

[0020] Optionally, determining a final analysis result according to the first analysis result and the second analysis result specifically includes:

[0021] Based on the first analysis result, determining a first automatic mode, and based on the second analysis result, determining a second automatic mode;

[0022] Displaying the first automatic mode and the second automatic mode to the target user;

[0023] In response to the automatic mode selection operation of the target user, a final analysis result is determined from the first analysis result and the second analysis result.

[0024] Optionally, according to the analysis result, controlling the game character of the target user to perform an automatic level-breaking task specifically includes:

[0025] Using a pre-trained image recognition model, the automatic level-breaking image of the target user is recognized to obtain a recognition result;

[0026] According to the recognition result and the analysis result, the game character of the target user is controlled to perform an automatic level-breaking task.

[0027] Optionally, according to the recognition result and the analysis result, controlling the game character of the target user to perform an automatic level-breaking task specifically includes:

[0028] When the obstacle type in the automatic level-breaking task is a dynamic obstacle, controlling the game character of the target user to avoid the obstacle according to the recognition result;

[0029] When the obstacle type in the automatic level-breaking task is a static obstacle, the game character of the target user is controlled to avoid the obstacle according to the analysis result.

[0030] Optionally, training an image recognition model includes:

[0031] Obtaining images of several users in a parkour game as training samples, and determining dynamic obstacles in the training samples as labels of the training samples;

[0032] Inputting the training sample into an image recognition model to obtain a prediction result of the training sample output by the image recognition model;

[0033] The image recognition model is trained according to the prediction result and the label.

[0034] This specification provides a game character behavior control device, the device comprising:

[0035] A game behavior data acquisition module is used to acquire game behavior data of several users in the parkour game when a preset time arrives;

[0036] An analysis result determination module, used to perform statistical analysis on the game behavior data to determine analysis results, wherein the analysis results include the user's preferred obstacle avoidance actions;

[0037] The task execution module is used to control the game character of the target user to execute the automatic level-breaking task according to the analysis result when the target user turns on the automatic level-breaking mode.

[0038] Optionally, the game behavior data includes at least one of a level, a parkour path, an obstacle avoidance action, a level-clearing time, a number of failures, and a number of parkour path repetitions.

[0039] Optionally, the analysis result determination module is specifically used to perform statistical analysis on the game behavior data of several users other than the target user to determine a first analysis result; perform statistical analysis on the game behavior data of the target user to determine a second analysis result; and determine a final analysis result based on the first analysis result and the second analysis result.

[0040] Optionally, the analysis result determination module is specifically used to determine a first level pass rate of the target user; and determine second level pass rates of several users, and determine an average level pass rate based on the second level pass rates of the several users; when the first level pass rate of the target user is not greater than the average level pass rate, determine the first analysis result as the final analysis result; when the first level pass rate of the target user is greater than the average level pass rate, determine the second analysis result as the final analysis result.

[0041] Optionally, the analysis result determination module is specifically used to determine a first automatic mode based on the first analysis result, and to determine a second automatic mode based on the second analysis result; display the first automatic mode and the second automatic mode to the target user; and determine a final analysis result in the first analysis result and the second analysis result in response to the automatic mode selection operation of the target user.

[0042] Optionally, the task execution module is specifically used to identify the automatic level-breaking image of the target user through a pre-trained image recognition model to obtain a recognition result; and control the game character of the target user to perform the automatic level-breaking task based on the recognition result and the analysis result.

[0043] Optionally, the task execution module is specifically used to, when the obstacle type in the automatic level-breaking task is a dynamic obstacle, control the game character of the target user to avoid the obstacle according to the recognition result; when the obstacle type in the automatic level-breaking task is a static obstacle, control the game character of the target user to avoid the obstacle according to the analysis result.

[0044] Optionally, the device further comprises:

[0045] The training module is used to obtain images of several users in a parkour game as training samples, and determine dynamic obstacles in the training samples as labels of the training samples; input the training samples into an image recognition model to obtain a prediction result of the training samples output by the image recognition model; and train the image recognition model according to the prediction result and the label.

[0046] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned game character behavior control method is implemented.

