A method and system for generating textures in antique-style stone-pressed products

Abstract

This invention relates to the field of texture generation technology, and provides a method and system for generating textures for antique stone-pressed products. The method includes: setting a non-uniform occurrence mechanism for geological evolution events on the texture surface to adjust the occurrence probability of each geological evolution event in a set of geological evolution events in the corresponding region of the texture surface; performing iterative evolution based on the set of geological evolution events, the non-uniform occurrence mechanism, and the adjusted occurrence probabilities; wherein, the iterative evolution includes: executing corresponding geological evolution events according to triggering conditions, and causing interactions between the geological evolution events to cumulatively change the texture surface morphology, thereby forming a non-periodic texture with multi-scale characteristics. This invention improves the randomness and quality of texture generation for antique stone-pressed products.

Description

Technical Field

[0001] This invention relates to the field of texture generation technology, and specifically to a method and system for generating textures for antique stone-pressed products. Background Technology

[0002] In the production of antique-style stone-pressed products, traditional techniques struggle to capture the unique randomness, rich layering, and unpredictable natural beauty of natural stone. This results in monotonous, repetitive textures, a lack of vibrancy, and blurred or unevenly distributed details, making the final product visually vastly different from natural stone. To overcome these limitations, the industry has begun actively exploring the use of digital technology to innovate texture generation methods. In some large-scale, long-term architectural projects, experienced architects and art designers have begun to raise subtle feedback. They have found that although the textures of individual products and local splicing areas appear random and seamless, when observing these laid-out antique stone surfaces over extended periods and on a large scale, they always sense a subtle "unnatural" regularity. This regularity is not a visually perceptible repeating pattern, but a deeper, statistically significant "pseudo-random" characteristic. For example, the distribution density, length, curvature, and other characteristics of program-generated "cracks" or "veins" may present an overly uniform or predictable distribution pattern on a macroscopic scale, lacking the true chaos and unpredictability of natural stone, which is formed by the random evolution of geological processes. This bias stems from the inherent deterministic logic and finite parameter space of computational methods when simulating natural randomness. Typical pseudo-random number generators or noise functions, even if they exhibit randomness locally, will reveal their inherent periodicity or statistical bias under sufficiently large sample spaces or long-term series. For example, the frequency of certain texture features may be too even, or their spatial distribution may lack the clusters or sparse areas common in natural stone, making the overall visual effect lack a true sense of "unexpectedness" and "vitality." This subtle sense of "pseudo-randomness" can cause an indescribable visual fatigue in observers after prolonged staring, and they may even subconsciously perceive that this is not real natural stone, thus weakening the product's "antique" charm and artistic value. This phenomenon is difficult to detect initially and is only keenly observed when the product is used on a large scale, observed over a long period, and evaluated by professionals with a deep understanding of natural materials. It is not a physical defect, but rather a subtle deviation from "true randomness" caused by the inherent logic and limited parameter space of the calculation method when simulating natural randomness. To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0003] This application discloses a method and system for generating textures for antique stone-pressed products, aiming to solve the problems of single texture, high repetition, unnatural edge connection, and lack of true randomness and unpredictable heterogeneity in existing antique stone-pressed product texture generation technologies. The technical solution of this application is as follows: In a first aspect, this application discloses a method for generating textures in antique stone-pressed products, comprising the following steps: To obtain the textured surface of antique stone-pressed products; Define a set of geological evolution events to simulate the geological evolution process; each geological evolution event in the set includes triggering conditions, scope of action, and rules for changing the morphology of the textured surface. A non-uniform occurrence mechanism for geological evolution events on the texture surface is established to adjust the occurrence probability of each geological evolution event in the corresponding region of the texture surface. Iterative evolution is carried out based on the set of geological evolution events, the non-uniform occurrence mechanism, and the adjusted occurrence probability. The iterative evolution includes: executing the corresponding geological evolution events according to the triggering conditions, and making the geological evolution events interact with each other to change the texture surface morphology in a cumulative manner, so as to form a non-periodic texture with multi-scale characteristics. This technical solution can simulate the natural geological evolution process and generate antique stone textures with multi-scale characteristics and non-periodicity. It effectively solves the problems of traditional methods such as single texture, high repetition and lack of true randomness in programmed texture generation, making the product present a more natural chaos and unpredictability on a macroscopic level. Secondly, this application also discloses an antique stone-pressed product texture generation system for performing the above-mentioned antique stone-pressed product texture generation method, including: The texture surface acquisition module is used to acquire the texture surface of antique stone-pressed products; The Geological Evolution Events module is used to define a set of geological evolution events that simulate the geological evolution process. Each geological evolution event in the geological evolution event set includes triggering conditions, scope of action, and rules for changing the morphology of the textured surface. The non-uniform occurrence mechanism module is used to set the non-uniform occurrence mechanism of geological evolution events on the texture surface, so as to adjust the occurrence probability of each geological evolution event in the geological evolution event set in the corresponding region of the texture surface. The iterative evolution execution module is used to perform iterative evolution based on the set of geological evolution events, the non-uniform occurrence mechanism, and the adjusted occurrence probability. The iterative evolution includes: executing the corresponding geological evolution events according to the triggering conditions, and causing the geological evolution events to interact with each other, so as to change the texture surface morphology of the texture surface in a cumulative manner, so as to form a non-periodic texture with multi-scale features. This technical solution provides a system for generating textures in antique stone-pressed products. Through modular design, it effectively supports the simulation and iterative evolution of geological events, thereby generating textures with multi-scale characteristics and non-periodicity. This solves the shortcomings of traditional methods and existing procedural generation techniques in terms of texture realism and randomness. Beneficial Effects: The method for generating textures for antique stone-pressed products disclosed in this application obtains the textured surface of the antique stone-pressed product and sets a set of geological evolution events simulating geological evolution processes. Each event includes triggering conditions, scope of action, and morphological change rules. Based on this, a non-uniform occurrence mechanism for geological evolution events on the textured surface is established to adjust the probability of occurrence of each event in the corresponding area. Finally, based on the geological evolution event set, the non-uniform occurrence mechanism, and the adjusted occurrence probabilities, iterative evolution is performed. Geological evolution events are executed according to the triggering conditions, and their interactions are made to cumulatively change the textured surface morphology, thereby forming a non-periodic texture with multi-scale characteristics. This method effectively solves the problems of monotonous textures, high repetition, unnatural edge connections, and lack of true randomness and unpredictable heterogeneity in the macroscopic appearance of antique stone-pressed products in existing technologies. By simulating real geological evolution processes, this application can generate highly random, richly layered textures with unpredictable heterogeneity in the macroscopic appearance, avoiding the "pseudo-randomness" phenomenon and visual fatigue common in traditional methods and existing procedural generation technologies. This technical solution makes antique stone-pressed products visually closer to the natural chaos and vitality of natural stone, greatly enhancing the artistic value and market competitiveness of the products. Attached Figure Description

[0004] Figure 1 This is a flowchart of a method for generating textures in an antique stone-pressed product according to one embodiment of the present invention; Figure 2 This is a flowchart of a method for generating textures in an antique stone-pressed product according to another embodiment of the present invention; Figure 3 This is a system block diagram of an antique stone-pressed product texture generation system according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Antique stone-pressed product texture generation system; 11. Texture surface acquisition module; 12. Geological evolution event module; 13. Non-uniform occurrence mechanism module; 14. Iterative evolution execution module. Detailed Implementation

