Game character control method and device, storage medium and electronic device

Abstract

This disclosure provides a game character control method, device, storage medium, and electronic device, relating to the field of game technology. The game character control method includes: in response to triggering a game character transfer event, determining a transfer time range for the non-player character based on the movement speed of a non-player character, the movement speed of a second game character, and the distance between the non-player character and the second game character; within the transfer time range of the non-player character, determining a target transfer object from candidate transfer objects, and controlling the non-player character to use the target transfer object for transfer. This disclosure controls the transfer of the non-player character based on the game's running state, which can improve the intelligence level of the non-player character's transfer performance to a certain extent.

Description

Technical Field

[0001] This disclosure relates to the field of game technology, and in particular to a game character control method, a game character control device, a computer-readable storage medium, and an electronic device. Background Technology

[0002] In multiplayer online games, a game can only start when enough players are matched. To avoid long matchmaking times, non-player characters are introduced into the game.

[0003] In related technologies, non-player game characters are typically controlled to make transfers (change direction of travel) based on their distance from the transfer object when a specific game event occurs. The timing of the transfer and the selection of the transfer object are relatively rigid, with a low degree of intelligence, and are easily recognized by game players.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure provides a game character control method, a game character control device, a computer-readable storage medium, and an electronic device, thereby overcoming, to at least a certain extent, the problem of low intelligence in the control of non-player characters in related technologies.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to a first aspect of this disclosure, a game character control method is provided, the game including a first game character and a second game character that tracks the first game character, the first game character including a non-player character, the method including: in response to triggering a game character transfer event, determining a transfer time range for the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character; within the transfer time range of the non-player character, determining a target transfer object from candidate transfer objects, and controlling the non-player character to use the target transfer object for transfer.

[0008] According to a second aspect of this disclosure, a game character control device is provided, characterized in that the game includes a first game character and a second game character that tracks the first game character, the first game character including a non-player character, the device comprising: a time range determination module, configured to, in response to triggering a game character transfer event, determine a transfer time range for the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character; and a character transfer control module, configured to, within the transfer time range of the non-player character, determine a target transfer object from candidate transfer objects, and control the non-player character to use the target transfer object for transfer.

[0009] According to a third aspect of this disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the above-described game character control method and its possible implementations.

[0010] According to a fourth aspect of this disclosure, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the above-described game character control method and its possible implementations.

[0011] The technical solution disclosed herein has the following beneficial effects:

[0012] In the aforementioned game character control process, in response to a triggered game character transfer event, the transfer time range for the non-player character is determined based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character. Within the non-player character's transfer time range, a target transfer object is selected from the candidate transfer objects, and the non-player character is controlled to use the target transfer object for transfer. This disclosure controls the transfer of the non-player character based on game running states such as character running speed and distance between characters, making the non-player character's operation performance closer to that of a real player. This can improve the intelligence level of the non-player character's transfer performance to a certain extent, thereby enhancing the player's game participation experience.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0015] Figure 1 A flowchart illustrating one of the exemplary embodiments of the present disclosure of a game character control method is shown.

[0016] Figure 2 This diagram illustrates a flowchart of one exemplary embodiment of the present disclosure, showing how to control a non-player character to perform a transfer using a target transfer object;

[0017] Figure 3 This diagram illustrates a flowchart of one exemplary embodiment of the present disclosure for determining a target transfer object from candidate transfer objects;

[0018] Figure 4 This diagram illustrates one of the sample transfer times according to an exemplary embodiment of the present disclosure.

[0019] Figure 5 This diagram illustrates the architecture of a supervised learning network according to one of the exemplary embodiments of this disclosure;

[0020] Figure 6 This diagram illustrates a flowchart of one exemplary embodiment of the present disclosure for determining a target transit object based on the transit probability corresponding to a candidate transit object;

[0021] Figure 7 This diagram illustrates a flowchart of one exemplary embodiment of the present disclosure for determining a target transit object at a target transit time.

[0022] Figure 8 This diagram illustrates a structural block diagram of one of the game character control devices according to this exemplary embodiment;

[0023] Figure 9 This exemplary embodiment shows an electronic device for implementing the above-described game character control method. Detailed Implementation

[0024] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0025] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] In this article, "first," "second," etc., are labels for specific objects, rather than limiting the number or order of objects.

[0027] In related technologies, the timing of non-player character transfers and the selection of transfer targets in games are relatively rigid and have a low level of intelligence, making them easily recognizable by game players and thus affecting the player's gaming experience.

[0028] In view of one or more of the above-mentioned problems, exemplary embodiments of this disclosure provide a game character control method, a game character control device, a computer-readable storage medium, and an electronic device, which can be applied to game scenarios containing non-player characters.

[0029] In one embodiment of this disclosure, the game character control method can run on a local terminal device or a server. When the game character control method runs on a server, it can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a server and a client device.

[0030] In one alternative implementation, various cloud applications, such as cloud gaming, can run under the cloud interaction system. Taking cloud gaming as an example, cloud gaming refers to a gaming method based on cloud computing. In the cloud gaming operating mode, the game program's execution and the game screen presentation are separated. The storage and execution of the game character control method are completed on the cloud gaming server. The client device is used for data reception, transmission, and game screen presentation. For example, the client device can be a display device with data transmission capabilities located close to the user, such as a mobile terminal, television, computer, or PDA; however, the execution of the game character control method is performed by the cloud gaming server in the cloud. During gameplay, the player operates the client device to send operation commands to the cloud gaming server. The cloud gaming server runs the game according to the operation commands, encodes and compresses game screen data, returns it to the client device via the network, and finally, the client device decodes and outputs the game screen.

[0031] In one alternative implementation, taking a game as an example, the local terminal device stores the game program and is used to display the game screen. The local terminal device is used to interact with the player through a graphical user interface (GUI), i.e., conventionally downloading, installing, and running the game program via an electronic device. The local terminal device can provide the GUI to the player in various ways, such as rendering it on the terminal's display screen or providing it to the player via holographic projection. For example, the local terminal device can include a display screen for displaying the GUI, which includes game screens, and a processor for running the game, generating the GUI, and controlling the display of the GUI on the display screen.

[0032] This disclosure provides a method for controlling a game character, such as... Figure 1 As shown, the game includes a first game character and a second game character that tracks the first game character. The first game character includes a non-player character, and may specifically include the following steps S110 to S120:

[0033] Step S110: In response to triggering a game character transfer event, determine the transfer time range of the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character.

[0034] Step S120: Within the transfer time range of the non-player character, determine the target transfer object from the candidate transfer objects, and control the non-player character to use the target transfer object for transfer.

