Virtual scene processing method, device, equipment, storage medium and program product

Abstract

The application discloses a virtual scene processing method, device, equipment, storage medium and program product. The method comprises the following steps: obtaining first scene information of a first virtual scene to be evaluated for interaction difficulty; determining a target virtual scene similar to the first virtual scene from a plurality of second virtual scenes determined in advance according to the first scene information; inputting the interaction difficulty of the target virtual scene, second scene information of the target virtual scene and the first scene information into a difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene. In this way, the first scene information of the first virtual scene is obtained, the second virtual scene with labeled difficulty is matched, and the difficulty prediction model is inputted to automatically predict the interaction difficulty of the first virtual scene, thereby effectively improving the accuracy of game interaction difficulty evaluation and guaranteeing the fairness of game battles.

Description

Technical Field

[0001] This application relates to the field of game technology, specifically to a method for processing virtual scenes, a device for processing virtual scenes, an electronic device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] In related technologies, assessing the interaction difficulty or balance of virtual scenes (such as map scenes corresponding to gameplay) often relies on the subjective experience of designers, or requires collecting and analyzing a large amount of user interaction data after the scene goes live before feedback and adjustments can be made. The former lacks objective and unified quantitative standards, making it difficult to guarantee the reliability and consistency of the assessment results; the latter leads to a serious delay in the assessment process, and obtaining effective data requires servers to handle massive amounts of real user interaction data, consuming huge computing and storage resources, and extending the development cycle. Summary of the Invention

[0003] This application provides a method for processing virtual scenes, a device for processing virtual scenes, an electronic device, a computer-readable storage medium, and a computer program product. By acquiring the first scene information of a first virtual scene, matching it with a second virtual scene whose difficulty has been marked, and inputting it into a difficulty prediction model, the interaction difficulty of the scene to be evaluated is automatically predicted, which effectively improves the accuracy of the difficulty evaluation of the first virtual scene and ensures the fairness of the game.

[0004] On one hand, embodiments of this application provide a method for processing virtual scenes, the method comprising: Obtain the first scene information of the first virtual scene whose interaction difficulty needs to be evaluated; Based on the first scene information, a target virtual scene similar to the first virtual scene is determined from a plurality of predetermined second virtual scenes, wherein each second virtual scene has a predetermined interaction difficulty; The interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information are input into the difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene. The scene information is used to characterize the spatial features of the corresponding virtual scene.

[0005] On the other hand, embodiments of this application provide a virtual scene processing apparatus, the apparatus comprising: The acquisition module is used to acquire the first scene information of the first virtual scene whose interaction difficulty is to be evaluated. The determining module is used to determine a target virtual scene similar to the first virtual scene from a plurality of predetermined second virtual scenes based on the first scene information, wherein each second virtual scene has a determined interaction difficulty; The processing module is used to input the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene and the first scene information into the difficulty prediction model for prediction processing, so as to obtain the interaction difficulty of the first virtual scene. The scene information is used to characterize the spatial features of the corresponding virtual scene.

[0006] On the other hand, embodiments of this application provide a computer-readable storage medium storing a computer program adapted for loading by a processor to execute the virtual scene processing method as described in any of the above embodiments.

[0007] On the other hand, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the virtual scene processing method as described in any of the above embodiments by calling the computer program stored in the memory.

[0008] On the other hand, embodiments of this application provide a computer program product, including computer instructions, which, when executed by a processor, implement the virtual scene processing method as described in any of the above embodiments.

[0009] The virtual scene processing method, virtual scene processing device, electronic device, computer-readable storage medium, and computer program product provided in this application embodiment obtain first scene information of a first virtual scene whose interaction difficulty is to be evaluated, determine a target virtual scene with a high matching degree from a plurality of predetermined second virtual scenes based on the first scene information, and then input the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information into a difficulty prediction model, so that the difficulty prediction model completes prediction processing based on the input information and outputs the interaction difficulty of the first virtual scene, thereby realizing the automated and standardized evaluation of the interaction difficulty of virtual scenes. Compared to solutions that rely on subjective human experience, adjust based on player feedback after game launch, or simply calculate distance features of scene interaction points, directly filtering similar target virtual scenes based on quantitative scene information helps reduce evaluation errors caused by subjective human judgment. Utilizing a difficulty prediction model for prediction processing ensures the efficiency and stability of scene difficulty assessment to a certain extent. Furthermore, using relevant data from highly similar target virtual scenes as the basis for prediction effectively improves the accuracy of the first virtual scene difficulty assessment results. This, to a certain extent, avoids the additional labor costs incurred by repeated modifications due to balance defects after scene launch, unifies the difficulty assessment standards for virtual scenes, enhances the persuasiveness of scene assessment results, and thus, to a certain extent, ensures the fairness of asymmetrical competitive games, stabilizes the core competitive experience, and optimizes the player experience. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the virtual scene processing method provided in the embodiments of this application.

[0012] Figure 2 This is a box plot diagram of the feature bins provided in the embodiments of this application.

[0013] Figure 3 This is a schematic diagram of feature binning provided for an embodiment of this application.

[0014] Figure 4 This is a schematic diagram of bucketed recall based on multi-dimensional features provided in an embodiment of this application.

[0015] Figure 5 This is a schematic diagram of the coarse-row model encoding provided in an embodiment of this application.

[0016] Figure 6 This is a schematic diagram illustrating coarse-rank similarity calculation and recall for embodiments of this application.

[0017] Figure 7 This is a schematic diagram of the fine-sorting model encoding provided in the embodiments of this application.

[0018] Figure 8 This is a schematic diagram illustrating the similarity calculation during the fine-ranking stage, as provided in an embodiment of this application.

[0019] Figure 9 This is a schematic diagram of similarity data in the fine-ranking stage provided in an embodiment of this application.

[0020] Figure 10 This is a schematic diagram illustrating the intensity level prediction based on Few-shot, as provided in an embodiment of this application.

[0021] Figure 11 This is a schematic diagram of the structure of the virtual scene processing device provided in the embodiments of this application.

[0022] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] This application provides a method for processing virtual scenes, a device for processing virtual scenes, an electronic device, a computer-readable storage medium, and a computer program product. Specifically, the virtual scene processing method of this application can be executed by an electronic device, which can be a terminal or a server. The terminal can be a smartphone, tablet computer, laptop computer, smart TV, wearable smart device, smart vehicle terminal, etc. The terminal can also include a client, which can be a browser client, instant messaging client, or mini-program, etc. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0025] It should be noted that, in this embodiment, the execution entity of the virtual scene processing method can be a terminal device or a server. The terminal device can be a local terminal device or a client device in a cloud platform. This embodiment does not limit the type of execution entity.

[0026] For example, when the virtual scene processing method runs on a terminal device, the terminal device may include a display screen and a processor. The display screen is used to present a graphical user interface (GUI) and receive instructions generated by the user interacting with the GUI. The processor is used to store applications, generate the GUI, respond to instructions, and control the display of the GUI on the display screen. When the user operates the GUI through the display screen, the GUI can control the local content of the terminal device in response to the received operation instructions. The terminal device can provide the GUI to the user in various ways, such as rendering it on the terminal device's display screen or presenting the GUI through holographic projection.

[0027] For example, when the processing method for the virtual scene runs on a server, this method can be implemented and executed based on a cloud system. The cloud system includes servers and client devices. The application's runtime and the graphical user interface (GUI) presentation are separate. The storage and execution of the virtual scene processing method are completed on the server. The GUI presentation is completed on the client, which is primarily used for data reception, transmission, and GUI presentation. For example, the client can be a display device with data transmission capabilities located close to the user, such as a mobile terminal, television, computer, PDA, personal digital assistant, or head-mounted display. However, the terminal device performing data processing is the server in the cloud. During this process, the user operates the client to send instructions to the server. The server executes the instructions, encodes and compresses the GUI data, returns it to the client via the network, and finally, the client decodes and outputs the GUI.

[0028] It should be noted that, in this embodiment, the execution entity of the virtual scene processing method can be a terminal device or a server. The terminal device can be a local terminal device or a client device in the aforementioned cloud system. This embodiment does not limit the type of execution entity.

[0029] The technical solution of this application will be described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0030] In game development, especially in game genres where fairness is crucial, assessing the difficulty and balance of newly designed map scenarios is a critical task. Common assessment methods have several limitations. For example, methods relying on human experience lack stable, quantifiable algorithms, their conclusions are heavily influenced by individual subjectivity, are difficult to programmatically implement, and cannot meet the demands of rapid development iterations for automated assessment tools. Another approach requires waiting until the scenario is online and then collecting and analyzing large amounts of player data for feedback and adjustments. This method is not only time-consuming and inefficient, but also requires continuous server operation and processing of massive amounts of match logs to obtain effective statistical samples, consuming considerable computing and storage resources. Furthermore, some attempts at automation rely solely on simple geometric distance features within the scenario, resulting in overly simplistic assessment models that fail to capture the complex spatial and interactive relationships that influence game difficulty. This leads to unreliable assessment results, failing to provide effective data support for refined scenario balance adjustments, potentially causing instability in the online game environment.

[0031] For the above issues, please refer to Figure 1 , Figure 1 This is a flowchart illustrating a virtual scene processing method provided in an embodiment of this application. It should be noted that the steps shown may be executed in a logical order different from that shown in the flowchart. The method includes: 110: Obtain the first scene information of the first virtual scene whose interaction difficulty needs to be evaluated; 120: Based on the first scene information, determine a target virtual scene similar to the first virtual scene from a plurality of pre-determined second virtual scenes, wherein each second virtual scene has a determined interaction difficulty; 130: Input the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information into the difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene; wherein, the scene information is used to characterize the spatial features of the corresponding virtual scene.

[0032] Specifically, when evaluating the rationality of virtual scene design, i.e., the difficulty of interaction, some technical solutions often rely on human experience for assessment, which is highly subjective and lacks persuasiveness. Furthermore, some solutions require adjustments to the virtual scene after its launch based on player feedback, resulting in significant manpower and time costs and low development efficiency. In addition, some solutions simply calculate basic features such as the distance between interaction points within the scene, failing to accurately reflect the actual interaction difficulty and thus failing to meet the evaluation requirements of virtual scene design, impacting player experience. Based on these problems, this application provides a method for processing virtual scenes. By acquiring the feature information of the virtual scene to be evaluated, matching it with similar reference virtual scenes labeled with difficulty, and then inputting the relevant information into a difficulty prediction model, the method automatically predicts the interaction difficulty of the scene to be evaluated, achieving objective and accurate evaluation during the development phase.

[0033] In some embodiments, a virtual scene can be understood as a scene built in a virtual environment that has spatial structure and interactive functions. It is typically used in virtual interaction fields such as games and includes various interactive objects and spatial layouts.

[0034] In some embodiments, the first virtual scene can be understood as a newly designed game map interaction area or interaction scene that requires an evaluation of the reasonableness of interaction difficulty during the game development process.

[0035] In some embodiments, scene information can be understood as a set of various quantitative data used to characterize the spatial features of a virtual scene, which can be used to assess the interaction difficulty of the virtual scene.

[0036] In some embodiments, the first scene information can be understood as quantitative data used to describe the attribute characteristics of the first virtual scene, including the length of the collision contour of the interaction point, the distance between the frame points, the scene image and the scene description, etc.

[0037] In some embodiments, spatial features can be understood as the spatial attributes of a virtual scene, such as its spatial size, object distribution, structural layout, and range of movable paths.

[0038] In some embodiments, a plurality of pre-determined second virtual scenes are game reference scenes that have been prepared in advance and labeled with interaction difficulty, forming a standard sample library for evaluating the game interaction difficulty.

[0039] In some embodiments, the second scene information can be understood as feature data corresponding to the second virtual scene, which is consistent with the dimensions and type of the first scene information.

[0040] In some embodiments, the target virtual scene can be understood as a reference scene selected from the second virtual scene that has a high degree of similarity to the features of the first virtual scene.

[0041] In some embodiments, interaction difficulty can be understood as a quantitative level that measures the advantage of the virtual scene in combat between different factions, and is used to reflect the impact of the virtual scene on the balance of the two factions in the game.

[0042] In some embodiments, the difficulty prediction model can be understood as an intelligent model trained based on a large language model combined with a small number of samples, which can predict the interaction difficulty of a new virtual scene based on reference scene information.

[0043] In some embodiments, the prediction process can be understood as the computational process by which the difficulty prediction model combines the interaction difficulty of the input reference virtual scene, the reference scene information, and the scene information to be evaluated, and derives the interaction difficulty of the virtual scene to be evaluated through feature comparison and logical reasoning.

[0044] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description, namely: In the virtual scene design process, if it is necessary to evaluate the rationality of the newly designed virtual scene, i.e. the interaction difficulty, it is necessary to first obtain the first scene information corresponding to the first virtual scene whose interaction difficulty needs to be evaluated, i.e. the newly designed virtual scene. The first scene information is quantitative data that characterizes the attributes of the virtual scene.

[0045] Subsequently, using several pre-defined second virtual scenarios as reference samples, and based on the acquired first scenario information, target virtual scenarios with a high degree of matching with the first scenario information features are selected from the reference samples. The second virtual scenarios are historical reference scenarios that have been validated in practice and have completed difficulty calibration, possessing mature difficulty data and feature parameters. The target virtual scenarios are reference samples that highly match the features of the first virtual scenario.

[0046] Finally, the selected target virtual scene's labeled interaction difficulty, the target virtual scene's corresponding second scene information, and the first virtual scene's first scene information are all input into the pre-trained difficulty prediction model. The difficulty prediction model combines the interaction difficulty of the target virtual scene, the feature association between the second scene information and the first scene information, and finally calculates the interaction difficulty of the first virtual scene.

[0047] Thus, in this embodiment of the application, by obtaining the first scene information of the first virtual scene whose interaction difficulty is to be evaluated, and determining the target virtual scene with a high matching degree from a plurality of predetermined second virtual scenes based on the first scene information, the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information are input into the difficulty prediction model, so that the difficulty prediction model completes the prediction processing based on the input information and outputs the interaction difficulty of the first virtual scene, thereby realizing the automated and standardized evaluation of the interaction difficulty of the virtual scene. Compared to solutions that rely on subjective human experience, adjust based on player feedback after game launch, or simply calculate distance features of scene interaction points, directly filtering similar target virtual scenes based on quantitative scene information helps reduce evaluation errors caused by subjective human judgment. Utilizing a difficulty prediction model for prediction processing ensures the efficiency and stability of scene difficulty assessment to a certain extent. Furthermore, using relevant data from highly similar target virtual scenes as the basis for prediction effectively improves the accuracy of the first virtual scene difficulty assessment results. This, to a certain extent, avoids the additional labor costs incurred by repeated modifications due to balance defects after scene launch, unifies the difficulty assessment standards for virtual scenes, enhances the persuasiveness of scene assessment results, and thus, to a certain extent, ensures the fairness of asymmetrical competitive games, stabilizes the core competitive experience, and optimizes the player experience.

[0048] In some embodiments provided in this application, each virtual scene includes an interactive object and two collision objects corresponding to the interactive object, with the two collision objects separated by the interactive object; the scene information of the virtual scene includes the reference distance corresponding to each collision object, as well as the first stand distance and the second stand distance corresponding to the interactive object; The reference distance corresponding to the collision object is the length of the closed path formed around the collision object according to the preset pathfinding rules. The first frame point distance is the distance between the interactive object in the virtual scene and the first designated functional point, and the second frame point distance is the distance between the interactive object in the virtual scene and the second designated functional point.

[0049] Specifically, in the standardization and difficulty assessment of virtual scenes, the lack of a clear definition of the structural composition and feature data of virtual scenes makes it impossible to accurately extract the spatial features of virtual scenes. This results in a lack of unified quantitative basis for virtual scene classification, and deviations are prone to occur during the matching of similar virtual scenes, affecting the accuracy of virtual interaction difficulty prediction. Based on the above problems, in some embodiments provided in this application, by clarifying the specific structural composition of virtual scenes and defining specific quantitative dimensions of scene information, a standardized scene feature description system is constructed using the collision object surround distance and frame distance as feature indicators, providing reliable data support for scene classification and similarity matching.

