A Study on the Spatial Renewal of Atypical Traditional Villages Based on Modular Intelligent Grouping—Yuguang Village in China as an Example ^†

Abstract

There are a large number of atypical traditional villages in Huizhou, China. These villages are facing a huge increase in spatial renewal and construction due to the problems of fragmented historical space and unbalanced spatial structure. Therefore, taking Yuguang Village as an example, this paper proposes an efficient spatial renewal method: by analyzing the space of traditional villages, simulated villages, and modern villages, three modules of traditional “continuation”, transitional “integration”, and modern “reconstruction” are constructed, and the “discrete aggregation” algorithm is used to combine and simulate these modules in various ways. Finally, the generated results are reasonably analyzed by the fractal theory. The application results of the methods above show that, based on the regional setting of spatial combination and the differential analysis of spatial distribution, intelligent organization technology can weaken the dual separation status of traditional and modern, and realize the targeted and reasonable evaluation of spatial transition and synergistic effect. The technical route can provide innovative and efficient design reference for the renewal of a large number of atypical traditional villages and other types of villages, which has universal and extensible applicability.

1. Introduction

China is a vast country with a wide distribution of traditional villages of various types and deep cultural heritage. Traditional villages usually have a long history, and the overall space can reflect the characteristics of a specific historical period, with high historical value; they have a certain amount of tangible and intangible cultural heritage, and well-preserved historical monuments, which can show the characteristics of the historical spatial pattern of traditional villages, with outstanding cultural value; their residents have a strong sense of identity with traditional culture; and the protection and development of the villages have a certain potential for development and the scientific research, with rich social value [1,2,3]. Village conservation and development work has certain potential for development and the scientific research, and is rich in social value [4]. However, with the acceleration of urbanization, traditional culture has been gradually marginalized by modern society. This trend has not only led to the lack of protection measures for early traditional villages, but also made the public’s awareness of the protection of historical and cultural relics and other aspects relatively weak [5]. Under this influence, in some traditional villages, the human environment and historical spatial patterns have suffered damage, historical monuments have been dismantled or modernized, traditional cultural space cannot be expressed with modern styles, and traditional vernacular culture is continuously being eroded, which has led to the existence of a large number of atypical traditional villages that are numerous and widely distributed, and still retain some traditional cultures, with a unique research value, even though they are facing modernization-induced erosion.

The so-called atypical traditional villages, compared with the traditional villages in the general sense in this study, refer to traditional villages with fewer and fragmented historical monuments, where the historical spatial structure has been destroyed due to modernization, and where the historical and cultural value still exists, but the living heritage is weak [6,7,8]. Despite the fact that the lack of the early awareness of protection has led to the damage to their historical spatial structure and traditional space, and some important historical relics have been destroyed or demolished, these atypical traditional villages still retain the memories of traditional vernacular culture, and their historical and cultural inheritance still exists in the spatial texture despite the issue [9,10,11]. Figure 1 lists the differences between traditional villages and atypical traditional villages.

On this basis, this study selected atypical traditional villages in the Huizhou region of China for the research. First, the Huizhou region of China is an important birthplace of Huizhou culture, and its profound cultural heritage has bred a large number of traditional villages with Huizhou characteristics [12]. As an important carrier of Huizhou culture, these villages demonstrate the unique historical and humanistic space of the region, providing valuable cases and data support for the protection and renewal of traditional villages in China. Second, unlike traditional villages, such as Xidi Village and Hong Village, which insist on “museum-type” protection [13,14], the atypical traditional villages in Huizhou enjoy greater freedom of transformation and opportunities for innovative development because they are less restricted by the protection policies, and can accommodate the realistic demands for village development and the operational needs for transforming spatial patterns and functional replacement of the villages, which are brought about by modernization development [15]. Thirdly, although the atypical traditional villages in Huizhou still retain some relatively intact traditional spaces in the process of high-speed urbanization, there are still homogenization problems, such as the fragmentation of historical space and an imbalance of spatial structure in general [16,17], which serve as important references in the field of the village spatial renewal research worldwide.

Specifically, the atypical traditional villages in Huizhou have insufficient overall planning of their village layout, insufficient organization of spatial texture, and an uneven distribution of functional areas in their long-term development [18,19], and are faced with the problems of mixing the old and new in the layout, disordered spatial renewal through demolition and construction, and functionality that cannot adapt to the development, and so on [20,21]. These problems are prevalent in a large number of atypical traditional villages that need to be renewed, hindering the overall development of the region.

