Graph generation method, device and equipment and computer readable storage medium

Abstract

The application provides a pattern generation method and device, equipment and a computer readable storage medium; the method comprises: performing state analysis processing on a to-be-filled chessboard to obtain a plurality of state indicators of the to-be-filled chessboard; determining a situation score of the to-be-filled chessboard based on the plurality of state indicators of the to-be-filled chessboard; determining a pattern combination corresponding to the to-be-filled chessboard based on the situation score, and releasing the pattern combination to the to-be-filled chessboard; wherein the pattern combination is used to be filled to an unfilled area unit of the to-be-filled chessboard as a whole through control operation. Through the application, the adaptability of the released pattern combination to the to-be-filled chessboard can be improved.

Description

Technical Field

[0001] This application relates to computer technology, and more particularly to a method, apparatus, device, and computer-readable storage medium for generating graphics. Background Technology

[0002] In games involving matching shapes on a board, shapes are often randomly generated. Users fill the board with these randomly generated shapes, aiming to eliminate any shape in a row or column once it is filled. While this allows users to play normally, the random generation of shapes can easily lead to a dead end, where no shape can be filled. This can discourage users from continuing to play, reducing the game's playability and user engagement. Summary of the Invention

[0003] This application provides a graphic generation method, apparatus, and computer-readable storage medium that can improve the adaptability of the released graphic combination to the chessboard to be filled.

[0004] The technical solution of this application embodiment is implemented as follows:

[0005] This application provides a method for generating graphics, including:

[0006] A state analysis process is performed on the chessboard to be filled to obtain multiple state indicators of the chessboard to be filled.

[0007] Based on multiple state indicators of the chessboard to be filled, the situation score of the chessboard to be filled is determined;

[0008] Based on the situation score, determine the graphic combination corresponding to the chessboard to be filled, and release the graphic combination to the chessboard to be filled;

[0009] The graphic combination is used to be filled into the unfilled area units of the chessboard to be filled through a control operation.

[0010] This application provides a graphics generation apparatus, including:

[0011] The analysis module is used to perform state analysis processing on the chessboard to be filled, and obtain multiple state indicators of the chessboard to be filled.

[0012] The first determining module is used to determine the situation score of the chessboard to be filled based on multiple state indicators of the chessboard to be filled.

[0013] The second determining module is used to determine the graphic combination corresponding to the chessboard to be filled based on the situation score, and release the graphic combination to the chessboard to be filled; wherein, the graphic combination is used to be filled into the unfilled area units of the chessboard to be filled by control operation.

[0014] In some embodiments, the analysis module is further configured to perform a filling ratio analysis on the chessboard to be filled to obtain the density of the chessboard to be filled; perform a difficulty prediction process on the chessboard to be filled to obtain the difficulty of eliminating the chessboard to be filled; perform a risk area identification process on the chessboard to be filled to obtain the risk level of the chessboard to be filled; and use the density of the chessboard to be filled, the difficulty of eliminating the chessboard to be filled, and the risk level of the chessboard to be filled as multiple state indicators of the chessboard to be filled.

[0015] In some embodiments, the analysis module is further configured to obtain a first number of already filled area units in the chessboard to be filled and a second number of area units in the chessboard to be filled; and to use the ratio of the first number to the second number as the density of the chessboard to be filled.

[0016] In some embodiments, the analysis module is further configured to perform the following processing for each row of the chessboard to be filled: determine a third number of unfilled area units in the row, and obtain the row elimination difficulty positively correlated with the third number; perform the following processing for each column of the chessboard to be filled: determine a fourth number of unfilled area units in the column, and obtain the column elimination difficulty positively correlated with the fourth number; and perform a fusion processing on the row elimination difficulty of multiple rows and the column elimination difficulty of multiple columns to obtain the elimination difficulty of the chessboard to be filled.

[0017] In some embodiments, the analysis module is further configured to perform risk region identification processing on the chessboard to be filled to obtain the risk regions of the chessboard to be filled; determine the risk level of each risk region based on the openness of each risk region; and fuse the risk levels of multiple risk regions to obtain the risk level of the chessboard to be filled.

[0018] In some embodiments, the first determining module is further configured to determine the weight of each state indicator based on the interaction strategy type corresponding to the chessboard to be filled; and to perform weighted fusion of multiple state indicators based on the weight of each state indicator to obtain the situation score of the chessboard to be filled.

[0019] In some embodiments, the second determining module is further configured to remove risky graphic combinations from the candidate graphic combinations; and based on the situation score, determine the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations.

[0020] In some embodiments, the second determining module is further configured to: obtain candidate graphic combinations whose area exceeds a first threshold as the risk graphic combination; obtain candidate graphic combinations whose length of any side exceeds a second threshold as the risk graphic combination; and obtain candidate graphic combinations whose shape complexity exceeds a third threshold as the risk graphic combination.

[0021] In some embodiments, the second determining module is further configured to acquire the operation record of the object that triggered the control operation and the filling failure record; generate candidate failure regions of the chessboard to be filled based on the operation record and the filling failure record; determine the generation strategy of the graphic combination based on the situation score and the candidate failure regions; and determine the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations based on the generation strategy.

[0022] This application provides an electronic device, including:

[0023] Memory, used to store executable instructions;

[0024] The processor, when executing executable instructions stored in the memory, implements the graphics generation method provided in the embodiments of this application.

[0025] This application provides a computer-readable storage medium storing executable instructions for inducing a processor to execute and implement the graphics generation method provided in this application.

[0026] This application provides a computer program product, including a computer program or computer executable instructions, which, when executed by a processor, implements the graphics generation method provided in this application.

[0027] The embodiments of this application have the following beneficial effects:

[0028] The state analysis process of the chessboard to be filled yields multiple state indicators. Based on these indicators, a situation score is determined. These indicators accurately reflect the current state of the chessboard, ensuring the accuracy of the subsequent situation score. Based on the situation score, a corresponding graphic combination is determined and released to the chessboard. The state indicators reflect the current situation score of the chessboard, and releasing a graphic combination that matches the current chessboard prevents it from entering a dead end, thus improving the game's playability. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the architecture of the graphics generation system 100 provided in an embodiment of this application;

[0030] Figure 2 This is a schematic diagram of the structure of the electronic device 500 provided in the embodiments of this application;

[0031] Figure 3A This is a first flowchart illustrating the graphic generation method provided in this application embodiment;

[0032] Figure 3B This is a second flowchart illustrating the graphic generation method provided in this application embodiment;