[0005] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. This application proposes a method for generating textures in antique stone-pressed products, combining... Figure 1 As shown, it includes: S1, Obtain the textured surface of the antique stone-pressed product; S2, define a set of geological evolution events to simulate the geological evolution process; each geological evolution event in the geological evolution event set includes triggering conditions, scope of action, and rules for changing the morphology of the textured surface. S3, set the non-uniform occurrence mechanism of geological evolution events on the texture surface, so as to adjust the occurrence probability of each geological evolution event in the corresponding region of the texture surface; S4, based on the set of geological evolution events, the non-uniform occurrence mechanism and the adjusted occurrence probability, performs iterative evolution; wherein, iterative evolution includes: executing the corresponding geological evolution events according to the triggering conditions, and making the geological evolution events interact with each other, so as to change the texture surface morphology of the texture surface in a cumulative manner, so as to form a non-periodic texture with multi-scale features. To facilitate understanding of the method for generating textures in antique stone-pressed products described in this application, the key terms used in this document will be explained in a unified manner. "Textured surface" refers to the target surface in antique stone-pressed products used to bear the surface undulations and material variations. The textured surface can be represented by a digital model, including a height field model, a mesh model, or a voxel data model, to store and express the geometric morphology and material properties corresponding to the texture. The geometric morphology information can include surface height, slope, curvature, fracture morphology, or local undulations, while the material properties information can include color distribution, roughness, mineral composition distribution, or regional material differences. By jointly describing the geometric morphology and material properties of the textured surface, a unified object can be provided for simulating subsequent geological evolution processes. A "geological evolution event set" refers to a collection of events used to simulate natural geological processes. Each geological evolution event in the set corresponds to a specific geological process, including weathering, erosion, deposition, faulting, and mineral crystallization. Each geological evolution event includes triggering conditions, an area of ​​influence, and morphological change rules. The triggering conditions define the conditions required for the corresponding geological evolution event to enter the execution state and can be related to iteration stages, local geometric features, local material properties, or other evolutionary state parameters. The area of ​​influence defines the region of impact of the geological evolution event on the texture surface. The morphological change rules define the specific changes the geological evolution event makes to the texture surface, including changes to geometric information and changes to material properties. By setting these parameters for different geological evolution events, the texture formation process can have clear evolutionary units and controllable change paths. The "non-uniform occurrence mechanism" refers to a control mechanism used to adjust the probability of geological evolution events occurring in different regions of a textured surface. This mechanism does not cause geological evolution events to trigger with the same probability in all regions. Instead, it differentiates the event occurrence tendency in different regions by combining the local state of the textured surface, a preset spatial distribution pattern, or previous evolution results. Through this method, the location and density of geological evolution events on the textured surface can exhibit non-uniform characteristics such as local concentration, local sparseness, banded distribution, or blocky distribution, thus making the generated result closer to the regional heterogeneity commonly found in natural stone textures. "Iterative evolution" refers to the continuous evolutionary process in which geological evolution events act on a textured surface according to a preset order, triggering relationship, and action rules. In this process, each geological evolution event is not executed in isolation, but rather uses the texture state formed by the preceding events as input conditions for subsequent events, creating a state transfer and superposition relationship between different events. Since each iteration changes the local morphology and material distribution of the textured surface, the triggering conditions, scope of action, and morphological changes of subsequent geological evolution events are also adjusted accordingly. Therefore, the overall texture formation process exhibits continuous accumulation and dynamic correlation. "Multi-scale features" refer to the discernible structural variations and levels of detail on a textured surface at different observation scales. At smaller scales, textured surfaces may exhibit detailed features such as grains, pores, fine pits, or microcracks; at intermediate scales, they may display fracture networks, mineral spots, localized sedimentary zones, or banded structures; at larger scales, they may exhibit overall undulating trends, directional structures, or regional texture patterns. These features at different scales are not independent of each other but are gradually superimposed through an iterative evolution process. "Aperiodic texture" refers to a textured surface where the pattern distribution does not exhibit fixed repeating units or a regular periodic structure. Even when spread over a large area, it does not produce obvious repetitive rhythms or mechanical replication traces. This feature makes the generated result closer to the natural texture of natural stone surfaces. Based on the above terminology definitions, the method for generating textures in antique stone-pressed products described in this application revolves around four stages: texture surface construction, geological evolution event set setting, non-uniform occurrence mechanism setting, and iterative evolution execution. By simulating the local differences, stage accumulation, and event coupling relationships in the natural geological process, the texture surface gradually forms a non-periodic texture with multi-scale characteristics. During implementation, the textured surface of the antique stone-pressed product is first acquired. This textured surface can be an initial blank surface or a pre-defined basic geometric surface. The initial blank surface provides the foundational bearing surface for subsequent geological evolution, while the pre-defined basic geometric surface introduces a priori terrain contours or basic undulations. The textured surface can be acquired by importing a basic geometric model file or by generating an initial digital surface model programmatically. For example, a two-dimensional height field grid with a preset resolution can be constructed, and each grid point can record its initial height value to form the initial textured surface; alternatively, a three-dimensional mesh model can be imported, serving as the initial object of subsequent geological evolution. By pre-determining the representation of the textured surface, a unified data foundation can be provided for the impact of geological evolution events on geometric morphological and material property information. After acquiring the textured surface, a set of geological evolution events is established to simulate the geological evolution process. Each geological evolution event in this set has its own triggering conditions, scope of action, and morphological change rules, allowing different events to participate in texture construction according to their respective logics during subsequent evolution. For example, weathering events can be triggered for areas with steep slopes or high surface exposure, reducing local height, increasing roughness, or altering surface particle distribution within the corresponding area; fracture generation events can be triggered for areas where local stress accumulation reaches a threshold, expanding linear or network fractures along a predetermined direction or local weak surfaces, while simultaneously changing the morphology and material properties of the fracture neighborhood; mineral deposition events can be triggered for local depressions, fluid convergence areas, or areas where preset mineral formation conditions are met, increasing the proportion of mineral components, adjusting color distribution, or forming vein-like structures within the corresponding area. By establishing separate action logics for different geological evolution events, the texture generation process can have an evolutionary basis corresponding to natural geological processes. After setting the geological evolution event set, a non-uniform occurrence mechanism for geological evolution events on the textured surface is further established to adjust the probability of occurrence of various geological evolution events in different regions of the textured surface. This non-uniform occurrence mechanism is used to change the tendency of texture features to spread evenly under traditional uniform random distribution, making the spatial distribution of geological evolution events more consistent with the natural texture formation patterns. In specific implementation, an event occurrence probability distribution map can be preset, so that different regions of the textured surface correspond to different base probabilities; alternatively, the event occurrence probability can be dynamically adjusted based on the current geometric morphology and material properties of the textured surface. For example, in areas with large elevation fluctuations and significant slope changes, the probability of weathering or erosion events is increased; in areas with low mineral density, significant local depressions, or existing fracture neighborhoods, the probability of mineral deposition or crystallization events is increased. Through this mechanism, the spatial triggering of geological evolution events is not isolated and random, but corresponds to the current state of the textured surface, thereby allowing local aggregate structures, sparse areas, striped areas, and transitional areas to form naturally during the evolution process. After establishing the non-uniform occurrence mechanism, the textured surface undergoes iterative evolution based on the geological evolution event set, the non-uniform occurrence mechanism, and the adjusted event probability. During this iterative evolution, the system checks in each iteration whether the current textured surface meets the triggering conditions corresponding to various geological evolution events. When the conditions are met, the system determines the location and scope of the corresponding geological evolution event based on the non-uniform occurrence mechanism, and updates the geometric morphology and material properties of the textured surface according to the morphological change rules of that geological evolution event. As the iteration progresses, a continuous influence relationship is formed between different geological evolution events; that is, the texture state changed by a preceding event participates in the judgment of the triggering conditions of subsequent events and affects the scope and effect of subsequent events. For example, a local depression formed by a preceding weathering event can further increase the probability of subsequent sedimentary or mineral deposition events occurring in that area; a fracture structure formed by a preceding faulting event can change the propagation path and boundary of subsequent erosion, crystallization, or material migration events. Therefore, the textured surface is no longer a simple repetition of a single rule during continuous iteration, but rather forms a cumulative evolutionary state with a correlation between preceding and subsequent events. In the early stages of iterative evolution, geological events can act on a larger area, forming the overall undulation trend and basic structural morphology of the textured surface. As evolution progresses, the scale of action gradually becomes more refined. Events such as localized weathering, the formation of fine fractures, grain erosion, or localized mineral deposition continue to superimpose on the existing basic structure, resulting in finer-scale localized changes. Simultaneously, sedimentation, mineral enrichment, color migration, and roughness variations at the material property level further participate in surface formation, giving the textured surface not only rich layers in geometric morphology but also regional differences in material expression. Through this evolutionary process of gradual superposition from large-scale structures to small-scale details, the final textured surface possesses identifiable multi-scale characteristics at the macro, meso, and micro levels, and the overall texture distribution does not exhibit regular repetition, thus forming a non-periodic texture with a natural feel. Optional, combined Figure 2 As shown, the steps of executing corresponding geological evolution events according to triggering conditions and causing interactions between these events to cumulatively change the texture surface morphology to form a non-periodic texture with multi-scale characteristics include: A1, during the execution of the corresponding geological evolution event based on the triggering conditions, monitor the geometric morphological features and material property features of the textured surface; A2, based on the geometric morphology and material properties of the textured surface, extracts the local height undulation, crack network connection characteristics and mineral spot density of the textured surface, and forms height undulation index, crack index and mineral spot index respectively. A3 compares the height undulation index, crack index, and mineral spot index with the preset heterogeneity target range to obtain the comparison results; where heterogeneity is used to characterize the undulation difference of the textured surface in a multi-scale spatial range, the complexity of the crack structure, and the uneven distribution of mineral spots, and the heterogeneity target range is the configuration of the target value range of the height undulation index, crack index, and mineral spot index. A4. When the comparison results indicate a deviation from the heterogeneity target range, adjust the random number generation bias of subsequent incomplete geological evolution events; where random numbers are random sampling variables used to randomly sample the occurrence location, direction, intensity, or scale of geological evolution events; random number generation bias is to adjust the distribution parameters of the random sampling variables to change the bias of the geological evolution event sampling results. A5, based on the adjusted random number generation bias and subsequent incomplete geological evolution events, continues to iterate and evolve, forming multi-scale, non-periodic textures with unpredictable heterogeneity in the process. Specifically, during the execution of corresponding geological evolution events based on triggering conditions, the geometric morphological features and material properties of the textured surface are monitored. The geometric morphological features characterize the surface morphology and may include surface undulations, slope, curvature, roughness, etc.; the material properties characterize the material distribution of the textured surface and may include mineral composition, color distribution, gloss, etc. By continuously monitoring these features, the state information of the textured surface at the current evolutionary stage can be obtained, allowing subsequent identification and adjustment of texture heterogeneity to be based on the current surface state, rather than simply proceeding unidirectionally according to preset event rules. Based on the monitored geometric morphological and material property characteristics, the local height undulations, fracture network connectivity features, and mineral spot density of the textured surface are further extracted. The local height undulations characterize the degree of height variation and spatial distribution in local areas of the textured surface, reflecting morphological differences caused by erosion, deposition, or localized exfoliation. The fracture network connectivity features characterize the length, density, connectivity, branching degree, and directional organization of fracture structures, reflecting the structural complexity of the textured surface under fracture, weathering, or stress release. The mineral spot density characterizes the uniformity, aggregation, or sparsity of mineral components on the textured surface, reflecting the variation of material properties in different regions. To facilitate subsequent quantitative analysis, the local height undulations are converted into height undulation indices, the fracture network connectivity features into fracture indices, and the mineral spot density into mineral spot indices, thus forming heterogeneity characterization parameters that can be used for comparative analysis. Heterogeneity is used to characterize the morphological differences, structural complexity, and material distribution non-uniformity of textured surfaces across multiple spatial scales. This heterogeneity is not limited to fluctuations in a single feature, but rather manifests as a comprehensive spatial representation of local height variations, fracture structure complexity, and non-uniform mineral spot distribution. The heterogeneity target range is a pre-defined range of target values ​​or distribution for the height variation index, fracture index, and mineral spot index, used to define the state the current textured surface should achieve at the heterogeneity level. For example, a corresponding fluctuation range can be set for the height variation index, a corresponding complexity and connectivity range for the fracture index, and a corresponding aggregation and non-uniform distribution range for the mineral spot index. Comparing the extracted height variation index, fracture index, and mineral spot index with the pre-defined heterogeneity target range yields a comparison result, which characterizes the degree of matching and deviation direction between the current textured surface heterogeneity and the target state. When the comparison results indicate that the heterogeneity of the current textured surface deviates from the preset target range of heterogeneity, the random number generation bias for subsequent unfinished geological evolution events is adjusted. The random numbers are used to randomly sample the location, direction, intensity, scale, or expansion path of geological evolution events; the random number generation bias is used to change the distribution parameters, sampling tendency, or spatial weights used in the random sampling process, so that the execution results of subsequent geological evolution events, while retaining randomness, are adjusted towards the target heterogeneity state. This adjustment does not cancel the random process, but rather applies directional guidance to the random sampling results while maintaining the naturalness of the evolution. For example, when a local area of ​​a textured surface is too flat, the sampling weight of erosion or weathering events in the relevant area can be increased, and their intensity or scale of action can be enhanced; when there is insufficient fracture structure, the sampling probability of fracture generation events can be increased, or the bias of directional sampling and extension length sampling can be adjusted to make fractures more likely to form branching and connected structures; when the distribution of mineral spots is too uniform, the sampling tendency of mineral deposition events or mineral aggregation events