A method for dynamically realizing complex irregular models based on the wave function collapse algorithm

Abstract

The present invention discloses a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core, and relates to the field of sandbox map algorithm generation. The present invention includes setting data rules in the normal direction corresponding to the data module; classifying data modules with the same characteristics to generate corresponding sets; retrieving data modules with determined contents and processing the data modules according to the data rules; while processing the data modules, monitoring and judging whether each processed data module complies with the rules, and when the data modules do not comply with the rules, clearing the processing contents of the data modules that do not comply with the rules to the corresponding data modules with determined contents. The present invention meets the personalized design needs of users, solves the problem of a large number of repetitive spatial stacking, and decomposes complex and large screening algorithm tasks into small and simple algorithms, thereby reducing the difficulty of algorithm implementation, effectively saving time, and improving work efficiency.

Description

Technical Field

[0001] The present invention relates to the field of sandbox map algorithm generation, and in particular to a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core. Background Art

[0002] Generating complex maps in the sandbox domain places extremely high demands on the speed of program algorithms, CPU consumption, and model resource vertices. For this, traditional brute force retrieval algorithms have the following problems in the existing technology:

[0003] 1. Self-retrieval under this constant constraint has a great performance cost;

[0004] 2. There are also great reading difficulties in the specification and understanding of the program;

[0005] 3. Difficulty in customizing algorithm-derived models;

[0006] 4. It is difficult to quickly handle complex pattern recognition and prediction problems in mathematical modeling and analysis;

[0007] 5. Difficulty in obtaining flexibility and adapting to various complex models;

[0008] The above problems limit the generation and development of highly liberalized maps, so a new and fast improvement technical solution is needed. Summary of the invention

[0009] The present invention provides a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core, solving the problems of the prior art.

[0010] In a first aspect, the present invention provides a method for dynamically implementing a complex irregular model with a wave function collapse algorithm as the core, wherein the method comprises the following steps in generating a sandbox map using a data module:

[0011] Set the data rules corresponding to the normal direction of the data module. The data module is the selected map template, including the terrain module, the building module, the character module and the directional interaction module. The data rules are based on the preset interactivity of different data modules and use the wave function collapse algorithm to achieve the presentation mode set on the sandbox map. The presentation mode includes the spatial position relationship and the belonging relationship. The spatial position relationship includes the position settings of up, down, left, right, inside and outside. The belonging relationship includes the way of merging and being fused into an overall module.

[0012] Classification: Classify data modules with the same characteristics into corresponding sets, and classify modules with the same data content characteristics, including color and shape;

[0013] Retrieve the data modules for the determined content and process the data modules according to the data rules. The processing order is: retrieve the data modules to be processed, integrate the presentation methods between the data modules based on the data rules, and repeatedly retrieve, classify, and integrate the data modules on all sandbox maps;

[0014] While processing data modules, monitor and determine whether each processed data module complies with the data rules. When a data module does not comply with the data rules, clear the data module that does not comply with the data rules to the corresponding determined content data module, that is, delete the data modules to be processed that do not comply with the data rules with other surrounding data modules.

[0015] Furthermore, it includes modularizing the data to be processed, defining the 3D vertices of the data model corresponding to the data modules, and setting the data rules on the 3D vertices for different types of data modules to provide direction discrimination.

[0016] Furthermore, the data rules for setting the data module corresponding to the normal direction include:

[0017] Basic constraint rules are set for the data model corresponding to the data module. The basic constraint rules are the processing content of the data module. The processing content reduces the number of data modules to be processed according to the basic constraint rules. The basic constraint rules include that the data modules to be processed are the same type or type-related data modules of adjacent data modules.

[0018] Furthermore, the step of classifying the data modules with the same characteristics to generate corresponding sets includes:

[0019] The data modules corresponding to the processing contents are detected, and the data modules corresponding to the data models of the same processing contents are classified as the same data set.

[0020] Furthermore, the wave function collapse algorithm is used to implement the presentation method set on the sandbox map, including calculating the entropy value of the data module set, including calculating the number of initial data modules with optional processing content in the set, and determining the entropy value based on whether the processing content of the data module in the data set is locked. The locked data content is the data content in the corresponding data module after monitoring that subsequent data modules comply with the rules after processing according to the data rules.