[0033] Figure 4 This is a schematic diagram of the chessboard to be filled provided in the embodiments of this application. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0036] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0038] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0039] 1) Block Puzzle: A type of puzzle game with the core gameplay of combining, filling, or eliminating geometric blocks. Specifically, you can use pre-shaped blocks (usually multi-cell polygons, such as four-cell blocks, five-cell blocks, etc.) as the objects of manipulation. By placing, rotating, and splicing, you can make the blocks form complete rows and columns within a fixed area, fill specific spaces, or achieve elimination conditions. The game scene is usually a closed rectangular grid (such as a 10×20 grid area), an irregular outline area (such as a puzzle that fills in animal shapes), or a boundless space where you need to avoid stacking and overflowing. The blocks are composed of basic cells. Common shapes include four-cell blocks: 7 classic shapes (I, O, T, L, J, S, Z), which are the core elements of games such as Tetris; and five-cell blocks: 12 shapes, which are mostly used in more complex puzzle games. Customizable shapes: such as triangles and hexagonal blocks, adaptable to different grid rules; when a block fills a row / column or a specific area, that area is eliminated and points are awarded; completely cover the target area with a given block, and you pass the level without any gaps; by placing blocks strategically to avoid exceeding the area boundaries, the longer you survive, the higher your score; blocks can usually only be translated and rotated (some games support flipping), and once placed, they cannot be moved (some games offer a limited number of "undo" functions); simple shapes and spacious areas are provided in the early stages of the game, while increasing the complexity of the blocks as the level progresses (such as introducing more irregular shapes), reducing the operating space, or increasing the falling speed of the blocks; extra points are awarded for actions such as continuously eliminating multiple rows, quickly eliminating blocks, and passing the level with fewer blocks; the game can also include power-ups such as "eliminate a row," "rotate an area," and "replace a block" to reduce the difficulty of getting stuck.

[0040] 2) Fillable Board: The core game area based on a grid, used for placing blocks or pieces. The fillable board is a grid-like space with a fixed number of rows and columns. Each grid cell (such as a square or hexagon) serves as the smallest unit for placing blocks. Players must complete game objectives (such as filling, eliminating, or matching) within the grid by placing and assembling blocks (or pieces). The fillable board is divided into equally sized grid cells (such as 8×8, 9×9, 10×20, etc.) by clear horizontal and vertical lines. Each cell has fixed coordinates (such as row number and column number). The blocks placed by the player must completely occupy the grid cell and cannot exceed the row / column of the fillable board. The size of the game board directly affects the game's difficulty. For example, a small board (e.g., 5x5) has fewer grid cells, resulting in a lower margin for error in block placement and requiring more precise planning. A large board (e.g., 15x15) has more space, but block stacking can complicate later operations (e.g., eliminating multiple layers of blocks). Each grid cell on the board to be filled has a clear state, including an empty state (the target area where blocks can be placed), a filled state (the area covered by blocks, usually marked by color or pattern), and a locked / obstacle state (some levels have fixed obstacles (e.g., black blocks) that cannot be covered, increasing the difficulty of layout). As the player operates, the board will provide real-time feedback on state changes: in elimination games, a filled row / column of grid will disappear (e.g., the grid is cleared after a row is eliminated in Tetris); in filling games, the board will display "fill percentage" or "number of remaining empty cells"; when failing, areas outside the board boundaries or where blocks cannot be placed will trigger visual warnings (e.g., flashing borders).

[0041] 3) Status Indicators: Key parameters used to quantify and describe the current state of the chessboard. They intuitively reflect the game progress, remaining difficulty, and operational space, serving as the core basis for players to formulate strategies and determine victory or defeat. Status indicators can include the number of blank units, the percentage of filled units, the size of consecutive blank areas, the distribution density of obstacle units, the height of block stacking, the shape complexity of blank units, and the number of eliminateable areas. Among these, the number of blank units is the total number of grid units on the chessboard that are not covered by blocks (i.e., the remaining space where blocks can be placed). The higher the number of blank units, the greater the operational space and the higher the short-term error tolerance (e.g., when there are 50 blank units remaining on a 10×10 chessboard, placing blocks is more flexible). The lower the value (e.g., when there are 5 blank units remaining), the higher the requirements for matching the shape of subsequent blocks, and the easier it is to lose the game due to "no suitable blocks". The percentage of filled units is the percentage of the number of units covered by blocks out of the total number of effective grid units (excluding obstacles). The percentage of filled units intuitively reflects the filling progress (e.g., 80% percentage means that the goal of filling is close to being achieved). The size of consecutive blank areas is the largest connected area (horizontal / vertical / ...) formed by adjacent blank units. The number of diagonally adjacent units (due to game rules potentially limiting diagonal placement) and the size of consecutive blank areas determine the maximum size of a block that can be placed. For example, if the maximum consecutive blank area is 4 units, the player cannot place a 5-unit "long block" and must prioritize eliminating surrounding blocks to expand the space. Scattered connected areas (such as multiple small areas of 2-3 units) increase the difficulty of block combinations, requiring more precise planning of the splicing order. The distribution density of obstacle units refers to the number and positional distribution characteristics of fixed obstacle units (uncoverable grids) on the board (such as concentrated at the edge / center, scattered / dense). The distribution density of obstacle units directly increases the difficulty of layout. For example, dense obstacles in the center will divide blank areas, making it difficult to place large blocks. Edge obstacles may restrict the conventional strategy of "placing blocks along the edge," forcing players to adjust their layout logic.

[0042] 4) Graphic Combinations: Graphic combinations refer to the arrangement and combination of blocks of different shapes and sizes to fill the blank areas of the chessboard. The core of graphic combinations is to solve the problem of blank areas that cannot be matched by a single block through "multi-block collaborative placement". For example, an L-shaped blank area (3 units) cannot be filled by a 2-unit block alone. It needs to be covered by a combination of "1 2-unit block + 1 1-unit block"; or by rotating or flipping blocks (such as rotating a T-shaped block 90° and then combining it with a Z-shaped block) to fill irregular spaces. Its essence is to use the geometric characteristics (shape, symmetry, size) of blocks to achieve "full coverage of blank areas" or "achievement of elimination conditions"; graphic combinations can include single block combinations, double block combinations, and multi-block combinations. Among them, a single block combination refers to using only a single block to match a blank area (the simplest combination form), where the shape of the blank area perfectly matches the shape of the single block (such as a 2×2 square blank matching a 2×2 block), without the need for additional blocks; a double block combination covers a blank area by splicing two blocks together, such as an L-shaped block + a small square block, filling blank areas with corners, or two 2-unit long strip blocks spliced ​​horizontally to form a 4-unit long strip, covering a 1×4 blank area; a multi-block combination is the coordinated combination of three or more blocks, used to cover complex or scattered blank areas, suitable for blank areas that are fragmented (such as multiple 2-3 unit small areas) or have notches or isolated units.