[0035] In the aforementioned game character control method, the transfer of non-player characters is controlled based on game running status such as character running speed and distance between characters. This makes the operation performance of non-player characters closer to that of real players, which can improve the intelligence of the transfer performance of non-player characters to a certain extent, thereby enhancing the player's game participation experience.

[0036] It should be noted that the first game character is the game character being tracked, which may include non-player characters. Here, a non-player character (NPC) refers to a character in the game controlled by the system to simulate a player. The second game character is a game character in the game that can be used to track or capture the first game character. The "transfer" in this disclosure can be an operation that changes the direction of movement of a game character in the game scene, such as vaulting over a virtual window, or it can be placing virtual props that hinder the progress of the second game character.

[0037] The following is about Figure 1 Each step in the process will be explained in detail.

[0038] In step S110, in response to triggering a game character transfer event, the transfer time range of the non-player character is determined based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character.

[0039] The game character relay event can be one or more pre-set game events used to trigger a non-player character to relay. For example, game character relay events may include, but are not limited to: the first game character being attacked, the second game character being attacked, the first game character relaying, and the second game character losing the first game character's line of sight for a period of time. It should be noted that the game character relay events described here are merely illustrative. In actual applications, different game character relay events can be set by referencing the timing when a real player controls the first game character to relay; no specific limitations are made here.

[0040] Understandably, in order to enable the second game character to track the first game character, the base movement speed of the second game character can be greater than that of the first game character. For example, the base movement speed of the second game character is 20% higher than that of the first game character. Here, base movement speed refers to the default movement speed preset for the game character.

[0041] The movement speed of the non-player character refers to the current movement speed of the non-player character in the game; the movement speed of the second game character refers to the current movement speed of the second game character in the game; the distance between the non-player character and the second game character can be the straight-line distance between the non-player character and the second game character, or the shortest path distance between the non-player character and the second game character.

[0042] For example, it can be calculated This yields the transit time range (0, t). Here, t is the maximum transit time, and d is the distance between the non-player character and the second game character. This refers to the movement speed of the second game character. This refers to the movement speed of non-player characters.

[0043] The transit time range refers to the time frame during which a non-player character transits. If a non-player character fails to transit within this time range, they may be tracked by a second game character. By defining the transit time range, the timing of non-player character transits is correlated with the actual game state, increasing the flexibility of their transit timing and enhancing the game's intelligence to some extent, making non-player characters less easily identifiable.

[0044] In an optional implementation, the determination of the transit time range for the non-player character in step S110 based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character can be achieved through the following steps: if there is a transitable object between the non-player character and the second game character, determine the time it takes for the second game character to pass through the transitable object; determine the transit time range for the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, the distance between the non-player character and the second game character, and the time it takes for the second game character to pass through the transitable object.

[0045] The term "transferable object" refers to a virtual object that can obstruct the movement of a second game character, rather than a non-player character. It should be noted that the transferable object disclosed herein refers to a virtual object that helps a non-player character create distance between themselves and the second game character, such as a virtual window in the game that can separate the first and second game characters, or a virtual item that can be used to attack the second game character.

[0046] For example, it can be calculated This yields the transit time range (0, t). Here, t is the maximum transit time for the non-player character, and d is the distance between the non-player character and the second game character. This refers to the movement speed of the second game character. This refers to the movement speed of non-player characters. The time it takes for the second game character to pass through the transitable object.

[0047] If there are transit points between the non-player character and the second character, the second character will encounter obstacles when chasing the non-player character, requiring additional time. By considering the time parameter for the second character to pass through transit points, the transit time range for the non-player character is determined more accurately and reasonably.

[0048] In step S120, within the transfer time range of the non-player character, the target transfer object is determined from the candidate transfer objects, and the non-player character is controlled to use the target transfer object for transfer.

[0049] Among them, the candidate transfer object refers to the available transfer object in the game scene, and the target transfer object is the transfer object that the non-player character will transfer to.

[0050] In an optional implementation, in step S120, within the transfer time range of the non-player character, a target transfer object is determined from the candidate transfer objects, and the non-player character is controlled to use the target transfer object for transfer, such as... Figure 2 As shown, this can be achieved through the following steps:

[0051] Step S210: Within the transit time range of non-player characters, determine the target transit time of non-player characters;

[0052] Step S220: At the target transfer time, determine the target transfer object from the candidate transfer objects, and control the non-player character to use the target transfer object for transfer.

[0053] Specifically, after determining the transfer time range, a target transfer time can be set for non-player characters within that transfer event range. The target transfer time refers to the timing when a non-player character performs the transfer.

[0054] Determining the target transfer time within this transfer time range links the transfer timing of non-player characters with the actual game state, making the transfer timing of non-player characters more human-like. This can improve the game's intelligence to a certain extent and make non-player characters less likely to be identified.

[0055] In one optional implementation, in step S210, determining the target transit time of the non-player character within the transit time range can be achieved through the following steps: within the transit time range of the non-player character, in response to the occurrence of a preset game event, taking the occurrence time of the preset game event as the target transit time; and / or within the transit time range of the non-player character, determining the target transit time of the non-player character based on a preset time period.

[0056] Preset game events refer to pre-defined game events that may affect the game's progress or outcome. Examples include: healing the first game character, unleashing a skill, and starting to repair a virtual cipher machine.

[0057] The preset time period refers to a pre-set time interval. This preset time period can be calculated from the start of the game or from the moment a game character's transition event is triggered; there is no specific limitation here.

[0058] It should be noted that since the game's transit time may include multiple target transit times, during actual gameplay, the target transit times of non-player characters can be updated in real time according to the game's progress, so that non-player characters can make dynamic transits and further improve the timeliness of transit control.

[0059] Determining the timing of non-player character transfers from different perspectives helps improve the flexibility of non-player character transfers.

[0060] In an optional implementation, in step S220, at the target transfer time, a target transfer object is determined from the candidate transfer objects, and a non-player character is controlled to use the target transfer object for transfer, such as... Figure 3 As shown, the specific steps may include:

[0061] Step S310: At the target transit time, determine the transit probability corresponding to the candidate transit object;

[0062] Step S320: Based on the transfer probability corresponding to the candidate transfer object, determine the target transfer object, and control the non-player character to use the target transfer object for transfer.

[0063] Figure 3 The steps shown quantify the selection process of the target transit object by determining the transit probability of each candidate transit object, which can ensure the accuracy of the selection of the target transit object to a certain extent.

[0064] Specifically, in step S310, at the target transit time, the transit probability corresponding to the candidate transit object is determined.

[0065] The transfer probability refers to the probability that a non-player character will use the corresponding candidate transfer object as the target transfer object for transfer.