[0047] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned game character behavior control method when executing the program.

[0048] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0049] It can be seen from the game character behavior control method provided in this specification that this method can obtain the game behavior data of several users in the parkour game when the preset moment arrives, perform statistical analysis on the game behavior data, and determine the analysis results, which include the user's preferred obstacle avoidance actions. When the target user turns on the automatic level-breaking mode, the target user's game character is controlled to perform the automatic level-breaking task according to the analysis results. This method determines the user's preferred game behavior through the user's game behavior data, and uses it as the behavior of the user's game character during automatic level-breaking, so that the user's game character's level-breaking behavior is closer to the real user's game behavior. It can also be replaced according to the cycle, so that the behavior of the user's game character in the automatic level-breaking mode can be flexibly changed and more intelligent, thereby improving the user's gaming experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0051] Figure 1 A flowchart of a method for controlling the behavior of a game character provided in this specification;

[0052] Figure 2 A schematic diagram of a path fork provided in this manual;

[0053] Figure 3 A schematic diagram showing the automatic mode provided for this manual;

[0054] Figure 4 Obstacle diagrams provided for this manual;

[0055] Figure 5 A schematic diagram of a game character behavior control device provided in this specification;

[0056] Figure 6 A method corresponding to the Figure 1 Schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0058] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0059] Figure 1 The following is a flow chart of a method for controlling the behavior of a game character provided in this specification, comprising the following steps:

[0060] S100: When a preset time arrives, obtain game behavior data of several users in the parkour game.

[0061] Generally speaking, the automatic level-breaking mode in parkour games controls the user's game character to automatically break through levels based on pre-set level-breaking actions and level-breaking paths. The level-breaking behavior is relatively simple and the user's gaming experience is poor.

[0062] Therefore, this specification provides a method for controlling the behavior of a game character, and the execution subject of this specification is a pre-built universal cloud business storage system. The execution subject of this specification can be a computing device that performs statistical analysis on game behavior data, such as a server, a personal computer (PC), etc., and can also be other electronic devices with computing capabilities. This specification does not limit this. For the sake of convenience, this specification is described with the server as the execution subject.

[0063] When the preset time arrives, the server can obtain the game behavior data of several users in the parkour game, and the game behavior data includes at least one of the following: the level, the parkour path, the obstacle avoidance action, the level time, the number of failures, and the number of parkour path repetitions. The parkour path refers to the path that the user controls the game character to run in the level, and the length of the path varies according to the different progress of the user. The obstacle avoidance action includes jumping, continuous jumping, shoveling, etc. The number of parkour path repetitions refers to the number of times the path is repeatedly selected at the same position in the same level.

[0064] For example, Figure 2 A schematic diagram of a path fork provided in this manual, such as Figure 2 shown.

[0065] If the level is the first fork in the second level, there are three paths: left, middle, and right. The user chooses to go left according to personal preference. If the user fails to pass the second level or other circumstances cause the user to try the second level again, then when facing the first fork in the second level, the user chooses to go left again. Then, the number of times the parkour path is repeated is 2.

[0066] It should be noted that the game behavior data may also include the user's obstacle avoidance time, etc. This manual does not limit this, as long as the data is the game behavior data of the user starting to pass the level. Game behavior data are all obtained with user authorization, and the preset time refers to the time when the data is obtained in the preset period, that is, the server can obtain the user's game behavior data according to the period, and the preset period can be set as needed, such as one day, 12 hours, etc.

[0067] S102: Perform statistical analysis on the game behavior data to determine analysis results, where the analysis results include the user's preferred obstacle avoidance actions.

[0068] In one or more embodiments of the present description, the server may perform statistical analysis on the game behavior data to determine the analysis results, which include the user's preferred obstacle avoidance actions, preferred paths, preferred forks, etc. The server determines the preferred game behaviors of several users through statistical analysis for subsequent use in the automatic level-breaking mode. Preferred behavior refers to the behavior performed by users with a relatively high proportion after statistical analysis, and a proportion greater than a preset proportion value is a relatively high proportion. For example, if the preset proportion value is 50%, the server obtains the game behavior data of 10 users, and determines through statistical analysis that for the first fork in the third level, there are 8 users who choose to go right, which is more than 50%, and has a relatively high proportion. The behavior of choosing to go right at the fork is a preferred behavior.