[0050] In some embodiments, an interactive object can be understood as an interactive component in a virtual scene that performs the function of spatial separation. It is a carrier for virtual characters to perform interactive operations and is used to separate two colliding objects to form independent confrontation areas.

[0051] In some embodiments, a collision object can be understood as an object in a virtual scene that has a physical collision volume and can block the movement path of a virtual character.

[0052] In some embodiments, the reference distance can be understood as the total length of the path taken by the virtual character to complete a full circle around the outer contour of the collision object according to a preset pathfinding rule, and is used to quantify the spatial scale of the collision object.

[0053] In some embodiments, the first stand-up distance can be understood as the spatial length between the interactive object and a first designated functional point within the virtual scene, i.e., the path length required for a virtual character to complete a stand-up operation at the first designated functional point of a collision object in the virtual scene. In some embodiments, the second stand-up distance can be understood as the spatial length between the interactive object and a second designated functional point within the virtual scene, i.e., the path length required for a virtual character to complete a stand-up operation at the second designated functional point of another collision object in the virtual scene, together with the first stand-up distance, reflecting the stand-up characteristics and the balance of confrontation in the scene.

[0054] In some embodiments, a designated function point can be understood as a pre-defined location node in a virtual scene that has a specific function.

[0055] To more clearly illustrate the virtual scene processing method provided in this application embodiment, please refer to the following exemplary description: First, all predetermined virtual scenes are uniformly structurally defined. Each virtual scene includes an interactive object and two collision objects corresponding to the interactive object. The interactive object is located between the two collision objects, separating the two collision objects into two independent spatial regions.

[0056] When a virtual character moves within the game, it cannot directly enter the area of ​​another collision object in the virtual scene from the area of ​​one collision object. It needs to complete a specified interaction action with the interaction object in order to pass between the two areas.

[0057] Given a clear understanding of the basic structure of the virtual scene, it is necessary to extract scene information for each virtual scene. The scene information includes two types of quantitative data: one is the reference distance corresponding to each collision object, and the other is the distance between the first and second standoff points corresponding to the interactive object, in order to characterize the features of the virtual scene from the two dimensions of spatial scale and adversarial operation.

[0058] The reference distance corresponding to the collision object is obtained by calculating the total path length of the virtual character moving in a complete circle along the outer contour of the collision object. It can reflect the overall spatial size of the collision object and is used to measure the spatial scale of the area.

[0059] The first stand-up distance is the total path length that a virtual character needs to move to perform a stand-up operation at the first designated functional point of a collision object in a virtual scene. The first stand-up distance reflects the ease of performing combat operations within the collision object area. The second stand-up distance corresponds to the total path length that a virtual character needs to move to perform a stand-up operation at the second designated functional point of another collision object in the virtual scene. The second stand-up distance reflects the ease of performing combat operations within the collision object area.

[0060] Understandably, by extracting and standardizing the reference distance, the distance to the first stand-up point, and the distance to the second stand-up point, the abstract spatial features of the virtual scene can be transformed into specific, quantifiable values, forming standardized scene information. This scene information can serve as the basis for dividing scene sets, ensuring that each virtual scene can be accurately categorized, and providing a reliable data foundation for retrieving similar virtual scenes and predicting interaction difficulty.

[0061] Thus, in this embodiment, by clearly defining the composition and spatial separation of the virtual scene, and standardizing the quantitative dimensions and specific calculation methods of scene information, a standardized definition of the reference virtual scene and unified collection of feature data are achieved. Compared to methods with vague definitions of virtual scene structure and a lack of unified standards for scene feature extraction, fixing and defining the components and spatial separation relationships of the virtual scene ensures that all reference scenes maintain a consistent spatial layout logic, thereby strengthening the standardization of the adversarial structure of the virtual scene to a certain extent. Furthermore, clearly defining the four quantitative indicators of scene information covers two types of features: scene spatial scale and operational efficiency, effectively avoiding the problem that simple features cannot reflect the true intensity of the scene. The unified distance calculation rules effectively ensure the consistency and comparability of feature data for all reference scenes, solving to a certain extent the problems of chaotic definitions and non-standardized feature quantification of reference virtual scenes. This ensures the objectivity and accuracy of scene set division and similar scene matching, thereby improving the stability and credibility of virtual scene interaction difficulty assessment to a certain extent.

[0062] In some embodiments provided in this application, the determination of the interaction difficulty of the second virtual scene includes: determining the target movement distance of the second virtual character within the action execution time based on the action execution time of the first virtual character performing the target interaction action through the interaction object in the second virtual scene and the movement speed of the second virtual character, wherein the game faction of the second virtual character is different from that of the first virtual character, and the second virtual character cannot perform the target interaction action through the interaction object; The interaction difficulty of the second virtual scene is determined based on the relationship between the target's movement distance and the reference distances corresponding to the two colliding objects in the second virtual scene.

[0063] Specifically, the interaction difficulty of the second virtual scene lacks a scientific basis for determination. Relying solely on human experience or simple distance statistics leads to distorted difficulty data, failing to provide a reliable and effective data basis for predicting the difficulty of the first virtual scene and reducing the credibility of the evaluation results. Based on the above problems, in some embodiments provided in this application, the target movement distance of the second virtual character is obtained by combining the attribute differences of virtual characters from different factions within the game and using the interaction action time and movement speed of virtual characters from different factions as the calculation basis. Then, the target movement distance is numerically compared with the surrounding distance of collision objects within the virtual scene, and the interaction difficulty of the second virtual scene is quantitatively determined through objective numerical relationships.

[0064] In some embodiments, the interactive objects in the second virtual scene can be understood as interactive components that perform spatial separation functions in the second virtual scene. They are carriers for virtual characters to perform interactive operations and are used to separate two colliding objects to form independent confrontation areas.

[0065] In some embodiments, the first virtual character can be understood as a character from a certain faction in a virtual scene who can perform interactive actions using interactive objects. In some embodiments, the second virtual character can be understood as a character from an opposing faction in the game who belongs to a different faction than the first virtual character and cannot perform interactive actions using interactive objects.

[0066] In some embodiments, the target interactive action can be understood as a game interactive behavior completed by the first virtual character operating an interactive object, used to trigger character state switching and area passage. For example, the vaulting action and the window vaulting action.

[0067] In some embodiments, the action execution time can be understood as the total time consumed by the first virtual character from triggering the target interaction action to the completion of the action, used to measure interaction efficiency. For example, the lag time of the first virtual character after performing a flip-board or flip-window interaction.

[0068] In some embodiments, movement speed can be understood as the distance that the second virtual character can move per unit time within the virtual scene, and is used to calculate the character's movement distance.

[0069] In some embodiments, the target movement distance can be understood as the total path length that the second virtual character can move during the time the first virtual character performs the target interaction action. For example, the distance that the second virtual character can move during the interaction freeze period of the first virtual character.

[0070] In some embodiments, game factions can be understood as different opposing sides within the game, with virtual characters from different factions possessing different operational abilities and functional roles. For example, game factions are divided into survivor factions and hunter factions.

[0071] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description, namely: when assessing the interaction difficulty of the virtual scene, data such as the movement speed and action execution time of the first virtual character, i.e. the survivor, and the second virtual character, i.e. the hunter, are first collected.

[0072] Next, a first virtual character and a second virtual character belonging to different game factions are selected as the computing subjects. The first virtual character can perform target interaction actions such as flipping over a board or flipping through a window in the second virtual scene through interactive objects, while the second virtual character does not have the ability to perform such interaction actions.

[0073] Subsequently, the reference distances corresponding to the two colliding objects in the second virtual scene are obtained, i.e., the envelope data corresponding to each interaction point, and are denoted as follows: and Next, based on the execution time of the first virtual character's target interaction action and the movement speed of the second virtual character, the maximum path length that the second virtual character can move within the execution time of the target interaction action, i.e., the target movement distance, is obtained through numerical calculation. The distance the target moves can reflect the second virtual character's pursuit ability during the interaction window.

[0074] Furthermore, in some embodiments, the target movement distance can be determined based on the position of the interactive object in the game's virtual scene. The maximum path length that the second virtual character can move within the execution time of the target interaction action, or the target movement distance. This represents the maximum path length that the second virtual character can move within the execution time of the interaction between the two targets.

[0075] Next, the calculated target movement distance is compared with the reference distances corresponding to the two clearly identified collision objects in the second virtual scene. Based on the numerical comparison results, the interaction difficulty corresponding to the second virtual scene is determined, providing an accurate and unified basis for scene classification and similar scene matching.

[0076] For example, define two dimensionless ratios to simplify the judgment:

[0077]

[0078] When the target's movement distance is much smaller than the two reference distances, it indicates that the first virtual character has ample spatial operational advantage, and the interaction difficulty is higher for the second virtual character. That is to say This indicates that the first virtual character has ample space for maneuver, while the interaction difficulty is higher for the second virtual character.

[0079] In this embodiment, 10 is a preset target movement distance multiple. In practical applications, it can be adjusted according to parameters such as game character speed and interaction stun time, and is not limited to this.

[0080] When the target's movement distance is much greater than one of the reference distances and much less than the other reference distance, the spatial advantages on both sides of the scene are balanced, and the interaction difficulty is at a medium level. That is to say This indicates that the interaction difficulty corresponding to the current second virtual scene is at a medium level.

[0081] When the target's movement distance is much greater than the two reference distances, the second virtual character has a greater advantage in pursuit, and the interaction difficulty is higher for the first virtual character. The time indicates that the interaction difficulty is higher for the first virtual character.

[0082] Table 1 Interaction Difficulty Level Score Table

[0083] As shown in Table 1 above, in some embodiments, the difficulty rating of the interaction can be based on a scale of 1 to 5, with 5 being the highest difficulty and 1 being the lowest. Right now This indicates that the two colliding objects connected by the interaction point are both relatively long, and a difficulty rating of 5 points is given. If Right now This indicates that the collision length is longer on one side and shorter on the other, thus earning a difficulty rating of 3 points. If and The lengths are all relatively short. A difficulty rating of 2 points is assigned. Additionally, in some embodiments, the 4-point and 1-point difficulty ratings are not used during the standard template library establishment phase and are reserved for subsequent evaluation stages.

[0084] For example, suppose in a certain virtual scene, the distance the second virtual character, i.e., the supervisor, moves during the interaction freeze, i.e., the time of action execution, is... Meters, the envelope lengths of the two collision objects (obstacles) on either side of a certain board (i.e., the interactive object) are respectively: reference distance meters, reference distance Meters. Calculations can be made:

[0085]

[0086] It can be determined that the interaction difficulty of the virtual scene meets the 3-point condition. The second virtual character can quickly cross the short side, but the long side still constitutes an effective obstacle. The interaction point intensity is moderate and relatively balanced for the first and second virtual characters.

[0087] For example, in a virtual scene, consider the target movement distance of a second virtual character during a single interaction freeze. Meters. The envelope lengths of the two sides of a certain first reference interaction object are respectively: reference distance. meters, reference distance Meters. Calculations can be made:

[0088]

[0089]

[0090] It can be determined that the interaction difficulty of this virtual scene does not meet the criteria of 5 or 3 points, and the reference distances on both sides are both less than the single-time stiff movement distance of the second virtual character, so 2 points are awarded. At this time, the second virtual character has a clear advantage in pursuit, and the interaction difficulty is relatively unfavorable to the first virtual character.

[0091] Understandably, by relying on objective data to calculate the interaction difficulty of virtual scenes, and deeply combining the characteristics of the character faction with the features of the scene space, a standardized difficulty quantification process can be formed, so that the interaction difficulty of each second virtual scene can be accurately defined, providing a reliable and unified difficulty benchmark for scene classification and similarity matching.

[0092] Thus, in this embodiment, the target movement distance is calculated by combining the execution time of the interaction action of the first virtual character belonging to different game factions with the movement speed of the second virtual character. The target movement distance is then compared with the reference distances corresponding to the two collision objects in the second virtual scene to ultimately determine the interaction difficulty of the second virtual scene, thereby achieving a quantitative assessment of the interaction difficulty of asymmetrical competitive virtual scenes. Compared to schemes that rely on subjective human experience to determine scene difficulty, the calculation method combining the differences in abilities of characters from different factions aligns with the competitive logic of asymmetrical competition, making the difficulty assessment results consistent with real combat situations. Furthermore, the objective numerical calculation based on action execution time and movement speed effectively eliminates assessment bias caused by subjective human judgment. Moreover, the method of determining interaction difficulty based on the relationship between the target movement distance and the reference distances corresponding to the two collision objects forms a unified and standardized assessment rule, effectively solving the problem of subjective and one-sided difficulty assessment in asymmetrical competitive virtual scenes, which fails to truly reflect the differences in faction confrontation. This is achieved by transforming the abstract "interaction time" into a "specific spatial distance" that the opponent (second virtual character) can pursue within the confrontation window. By introducing two dynamic parameters that directly affect the outcome of the confrontation—character movement speed and interaction action execution time—the calculated target movement distance can be dynamically and accurately reflected in a specific interaction, showing the actual opportunity gained by the pursuer. This provides a dynamic and practical quantitative benchmark for subsequent comparison with static scene spatial features (reference distance), overcoming the limitations of evaluating solely based on static map size.

[0093] In some embodiments provided in this application, the action execution time is the interaction freeze time generated when the first virtual character performs a target interactive action through an interactive object in the second virtual scene; the step of determining the target movement distance of the second virtual character within the action execution time based on the action execution time of the first virtual character performing the target interactive action through an interactive object in the second virtual scene and the movement speed of the second virtual character includes: If there are no other interactive objects associated with the interactive object in the second virtual scene in the area where the interactive object is located, the target movement distance is determined based on the single interaction freeze time and the movement speed of the second virtual character. If there are other interactive objects associated with the interactive object in the second virtual scene in the area where the interactive object is located, the target movement distance is determined based on the time of multiple interaction freezes and the movement speed of the second virtual character.

[0094] Specifically, the calculation method for target movement distance in virtual scenes is too simplistic. It fails to adjust the calculation logic based on the actual layout state of interactive objects, making it unsuitable for the interaction mechanisms of different game scenarios. This results in the calculated movement distance not matching the actual movement of characters in combat, thus affecting the accuracy of virtual scene interaction difficulty assessment and failing to accurately reflect the actual interaction characteristics of the scene. Based on the above problems, in some embodiments provided in this application, the action execution time is explicitly defined as the interaction freeze time. The existence of associated interactive objects in the second virtual scene is used as the criterion for determination. Single interaction freeze time or multiple interaction freeze times are used, combined with the movement speed of the second virtual character, to differentially calculate the target movement distance that conforms to the actual scene.

[0095] In some embodiments, the interaction freeze time can be understood as the duration of a brief freeze state that occurs when the first virtual character performs a target interaction action. During the interaction freeze time, the first virtual character cannot perform other operations.

[0096] In some embodiments, movement speed can be understood as the distance that the second virtual character can move per unit time in the virtual scene.

[0097] In some embodiments, other associated interactive objects can be understood as similar interactive carriers that are located in the same area as the interactive objects in the second virtual scene and have spatial or functional linkage relationships.

[0098] In some embodiments, the single interaction freeze time can be understood as the freeze duration generated when the first virtual character performs a target interaction action once. In some embodiments, the multiple interaction freeze time can be understood as the total freeze duration generated when the first virtual character performs multiple target interaction actions consecutively.

[0099] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description, namely: First, in practical applications, the action execution time is defined, and it is clarified that the action execution time is the interaction freeze time that the first virtual character enters a frozen state when it performs the target interaction action through the interaction object in the second virtual scene. The interaction freeze time is a fixed interaction mechanism parameter in the game, which can truly reflect the no-operation window when the first virtual character completes the interaction action.

[0100] When calculating the target movement distance of the second virtual character, it is necessary to first determine the scene layout, that is, to determine whether there are other interactive objects associated with the interactive object in the area where the interactive object is located in the second virtual scene.

[0101] When it is determined that there are no associated interactive objects in the area, it means that the interactive objects in this second virtual scene are independently laid out individuals. The first virtual character only needs to perform the target interactive action once to complete the corresponding operation, generating one interaction freeze time. At this time, the single interaction freeze time is used as the calculation basis. The single interaction freeze time is multiplied by the movement speed of the second virtual character, and the resulting value is the target movement distance that the second virtual character can move in this scene.

