Multi-dimensional puzzle game generation method, system and equipment

Abstract

The invention is suitable for the technical field of games, and provides a multi-dimensional puzzle game generation method, system and equipment, and the method comprises the steps: building a plurality of dimension types for a puzzle game; historical behavior data of a player is obtained, and the difficulty level of the player in the game process is obtained based on the historical behavior data; based on the difficulty level, determining a puzzle solving dimension and a switching dimension in the game process of the player, and determining a switching rule between the puzzle solving dimension and the switching dimension at the same time; based on the puzzle solving dimension, generating a puzzle instance, loading the puzzle instance into a game scene corresponding to the puzzle solving dimension, and enabling the puzzle instance to respond to switching between the puzzle solving dimension and the switching dimension in real time; according to the generated multi-dimensional puzzle game, a player can switch different dimensions in real time, the complexity and strategic performance of the game are improved, and therefore it is ensured that game experience of the player has enough challenging performance.

Description

Technical Field

[0001] The present application belongs to the field of game technology, and in particular relates to a method, system and device for generating a multi-dimensional puzzle game. Background Art

[0002] In the field of modern game design, with the advancement of technology and the improvement of players' aesthetic level, players' demand for game innovation and deep interactive experience is increasing.

[0003] Existing puzzle games are often limited to a single-dimensional operation, typically involving only spatial or temporal manipulation. For example, the Portal series of games is a puzzle-solving game based on spatial teleportation, featuring spatial switching and puzzle-solving elements. It primarily focuses on spatial teleportation, rather than multi-dimensional switching. Braid, on the other hand, is a platform game that utilizes time manipulation to solve puzzles, involving temporal manipulation, primarily focusing on temporal manipulation, rather than switching between multiple dimensions. Another game, Quantum Break, combines time manipulation with action-adventure elements, featuring temporal manipulation, but focuses more on action and time manipulation, rather than multi-dimensional puzzle-solving.

[0004] The limitations of the above-mentioned games make it difficult to satisfy the current players' pursuit of more complex, challenging and novel gaming experiences. Therefore, this application proposes to develop a game that can switch puzzles in multiple dimensions to satisfy the players' gaming experience. Summary of the Invention

[0005] The embodiments of the present application provide a method, system, and device for generating a multi-dimensional puzzle game, which can develop a game that can switch between puzzles in multiple dimensions to satisfy the player's gaming experience.

[0006] In a first aspect, an embodiment of the present application provides a method for generating a multi-dimensional puzzle game, comprising:

[0007] Establish multiple dimension types for puzzle games;

[0008] Obtaining historical behavior data of the player, and obtaining the difficulty level of the player during the game based on the historical behavior data;

[0009] Based on the difficulty level, determining a puzzle-solving dimension and a switching dimension during the player's game process, and determining a switching rule between the puzzle-solving dimension and the switching dimension;

[0010] Generate a puzzle instance based on the puzzle-solving dimension, load the puzzle instance into the game scene corresponding to the puzzle-solving dimension, and the puzzle instance responds to the switching between the puzzle-solving dimension and the switching dimension in real time

[0011] Among them, the puzzle-solving dimension is the basic dimension type when the player solves the puzzle, and the switching dimension is any one or more dimension types other than the puzzle-solving dimension among the multiple dimension types. Different puzzle-solving dimensions correspond to different game scenes and puzzle types.

[0012] Furthermore, the puzzle game is provided with multiple dimension types, including:

[0013] Establishing a dimension manager, and generating different dimension types by modifying dimension attributes in the dimension manager, wherein the dimension attributes include physical attributes and environmental attributes;

[0014] In different puzzle dimensions, different game scenes and puzzle types are generated based on the physical properties and the environmental properties. The dimension types include normal dimension, gravity dimension and time dimension. The puzzle types include sliding block puzzles, number puzzles and path planning.

[0015] Furthermore, the generation of different game scenes and puzzle types includes:

[0016] Divide the game scene into multiple basic components, and load scene attributes of the corresponding dimension type for the basic components according to the dimension attributes of the dimension type, wherein the scene attributes include environmental factors, art resources and exchange logic;

[0017] Different basic components correspond to different dimension types. When switching dimensions, the game scene is dynamically adjusted by adjusting the scene properties of the basic components.

[0018] Machine learning algorithms are used to analyze players' historical preference data to obtain players' preferred puzzle types.

[0019] Furthermore, the acquiring of the player's historical behavior data and obtaining the player's difficulty level during the game based on the historical behavior data includes:

[0020] Collecting historical behavior data of players, including player level, number of puzzles solved, puzzle-solving success rate, average puzzle-solving time, and game performance, where the game performance refers to the player's puzzle-solving status within a preset time period;

[0021] Based on the player level, set a basic difficulty for the player;

[0022] Dynamically adjust the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time;

[0023] Based on the basic difficulty and the difficulty coefficient, the difficulty level of the player during the game is determined.