[0069] In addition, the preferred path may be composed of a preset number of preferred forks, and the preset number is related to the number of forks set in the level. For example, if there are multiple paths in level 4, but no matter which path the user takes, he needs to pass through 10 forks to pass the level, then the preset number is 10, and the server can determine the preferred forks corresponding to the 10 forks that the user needs to pass from the starting point to the end point through statistical analysis, and obtain the preferred path.

[0070] S104: When the target user turns on the automatic level-breaking mode, the game character of the target user is controlled to perform the automatic level-breaking task according to the analysis result.

[0071] When the target user turns on the automatic level-breaking mode, the server can first determine the current level of the target user. From the analysis results, the preferred obstacle avoidance action and preferred path of the current level are determined, and then the game character of the target user is controlled to avoid obstacles according to the preferred obstacle avoidance action, and the game character of the target user is controlled to automatically break through the level according to the preferred path, and finally the automatic level-breaking is completed.

[0072] based on Figure 1 The game character behavior control method shown in the figure can obtain the game behavior data of several users in the parkour game when the preset time arrives, perform statistical analysis on the game behavior data, and determine the analysis results, which include the user's preferred obstacle avoidance actions. When the target user turns on the automatic level-breaking mode, the game character of the target user is controlled to perform the automatic level-breaking task according to the analysis results. This method determines the user's preferred game behavior through the user's game behavior data, and uses it as the behavior of the user's game character when automatically breaking through the level, so that the level-breaking behavior of the user's game character is closer to the game behavior of the real user. It can also be replaced according to the cycle, so that the behavior of the user's game character in the automatic level-breaking mode can be flexibly changed and more intelligent.

[0073] In order to further determine the analysis results for the target user, so that the server can control the game behavior of the target user's game character when automatically passing the level according to the analysis results to be closer to the target user, for step S102, when there is statistical analysis of the game behavior data, the game behavior data of the target user and the game behavior data of other users can be analyzed separately. That is, statistical analysis is performed on the game behavior data of several users other than the target user to determine the first analysis result, and statistical analysis is performed on the game behavior data of the target user to determine the second analysis result. According to the first analysis result and the second analysis result, the final analysis result is determined. The first analysis result is obtained by analyzing the game behavior data of several users who have passed the level, but the target user may not have passed the level yet, and the second analysis result is obtained by inference based on the historical game behavior data of the target user. The difference is that the first analysis result is obtained by statistical analysis of real data, while the second analysis result is obtained by inference.

[0074] For example, there are 12 levels in a parkour game. There are users who have successfully passed all the levels, but the target user has only passed the first 7 levels, leaving 5 levels to be passed. Then, the second analysis result is inferred based on the game behavior data of the target user when passing the first 7 levels. The second analysis result includes the user's preferred path and preferred obstacle avoidance actions in the subsequent 5 levels, rather than the target user's actual level-passing behavior in the subsequent 5 levels. The first analysis result is obtained by statistically analyzing the game behavior data of users who have already conquered each level. That is, users may include users who have passed the level, or users who have only conquered but not passed the level.

[0075] Furthermore, the second analysis result can be obtained through a pre-constructed behavior prediction model. Specifically, the characteristic parameters of the target user's game behavior data are determined, and the characteristic parameters are input into the behavior prediction model to obtain the second analysis result output by the behavior prediction model. Of course, the game behavior data of the target user can also be statistically analyzed to determine the statistical results, and the second analysis result is determined based on the statistical results. For example, following the above example, when analyzing the target user, the determined statistical results show that the target user encountered obstacles 500 times, of which 400 times the target user chose to jump to avoid, 50 times to shovel, and 50 times to jump continuously. Then, the preferred obstacle avoidance action in the second analysis result is jumping.