in different areas can be changed, so that the distribution of mineral spots shows more obvious aggregation and dispersion changes. Based on the adjusted random number generation bias and subsequent unfinished geological evolution events, the system continues iterative evolution. Since the random number generation bias has been corrected according to the current heterogeneity deviation of the textured surface, the random sampling results of subsequent geological evolution events in terms of location, direction, intensity, and scale will change accordingly. This causes the subsequent evolution path of the textured surface to no longer remain in the original uniform expansion state, but gradually develop towards a direction with stronger local variability, richer structural layers, and higher material distribution complexity. Through this feedback adjustment process, the textured surface can form multi-scale, non-periodic texture features with unpredictable heterogeneity during continuous evolution. In some preferred embodiments, it is assumed that during the iterative evolution of the texture of the antique stone-molded product, the system monitors that the height undulation index of the texture surface is consistently below the lower limit of the heterogeneity target range, the crack index indicates that the crack network connection characteristics are too simple, and the mineral spot index indicates that the mineral spot density distribution is too uniform. These results indicate that the current texture surface exhibits insufficient undulation, a simple structure, and insufficient material distribution variation in local areas, and has not yet achieved the natural roughness, structural complexity, and material heterogeneity that antique stone textures should possess. Based on the above comparison results, the system determines that the heterogeneity of the current textured surface deviates from the preset target range, and adjusts the random number generation bias of subsequent incomplete geological evolution events accordingly. For erosion events, the probability of their occurrence in the current locally flat area can be increased, and the corresponding intensity or scale of action can be increased, making it easier for subsequent evolution to form depressions, undulations, and rough structures in the relevant areas; for fracture generation events, the directional sampling bias and length sampling bias can be adjusted, making it easier for them to form branching or interconnected fracture networks based on existing fractures; for mineral deposition events or mineral aggregation events, the spatial location sampling bias can be adjusted, causing mineral spots to form local aggregation, local dispersion, or discontinuous distribution in some areas, thereby weakening the original uniform distribution characteristics. After the random number generation bias adjustment is completed, subsequent geological evolution events continue to be executed. With the intensification of erosion events, the textured surface gradually develops more significant local height variations and surface roughness changes; with the bias adjustment of fracture generation events, the fracture network gradually forms more complex branching and connectivity relationships; with the bias adjustment of mineral-related events, mineral spots form a more natural clustering and dispersion distribution and regional differences on the textured surface. By incorporating current surface condition monitoring, heterogeneity index extraction, target range comparison, and random number generation bias adjustment into the same iterative evolution process, the system can continuously correct the evolution direction during texture evolution, resulting in a final antique stone-pressed product texture with richer layering, a stronger sense of natural heterogeneity, and more obvious non-repetitive features in visual effect. Optionally, the steps for defining the non-uniform occurrence mechanism of geological evolution events on textured surfaces include: Continuously monitor the statistical distribution characteristics of textured surfaces in different spatial ranges; The identification results are obtained by identifying cases where the statistical distribution characteristics tend to be uniform or lack extreme values ​​on a macro scale. Based on the identification results, generate a region probability weight distribution; The trigger probability and intensity of geological evolution events are adjusted based on the regional probability weight distribution. Based on the adjusted trigger probability and intensity, a non-uniform occurrence mechanism for the location of geological evolution events is set and adjusted. Specifically, continuously monitoring the statistical distribution characteristics of textured surfaces across different spatial ranges refers to the system's real-time or periodic data collection and analysis of the textured surface's geometric morphology (such as height variations, roughness, and crack density) and material properties (such as mineral spot distribution and color variations) during texture evolution. These statistical distribution characteristics can include mean, variance, skewness, kurtosis, spatial autocorrelation, fractal dimension, etc., to quantify the complexity and heterogeneity of the texture at different regions and scales. The aim is to obtain comprehensive information about the current state of the textured surface, providing a data foundation for subsequent adaptive adjustments. The identification of statistical distribution characteristics that tend to be uniform or lack extreme values ​​on a macroscopic scale refers to analyzing the monitored statistical distribution characteristics to determine whether there are large areas of flatness, sparsely fractured areas, or areas with overly uniform mineral spot distribution on the textured surface. For example, when the height variation variance of a certain macroscopic area is lower than a preset threshold, or the connectivity index of the fracture network is too low, or the entropy value of the mineral spot density distribution is too small, the area can be considered to be uniform or lacking extreme values. This identification result is used to indicate areas that require intervention and adjustment during texture evolution. In practical applications, generating a regional probability weight distribution based on the identification results refers to dynamically generating a spatially distributed weight map based on the identified regions that tend to be uniform or lack extreme values. In this weight map, regions identified as overly uniform or lacking extreme values ​​are assigned higher weights, while regions with rich texture features are assigned lower weights. This regional probability weight distribution directly affects the probability and intensity of subsequent geological evolution events, aiming to guide the evolutionary process towards greater heterogeneity and complexity. Furthermore, adjusting the triggering probability and intensity of geological evolution events based on the regional probability weight distribution means applying the generated regional probability weight distribution to each event in the geological evolution event set. For example, in areas with higher weights, the triggering probability of specific types of geological evolution events (such as erosion, weathering, and fissure formation) can be increased, while their intensity (such as erosion depth, fissure length, and the amplitude of speckle density variation) can also be enhanced. Conversely, in areas with lower weights, these parameters may be reduced. The aim is to avoid excessive homogenization of local texture areas by finely controlling the occurrence tendency and impact of events. Therefore, setting and adjusting the non-uniform occurrence mechanism of geological evolution events based on the adjusted trigger probability and intensity means ultimately integrating these dynamically adjusted parameters into the sampling process of geological evolution events. For example, when selecting the occurrence location of geological evolution events, a weighted random sampling method can be used to make events more likely to occur in areas where increased complexity is needed. This mechanism ensures that the occurrence of geological evolution events is no longer completely random or statically preset, but can be intelligently adjusted according to the real-time state of the textured surface, thereby forming a non-uniform texture that is more in line with natural laws. Optionally, when the comparison results indicate a deviation from the heterogeneity target range, after adjusting the random number generation bias for subsequent incomplete geological evolution events, the method further includes: During each geological evolution event after adjusting the random number generation bias, the geometric morphological features and material property features of local areas on the textured surface are monitored; Calculate the statistical distribution characteristics of local area height undulation index, fracture index, and mineral spot index; The statistical distribution characteristics are compared with the preset heterogeneity target range to verify the local area and obtain the statistical distribution comparison results of the local area. When the statistical distribution comparison results indicate a deviation from the preset heterogeneity target range, the random number generation bias of subsequent geological evolution events is readjusted. Specifically, after each geological evolution event is executed and the random number generation bias is adjusted, the system immediately monitors a local area of ​​the textured surface. This monitoring aims to acquire the geometric and material properties of this local area, such as local height variations, fracture distribution, and mineral spot density. The local area refers to a sub-region on the textured surface with a specific spatial range and boundaries; its size and location can be dynamically set according to the texture's fineness and monitoring requirements. Furthermore, for the monitored local areas, the statistical distribution characteristics of their height undulation index, fracture index, and mineral spot index are calculated. These statistical distribution characteristics can include mean, variance, skewness, kurtosis, etc., to quantify the texture characteristics of the local area. For example, the statistical distribution of the height undulation index can reflect the flatness or ruggedness of the local area; the statistical distribution of the fracture index can characterize the density, connectivity, or directionality of the local fracture network; and the statistical distribution of the mineral spot index can reveal the degree of aggregation or dispersion of local mineral spots. Subsequently, the statistical distribution characteristics of these local areas are compared with the preset heterogeneity target range. This comparison process is a "verification" of the local areas, aiming to verify whether the local areas still meet the expected heterogeneity requirements after overall adjustments. The heterogeneity target range is a configuration of target value intervals for height undulation indicators, fracture indicators, and mineral spot indicators, used to ensure that the undulation differences of texture, the complexity of fracture structure, and the non-uniformity of mineral spot distribution within a multi-scale spatial range meet the expectations. Through comparison, the statistical distribution comparison results of the local areas can be obtained. When the statistical distribution comparison results of a local area deviate from the preset heterogeneity target range, the system will readjust the random number generation bias of subsequent unfinished geological evolution events. This readjustment is a fine-grained intervention targeting local deviations, aiming to guide the deviated local areas back within the target range. Adjusting the random number generation bias can change the bias of random sampling results of geological evolution events in terms of occurrence location, direction of action, intensity, or scale of action, thereby more precisely controlling the evolution direction of local textures. Optionally, the steps for defining the non-uniform occurrence mechanism of geological evolution events on textured surfaces include: Continuously monitor the statistical distribution characteristics of textured surfaces across multiple spatial ranges; these statistical distribution characteristics include microscale particle distribution and pore structure features, as well as macroscale fracture orientation and mineral vein extension features. Identify situations where statistical distribution characteristics tend to be uniform or lack extreme values ​​across different spatial scales; Based on the statistical distribution characteristics, which tend to be uniform or lack extreme values ​​at different spatial scales, scale weight configurations are generated. Based on scale weight configuration, the triggering probability and intensity of geological evolution events are adjusted; Based on the adjusted triggering probability and intensity, the non-uniform occurrence mechanism of geological evolution events is adjusted. Specifically, when continuously monitoring the statistical distribution characteristics of textured surfaces across multiple spatial scales, "multiple spatial scales" refers to continuous or discrete spatial scales ranging from extremely small local areas (e.g., microscale) to larger overall areas (e.g., macroscale). Statistical distribution characteristics at the microscale can include quantitative analysis of the size, shape, arrangement, and porosity of particles on the textured surface. Statistical distribution characteristics at the macroscale can involve assessing the length, width, direction, and interlacing patterns of cracks on the textured surface, as well as the distribution density and extension direction of mineral spots or veins. Monitoring these characteristics aims to comprehensively understand the structure and composition of textured surfaces at different levels of refinement. Identifying situations where statistical distribution characteristics tend to be uniform or lack extreme values ​​across different spatial scales involves analyzing the monitored statistical distribution characteristics to determine whether, at a specific scale, a certain aspect of the texture (such as particle distribution) is overly uniform, lacking the randomness and variability commonly found in nature, or whether extreme values ​​(such as exceptionally large or small particles, or exceptionally long or short fissures) are missing. For example, if the particle distribution at the microscale is too uniform, it may mean that the area lacks randomness in natural sedimentation or erosion processes; if the fissure orientation at the macroscale is too uniform, it may lack the complexity under geological stress. Based on the statistical distribution characteristics of texture features tending towards uniformity or lacking extreme values ​​at different spatial scales, a scale weight configuration is generated. The scale weight configuration can be understood as a set of parameters used to quantify the heterogeneity requirements of texture features at different spatial scales. When texture features at a certain scale tend towards uniformity or lack extreme values, the corresponding scale weight is set to a higher value to indicate that more variation or extreme events need to be introduced at that scale. Conversely, if the texture features at a certain scale are already sufficiently complex and heterogeneous, the corresponding scale weight can be set to a lower value. Based on scale-weighted configuration, the trigger probability and intensity of geological evolution events are adjusted accordingly. For example, in a micro-region, if the grain distribution is too uniform, the trigger probability of geological evolution events related to grain deposition or erosion may be increased, or their intensity may be enhanced, to introduce more randomness and variation. Similarly, in a macro-region, if the fracture orientation is too regular, the trigger probability or intensity of geological evolution events related to faulting or weathering may be adjusted to generate a more complex fracture network. Ultimately, based on the adjusted triggering probabilities and intensity of action, the non-uniform occurrence mechanism of geological evolution events was further refined. This means that geological evolution events no longer occur completely randomly on textured surfaces, but rather occur biasedly in specific regions with specific probabilities and intensities, according to the heterogeneity requirements of different spatial scales. For example, in areas where increased micro-roughness is required, events related to wear or deposition are more likely to occur; in areas where macro-fractures need to be introduced, events related to fracture occur more frequently. Optionally, the step of continuously monitoring the statistical distribution characteristics of the textured surface across multiple spatial ranges may include the following. Based on the current geological evolution event type and evolution stage of the textured surface, select and enable spatial sampling probes; each spatial sampling probe is configured with sampling range and feature extraction rules; Statistical analysis is performed on the local texture samples collected by each spatial sampling probe to obtain the geometric morphological features and material property features at different spatial scales; The geometric shape features and material property features are compared with the preset heterogeneity reference range to obtain the spatial sampling comparison results; When the spatial sampling comparison results indicate an out-of-range deviation, adjust the spatial position of the spatial sampling probe, the sampling range, or the feature extraction rules to recapture local texture samples where the deviation in the spatial sampling comparison results is within the preset heterogeneity reference range. The spatial sampling probe can be understood as a virtual or physical sensor or analysis unit designed to collect and analyze data in specific areas of a textured surface. Each spatial sampling probe is configured with a specific sampling range, which defines the spatial boundary for data collection. This range can be, for example, a rectangular area, a circular area, or a local area of ​​arbitrary shape on the textured surface. Furthermore, each probe is configured with feature extraction rules, which define the methods for extracting geometric and material properties from the collected data. These rules may include calculating local height variations, fracture density, and mineral spot distribution. Specifically, when performing statistical analysis on local texture samples acquired by each spatial sampling probe, geometric morphological features and material properties at different spatial scales can be obtained. Geometric morphological features may include, but are not limited to, local curvature, surface roughness, and fractal dimension, used to quantify the geometric complexity of the textured surface. Material property features may include mineral composition, color distribution, and gloss, used to describe the material composition and visual appearance of the textured surface. The acquisition of these features aims to comprehensively characterize the properties of local texture samples. Furthermore, the acquired geometric and material properties are compared with a preset heterogeneity reference range. The heterogeneity reference range is a pre-defined target interval used to measure the undulation differences, fracture structure complexity, and uneven distribution of mineral spots on the textured surface across multiple spatial scales. Through this comparison, spatial sampling comparison results are obtained, indicating whether the features of the current local texture sample meet the expected heterogeneity requirements. When spatial sampling comparison results indicate out-of-range deviations—meaning the features of the current local