[0024] Furthermore, dynamically adjusting the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time includes:

[0025] If the puzzle-solving success rate is higher than a success rate threshold and the average puzzle-solving time is lower than a time threshold, generating a first difficulty coefficient, wherein the first difficulty coefficient is used to increase the difficulty of the puzzle;

[0026] If the puzzle-solving success rate is lower than a success rate threshold or the average puzzle-solving time is higher than a time threshold, generating a second difficulty coefficient, wherein the second difficulty coefficient is used to reduce the difficulty of the puzzle;

[0027] The calculation formula of the first difficulty coefficient is as follows:

[0028] d1=i+(s-s0)×w1

[0029] The calculation formula of the second difficulty coefficient is as follows:

[0030]

[0031] Among them, d1 represents the first difficulty coefficient, d2 represents the second difficulty coefficient, i represents the basic difficulty coefficient, i = number of solved puzzles / number of puzzles, the number of puzzles represents the preset number of puzzles to be solved under the player level, w1 represents the first adjustment factor, which is used to balance the coefficient of the player level and the game performance, and w2 represents the second adjustment factor, which is used to balance the coefficient of the puzzle-solving success rate and the average puzzle-solving time.

[0032] Furthermore, the dynamically adjusting the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time also includes:

[0033] Through the expert evaluation method, the weight ratio of the puzzle-solving success rate and the average puzzle-solving time in the difficulty coefficient adjustment process is obtained;

[0034] Obtaining a basic time impact value based on the average puzzle-solving time and the puzzle difficulty benchmark time, wherein the puzzle difficulty benchmark time is the time taken to solve a preset puzzle;

[0035] Obtaining the second adjustment factor based on the weight ratio and the basic time impact value;

[0036] The calculation formula of the basic time impact value is as follows:

[0037] Basic time impact value = (average puzzle solving time / puzzle difficulty benchmark time) - 1

[0038] The calculation formula of the second adjustment factor is as follows:

[0039] w2 = weight ratio × basic time impact value.

[0040] Furthermore, the determining of the puzzle-solving dimension and the switching dimension during the player's game process based on the difficulty level, and the determining of the switching rules between the puzzle-solving dimension and the switching dimension, include:

[0041] Establishing a difficulty level table, the difficulty level table including a basic difficulty and a difficulty coefficient range corresponding to each difficulty level, wherein the basic difficulty corresponds to the puzzle-solving dimension, the difficulty coefficient corresponds to a switching attribute of the switching dimension, and the switching attribute includes the number of switching dimensions and a switching frequency between the puzzle-solving dimension and the switching dimension;

[0042] Based on the switching attributes, a switching rule between the puzzle-solving dimension and the switching dimension is determined.

[0043] Furthermore, the method for generating the multi-dimensional puzzle game further includes:

[0044] Acquiring real-time behavior data of a player, and establishing a player performance prediction model based on the real-time behavior data, wherein the player performance prediction model is used to predict the player's potential performance in different puzzle attributes;

[0045] Based on the player performance prediction model, puzzle attributes of puzzles solved by the player during the game are dynamically adjusted, wherein the puzzle attributes include difficulty level and puzzle type.

[0046] In a second aspect, an embodiment of the present application provides a system for developing a multi-dimensional puzzle game, comprising:

[0047] The first processing module is used to establish multiple dimension types for the puzzle game;

[0048] The second processing module is used to obtain the player's historical behavior data and obtain the player's difficulty level during the game based on the historical behavior data;

[0049] A third processing module is configured to determine, based on the difficulty level, a puzzle-solving dimension and a switching dimension during the player's game process, and determine a switching rule between the puzzle-solving dimension and the switching dimension;

[0050] The fourth processing module is used to generate a puzzle instance based on the puzzle dimension, load the puzzle instance into the game scene corresponding to the puzzle dimension, and the puzzle instance responds to the switching between the puzzle dimension and the switching dimension in real time.

[0051] Among them, the puzzle-solving dimension is the basic dimension type when the player solves the puzzle, and the switching dimension is any one or more dimension types other than the puzzle-solving dimension among the multiple dimension types. Different puzzle-solving dimensions correspond to different game scenes and puzzle types.

[0052] In a third aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for generating a multi-dimensional puzzle game when executing the computer program.

[0053] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0054] The present application discloses a method for generating a multi-dimensional puzzle game. The method establishes multiple dimension types in the puzzle game, sets a puzzle-solving dimension and a switching dimension during the puzzle generation process, and enables players to solve corresponding puzzles by switching between the puzzle-solving dimension and the switching dimension. This increases the complexity and strategy of the game, thereby ensuring that the player's gaming experience is sufficiently challenging. In addition, the method determines the player's difficulty level based on the player's historical behavior data. The difficulty level is associated with the puzzle-solving dimension and the switching dimension, thereby providing players with puzzles that match their current skill level. At the same time, the difficulty of the puzzle is measured by the switching properties of the switching dimension, thereby increasing the complexity of the generated puzzle and thus increasing the challenge of the puzzle. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 This is a flow chart of a method for generating a multi-dimensional puzzle game provided by one embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of the structure of a multi-dimensional puzzle game development system provided by one embodiment of the present invention;

[0058] Figure 3 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0060] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0061] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0062] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0063] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0064] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0065] See also Figure 1 As shown, the present invention is a method for generating a multi-dimensional puzzle game, comprising the following steps:

[0066] S100, establishing multiple dimension types for puzzle games;

[0067] The multi-dimensional puzzle game of the present application allows players to switch between different dimensions in real time during the game. Switching between different dimensions affects the player's perspective and movement, and at the same time affects the solution to the puzzle. Multi-dimensional switching increases the complexity and strategy of the game, thereby ensuring that the player's gaming experience is sufficiently challenging.