[0043] This application provides a method, apparatus, device, and computer-readable storage medium for generating graphics, which can improve the adaptability of the released graphic combination to the chessboard to be filled. The exemplary application of the graphics generation device provided in this application is described below. The device provided in this application can be implemented as various types of user terminals such as laptops, tablets, desktop computers, set-top boxes, and mobile devices (e.g., mobile phones, portable music players, personal digital assistants, dedicated messaging devices, portable gaming devices), or as a server. The exemplary application when the device is implemented as a server will be described below.

[0044] See Figure 1 , Figure 1 This is a schematic diagram of the architecture of the graphics generation system 100 provided in the embodiments of this application. In order to support a graphics generation application, the terminal 400 connects to the server 200 through the network 300. The network 300 can be a wide area network or a local area network, or a combination of the two.

[0045] Terminal 400 is used to display the chessboard to be filled in a graphical interface, obtain the current chessboard information of the chessboard to be filled, and transmit the chessboard information to server 200 through network 300. Server 200 is used to perform state analysis processing on the chessboard to be filled based on the chessboard information transmitted by network 300 to obtain multiple state indicators of the chessboard to be filled; determine the position score of the chessboard to be filled based on the multiple state indicators of the chessboard to be filled; determine the corresponding graphic combination of the chessboard to be filled based on the position score, and release the graphic combination to the chessboard to be filled.

[0046] In some embodiments, server 200 may be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing 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, CDN, and big data and artificial intelligence platforms. Terminal 400 may be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the invention.

[0047] See Figure 2 , Figure 2 This is a schematic diagram of the structure of the electronic device 500 provided in the embodiments of this application, with the electronic device 500 as an example. Figure 1 Taking server 200 as an example, Figure 2 The illustrated electronic device 500 includes at least one processor 510, a memory 550, at least one network interface 520, and a user interface 530. The various components in the electronic device 500 are coupled together via a bus system 540. It is understood that the bus system 540 is used to implement communication between these components. In addition to a data bus, the bus system 540 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 2 The general labeled all buses as Bus System 540.

[0048] The processor 510 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0049] User interface 530 includes one or more output devices 531 that enable the presentation of media content, including one or more speakers and / or one or more visual displays. User interface 530 also includes one or more input devices 532, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.

[0050] The memory 550 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state storage, hard disk drives, optical disk drives, etc. The memory 550 may optionally include one or more storage devices physically located away from the processor 510.

[0051] The memory 550 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), and the volatile memory may be random access memory (RAM). The memory 550 described in this application embodiment is intended to include any suitable type of memory.

[0052] In some embodiments, memory 550 is capable of storing data to support various operations, examples of which include programs, modules, and data structures or subsets or supersets thereof, as illustrated below.

[0053] Operating system 551 includes system programs for handling various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, driver layer, etc., for implementing various basic business functions and handling hardware-based tasks;

[0054] The network communication module 552 is used to reach other electronic devices via one or more (wired or wireless) network interfaces 520, exemplary network interfaces 520 including: Bluetooth, WiFi, and Universal Serial Bus (USB), etc.

[0055] Presentation module 553 is used to enable the presentation of information (e.g., user interface for operating peripheral devices and displaying content and information) via one or more output devices 531 (e.g., display screen, speaker, etc.) associated with user interface 530.

[0056] The input processing module 554 is used to detect and translate one or more user inputs or interactions from one or more input devices 532.

[0057] In some embodiments, the apparatus provided in this application can be implemented in software. Figure 2 A graphics generation apparatus 555 stored in memory 550 is shown. This apparatus can be software in the form of programs and plug-ins, and includes the following software modules: an analysis module 5551, a first determination module 5552, and a second determination module 5553. These modules are logically linked and can therefore be arbitrarily combined or further separated according to their implemented functions. The functions of each module will be described below.

[0058] In other embodiments, the apparatus provided in this application can be implemented in hardware. As an example, the apparatus provided in this application can be a processor in the form of a hardware decoding processor, which is programmed to execute the intent recognition method provided in this application. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0059] The graphic generation method provided in this application will be described in conjunction with exemplary applications and implementations of the terminals provided in the embodiments of this application.

[0060] See Figure 3A , Figure 3A This is a flowchart illustrating the graphic generation method provided in the embodiments of this application, which will be combined with... Figure 3A The steps shown are explained.

[0061] In step 101, the chessboard to be filled is subjected to state analysis processing to obtain multiple state indicators of the chessboard to be filled.

[0062] As an example, the game board to be filled can be a core game area based on a grid (using the grid as regional units) for placing blocks or pieces. The game board is a grid-like space consisting of a fixed number of rows and columns. Each grid cell (such as a square or hexagonal cell) is the smallest unit for placing blocks. Players must complete game objectives (such as filling, eliminating, matching, etc.) within the grid by placing and assembling blocks (or pieces). The game board is divided into equally sized grid cells (such as 8×8, 9×9, 10×20, etc.) by clear horizontal and vertical lines. Each cell has fixed coordinates (such as row number and column number). The blocks placed by the player must completely occupy the grid cell and cannot exceed the row / column of the game board. The size of the game board directly affects the game's difficulty. For example, a small board (e.g., 5x5) has fewer grid cells, resulting in lower tolerance for errors in block placement and requiring more precise planning. A large board (e.g., 15x15) offers more space, but may lead to more complex later operations due to block stacking (e.g., needing to eliminate multiple layers of blocks). Each grid cell on the board to be filled has a clear state distinction, including an empty state (the target area where blocks can be placed), a filled state (the area covered by blocks, usually marked by color or pattern), and a locked / obstacle state (some levels contain fixed obstacles (e.g., black blocks) that cannot be covered, increasing the difficulty of layout). As the player operates, the board will provide real-time feedback on state changes: in elimination games, a filled row / column of grid will disappear (e.g., the grid is cleared after a row is eliminated in Tetris); in filling games, the board will display "fill percentage" or "number of remaining empty cells"; upon failure, areas outside the board boundaries or where blocks cannot be placed will trigger visual warnings (e.g., flashing borders). Figure 4 In the diagram, 401 represents the entire chessboard to be filled, and 402 represents a region within the chessboard to be filled.