[0021] Furthermore, while processing the data modules, monitoring and judging whether each processed data module complies with the rules, when the data module does not comply with the rules, clearing the processing content of the data module that does not comply with the rules to the corresponding determined content data module, also includes:

[0022] The data is judged to see whether it meets the rules. The corresponding rules are used to find out whether the predicted processing content is consistent with the processed content. If they are inconsistent, the entropy values ​​generated on different data modules during the corresponding processing are modified, and the entropy value of the corresponding data set is updated.

[0023] Furthermore, it also includes that for each data module, when the next adjacent data module of the data module does not conform to the processing result of the next adjacent data module according to the data rule processing content, it falls back to the data module that conforms to the processing result and is consistent, and reselects the direction with the smallest entropy as the next processing direction for the next adjacent data module.

[0024] In a second aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0025] The present invention provides a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core, achieving the following technical excellence and progress:

[0026] 1. Effectively utilize resources, reduce package size, and control the number of models;

[0027] 2. Optimize user experience, easy operation, and automatic model generation;

[0028] 3. Add various constraint rules to increase the complexity of the model structure and meet users' personalized design needs;

[0029] 4. Process complex mathematical models and large-scale data sets to achieve automatic generation of highly customized 3D maps;

[0030] 5. Fixed rules to solve the problem of large number of repetitive space stacking;

[0031] 6. Decompose complex and large screening algorithm tasks into small and simple algorithms, thereby reducing the difficulty of algorithm implementation, processing complex pattern recognition and prediction problems faster than traditional algorithms, effectively saving time and improving work efficiency;

[0032] 7. A set of parameters based on model vertices and normals was developed to standardize production specifications and reduce the difficulty of model prototype production. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0034] Figure 1A flow chart of a method for dynamically implementing a complex irregular model with a wave function collapse algorithm as the core is provided for an exemplary embodiment of the present invention.

[0035] Figure 2 A collapse flow chart is provided for an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0037] Explanation of concepts and terms in the embodiments of the present invention:

[0038] Model vertex: the vertex of the 3D model;

[0039] Model normal: the vertical direction of the surface;

[0040] Basic constraint rules: For example, when choosing seats in a classroom, only B can sit next to A, and only his friends can sit in front of A.

[0041] Algorithm collapse: filter out qualified data based on constraint rules;

[0042] Propagation: After the candidate for a seat is determined, the set of optional objects for other seats can be processed through basic constraint rules plus propagation.

[0043] The shortcomings of existing screening algorithm technology are that it cannot effectively save resources, the model is difficult to make and reuse, the algorithm takes a long time, the logic is difficult to read after the program code is implemented, and more importantly, the scene generation algorithm for complex map modules is not clear enough. In response to these shortcomings, the present invention quotes the concept of wave function collapse in physics, and makes regular restrictions on the vertices and normals of the model, so as to better adapt to the splicing of various complex models, reduce the number of homogenized models, and optimize the algorithm's search difficulty. The present invention refers to local search algorithms such as walkSAT and Opt algorithms, backtracking algorithms, branch and bound methods, MaxWalkSat, and C++ terrain splicing algorithms, and finally adopts the logic of wave function collapse in quantum mechanics as the basic theoretical guidance to meet the needs of highly customized complex maps for projects.

[0044] The steps for implementing the algorithm are roughly as follows:

[0045] 1. Constraint rules, formulate the restriction conditions of the data block;

[0046] 2. Propagation: When a piece of data is determined and propagated through rules, the optional object set of other data can be processed;

[0047] 3. Backtracking: When the algorithm makes an error or conflicts during the propagation process and cannot find the data for the specified rule, in order to avoid wasting resources, the wrong solution is eliminated, and then backtracked and reselected until the correct data is found.