[0068] In some embodiments, the above step S100 includes:

[0069] Establishing a dimension manager, and generating different dimension types by modifying dimension attributes in the dimension manager, wherein the dimension attributes include physical attributes and environmental attributes;

[0070] In different puzzle dimensions, different game scenes and puzzle types are generated based on the physical properties and the environmental properties. The dimension types include normal dimension, gravity dimension and time dimension. The puzzle types include sliding block puzzles, number puzzles and path planning.

[0071] In this embodiment, a dimension manager is provided to define the physical and environmental properties of different dimension types, ensuring seamless player transitions between dimensions and enriching the game mechanics. Furthermore, players can customize the physical and environmental properties through the dimension manager, generating a custom dimension. This custom dimension can then be incorporated into puzzle creation, enhancing player engagement.

[0072] Specifically, physical properties include gravity parameters, time parameters, and space parameters, and environmental properties include light intensity, hue, etc. Therefore, multiple dimension types are based on normal dimensions. By adjusting the parameter values of physical properties and environmental properties, different dimensions are generated. For example, by adjusting the gravity parameters, a gravity dimension can be generated, such as setting a low gravity dimension with a gravity parameter of 20% of the gravity of the normal dimension. When defining dimension attributes through the dimension manager, you need to enter the dimension name and the corresponding dimension attribute parameters, and save to generate a dimension type.

[0073] In this embodiment, when any one of the multiple dimension types is used as the puzzle-solving dimension, different game scenes and puzzle types can be generated according to the dimension type of the puzzle-solving dimension. For example, when the normal dimension is used as the puzzle-solving dimension, the game scene is set to a familiar environment in daily life, such as a city street, park, school, etc. The puzzle type is a slider puzzle, a digital puzzle, or a path planning puzzle under normal physical conditions. If the gravity dimension is used as the puzzle-solving dimension, the game scene is a simulated space environment or a special laboratory. Players can move and operate objects under low gravity or anti-gravity conditions by setting the parameter values of the physical properties. The puzzle type is a puzzle designed according to the gravity characteristics. For example, in a dimension with low gravity as the gravity characteristic, the slider of the slider puzzle becomes light and can be easily moved, and the puzzle matching is completed. If the time dimension is used as the puzzle-solving dimension, the game scene is a special area where time is slowed down or accelerated, in which players can perform slow motion or fast motion operations. The corresponding puzzle type is a puzzle that requires complex operations or observations under special time characteristics. For example, the sliding block puzzle type corresponding to the time dimension with a decelerated time characteristic requires capturing the details of the complete puzzle in a fast-moving object or scene to help players better observe and analyze.

[0074] In some embodiments, generating different game scenes and puzzle types includes:

[0075] Divide the game scene into multiple basic components, and load scene attributes of the corresponding dimension type for the basic components according to the dimension attributes of the dimension type, wherein the scene attributes include environmental factors, art resources and exchange logic;

[0076] Different basic components correspond to different dimension types. When switching dimensions, the game scene is dynamically adjusted by adjusting the scene properties of the basic components.

[0077] Machine learning algorithms are used to analyze players' historical preference data to obtain players' preferred puzzle types.

[0078] In this embodiment, loading environmental factors that match the dimensional attributes, such as lighting, weather, temperature, etc., can create a more realistic and vivid game world. At the same time, according to the dimensional attributes, corresponding art resources are selected and loaded, such as terrain maps, vegetation models, architectural styles, etc., to make the game scene more visually rich and attractive. In addition, by setting exchange logic related to the dimensional attributes, such as the character's movement speed on different terrains, the changes of items in different climates, etc., the interactivity and playability of the game can be further increased.

[0079] In this embodiment, the game scene can be used to coordinate the interaction between puzzle instances and other game elements. By dynamically adjusting the display and interaction of puzzles in different dimension types, the game scene can be dynamically changed. Other game elements include characters, UI, and sound effects. Specifically, according to the dimensional attributes of the dimension type, the corresponding scene attributes are added to these basic components. This is the key to achieving dynamic and diverse game scenes. Dimensional attributes may include terrain, climate, time of day, etc. These attributes determine the basic characteristics and atmosphere of the game scene.

[0080] In this embodiment, the game scene is divided into multiple basic components. According to the dimensional attributes of the dimension type corresponding to the basic component, the corresponding scene attributes can be loaded and generated. Thus, different basic components can represent the same dimension type or different dimension types. When switching dimensions, each basic component can be switched separately. By switching the dimensional attributes of the dimension, the game scene in the basic component is adjusted. For example, in a sliding block puzzle game in which the puzzle dimension is the normal dimension, during the puzzle-solving process of one of the basic components, the player needs to switch from the normal dimension to the time dimension, and then capture the instantly changing puzzle elements. At this time, the above-mentioned basic component to be switched will load different scene attributes, art resources and interaction logic according to the time characteristics in the time dimension. In summary, in a game scene, after the dimension switching, different basic components present different styles and layouts in different dimension types.

[0081] In this embodiment, by dividing the game scenes, the game can be modularly designed. Each basic component carries specific functions and attributes. This makes the game structure clearer, helps reduce unnecessary resource loading and rendering, improves the game's operating efficiency and performance, and facilitates management and maintenance for developers. In addition, modular basic components make the game scenes more flexible during the development process. Developers can easily add, delete, or modify basic components as needed to cope with changes and updates to game content. At the same time, because each basic component is independent, it can be easily reused or expanded to adapt to different game scenes and needs.