[0102] When it is determined that there are related interactive objects in the area, it means that multiple interactive objects in the area form a linked layout. The first virtual character needs to perform multiple target interactive actions in succession, which will generate multiple interaction freeze times. At this time, the sum of multiple interaction freeze times needs to be used as the basis for calculation, and then multiplied by the movement speed of the second virtual character to finally obtain the target movement distance adapted to the linked scene.

[0103] Understandably, by determining the scene association state, different calculation rules are divided so that the calculation results of the target movement distance conform to the scene characteristics of different layouts. To a certain extent, this ensures that the calculation values ​​in each scene can match the movement ability of the second virtual character in actual combat, providing real and accurate basic data support for the determination of interaction difficulty.

[0104] Thus, in this embodiment, the action execution time is defined as the interaction freeze time generated by the first virtual character performing the target interaction action. Based on whether there are other related interaction objects in the area where the interaction object is located in the second virtual scene, the target movement distance is calculated by combining the single interaction freeze time or multiple interaction freeze times with the movement speed of the second virtual character, thereby achieving adaptive calculation of the target movement distance of the second virtual character. Compared to the scheme in virtual scene processing that uses a fixed duration to uniformly calculate the character's movement distance without adjusting the calculation rules according to the actual layout state of the interaction objects, this method allows the calculation process of the target movement distance to match the actual distribution and linkage state of the interaction objects within the virtual scene. This improves the fit and accuracy of the target movement distance calculation results to a certain extent. Furthermore, calculating the target movement distance based on quantitative game data such as the interaction freeze time and the movement speed of the second virtual character ensures the reliability of the target movement distance to a certain extent. In addition, the differentiated calculation rules can adapt to various interaction object scenarios, improving the comprehensiveness and applicability of the target movement distance calculation to a certain extent, and providing stable and reliable data support for accurately determining the difficulty of virtual scene interactions.

[0105] In some embodiments provided in this application, the above-mentioned determination of the interaction difficulty of the second virtual scene based on the relationship between the target movement distance and the reference distances corresponding to the two collision objects in the second virtual scene includes at least one of the following steps: when the product of the target movement distance and the preset multiple is less than the smaller of the two reference distances, the interaction difficulty of the second virtual scene is determined to be the first difficulty level; If the product of the target movement distance and the preset multiple is greater than one of the two reference distances and less than the other, the interaction difficulty of the second virtual scene is determined to be the second difficulty level. If both reference distances are less than the preset distance threshold, the interaction difficulty of the second virtual scene is determined to be the third difficulty level.

[0106] Specifically, the determination of the difficulty of virtual scene interaction lacks a unified quantitative standard, making it impossible to classify based on the spatial distance characteristics of the scene. The determination process relies on subjective judgment, resulting in a discrepancy between the difficulty level classification and the actual interaction characteristics of the scene, thus failing to meet the objective requirements of virtual scene interaction difficulty assessment. Based on the above problems, in some embodiments provided in this application, the target movement distance magnified by a preset factor is used as the determination basis. Combined with the comparison results of the reference distances corresponding to the two colliding objects in the second virtual scene, three difficulty levels—a first difficulty level, a second difficulty level, and a third difficulty level—are classified into three quantitative scenarios. Simultaneously, a preset distance threshold is added to determine the special case where both reference distances are too short, thus constructing a standardized quantitative classification scheme for the difficulty of second virtual scene interaction.

[0107] In some embodiments, the preset multiplier can be understood as a pre-defined numerical multiplier used to magnify the target's movement distance, in order to perform magnification calculations on the target's movement distance. For example, magnifying the target's movement distance by 10 times.

[0108] In some embodiments, the preset distance threshold can be understood as a pre-set numerical standard used to determine whether two reference distances are too short. For example, the preset distance threshold can be set as the length of the target movement distance.

[0109] In some embodiments, the first difficulty level can be understood as the highest level of interaction difficulty in the second virtual scene, representing that the scene has the highest level of interactive operation difficulty. In some embodiments, the second difficulty level can be understood as a medium level of interaction difficulty in the second virtual scene, representing that the scene has a medium level of interactive operation difficulty. In some embodiments, the third difficulty level can be understood as a low level of interaction difficulty in the second virtual scene, representing that the scene has a low level of interactive operation difficulty.

[0110] In some embodiments, the size relationship can be understood as the numerical comparison result formed between the target movement distance magnified by a preset factor and two reference distances.

[0111] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: The difficulty of interaction in the second virtual scene is determined based on a quantitative comparison of spatial distance. The judgment process follows preset numerical rules and has no subjective intervention. First, the product of the target's movement distance and a preset multiple is calculated to obtain the magnified target movement value, which is then compared with the reference distances corresponding to the two collision objects in the second virtual scene.

[0112] When the magnified target movement value is less than the smaller of the two reference distances, the interaction difficulty of the second virtual scene can be determined to be at the first difficulty level. In this case, the surrounding movement distance of the two colliding objects in the second virtual scene is much greater than the movement distance of the virtual character during the execution of the interaction action. The virtual character has a greater spatial advantage in completing the interaction action in the scene, and the difficulty of the interaction operation reaches the highest level.

[0113] When the magnified target movement value is greater than one of the two reference distances while being less than the other, the interaction difficulty of the second virtual scene is determined to be at the second difficulty level. In this case, the orbital movement distances of the two colliding objects in the second virtual scene are of different lengths, the virtual character has only a partial advantage in the interaction space, and the difficulty of the interaction operation is at a medium level.

[0114] Furthermore, when both reference distances are less than a preset distance threshold, the interaction difficulty of the second virtual scene is determined to be at the third difficulty level. In this case, the orbital movement distances of the two colliding objects in the second virtual scene are both too short, the space constraints for the virtual character to complete the interaction are small, and the difficulty of the interaction operation is at a low level.

[0115] The three judgment logics mentioned above are independent of each other and cover different spatial features of the second virtual scene. The difficulty level is completed through fixed numerical calculation and comparison rules. Quantitative indicators are used to replace subjective judgment to ensure that each second virtual scene can be classified into the corresponding difficulty level and match the interaction difficulty attributes of different spatial feature scenes.

[0116] Thus, in this embodiment, based on the comparison results of the product of the target movement distance and a preset multiple, the reference distances corresponding to the two collision objects in the second virtual scene, and the comparison results of the two reference distances and preset distance thresholds, the interaction difficulty of the second virtual scene is determined in three independent scenarios, correspondingly classifying a first difficulty level, a second difficulty level, and a third difficulty level. This achieves standardized quantitative grading of the interaction difficulty of the second virtual scene. Compared to the evaluation method that relies on subjective judgment of virtual scene interaction difficulty based on human experience, this effectively reduces the dependence of the virtual scene interaction difficulty determination process on subjective human judgment, thereby improving the objectivity and accuracy of virtual scene interaction difficulty assessment to a certain extent. Moreover, the division of interaction difficulty levels is based on quantitative data such as target movement distance, reference distance, preset multiple, and preset distance threshold, providing reliable data support for the determination of difficulty levels and ensuring the reliability and consistency of the difficulty level determination results to a certain extent. In addition, multiple determination scenarios can adapt to second virtual scenes with different spatial distance characteristics, meeting the interaction difficulty assessment needs of various virtual scenes to a certain extent, and improving the comprehensiveness and practical applicability of virtual scene interaction difficulty determination.

[0117] In some embodiments provided in this application, the method further includes: dividing the multiple second virtual scenes into multiple scene sets according to the second scene information of each second virtual scene; The above-mentioned step of determining a target virtual scene similar to the first virtual scene from a predetermined plurality of second virtual scenes based on the first scene information includes: determining a set of target scenes corresponding to the first scene information from a plurality of scene sets based on the first scene information; The second virtual scene in the target scene set is determined as the target virtual scene.

[0118] Based on the second scene information corresponding to each second game scene, all pre-determined second game scenes are divided into multiple different scene sets. The second game scenes within each scene set possess highly similar feature parameters. By pre-dividing the scene sets, a massive number of reference scenes are categorized according to feature similarity, establishing an efficient index structure for subsequent similarity matching. When a new scene needs to be matched, it is not necessary to traverse all reference scenes; only the set to which the features of the new scene belong needs to be calculated. This quickly narrows the search scope to one or a few sets with similar features, fundamentally solving the problem of large computational load and long time consumption in full-scale retrieval, laying the foundation for rapidly determining the target game scene.

[0119] Specifically, in the process of evaluating the interaction difficulty of virtual scenes, directly filtering similar scenes from all predetermined second virtual scenes results in an excessively large search range, leading to low filtering efficiency. Furthermore, the irregular comparison of all scenes makes it difficult to guarantee the accuracy of similar scene matching, thus reducing the overall efficiency and accuracy of game difficulty evaluation. Based on these problems, in some embodiments provided in this application, the second virtual scenes are first categorized into multiple scene sets according to features. Then, the first scene information of the scene to be evaluated is matched against the corresponding target scene set, and the target virtual scene is determined from the target scene set, achieving efficient and accurate filtering of similar scenes.

[0120] In some embodiments, a scene set can be understood as a group of scenes formed after classifying the second virtual scenes according to a unified feature dimension, where virtual scenes within the same set have highly similar feature attributes. In some embodiments, a target scene set can be understood as a set of scenes that highly match the information features of the first scene, which is a limiting range for selecting target virtual scenes.

[0121] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: Based on the second scene information corresponding to each second virtual scene, all pre-determined second virtual scenes are divided into multiple different scene sets, with the second virtual scenes within each scene set possessing highly similar feature parameters. Then, based on the feature attributes of the first scene information of the first virtual scene, a target scene set matching the first scene information is determined from the divided scene sets. Finally, all second virtual scenes included in the target scene set are identified as target virtual scenes similar to the first virtual scene, achieving preliminary screening of similar scenes.

[0122] Thus, in this embodiment, multiple second virtual scenes are first classified into scene sets based on the second scene information. Then, the target scene set that matches is located from the classified scene sets based on the first scene information. Finally, the second virtual scenes within the target scene set are determined as the target virtual scenes, thereby achieving hierarchical filtering and positioning of target virtual scenes. Compared to directly performing global similarity matching in all second virtual scenes, pre-classifying the second virtual scenes into scene sets can effectively narrow the search range of similar scenes. At the same time, target matching based on scene sets can effectively improve the efficiency of similar scene filtering. Furthermore, the process of uniformly relying on scene information to complete classification and matching can effectively eliminate errors caused by subjective human judgment, thereby improving the objectivity and stability of the target virtual scene determination process to a certain extent. This effectively solves the problems of excessively long similarity retrieval time and low matching accuracy, and thus improves the overall efficiency and reliability of the first virtual scene interaction difficulty assessment to a certain extent, reducing the resource consumption in the scene development and testing stages.

[0123] In some embodiments provided in this application, the multiple scene sets include at least one of multiple first scene sets, multiple second scene sets, and multiple third scene sets; the multiple first scene sets are obtained by dividing the second scene information by two reference distances, the multiple second scene sets are obtained by dividing the second scene information by the first frame point distance and the second frame point distance, and the multiple third scene sets are obtained by dividing the second scene information by two reference distances, the first frame point distance, and the second frame point distance.

[0124] Specifically, in the process of dividing multiple second virtual scenes into scene sets, the lack of a clear scene set classification system and standardized division dimensions makes it impossible to perform hierarchical classification based on different quantitative dimensions of scene spatial characteristics. This results in a lack of specificity and rationality in the division of scene sets, making it difficult to quickly match scene sets with similar characteristics to the first virtual scene, reducing the efficiency and accuracy of similar scene selection, and failing to provide an efficient sample retrieval basis for assessing the difficulty of virtual scene interaction. Based on the above problems, in some embodiments provided in this application, it is clarified that multiple scene sets include at least one of a first scene set, a second scene set, and a third scene set. Furthermore, it is stipulated that the three types of scene sets are divided based on the reference distance of collision objects, the distance to the standpoint of interactive objects, and a combination of the two distances, respectively, constructing a multi-dimensional hierarchical classification system for scene sets, effectively solving the problem of the lack of clear basis for scene set division.

[0125] In some embodiments, the first scene set can be understood as a group of virtual scenes divided according to the reference distances corresponding to collision objects in the second virtual scene, and the spatial scale of all second virtual scenes within the first scene set is highly similar. In some embodiments, the second scene set can be understood as a group of virtual scenes divided according to the distances to stand points corresponding to interactive objects in the second virtual scene, and the overall spatial position characteristics of interactive objects in all second virtual scenes within the second scene set are highly similar.

[0126] In some embodiments, the third scene set can be understood as a group of virtual scenes formed by comprehensively dividing the collision objects in the second virtual scene and the stand distances corresponding to the interactive objects. The spatial and operational comprehensive relative characteristics of all second virtual scenes in the third scene set are highly similar.

[0127] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description, namely: multiple scene sets include at least one of multiple first scene sets, multiple second scene sets, and multiple third scene sets. In practical applications, a single type of scene set can be flexibly selected for division according to the specific needs of virtual scene interaction difficulty assessment, or multiple scene sets can be combined, which improves the adaptability and flexibility of scene classification to a certain extent.

[0128] Meanwhile, the specific criteria for dividing the three types of scene sets are clarified in turn. Multiple first scene sets are divided by the reference distances corresponding to two collision objects in the second virtual scene. This division method focuses on the spatial surrounding size characteristics of collision objects in the virtual scene. Second virtual scenes with similar collision object size characteristics are grouped into the same first scene set to reflect the differences in the spatial barrier structure of the virtual scene.

[0129] Multiple sets of second scenes are obtained by dividing the distance between the first frame point and the second frame point corresponding to the interactive object in the second virtual scene. This division method focuses on the spatial positional relationship characteristics between the interactive object and the specified functional point in the virtual scene, and groups the second virtual scenes with similar interactive object positional characteristics into the same set of second scenes to reflect the differences in the interactive operation position of the virtual scene.

[0130] Multiple sets of third scenes are divided by four features: two reference distances, the distance to the first stand point, and the distance to the second stand point in the second virtual scene. This division method is based on the dual dimensions of collision object size features and interaction object position features. It takes into account both the spatial obstruction structure of the virtual scene and the position attributes of the interaction operation, and groups second virtual scenes with highly similar comprehensive spatial features into the same set of third scenes.

[0131] Thus, in this embodiment, multiple second virtual scenes are divided into multiple scene sets based on the second scene information of the second virtual scene. These scene sets include at least one of multiple first scene sets, multiple second scene sets, and multiple third scene sets. Multiple first scene sets are obtained by dividing the second scene information using two reference distances. Multiple second scene sets are obtained by dividing the second scene information using the first and second frame point distances. Multiple third scene sets are obtained by dividing the second scene information using two reference distances, the first frame point distance, and the second frame point distance. This achieves standardized classification of multiple second virtual scenes based on multi-dimensional spatial features. Compared to schemes that rely on manual experience to divide scene sets or classify scenes using a single simple distance feature, this effectively reduces the dependence of scene set division on subjective human judgment, thereby improving the efficiency and standardization of scene classification to a certain extent. Furthermore, the division of scene sets can be determined based on quantitative spatial feature data such as the reference distance corresponding to the collision object and the frame point distance corresponding to the interaction object, providing reliable data support for scene classification and ensuring the accuracy and rationality of scene set classification to a certain extent.

[0132] Furthermore, the multi-dimensional scene set division can comprehensively cover the spatial characteristics of virtual scenes in asymmetric competitive games, providing a stable and reliable classification basis for target scene set matching and similar virtual scene selection. To a certain extent, it improves the scientificity and objectivity of virtual scene interaction difficulty assessment, reduces the deviation in interaction difficulty assessment caused by unreasonable classification, and lowers the repetitive labor cost of map design adjustment.

[0133] In some embodiments provided in this application, the step of dividing multiple second virtual scenes into multiple scene sets based on the second scene information of each second virtual scene includes: determining a first average distance between two reference distances of the second scene information; Based on the first average distance corresponding to each second virtual scene, multiple sets of first scenes are determined, and the multiple second virtual scenes are divided into multiple sets of first scenes.