[0063] As an example, the status indicators of the chessboard to be filled can be key parameters that quantify and describe the current state of the chessboard. They can intuitively reflect the game progress, remaining difficulty, and operational space, and are the core basis for players to formulate strategies and judge the outcome. Status indicators can include the number of blank units, the percentage of filled units, the size of continuous blank areas, the distribution density of obstacle units, the height of block stacking, the shape complexity of blank units, and the number of areas that can be eliminated. Among them, the number of blank units is the total number of grid units on the chessboard that are not covered by blocks (i.e., the remaining space where blocks can be placed). The higher the number of blank units, the greater the operational space and the higher the short-term error tolerance (e.g., when there are 50 blank units left on a 10×10 chessboard, the placement of blocks is more flexible). The lower the value (e.g., when there are 5 blank units left), the higher the requirements for matching the shape of subsequent blocks, and the easier it is to lose the game due to "no suitable blocks". The percentage of filled units is the percentage of the number of units covered by blocks to the total number of effective grids (excluding obstacles). The percentage of filled units intuitively reflects the filling progress (e.g., 80% percentage means that the goal of filling is close to being achieved). The size of continuous blank areas is the largest connected area (horizontal / shortage) formed by adjacent blank units. Vertically / diagonally adjacent (the number of diagonally adjacent units may be limited by game rules), the size of the continuous blank area determines the maximum size of the block that can be placed. For example, if the maximum continuous blank area is 4 units, the player cannot place a 5-unit "long block" and must prioritize eliminating surrounding blocks to expand the space. Scattered connected areas (such as multiple small areas of 2-3 units) increase the difficulty of block combination and require more precise planning of the splicing order. The distribution density of obstacle units refers to the number and positional distribution characteristics of fixed obstacle units (uncoverable grids) on the board (such as concentrated at the edge / center, scattered / dense). The distribution density of obstacle units directly increases the difficulty of layout. For example, dense obstacles in the center will divide the blank area, making it difficult to place large blocks. Edge obstacles may restrict the conventional strategy of "placing blocks along the edge" and force players to adjust their layout logic.

[0064] In some embodiments, the state analysis processing of the chessboard to be filled in step 101, and the resulting multiple state indicators of the chessboard to be filled, can be obtained through methods such as... Figure 3B Steps 1011 to 1014 shown are implemented.

[0065] In step 1011, the chessboard to be filled is subjected to a filling ratio analysis to obtain the density of the chessboard to be filled.

[0066] In some embodiments, the process of performing a filling ratio analysis on the chessboard to be filled in step 1011 to obtain the density of the chessboard to be filled can be achieved by the following technical solution: obtaining a first number of already filled area units in the chessboard to be filled and a second number of area units in the chessboard to be filled; and using the ratio of the first number to the second number as the density of the chessboard to be filled.

[0067] As an example, the density of the chessboard to be filled refers to the ratio of the number of elements already filled on the chessboard (such as blocks, numbers, puzzle pieces, characters, etc.) to the total capacity of the chessboard. It is used to measure the degree of filling of the chessboard or the scarcity of remaining blank space. The state of the chessboard can be intuitively judged by the density value. When the density is close to 0, the chessboard is almost blank, with sufficient filling space and high strategic flexibility. When the density is close to 1, the chessboard has been largely filled, with little remaining blank space, low error tolerance, and may face the risk of a "dead end" (such as when the density of Tetris is too high, it is difficult to place new blocks).

[0068] As an example, the density of the chessboard to be filled can be a representation of the proportion of already filled area units in the chessboard to be filled. In this case, the first number of already filled area units in the chessboard to be filled and the second number of all area units included in the chessboard to be filled can be obtained. By determining the ratio of the first number to the second number, the density of the chessboard to be filled can be obtained. For example, if the first number is 30 and the second number is 100, then the density of the chessboard to be filled is 0.3.

[0069] In step 1012, the elimination difficulty prediction process is performed on the chessboard to be filled to obtain the elimination difficulty of the chessboard to be filled.

[0070] In some embodiments, the elimination difficulty prediction process for the chessboard to be filled in step 1012, to obtain the elimination difficulty of the chessboard to be filled, can be achieved by the following technical solution: For each row in the chessboard to be filled, perform the following processing: determine the third number of unfilled area units in the row, and obtain the row elimination difficulty positively correlated with the third number; For each column in the chessboard to be filled, perform the following processing: determine the fourth number of unfilled area units in the column, and obtain the column elimination difficulty positively correlated with the fourth number; perform a fusion processing on the row elimination difficulty of multiple rows and the column elimination difficulty of multiple columns to obtain the elimination difficulty of the chessboard to be filled.

[0071] As an example, since the way to eliminate graphics in the chessboard to be filled is that the area cells of any row or column in the chessboard to be filled are all filled, the graphics in the area cells of that row or column can be eliminated, and the area cells of that row or column can be updated to unfilled area cells. Therefore, the fewer unfilled area cells in any row or column in the chessboard to be filled, the closer that row or column is to elimination, and the easier it is to eliminate. Therefore, we can obtain the unfilled area cells of each row of the chessboard to be filled, and then determine the row elimination difficulty based on the number of unfilled area cells in each row. Similarly, we can obtain the unfilled area cells of each column of the chessboard to be filled, and determine the column elimination difficulty based on the number of unfilled area cells in each column. Finally, we can merge the row elimination difficulty and the column elimination difficulty to obtain the elimination difficulty of the chessboard to be filled. For the specific elimination difficulty of the chessboard to be filled, please refer to the following formula (1):

[0072] (1)

[0073] In formula (1), This can reduce the difficulty of filling the chessboard. The number of unfilled cells in each row. The number of unfilled area cells in each column. To eliminate the difficulty of the process, Eliminate the difficulty of the column.

[0074] In step 1013, the risk area identification process is performed on the chessboard to be filled to obtain the risk level of the chessboard to be filled.

[0075] As an example, in a chessboard to be filled, risk areas refer to specific regions that, due to their location characteristics, shape limitations, or strategic value, may significantly increase the probability of a dead end or restrict subsequent operational space after being filled. Identifying and avoiding these areas is crucial to improving the game's success rate. Their core characteristics include: difficulty in eliminating or adjusting them through conventional operations after filling; requiring specific conditions for effective utilization; and having a decisive impact on the stability of the overall structure. Risk areas typically have strict requirements regarding the shape, order, or position of the filling elements. For example, in Tetris, if a 2-width channel forms a high-low structure, it can only be filled with specific S-shaped blocks; otherwise, the area can never be eliminated.

[0076] In some embodiments, the risk region identification process of the chessboard to be filled in step 1013 to obtain the risk level of the chessboard to be filled can be achieved by the following technical solution: performing risk region identification processing on the chessboard to be filled to obtain the risk regions of the chessboard to be filled; determining the risk level of each risk region based on the openness of each risk region; and fusing the risk levels of multiple risk regions to obtain the risk level of the chessboard to be filled.