texture sample exceed the heterogeneity reference range—the system dynamically adjusts the spatial position of the spatial sampling probe, the sampling range, or the feature extraction rules. For example, if the texture of a certain region is found to be too smooth and does not meet the expected roughness range, the probe's sampling range can be adjusted to cover a larger area to capture more details, or the feature extraction rules can be adjusted to analyze the microstructure of that region more precisely. The purpose of this adjustment is to recapture local texture samples whose feature deviations are within the preset heterogeneity reference range, thereby ensuring that subsequent monitoring and evolution processes are based on accurate and representative texture data. Optionally, after comparing the geometric features and material properties with a preset heterogeneity reference range to obtain the spatial sampling comparison results, the method further includes: Continuously track the changing trend and fluctuation range of spatial sampling comparison results within a preset time period; Based on the trend and fluctuation range, it is determined whether the spatial sampling comparison results oscillate repeatedly or continue to hover in the edge region of the heterogeneity reference range, and the oscillation judgment result is obtained. Calculate the stability index based on the oscillation assessment results; When the stability index is lower than the preset threshold, the real-time adjustment of the spatial sampling probe is suspended and the observation period begins. During the observation period, maintain the current spatial sampling probe settings and continuously monitor the effect of texture evolution; Based on the effect of texture evolution during the observation period, the heterogeneity reference range is corrected by a preset amplitude to obtain the corrected heterogeneity reference range. The geometric features and material properties are re-compared with the corrected heterogeneity reference range to obtain the updated spatial sampling comparison results. Specifically, continuously tracking the trend and fluctuation amplitude of spatial sampling comparison results within a preset time period means that the system records and analyzes the numerical changes of the spatial sampling comparison results over a period of time, quantifying their trend and volatility through methods such as sliding window averaging and standard deviation calculation. The preset time period can be configured according to the speed and complexity of texture evolution to ensure that meaningful dynamic changes are captured. Furthermore, based on the trend and fluctuation amplitude, it determines whether the spatial sampling comparison results oscillate repeatedly or continuously hover in the edge region of the heterogeneous reference range, obtaining an oscillation judgment result. This aims to identify situations where the comparison results, although not significantly exceeding the range, fluctuate unstablely near the boundary of the reference range. For example, a threshold can be set; when the comparison results frequently cross within a certain percentage range of the upper and lower limits of the reference range, it is judged as oscillation or hovering. Based on the oscillation judgment results, a stability index is calculated. The stability index can be understood as a quantitative indicator measuring the stability of spatial sampling comparison results over a period of time. For example, it can be calculated comprehensively based on the frequency, amplitude, and duration of oscillations, aiming to provide an objective numerical value to assess the stability of the current texture evolution state. In practical applications, when the stability index falls below a preset threshold, real-time adjustments to the spatial sampling probe are paused, and an observation period is initiated. This means the system no longer immediately responds to minor deviations in the comparison results by adjusting the probe, but instead temporarily freezes the probe settings to avoid excessive intervention. The observation period can be set to a fixed time or until the texture evolution effect reaches a certain stable state. During the observation period, the current spatial sampling probe settings are maintained, and the texture evolution effect is continuously monitored. This aims to assess whether the texture evolution can stabilize on its own or exhibit the expected changes without real-time adjustments. Monitoring the texture evolution effect can include continuous evaluation of the macroscopic visual effect of the texture surface, key geometric features, or material properties. Therefore, based on the texture evolution effect during the observation period, the heterogeneity reference range is corrected by a preset magnitude to obtain the corrected heterogeneity reference range. If the texture evolution during the observation period indicates that the current heterogeneity reference range may be too restrictive or not fully aligned with actual evolutionary needs, it can be fine-tuned, such as by expanding or shrinking its boundaries, to better suit the texture generation target. Preset amplitude corrections ensure the controllability of the correction process. Finally, the geometric features and material properties are re-compared with the corrected heterogeneity reference range to obtain updated spatial sampling comparison results. In this way, the system can perform subsequent texture evaluation and probe adjustments based on a more accurate reference range that better suits the current evolutionary state, thereby improving the accuracy and stability of texture generation. Optionally, the steps for selecting and enabling a spatial sampling probe based on the current geological evolution event type and evolutionary stage of the textured surface include: After each geological evolution event is executed, the local geometric complexity and material property distribution of the textured surface are evaluated to obtain the corresponding texture evaluation results; Based on the texture evaluation results, select and activate the spatial sampling probe; When a change in the type or stage of a geological evolution event is detected, the combination and corresponding configuration of the spatial sampling probes are adjusted or switched. Specifically, after each geological evolution event, the system evaluates a local area of ​​the textured surface. This evaluation aims to obtain a detailed state of the current texture, including the complexity of its geometry and the distribution of material properties. Local geometric complexity can be understood as the richness of structural features such as undulations, folds, and fissures on the textured surface at both the micro and macro levels. The distribution of material properties refers to the spatial arrangement of mineral components, colors, luster, etc., on the textured surface. Through a comprehensive evaluation of these features, a texture evaluation result can be obtained, which can fully reflect the characteristics of the current texture. The spatial sampling probes are tools used to collect local samples of the textured surface. Each probe is pre-configured with a specific sampling range and feature extraction rules to adapt to different types and scales of texture features. Based on the texture evaluation results, the system can intelligently select and activate one or more spatial sampling probes that best suit the current texture state. For example, if the evaluation results show that the texture has a highly complex crack network, a probe specifically designed to capture crack features may be selected; if the results show uneven distribution of mineral spots, a probe adept at analyzing material properties may be selected. Furthermore, geological evolution is a dynamic process, and the types of geological evolution events (e.g., erosion, deposition, faulting) and the stages of evolution (e.g., early, middle, and late stages) are constantly changing. When the system detects a change in the type or stage of a geological evolution event, the original combination or configuration of spatial sampling probes may no longer be applicable. Therefore, the solution in this application can adjust or switch the combination and corresponding configuration of spatial sampling probes in a timely manner. For example, when transitioning from a stage dominated by erosion to a stage dominated by deposition, the sampling probes may shift their focus from surface wear and depressions to particle accumulation and bedding structure. This dynamic adjustment ensures that the sampling probes can always efficiently and accurately capture key texture information of the current evolution stage. Optionally, after each geological evolution event, the step of evaluating the local geometric complexity and material property distribution of the textured surface and obtaining the corresponding texture evaluation results specifically includes: After each geological evolution event is executed, a multi-resolution height field of a local region on the textured surface is obtained; Geometric features are extracted from the multi-resolution height field to obtain the local curvature distribution, surface roughness statistics, and fractal dimension, which are used as geometric features. Material property scanning is performed on local areas of the textured surface to obtain the relative content of mineral components, color distribution uniformity, and gloss variation range, which are used as material property features. By fusing geometric features and material property features, a local texture feature vector is formed. This local texture feature vector is then used as input for evaluating local geometric complexity and material property distribution to obtain the corresponding texture evaluation results. Among them, acquiring a multi-resolution height field of a local area of ​​a textured surface refers to collecting and representing the height information of a specific area of ​​the textured surface using sensors or algorithms of multiple resolutions. A multi-resolution height field can capture the undulations of the textured surface at different scales, from fine granular structures to macroscopic wave-like undulations. Furthermore, geometric features are extracted from the multi-resolution height field to quantify the geometric properties of the textured surface. Local curvature distribution reflects the degree and direction of surface bending, surface roughness statistics characterize the degree of microscopic unevenness, and fractal dimension is used to describe the self-similarity and complexity of the texture at different scales. These geometric features collectively constitute a comprehensive description of the textured surface morphology. Simultaneously, performing material property scanning on local areas of the textured surface is to obtain the material composition and optical properties of the textured surface. The relative content of mineral components reveals the proportion of different minerals in the texture, the uniformity of color distribution reflects the uniformity or variation pattern of texture color, and the range of gloss variation characterizes the surface's light reflection properties. These material property characteristics provide important information about the texture material composition and visual appearance. Finally, the acquired geometric features and material property features are fused to form a comprehensive local texture feature vector. This feature vector serves as input for evaluating local geometric complexity and material property distribution. It is processed through a specific evaluation model or algorithm to obtain a texture evaluation result that comprehensively reflects the current state of the textured surface. This evaluation result provides a data foundation for the subsequent selection and configuration of spatial sampling probes. This application proposes a texture generation system for antique stone-pressed products, used to execute the aforementioned texture generation method for antique stone-pressed products, combined with... Figure 3 As shown, the texture generation system 1 for antique stone-pressed products includes: Texture surface acquisition module 11 is used to acquire the texture surface of the antique stone-pressed product; The geological evolution event module 12 is used to set a set of geological evolution events to simulate the geological evolution process; each geological evolution event in the geological evolution event set includes triggering conditions, scope of action, and rules for changing the morphology of the textured surface. The non-uniform occurrence mechanism module 13 is used to set the non-uniform occurrence mechanism of geological evolution events on the texture surface, so as to adjust the occurrence probability of each geological evolution event in the geological evolution event set in the corresponding area of ​​the texture surface. The iterative evolution execution module 14 is used to perform iterative evolution based on the set of geological evolution events, the non-uniform occurrence mechanism, and the adjusted occurrence probability. The iterative evolution includes: executing the corresponding geological evolution events according to the triggering conditions, and making the geological evolution events interact with each other to change the texture surface morphology of the texture surface in a cumulative manner, so as to form a non-periodic texture with multi-scale features. In some embodiments of this application, the above-mentioned antique stone-pressed product texture generation system achieves texture generation through the coordinated operation of its various functional modules. Specifically, the texture surface acquisition module is used to acquire the texture surface of the antique stone-pressed product. The function of this module corresponds to the step of acquiring the texture surface in the above method implementation. It should be emphasized that this texture surface acquisition module can be implemented as a data input interface, such as a file reader, to load predefined or user-provided texture surface data; or it can be implemented as a generator to create an initial flat or simply undulating digital surface based on basic parameters (such as size and resolution). The geological evolution event module is used to define a set of geological evolution events that simulate geological evolution processes. The function of this module corresponds to the steps in setting the geological evolution event set in the above-described method implementation. It is important to emphasize that the geological evolution event module can be implemented as an event configurator, allowing users to define and manage different geological evolution events and their parameters through a graphical interface or configuration file; or it can be implemented as a preset event library containing various standard geological event templates for system use. The non-uniform occurrence mechanism module is used to set the non-uniform occurrence mechanism of geological evolution events on the textured surface, so as to adjust the occurrence probability of each geological evolution event in the set of geological evolution events in the corresponding region of the textured surface. The function of this module corresponds to the step of setting the non-uniform occurrence mechanism in the above method implementation. It should be emphasized that the non-uniform occurrence mechanism module can be implemented as a probability distribution manager to load or generate a spatial probability weight map and sample the occurrence location of the event according to the weight map; or it can be implemented as a real-time analyzer to dynamically calculate and update the local occurrence probability of the event according to the current state of the textured surface (such as local height, slope, material distribution). The iterative evolution execution module is used to perform iterative evolution based on a set of geological evolution events, a non-uniform occurrence mechanism, and adjusted occurrence probabilities. Iterative evolution includes: executing corresponding geological evolution events according to triggering conditions, and enabling interactions between these events to cumulatively change the texture surface morphology, forming a non-periodic texture with multi-scale characteristics. The function of this module corresponds to the iterative evolution steps in the aforementioned method implementation. It is important to emphasize that the iterative evolution execution module can be implemented as a main control unit, responsible for scheduling and coordinating the work of other modules, managing the iteration loop, and executing geological evolution events according to triggering conditions and non-uniform occurrence mechanisms. It is also responsible for recording and accumulating the morphological changes of the texture surface until a preset evolution termination condition is reached. The antique stone-pressed product texture generation system proposed in this application effectively solves the "pseudo-random" problem of textures in existing antique stone-pressed products at the macroscopic scale through its modular design. Traditional procedural texture generation systems, although capable of generating textures with a certain degree of randomness and hierarchy, suffer from inherent deterministic logic and a limited parameter space, resulting in statistically overly uniform or predictable distribution patterns. They lack the true chaos and unpredictability of natural stone, which evolves randomly through geological processes. This application's system achieves a detailed simulation of real geological evolution processes by introducing a texture surface acquisition module, a geological evolution event module, a non-uniform occurrence mechanism module, and an iterative evolution execution module. The geological evolution event module can set diverse geological events, while the non-uniform occurrence mechanism module can dynamically adjust the probability of event occurrence based on the local features of the texture surface, thereby breaking the uniformity of traditional methods in spatial distribution. Furthermore, the iterative evolution execution module simulates the interaction and cumulative changes between geological events, making the texture formation process more dynamic, and the final generated texture exhibits rich detail and unpredictable heterogeneity at multiple scales. Therefore, the system of this application can generate truly non-periodic textures, avoiding visual fatigue and weakening of artistic value caused by the "pseudo-random" characteristics in traditional methods. This allows antique stone-pressed products to maintain their inherent naturalness and artistic value when observed over a long period and over a wide area, thereby greatly enhancing the "antique" charm and market competitiveness of the products. The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for generating textures in antique stone-pressed products, characterized in that, include: To obtain the textured surface of antique stone-pressed products; A set of geological evolution events is defined to simulate the geological evolution process; each geological evolution event in the set includes triggering conditions, scope of action, and rules for changing the morphology of the textured surface. A non-uniform occurrence mechanism for geological evolution events on the textured surface is established to adjust the occurrence probability of each geological evolution event in the set of geological evolution events in the corresponding region on the textured surface; Based on the set of geological evolution events, the non-uniform occurrence mechanism, and the adjusted occurrence probability, iterative evolution is performed; wherein, the iterative evolution includes: executing corresponding geological evolution events according to triggering conditions, and causing interactions between geological evolution events to cumulatively change the texture surface morphology of the texture surface to form a non-periodic texture with multi-scale features.