[0134] Specifically, when dividing the second virtual scene into scene sets, there is a lack of standardized quantitative criteria. Relying solely on the reference distance of a single collision object for division can easily lead to significant differences in scene features within the scene set, resulting in insufficient scene classification accuracy. This, in turn, reduces the matching efficiency of the target scene set and the filtering effect of similar scenes. To address these issues, in some embodiments provided in this application, a first average distance is calculated based on the second scene information and used as the classification criterion. This standardizes the division of multiple second virtual scenes into corresponding scene sets, improving the accuracy of scene classification.

[0135] In some embodiments, the first average distance can be understood as the arithmetic mean of the reference distances corresponding to the two colliding objects in the second virtual scene, used to characterize the overall spatial scale of the second virtual scene.

[0136] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description, namely: when dividing the set to which the second virtual scene belongs, the second scene information is used as the basic data, and standardized index calculation and classification operations are performed for each independent second virtual scene.

[0137] First, extract the second scene information of the current second virtual scene and obtain basic quantitative data such as the reference distances corresponding to the two collision objects. The two reference distances can characterize the features of the virtual scene from the dimension of the spatial scale of the collision objects.

[0138] Subsequently, a quantitative index of the first average distance is calculated based on two reference distances, and this first average distance is used as the basis for dividing the first scene set to meet the classification requirements of the first scene set. Specifically, the first average distance index is calculated by adding the two reference distances and dividing by two to obtain an average value that can represent the overall collision space size of the scene, i.e., the first average distance. The first average distance eliminates the local deviation of the size of individual collision objects and fully reflects the spatial scale of the overall collision objects in the scene.

[0139] After calculating the aforementioned quantitative indicators, the first scene set is divided based on the first average distance. Then, all second virtual scenes are categorized according to their numerical ranges, ensuring a high degree of similarity in scene spatial scale within the same first scene set. After categorization, all second virtual scenes are assigned to their corresponding first scene sets, achieving standardized classification of the reference virtual scenes.

[0140] Thus, in this embodiment, a first average distance quantification index is first calculated based on the second scene information of the second virtual scene. Then, the first average distance is used as the dividing criterion to classify multiple second virtual scenes into corresponding scene sets, thereby achieving standardized classification of reference virtual scenes. Compared to relying on human experience to classify scene categories or using only a single simple distance feature for scene classification, using the first average distance as the basis can integrate second virtual scenes with similar spatial scales, thereby improving the standardization and rationality of the first scene set division to a certain extent. This effectively eliminates classification bias caused by subjective human judgment, solves the problems of ambiguous classification criteria for second virtual scenes and excessive differences in features within scene sets, and thus improves the accuracy and processing efficiency of target scene set matching to a certain extent, ensuring the objectivity and stability of virtual scene interaction difficulty assessment.

[0141] In some embodiments provided in this application, the step of dividing multiple second virtual scenes into multiple scene sets based on the second scene information of each second virtual scene includes: determining the first frame distance and the second average distance of the second frame distance of the second scene information; Based on the second average distance corresponding to each second virtual scene, multiple sets of second scenes are determined, and the multiple second virtual scenes are divided into multiple sets of second scenes.

[0142] Specifically, in the process of dividing the second scene set based on the distance of interactive object standpoints, using only the distance of a single standpoint as the dividing criterion cannot comprehensively reflect the overall spatial positional relationship between the interactive object and two specified functional points within the scene. Furthermore, the lack of a unified quantitative dividing standard makes it difficult to reasonably classify multiple second virtual scenes according to the positional characteristics of the interactive objects, resulting in chaotic classification of the second scene set. The positional characteristics of scenes within the second scene set vary significantly, making it impossible to accurately match the spatial positional attributes of the scenes. This reduces the efficiency and accuracy of target scene set retrieval and similar virtual scene filtering, thereby affecting the reliability and scientific validity of the virtual scene interaction difficulty assessment results. Based on the above problems, in some embodiments provided in this application, by calculating the second average distance between the first standpoint distance and the second standpoint distance of the second scene information, and using this comprehensive average distance as a unified quantitative standard to determine multiple second scene sets, all second virtual scenes are then divided into corresponding second scene sets according to the second average distance. This solves the problems of lack of standardized basis and unreasonable classification in the division of second scene sets.

[0143] In some embodiments, the second average distance can be understood as the arithmetic mean of the distance between the first frame point and the distance between the second frame point in the second scene information, used to characterize the overall spatial location features of the interactive object in the second virtual scene.

[0144] To more clearly illustrate the virtual scene processing method provided in this application embodiment, please refer to the following exemplary description: First, all predetermined second virtual scenes are traversed. For each independent second virtual scene, the first frame distance and the second frame distance are extracted from the corresponding second scene information. The arithmetic mean of the extracted first frame distance and the second frame distance is calculated to obtain the second average distance of the second virtual scene. The second average distance integrates the position association information between the interactive object and two specified functional points, and can objectively and comprehensively reflect the overall spatial position characteristics of the interactive object in the virtual scene. It effectively eliminates the local deviation of the single frame distance and improves the completeness and representativeness of the position feature representation.

[0145] After calculating the second average distance for all second virtual scenes, multiple sets of second scenes are determined according to the numerical distribution pattern and range of all second average distances, following a unified and fixed numerical interval rule. Each numerical interval corresponds to an independent set of second scenes, ensuring that the classification criteria for all sets of second scenes remain consistent and guaranteeing the standardization and fairness of the classification rules.

[0146] Finally, each second virtual scene is categorized into its corresponding second scene set according to the numerical range of its corresponding second average distance. This ensures that all second virtual scenes within the same second scene set maintain a high degree of similarity in the overall spatial location characteristics of the interactive objects, while different second scene sets correspond to different levels of location characteristics, forming a hierarchical and feature-unified second scene set system.

[0147] Thus, in this embodiment, the second average distance between the first and second frame point distances of the second scene information is first determined. Multiple sets of second scenes are then determined based on the second average distance corresponding to each second virtual scene, and these multiple virtual scenes are divided into multiple sets. This achieves standardized classification of multiple virtual scenes based on the frame point distance feature of the interactive objects. Compared to the scheme of classifying scenes using a simple feature of a single frame point distance, this effectively reduces the reliance on subjective human judgment in scene set division, thereby improving the efficiency and standardization of scene classification to a certain extent. Furthermore, the division of the second scene sets can be determined based on the quantitative data of the second average distance calculated from the first and second frame point distances, ensuring that the scene classification results are supported by objective data and guaranteeing the accuracy and rationality of scene set classification to a certain extent.

[0148] In addition, the second average distance can comprehensively reflect the overall spatial location characteristics of the interactive object and the two specified functional points, effectively ensuring the consistency of the interactive location characteristics of the second virtual scene within the same second scene set. It provides a stable and reliable basis for the location feature classification for target scene set matching and similar virtual scene screening, which to a certain extent improves the scientificity and objectivity of virtual scene interaction difficulty assessment, reduces the deviation in interaction difficulty assessment caused by unreasonable classification, and reduces the repetitive labor cost of game map design adjustment.

[0149] In some embodiments provided in this application, the step of dividing multiple second virtual scenes into multiple scene sets based on the second scene information of each second virtual scene includes: determining a first average distance between two reference distances of the second scene information, and a second average distance between the first frame distance and the second frame distance of the second scene information; Determine the first ratio between the first average distance and the corresponding second average distance of the second virtual scene; Based on the first ratio corresponding to each second virtual scene, a set of multiple third scenes is determined, and the multiple second virtual scenes are divided into multiple sets of third scenes.

[0150] Specifically, in the process of comprehensively dividing the third scene set based on the reference distance corresponding to the collision object and the stand distance corresponding to the interaction object, using the average distance of a single dimension as the division basis fails to reflect the proportional relationship between the size characteristics of the collision object and the positional characteristics of the interaction object. Furthermore, the lack of a comprehensive and quantitative unified division standard makes it difficult to reasonably classify multiple second virtual scenes according to their overall comprehensive spatial characteristics. This results in an unreasonable classification of the third scene set, with significant differences in the comprehensive spatial characteristics of scenes within the third scene set, making it impossible to match the comprehensive spatial attributes of the scenes. This reduces the accuracy of target scene set retrieval and similar virtual scene selection, thereby affecting the accuracy and scientific nature of virtual scene interaction difficulty assessment. Based on the above problems, in some embodiments provided in this application, a first average distance and a second average distance of the second virtual scene are calculated respectively, and then a first ratio of the first average distance to the second average distance is calculated. The first ratio is used as a quantitative standard to determine multiple third scene sets. Finally, all second virtual scenes are divided into the corresponding third scene sets according to the first ratio. This, to a certain extent, solves the problem of the lack of a comprehensive quantitative division basis and unreasonable classification of the third scene set.

[0151] In some embodiments, the first ratio can be understood as the calculated ratio of the first average distance to the second average distance in the second virtual scene, used to comprehensively reflect the correlation characteristics between scene collision size and interaction position.

[0152] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: First, traverse the predetermined second virtual scene. For each independent second virtual scene, extract the reference distances corresponding to the two collision objects and the first and second stand distances corresponding to the interaction object from the corresponding second scene information, so as to fully characterize the spatial attributes of the second virtual scene from the two dimensions of collision object size and interaction object position.

[0153] Subsequently, the two extracted reference distances are arithmetically averaged. The resulting first average distance can comprehensively reflect the overall surrounding size characteristics of the two colliding objects in the second virtual scene, eliminating the local deviation of the reference distance of a single colliding object and improving the overall representation of the scene collision size.

[0154] Meanwhile, by arithmetically averaging the distances of the first and second standpoints, the resulting second average distance can comprehensively reflect the overall spatial positional relationship between the interactive object and the two designated functional points within the second virtual scene, thus compensating for the deficiency that the distance of a single standpoint can only reflect a one-sided positional relationship.

[0155] After calculating the above two types of average distances, the first average distance is used as the dividend and the second average distance is used as the divisor to perform a division operation to obtain the first ratio of the second virtual scene. The first ratio can comprehensively reflect the correlation ratio between the size of the scene collision object and the position of the interaction object. It retains the feature information of the collision size to a certain extent and takes into account the feature information of the interaction position, thus realizing the fusion representation of the two-dimensional spatial features.

[0156] After obtaining the first ratio of all second virtual scenes, based on the numerical distribution range and distribution pattern of all first ratios, multiple sets of third scenes are determined according to a unified and fixed numerical interval rule. Each numerical interval corresponds to an independent set of third scenes to ensure that the division criteria of all sets of third scenes are consistent.

[0157] Finally, each second virtual scene is classified into the corresponding third scene set according to the numerical range of the corresponding first ratio, so that all second virtual scenes in the same third scene set maintain a high degree of similarity in the comprehensive spatial features of collision object size and interaction object position. Different third scene sets correspond to different levels of comprehensive spatial features, forming a third scene set system with clear hierarchy and unified features.

[0158] Thus, in this embodiment, the first average distance between two reference distances of the second scene information and the second average distance between the first frame distance and the second frame distance are first determined. Then, the first ratio between the first average distance and the second average distance of the second virtual scene is determined. Based on the first ratio of each second virtual scene, multiple third scene sets are determined, and the multiple second virtual scenes are divided into multiple third scene sets. This achieves standardized classification of multiple second virtual scenes based on the comprehensive features of collision object size and interaction object position. Compared with the scheme of scene classification based on a single distance feature, this effectively reduces the dependence of scene set division on human subjective judgment, thereby improving the efficiency and standardization of scene classification to a certain extent. Furthermore, the division of the third scene set is determined based on the quantitative data of the first ratio calculated from the first average distance and the second average distance, providing objective data support for scene classification and ensuring the accuracy and rationality of scene set classification to a certain extent.

[0159] Furthermore, the first ratio can comprehensively reflect the correlation characteristics between the overall size of the colliding object and the overall position of the interactive object, which to a certain extent ensures the consistency of the second virtual scene in the same third scene set in terms of comprehensive spatial characteristics. This provides a stable and reliable comprehensive feature classification basis for target scene set matching and similar virtual scene screening, thereby improving the scientificity and objectivity of virtual scene interaction difficulty assessment to a certain extent, reducing the deviation in interaction difficulty assessment caused by unreasonable classification, and reducing the repetitive labor cost of game map design adjustment.

[0160] In some embodiments provided in this application, a plurality of second virtual scenes are divided into a plurality of scene sets based on at least one of a first average distance, a second average distance, and a first ratio for each second virtual scene, including at least one of the following steps: determining a plurality of first scene sets based on the first average distance of each second virtual scene, and dividing the plurality of second virtual scenes into a plurality of first scene sets; Based on the second average distance of each second virtual scene, multiple sets of second scenes are determined, and the multiple second virtual scenes are divided into multiple sets of second scenes; Based on the first ratio of each second virtual scene, a plurality of third scene sets are determined, and the plurality of second virtual scenes are divided into a plurality of third scene sets.

[0161] Specifically, when dividing a second virtual scene into scene sets based on second scene information, the differences in features within each scene set are significant, resulting in poor adaptability and selectivity of scene set division, making it difficult to meet the diverse needs of virtual scene evaluation. To address these issues, some embodiments provided in this application use a first average distance, a second average distance, and a first ratio as independent division criteria to generate a first scene set, a second scene set, and a third scene set. Single or multiple scene set combinations can be selected to achieve multi-dimensional standardized scene classification, improving the applicability of scene classification.

[0162] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: when classifying the second virtual scene, the scene set is divided in an independent and standardized manner based on the first average distance, the second average distance, and the first ratio. In practical applications, one or more division methods can be flexibly selected and combined according to actual evaluation needs.

[0163] The first division method is based on the first average distance, which represents the overall spatial scale of the second virtual scene. First, the first average distance value of the second virtual scene is calculated, and then... (The sentence is incomplete and requires more context to translate accurately.) Figure 4The numerical distribution pattern is analyzed using methods such as quantiles. Continuous and non-overlapping numerical intervals are set. Each numerical interval corresponds to an independent first scene set. Each second virtual scene is assigned to the corresponding first scene set according to the interval to which the first average distance belongs, ensuring that virtual scenes within the same first scene set maintain a high degree of consistency in spatial scale.

[0164] The second division method is based on the second average distance, which represents the overall spatial location characteristics of the interactive objects in the second virtual scene. First, the second average distance values ​​of all second virtual scenes are collected, and a reasonable range is set in combination with the numerical distribution characteristics. Each range corresponds to a set of second scenes. The second virtual scenes are assigned to the set of second scenes in the corresponding range according to the second average distance, so that the scenes in the same set of second scenes maintain the same overall spatial location characteristics.

[0165] The third division method is based on the first ratio, which balances the relative characteristics of spatial scale and operational efficiency. First, the first ratio value of all second virtual scenes is calculated. According to the distribution law of the ratio, the numerical intervals are divided. Each interval corresponds to a third scene set. The second virtual scenes are assigned to the corresponding third scene set according to the first ratio, so that the scenes in the same third scene set remain similar in terms of comprehensive relative characteristics.

[0166] Thus, in this embodiment, the first average distance, the second average distance, and the first ratio are used as independent dividing criteria to generate corresponding first scene sets, second scene sets, and third scene sets, respectively. This supports the use of a single dividing method or a combination of multiple dividing methods, thereby achieving multi-dimensional standardized classification of the second virtual scene. Compared to relying on human experience to classify scene categories or using only a single simple feature for scene classification, the setting of multiple independent dividing methods effectively improves the flexibility and adaptability of scene classification. Furthermore, the numerically quantified dividing criteria effectively eliminate classification bias caused by subjective human judgment, solving the problems of single-dimensional scene classification and excessive differences in features within scene sets in traditional methods. This, to a certain extent, improves the efficiency and accuracy of target scene set matching, ensures the objectivity and stability of virtual scene interaction difficulty assessment, and optimizes the fairness of game battles and the player's gaming experience.