[0082] In this embodiment, player game behavior data on various puzzle types is collected from data sources such as game logs and player behavior records. The game behavior data includes key indicators such as the player's game time, completion rate, number of attempts, and success rate for different puzzle types. These key indicators are used as historical preference data for analysis. Specifically, the historical preference data is cleaned to remove invalid, abnormal, or duplicate data, and the data is standardized to ensure that data from different sources and scales can be compared and analyzed. Preference features that reflect player preferences are extracted from the standardized data. The preference features include the percentage of player game time on a certain puzzle type, the average completion rate, and the ratio of the number of attempts to the success rate. Based on the preference features, a preference weight is calculated for each puzzle type. The preference weight calculation can use a weighted average method, such as assigning a weight coefficient to each preference feature, then adding the product of each feature value and the corresponding weight coefficient to obtain a preference weight for each puzzle type. Based on the preference weights, multiple puzzle types are ranked to determine the player's preferred puzzle type.

[0083] In this embodiment, the player's current game status is evaluated, such as game progress, unlocked puzzle types, remaining time, etc., and the puzzle type selection strategy is adjusted according to the game context, such as giving priority to puzzle types related to the current game progress, or selecting puzzle types that are moderately time-consuming based on the remaining time.

[0084] S200: Obtain historical behavior data of the player, and based on the historical behavior data, obtain the difficulty level of the player during the game;

[0085] In this embodiment, the difficulty of the generated puzzles is dynamically adjusted based on the player's game progress, game performance, and skill level. At this time, as the player continues to advance in the game, the complexity and challenge of the puzzles will also change accordingly to maintain the fun and challenge of the game. Therefore, it is necessary to obtain the player's historical behavior data. Through the player's historical behavior data, the player's difficulty level during the game is determined, and puzzles of corresponding difficulty are dynamically generated.

[0086] In some embodiments, step S200 includes:

[0087] Collecting historical behavior data of players, including player level, number of puzzles solved, puzzle-solving success rate, average puzzle-solving time, and game performance, where the game performance refers to the player's puzzle-solving status within a preset time period;

[0088] Based on the player level, set a basic difficulty for the player;

[0089] Dynamically adjust the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time;

[0090] Based on the basic difficulty and the difficulty coefficient, the difficulty level of the player during the game is determined.

[0091] In this embodiment, the number of basic difficulties is determined based on the number of preset player levels and the number of dimension types. Specifically, in the difficulty level table, a basic difficulty range is defined for each player level. For example, player levels 1-10 correspond to basic difficulty 1, player levels 11-20 correspond to basic difficulty 2, and so on, to determine the basic difficulty corresponding to the player level.

[0092] In this embodiment, multiple levels of difficulty coefficients are set in each basic difficulty. Within the same basic difficulty, multiple levels of difficulty coefficients are set, and each level of difficulty coefficient corresponds to a different difficulty. The difficulty level under the same basic difficulty is dynamically adjusted based on the player's puzzle-solving success rate and average puzzle-solving time, thereby providing players with puzzles that match their current skill level. Players will not feel bored because they are too simple, nor will they feel frustrated because they are too difficult. This ensures that the game is attractive to players of different levels and increases the replayability of the game.

[0093] In this embodiment, the basic difficulty and the difficulty coefficient are combined to generate the difficulty level during the player's game process. It can be understood that different difficulty levels correspond to different puzzle-solving difficulties.

[0094] In a preferred embodiment, the difficulty level table is as follows:

[0095]

[0096] Specifically, a player's player level is 2. In the above difficulty level table, the corresponding basic difficulty is 1. Based on the puzzle-solving success rate and average puzzle-solving time in the player's historical behavior data, the corresponding difficulty coefficient is calculated to be 0.05. It can be seen from the above table that when the difficulty coefficient is 0.05 and the basic difficulty is 1, the corresponding difficulty level is 1.1. In this difficulty level, the difficulty of each puzzle type is relatively simple, and there are fewer steps to solve it.

[0097] It can be understood that each difficulty level corresponds to a puzzle library, which includes puzzles of each difficulty level corresponding to each puzzle type. When a new puzzle game needs to be generated, after determining the puzzle type in the current game, the corresponding puzzle can be randomly called from the puzzle library and loaded into the game scene for players to solve.

[0098] In some embodiments, dynamically adjusting the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time includes:

[0099] If the puzzle-solving success rate is higher than a success rate threshold and the average puzzle-solving time is lower than a time threshold, generating a first difficulty coefficient, wherein the first difficulty coefficient is used to increase the difficulty of the puzzle;

[0100] If the puzzle-solving success rate is lower than a success rate threshold or the average puzzle-solving time is higher than a time threshold, generating a second difficulty coefficient, wherein the second difficulty coefficient is used to reduce the difficulty of the puzzle;

[0101] The calculation formula of the first difficulty coefficient is as follows:

[0102] d1=i+(s-s0)×w1

[0103] The calculation formula of the second difficulty coefficient is as follows:

[0104]

[0105] Among them, d1 represents the first difficulty coefficient, d2 represents the second difficulty coefficient, i represents the basic difficulty coefficient, i = number of solved puzzles / number of puzzles, the number of puzzles represents the preset number of puzzles to be solved under the player level, w1 represents the first adjustment factor, which is used to balance the coefficient of the player level and the game performance, and w2 represents the second adjustment factor, which is used to balance the coefficient of the puzzle-solving success rate and the average puzzle-solving time.