[0066] In one optional implementation, at the target transfer time, the transfer probability corresponding to the candidate transfer object is determined, which can be achieved through the following steps: at the target transfer time, one or more game data corresponding to the target transfer time are obtained; the game data corresponding to the target transfer time are input into the supervised learning network, and the transfer probability corresponding to the candidate object is output.

[0067] Among them, one or more game data corresponding to the target transit time refers to the game data corresponding to a specific parameter item of the target transit time.

[0068] For example, taking a target transit time determined based on a preset time period as an example, multiple game data such as the character's position data, the character's orientation data, the character's skill cooldown time data, and the cipher machine decryption progress data corresponding to the target transit time can be obtained.

[0069] For example, taking the target transit time determined based on a preset game event as an example, one or more game data corresponding to the preset game event can be obtained, such as the time data for treating the first game character.

[0070] The supervised learning network can output the transfer probability of each candidate object based on the game data corresponding to the target transfer time.

[0071] The transit probability of candidate transit objects is determined by considering multiple game data points corresponding to the target transit time. This comprehensive approach provides a more accurate and comprehensive reference basis for the selection of target transit objects.

[0072] In one alternative implementation, before inputting the game data corresponding to the target transit time into the supervised learning network, the following step may be performed: normalizing the game data corresponding to the target transit time.

[0073] For example, taking character position data as an example, the character's three-dimensional position coordinates can be divided by the maximum coordinate value of the game scene map to achieve normalization of the character's position data.

[0074] For example, taking the cipher machine decryption progress as an example, the current cipher machine progress can be divided by 100 to achieve normalization of the cipher machine progress data.

[0075] Since the game data may differ in numerical value and dimensionality, normalizing the game data before inputting it into the supervised learning network can prevent the supervised learning network from being slow in gradient descent updates, thus affecting processing efficiency.

[0076] It should be noted that before using a supervised learning network, it can be trained and its configuration parameters updated to continuously improve the accuracy of the supervised learning network.

[0077] In one optional implementation, before inputting the game data corresponding to the target transfer time into the supervised learning network, the following steps may be performed: based on the game recording data corresponding to the sample player character, determine the sample transfer time of the sample player character, and obtain one or more game data corresponding to the sample transfer time, as well as the pre-transfer object corresponding to the sample player character at the sample transfer time; based on the game data corresponding to the sample transfer time and the pre-transfer object corresponding to the sample player character at the sample transfer time, train the supervised learning network.

[0078] Here, the sample player character can be the first player character controlled by a real player in a historical game. The sample transfer moment can be the time when the sample player character makes a transfer. The pre-transfer object refers to the transfer object that the sample player character will use when making the transfer. One or more game data corresponding to the sample transfer moment can include the game data corresponding to specific parameter items of the sample transfer moment.

[0079] Optionally, the sample transit time of the sample player character can be determined through the following steps: based on the game recording data corresponding to the sample player character, determine the transit time range of the sample player character when the game character transit event is triggered; within the transit time range of the sample player character, determine the sample transit time of the sample player character.

[0080] The game match recording data corresponding to the sample player character refers to the game data obtained by recording the historical game matches of the sample player character.

[0081] Specifically, during a game match between sample players, game data corresponding to one or more specific parameters can be recorded based on a preset time period. Game data corresponding to the time when a preset game event occurs can also be recorded to obtain game match recording data.

[0082] Optionally, the game recording data can be stored in at least two parts. One part can store game data recorded at preset time intervals, such as character position data, character orientation data, character skill cooldown time data, cipher machine decryption progress data, etc. The other part can store game data recorded at the time of preset game event recording, such as game data corresponding to the game event of healing the first game character, game data corresponding to the game event of skill release, game data corresponding to the game event of starting to repair the virtual cipher machine, etc.

[0083] Specifically, the transition time range of the sample player can be determined based on the sample player's movement speed when the game character transition event is triggered, the movement speed of the game character tracking the sample player, and the distance between the sample player and the game character tracking the sample player. The game character tracking the sample player can be the second game character in the sample player's game.

[0084] For example, it can be calculated The transit time range is obtained (0, ).in, The maximum transit time for the sample players. The distance between the sample player character and the second game character. To track the movement speed of the game characters of the sample player characters, The movement speed of the sample player character.

[0085] Furthermore, if there is a transferable object between the sample player character and the game character tracking the sample player character when the sample player character triggers the game character transfer event, the time it takes for the game character tracking the sample player character to pass through the transferable object can also be determined; based on the sample player character's movement speed when the game character transfer event is triggered, the movement speed of the game character tracking the sample player character, the distance between the sample player character and the game character tracking the sample player character, and the time it takes for the game character tracking the sample player character to pass through the transferable object, the transfer time range of the sample player can be determined.

[0086] For example, it can be calculated The transit time range is obtained (0, ).in, The maximum transit time for the sample players. The distance between the sample player character and the second game character. To track the movement speed of the game characters of the sample player characters, For the movement speed of the sample player characters, To track the time it takes for a sample player's game character to pass through a transitable object.

[0087] Specifically, within the transit time range of the sample player character, determining the sample transit time can be achieved through the following steps: within the transit time range of the sample player character, taking the occurrence time of a preset game event as the sample transit time of the sample player character; and / or within the transit time range of the sample player character, determining the sample transit time of the sample player character based on a preset time period.

[0088] For example, such as Figure 4 As shown, a schematic diagram of sample transit time is provided. The calculation can start from the transit time trigger time 401 of the game character, and take the periodic time 402 within the transit time range of the sample player character and the preset game event occurrence time 403 as the sample transit time.

[0089] After determining the sample transfer time, one or more game data corresponding to the sample transfer time can be extracted from the game recording data. Based on the game data corresponding to the sample transfer time and the pre-transfer object corresponding to the sample player character at the sample transfer time, a supervised learning network can be trained.

[0090] Specifically, the game data corresponding to the sample transfer time can be normalized and then input into the supervised learning network. Based on the output of the supervised learning network and the pre-transfer object corresponding to the sample player character at the sample transfer time, the supervised learning network can be optimized so that it can simulate the player's selection of transfer object.

[0091] Optionally, the supervised learning network may include a first network layer, a connection layer, a second network layer, an activation function layer, and an output layer; the first network layer is used to transform the input game data into feature vectors; the connection layer is used to concatenate the feature vectors corresponding to the game data output by the first network layer; the second network layer is used to reduce the dimensionality of the concatenated feature vectors output by the connection layer; the activation function layer is used to map the feature vectors output by the second network layer to the output layer; and the output layer is used to output the transit probability corresponding to the candidate object.

[0092] The first network layer can be an ANN (Artificial Neural Network) layer. Optionally, each game data point can be input into the first network layer in parallel, and the corresponding feature vector for each game data point can be output. The feature vector can be used to represent the characteristics of the game data and can be a multi-dimensional vector. For example, ... Figure 5 The first network layer 501 is shown in the diagram.