[0167] In some embodiments provided in this application, the step of determining the target scene set corresponding to the first scene information from multiple scene sets based on the first scene information includes: determining the first target scene set corresponding to the first virtual scene from multiple first scene sets based on the third average distance of two reference distances of the first scene information; Based on the fourth average distance between the first and second frame points in the first scene information, determine the set of second target scenes corresponding to the first virtual scene from multiple sets of second scenes; Based on the second ratio of the third average distance to the fourth average distance, the set of third target scenes corresponding to the first virtual scene is determined from multiple sets of third scenes; The union of the first set of target scenes, the second set of target scenes, and the third set of target scenes is determined as the target scene set.

[0168] Specifically, in the virtual scene similarity matching process, matching a single scene set cannot comprehensively filter similar scenes, easily missing high-quality reference samples, resulting in incomplete screening of target virtual scenes and affecting the effectiveness of interaction difficulty prediction. Based on the above problems, in some embodiments provided in this application, three integrated features—a third average distance, a fourth average distance, and a second ratio of the third average distance to the fourth average distance—are calculated based on the first scene information of the first virtual scene. These features are then matched against the first target scene set, the second target scene set, and the third target scene set, respectively. The union of the three target scene sets is taken as the target scene set, achieving multi-dimensional similarity scene screening.

[0169] In some embodiments, the third average distance can be understood as the arithmetic mean of the reference distances corresponding to the two colliding objects in the first virtual scene, used to characterize the overall spatial scale of the first virtual scene. In some embodiments, the fourth average distance can be understood as the arithmetic mean of the distance between the first frame point and the distance between the second frame point in the first scene information in the first virtual scene, used to characterize the overall spatial position characteristics of the interactive objects in the first virtual scene.

[0170] In some embodiments, the second ratio can be understood as the ratio of the third average distance to the fourth average distance in the first virtual scene, used to characterize the relative relationship between the spatial scale and operational efficiency of the first virtual scene.

[0171] In some embodiments, the first target scene set can be understood as a first scene set matching a third average distance, with the internal scene space scale being highly similar to that of the first virtual scene. In some embodiments, the second target scene set can be understood as a second scene set matching a fourth average distance, with the internal scene setup efficiency being highly similar to that of the first virtual scene.

[0172] In some embodiments, the third target scene set can be understood as a third scene set that matches the second ratio, and the comprehensive features of the internal scenes are highly similar to those of the first virtual scene.

[0173] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: when filtering target virtual scenes, the first scene information of the first virtual scene is used as the basic data, and the calculation of three indicators and the matching of three types of scene sets are completed in sequence, and finally, a highly similar target virtual scene is obtained.

[0174] First, the reference distances corresponding to the two colliding objects in the first scene information are extracted. The two reference distances are added together and divided by two to obtain the third average distance, which can fully reflect the overall spatial scale of the first virtual scene. Based on the third average distance, the set with matching numerical ranges is searched in the pre-divided sets of multiple first scenes. This set is determined as the first target scene set. All second virtual scenes in the first target scene set are highly similar to the first virtual scene in terms of spatial scale.

[0175] Next, the distances of the first and second frame points in the first scene information are extracted, the two values ​​are added together and divided by two to obtain the fourth average distance, which can comprehensively reflect the overall spatial position characteristics of the interactive objects in the first virtual scene. Based on the fourth average distance, in the multiple pre-divided sets of second scenes, the set with matching numerical intervals is searched and the set is determined as the second target scene set. All second virtual scenes in the second target scene set are highly similar to the first virtual scene in terms of overall spatial position characteristics.

[0176] Subsequently, the obtained third average distance is used as the numerator and the fourth average distance is used as the denominator. The two values ​​are divided to obtain the second ratio. This second ratio can balance the relative relationship between spatial scale and operational efficiency and reflect the comprehensive characteristics of the scene. Based on this second ratio, in the multiple pre-divided third scene sets, the set with matching numerical intervals is searched and the set is determined as the third target scene set. All second virtual scenes in the third target scene set are highly similar to the first virtual scene in terms of comprehensive relative characteristics.

[0177] After determining the three sets of target scenarios, the first set of target scenarios, the second set of target scenarios, and the third set of target scenarios are merged, and duplicate scenario data is removed to obtain the target scenario set.

[0178] Thus, in this embodiment, the first, second, and third target scene sets are matched based on three quantitative indicators: the third average distance, the fourth average distance, and the second ratio. The union of these three target scene sets is then used to determine the target scene set, thereby achieving multi-dimensional similarity scene retrieval for the scene to be evaluated. Compared to schemes that rely solely on a single feature dimension for global retrieval, the multi-dimensional parallel matching method effectively narrows the retrieval range of similar scenes. The unified quantitative matching standard eliminates, to some extent, the retrieval bias caused by subjective human judgment, solving the problems of single scene matching dimensions, excessively large retrieval range, and insufficient retrieval accuracy. This improves the efficiency and accuracy of target virtual scene screening and difficulty prediction, thereby ensuring the fairness of game battles and enhancing the player's gaming experience to a certain extent.

[0179] In some embodiments provided in this application, the scene information further includes scene images of the virtual scene and scene description information of the virtual scene; the above-mentioned determination of the second virtual scene in the target scene set as the target virtual scene includes: encoding the first scene information and the second scene information of each second virtual scene in the target scene set respectively to obtain the first scene encoding information of the first scene information and the second scene encoding information of each second scene information in the target scene set; Calculate the similarity between the first scene encoding information and each second scene encoding information to obtain the first similarity between each second virtual scene and the first virtual scene in the target scene set; Based on the first similarity of each second virtual scene, the target virtual scene in the target scene set is determined.

[0180] Specifically, the target scene set includes multiple second virtual scenes. The feature matching degrees between different virtual scenes and the first virtual scene vary. Directly using all scenes in the target scene set as target virtual scenes results in invalid data with low matching degrees, leading to inconsistent quality of reference samples and affecting the prediction of interaction difficulty. To address these issues, some embodiments provided in this application expand the dimension of scene information to include scene images and scene description information. Standardized encoding processes are performed on the first scene information and the second scene information of each second virtual scene to generate scene encoding information with a unified dimension. Then, by calculating the similarity between the encoded information, an objective and accurate first similarity degree is obtained. Based on the first similarity degree value, virtual scenes with high matching degrees are selected as target virtual scenes, improving the reliability of the reference data.

[0181] In some embodiments, the first similarity can be understood as a numerical value used to quantify the feature matching degree between the second virtual scene and the first virtual scene. The magnitude of the numerical value can reflect the level of scene similarity. The higher the first similarity value, the closer the features of the corresponding second virtual scene are to those of the first virtual scene.

[0182] In some embodiments, encoding processing can be understood as the process of converting non-standardized scene information into a unified format numerical code, thereby realizing a unified digital expression of scene features to eliminate information differences and improve computational accuracy.

[0183] In some embodiments, the first scene encoding information can be understood as a standardized numerical code obtained after encoding the first scene information, including all features of the first virtual scene. In some embodiments, the second scene encoding information can be understood as a standardized numerical code obtained after encoding the second scene information, and resides in the same feature space as the first scene encoding information.

[0184] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description, namely: the scene information, in addition to the original spatial feature data, also includes scene images and scene description information of the virtual scene.

[0185] When determining the target virtual scene, the complete first scene information corresponding to the first virtual scene to be evaluated is first extracted. The first scene information includes quantitative features such as the scale of scene collision objects and the distance of the stand-up operation. At the same time, each second virtual scene in the target scene set is traversed, and the complete second scene information of the corresponding dimension is extracted one by one. All second scene information is completely consistent with the first scene information in terms of feature dimensions, data format and calculation logic, which ensures the fairness and accuracy of similarity calculation.

[0186] Subsequently, a fixed-parameter encoding model is used to transform the first scene information into fixed-dimensional first scene encoded information to eliminate format differences and dimensional interference in the original data. Simultaneously, for each second virtual scene in the target scene set, the same fixed-parameter encoding model is used to transform the second scene information of the second virtual scene into corresponding second scene encoded information. The second scene encoded information and the first scene encoded information reside in the same feature space, with unified feature dimensions and numerical rules, allowing for direct similarity calculation. Furthermore, the above encoding process is repeatable; the same scene information can be processed multiple times to obtain consistent encoding results, effectively ensuring the accuracy of similarity calculation.

[0187] After encoding, the similarity between the encoded information of the first scene and the encoded information of each second scene in the target scene set is calculated. The result of each calculation is the first similarity between the corresponding second virtual scene and the first virtual scene. The higher the first similarity value, the closer the features of the corresponding second virtual scene are to the first virtual scene, and the higher the reference value of the interaction difficulty provided to the first virtual scene.

[0188] Finally, after completing the first similarity calculation for all second virtual scenes, based on the calculated first similarity values, second virtual scenes that meet the requirements are selected from the target scene set according to the established filtering rules and determined as target virtual scenes, which are used to provide a reference for predicting the interaction difficulty of the first virtual scene.

[0189] In some embodiments, the filtering rules are uniform and fixed, but can be flexibly adjusted according to actual evaluation needs to ensure that the selected target virtual scenes all have a high degree of feature matching.

[0190] In some embodiments, all second virtual scenes in the target scene set can be sorted from high to low according to the first similarity value, and a fixed number of virtual scenes at the top of the sort can be selected as target virtual scenes.

[0191] In some embodiments, a first similarity threshold can be set, and virtual scenes with values ​​greater than the threshold can be selected as target virtual scenes.

[0192] The target virtual scenes obtained after screening can be used as input data for the difficulty prediction model, providing reliable reference support for the interaction difficulty assessment of the first virtual scene.

[0193] Understandably, standardized coding processes can transform the diverse features of a scene into a unified and computable numerical vector, effectively eliminating computational biases caused by subjective human judgment. The unified structure of scene coding information ensures the consistency and fairness of similarity calculation to a certain extent. Furthermore, selecting target virtual scenes based on similarity improves the fit between the selection results and the features of the first virtual scene to a certain extent.

[0194] Furthermore, the comparison based on encoded vectors effectively avoids the problem of one-sided similarity judgment caused by single feature comparison, and to a certain extent solves the problem of insufficient accuracy in scene similarity calculation, ensuring the stability and accuracy of target virtual scene selection, and thus improving the scientificity and objectivity of the first virtual scene interaction difficulty assessment to a certain extent.

[0195] Thus, in this embodiment, standardized scene encoding information is generated by encoding the first scene information and the second scene information of each second virtual scene in the target scene set. Then, a similarity calculation is performed based on the scene encoding information to obtain a first similarity degree. Finally, the target virtual scene is determined based on the first similarity degree, thereby achieving quantitative calculation of the similarity between scenes and selection of target scenes. Compared to directly using all second virtual scenes in the target scene set, calculating the first similarity degree based on the first and second scene information effectively eliminates the selection bias caused by subjective human judgment. Furthermore, by selecting and retaining second virtual scenes that match the features of the first virtual scene based on the first similarity degree, and eliminating invalid reference scenes with low matching degree, the problem of scene redundancy and insufficient selection accuracy within the target scene set is effectively solved. This, to a certain extent, improves the accuracy and objectivity of the interaction difficulty assessment of the first virtual scene, ensuring the fairness of game battles.

[0196] In some embodiments provided in this application, the above-described steps of encoding the first scene information and the second scene information of each second virtual scene in the target scene set to obtain the first scene encoding information of the first scene information and the second scene encoding information of each second scene information in the target scene set include: encoding the scene image and scene description information of the first virtual scene and the scene image and scene description information of each second virtual scene in the target scene set to obtain the first image encoding representation and the first description information encoding representation of the first virtual scene, and the second image encoding representation and the second description information encoding representation of each second virtual scene; The first image coding representation and the first description information coding representation are spliced ​​together to obtain the first scene coding information; The second image encoding representation and the second description information encoding representation of each second virtual scene are spliced ​​together to obtain the second scene encoding information of each second scene.

[0197] Specifically, scene information includes two types of heterogeneous data: visual images and text descriptions. Encoding a single modality cannot fully extract the features of the scene, resulting in scene encoding information that cannot comprehensively reflect the attributes of the virtual scene. This leads to significant deviations in similarity calculation results, affecting the effectiveness of similar scene selection. To address these issues, some embodiments provided in this application split the scene information into scene images and scene descriptions, encode them separately, generate corresponding coded representations, and then concatenate them to obtain scene encoding information that includes both visual and textual features, thus improving the accuracy of scene similarity calculation.

[0198] In some embodiments, the scene image of the first virtual scene can be understood as the visual image data of the first virtual scene, used to reflect the visual layout and structural form of the scene. In some embodiments, the scene description information of the first virtual scene can be understood as the text description data of the first virtual scene, used to describe the type, location, and other attributes of the virtual scene.

[0199] In some embodiments, the scene image of the second virtual scene can be understood as the visual image data of the second virtual scene, and is consistent with the type of the first scene image. In some embodiments, the scene description information of the second virtual scene can be understood as the text description data of the second virtual scene, and is consistent with the type of the first scene description information.

[0200] In some embodiments, encoding processing can be understood as the process of converting image and text data into standardized numerical representations to unify data formats and realize the digital expression of scene features.

[0201] In some embodiments, the first image encoding representation can be understood as a numerical representation generated after encoding the scene image of the first virtual scene, representing the visual features of the scene.

[0202] In some embodiments, the first description information encoding representation can be understood as a numerical representation generated after encoding the scene description information of the first virtual scene, representing the semantic features of the scene text.

[0203] In some embodiments, the second image encoding representation can be understood as a numerical representation generated after encoding the scene image of the second virtual scene, used for comparing visual features.

[0204] In some embodiments, the second description information encoding representation can be understood as a numerical representation generated after encoding the scene description information of the second virtual scene, which is used to compare text features.

[0205] In some embodiments, the stitching process can be understood as the process of merging the image coding representation and the description information coding representation into a complete scene coding information.

[0206] In some embodiments, the first scene coding information can be understood as the coded data obtained by concatenating the first image coding representation and the first description information coding representation. In some embodiments, the second scene coding information can be understood as the coded data obtained by concatenating the second image coding representation and the second description information coding representation.

[0207] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: the scene information includes scene image and scene description information. When encoding the first scene information and the second scene information to obtain scene encoding information that can reflect the first scene information and the second scene information, the scene information is split into scene image and description information and encoded separately.

[0208] For visual image information, i.e. scene images, a dedicated image coding model is used to extract features and transform vectors in the scene images of the first virtual scene, generating a first image coding representation that reflects the visual features of the first virtual scene, such as spatial structure, layout of collision objects, and distribution of interaction points. At the same time, the scene images of each second virtual scene in the target scene set are processed using the same coding model and parameter settings to generate corresponding second image coding representations. The vector dimension of all image coding representations is kept consistent, effectively ensuring the unified quantitative expression of visual features.

[0209] For text-based descriptive information, i.e. scene description information, a dedicated text encoding model is used to perform semantic extraction and vector transformation on the scene description information of the first virtual scene. This generates a first description information encoding representation that accurately reflects the semantic features of the first virtual scene type, interactive object attributes, regional location, and functional positioning. Simultaneously, the scene description information of each second virtual scene is encoded using the same rules to generate a corresponding second description information encoding representation. The vector dimensions of the text encoding representation and the image encoding representation are mutually adapted.

[0210] After completing the categorized encoding, for the first virtual scene, the first image encoding representation and the first description information encoding representation corresponding to the first virtual scene are merged in a fixed order to form the first scene encoding information that integrates visual features and text semantic features. The first scene encoding information includes all the features of the first virtual scene, effectively eliminating the feature loss problem caused by a single information type.

[0211] For each second virtual scene within the target scene set, the second image encoding representation and the second description information encoding representation corresponding to the second virtual scene are concatenated according to the concatenation order and rules of the first image encoding representation and the first description information encoding representation to generate the corresponding second scene encoding information. The dimensional structure of the second scene encoding information is consistent with that of the first scene encoding information, and it can be directly used for similarity calculation.