[0106] In this embodiment, when the puzzle-solving success rate is higher than the success rate threshold and the average puzzle-solving time is lower than the time threshold, it indicates that the player's game performance is good, and the difficulty coefficient can be appropriately increased. Specifically, by setting a first difficulty coefficient, the first difficulty coefficient is measured by the puzzle-solving success rate to ensure that the difficulty of the generated puzzle is within the player's game level. At the same time, by setting a first adjustment factor, the player level and game performance are balanced, so that the overall difficulty level of the game is in a steadily increasing trend, so that the player will neither feel frustrated due to a sudden increase in game difficulty nor feel bored due to the unchanged difficulty, thereby balancing the player's progress speed and the challenge of the game.

[0107] In this embodiment, when the puzzle-solving success rate is lower than the success rate threshold or the average puzzle-solving time is higher than the time threshold, it means that the player has encountered difficulties in the puzzle-solving process and the game difficulty needs to be appropriately reduced. Specifically, by setting a second difficulty coefficient, wherein the second difficulty coefficient is associated with the average puzzle-solving time and the puzzle-solving success rate, the puzzle-solving success rate and the puzzle difficulty are balanced by (1-s), and the puzzle-solving time and the puzzle difficulty are balanced by (t-t0). At the same time, by setting the first adjustment factor, the player level and game performance are balanced. On this basis, the second adjustment factor is set to ensure that the time factor and the success rate factor have considerable weight when adjusting the difficulty coefficient, so as to balance the impact of the puzzle-solving success rate and the average puzzle-solving time on the game difficulty.

[0108] In this embodiment, i represents the basic difficulty coefficient, which is calculated as follows: i = number of puzzles solved / number of puzzles, where the number of puzzles is the preset number of puzzles for the player level. Specifically, under a preset situation, the player must solve a certain number of puzzles before advancing to the next difficulty level. This number of puzzles is derived from the puzzle-solving data of all players in the game and has a certain degree of universality, reflecting the puzzle-solving ability and progress of most players. At the same time, through this basic difficulty coefficient, developers can more easily measure and adjust subsequent difficulty settings to ensure that the overall difficulty curve of the game meets design expectations.

[0109] In some embodiments, dynamically adjusting the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time further includes:

[0110] Through the expert evaluation method, the weight ratio of the puzzle-solving success rate and the average puzzle-solving time in the difficulty coefficient adjustment process is obtained;

[0111] Obtaining a basic time impact value based on the average puzzle-solving time and the puzzle difficulty benchmark time, wherein the puzzle difficulty benchmark time is the time taken to solve a preset puzzle;

[0112] Obtaining the second adjustment factor based on the weight ratio and the basic time impact value;

[0113] The calculation formula of the basic time impact value is as follows:

[0114] Basic time impact value = (average puzzle solving time / puzzle difficulty benchmark time) - 1

[0115] The calculation formula of the second adjustment factor is as follows:

[0116] w2 = weight ratio × basic time impact value.

[0117] In this embodiment, the second adjustment factor is used to adjust the impact of the player's average puzzle-solving time on the difficulty coefficient, so as to ensure that the time factor and the success rate factor have considerable weight when adjusting the difficulty coefficient, that is, when the player's average puzzle-solving time increases, the difficulty coefficient should be reduced accordingly to reflect the player's lack of puzzle-solving speed; conversely, when the average puzzle-solving time decreases, the difficulty coefficient should be increased accordingly to reflect the player's improvement in puzzle-solving speed. On this basis, the relative importance between the puzzle-solving success rate and the average puzzle-solving time needs to be determined. Therefore, in this embodiment, the weights of the puzzle-solving success rate and the average puzzle-solving time in the difficulty coefficient adjustment are determined by adopting an expert evaluation method. In some other embodiments, the determination is also made by adopting methods such as player feedback or data analysis.

[0118] In this embodiment, a basic time impact value is also provided. The above-mentioned basic time impact value represents the influence of the average puzzle-solving time on the difficulty coefficient in the absence of a second adjustment factor, thereby balancing the influence of the time factor on the difficulty coefficient. Specifically, by recording the average time of a large number of players in the puzzle-solving process, the puzzle difficulty benchmark time of the puzzle is obtained, thereby obtaining the basic time impact value. Specifically, the calculation formula of the above-mentioned basic time impact value compares the average puzzle-solving time of the player with the puzzle difficulty benchmark time to calculate a relative time impact value.

[0119] In one embodiment, for the first adjustment factor, an association model is established by analyzing the relationship between the player level and the game performance. The association model is used to simulate the impact of the adjustment of the player level on the player performance under different first adjustment factors. Specifically, based on the results of the simulation experiment, the effect of the difficulty coefficient adjustment under different first adjustment factors is evaluated, so as to determine the final first adjustment factor for the calculation of the first difficulty coefficient and the second difficulty coefficient.

[0120] S300, determining a puzzle-solving dimension and a switching dimension during the player's game process based on the difficulty level, and determining a switching rule between the puzzle-solving dimension and the switching dimension;

[0121] In this embodiment, during the puzzle generation process, a puzzle-solving dimension and a switching dimension are set, and the player's solution to the corresponding puzzle is achieved by switching between the puzzle-solving dimension and the switching dimension. In addition, the player's difficulty level is associated with the puzzle-solving dimension and the switching dimension, thereby providing players with puzzles that match their current skill level. At the same time, the difficulty of the puzzle is measured by the switching attributes of the switching dimension, thereby increasing the complexity of the puzzle and thus the challenge of the puzzle.