[0048] The principle of the phenomenon of wave function collapse is as follows: Draw 10 lines horizontally and vertically on a piece of white paper to divide it into 81 equal grids. There are 5 colors: red, yellow, green, blue and purple. It is stipulated that the grid above red can only be filled with yellow, the grid below yellow can only be filled with green, and the grid to the left of green can only be filled with purple. In this way, fill each grid on the paper with color, and you can get a variety of color palettes. The probability of each grid being filled is 1 / 81. When one of the grids is filled with red, then this grid is locked as red, and the probability of the grid above red being filled with yellow is greatly increased (not filled). Therefore, such a phenomenon of changing from chaos to certainty is expressed in a term called wave function collapse. Therefore, the core principle of the algorithm is to dynamically reduce the range of colors filled in each grid until all grids are filled with colors. In the principle metaphor overview, the relevant technical feature steps are extracted, including: 1. Constraint rules, formulate rules for the color of the grid to be filled. The grid above red can only be filled with yellow, the grid below yellow can only be filled with green, and the left side of green can only be filled with purple, in order to expect the coloring board under the desired rules; 2. Propagation. When a grid is filled, add rules for propagation, that is, screen whether the grid next to it is filled and the set of objects that can be filled. After the screening is completed, you can know what color to fill. In the 2D world, an object has 4 edges (front, back, left, right, and right), while in the 3D world, an object has 6 faces (front, back, left, right, and up and down). Each face corresponds to a data, so the algorithm propagation idea of ​​wave function collapse can be propagated according to the data of the collapsed position. Put the rules in and process the optional set of each data; 3. Backtracking. When a grid is about to be filled with red, its top can only be yellow, but the grid below yellow has been filled with blue, so this propagation is contrary to the constraint rules. It is hoped that the wrong solution can be eliminated and then retreat. Select again. This is the meaning of backtracking. When sorting out the algorithm, it was found that for simple models, there is less demand for basic art model pieces that make up the model, and there are fewer possibilities for conditional screening, which can quickly meet the map generation. However, for complex map models such as roofs, the occlusion of the model will derive various scenes, so the constraint rules of the model need to be more detailed, which places high demands on the art model. For this, the blocks that make up the model are numbered, and the constraints are reduced to meet the needs.

[0049] The specific application scenario of the present invention is customized map generation.

[0050] The present invention provides a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core, aiming to solve the above technical problems of the prior art.

[0051] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0052] Example 1: Figure 1 As shown, the present invention provides a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core. Figure 1 Including:

[0053] The preceding steps are as follows: setting data rules in the normal direction corresponding to the data module; classifying data modules with the same characteristics to generate corresponding sets; including modular processing of the data to be processed, and defining the 3D vertices of the data model corresponding to the data module, wherein setting data rules in the normal direction corresponding to the data module includes: setting basic constraint rules for the data module corresponding to the data model, wherein the basic constraint rules are the processing content for the data module, and the processing content reduces the number of data modules to be processed according to the basic constraint rules. Explanation: dynamically making the range of candidate objects for each grid smaller and smaller, until finally all grids can select suitable objects, for example: each grid is filled with color, that is, corresponding to the collapsed object; and dynamically reducing the range of candidate objects is achieved through the following strategies: through constraint rules, propagation and backtracking;

[0054] The data modules with the same characteristics are classified to generate corresponding sets, including: detecting the data modules corresponding to the processing content, and classifying the data modules corresponding to the data models with the same processing content into the same data set.

[0055] The processing steps are as follows: control the selection of the data module corresponding to the direction with the minimum entropy value, and perform collapse, wherein the collapse is the data processing content according to the data rules, wherein the data grid is propagated according to the data rules, and during the propagation process, each processed data module is monitored and judged whether it complies with the rules, if successful, the next collapse is continued, if failed, the data module corresponding to the direction with the minimum entropy value selected last time is returned to update the entropy value sorting, and the data module corresponding to the direction with the minimum entropy value after the update is selected. In the process of data propagation, that is, the collapse process, the data module with determined content is retrieved and the data module is processed according to the data rules; while processing the data module, each processed data module is monitored and judged whether it complies with the rules, and when the data module does not comply with the rules, the processing content of the data module that does not comply with the rules to the corresponding data module with determined content is cleared.