Domestic and foreign researchers have conducted some in-depth studies on these problems and achieved corresponding results. Early studies focused on the theoretical discussion under the framework of village structure [22], and mainly discussed the evolution and development of the overall spatial structure of villages under multi-dimensional influencing factors, such as urbanization [23] and the updating direction of spatial nodes under structural optimization [24]. However, due to the limitation of related technologies, the research scope is often limited to the qualitative research of a single or a few categories of villages [25], lacking solutions to the common problems of a large number of traditional villages. In recent years, with the rapid development of computer technologies, such as intelligent algorithms, the focus of the village spatial research has gradually shifted from traditional qualitative analysis to the use of advanced technologies for forward-looking spatial evolution prediction [26], plot planning and layout generation [27,28], and morphological data extraction [29]. This transformation greatly promoted the improvement of the quantitative research in the field of village space [30]. As a dynamic algorithm model based on data and operation rules [31], digital technology is widely used in the field of art and design with its advantages of strong flexibility [32], dynamic visualization [33], and efficient workflow [34], especially in process simulation [35] and evolution prediction [36]. Through mathematical formulas and computer programs, digital technologies are able to generate artistic forms of images, patterns, or animations. At the same time, the inherent randomness and unpredictability of digital algorithms can create unique and experimental works. Moreover, the use of digital technology is not limited to the field of art. Scholars have also combined digital algorithms, such as machine learning, Geographic Information System (GIS), remote sensing technology, and big data analysis with village spatial planning and management, thus deepening the understanding of village spatial structure and providing new tools and methods for the sustainable development of villages [37]. In practical applications, machine learning technology can effectively identify the spatial patterns and evolution rules of villages by processing large-scale geographical and socio-economic data [38]. For example, the Support Vector Machine (SVM) and random forest algorithm perform well in predicting land use changes and optimizing village layout, providing a scientific basis for planning [39]. At the same time, by analyzing geospatial data and population distribution information, machine learning algorithms can predict the demand and use efficiency of infrastructure, for example, using machine learning models to evaluate the accessibility of transportation networks in rural areas and propose optimization recommendations [40]. These techniques not only improve the accuracy of planning, but also promote the transmission of economic benefits and cultural heritage [41]. In addition, digital technology shows various advantages in the spatial renewal of villages. After digital technology intervenes and extracts the material spatial characteristics and action mechanism of rural development and transformation, a spatial digital governance technology system based on rural multi-agent collaborative governance can be constructed [42], and areas suitable for development and in need of protection can be identified, so as to formulate more scientific and reasonable planning schemes [43]. For the cultural protection of traditional villages, digital technology provides new opportunities. From the perspective of the whole life cycle, the coupling mechanism between traditional villages and digital twins is constructed [44], which realizes the visualization, monitoring, diagnosis, decision-making, interaction, and experience of traditional villages [45]. This digital protection method not only retains the historical and cultural value of villages, but also provides technical support for the live inheritance [46]. The application of related digital algorithm models in village spatial renewal has gradually become a research hotspot. For example, the cellular automata model is used to explore the spatial characteristics and evolution rules of villages [47]. Discrete dynamic technology realizes spatial layout reconstruction by collecting village surface data [48]. Image-based algorithms analyze village form development by extracting the relationship between spatial elements [49]. These studies further expand the technological path of village spatial renewal. However, the current intelligent research still faces many challenges in the issue of village spatial renewal. The high learning threshold and learning cost make it difficult for basic designers to quickly master and apply related technologies. The research on the spatial characteristics of villages is not sufficient. The large amount of renewal construction is also difficult to solve quickly with the existing technology. Therefore, how to apply digital design to the spatial renewal research of a large number of atypical traditional villages in a more friendly and efficient way for basic designers has become an urgent problem to be solved. Future research needs to further strengthen the theoretical discussion and empirical research to promote the deep integration of village spatial research and digital technology, and provide more targeted solutions for the sustainable development of villages.

In this regard, the innovation of this research is to propose a new technical framework for the dilemma of the transition from atypical traditional villages to modern life. The current method of coordinated updating of traditional and modern features mainly relies on the insertion of layout points under human investigation, which leads to the blockage problem of the smooth transition of the new and old spatial features. In this study, digital technology was used to ensure the orderly connection between the old and new styles and their potential transition forms, and the diversified adjustment of the overall spatial style of atypical traditional villages was realized. In addition, considering that the development of a large number of atypical traditional villages is deeply affected and restricted by the regional economy, there is a lack of intelligent, low-cost, and universal solutions. Therefore, a combined spatial renewal system is studied and developed to assist in guiding the conservation and renewal of a large number of atypical traditional villages. At the same time, the current intelligent technology for the village space research is still at the surface stage of data collection and mechanical generation, and fails to fully combine the specific background of villages and the needs of people for in-depth analysis. Therefore, this paper establishes a set of basic-operation base map rules combining village background and crowd needs, and explores the generation method of the multi-type and diverse intelligent fabric of future village space. Through these innovative measures, the research not only breaks through the limitations of the existing research methods, but also provides more scientific and flexible design reference, promotes the overall protection of cultural heritage, and provides designers with lower-cost and more efficient support tools, which better balances the relationship between traditional heritage protection and modern life needs.