[0077] As an example, the chessboard to be filled is identified, and the blank areas surrounded by filled area units are identified. Irregularly shaped or too narrow blank areas can be identified as risk areas, such as a narrow area of ​​1 area unit x 2 area units, a small Z-shaped gap, and irregular areas that need to be filled with a specific shape, such as a T-shaped area. Then, for each risk area, the directions that can enter the risk area are determined. The more directions that can enter the risk area, the higher the openness of the risk area. The risk level of the risk area is inversely proportional to the openness. Finally, the risk levels of each risk area are merged to obtain the risk level of the chessboard to be filled. The risk level of the chessboard to be filled can be obtained by the following formula (2):

[0078] (2)

[0079] In formula (2), The risk level of the chessboard to be filled. In order to be able to enter the risk area direction, Risk area openness, Risk area The level of risk.

[0080] In step 1014, the density of the chessboard to be filled, the difficulty of eliminating the chessboard to be filled, and the risk level of the chessboard to be filled are used as multiple state indicators of the chessboard to be filled.

[0081] The above method can accurately determine the state indicators of the three dimensions of the chessboard to be filled, namely density, elimination difficulty, and risk level, thereby improving the accuracy of the subsequent situation score.

[0082] In step 102, the position score of the chessboard to be filled is determined based on multiple state indicators of the chessboard to be filled.

[0083] In some embodiments, determining the situation score of the chessboard to be filled based on multiple state indicators in step 102 can be achieved by the following technical solution: determining the weight of each state indicator based on the interaction strategy type corresponding to the chessboard to be filled; and weighting and fusing multiple state indicators based on the weight of each state indicator to obtain the situation score of the chessboard to be filled.

[0084] As an example, the interaction strategy type can be a strategy selected by the user to fill the chessboard with graphic combinations, which can include challenging, conservative, etc.

[0085] As an example, the method for determining the position score of the chessboard to be filled can be found in the following formula (3):

[0086] (3)

[0087] In formula (3), Rate the position of the chessboard to be filled. The density of the chessboard to be filled. The difficulty of eliminating the chessboard to be filled. The risk level of the chessboard to be filled. , and Weights of status indicators.

[0088] For challenging interaction strategies, it can improve The value increases the emphasis on possible clearing operations, encourages multiple fill attempts at a time, and reduces... The weighting of this approach increases tolerance for potentially dead-end situations, accepting higher risks in pursuit of higher rewards, such as eliminating multiple rows at once. For conservative interaction strategies, this approach increases... The weighting of the board, maintaining its openness, and prioritizing ensuring sufficient space are all factors that could improve the game. The weighting of these factors enhances the defense against deadlocks, ensuring that each move leaves room for the next step and reduces the potential for a complete collapse. The weighting of [something] is reduced, with less focus on immediate clearing and a preference for clearing things up whenever possible.

[0089] In step 103, based on the situation score, a graphic combination corresponding to the chessboard to be filled is determined, and the graphic combination is released to the chessboard to be filled; wherein, the graphic combination is used to be filled into the unfilled area units of the chessboard to be filled by control operation.

[0090] As an example, graphic combination refers to the arrangement and combination of blocks of different shapes and sizes to fill the blank areas of a chessboard. The core of graphic combination is to solve the problem of blank areas that a single block cannot match by "multiple blocks co-placing". For example, an L-shaped blank area (3 units) cannot be filled by a 2-unit block alone, and needs to be covered by a combination of "1 2-unit block + 1 1-unit block"; or by rotating or flipping blocks (such as rotating a T-shaped block 90° and then combining it with a Z-shaped block) to fill irregular spaces. Its essence is to use the geometric characteristics (shape, symmetry, size) of blocks to achieve "full coverage of blank areas" or "achievement of elimination conditions"; graphic combinations can include single block combinations, double block combinations, and multi-block combinations. Among them, a single block combination refers to using only a single block to match a blank area (the simplest combination form), where the shape of the blank area perfectly matches the shape of the single block (such as a 2×2 square blank matching a 2×2 block), without the need for additional blocks; a double block combination covers a blank area by splicing two blocks together, such as an L-shaped block + a small square block, filling blank areas with corners, or two 2-unit long strip blocks spliced ​​horizontally to form a 4-unit long strip, covering a 1×4 blank area; a multi-block combination is the coordinated combination of three or more blocks, used to cover complex or scattered blank areas, suitable for blank areas that are fragmented (such as multiple 2-3 unit small areas) or have notches or isolated units.

[0091] As an example, control operations can include basic movement operations, shape adjustment operations, and combination and fixation operations. Basic movement operations include directional movement, specifically using keyboard arrow keys (up, down, left, right), touchscreen swiping, or mouse dragging to control the block's horizontal and vertical movement (or accelerated descent) within the board, adjusting the block's initial position. Before the block is fixed, small movements are used to avoid overlapping with existing blocks or precisely align with blank areas. Shape adjustment operations are also included, specifically using specific keyboard keys (such as the "up arrow" and "spacebar") or touchscreen double-clicking and gesture rotation to change the block's orientation (e.g., the 7 types of Tetris blocks can be rotated to 4 different angles) to fit different shaped blank areas. A few games support changing the block's shape under specific conditions (e.g., special blocks in "Super Tetris"), increasing combination flexibility. Combination and fixation operations are also included, specifically after the block has moved to the target position and its shape has been adjusted, using the "down arrow" (press and hold), "confirm" button, or releasing the mouse / ... Stop dragging to fix the block in its current position and form a stable combination with existing blocks.

[0092] In some embodiments, determining the graphic combination corresponding to the chessboard to be filled based on the situation score in step 103 can be achieved by the following technical solution: deleting risky graphic combinations from the candidate graphic combinations; and determining the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations based on the situation score.

[0093] As an example, the situation score reflects the current state of the board to be filled. Different situation scores determine corresponding graphic combinations. For instance, if the situation score is above a first threshold, it indicates that there are few filled areas on the board, requiring rapid filling. In this case, an expansionary graphic combination can be selected from the candidate combinations, including large blocks (which can effectively fill large areas with a single placement, but incorrect placement can lead to difficulties) and open blocks (with branches in multiple directions, capable of connecting and expanding different open areas). On the other hand, if the situation score is below the first threshold but above the second threshold, it indicates that the board is suitable for subsequent layouts and requires accurate elimination of graphics. In this case, a preparatory graphic combination can be selected from the candidate combinations, including blocks that help the player approach completing a row or column, preparatory blocks for clearing rows in later rounds, and area optimization blocks that, when placed, bring the board closer to clearing entire rows or columns. For example, if the situation score is below the second threshold, it indicates that there are many already filled areas in the current chessboard, which is likely to lead to a dead end. You can choose a simple graphic combination from the candidate graphic combinations, including small straight squares for filling edges or small gaps, as well as flexible shape squares (L-shaped or T-shaped, which can be used to solve irregularly shaped gaps).