[0056] It also includes calculating the entropy value of the data module set, including calculating the number of initial data modules with optional processing content in the set, determining the entropy value based on whether the processing content of the data module in the data set is locked, and the locked data content is the data content in the corresponding data module in which the subsequent data modules are monitored to comply with the rules after processing according to the data rules. While processing the data module, monitoring and judging whether each processed data module complies with the rules, when the data module does not comply with the rules, clearing the processing content of the data module that does not comply with the rules to the corresponding content data module, also includes: judging whether the data complies with the rules is to find out whether the predicted processing content is consistent with the processed content according to the corresponding rules, and when it is inconsistent, modifying the entropy value generated on different data modules in the corresponding processing process, and updating the entropy value of the corresponding data set. It also includes for each data module, when the next adjacent data module of the data module does not comply with the processing result of the next adjacent data module according to the data rule processing content, falling back to the data module that complies with the processing result and is consistent, and reselecting the direction with the minimum entropy as the processing direction of the next adjacent data module next time.

[0057] Embodiment 2:

[0058] This embodiment 2 is implemented based on the embodiment 1, and the algorithm is used to fill the grid, and the algorithm allows each grid to select a suitable color, and the number of selectable colors is getting smaller and smaller. The specific embodiment content is as follows:

[0059] Box: a box of each color, which is a generated block in the map. It contains the following information: location, optional module set, 6 directions, map reference, final selected module, whether it has collapsed, and whether it has been colored.

[0060] Module Prototype (ModuleProtoType): Contains information about 6 faces (FaceDetails), which are top, bottom, left, right, front, and back. Each face corresponds to a connection ID, which is ConnectorID. Only those with the same ConnectorID can be connected to each other.

[0061] The most basic constraint rule is that only the same ConnectorID can connect to each other;

[0062] The module prototype contains information:

[0063] 1. 6 face information: divided into horizontal face and vertical upper and lower face. By calculating the vertices of the face mesh in the direction of the normal, the face information will be generated;

[0064] 2. Surface information includes:

[0065] (1) Walkable: (whether walking is allowed);

[0066] (2) Connector: ConnectorID. Only the same ConnectorID can be connected.

[0067] (3) Fingerprint: Fingerprint, which is calculated by mesh vertices to obtain a vector4 array, used to generate ConnectorIDs in batches;

[0068] (4)ModulePrototypes: The prototypes of neighboring modules that you want to exclude.

[0069] 3. Probability of being randomly selected:

[0070] Module: It is obtained by rotating ModuleProtoType (module prototype) around the Y axis. Rotating around the Y axis by 0, 90, 180, and 270 degrees respectively will give 4 modules, so that the resources of the module prototype can be reused. Module is the block finally generated on the map.

[0071] Modules contain information such as Figure 1 As shown:

[0072] 1. Name: module name (tree, water, grass, etc.);

[0073] 2. ModuleProtoType: the module prototype that constitutes it;

[0074] 3. Rotation: Degree of rotation around the Y axis, 0-0 1-90 2-180 3-270;

[0075] 4, ModuleSet[]: Optional module set of 6 directional neighboring blocks (it is an array);

[0076] 5. PLogP: probability p*log(p), needed when calculating entropy.

[0077] Explanation of module collection:

[0078] That is, the module set, which uses the bit array method to encapsulate all pre-processed modules into the set. The ModuleSet in the box represents all optional modules of the current box;

[0079] The module collection contains information:

[0080] 1, datalong[]: bit array, 1 bit corresponds to 1 module index, 0 means not selectable, 1 means selectable;

[0081] 2. entropy: entropy;

[0082] 3. entropyOutdated: whether entropy is expired (need to be recalculated next time);

[0083] 4, Count: the total number of optional modules;

[0084] Collapse process: Controller, e.g. Figure 2 As shown, it is responsible for generating and managing all blocks on the map;

[0085] s1, first obtain all the blocks that need to be processed;

[0086] s2, then calculate the entropy of the optional set in each block and find the block with the smallest entropy value;

[0087] s3, find the block with the smallest entropy value and collapse it;

[0088] That is, generate a specific model (grass, water block, tree block); collapse this block, which means randomly selecting a module from ModuleSet (optional module set) (the greater the probability of a module, the easier it is to be selected);

[0089] s4, backtrack if not found;

[0090] s5, after the collapse is completed, the ModuleSet of the neighboring blocks in 6 directions needs to be propagated according to the rules;

[0091] s6, delete the current block from the current set of optional modules;

[0092] s7, start propagating the neighboring blocks that affect the current block.