[0093] The connection layer concatenates the feature vectors corresponding to the various game data outputs from the first network layer, outputting a concatenated feature vector. For example, as shown... Figure 5 The connection layer 502 is shown in the diagram.

[0094] The second network layer can be an ANN layer, which can reduce the dimensionality of the feature vectors. It should be noted that in practical applications, one or more second network layers can be configured after the connection layer as needed; this disclosure does not specify a particular number of second network layers. For example, such as... Figure 5 The second network layer 503 is shown in the diagram.

[0095] The activation function layer can be a softmax activation function layer, which can present the results of multi-class classification as probabilities and output them through the output layer. For example, such as... Figure 5 The activation function layer 504 is shown in the diagram.

[0096] The output layer can be used to output the transit probability corresponding to the candidate object.

[0097] For example, as shown in Table 1, the transit probabilities corresponding to transit objects 1 to m can be output, where m is the number of candidate transit objects:

[0098] Table 1

[0099]

[0100] It should be noted that the sum of the transit probabilities corresponding to transit objects 1 to m is 1.

[0101] Specifically, in step S320, the target transfer object is determined based on the transfer probability corresponding to the candidate transfer object, and the non-player character is controlled to use the target transfer object for transfer.

[0102] For example, the candidate transit object with the highest transit probability value can be used as the target transit object.

[0103] In one optional implementation, the target transit object is determined based on the transit probability corresponding to the selected transit object, such as... Figure 6 As shown, it can also be achieved through the following steps:

[0104] Step S610: Determine the winning difficulty value for the second game character at the target transit time;

[0105] Step S620: Based on the winning difficulty value of the second game character and the transfer probability corresponding to the candidate transfer object, determine the target transfer object.

[0106] By combining the difficulty of winning with the second game character, the intensity of the game battles can be dynamically adjusted, enhancing the player's competitive gaming experience.

[0107] For example, if the second game character has a lower difficulty level to win, a transfer target with a higher probability of success can be selected to reduce the second game character's winning advantage; if the second game character has a higher difficulty level to win, a transfer target with a lower probability of success can be selected to increase the second game character's winning advantage, thereby balancing the winning advantages between the first and second game characters and enhancing the competitive gaming experience.

[0108] Specifically, in step S610, the winning difficulty value of the second game character at the target transit time is determined.

[0109] The winning difficulty value refers to the difficulty level for the second game character to win the game.

[0110] Taking a puzzle game as an example, in one optional implementation, the winning difficulty value of the second game character at the target transition time can be determined through the following steps: based on the game puzzle progress corresponding to the target transition time and the character status parameters corresponding to the target transition time, determine the winning difficulty value of the second game character at the target transition time.

[0111] In this context, "game decryption progress" refers to the progress of code deciphering within the game. It's important to note that for the second player character, acting as the tracker, a lower game decryption progress gives them a greater advantage in winning; conversely, a higher game decryption progress results in a lower advantage for the second player character.

[0112] Among them, character status parameters refer to parameters related to the game status of the first character, such as the number of available equipment and the character's fear value. It should be noted that the worse the first character's status, the lower the first character's winning advantage; the better the first character's status, the higher the first character's winning advantage.

[0113] For example, e = g / (a) can be calculated. 100)-f / (b 100) Obtain the winning difficulty value for the second game character. Where e is the winning difficulty value after normalizing the sum of the decoding progress of all virtual cipher machines in the game and the sum of the status values ​​of the first game character, g is the sum of the decoding progress of all virtual cipher machines in the game, a is the number of virtual cipher machines in the game, f is the sum of the status values ​​of the first game character, and b is the number of first game characters in the game. It should be noted that this disclosure uses the example of a higher status value as the first game character's status worse. In practical applications, the formula for calculating the winning difficulty value can be adaptively adjusted according to specific needs; no specific limitations are made here.

[0114] In the above steps, the difficulty of winning for the second game character was quantified by comprehensively considering the game's puzzle-solving progress and character status parameters, so as to dynamically adjust the game difficulty and enhance the player's gaming experience.

[0115] Specifically, in step S620, the target transfer object is determined based on the winning difficulty value of the second game character and the transfer probability corresponding to the selected transfer object.

[0116] In one optional implementation, the determination of the target transfer object based on the winning difficulty value of the second game character and the transfer probability corresponding to the candidate transfer object can be achieved through the following steps: If the winning difficulty value of the second game character is less than a preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the maximum transfer probability, wherein the maximum transfer probability is the maximum value among the transfer probabilities corresponding to the candidate transfer objects; If the winning difficulty value of the second game character is greater than the preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the minimum transfer probability, wherein the minimum transfer probability is the minimum value among the transfer probabilities corresponding to the candidate transfer objects; If the winning difficulty value of the second game character is equal to the preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the maximum transfer probability and the minimum transfer probability.

[0117] The preset difficulty threshold refers to a pre-set difficulty threshold that can be located between the maximum and minimum difficulty values.

[0118] For example, if e is the normalized winning difficulty value after considering the sum of the decoding progress of each virtual cipher machine in the game and the sum of the state values ​​of the first game character, then 0 can be used as the preset difficulty value. If e < 0, it indicates that the second game character has a lower winning difficulty and is in an advantageous state. In this case, the target transfer object can be determined from the candidate transfer objects based on the maximum transfer probability to reduce the winning advantage of the second game character. If e > 0, it indicates that the second game character has a higher winning difficulty and is in a disadvantageous state. In this case, the target transfer object can be determined from the candidate transfer objects based on the minimum transfer probability to enhance the winning advantage of the second game character. If e = 0, it indicates that the winning difficulty of the second game character is comparable to that of the first game character, and the two are in a balanced state. In this case, the target transfer object can be determined from the candidate transfer objects based on the maximum transfer probability and the minimum transfer probability.

[0119] The above steps achieve a dynamic balance of winning advantages between the first and second game characters, further enhancing the player's competitive gaming experience.

[0120] In one optional implementation, the above-mentioned determination of the target transit object from candidate transit objects based on the maximum transit probability can be achieved through the following steps: determining the transit object selection deviation probability based on the winning difficulty value of the second game character; determining the probability difference between the maximum transit probability and the transit object selection deviation probability; determining a first probability range based on the probability difference and the maximum transit probability; and determining the target transit object from candidate transit objects whose transit probabilities are within the first probability range.

[0121] The transit target selection deviation probability refers to the allowable probability deviation value when selecting a target transit target. The first probability range refers to the probability range formed by the probability difference and the maximum transit probability.