[0212] Thus, in this embodiment, by encoding the image information and description information of the virtual scene separately and then concatenating the two types of encoded representations to generate scene encoded information, a multimodal fusion expression of scene features is achieved. Compared to schemes that only encode a single type of scene information, scene image encoding can preserve the visual spatial structure features of the scene, while scene description information encoding can carry the textual semantic features of the scene. The concatenation of the two types of encoded representations can integrate scene features from both visual and semantic dimensions, making the scene encoded information more closely match the actual scene attributes. Furthermore, the unified encoding and concatenation rules ensure the comparability and consistency of different scene encoded information, effectively solving the problem of incomplete coverage of single-modal encoded features leading to deviations in similarity calculation. This improves the accuracy and reliability of the first similarity determination to a certain extent, thereby enhancing the rationality of target virtual scene selection and providing reliable data support for predicting the interaction difficulty of virtual scenes. It also optimizes the overall effect of asymmetric competitive game map balance evaluation.

[0213] In some embodiments provided in this application, determining the target virtual scene in the target scene set based on the first similarity of each second virtual scene includes: determining the second similarity between the first preset number of second virtual scenes and the first virtual scene based on the first scene information and the second scene information of the first preset number of second virtual scenes in the target scene set whose first similarity is higher than the first threshold; A second preset number of second virtual scenes with a second similarity level higher than a second threshold are identified as target virtual scenes.

[0214] Specifically, the target scene set includes a large number of second virtual scenes. Relying on a single similarity score is insufficient to filter scenes with inadequate feature matching to the scene being evaluated, leading to a risk of feature bias in the screening results and making it difficult to guarantee the optimality of the target virtual scenes, thus affecting the reliability of the interaction difficulty prediction. Based on the above problems, in some embodiments provided in this application, after initially screening a first preset number of second virtual scenes based on a first similarity score, a second preset number of target virtual scenes are then screened from the second virtual scenes based on a second similarity score, gradually optimizing the sample quality and balancing screening efficiency and matching accuracy.

[0215] In some embodiments, the first threshold can be understood as a numerical standard for determining whether the second virtual scene has a preliminary similarity in the initial screening. Second virtual scenes with a similarity greater than the first threshold enter the second screening stage. For example, a second virtual scene with a first similarity greater than 0.7 to the first virtual scene is selected to enter the second screening stage.

[0216] In some embodiments, the first preset number can be understood as a threshold for the number of samples to be screened in the preliminary screening stage, used to limit the selection scale of the second virtual scenes. For example, 8. In some embodiments, the first preset number of second virtual scenes can be understood as highly similar candidate virtual scenes obtained through preliminary screening based on the first similarity level.

[0217] In some embodiments, the second threshold can be understood as a numerical standard for determining whether the second virtual scene in the secondary screening has a high degree of similarity. The second virtual scene with a similarity greater than the second threshold is the final target virtual scene. For example, the second virtual scene with a similarity greater than 0.85 to the first virtual scene is selected as the final target virtual scene.

[0218] In some embodiments, the second similarity can be understood as the quantization result of secondary feature matching between a first preset number of second virtual scenes and a first virtual scene.

[0219] In some embodiments, the second preset quantity can be understood as a threshold for the number of samples to be screened in advance for the secondary screening stage, used to limit the selection scale of the final target virtual scene, and the value is less than the first preset quantity. For example, 2.

[0220] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: when filtering target virtual scenes, firstly, all second virtual scenes in the target scene set are traversed, and the first similarity value corresponding to each second virtual scene and the first virtual scene is retrieved. The first similarity value can reflect the basic feature matching between scenes.

[0221] Subsequently, all second virtual scenes are sorted from highest to lowest according to their first similarity score. After sorting, a first preset number of second virtual scenes with a first similarity score higher than a first threshold are selected. These first preset number of second virtual scenes have a high degree of basic feature matching, thus completing the initial candidate sample screening. Understandably, this initial screening narrows down the range of candidate scenes, eliminates invalid reference scenes with significant feature differences, effectively reduces the computational load of secondary screening, and improves the overall scene processing efficiency.

[0222] Subsequently, for each selected second virtual scene, the first scene information of the first virtual scene and the second scene information of the selected second virtual scene are extracted, and the second similarity between each selected second virtual scene and the first virtual scene is calculated.

[0223] After calculating the second similarity of all selected second virtual scenes, they are sorted again from high to low according to their values. A second preset number of second virtual scenes with a second similarity higher than the second threshold are selected. The second preset number is a fixed value that is set in advance and is less than the first preset number, to ensure that the scenes selected in the end have the highest feature matching degree.

[0224] The second virtual scene obtained through the above two-stage screening is the final target virtual scene used for difficulty prediction. It can be directly input into the difficulty prediction model to complete the interaction difficulty assessment of the first virtual scene.

[0225] Thus, in this embodiment, a first preset number of second virtual scenes with high matching degree are selected based on the first similarity degree to complete the initial screening. Then, a second screening is conducted on the first preset number of second virtual scenes based on the second similarity degree to determine the final target virtual scene, thereby realizing a two-level progressive screening of similar scenes. Compared with a single screening method, the initial screening based on the first similarity degree can quickly eliminate scenes with low feature matching degree, narrowing the data processing scope of the second screening and effectively reducing the overall computation time and system resource consumption. The second screening based on the second similarity degree can further improve the accuracy of scene matching based on the initial screening, thereby solving to some extent the problem of insufficient accuracy of single screening or low processing efficiency caused by excessive data volume, and thus improving the accuracy and efficiency of the first virtual scene interaction difficulty assessment to a certain extent.

[0226] In some embodiments provided in this application, the step of determining the second similarity between the first preset number of second virtual scenes and the first virtual scene based on the first scene information and the second scene information of the first preset number of second virtual scenes in the target scene set with a first similarity higher than the first threshold includes: encoding the first scene information and the second scene information of the first preset number of second virtual scenes respectively to obtain the first scene encoding information of the first scene information and the third scene encoding information of the second scene information of the first preset number of second virtual scenes; Calculate the similarity between the first scene encoding information and each third scene encoding information to obtain the second similarity between the first virtual scene and the first virtual scene for a first preset number of second virtual scenes.

[0227] Specifically, when calculating the secondary similarity of the first preset number of second virtual scenes obtained from the initial selection, directly using the original scene information for feature comparison leads to problems such as inconsistent information formats and incomplete feature extraction, resulting in distorted matching results. Furthermore, simple numerical comparison cannot fully capture the deep attributes of the scene, making the calculation results of the secondary similarity lack objectivity and accuracy, thus affecting the effectiveness of the secondary screening. Based on the above problems, in some embodiments provided in this application, the first scene information and the second scene information of the first preset number of second virtual scenes are encoded separately to generate scene encoding information of a unified dimension. By calculating the similarity between the two sets of scene encoding information, an accurate and reliable secondary similarity is obtained, providing a reliable basis for the screening of target virtual scenes.

[0228] In some embodiments, encoding processing can be understood as the process of converting non-standardized scene information into a unified format numerical code, which can realize the digital and unified expression of scene features, thereby eliminating information differences and improving calculation accuracy.

[0229] In some embodiments, the third scene encoding information can be understood as a standardized numerical code obtained by encoding the second scene information of each initially selected second virtual scene, which is in the same feature space as the first scene encoding information.

[0230] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: when calculating the second similarity, standardized scene information processing should be the core, and unified calculation rules and processing procedures should be adopted throughout the process to ensure the objectivity and stability of the calculation results.

[0231] First, for the first virtual scene to be evaluated, complete first scene information is extracted. The first scene information includes quantitative features such as scene space scale and operation efficiency. At the same time, for each of the first preset number of second virtual scenes that have been initially screened, complete second scene information of the corresponding dimension is extracted to ensure that the feature dimensions, calculation methods and data formats of the first scene information and the second scene information are consistent, so that they have the basic conditions for direct comparison.

[0232] Subsequently, the first scene information and the second scene information are encoded separately. The encoding process adopts a unified encoding model and parameter settings to transform the unstructured scene feature data into a fixed-dimensional vector form to eliminate the computational differences caused by the original data format. Specifically, the first scene information is encoded to generate first scene encoded information, and the second scene information of each initially selected second virtual scene is encoded to generate corresponding third scene encoded information. All third scene encoded information and first scene encoded information are in the same feature space, and similarity calculation can be performed directly.

[0233] Understandably, encoding processes are stable and repeatable. The same scene information can be encoded multiple times to obtain consistent vector results, which effectively ensures the rigor of the calculation process.

[0234] After encoding, the similarity score between the encoded information of the first scene and the encoded information of each third scene is calculated. This second similarity score reflects the degree of matching of detailed features between each initially selected second virtual scene and the first virtual scene. In some embodiments, the second similarity score can be directly used as a quantitative basis for secondary screening of target virtual scenes. The higher the value of the second similarity score, the closer the features of the corresponding second virtual scene are to the first virtual scene, and the higher its reference value.

[0235] Thus, in this embodiment, standardized coded information is generated by encoding the first scene information and the second scene information of the first preset number of second virtual scenes respectively. Then, a similarity calculation is performed based on the coded information to obtain the second similarity degree. This realizes the quantitative calculation of scene similarity degree in the secondary screening stage, transforms scene features into standardized high-dimensional vectors, effectively eliminates the errors and deviations caused by manual judgment, and makes the calculation result of the second similarity degree more in line with the actual scene matching requirements. This effectively solves the problems of fuzzy similarity degree judgment and insufficient precision in the secondary screening, improves the accuracy of target virtual scene screening to a certain extent, and further improves the credibility and scientificity of interaction difficulty assessment to a certain extent, ensuring the fairness of game battles.

[0236] In some embodiments provided in this application, the scene information further includes scene images and scene description information. The steps described above, which encode the first scene information and the second scene information of the first preset number of second virtual scenes respectively to obtain the first scene encoding information of the first scene information and the third scene encoding information of the first preset number of second virtual scenes, include: annotating the collision objects in the scene images of the first preset number of second virtual scenes to obtain annotated images. The scene images and scene description information of the first virtual scene and the labeled images and scene description information of each second virtual scene are encoded to obtain the third image encoding representation and the third description information encoding representation of the first virtual scene, and the fourth image encoding representation and the fourth description information encoding representation of each second virtual scene. The third image coding representation and the third description information coding representation are spliced ​​together to obtain the first scene coding information; The fourth image encoding representation and the fourth description information encoding representation of each second virtual scene are spliced ​​together to obtain the third scene encoding information of a first preset number of second scene information.

[0237] Specifically, when calculating the similarity between a first preset number of second virtual scenes and a first virtual scene, the scene images do not highlight the collision object features that affect the interaction difficulty, and the encoding model cannot focus on the scene interaction contours, resulting in a large deviation in the similarity calculation results and reducing the reliability of game difficulty prediction. Based on the above problems, in some embodiments provided in this application, collision objects in the first preset number of second virtual scenes are first annotated and enhanced, and then the scene images and scene description information are encoded separately. The two types of encoded representations are then concatenated to obtain complete scene encoding information, thereby improving the accuracy of the second similarity calculation.

[0238] In some embodiments, annotation processing can be understood as a visual enhancement operation that marks the outlines and positions of colliding objects in a scene image. In some embodiments, annotated imagery can be understood as a scene imagery that, after annotation processing, highlights the features of colliding objects.

[0239] In some embodiments, encoding processing can be understood as a process of converting scene images and text information into standardized numerical representations for the purpose of unifying data formats.

[0240] In some embodiments, the third image coding representation can be understood as a numerical representation obtained after encoding the scene image of the first virtual scene, representing the visual features of the scene to be evaluated.

[0241] In some embodiments, the third description information encoding representation can be understood as the numerical representation obtained after encoding the scene description information of the first virtual scene, representing the text features of the scene to be evaluated.

[0242] In some embodiments, the fourth image coding representation can be understood as a numerical representation obtained after the labeled image has been coded, representing the visual features of the reference virtual scene.

[0243] In some embodiments, the fourth description information encoding representation can be understood as a numerical representation obtained after encoding the scene description information of each initially selected second virtual scene, representing the text features of the reference scene.

[0244] In some embodiments, stitching can be understood as a process of merging image encoding representation and description information encoding representation into a complete set of encoded data, used to integrate multimodal scene features.

[0245] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please refer to the following exemplary description: The scene information corresponding to the virtual scene includes scene images and scene description information. When performing encoding processing, the two collision objects in the scene images of the first preset number of second virtual scenes are first annotated. The outline range and spatial position of the two collision objects are marked in the image. The evaluation elements are highlighted by visual enhancement and irrelevant background interference is eliminated to obtain an annotated image that clearly highlights the collision features.

[0246] Subsequently, the scene images of the first virtual scene are used to extract features using a visual coding model and other methods, and transformed into a third image coding representation. At the same time, the scene description information of the first virtual scene is processed using a text coding model and other methods, and transformed into a third description information coding representation.

[0247] The same encoding logic is applied to the labeled images and scene description information of the first preset number of second virtual scenes to convert the labeled images into fourth image encoding representations and the scene description information of the first preset number of second virtual scenes into fourth description information encoding representations.

[0248] In some embodiments, the above encoding process uses a unified model and parameters to ensure that vectors are comparable within the same feature space.

[0249] Next, the third image encoding representation and the third descriptive information encoding representation are concatenated to obtain the first scene encoding information that integrates both visual and textual features. Then, the fourth image encoding representation and the fourth descriptive information encoding representation corresponding to the first preset number of second virtual scenes are concatenated to obtain the corresponding third scene encoding information. Finally, based on the third scene encoding information and the third scene encoding information corresponding to the first preset number of second virtual scenes, the second similarity between the first virtual scene and the first preset number of second virtual scenes is calculated.

[0250] Understandably, after annotation enhancement and encoding splicing, the scene encoding information can completely and accurately carry the spatial features that affect the difficulty of interaction, providing a reliable data foundation for similarity calculation.

[0251] Thus, in this embodiment, by annotating collision objects in the scene images of a first preset number of second virtual scenes to generate exclusive annotated images, and then encoding the scene images and scene description information respectively, and splicing the two types of encoded representations to generate unified scene encoding information, the enhanced extraction and multimodal fusion encoding of virtual scene structural features are achieved. Compared with the ordinary encoding method that does not annotate collision objects, the annotation processing for two collision objects can highlight the spatial structure in the scene that affects the intensity of the confrontation, effectively avoiding the interference of irrelevant visual information on the encoding results. At the same time, the splicing processing of scene image encoding and scene description information encoding can integrate visual spatial features and text semantic features, effectively improving the matching degree of scene encoding information with the scene evaluation requirements of asymmetric competitive games. Moreover, the unified annotation encoding and splicing rules effectively ensure the dimensional consistency and comparability of different scene encoding information, and to a certain extent improve the precision and accuracy of the second similarity determination, thereby ensuring the accuracy of target virtual scene selection to a certain extent, and providing reliable feature data support for the prediction of virtual scene interaction difficulty.

[0252] In some embodiments provided in this application, the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene and the first scene information are input into the difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene, including: splicing the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, the first scene information and the pre-configured prompt information template to obtain model input information; The model input information is input into the difficulty prediction model so that the difficulty prediction model can perform prediction processing to obtain the interaction difficulty of the first virtual scene. The prediction processing includes predicting the interaction difficulty of the first virtual scene based on the model input information.

[0253] Specifically, in the process of predicting the difficulty of a virtual scene, directly inputting the interaction difficulty of the target virtual scene, the information of the second scene, and the information of the first scene into the difficulty prediction model results in inconsistent input formats between the reference data and the data to be evaluated. This causes the difficulty prediction model to be unable to quickly understand the prediction task, leading to low prediction efficiency and insufficient accuracy. Based on the above problems, in some embodiments provided in this application, the difficulty data of the target virtual scene, the information of the second scene, the information of the first scene, and the prompt information template are standardized and concatenated to form standardized model input information. This model input information is then input into the difficulty prediction model, which performs automated reasoning and outputs the interaction difficulty of the first virtual scene.

[0254] In some embodiments, the prompt message template can be understood as a fixed text structure pre-designed according to the scenario difficulty assessment logic, used to standardize the format of the input information and reasoning logic of the difficulty prediction model, and guide the difficulty prediction model to complete the prediction of the game difficulty.

[0255] In some embodiments, splicing can be understood as the process of integrating scattered difficulty values, scene information, text templates, and other content into continuous and complete input content for the difficulty prediction model in a fixed order.