2. The method for generating textures in an antique stone-pressed product according to claim 1, characterized in that, The step of executing corresponding geological evolution events according to triggering conditions and causing interactions between these events to cumulatively change the texture surface morphology to form a non-periodic texture with multi-scale characteristics includes: During the execution of the corresponding geological evolution event based on the triggering conditions, the geometric morphological features and material property features of the textured surface are monitored; Based on the geometric morphology and material properties of the textured surface, the local height undulation, crack network connection features, and mineral spot density of the textured surface are extracted, and height undulation index, crack index, and mineral spot index are formed respectively. The height undulation index, crack index, and mineral spot index are compared with the preset heterogeneity target range to obtain the comparison results; wherein, heterogeneity is used to characterize the undulation difference of the textured surface in a multi-scale spatial range, the complexity of the crack structure, and the uneven distribution of mineral spots, and the heterogeneity target range is configured as the target value range of the height undulation index, crack index, and mineral spot index. When the comparison results indicate a deviation from the heterogeneity target range, the random number generation bias of subsequent incomplete geological evolution events is adjusted; wherein, the random number is a random sampling variable used to randomly sample the occurrence location, direction of action, intensity of action, or scale of action of geological evolution events; the random number generation bias is to adjust the distribution parameters of the random sampling variable to change the bias of the geological evolution event sampling results; Based on the adjusted random number generation bias and subsequent incomplete geological evolution events, iterative evolution continues, forming multi-scale, non-periodic textures with unpredictable heterogeneity in the process.