[0122] For example, if we take 'e' as the winning difficulty value after normalizing the sum of the decoding progress of each virtual cipher machine in the game and the sum of the state values ​​of the first game character, and 0 as the preset difficulty value, when 'e < 0,' λ / e can be used as the probability of deviation in selecting the transfer target. Here, λ can be a preset constant greater than 0, a hyperparameter that can be set empirically. After determining the probability of deviation in selecting the transfer target, the probability difference between the maximum transfer probability and the probability of deviation in selecting the transfer target can be obtained by calculating (α - λ / e), where α is the maximum transfer probability. Furthermore, (α - λ / e, α) can be used as the first probability range.

[0123] Since there may be multiple candidate transit points whose transit probabilities fall within the first probability range, for example, candidate transit points whose transit probabilities fall within the first probability range (α-λ / e, α) are... , … At this point, a candidate transit object can be randomly selected from those with transit probabilities within the first probability range and used as the target transit object.

[0124] If the winning difficulty value of the second game character is less than the preset difficulty threshold, the game can be dynamically balanced to a certain extent and the competitive experience can be enhanced by selecting a transfer object with a probability value close to the maximum transfer probability.

[0125] In one optional implementation, the above-mentioned determination of the target transit object from candidate transit objects based on the minimum transit probability can be achieved through the following steps: determining the transit object selection deviation probability based on the winning difficulty value of the second game character; determining the probability sum of the minimum transit probability and the transit object selection deviation probability; determining a second probability range based on the probability sum and the minimum transit probability; and determining the target transit object from candidate transit objects whose transit probabilities are within the second probability range.

[0126] The second probability range refers to the probability range consisting of the probability difference and the minimum transit probability.

[0127] For example, if we take 'e' as the winning difficulty value after normalizing the sum of the decoding progress of each virtual cipher machine in the game and the sum of the state values ​​of the first game character, and 0 as the preset difficulty value, when 'e>0', λ / e can be used as the probability of deviation in selecting the transfer target. Here, λ can be a preset constant greater than 0, a hyperparameter that can be set empirically. After determining the probability of deviation in selecting the transfer target, the sum of probabilities between the minimum transfer probability and the probability of deviation in selecting the transfer target can be obtained by calculating (β+λ / e), where β is the minimum transfer probability. Furthermore, (β, β+λ / e) can be used as the second probability range.

[0128] Since there may be multiple candidate transit objects whose transit probabilities fall within the second probability range, one of the candidate transit objects whose transit probabilities fall within the second probability range can be randomly selected as the target transit object.

[0129] If the winning difficulty value of the second game character is greater than the preset difficulty threshold, the game can be dynamically balanced to a certain extent and the competitive experience can be enhanced by selecting a transfer object with a probability value close to the minimum transfer probability.

[0130] In one optional implementation, the above-mentioned determination of the target transit object from the candidate transit objects based on the maximum transit probability and the minimum transit probability can be achieved through the following steps: determining a third probability range based on the maximum transit probability and the minimum transit probability; determining the target transit object from the candidate transit objects whose transit probabilities are within the third probability range.

[0131] The third probability range can be a subset of the probability range formed by the minimum transit probability and the maximum transit probability.

[0132] For example, if we take 'e' as the winning difficulty value after normalizing the sum of the decoding progress of each virtual cipher machine in the game and the sum of the state values ​​of the first game character, with 0 being the preset difficulty value and 'e=0', a probability reference value can be obtained by calculating α / λ+β / λ. Based on this probability reference value, the target transfer object is determined from the candidate transfer objects. Optionally, the transfer object with the transfer probability closest to the probability reference value can be used as the target transfer object. Optionally, a third probability range containing the probability reference value can also be determined, and a selected object can be randomly selected from the candidate transfer objects whose transfer probabilities fall within the third probability range as the target transfer object. This maintains the balance of the game to a certain extent and further enhances the competitive gaming experience.

[0133] like Figure 7 As shown, a flowchart is provided for determining the target transit object at the target transit time, which may specifically include the following steps S701 to S707:

[0134] Step S701: At the target transit time, obtain one or more game data corresponding to the target transit time, and normalize the game data corresponding to the target transit time.

[0135] Step S702: Input the game data corresponding to the target transfer time into the supervised learning network and output the transfer probability corresponding to the candidate object;

[0136] Step S703: Determine the winning difficulty value of the second game character at the target transfer time based on the game decryption progress corresponding to the target transfer time and the character status parameters corresponding to the target transfer time.

[0137] Step S704: Determine the relationship between the winning difficulty value of the second game character and the preset difficulty threshold;

[0138] If the winning difficulty value of the second game character is less than the preset difficulty threshold, proceed to step S705; if the winning difficulty value of the second game character is greater than the preset difficulty threshold, proceed to step S706; if the winning difficulty value of the second game character is equal to the preset difficulty threshold, proceed to step S707.

[0139] Step S705: Based on the maximum transit probability, determine the target transit object from the candidate transit objects;

[0140] Step S706: Based on the minimum transit probability, determine the target transit object from the candidate transit objects;

[0141] Step S707: Based on the maximum transit probability and the minimum transit probability, determine the target transit object from the candidate transit objects.

[0142] Figure 8 A game character control device 800 is shown in an exemplary embodiment of this disclosure. The game includes a first game character and a second game character that tracks the first game character. The first game character includes a non-player character, such as... Figure 8 As shown, the game character control device 800 may include:

[0143] The time range determination module 810 is used to respond to the triggering of the game character transfer event and determine the transfer time range of the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character.

[0144] The character transfer control module 820 is used to determine the target transfer object from the candidate transfer objects within the transfer time range of non-player characters, and control the non-player characters to use the target transfer object for transfer.

[0145] In an optional implementation, based on the aforementioned scheme, the time range determination module 810 can be configured to: if there is a transferable object between the non-player game character and the second game character, determine the time it takes for the second game character to pass through the transferable object; and determine the transfer time range of the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, the distance between the non-player character and the second game character, and the time it takes for the second game character to pass through the transferable object.

[0146] In an optional implementation, based on the aforementioned scheme, the character transfer control module 820 may include: a transfer time determination module, used to determine the target transfer time of a non-player character within the transfer time range of the non-player character; and a transfer object selection module, used to determine the target transfer object from the candidate transfer objects at the target transfer time, and control the non-player character to use the target transfer object for transfer.

[0147] In an optional implementation, based on the aforementioned scheme, the transit time determination module can be configured to: within the transit time range of a non-player character, in response to the occurrence of a preset game event, take the occurrence time of the preset game event as the target transit time; and / or within the transit time range of a non-player character, determine the target transit time of the non-player character based on a preset time period.

[0148] In an optional implementation, based on the aforementioned scheme, the transit object selection module may include: a probability determination module, used to determine the transit probability corresponding to the candidate transit object at the target transit time; and a transit object determination module, used to determine the target transit object based on the transit probability corresponding to the candidate transit object, and control non-player characters to use the target transit object for transit.