[0093] There are basic blocks and variant blocks (i.e., changes to the basic blocks); after the collapse is completed, the basic block is a square grass, and the basic block is replaced with the four walls of the house. At this time, a roof needs to be added to the four walls, and some replacement operations can be performed on the basic block according to the direction of the normal and the type of the basic block; with only a simple change to a basic block, it is difficult to achieve perfect replacement of complex combined basic blocks, so each surface information is configured to cope with complex changes.

[0094] In the several embodiments provided by the present invention, it should be understood that the disclosed medium and method can be implemented in other ways. For example, the medium embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another medium, or some features can be ignored or not executed.

[0095] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0097] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0098] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or media. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0099] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0100] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

[0101] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention is indicated by the claims above.

Claims

1. A method for dynamically implementing complex irregular models based on a wave function collapse algorithm, characterized in that: The process of generating a sandbox map using the application data module includes the following steps: Setting data rules in the normal direction corresponding to the data module, including: setting basic constraint rules for the data model corresponding to the data module, the basic constraint rules are the processing content of the data module, the processing content reduces the number of data modules to be processed according to the basic constraint rules, and the basic constraint rules include that the data module to be processed is the same type or type-related data module of the adjacent data module; the data module is a selected map template, including a terrain module, a building module, a character module and a directional interaction module, the data rule is to use a wave function collapse algorithm based on the preset interactivity of different data modules to realize the presentation method set on the sandbox map, the presentation method includes spatial position relationship and belonging relationship, the spatial position relationship includes the position setting of up, down, left, right, inside and outside, and the belonging relationship includes the way of merging and being fused into an integral module; Classification: Classify data modules with the same characteristics into corresponding sets, and classify modules with the same data content characteristics, including color and shape; Retrieve the data module of the determined content and process the data module according to the data rules. The processing order is: retrieve the data module to be processed, integrate the presentation methods between the data modules based on the data rules, and repeatedly retrieve, classify, and integrate the data modules on all sandbox maps. Specifically, it includes: modularizing the data to be processed, defining the 3D vertices of the data model corresponding to the data module, and setting the data rules on the 3D vertices to provide direction discrimination for different types of data modules; While processing the data module, monitor and judge whether each processed data module complies with the data rules. When the data module does not comply with the data rules, clear the processing content of the data module that does not comply with the data rules to the corresponding determined content data module, that is, delete the data modules to be processed that do not comply with the data rules with other surrounding data modules, specifically including: judging whether the data complies with the data rules to find out whether the predicted processing content is consistent with the processed content according to the corresponding rules, and when they are inconsistent, modify the entropy values ​​generated on different data modules in the corresponding processing process, and update the entropy value of the corresponding data set; Among them, the wave function collapse algorithm is used to implement the presentation method set on the sandbox map, including calculating the entropy value of the data module set, including calculating the number of initial data modules with optional processing content in the set, and determining the entropy value based on whether the processing content of the data module in the data set is locked. The locked data content is the data content in the corresponding data module after monitoring that subsequent data modules comply with the data rules after processing according to the data rules.

2. According to claim 1, a method for dynamically realizing complex irregular models with wave function collapse algorithm as the core is characterized in that: The step of classifying data modules with the same characteristics to generate corresponding sets includes: The data modules corresponding to the processing contents are detected, and the data modules corresponding to the data models of the same processing contents are classified as the same data set.

3. According to claim 2, a method for dynamically realizing complex irregular models with a wave function collapse algorithm as the core is characterized in that: It also includes, for each data module, when the next adjacent data module of the data module does not conform to the processing result of the next adjacent data module according to the data rule processing content, falling back to the data module that conforms to the processing result and is consistent, and reselecting the direction with the smallest entropy as the next processing direction for the next adjacent data module.

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