[0122] In some embodiments, step S300 includes:

[0123] Establishing a difficulty level table, the difficulty level table including a basic difficulty and a difficulty coefficient range corresponding to each difficulty level, wherein the basic difficulty corresponds to the puzzle-solving dimension, the difficulty coefficient corresponds to a switching attribute of the switching dimension, and the switching attribute includes the number of switching dimensions and a switching frequency between the puzzle-solving dimension and the switching dimension;

[0124] Based on the switching attributes, a switching rule between the puzzle-solving dimension and the switching dimension is determined.

[0125] In this embodiment, the basic difficulty is associated with the puzzle-solving dimension, and each level of basic difficulty corresponds to a dimension type. For example, in the above-mentioned difficulty level table, when the basic difficulty is 1, the puzzle-solving dimension corresponding to the puzzle is the normal dimension. Based on the normal dimension, the corresponding game scene and puzzle are generated. At the same time, the dimension switching is performed through the switching dimensions preset by the basic components in the game scene. The difficulty of the generated puzzle is measured by the switching properties of each switching dimension. For example, when the difficulty coefficient is 0.0-0.1, the number of its switching dimensions is one. In the process of puzzle solving, it is necessary to switch between the above-mentioned switching dimensions and the puzzle-solving dimensions to obtain the final puzzle-solving result. It can be understood that the switching efficiency between the switching dimensions and the puzzle-solving dimensions can also be used to measure the difficulty of the generated puzzle. The higher the difficulty level corresponding to the player's player level, the higher the number of switching dimensions and the switching frequency between dimensions, so that the puzzle has more corresponding solution steps, increases the complexity of the puzzle, and thus increases the challenge of the puzzle.

[0126] In this embodiment, switching rules between the puzzle-solving dimension and the switching dimension are preset, so that players can switch between different dimensions to solve puzzles. For example, in the process of solving a sliding block puzzle, the normal dimension is used as the puzzle-solving dimension, and the slider puzzle operates normally in the normal dimension. Players can move the slider, but some of the basic components are set to the switching dimension and are locked and cannot be moved. For example, one of the basic components is set to a low-gravity dimension. The player must switch it to the low-gravity dimension before the puzzle slider can become light and the originally locked slider can be moved. Moreover, according to the physical properties of the low-gravity dimension, the movement speed of the puzzle slider is slower than that of the normal dimension. For example, one of the basic components is set to a time-accelerated dimension. When switching to the time-accelerated dimension, the puzzle slider moves faster according to the set physical properties. In some embodiments, certain time-limited mechanisms can be triggered through the above-mentioned time-accelerated dimension, thereby increasing the difficulty of the game experience.

[0127] In this embodiment, a script is written to bind dimension switching events for switching between dimensions. For example, in one embodiment, when the player presses the "L" key in the puzzle dimension, the `SwitchDimension("Low Gravity Dimension")` function is called to implement the switch. It can be understood that each dimension type is corresponding to a trigger button. When the player triggers the trigger button during the game, the above-mentioned SwitchDimension function is called to perform a preliminary dimension call, and then the player's current dimension type is obtained. The final dimension switching is implemented by calling the SetDimensionInteraction function. For example, in one embodiment, when the player switches from the "Low Gravity Dimension" to the "Time Slowdown Dimension", based on the rule that the player's speed is halved, the dimension switch is implemented by calling `SetDimensionInteraction("Low Gravity Dimension", "Time Slowdown Dimension", "speedHalve")`, where "speedHalve" represents the preset switching rule called.

[0128] S400: Generate a puzzle instance based on the puzzle-solving dimension, load the puzzle instance into a game scene corresponding to the puzzle-solving dimension, and the puzzle instance responds to switching between the puzzle-solving dimension and the switching dimension in real time.

[0129] In this embodiment, a puzzle instance is a specific puzzle object generated based on the puzzle type and specific parameters. It is the concretization and implementation of the puzzle, containing specific puzzle data and logic, such as the layout, initial state, and target state of a sliding block puzzle, and thus providing players with the actual solution. The game scene, on the other hand, is the container for game content, displaying the current game state. It presents the player with the visual environment and interactive interface within the game, allowing players to interact with game objects. The interactive interface includes objects, characters, and environments within the scene.

[0130] In this embodiment, the puzzle instance is loaded into the game scene, and the player can operate the puzzle in the game scene, such as moving sliders, rotating parts, etc. The game scene is responsible for processing the player's operations and updating the display according to the logic of the puzzle instance to provide instant feedback. At the same time, the game scene not only contains puzzles, but may also contain characters, story backgrounds and environments, so that the puzzles are integrated with the overall plot of the game. In addition, the puzzle instance can be affected by environmental factors in the game scene, such as physical effects, light and shadow changes in the scene, etc., to increase the challenge and fun of the puzzle. In addition, the game scene can save the player's operations and progress, and support the game's pause, resume and archive functions. Specifically, by calling the `GeneratePuzzle("PuzzleType",difficultyLevel)` function, a puzzle is generated according to the specified difficulty level and puzzle type, where PuzzleType represents the puzzle type and difficultyLevel represents the difficulty level.