[0149] In an optional implementation, based on the aforementioned scheme, the probability determination module can be configured to: acquire one or more game data corresponding to the target transfer time at the target transfer time; input the game data corresponding to the target transfer time into the supervised learning network, and output the transfer probability corresponding to the candidate object.

[0150] In an optional implementation, based on the aforementioned scheme, before inputting the various game data corresponding to the target transfer time into the supervised learning network, the game character control device 800 may further include: a data normalization module, used to normalize the various game data corresponding to the target transfer time.

[0151] In an optional implementation, based on the aforementioned scheme, before inputting the game data corresponding to the target transfer time into the supervised learning network, the game character control device 800 may further include: a sample data acquisition module, used to determine the sample transfer time of the sample player character based on the game recording data corresponding to the sample player character, and acquire one or more game data corresponding to the sample player character at the sample transfer time, as well as the pre-transfer object corresponding to the sample player character at the sample transfer time; and a network training module, used to train the supervised learning network based on the game data corresponding to the sample transfer time and the pre-transfer object corresponding to the sample player character at the sample transfer time.

[0152] In an optional implementation, based on the aforementioned scheme, the sample data acquisition module includes: a sample time range determination module, used to determine the transit time range of the sample player character when the game character transit event is triggered; and a marker time determination module, used to determine the sample transit time of the sample player character within the transit time range of the sample player character.

[0153] In an optional implementation, based on the aforementioned scheme, the marker time determination module can be configured to: within the transit time range of the sample player character, take the occurrence time of a preset game event as the sample transit time of the sample player character; and / or within the transit time range of the sample player character, determine the sample transit time of the sample player character based on a preset time period.

[0154] In an optional implementation, based on the aforementioned scheme, the supervised learning network includes a first network layer, a connection layer, a second network layer, an activation function layer, and an output layer; the first network layer is used to convert the input game data into feature vectors; the connection layer is used to concatenate the feature vectors corresponding to the game data output by the first network layer; the second network layer is used to reduce the dimension of the concatenated feature vectors output by the connection layer; the activation function layer is used to map the feature vectors output by the second network layer to the output layer; and the output layer is used to output the transfer probability corresponding to the candidate object.

[0155] In an optional implementation, based on the aforementioned scheme, the transit object determination module may include: a difficulty value determination module, used to determine the winning difficulty value of the second game character at the target transit time; and a winning difficulty adjustment module, used to determine the target transit object based on the winning difficulty value of the second game character and the transit probability corresponding to the candidate transit object.

[0156] In an optional implementation, based on the aforementioned scheme, the difficulty value determination module can be configured to: determine the winning difficulty value of the second game character at the target transfer time based on the game decryption progress corresponding to the target transfer time and the character status parameters corresponding to the target transfer time.

[0157] In an optional implementation, based on the aforementioned scheme, the winning difficulty adjustment module may include: a first adjustment module, configured to determine a target transfer object from candidate transfer objects based on the maximum transfer probability if the winning difficulty value of the second game character is less than a preset difficulty threshold; wherein the maximum transfer probability is the maximum value among the transfer probabilities corresponding to the candidate transfer objects; a second adjustment module, configured to determine a target transfer object from candidate transfer objects based on the minimum transfer probability if the winning difficulty value of the second game character is greater than the preset difficulty threshold; wherein the minimum transfer probability is the minimum value among the transfer probabilities corresponding to the candidate transfer objects; and a third adjustment module, configured to determine a target transfer object from candidate transfer objects based on the maximum transfer probability and the minimum transfer probability if the winning difficulty value of the second game character is equal to the preset difficulty threshold.

[0158] In an optional implementation, based on the aforementioned scheme, the first adjustment module can be configured to: determine the probability of deviation in the selection of a transfer object based on the winning difficulty value of the second game character; determine the probability difference between the maximum transfer probability and the probability of deviation in the selection of a transfer object; determine a first probability range based on the probability difference and the maximum transfer probability; and determine the target transfer object from the candidate transfer objects whose transfer probabilities are within the first probability range.

[0159] In an optional implementation, based on the aforementioned scheme, the second adjustment module can be configured to: determine the probability of deviation in selecting a transfer object based on the winning difficulty value of the second game character; determine the sum of the probabilities of the minimum transfer probability and the probability of deviation in selecting a transfer object; determine a second probability range based on the sum of probabilities and the minimum transfer probability; and determine the target transfer object from the candidate transfer objects whose transfer probabilities are within the second probability range.

[0160] In an optional implementation, based on the aforementioned scheme, the third adjustment module can be configured to: determine a third probability range based on the maximum transit probability and the minimum transit probability; and determine the target transit object from the candidate transit objects whose transit probabilities are within the third probability range.

[0161] The specific details of each module in the aforementioned game character control device 800 have been described in detail in the method section of the implementation method. Any undisclosed details can be found in the implementation method section of the method section, and therefore will not be repeated here.

[0162] Exemplary embodiments of this disclosure also provide a computer-readable storage medium storing a program product capable of implementing the game character control method described above. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on an electronic device, causes the electronic device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0163] The program product may be a portable compact disc read-only memory (CD-ROM) and include program code, and may run on an electronic device, such as a personal computer. However, the program product disclosed herein is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0164] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0165] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0166] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF (Radio Frequency), etc., or any suitable combination thereof.

[0167] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0168] Exemplary embodiments of this disclosure also provide an electronic device capable of implementing the above-described game character control method. Referring below... Figure 9 To describe an electronic device 900 according to such an exemplary embodiment of the present disclosure. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0169] like Figure 9 As shown, the electronic device 900 can be represented as a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.

[0170] The storage unit 920 stores program code, which can be executed by the processing unit 910, causing the processing unit 910 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0171] Specifically, the processing unit 910 may perform the following steps:

[0172] In response to the triggering of a game character transfer event, the transfer time range of the non-player character is determined based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character.

[0173] Within the transfer timeframe of non-player characters, determine the target transfer object from the candidate transfer objects, and control the non-player characters to use the target transfer object for transfer.

[0174] In an optional implementation, based on the aforementioned scheme, determining the transit time range of the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character can be achieved through the following steps: if there is a transitable object between the non-player character and the second game character, determine the time it takes for the second game character to pass through the transitable object; determine the transit time range of the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, the distance between the non-player character and the second game character, and the time it takes for the second game character to pass through the transitable object.

[0175] In an optional implementation, based on the aforementioned scheme, the process of determining the target transfer object from candidate transfer objects within the transfer time range of the non-player character and controlling the non-player character to use the target transfer object for transfer can be achieved through the following steps: determining the target transfer time of the non-player character within the transfer time range of the non-player character; determining the target transfer object from candidate transfer objects at the target transfer time and controlling the non-player character to use the target transfer object for transfer.