[0094] By removing risky graphic combinations from the candidate graphic combinations, we can avoid graphic combinations that are difficult to fill into the board, thus improving the game's playability.

[0095] In some embodiments, before removing risky graphic combinations from the candidate graphic combinations, the following technical solutions may also be performed: obtaining candidate graphic combinations whose area exceeds a first threshold as the risky graphic combinations; obtaining candidate graphic combinations whose length of any side exceeds a second threshold as the risky graphic combinations; obtaining candidate graphic combinations whose shape complexity exceeds a third threshold as the risky graphic combinations.

[0096] As an example, graphic combinations that are too large in area or too long in one direction are difficult to fit into the unfilled areas of the chessboard to be filled. Therefore, candidate graphic combinations with an area exceeding the first threshold can be regarded as risky graphic combinations, as can candidate graphic combinations with any side length exceeding the second threshold.

[0097] As an example, with a first threshold of 10 area units, candidate graphic combination A is a rectangle consisting of 4 area units x 4 area units. Therefore, the area of ​​candidate graphic combination A is 16 area units, and it can be classified as a risky graphic combination. With a second threshold of 7 area units, candidate graphic combination B is a T-shaped combination consisting of a long strip of 1 area unit x 5 area units and a long strip of 1 area unit x 7 area units. It can be determined that the length of candidate graphic combination B at one angle exceeds the second threshold, and therefore, candidate graphic combination B is classified as a risky graphic combination.

[0098] In some embodiments, the step 103 of determining the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations based on the situation score can be implemented by the following technical solution: obtaining the operation record of the object that triggered the control operation and the filling failure record; generating candidate failure regions of the chessboard to be filled based on the operation record and the filling failure record; determining the generation strategy of the graphic combination based on the situation score and the candidate failure regions; and determining the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations based on the generation strategy.

[0099] As an example, obtaining the operation records and fill failure records of an object can reflect the state of the areas where the object is prone to fill failure. Then, areas in the chessboard to be filled that are similar to the states of areas prone to fill failure can be used as candidate failure areas. Afterwards, by selecting a combination of graphics that matches the candidate failure areas from the candidate graphic combinations, the user can avoid fill failures.

[0100] As an example, the situation score, object operation records, and filling failure records can be input into a pre-trained artificial intelligence model. The artificial intelligence model can identify the candidate failure areas included in the chessboard to be filled, and select the graphic combination that matches the candidate failure area from the candidate graphic combination based on the filled area units in the candidate failure area.

[0101] By using the above method, a combination of graphics that fits the chessboard to be filled can be obtained, thus improving the game's playability.

[0102] The following will describe an exemplary application of the embodiments of this application in a real-world application scenario.

[0103] In practical applications, three blocks appear each round. Players drag and drop the blocks to place them on the board. The system immediately analyzes the current board state and calculates a multi-dimensional situation score. The block-producing module selects three more suitable combinations from the block pool based on the situation score and presents them to the player. High-risk situations will trigger "conservative combinations," while low-risk situations will allow for the selection of "challenging combinations." If a behavioral model is integrated, players can perceive that the block-producing logic gradually aligns with their strategic preferences.

[0104] Specifically, the current chessboard state (10×10 array) is first obtained, and then indicators such as empty space density, number of dead corners, probability of clearing a move, and number of empty spaces are determined based on the current chessboard state. Then, a comprehensive score is generated through a weighted formula. The method for generating the comprehensive score can be found in the following formula (4):

[0105] (4)

[0106] In formula (4), Rate the position of the chessboard to be filled. The density of the chessboard to be filled. The difficulty of eliminating the chessboard to be filled. The risk level of the chessboard to be filled. , and Weights of status indicators.

[0107] For challenging interaction strategies, it can improve The value increases the emphasis on possible clearing operations, encourages multiple fill attempts at a time, and reduces... The weighting of this approach increases tolerance for potentially dead-end situations, accepting higher risks in pursuit of higher rewards, such as eliminating multiple rows at once. For conservative interaction strategies, this approach increases... The weighting of the board, maintaining its openness, and prioritizing ensuring sufficient space are all factors that could improve the game. The weighting of these factors enhances the defense against deadlocks, ensuring that each move leaves room for the next step and reduces the potential for a complete collapse. The weighting of [something] is reduced, with less focus on immediate clearing and a preference for clearing things up whenever possible.

[0108] As an example, the density of the chessboard to be filled can be a representation of the proportion of already filled area units in the chessboard to be filled. In this case, the first number of already filled area units in the chessboard to be filled and the second number of all area units included in the chessboard to be filled can be obtained. By determining the ratio of the first number to the second number, the density of the chessboard to be filled can be obtained. For example, if the first number is 30 and the second number is 100, then the density of the chessboard to be filled is 0.3.

[0109] As an example, since the way to eliminate graphics in the chessboard to be filled is that the area cells of any row or column in the chessboard to be filled are all filled, the graphics in the area cells of that row or column can be eliminated, and the area cells of that row or column can be updated to unfilled area cells. Therefore, the fewer unfilled area cells in any row or column in the chessboard to be filled, the closer that row or column is to elimination, and the easier it is to eliminate. Therefore, we can obtain the unfilled area cells of each row of the chessboard to be filled, and then determine the row elimination difficulty based on the number of unfilled area cells in each row. Similarly, we can obtain the unfilled area cells of each column of the chessboard to be filled, and determine the column elimination difficulty based on the number of unfilled area cells in each column. Finally, we can merge the row elimination difficulty and the column elimination difficulty to obtain the elimination difficulty of the chessboard to be filled. For the specific elimination difficulty of the chessboard to be filled, please refer to the following formula (5):

[0110] (5)

[0111] In formula (5), This can reduce the difficulty of filling the chessboard. The number of unfilled cells in each row. The number of unfilled area cells in each column. To eliminate the difficulty of the process, Eliminate the difficulty of the column.

[0112] As an example, the chessboard to be filled is identified, and the blank areas surrounded by filled area units are identified. Irregularly shaped or too narrow blank areas can be identified as risk areas, such as a narrow area of ​​1 area unit x 2 area units, a small Z-shaped gap, and irregular areas that need to be filled with a specific shape, such as a T-shaped area. Then, for each risk area, the directions that can enter the risk area are determined. The more directions that can enter the risk area, the higher the openness of the risk area. The risk level of the risk area is inversely proportional to the openness. Finally, the risk levels of each risk area are merged to obtain the risk level of the chessboard to be filled. The risk level of the chessboard to be filled can be obtained by the following formula (6):

[0113] (6)

[0114] In formula (6), The risk level of the chessboard to be filled. In order to be able to enter the risk area direction, Risk area openness, Risk area The level of risk.