[0256] In some embodiments, the model input information can be understood as complete input content that has been spliced ​​and can be directly read and parsed by the difficulty prediction model.

[0257] To more clearly illustrate the virtual scene processing method provided in this application embodiment, please refer to the following exemplary description: When performing the first virtual scene difficulty prediction, firstly, a pre-configured prompt information template is obtained. This prompt information template is a structured text framework used to guide the difficulty prediction model to understand the logical relationship between the prediction task and the input data. For example, a basic prompt information template may contain the following fixed sentence: "The following feature information of the reference game scene is known as [Second Scene Information], and its labeled interaction difficulty is [Scene Interaction Difficulty]. Please predict its interaction difficulty level based on the feature information [First Scene Information] of the game scene to be evaluated." Then, the interaction difficulty corresponding to the determined target virtual scene, the second scene information of the target virtual scene, and the first scene information of the first virtual scene are filled and concatenated according to the placeholders in the template to obtain model input information with a clear structure and well-defined task. After concatenating the model input information, the generated model input information is directly fed into the difficulty prediction model. The difficulty prediction model has been trained with a large amount of standard scene data and has mature scene feature understanding and difficulty inference capabilities, so there is no need to adjust the parameters or retrain for this evaluation task.

[0258] After receiving the input information, the difficulty prediction model first parses the template guidance content in the input information to clarify the task objective, then extracts the difficulty and feature data of the target virtual scene as a reference, and reads the feature data of the first virtual scene. It then combines the reference information and the information to be evaluated to perform comprehensive reasoning. Based on the correspondence between scene feature similarity and reference difficulty, it outputs a stable and reliable prediction result, namely the interaction difficulty of the first virtual scene, thus realizing the automated prediction of virtual interaction difficulty.

[0259] It should be noted that the difficulty prediction model in this disclosure can be built based on a Large Language Model (LLM), utilizing its powerful contextual understanding and reasoning capabilities for prediction; it can also be other types of machine learning models, such as a regression prediction model built based on a Deep Neural Network (DNN), which learns the complex mapping relationship between reference scene features and difficulty to predict new scenes; or it can be built based on an ensemble learning model such as a Gradient Boosting Decision Tree (GBDT). As long as the model can output a difficulty prediction value for the latter based on the input reference scene information and the scene to be evaluated information, it falls within the scope of the difficulty prediction model in this disclosure.

[0260] Thus, in this embodiment, by combining the interaction difficulty of the target virtual scene, the second scene information, and the first scene information with a prompt information template to form standardized model input information, and then inputting the model input information into the difficulty prediction model to complete the prediction process, the automated and accurate prediction of the first virtual interaction difficulty is achieved. Compared with solutions that rely on manual experience assessment, adjust based on player combat data after launch, or only use simple distance feature assessment, standardized information splicing processing can organize diverse scene features and difficulty reference data into a unified input format that the model can recognize. This effectively ensures the standardization and completeness of data input, eliminates assessment bias caused by experience judgment, and improves the efficiency and objectivity of scene difficulty assessment to a certain extent. In turn, it ensures the fairness of asymmetrical competitive games to a certain extent and optimizes the player's gaming experience.

[0261] To more clearly illustrate the virtual scene processing method provided in the embodiments of this application, please also refer to... Figures 2 to 10 , Figures 2 to 10 The following is a schematic diagram illustrating an application scenario of the virtual scene processing method provided in this application embodiment: The following uses a map's interactive point as the first virtual scene to be evaluated, the first virtual character as a survivor, and the second virtual character as a hunter as an example to illustrate the virtual scene processing method provided in this application embodiment: As shown in Table 2, Table 2 contains basic data information for survivors and hunters. First, a standard template library was constructed and basic in-game data was collected. The scene in the template library is the second virtual scene, which is a reference scene with completed difficulty labeling. The collected data includes the basic movement speed of survivors and hunters, and the stun time after interaction at interaction points. Among them, the basic movement speed of survivors is 3.8 m / s (code unit is 45.00), the stun time after vaulting pallets is 1.17 seconds, and the stun time after vaulting windows is 0.87 seconds. The average basic movement speed of hunters is 4.64 m / s (code unit is 55.00).

[0262] Table 2 Basic Data Information for Survivors and Hunters

[0263] The data units include meters and codes. Meters are the units displayed to players, while codes are the actual units used for calculations within the game. The two are simply units that can be converted between each other.

[0264] Next, the quantization data of the second virtual scene is calculated. First, the envelope data corresponding to each interaction point is obtained. The envelope data is the length of the closed line connecting the colliders around the interaction point, which corresponds to the reference distance between the two colliding objects in the second scene information. Reference distance Then calculate the target movement distance of the regulator. , The survivor's interaction stun time is multiplied by the hunter's base movement speed.

[0265] As shown in Table 3, the target movement distance for the regulator during interaction freeze is as follows: Under a single interaction freeze, the regulator can move 64.35 yards to the pallet and 47.85 yards to the window; under two interaction freezes, the regulator can move 128.7 yards to the pallet and 95.7 yards to the window. The value is determined based on whether the board areas are associated. Unassociated board areas use a single interaction stiffness distance, while associated board areas use two interaction stiffness distances.

[0266] Table 3. Target movement distance of the regulator during interaction freeze.

[0267] Then, the interaction difficulty of the second virtual scene is labeled, using a 1-5 point rating system, with 5 points being the highest difficulty and 1 point being the lowest. Much smaller than min( , If 5 points are awarded, then it will be marked as 5. Only much larger , If one of them is much smaller than the other, it is marked with 3 points. and If both sides are relatively short, a score of 2 is given. Scores of 4 and 1 are not used when the standard template library is established, but will be used for subsequent evaluation. The difficulty rating result is the interaction difficulty of the second virtual scene.

[0268] For example, as shown in Table 4, Table 4 shows the target movement distance and reference distance. and reference distance The interaction difficulty is rated according to the classification.

[0269] Table 4 Based on target movement distance and reference distance and reference distance Interaction difficulty rating

[0270] Next, as Figure 2 and Figure 3 As shown, all data in the standard template library undergoes hierarchical quantization processing. A box plot is calculated for each column of feature data. Data is binned based on the quartiles P0, P25, P50, P75, and P100 of the box plot, using the average length (first average distance), the average shelf distance (second average distance), and the ratio of length to shelf distance (first ratio) as the binning process. For example... Figure 3 As shown, the length feature bucket interval is [0, 240, 280, 340, 800], the frame distance feature bucket interval is [0, 120, 180, 240, 720], and the length to frame ratio bucket interval is [1.0, 1.7, 2.0, 2.7, 4.5]. Each second virtual scene will be divided into one of the four buckets. At the same time, based on the first average distance, the second average distance, and the first ratio, multiple second virtual scenes are divided into the first scene set, the second scene set, and the third scene set, respectively.

[0271] Subsequently, multi-path similarity data retrieval was performed on the first virtual scene to be evaluated. The first scene information included the reference distances corresponding to the two colliding objects and the first and second frame distances of the interactive objects. The third average distance of the second reference distance, the fourth average distance of the first and second frame distances, and the second ratio of the third and fourth average distances were calculated. The bucket to which the new sample belonged was determined based on the multidimensional features of the new sample. Figure 4 As shown, in this embodiment, the average length of the new sample falls into length bin 4, the average distance of the shelf point falls into shelf point bin 3, and the ratio falls into ratio bin 1. Finally, all second virtual scenes in bins 4, 3, and 1 are recalled.

[0272] Next, as Figure 5 and Figure 6 As shown, a Contrastive Language-Image Pre-training (CLIP) model is used to encode the new samples and the second virtual scenes in the recall bins. The scene images and scene description information (System Prompt and User Prompt) are encoded separately, each resulting in a 512-dimensional vector. The three vectors are concatenated to generate a 512*3-dimensional embedding vector, which is the scene encoding information. The similarity between the new sample embedding vector and the recall sample embedding vector is calculated, and a first preset number of second virtual scenes with the highest similarity are selected to complete the initial screening.

[0273] Next, as Figure 7 , Figure 8 and Figure 9 As shown, two collision objects within the second scene images of the first preset number of second virtual scenes are labeled to highlight the collision outline corresponding to the interaction point. At the same time, the System Prompt and User Prompt content are further refined. The CLIP model is used again to encode the scene images and scene description information of the first virtual scene, the labeled images, and the scene description information of the second virtual scene to obtain the corresponding image encoding representation and description information encoding representation. After splicing, the third scene encoding information and the fourth scene encoding information are generated. The second similarity between the two is calculated. A second preset number of second virtual scenes with a second similarity higher than the second threshold are selected as the target virtual scenes.

[0274] Finally, as Figure 10 As shown, a new sample intensity level prediction based on few-shot learning is performed. Here, few-shot learning is a lightweight inference paradigm for large language models. In this embodiment, it relies on a small number of target virtual scenes that, after secondary screening, highly match the spatial features of the first virtual scene as labeled examples. The correspondence between scene spatial features and interaction difficulty is explicitly passed to the model through prompts. This allows the model to quickly learn the evaluation rules for virtual scene interaction difficulty without requiring parameter fine-tuning or gradient updates, or additional training data, thus adapting to the scene evaluation task of this embodiment.

[0275] In this embodiment, the Large Language Model (LLM) is used as the difficulty prediction model. In this embodiment, the LLM relies on the semantic understanding and logical reasoning capabilities formed by pre-training to parse the scene space features and difficulty annotation information in the model input information, autonomously extract the quantitative mapping relationship between scene features and interaction difficulty in the reference example, and then transfer and apply the mapping relationship to the feature analysis of the first virtual scene. Based on feature matching and logical deduction, the interaction difficulty prediction is completed, and the model's own parameters remain unchanged throughout the process.

[0276] In the process of predicting the intensity level of new samples based on Few-shot, all target virtual scenes, after being screened twice (coarse and fine ranking), are first used as valid reference examples. The second scene information and the determined interaction difficulty of each target virtual scene are extracted and organized into standardized example entries. Then, a standardized prompt template, constructed based on the prompt text project and including task description, example display, and input of the sample to be predicted, clarifies the task the model must perform: predicting the interaction difficulty of virtual scenes. Next, the organized interaction difficulty of the target virtual scene, the second scene information, and the first scene information of the first virtual scene are sequentially concatenated and integrated according to the fixed structure of the prompt information template to generate complete model input information that meets the LLM input requirements. Then, the model input information is input into the difficulty prediction model. The model quickly absorbs the scene features and difficulty correspondence rules from the examples through Few-shot context learning. Finally, the LLM analyzes and performs matching inference on the spatial features of the first virtual scene based on the learned evaluation rules, ultimately outputting the intensity level corresponding to the first virtual scene, which is the interaction difficulty of the first virtual scene.

[0277] To facilitate better implementation of the virtual scene processing method of this application embodiment, this application embodiment also provides a virtual scene processing apparatus. Please refer to... Figure 11 , Figure 11 This is a schematic diagram of the structure of a virtual scene processing device provided in an embodiment of this application. The virtual scene processing device 200 may include: an acquisition module 210, used to acquire first scene information of a first virtual scene whose interaction difficulty is to be evaluated; The determining module 220 is used to determine a target virtual scene similar to the first virtual scene from a plurality of pre-determined second virtual scenes based on the first scene information, wherein each second virtual scene has a determined interaction difficulty; The processing module 230 is used to input the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information into the difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene; wherein, the scene information is used to characterize the spatial features of the corresponding virtual scene.

[0278] In some embodiments, each virtual scene includes an interactive object and two collision objects corresponding to the interactive object, with the two collision objects separated by the interactive object; the scene information of the virtual scene includes the reference distance corresponding to each collision object, as well as the first stand-point distance and the second stand-point distance corresponding to the interactive object; the reference distance corresponding to the collision object is the length of the closed path formed around the collision object according to the preset pathfinding rules, the first stand-point distance is the distance between the interactive object and the first designated functional point in the virtual scene, and the second stand-point distance is the distance between the interactive object and the second designated functional point in the virtual scene.

[0279] In some embodiments, the processing module 230 can also be used to determine the target movement distance of the second virtual character within the action execution time based on the action execution time of the first virtual character performing the target interactive action through the interactive object in the second virtual scene and the movement speed of the second virtual character, and to determine the interaction difficulty of the second virtual scene based on the relationship between the target movement distance and the reference distances corresponding to the two collision objects in the second virtual scene, wherein the game faction of the second virtual character is different from that of the first virtual character, and the second virtual character cannot perform the target interactive action through the interactive object.

[0280] In some embodiments, the action execution time is the interaction freeze time generated when the first virtual character performs the target interaction action through the interaction object in the second virtual scene; the processing module 230 can also be used to determine the target movement distance based on the single interaction freeze time and the movement speed of the second virtual character when there are no other interaction objects associated with the interaction object in the area where the interaction object is located in the second virtual scene, and to determine the target movement distance based on the multiple interaction freeze times and the movement speed of the second virtual character when there are other interaction objects associated with the interaction object in the area where the interaction object is located in the second virtual scene.

[0281] In some embodiments, the processing module 230 can also be used to determine the interaction difficulty of the second virtual scene as a first difficulty level when the product of the target movement distance and the preset multiple is less than the smaller of the two reference distances, to determine the interaction difficulty of the second virtual scene as a second difficulty level when the product of the target movement distance and the preset multiple is greater than one of the two reference distances and less than the other, and to determine the interaction difficulty of the second virtual scene as a third difficulty level when both reference distances are less than a preset distance threshold.

[0282] In some embodiments, the processing module 230 can also be used to divide the multiple second virtual scenes into multiple scene sets according to the second scene information of each second virtual scene. The determining module 220 can also be used to determine the target scene set corresponding to the first scene information from the multiple scene sets according to the first scene information, and determine the second virtual scene in the target scene set as the target virtual scene.

[0283] In some embodiments, the multiple scene sets include at least one of multiple first scene sets, multiple second scene sets, and multiple third scene sets; the multiple first scene sets are obtained by dividing the second scene information by two reference distances, the multiple second scene sets are obtained by dividing the second scene information by the first frame point distance and the second frame point distance, and the multiple third scene sets are obtained by dividing the second scene information by two reference distances, the first frame point distance, and the second frame point distance.

[0284] In some embodiments, the processing module 230 may also be used to determine the first average distance between two reference distances of the second scene information, and determine a plurality of first scene sets according to the first average distance corresponding to each second virtual scene, and divide the plurality of second virtual scenes into a plurality of first scene sets.

[0285] In some embodiments, the processing module 230 may also be used to determine the second average distance between the first frame distance and the second frame distance of the second scene information, and determine a plurality of second scene sets according to the second average distance corresponding to each second virtual scene, and divide the plurality of second virtual scenes into a plurality of second scene sets.

[0286] In some embodiments, the processing module 230 may further be used to determine a first average distance between two reference distances of the second scene information, and a second average distance between the first frame distance and the second frame distance of the second scene information, and to determine a first ratio between the first average distance and the corresponding second average distance of the second virtual scene. Finally, based on the first ratio corresponding to each second virtual scene, a plurality of third scene sets are determined, and the plurality of second virtual scenes are divided into a plurality of third scene sets.

[0287] In some embodiments, the determining module 220 may further be used to determine a first target scene set corresponding to a first virtual scene from multiple first scene sets based on a third average distance of two reference distances of the first scene information; to determine a second target scene set corresponding to a first virtual scene from multiple second scene sets based on a fourth average distance of the distance between the first and second frame points of the first scene information; and to determine a third target scene set corresponding to a first virtual scene from multiple third scene sets based on a second ratio of the third average distance to the fourth average distance; and finally, to determine the target scene set by the union of the first target scene set, the second target scene set, and the third target scene set.

[0288] In some embodiments, the scene information further includes scene images of the virtual scene and scene description information of the virtual scene; the determining module 220 can also be used to encode the first scene information and the second scene information of each second virtual scene in the target scene set respectively to obtain the first scene encoding information of the first scene information and the second scene encoding information of each second scene information in the target scene set, calculate the similarity between the first scene encoding information and each second scene encoding information, obtain the first similarity degree between each second virtual scene in the target scene set and the first virtual scene, and determine the target virtual scene in the target scene set based on the first similarity degree of each second virtual scene.