[0176] In an optional implementation, based on the aforementioned scheme, determining the target transit time of a non-player character within the transit time range of a non-player character can be achieved through the following steps: within the transit time range of a non-player character, in response to the occurrence of a preset game event, taking the occurrence time of the preset game event as the target transit time; and / or within the transit time range of a non-player character, determining the target transit time of a non-player character based on a preset time period.

[0177] In an optional implementation, based on the aforementioned scheme, the process of determining the target transfer object from the candidate transfer objects at the target transfer time and controlling the non-player character to use the target transfer object for transfer can be achieved through the following steps: at the target transfer time, determining the transfer probability corresponding to the candidate transfer object; based on the transfer probability corresponding to the candidate transfer object, determining the target transfer object and controlling the non-player character to use the target transfer object for transfer.

[0178] In an optional implementation, based on the aforementioned scheme, the determination of the transit probability corresponding to the candidate transit object at the target transit time can be achieved through the following steps: at the target transit time, obtain one or more game data corresponding to the target transit time; input the game data corresponding to the target transit time into the supervised learning network, and output the transit probability corresponding to the candidate object.

[0179] In an alternative implementation, based on the aforementioned scheme, before inputting the game data corresponding to the target transit time into the supervised learning network, the following steps can also be performed: normalizing the game data corresponding to the target transit time.

[0180] In an optional implementation, based on the aforementioned scheme, before inputting the game data corresponding to the target transfer time into the supervised learning network, the following steps may also be performed: based on the game recording data corresponding to the sample player character, determine the sample transfer time of the sample player character, and obtain one or more game data corresponding to the sample transfer time, as well as the pre-transfer object corresponding to the sample player character at the sample transfer time; based on the game data corresponding to the sample transfer time and the pre-transfer object corresponding to the sample player character at the sample transfer time, train the supervised learning network.

[0181] In an optional implementation, based on the aforementioned scheme, the determination of the sample player character's transit time can be achieved through the following steps: determining the transit time range of the sample player character when the game character transit event is triggered; and determining the sample player character's transit time within the transit time range.

[0182] In an optional implementation, based on the aforementioned scheme, determining the sample transit time of a sample player character within the transit time range of the sample player character can be achieved through the following steps: within the transit time range of the sample player character, taking the occurrence time of a preset game event as the sample transit time of the sample player character; and / or within the transit time range of the sample player character, determining the sample transit time of the sample player character based on a preset time period.

[0183] In an optional implementation, based on the aforementioned scheme, the supervised learning network includes a first network layer, a connection layer, a second network layer, an activation function layer, and an output layer; the first network layer is used to convert the input game data into feature vectors; the connection layer is used to concatenate the feature vectors corresponding to the game data output by the first network layer; the second network layer is used to reduce the dimension of the concatenated feature vectors output by the connection layer; the activation function layer is used to map the feature vectors output by the second network layer to the output layer; and the output layer is used to output the transfer probability corresponding to the candidate object.

[0184] In an optional implementation, based on the aforementioned scheme, the determination of the target transit object based on the transit probability corresponding to the candidate transit object can be achieved through the following steps: determining the winning difficulty value of the second game character at the target transit time; determining the target transit object based on the winning difficulty value of the second game character and the transit probability corresponding to the candidate transit object.

[0185] In an optional implementation, based on the aforementioned scheme, the determination of the winning difficulty value of the second game character at the target transfer time can be achieved through the following steps: determining the winning difficulty value of the second game character at the target transfer time based on the game decryption progress corresponding to the target transfer time and the character status parameters corresponding to the target transfer time.

[0186] In an optional implementation, based on the aforementioned scheme, the determination of the target transfer object based on the winning difficulty value of the second game character and the transfer probability corresponding to the candidate transfer object can be achieved through the following steps: if the winning difficulty value of the second game character is less than a preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the maximum transfer probability, wherein the maximum transfer probability is the maximum value among the transfer probabilities corresponding to the candidate transfer objects; if the winning difficulty value of the second game character is greater than the preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the minimum transfer probability, wherein the minimum transfer probability is the minimum value among the transfer probabilities corresponding to the candidate transfer objects; if the winning difficulty value of the second game character is equal to the preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the maximum transfer probability and the minimum transfer probability.

[0187] In an optional implementation, based on the aforementioned scheme, the determination of the target transit object from candidate transit objects based on the maximum transit probability can be achieved through the following steps: determining the transit object selection deviation probability based on the winning difficulty value of the second game character; determining the probability difference between the maximum transit probability and the transit object selection deviation probability; determining a first probability range based on the probability difference and the maximum transit probability; and determining the target transit object from candidate transit objects whose transit probabilities are within the first probability range.

[0188] In an optional implementation, based on the aforementioned scheme, the determination of the target transit object from candidate transit objects based on the minimum transit probability can be achieved through the following steps: determining the transit object selection deviation probability based on the winning difficulty value of the second game character; determining the probability sum of the minimum transit probability and the transit object selection deviation probability; determining a second probability range based on the probability sum and the minimum transit probability; and determining the target transit object from candidate transit objects whose transit probabilities fall within the second probability range.

[0189] In an optional implementation, based on the aforementioned scheme, the determination of the target transit object from candidate transit objects based on the maximum transit probability and the minimum transit probability can be achieved through the following steps: determining a third probability range based on the maximum transit probability and the minimum transit probability; determining the target transit object from candidate transit objects whose transit probabilities are within the third probability range.

[0190] In the aforementioned game character control method, the transfer of non-player characters is controlled based on game running status such as character running speed and distance between characters. This makes the operation performance of non-player characters closer to that of real players, which can improve the intelligence of the transfer performance of non-player characters to a certain extent, thereby enhancing the player's game participation experience.

[0191] Storage unit 920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 921 and / or cache memory 922, and may further include read-only memory (ROM) 923.

[0192] Storage unit 920 may also include a program / utility 924 having a set (at least one) program module 925, such program module 925 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0193] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0194] Electronic device 900 can also communicate with one or more external devices 1000 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 900, and / or with any device that enables electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 960. Figure 9 As shown, network adapter 960 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although... Figure 9 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems.

[0195] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to the exemplary embodiments of this disclosure.