[0115] Next, high-risk combinations (such as those that are too large or have a closed shape) are eliminated from the pool of candidate blocks. The three most suitable combinations are selected according to the situation score corresponding to the current situation. The types of graphic combinations include: decompression type (avoiding dead ends), preparation type (laying the groundwork for the next step), and expansion type (used to open up areas).

[0116] Specifically, the situation score reflects the current state of the board to be filled. Different situation scores determine corresponding graphic combinations. For example, if the situation score is above the first threshold, it indicates that there are few filled areas on the board, requiring rapid filling. In this case, an expansionary graphic combination can be selected from the candidate combinations, including large blocks (which can effectively fill large areas with a single placement, but incorrect placement can lead to difficulties) and open blocks (with branches in multiple directions, capable of connecting and expanding different open areas). On the other hand, if the situation score is below the first threshold but above the second threshold, it indicates that the board is suitable for subsequent layouts and requires accurate elimination of graphics. In this case, a preparatory graphic combination can be selected from the candidate combinations, including blocks that help the player approach the completion of a row or column, preparatory blocks for clearing rows in later rounds, and area optimization blocks that, when placed, bring the board closer to the state of clearing an entire row or column. For example, if the situation score is below the second threshold, it indicates that there are many already filled areas in the current chessboard, which is likely to lead to a dead end. You can choose a simple graphic combination from the candidate graphic combinations, including small straight squares for filling edges or small gaps, as well as flexible shape squares (L-shaped or T-shaped, which can be used to solve irregularly shaped gaps).

[0117] Then, risky graphic combinations can be removed from the candidate graphic combinations. For example, if the first threshold is 10 area units, and candidate graphic combination A is a rectangle with 4 area units x 4 area units, then the area of ​​candidate graphic combination A is 16 area units, so candidate graphic combination A can be considered a risky graphic combination. If the second threshold is 7 area units, and candidate graphic combination B is a T-shaped graphic combination composed of a strip graphic with 1 area unit x 5 area units and a strip graphic with 1 area unit x 7 area units, it can be determined that the length of candidate graphic combination B at one angle exceeds the second threshold, so candidate graphic combination B can be considered a risky graphic combination.

[0118] Finally, by obtaining the object's operation records and filling failure records, the state of the areas where the object is prone to filling failure can be reflected. Then, areas in the chessboard to be filled that are similar to the states of areas prone to filling failure can be regarded as candidate failure areas. Afterwards, by selecting graphic combinations that match the candidate failure areas from the candidate graphic combinations, the user can avoid filling failure. The situation score, the object's operation records, and the filling failure records can be input into a pre-trained artificial intelligence model. The artificial intelligence model can identify the candidate failure areas included in the chessboard to be filled, and select graphic combinations that match the candidate failure areas from the candidate graphic combinations based on the already filled area units in the candidate failure areas.

[0119] The above methods can significantly enhance the game's strategic depth and sustainability. The mechanism for selecting graphic combinations can increase the average game duration and reduce player churn caused by "dead ends." It also increases user engagement and a sense of accomplishment, as players perceive that each decision is related to the situation's score, creating a feedback loop. Furthermore, it enhances user stickiness and replayability, providing a different experience in each game and supporting personalized block generation logic. Finally, it enriches monetization strategies, as the block generation mechanism can be combined with the item system.

[0120] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0121] The following description continues to illustrate the exemplary structure of the graphics generation apparatus 555 provided in the embodiments of this application as a software module. In some embodiments, such as... Figure 2 As shown, the software modules stored in the graphics generation device 555 of the memory 540 may include:

[0122] Analysis module 5551 is used to perform state analysis processing on the chessboard to be filled, and obtain multiple state indicators of the chessboard to be filled.

[0123] The first determining module 5552 is used to determine the situation score of the chessboard to be filled based on multiple state indicators of the chessboard to be filled.

[0124] The second determining module 5553 is used to determine the graphic combination corresponding to the chessboard to be filled based on the situation score, and release the graphic combination to the chessboard to be filled; wherein, the graphic combination is used to be filled into the unfilled area units of the chessboard to be filled by control operation.

[0125] In some embodiments, the analysis module 5551 is further configured to perform a filling ratio analysis on the chessboard to be filled to obtain the density of the chessboard to be filled; perform a difficulty prediction process on the chessboard to be filled to obtain the difficulty of eliminating the chessboard to be filled; perform a risk area identification process on the chessboard to be filled to obtain the risk level of the chessboard to be filled; and use the density of the chessboard to be filled, the difficulty of eliminating the chessboard to be filled, and the risk level of the chessboard to be filled as multiple state indicators of the chessboard to be filled.

[0126] In some embodiments, the analysis module 5551 is further configured to obtain a first number of already filled area units in the chessboard to be filled and a second number of area units in the chessboard to be filled; and to use the ratio of the first number to the second number as the density of the chessboard to be filled.

[0127] In some embodiments, the analysis module 5551 is further configured to perform the following processing for each row of the chessboard to be filled: determine a third number of unfilled area units in the row, and obtain the row elimination difficulty positively correlated with the third number; perform the following processing for each column of the chessboard to be filled: determine a fourth number of unfilled area units in the column, and obtain the column elimination difficulty positively correlated with the fourth number; and perform a fusion processing on the row elimination difficulty of multiple rows and the column elimination difficulty of multiple columns to obtain the elimination difficulty of the chessboard to be filled.

[0128] In some embodiments, the analysis module 5551 is further configured to perform risk region identification processing on the chessboard to be filled to obtain the risk regions of the chessboard to be filled; determine the risk level of each risk region based on the openness of each risk region; and fuse the risk levels of multiple risk regions to obtain the risk level of the chessboard to be filled.

[0129] In some embodiments, the first determining module 5552 is further configured to determine the weight of each of the state indicators based on the interaction strategy type corresponding to the chessboard to be filled; and to perform weighted fusion of multiple state indicators based on the weight of each of the state indicators to obtain the situation score of the chessboard to be filled.

[0130] In some embodiments, the second determining module 5553 is further configured to remove risky graphic combinations from the candidate graphic combinations; and based on the situation score, determine the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations.

[0131] In some embodiments, the second determining module 5553 is further configured to: obtain candidate graphic combinations whose area exceeds a first threshold as the risk graphic combination; obtain candidate graphic combinations whose length of any side exceeds a second threshold as the risk graphic combination; and obtain candidate graphic combinations whose shape complexity exceeds a third threshold as the risk graphic combination.