3. The method for generating textures in an antique stone-pressed product according to claim 1, characterized in that, The steps for defining the mechanism of non-uniform occurrence of geological evolution events on the textured surface include: Continuously monitor the statistical distribution characteristics of the textured surface in different spatial ranges; The identification result is obtained by identifying cases where the statistical distribution characteristics tend to be uniform or lack extreme values ​​on a macroscopic scale. Based on the identification results, a region probability weight distribution is generated; Based on the aforementioned regional probability weight distribution, the trigger probability and intensity of geological evolution events are adjusted. Based on the adjusted trigger probability and intensity, a non-uniform occurrence mechanism for the location of geological evolution events is set and adjusted.

4. The method for generating textures in an antique stone-pressed product according to claim 2, characterized in that, After the step of adjusting the random number generation bias of subsequent incomplete geological evolution events when the comparison result indicates a deviation from the heterogeneity target range, the method further includes: During each geological evolution event after adjusting the random number generation bias, the geometric morphological features and material property features of local areas on the textured surface are monitored; Calculate the statistical distribution characteristics of the height undulation index, fracture index, and mineral spot index of the local area; The statistical distribution characteristics are compared with the preset heterogeneity target range to verify the local area and obtain the statistical distribution comparison results of the local area. When the statistical distribution comparison result indicates a deviation from the preset heterogeneity target range, the random number generation bias of subsequent geological evolution events is readjusted.