[0131] In some embodiments, the method for generating a multi-dimensional puzzle game includes:

[0132] Acquiring real-time behavior data of a player, and establishing a player performance prediction model based on the real-time behavior data, wherein the player performance prediction model is used to predict the player's potential performance in different puzzle attributes;

[0133] Based on the player performance prediction model, puzzle attributes of puzzles solved by the player during the game are dynamically adjusted, wherein the puzzle attributes include difficulty level and puzzle type.

[0134] This embodiment also includes a dynamic puzzle generation method, which can adjust the puzzle attributes of the puzzle in real time according to the player's game style, skill level and progress, providing a personalized and continuously challenging game experience.

[0135] In this embodiment, during the game, the player's game behavior data is recorded through an event listener, the collected data is cached locally or in memory, and transmitted to the data analysis module periodically or in real time. The game behavior data includes puzzle-solving time, operation sequence, number of failures, and hint usage. The puzzle-solving time is the time taken from the start to the completion of each puzzle; the operation sequence is the sequence of operations performed by the player during the puzzle-solving process, which is used to analyze the problem-solving strategy; the number of failures is the number of times the player failed or retried in the puzzle; and the hint usage indicates whether the player used the hint function and the number of times. In the data analysis module, feature extraction is performed on the game behavior data to obtain time features, operation features, and error features. The time feature represents the average puzzle-solving time and puzzle-solving time distribution; the operation feature represents the average number of operations and commonly used problem-solving strategy patterns; and the error feature represents common error types and error frequency. Based on the above features, a player performance prediction model is established to estimate the player's potential performance in puzzles of different difficulty levels and types.

[0136] In this embodiment, the player's difficulty level is obtained, and based on the evaluation results of the above-mentioned player performance prediction model, a certain number of puzzle instances are pre-generated and stored in a cache for immediate call. When the player requests a new puzzle, the system can immediately call the pre-generated puzzle instance from the cache without the need for real-time calculation or loading, thereby greatly reducing waiting time and improving the smoothness and responsiveness of the game.

[0137] See also Figure 2 As shown, the present invention also provides a multi-dimensional puzzle game development system, the system comprising:

[0138] The first processing module 201 is used to establish multiple dimension types for the puzzle game;

[0139] The second processing module 202 is used to obtain the player's historical behavior data, and obtain the player's difficulty level during the game based on the historical behavior data;

[0140] The third processing module 203 is used to determine the puzzle-solving dimension and the switching dimension during the player's game process based on the difficulty level, and determine the switching rules between the puzzle-solving dimension and the switching dimension;

[0141] Fourth processing module 204: for generating a puzzle instance based on the puzzle-solving dimension, loading the puzzle instance into a game scene corresponding to the puzzle-solving dimension, wherein the puzzle instance responds in real time to switching between the puzzle-solving dimension and the switching dimension;

[0142] Among them, the puzzle-solving dimension is the basic dimension type when the player solves the puzzle, and the switching dimension is any one or more dimension types other than the puzzle-solving dimension among the multiple dimension types. Different puzzle-solving dimensions correspond to different game scenes and puzzle types.

[0143] It is understandable that if Figure 1 The contents of the embodiment of the method for generating a multi-dimensional puzzle game shown in the figure are applicable to the embodiment of the development system of the multi-dimensional puzzle game. The functions specifically implemented by the embodiment of the development system of the multi-dimensional puzzle game are similar to those of the embodiment of the method for generating a multi-dimensional puzzle game shown in the figure. Figure 1 The generation method of the multi-dimensional puzzle game shown in the embodiment is the same as that of the embodiment shown in the embodiment, and the beneficial effects achieved are the same as those of the embodiment shown in the embodiment. Figure 1 The beneficial effects achieved by the embodiment of the method for generating a multi-dimensional puzzle game shown are also the same.

[0144] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0146] See also Figure 3 As shown, an embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a method for generating a multi-dimensional puzzle game as described in any one of the above methods is implemented.

[0147] The computer device 3 may be a desktop computer, a notebook computer, a PDA, a cloud server or other computing devices. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that Figure 3 This is merely an example of the computer device 3 and does not constitute a limitation on the computer device 3 . The computer device 3 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 3 may also include input and output devices, network access devices, etc.

[0148] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0149] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 3. Furthermore, the memory 302 may include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is about to be output.

[0150] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for generating a multi-dimensional puzzle game as described in any one of the above methods is implemented.

[0151] In this embodiment, if the integrated unit is implemented in the form of 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 present application implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying computer program code to a camera / computer device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard drive, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0152] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for generating a multi-dimensional puzzle game, characterized in that: include: Establish multiple dimension types for puzzle games; Obtaining historical behavior data of the player, and obtaining the difficulty level of the player during the game based on the historical behavior data; Based on the difficulty level, determining a puzzle-solving dimension and a switching dimension during the player's game process, and determining a switching rule between the puzzle-solving dimension and the switching dimension; Generate a puzzle instance based on the puzzle-solving dimension, load the puzzle instance into a game scene corresponding to the puzzle-solving dimension, and the puzzle instance responds in real time to switching between the puzzle-solving dimension and the switching dimension; Among them, the puzzle-solving dimension is the basic dimension type when the player solves the puzzle, and the switching dimension is any one or more dimension types other than the puzzle-solving dimension among the multiple dimension types. Different puzzle-solving dimensions correspond to different game scenes and puzzle types.