[0196] Furthermore, the above figures are merely illustrative representations of the processes included in the methods according to exemplary embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0197] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0198] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0199] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A game character control method, characterized by, The game includes a first game character and a second game character that tracks the first game character. The first game character includes a non-player character. The method includes: In response to triggering a game character transit event, the transit time range of the non-player character is determined based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character. Within the transit time range of the non-player character, a target transit object is determined from the candidate transit objects, and the non-player character is controlled to use the target transit object for transit; The step of determining a target transfer object from candidate transfer objects within the transfer time range of the non-player character, and controlling the non-player character to use the target transfer object for transfer, includes: Within the transit time range of the non-player character, determine the target transit time of the non-player character; At the target transit time, determine the transit probability corresponding to the candidate transit object; Based on the transit probability corresponding to the candidate transit objects, a target transit object is determined, and the non-player character is controlled to use the target transit object for transit.

2. The method of claim 1, wherein, Determining the transit time range for the non-player character based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character includes: If there is a transferable object between the non-player character and the second game character, determine the time it takes for the second game character to pass through the transferable object; The transit time range of the non-player character is determined based on the movement speed of the non-player character, the movement speed of the second game character, the distance between the non-player character and the second game character, and the time it takes for the second game character to pass through the transit object.

3. The method of claim 1, wherein, Determining the target transit time for the non-player character within the transit time range includes: Within the transit time range of the non-player character, in response to the occurrence of a preset game event, the occurrence time of the preset game event is taken as the target transit time; and / or Within the transit time range of the non-player character, the target transit time of the non-player character is determined based on a preset time period.

4. The method of claim 1, wherein, Determining the transit probability corresponding to the candidate transit object at the target transit time includes: At the target transit time, acquire one or more game data corresponding to the target transit time; The game data corresponding to the target transfer time is input into the supervised learning network, and the transfer probability corresponding to the candidate transfer object is output.

5. The method of claim 4, wherein, Before inputting the game data corresponding to the target transit time into the supervised learning network, the method further includes: The game data corresponding to the target transit time are normalized.

6. The method according to claim 4, characterized in that, Before inputting the game data corresponding to the target transit time into the supervised learning network, the method further includes: Based on the game recording data corresponding to the sample player character, determine the sample transfer time of the sample player character, and obtain one or more game data corresponding to the sample player character at the sample transfer time, as well as the pre-transfer object corresponding to the sample player character at the sample transfer time. The supervised learning network is trained based on the game data corresponding to the sample transfer time and the pre-transfer object corresponding to the sample player character at the sample transfer time.

7. The method according to claim 6, characterized in that, Determining the sample transit time of the sample player character includes: Determine the transit time range of the sample player character when the game character transit event is triggered; Within the transit time range of the sample player character, determine the sample transit time of the sample player character.

8. The method of claim 7, wherein, Determining the sample transit time of the sample player character within the transit time range of the sample player character includes: Within the transit time range of the sample player character, the occurrence time of a preset game event is taken as the sample transit time of the sample player character; and / or Within the transit time range of the sample player character, the sample transit time of the sample player character is determined based on a preset time period.

9. The method of claim 4, wherein, The supervised learning network includes a first network layer, a connection layer, a second network layer, an activation function layer, and an output layer. The first network layer is used to convert the input game data into feature vectors. The connection layer is used to concatenate the feature vectors corresponding to the game data output by the first network layer. The second network layer is used to reduce the dimensionality of the concatenated feature vectors output by the connection layer. The activation function layer is used to map the feature vectors output by the second network layer to the output layer. The output layer is used to output the transfer probability corresponding to the candidate transfer object.

10. The method of claim 1, wherein, The step of determining the target transit object based on the transit probability corresponding to the candidate transit objects includes: Determine the winning difficulty value for the second game character at the target transit time; The target transfer object is determined based on the winning difficulty value of the second game character and the transfer probability corresponding to the candidate transfer object.

11. The method of claim 10, wherein, Determining the winning difficulty value of the second game character at the target transit time includes: Based on the game decryption progress corresponding to the target transfer time and the character status parameters corresponding to the target transfer time, the winning difficulty value of the second game character at the target transfer time is determined.

12. The method of claim 10, wherein, The step of determining the target transfer object based on the winning difficulty value of the second game character and the transfer probability corresponding to the candidate transfer object includes: If the winning difficulty value of the second game character is less than the preset difficulty threshold, a target transfer object is determined from the candidate transfer objects based on the maximum transfer probability, wherein the maximum transfer probability is the maximum value among the transfer probabilities corresponding to the candidate transfer objects; If the winning difficulty value of the second game character is greater than the preset difficulty threshold, a target transfer object is determined from the candidate transfer objects based on the minimum transfer probability, wherein the minimum transfer probability is the minimum value of the transfer probabilities corresponding to the candidate transfer objects; If the winning difficulty value of the second game character is equal to the preset difficulty threshold, the target transfer object is determined from the candidate transfer objects based on the maximum transfer probability and the minimum transfer probability.

13. The method of claim 12, wherein, The step of determining the target transit object from the candidate transit objects based on the maximum transit probability includes: Based on the winning difficulty value of the second game character, determine the probability of deviation in the selection of the transfer target; Determine the probability difference between the maximum transit probability and the transit target selection deviation probability; Based on the probability difference and the maximum transit probability, a first probability range is determined; The target transit object is determined from the candidate transit objects whose transit probabilities are within the first probability range.

14. The method of claim 12, wherein, The step of determining the target transit object from the candidate transit objects based on the minimum transit probability includes: Based on the winning difficulty value of the second game character, determine the probability of deviation in the selection of the transfer target; The probability of determining the minimum transit probability and the probability of deviation in the selection of the transit object is summed. Based on the aforementioned probability and the minimum transit probability, a second probability range is determined; The target transit object is determined from the candidate transit objects whose transit probability is within the second probability range.

15. The method according to claim 12, characterized in that, The step of determining the target transit object from the candidate transit objects based on the maximum transit probability and the minimum transit probability includes: Based on the maximum transit probability and the minimum transit probability, a third probability range is determined; The target transit object is determined from the candidate transit objects whose transit probability is within the third probability range.

16. A game character control apparatus characterized by comprising: The game includes a first game character and a second game character that tracks the first game character. The first game character includes a non-player character. The device includes: The time range determination module is used to determine the transit time range of the non-player character in response to the triggering of a game character transit event, based on the movement speed of the non-player character, the movement speed of the second game character, and the distance between the non-player character and the second game character. The character transfer control module is used to determine the target transfer object from the candidate transfer objects within the transfer time range of the non-player character, and control the non-player character to use the target transfer object for transfer; The role transfer control module is configured as follows: Within the transit time range of the non-player character, determine the target transit time of the non-player character; At the target transit time, determine the transit probability corresponding to the candidate transit object; Based on the transit probability corresponding to the candidate transit objects, a target transit object is determined, and the non-player character is controlled to use the target transit object for transit.

17. A computer readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 15.

18. An electronic device, comprising: include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 15.

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