5. The method for generating textures in an antique stone-pressed product according to claim 1, characterized in that, The steps for defining the mechanism of non-uniform occurrence of geological evolution events on the textured surface include: Continuously monitor the statistical distribution characteristics of the textured surface in multiple spatial ranges; the statistical distribution characteristics include microscale particle distribution and pore structure characteristics, as well as macroscale fracture orientation and mineral vein extension characteristics. Identify cases where the statistical distribution characteristics tend to be uniform or lack extreme values ​​at different spatial scales; Based on the statistical distribution characteristics, which tend to be uniform or lack extreme values ​​at different spatial scales, a scale weight configuration is generated. Based on the aforementioned scale weight configuration, the triggering probability and intensity of geological evolution events are adjusted; Based on the adjusted triggering probability and intensity, the non-uniform occurrence mechanism of geological evolution events is adjusted.

6. The method for generating textures in an antique stone-pressed product according to claim 5, characterized in that, The step of continuously monitoring the statistical distribution characteristics of the textured surface in multiple spatial ranges includes: Based on the current geological evolution event type and evolution stage of the textured surface, select and enable spatial sampling probes; each spatial sampling probe is configured with sampling range and feature extraction rules; Statistical analysis is performed on the local texture samples collected by each spatial sampling probe to obtain the geometric morphological features and material property features at different spatial scales; The geometric features and material properties are compared with a preset heterogeneity reference range to obtain spatial sampling comparison results. When the spatial sampling comparison results indicate an out-of-range deviation, adjust the spatial position of the spatial sampling probe, the sampling range, or the feature extraction rules to recapture local texture samples where the deviation in the spatial sampling comparison results is within the preset heterogeneity reference range.