[0076] The target user may select a corresponding automatic mode to automatically pass the level, that is, determine the final analysis result in response to the user's operation. The server may determine a first automatic mode based on the first analysis result, and determine a second automatic mode based on the second analysis result. The first automatic mode and the second automatic mode are displayed to the target user. In response to the automatic mode selection operation of the target user, the final analysis result is determined in the first analysis result and the second analysis result. In other words, the automatic modes corresponding to the first analysis result and the second analysis result are displayed to the user, and the user makes a selection.

[0077] Figure 3 The schematic diagram showing the automatic mode provided for this manual is as follows: Figure 3 As shown. The server displays the display results through the client of the target user, which can be a smartphone, PC, etc. Taking a smartphone as an example, the target user can long press the display boxes of the first automatic mode and the second automatic mode, and the client can display the explanation of the automatic mode. When the target user long presses the button of the first automatic mode, the explanation content "After selecting this mode, your game character will pass the level according to the preferred level-breaking behavior of players who have passed the level" can be displayed. When the target user long presses the button of the second automatic mode, the explanation content "After selecting this mode, your game character will pass the level according to your preferred level-breaking behavior" can be displayed.

[0078] The first pass rate of the target user can also be determined. In this case, the server needs to first determine the pass rate of the level that the target user has passed. Based on the pass rate of the level that the target user has passed, the first pass rate of the target user is determined. The server can perform a weighted average of the pass rates of each level that the target user has passed to obtain the first pass rate of the target user. Among them, the pass rate can be determined based on the number of failures of each level of the user, the pass time, and the preset level difficulty, and can also be determined according to other methods, which are not limited in this specification. For example, for the level A that the target user has passed, the game behavior data of the target user records that the level difficulty of the A level is relatively low, but the number of failures of the target user is relatively high, which is higher than the number of failures of other users to pass the A level, and the pass time is relatively long, then the pass rate of the A level of the target user is relatively low.

[0079] The server also needs to determine the second level pass rates of several users, and determine the average level pass rate based on the second level pass rates of several users. The second level pass rate of each user can also be determined by the level pass rate of the user's cleared levels.

[0080] The server can determine whether to select the first analysis result or the second analysis result by comparing the first level pass rate and the average level pass rate. When the first level pass rate of the target user is not greater than the average level pass rate, it means that the pass rate of the preferred game behavior of other users is higher. In order to improve the game experience of the target user, the first analysis result can be determined as the final analysis result. When the first level pass rate of the target user is greater than the average level pass rate, it means that the pass rate of the preferred game behavior of the target user himself is higher, and the second analysis result is determined as the final analysis result.

[0081] In addition, the first analysis result and the second analysis result can be combined according to the preset weight to obtain the final analysis result. The server controls the game character of the target user to perform the automatic breakthrough task according to the final analysis result. This is because by combining the behavior of other players, the behavior pattern of the target user can be supplemented and improved, so that the automatic mode can more comprehensively reflect the game behavior that the target user may take. In addition, the game behavior data of the target user may contain outliers or errors. Referring to the analysis results of other users can help identify and correct these anomalies and ensure that the behavior of the game character of the target user in the automatic mode is more reasonable. Of course, if the game behavior data of the target user is less, the accuracy of the analysis result determined by relying solely on the game behavior data of the target user is low. Combining the behavior data of other users can provide more behavior samples and improve the accuracy of the analysis results.

[0082] Obstacles include static obstacles and dynamic obstacles. The appearance time of dynamic obstacles is random, which leads to the random location of dynamic obstacles. Figure 4 The obstacle diagram provided for this manual is as follows: Figure 4 shown.

[0083] The cylinder is a dynamic obstacle, the dotted line is the path of the dynamic obstacle, and the combination of the rectangle and the circle is the game character. In order to further improve the user's gaming experience, a pre-trained image recognition model can also be deployed in the user's client. In the automatic level-breaking mode, the pre-trained image recognition model is used to recognize the automatic level-breaking image of the target user in real time to obtain a recognition result. Based on the recognition result and the analysis result, the game character of the target user is controlled in real time to perform the automatic level-breaking task. The recognition result includes the obstacle type. The server can determine the obstacle avoidance path based on the obstacle type, and then combine the obstacle avoidance path and the analysis result to control the game character of the target user to perform the automatic level-breaking task in real time.