[0289] In some embodiments, the determining module 220 can also be used to encode the scene image and scene description information of the first virtual scene and the scene image and scene description information of each second virtual scene in the target scene set, respectively, to obtain the first image encoding representation and the first description information encoding representation of the first virtual scene, and the second image encoding representation and the second description information encoding representation of each second virtual scene; to perform splicing processing on the first image encoding representation and the first description information encoding representation to obtain the first scene encoding information; and to perform splicing processing on the second image encoding representation and the second description information encoding representation of each second virtual scene to obtain the second scene encoding information of each second scene information.

[0290] In some embodiments, the determining module 220 may also be used to determine the second similarity between the first preset number of second virtual scenes and the first virtual scene based on the first scene information and the second scene information of the first preset number of second virtual scenes in the target scene set whose first similarity is higher than the first threshold, and to determine the second preset number of second virtual scenes whose second similarity is higher than the second threshold as the target virtual scene.

[0291] In some embodiments, the determining module 220 may further be used to encode the first scene information and the second scene information of the first preset number of second virtual scenes respectively to obtain the first scene encoding information of the first scene information and the third scene encoding information of the second scene information of the first preset number of second virtual scenes, and calculate the similarity between the first scene encoding information and each third scene encoding information to obtain the second similarity between the first preset number of second virtual scenes and the first virtual scene respectively.

[0292] In some embodiments, the scene information further includes scene images and scene description information. The determining module 220 can also be used to annotate collision objects in the scene images of a first preset number of second virtual scenes to obtain annotated images, encode the scene images and scene description information of the first virtual scene and the annotated images and scene description information of each second virtual scene respectively to obtain a third image encoding representation and a third description information encoding representation of the first virtual scene, and a fourth image encoding representation and a fourth description information encoding representation of each second virtual scene, splice the third image encoding representation and the third description information encoding representation to obtain first scene encoding information, and splice the fourth image encoding representation and the fourth description information encoding representation of each second virtual scene to obtain third scene encoding information of a first preset number of second scene information.

[0293] In some embodiments, the processing module 230 can also be used to splice the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, the first scene information, and the pre-configured prompt information template to obtain model input information, and input the model input information into the difficulty prediction model so that the difficulty prediction model can perform prediction processing to obtain the interaction difficulty of the first virtual scene, wherein the prediction processing includes predicting the interaction difficulty of the first virtual scene based on the model input information.

[0294] Each unit in the aforementioned virtual scene processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in the electronic device in hardware form, or stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to each unit.

[0295] The virtual scene processing device 200 can be integrated into a terminal or server that has memory and a processor and thus computing power, or the virtual scene processing device 200 can be the terminal or server.

[0296] Optionally, this application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the virtual scene processing method of the above-described method embodiments by calling the computer program stored in the memory.

[0297] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may be a terminal or a server. Figure 12As shown, the electronic device 300 includes a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 and the memory 302 are electrically connected. Those skilled in the art will understand that the electronic device structure shown in the figures does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0298] The processor 301 is the control center of the electronic device 300. It connects various parts of the electronic device 300 through various interfaces and lines. By running or loading software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device 300 and processes data, thereby performing overall processing of the electronic device 300.

[0299] In this embodiment of the application, the processor 301 in the electronic device 300 will load the instructions corresponding to the process of one or more computer programs into the memory 302 according to the following steps, and the processor 301 will run the computer programs stored in the memory 302 to realize various functions: obtaining the first scene information of the first virtual scene to be evaluated for interaction difficulty; Based on the first scene information, a target virtual scene similar to the first virtual scene is determined from a plurality of pre-determined second virtual scenes, wherein each second virtual scene has a predetermined interaction difficulty; The interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information are input into the difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene; wherein, the scene information is used to characterize the spatial features of the corresponding virtual scene.

[0300] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0301] Optional, such as Figure 12 As shown, the electronic device 300 also includes: a display screen 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. The processor 301 is electrically connected to the display screen 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307. Those skilled in the art will understand that... Figure 12 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0302] The display screen 303 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The display screen 303 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), generate corresponding operation commands, and execute the corresponding program. Optionally, the touch panel may include a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, and transmits the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 301, and can receive and execute commands from the processor 301. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 301 to determine the type of touch event. Subsequently, the processor 301 provides corresponding visual output on the display panel according to the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the display screen 303 to achieve input and output functions. However, in some embodiments, the touch panel and the display screen 303 can be implemented as two independent components to achieve input and output functions. That is, the display screen 303 can also be used as part of the input unit 306 to achieve input functions.

[0303] The radio frequency circuit 304 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices.

[0304] Audio circuitry 305 can be used to provide an audio interface between a user and an electronic device via a speaker and a microphone. Audio circuitry 305 converts received audio data into electrical signals, transmits them to the speaker, and the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuitry 305, converted back into audio data, and then processed by processor 301 before being transmitted via radio frequency circuitry 304 to, for example, another electronic device, or output to memory 302 for further processing. Audio circuitry 305 may also include an earphone jack to facilitate communication between peripheral headphones and electronic devices.

[0305] The input unit 306 can be used to receive input numbers, characters, or object feature information (such as fingerprints, irises, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0306] Power supply 307 is used to supply power to various components of electronic device 300. Optionally, power supply 307 can be logically connected to processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. Power supply 307 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0307] although Figure 12 As not shown in the diagram, the electronic device 300 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0308] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to a computer device, and the computer program causes the computer device to execute the corresponding processes in the virtual scene processing method described in the embodiments of this application; for brevity, further details are omitted here.

[0309] This application also provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding process in the virtual scene processing method described in the embodiments of this application. For simplicity, further details are omitted here.

[0310] This application also provides a computer program comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding process in the virtual scene processing method of this application. For brevity, further details are omitted here.

[0311] It should be understood that the processor in this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0312] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0313] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0314] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0315] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0316] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0317] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0318] In addition, the functional units in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0319] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer or a server) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0320] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing virtual scenes, characterized in that, The method includes: Obtain the first scene information of the first virtual scene whose interaction difficulty needs to be evaluated; Based on the first scene information, a target virtual scene similar to the first virtual scene is determined from a plurality of predetermined second virtual scenes, wherein each second virtual scene has a predetermined interaction difficulty; The interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information are input into the difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene. The scene information is used to characterize the spatial features of the corresponding virtual scene.

2. The method according to claim 1, characterized in that, Each virtual scene includes an interactive object and two collision objects corresponding to the interactive object, with the two collision objects separated by the interactive object; The scene information of the virtual scene includes the reference distance corresponding to each of the collision objects, and the first stand-up distance and the second stand-up distance corresponding to the interactive object; The reference distance corresponding to the collision object is the length of the closed path formed around the collision object according to the preset pathfinding rules. The first standpoint distance is the distance between the interactive object and the first designated function point in the virtual scene, and the second standpoint distance is the distance between the interactive object and the second designated function point in the virtual scene.

3. The method according to claim 2, characterized in that, Determining the interaction difficulty of the second virtual scene includes: Based on the action execution time of the first virtual character performing the target interactive action through the interactive object in the second virtual scene, and the movement speed of the second virtual character, the target movement distance of the second virtual character within the action execution time is determined. The game faction of the second virtual character is different from that of the first virtual character, and the second virtual character cannot perform the target interactive action through the interactive object. The interaction difficulty of the second virtual scene is determined based on the relationship between the target movement distance and the reference distances corresponding to the two collision objects in the second virtual scene.

4. The method according to claim 3, characterized in that, The action execution time is the interaction freeze time generated when the first virtual character performs the target interactive action through the interactive object in the second virtual scene; The step of determining the target movement distance of the second virtual character within the action execution time based on the action execution time of the first virtual character performing a target interactive action through an interactive object in the second virtual scene, and the movement speed of the second virtual character, includes: If there are no other interactive objects associated with the interactive object in the second virtual scene in the area where the interactive object is located, the target movement distance is determined based on the single interaction freeze time and the movement speed of the second virtual character; If there are other interactive objects associated with the interactive object in the second virtual scene in the area where the interactive object is located, the target movement distance is determined based on the time of multiple interaction freezes and the movement speed of the second virtual character.

5. The method according to claim 3, characterized in that, Determining the interaction difficulty of the second virtual scene based on the relationship between the target's movement distance and the reference distances corresponding to the two colliding objects in the second virtual scene includes at least one of the following steps: If the product of the target movement distance and the preset multiple is less than the smaller of the two reference distances, the interaction difficulty of the second virtual scene is determined to be the first difficulty level. If the product of the target movement distance and the preset multiple is greater than one of the two reference distances and less than the other, the interaction difficulty of the second virtual scene is determined to be the second difficulty level. If both reference distances are less than a preset distance threshold, the interaction difficulty of the second virtual scene is determined to be the third difficulty level.

6. The method according to claim 3, characterized in that, The method further includes: Based on the second scene information of each second virtual scene, the plurality of second virtual scenes are divided into a plurality of scene sets; The step of determining a target virtual scene similar to the first virtual scene from a predetermined plurality of second virtual scenes based on the first scene information includes: Based on the first scene information, determine the target scene set corresponding to the first scene information from the plurality of scene sets; The second virtual scene in the set of target scenes is determined as the target virtual scene.

7. The method according to claim 6, characterized in that, The multiple scene sets include at least one of multiple first scene sets, multiple second scene sets, and multiple third scene sets; The plurality of first scene sets are obtained by dividing the second scene information into two reference distances. The plurality of second scene sets are obtained by dividing the second scene information into a first frame point distance and a second frame point distance. The plurality of third scene sets are obtained by dividing the second scene information into the two reference distances, the first frame point distance, and the second frame point distance.

8. The method according to claim 7, characterized in that, The step of dividing the plurality of second virtual scenes into a plurality of scene sets based on the second scene information of each second virtual scene includes: Determine the first average distance between the two reference distances of the second scene information; Based on the first average distance corresponding to each second virtual scene, the plurality of first scene sets are determined, and the plurality of second virtual scenes are assigned to the plurality of first scene sets.

9. The method according to claim 7, characterized in that, The step of dividing the plurality of second virtual scenes into a plurality of scene sets based on the second scene information of each second virtual scene includes: Determine the second average distance between the first frame point distance and the second frame point distance in the second scene information; Based on the second average distance corresponding to each second virtual scene, the plurality of second scene sets are determined, and the plurality of second virtual scenes are divided into the plurality of second scene sets.

10. The method according to claim 7, characterized in that, The step of dividing the plurality of second virtual scenes into a plurality of scene sets based on the second scene information of each second virtual scene includes: Determine the first average distance between the two reference distances of the second scene information, and the second average distance between the first frame distance and the second frame distance of the second scene information; Determine a first ratio between the first average distance corresponding to the second virtual scene and the corresponding second average distance; Based on the first ratio corresponding to each second virtual scene, the plurality of third scene sets are determined, and the plurality of second virtual scenes are divided into the plurality of third scene sets.

11. The method according to claim 7, characterized in that, The step of determining the target scene set corresponding to the first scene information from the plurality of scene sets based on the first scene information includes: Based on the third average distance of the two reference distances of the first scene information, a first target scene set corresponding to the first virtual scene is determined from the plurality of first scene sets; Based on the fourth average distance between the first frame point distance and the second frame point distance of the first scene information, determine the second target scene set corresponding to the first virtual scene from the plurality of second scene sets; Based on the second ratio of the third average distance to the fourth average distance, a third target scene set corresponding to the first virtual scene is determined from the plurality of third scene sets; The target scene set is determined by the union of the first target scene set, the second target scene set, and the third target scene set.

12. The method according to claim 6, characterized in that, The scene information also includes scene images of the virtual scene and scene description information of the virtual scene; The step of determining the second virtual scene in the target scene set as the target virtual scene includes: The first scene information and the second scene information of each second virtual scene in the target scene set are encoded respectively to obtain the first scene encoding information of the first scene information and the second scene encoding information of each second scene information in the target scene set; Calculate the similarity between the first scene encoding information and each second scene encoding information to obtain the first similarity between each second virtual scene and the first virtual scene in the target scene set; The target virtual scene in the target scene set is determined based on the first similarity of each second virtual scene.

13. The method according to claim 12, characterized in that, The step of encoding the first scene information and the second scene information of each second virtual scene in the target scene set to obtain the first scene encoding information of the first scene information and the second scene encoding information of each second scene information in the target scene set includes: The scene image and scene description information of the first virtual scene and the scene image and scene description information of each second virtual scene in the target scene set are encoded to obtain the first image encoding representation and the first description information encoding representation of the first virtual scene, and the second image encoding representation and the second description information encoding representation of each second virtual scene. The first image encoding representation and the first description information encoding representation are spliced ​​together to obtain the first scene encoding information; The second image encoding representation and the second description information encoding representation of each second virtual scene are spliced ​​together to obtain the second scene encoding information of each second scene information.

14. The method according to claim 12, characterized in that, Determining the target virtual scene in the target scene set based on the first similarity of each second virtual scene includes: Based on the first scene information and the second scene information of a first preset number of second virtual scenes in the target scene set whose first similarity is higher than the first threshold, the second similarity between the first preset number of second virtual scenes and the first virtual scene is determined; The second preset number of the second virtual scenes with a similarity level higher than the second threshold are determined as the target virtual scene.

15. The method according to claim 14, characterized in that, The step of determining the second similarity between the first preset number of second virtual scenes and the first virtual scene based on the first scene information and the second scene information of the first preset number of second virtual scenes in the target scene set whose first similarity is higher than the first threshold includes: The first scene information and the second scene information of the first preset number of second virtual scenes are respectively encoded to obtain the first scene encoding information of the first scene information and the third scene encoding information of the second scene information of the first preset number of second virtual scenes. Calculate the similarity between the first scene encoding information and each of the third scene encoding information to obtain the second similarity between the first preset number of second virtual scenes and the first virtual scene.

16. The method according to claim 15, characterized in that, The scene information also includes scene images and scene description information. The process of encoding the first scene information and the second scene information of the first preset number of second virtual scenes to obtain first scene encoding information for the first scene information and third scene encoding information for the second scene information of the first preset number of second virtual scenes includes: The collision objects within the scene images of the first preset number of second virtual scenes are labeled to obtain labeled images; The scene image and scene description information of the first virtual scene and the labeled image and scene description information of each second virtual scene are encoded respectively to obtain the third image encoding representation and the third description information encoding representation of the first virtual scene, and the fourth image encoding representation and the fourth description information encoding representation of each second virtual scene. The third image encoding representation and the third description information encoding representation are spliced ​​together to obtain the first scene encoding information; The fourth image encoding representation and the fourth description information encoding representation of each second virtual scene are spliced ​​together to obtain the third scene encoding information of the first preset number of second scene information.

17. The method according to claim 1, characterized in that, The step of inputting the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, and the first scene information into a difficulty prediction model for prediction processing to obtain the interaction difficulty of the first virtual scene includes: The interaction difficulty of the target virtual scene, the second scene information of the target virtual scene, the first scene information, and the pre-configured prompt information template are spliced ​​together to obtain the model input information; The model input information is input into the difficulty prediction model so that the difficulty prediction model performs prediction processing to obtain the interaction difficulty of the first virtual scene. The prediction processing includes predicting the interaction difficulty of the first virtual scene based on the model input information.

18. A virtual scene processing device, characterized in that, The device includes: The acquisition module is used to acquire the first scene information of the first virtual scene whose interaction difficulty is to be evaluated. The determining module is used to determine a target virtual scene similar to the first virtual scene from a plurality of predetermined second virtual scenes based on the first scene information, wherein each second virtual scene has a determined interaction difficulty; The processing module is used to input the interaction difficulty of the target virtual scene, the second scene information of the target virtual scene and the first scene information into the difficulty prediction model for prediction processing, so as to obtain the interaction difficulty of the first virtual scene. The scene information is used to characterize the spatial features of the corresponding virtual scene.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted for loading by a processor to perform the virtual scene processing method as described in any one of claims 1-17.

20. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program, and the processor executing the virtual scene processing method according to any one of claims 1-17 by calling the computer program stored in the memory.

21. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the virtual scene processing method according to any one of claims 1-17.

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