7. The method for generating textures in an antique stone-pressed product according to claim 6, characterized in that, After the step of comparing the geometric features and material properties with a preset heterogeneity reference range to obtain the spatial sampling comparison result, the method further includes: Continuously track the changing trend and fluctuation range of the spatial sampling comparison results within a preset time period; Based on the trend and fluctuation amplitude, it is determined whether the spatial sampling comparison result oscillates repeatedly or continues to hover in the edge region of the heterogeneity reference range, and an oscillation judgment result is obtained. Calculate the stability index based on the oscillation assessment results; When the stability index is lower than the preset threshold, the real-time adjustment of the spatial sampling probe is suspended and the observation period begins. During the observation period, maintain the current spatial sampling probe settings and continuously monitor the effect of texture evolution; Based on the effect of texture evolution during the observation period, the heterogeneity reference range is corrected by a preset amplitude to obtain the corrected heterogeneity reference range. The geometric features and material properties are re-compared with the corrected heterogeneity reference range to obtain the updated spatial sampling comparison results.

8. The method for generating textures in an antique stone-pressed product according to claim 6, characterized in that, The step of selecting and activating the spatial sampling probe based on the current geological evolution event type and evolution stage of the textured surface includes: After each geological evolution event is executed, the local geometric complexity and material property distribution of the textured surface are evaluated to obtain the corresponding texture evaluation results; Based on the texture evaluation results, select and activate the spatial sampling probe; When a change in the type or stage of a geological evolution event is detected, the combination and corresponding configuration of the spatial sampling probes are adjusted or switched.

9. The method for generating textures in an antique stone-pressed product according to claim 8, characterized in that, The step of evaluating the local geometric complexity and material property distribution of the textured surface after each geological evolution event to obtain the corresponding texture evaluation result includes: After each geological evolution event is executed, a multi-resolution height field of a local region on the textured surface is obtained; Geometric features are extracted from the multi-resolution height field to obtain the local curvature distribution, surface roughness statistics, and fractal dimension, which are used as geometric features. Material property scanning is performed on local areas of the textured surface to obtain the relative content of mineral components, color distribution uniformity, and gloss variation range, which are used as material property features. The geometric features and material property features are fused to form a local texture feature vector, and the local texture feature vector is used as the input for evaluating the local geometric complexity and material property distribution to obtain the corresponding texture evaluation result.

10. A texture generation system for antique stone-pressed products, used to execute the texture generation method for antique stone-pressed products as described in claim 9, characterized in that, include: The texture surface acquisition module is used to acquire the texture surface of antique stone-pressed products; The geological evolution event module is used to set a set of geological evolution events that simulate the geological evolution process; each geological evolution event in the geological evolution event set includes triggering conditions, scope of action, and rules for changing the morphology of the textured surface. The non-uniform occurrence mechanism module is used to set the non-uniform occurrence mechanism of geological evolution events on the textured surface, so as to adjust the occurrence probability of each geological evolution event in the set of geological evolution events in the corresponding region on the textured surface; The iterative evolution execution module is used to perform iterative evolution based on the set of geological evolution events, the non-uniform occurrence mechanism, and the adjusted occurrence probability; wherein, the iterative evolution includes: executing the corresponding geological evolution events according to the triggering conditions, and causing the geological evolution events to interact with each other, so as to change the texture surface morphology of the texture surface in a cumulative manner, so as to form a non-periodic texture with multi-scale features.

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