[0084] Specifically, when the obstacle type in the automatic level-breaking task is a dynamic obstacle, the target user's game character is controlled to avoid the obstacle based on the recognition result, that is, the obstacle avoidance path is determined based on the recognition result, and the target user's game character is controlled to avoid the obstacle based on the obstacle avoidance path. When the obstacle in the automatic level-breaking task is a static obstacle, the server can directly control the target user's game character to avoid the obstacle based on the analysis result, and the preferred obstacle avoidance action in the analysis result is mainly for static obstacles. By combining the recognition result with the analysis result, the game behavior of the target user's game character is adjusted in real time to further improve the user's automatic level-breaking pass rate, thereby improving the user experience.

[0085] It should be noted that an AI robot can also be set up in the user's client, and the AI ​​robot can be used to control the game character of the target user to automatically pass levels.

[0086] This specification also provides a training method for an image recognition model. Specifically, a server obtains images of several users in a parkour game as training samples, and determines the dynamic obstacles in the training samples as labels for the training samples. The labels include instances of dynamic obstacles at various angles, sizes, and environments, so that the image recognition model can be generalized to new images. The training samples are input into the image recognition model to obtain the prediction results of the training samples output by the image recognition model. The prediction results are the recognition results of the obstacles in the training samples by the image recognition model. Afterwards, the image recognition model is trained based on the prediction results and the labels.

[0087] This specification also provides a method for training a behavior prediction model. First, the server obtains the game behavior data of the user, preprocesses the game behavior data, such as data cleaning, and then uses the preprocessed game behavior data as sample data. Determine the sample feature parameters of the sample data, input the sample feature parameters into the behavior prediction model, and obtain the prediction analysis results output by the behavior prediction model. The internal structure of the behavior prediction model may include a long short-term memory network, so that the behavior prediction model can predict the user's behavior based on the time series.

[0088] The above is one or more implementation methods of this specification, based on Figure 1 The present specification also provides a corresponding game character behavior control device, such as Figure 5 shown.

[0089] Figure 5 A schematic diagram of a game character behavior control device provided in this specification includes:

[0090] The game behavior data acquisition module 500 is used to acquire the game behavior data of several users in the parkour game when a preset time arrives;

[0091] An analysis result determination module 502 is used to perform statistical analysis on the game behavior data to determine analysis results, wherein the analysis results include the user's preferred obstacle avoidance actions;

[0092] The task execution module 504 is used to control the game character of the target user to execute the automatic level-breaking task according to the analysis result when the target user turns on the automatic level-breaking mode.

[0093] Optionally, the game behavior data includes at least one of a level, a parkour path, an obstacle avoidance action, a level-clearing time, a number of failures, and a number of parkour path repetitions.

[0094] Optionally, the analysis result determination module 502 is specifically used to perform statistical analysis on the game behavior data of several users other than the target user to determine a first analysis result; perform statistical analysis on the game behavior data of the target user to determine a second analysis result; and determine a final analysis result based on the first analysis result and the second analysis result.

[0095] Optionally, the analysis result determination module 502 is specifically used to determine a first level-clearing pass rate of the target user; and determine second level-clearing pass rates of several users, and determine an average level-clearing pass rate based on the second level-clearing pass rates of the several users; when the first level-clearing pass rate of the target user is not greater than the average level-clearing pass rate, determine the first analysis result as the final analysis result; when the first level-clearing pass rate of the target user is greater than the average level-clearing pass rate, determine the second analysis result as the final analysis result.

[0096] Optionally, the analysis result determination module 502 is specifically used to determine a first automatic mode based on the first analysis result, and to determine a second automatic mode based on the second analysis result; display the first automatic mode and the second automatic mode to the target user; and determine a final analysis result in the first analysis result and the second analysis result in response to the automatic mode selection operation of the target user.

[0097] Optionally, the task execution module 504 is specifically used to identify the automatic level-breaking image of the target user through a pre-trained image recognition model to obtain a recognition result; and control the game character of the target user to perform the automatic level-breaking task based on the recognition result and the analysis result.