2. The method according to claim 1, wherein The above-mentioned method establishes multiple dimension types for puzzle games, including: Establishing a dimension manager, and generating different dimension types by modifying dimension attributes in the dimension manager, wherein the dimension attributes include physical attributes and environmental attributes; In different puzzle dimensions, different game scenes and puzzle types are generated based on the physical properties and the environmental properties. The dimension types include normal dimension, gravity dimension and time dimension. The puzzle types include sliding block puzzles, number puzzles and path planning.

3. The method according to claim 2, wherein The generation of different game scenes and puzzle types includes: Divide the game scene into multiple basic components, and load scene attributes of the corresponding dimension type for the basic components according to the dimension attributes of the dimension type, wherein the scene attributes include environmental factors, art resources and exchange logic; Different basic components correspond to different dimension types. When switching dimensions, the game scene is dynamically adjusted by adjusting the scene properties of the basic components. Machine learning algorithms are used to analyze players' historical preference data to obtain players' preferred puzzle types.

4. The method according to claim 1, wherein The acquiring of the player's historical behavior data and obtaining the player's difficulty level during the game based on the historical behavior data includes: Collecting historical behavior data of players, including player level, number of puzzles solved, puzzle-solving success rate, average puzzle-solving time, and game performance, where the game performance refers to the player's puzzle-solving status within a preset time period; Based on the player level, set a basic difficulty for the player; Dynamically adjust the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time; Based on the basic difficulty and the difficulty coefficient, the difficulty level of the player during the game is determined.

5. The method according to claim 4, wherein The dynamically adjusting the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time includes: If the puzzle-solving success rate is higher than a success rate threshold and the average puzzle-solving time is lower than a time threshold, generating a first difficulty coefficient, wherein the first difficulty coefficient is used to increase the difficulty of the puzzle; If the puzzle-solving success rate is lower than a success rate threshold or the average puzzle-solving time is higher than a time threshold, generating a second difficulty coefficient, wherein the second difficulty coefficient is used to reduce the difficulty of the puzzle; The calculation formula of the first difficulty coefficient is as follows: d1=i+(s-s0)×w1 The calculation formula of the second difficulty coefficient is as follows: Among them, d1 represents the first difficulty coefficient, d2 represents the second difficulty coefficient, i represents the basic difficulty coefficient, i = number of solved puzzles / number of puzzles, the number of puzzles represents the preset number of puzzles to be solved under the player level, w1 represents the first adjustment factor, which is used to balance the coefficient of the player level and the game performance, and w2 represents the second adjustment factor, which is used to balance the coefficient of the puzzle-solving success rate and the average puzzle-solving time.

6. The method according to claim 5, wherein The dynamically adjusting the difficulty coefficient of the basic difficulty based on the puzzle-solving success rate and the average puzzle-solving time further includes: Through the expert evaluation method, the weight ratio of the puzzle-solving success rate and the average puzzle-solving time in the difficulty coefficient adjustment process is obtained; Obtaining a basic time impact value based on the average puzzle-solving time and the puzzle difficulty benchmark time, wherein the puzzle difficulty benchmark time is the time taken to solve a preset puzzle; Obtaining the second adjustment factor based on the weight ratio and the basic time impact value; The calculation formula of the basic time impact value is as follows: Basic time impact value = (average puzzle solving time / puzzle difficulty benchmark time) - 1 The calculation formula of the second adjustment factor is as follows: w2 = weight ratio × basic time impact value.

7. The method according to claim 1, wherein The determining of the puzzle-solving dimension and the switching dimension during the player's game process based on the difficulty level, and determining the switching rules between the puzzle-solving dimension and the switching dimension, includes: Establishing a difficulty level table, the difficulty level table including a basic difficulty and a difficulty coefficient range corresponding to each difficulty level, wherein the basic difficulty corresponds to the puzzle-solving dimension, the difficulty coefficient corresponds to a switching attribute of the switching dimension, and the switching attribute includes the number of switching dimensions and a switching frequency between the puzzle-solving dimension and the switching dimension; Based on the switching attributes, a switching rule between the puzzle-solving dimension and the switching dimension is determined.

8. The method according to claim 1, wherein The method further comprises: Acquiring real-time behavior data of a player, and establishing a player performance prediction model based on the real-time behavior data, wherein the player performance prediction model is used to predict the player's potential performance in different puzzle attributes; Based on the player performance prediction model, puzzle attributes of puzzles solved by the player during the game are dynamically adjusted, wherein the puzzle attributes include difficulty level and puzzle type.

9. A multi-dimensional puzzle game development system, characterized in that: include: The first processing module is used to establish multiple dimension types for the puzzle game; The second processing module is used to obtain the player's historical behavior data and obtain the player's difficulty level during the game based on the historical behavior data; A third processing module is configured to determine, based on the difficulty level, a puzzle-solving dimension and a switching dimension during the player's game process, and determine a switching rule between the puzzle-solving dimension and the switching dimension; A fourth processing module is configured to generate a puzzle instance based on the puzzle-solving dimension, load the puzzle instance into a game scene corresponding to the puzzle-solving dimension, and enable the puzzle instance to respond in real time to switching between the puzzle-solving dimension and the switching dimension; Among them, the puzzle-solving dimension is the basic dimension type when the player solves the puzzle, and the switching dimension is any one or more dimension types other than the puzzle-solving dimension among the multiple dimension types. Different puzzle-solving dimensions correspond to different game scenes and puzzle types.

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

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