[0132] In some embodiments, the second determining module 5553 is further configured to acquire the operation record of the object that triggered the control operation and the filling failure record; generate candidate failure regions of the chessboard to be filled based on the operation record and the filling failure record; determine the generation strategy of the graphic combination based on the situation score and the candidate failure regions; and determine the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations based on the generation strategy.

[0133] This application provides a computer program product or computer program that includes 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 graphics generation method described above in this application.

[0134] This application provides a computer-readable storage medium storing executable instructions. When these executable instructions are executed by a processor, they cause the processor to execute the graphics generation method provided in this application. For example, ... Figure 3A The method for generating graphics is shown.

[0135] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a variety of devices including one or any combination of the above-mentioned memories.

[0136] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0137] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0138] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0139] In summary, the embodiments of this application can achieve the following beneficial effects:

[0140] The state analysis process of the chessboard to be filled yields multiple state indicators. Based on these indicators, a situation score is determined. These indicators accurately reflect the current state of the chessboard, ensuring the accuracy of the subsequent situation score. Based on the situation score, a corresponding graphic combination is determined and released to the chessboard. The state indicators reflect the current situation score of the chessboard, and releasing a graphic combination that matches the current chessboard prevents it from entering a dead end, thus improving the game's playability.

[0141] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A method for generating graphics, characterized in that, The method includes: A state analysis process is performed on the chessboard to be filled to obtain multiple state indicators of the chessboard to be filled, including the difficulty of eliminating the chessboard to be filled. Based on the interaction strategy type corresponding to the chessboard to be filled, the weight of each state indicator is determined. The interaction strategy type includes challenging and conservative types. The weight of the elimination difficulty of the chessboard to be filled corresponding to the challenging interaction strategy type is higher than the weight of the elimination difficulty of the chessboard to be filled corresponding to the conservative interaction strategy type. Based on the weight of each of the state indicators, the multiple state indicators are weighted and fused to obtain the situation score of the chessboard to be filled. The following steps are taken: First, select candidate graphic combinations whose area exceeds a first threshold as risky graphic combinations. Second, select candidate graphic combinations whose length of any side exceeds a second threshold as risky graphic combinations. Third, select candidate graphic combinations whose shape complexity exceeds a third threshold as risky graphic combinations. Fourth, remove risky graphic combinations from the candidate graphic combinations. Fifth, based on the situation score, determine the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations and release the graphic combination to the chessboard to be filled. The graphic combination is used to be filled into the unfilled area units of the chessboard to be filled through a control operation.

2. The method according to claim 1, characterized in that, The process of performing state analysis on the chessboard to be filled yields multiple state indicators of the chessboard, including: The density of the chessboard to be filled is obtained by performing a filling ratio analysis on the chessboard to be filled. The elimination difficulty of the chessboard to be filled is predicted by performing elimination difficulty prediction processing on the chessboard to be filled. The risk area identification process is performed on the chessboard to be filled to obtain the risk level of the chessboard to be filled. The density of the chessboard to be filled, the difficulty of eliminating the chessboard to be filled, and the risk level of the chessboard to be filled are used as multiple state indicators of the chessboard to be filled.

3. The method according to claim 2, characterized in that, The process of performing a filling ratio analysis on the chessboard to be filled to obtain the density of the chessboard includes: Obtain the first number of already filled area units in the chessboard to be filled, and the second number of area units in the chessboard to be filled; The ratio of the first number to the second number is used as the density of the chessboard to be filled.

4. The method according to claim 2, characterized in that, The process of predicting the elimination difficulty of the chessboard to be filled, and obtaining the elimination difficulty of the chessboard to be filled, includes: For each row in the chessboard to be filled, the following process is performed: determine the third number of unfilled area units in the row, and obtain the row elimination difficulty that is positively correlated with the third number; For each column in the chessboard to be filled, perform the following processing: determine the fourth number of unfilled area units in the column, and obtain the column elimination difficulty that is positively correlated with the fourth number; The elimination difficulty of the row elimination difficulty of multiple rows and the elimination difficulty of the column elimination difficulty of multiple columns are fused to obtain the elimination difficulty of the chessboard to be filled.

5. The method according to claim 2, characterized in that, The step of identifying risk areas on the chessboard to be filled to obtain the risk level of the chessboard includes: The risk area of ​​the chessboard to be filled is identified by performing risk area identification processing on the chessboard to be filled. The risk level of each risk area is determined based on its openness. The risk levels of multiple risk areas are fused to obtain the risk level of the chessboard to be filled.

6. The method according to claim 1, characterized in that, The step of determining the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations based on the situation score includes: Retrieve the operation records of the object that triggered the control operation and the failure records for filling in the data; Based on the operation records and the filling failure records, candidate failure regions for the chessboard to be filled are generated; Based on the situation score and the candidate failure areas, the generation strategy for the graphic combination is determined; Based on the generation strategy, the graphic combination corresponding to the chessboard to be filled is determined from the remaining candidate graphic combinations.

7. A graphic generation device, characterized in that, The device includes: The analysis module is used to perform state analysis processing on the chessboard to be filled, and obtain multiple state indicators of the chessboard to be filled, including the difficulty of eliminating the chessboard to be filled. The first determining module is used to determine the weight of each state indicator based on the interaction strategy type corresponding to the chessboard to be filled. The interaction strategy type includes challenging and conservative types. The weight of the elimination difficulty of the chessboard to be filled corresponding to the challenging interaction strategy type is higher than the weight of the elimination difficulty of the chessboard to be filled corresponding to the conservative interaction strategy type. Based on the weight of each state indicator, multiple state indicators are weighted and fused to obtain the situation score of the chessboard to be filled. The second determining module is used to: acquire candidate graphic combinations whose area exceeds a first threshold as risk graphic combinations; acquire candidate graphic combinations whose length of any side exceeds a second threshold as risk graphic combinations; acquire candidate graphic combinations whose shape complexity exceeds a third threshold as risk graphic combinations; delete risk graphic combinations from the candidate graphic combinations; and, based on the situation score, determine the graphic combination corresponding to the chessboard to be filled from the remaining candidate graphic combinations and release the graphic combination to the chessboard to be filled; wherein, the graphic combination is used to be filled into the unfilled area units of the chessboard to be filled through control operations.

8. An electronic device, characterized in that, The electronic device includes: Memory is used to store executable instructions or computer programs. A processor, configured to execute computer-executable instructions or computer programs stored in the memory, implements the graphics generation method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, they implement the graphics generation method according to any one of claims 1 to 6.

10. A computer program product comprising computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer program are executed by a processor, the graphics generation method according to any one of claims 1 to 6 is implemented.

Images