[0098] Optionally, the task execution module 504 is specifically used to, when the obstacle type in the automatic level-breaking task is a dynamic obstacle, control the game character of the target user to avoid the obstacle according to the recognition result; when the obstacle type in the automatic level-breaking task is a static obstacle, control the game character of the target user to avoid the obstacle according to the analysis result.

[0099] Optionally, the device further comprises:

[0100] The training module 506 is used to obtain images of several users in the parkour game as training samples, and determine the dynamic obstacles in the training samples as labels of the training samples; input the training samples into the image recognition model to obtain the prediction results of the training samples output by the image recognition model; and train the image recognition model according to the prediction results and the labels.

[0101] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A game character behavior control method is provided.

[0102] This manual also provides Figure 6 The one shown corresponds to Figure 1 Schematic diagram of the structure of an electronic device. Figure 6As shown, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The game character behavior control method.

[0103] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0104] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0105] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0106] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0107] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0108] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0109] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that specifies the functions of a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0112] In a typical configuration, a computing device includes one or more processors (first chips), input / output interfaces, network interfaces, and memory.

[0113] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0114] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0115] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0116] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0118] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0119] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A game character behavior control method, characterized in that: The method comprises: When the preset time arrives, obtain the game behavior data of several users in the parkour game; Performing statistical analysis on the game behavior data to determine analysis results, wherein the analysis results include the user's preferred obstacle avoidance actions; When the target user turns on the automatic level-breaking mode, the game character of the target user is controlled to perform the automatic level-breaking task according to the analysis result.

2. The method according to claim 1, characterized in that The game behavior data includes at least one of a level, a parkour path, an obstacle avoidance action, a level-clearing time, a number of failures, and a number of parkour path repetitions.

3. The method according to claim 1, characterized in that Performing statistical analysis on the game behavior data to determine analysis results, specifically including: Performing statistical analysis on the game behavior data of several users other than the target user to determine a first analysis result; Performing statistical analysis on the game behavior data of the target user to determine a second analysis result; A final analysis result is determined according to the first analysis result and the second analysis result.

4. The method according to claim 3, characterized in that Determining a final analysis result according to the first analysis result and the second analysis result specifically includes: Determine a first level-clearing pass rate of the target user; and determine a second level-clearing pass rate of a plurality of users, and determine an average level-clearing pass rate based on the second level-clearing pass rates of the plurality of users; When the first level-clearing rate of the target user is not greater than the average level-clearing rate, determining the first analysis result as the final analysis result; When the first level-clearing rate of the target user is greater than the average level-clearing rate, the second analysis result is determined as the final analysis result.

5. The method according to claim 3, characterized in that Determining a final analysis result according to the first analysis result and the second analysis result specifically includes: Based on the first analysis result, determining a first automatic mode, and based on the second analysis result, determining a second automatic mode; Displaying the first automatic mode and the second automatic mode to the target user; In response to the automatic mode selection operation of the target user, a final analysis result is determined from the first analysis result and the second analysis result.

6. The method according to claim 1, characterized in that According to the analysis result, the game character of the target user is controlled to perform an automatic level-breaking task, specifically including: Using a pre-trained image recognition model, the automatic level-breaking image of the target user is recognized to obtain a recognition result; According to the recognition result and the analysis result, the game character of the target user is controlled to perform an automatic level-breaking task.

7. The method according to claim 6, characterized in that According to the recognition result and the analysis result, controlling the game character of the target user to perform an automatic level-breaking task specifically includes: When the obstacle type in the automatic level-breaking task is a dynamic obstacle, controlling the game character of the target user to avoid the obstacle according to the recognition result; When the obstacle type in the automatic level-breaking task is a static obstacle, the game character of the target user is controlled to avoid the obstacle according to the analysis result.

8. The method according to claim 6, characterized in that Train the image recognition model, including: Obtaining images of several users in a parkour game as training samples, and determining dynamic obstacles in the training samples as labels of the training samples; Inputting the training sample into an image recognition model to obtain a prediction result of the training sample output by the image recognition model; The image recognition model is trained according to the prediction result and the label.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

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