In summary, this study proposes an intelligent updating method for an atypical traditional village space, which has a good research purpose and significance: (1) This study plans to solve the spatial coordination problems encountered in the transition process of atypical traditional villages to modern life [50,51], and proposes a method of intelligent organization by constructing three types of old-and-new-attribute modules, so as to realize the orderly connection and diversified adjustment between the old and new spaces and transition forms. This will help to repair the damaged spatial layout and texture of villages, and better meet the needs of modern village development while protecting historical heritage. (2) This study intends to solve the problem of the mismatch between the space and development of atypical traditional villages [52]. According to the spatial distribution layout of functions in villages, the generation mechanism of spatial modules with different functional combinations is proposed to realize the reasonable distribution of functional areas in the village. The relevant methods can help local designers to plan the layout of village functional space at a low cost and quickly and efficiently, expand the space for commercial entertainment and public services, improve the quality of life of local villagers, and promote tourism to promote local economic development. (3) This study deals with the adaptive challenge of combining the village background and intelligent algorithm [53]. In view of the current situation that a large number of atypical traditional villages urgently need to be diversified and updated [54], a high-efficiency, intelligent, and universal module self-generation method system is proposed based on the spatial combination regionality setting. It can more effectively guide the overall spatial renewal and protection of atypical traditional villages.

2. Methodology

In view of the abovementioned background, this study introduces the “discrete aggregation” algorithm, aiming at constructing an algorithmic generation model that better fits the actual situation of atypical traditional villages. The “discrete aggregation” algorithm was divided into two steps: “discrete” and “aggregation” [55]. Objects can be simply understood as “discrete” parts that are “aggregated” together by certain rules [56]. Analogous to the village space, the study firstly developed “discrete” module units from the space of representative traditional villages, excellent cases of modern new countryside, and simulated villages themselves. These module units not only contain the inheritance and development of traditional elements, but also incorporate modern design concepts and technological means, and combine with the living needs of village residents. Next, this study further develops a detailed base map of modular “aggregation” rules after fully considering multiple influencing factors, such as natural geographical conditions, the development of people’s needs, and future development plans, to ensure that it is innovative and does not lose the integrity of the village texture [57,58,59]. Finally, based on different types of module combinations, this study applies the “discrete aggregation” algorithm to generate spatial modules to meet the needs of different types of atypical villages or the specific transformation needs of different areas within the village, establishes a quantitative evaluation framework based on the fractal theory, and attempts to apply the simulation results to the actual design process, so as to achieve the diversified intelligent renewal of village space through the flexible use of digital algorithms.

2.1. Sources and Classification of Modules

Based on the comprehensive consideration of the background situation of villages and the daily needs of the population, this study divides the basic elements of village space into four categories [60,61,62] according to the use function: (1) Residential living space: the daily living space of villagers, including new and old residential houses, etc.; (2) Public service space: village basic public service facilities and spaces that meet the basic daily life needs of villagers, including public toilets and express stations; (3) Commercial entertainment space: village external reception activities and leisure spaces for villagers, including ancestral temple academies, village activity centers, etc.; and (4) Open activity space: village activity squares and spaces that meet the basic transportation and activity requirements of villagers, including squares, parks, etc.

The module unit, as a combination space unit carrying a variety of elements, is established according to the functional types after discussing the classification of basic elements of village space. This study takes into full consideration the heritage value of historical buildings in atypical traditional villages. The modules were divided into three categories according to the basic living places and crowd behavior activity scenarios of the villages [63,64,65]: (1) The fixed-module unit is defined as the immovable and non-renewable combined space module unit in the village background, including the historical monuments and other non-renewable reserved buildings in the space, as well as the immovable areas, such as water entrances and ancient bridges. These modules are scattered in the basic living context of the atypical traditional village background. (2) The node module unit is defined as the combined space module unit in the village background, which mainly consists of open activity and commercial entertainment spaces, connects a small amount of public service and residential activity spaces, and is primarily distributed in the main public activity node areas of the village. (3) The basic module unit is defined as the combined space module unit in the village background, which mainly consists of living spaces and connects some public services, commercial entertainment, and open activity spaces. It is widely distributed throughout the village and primarily connected by street networks.

Under the three types of module units, according to the difference between traditional and modern space, the attributes of module units can be subdivided into three types [66]: (1) Traditional “continuation” attribute: refers to the continuation of traditional spatial patterns and historical space of ancient villages, and its modules mainly have traditional attributes; (2) Transitional “integration” attribute: refers to the modern functional space that is placed for the needs of the crowd on the basis of the continuation of the traditional spatial pattern space, and its modules are combinations of traditional and modern attributes; and (3) Modern “reconstruction” attribute: refers to the combination space of new functions in the modern, new, countryside, and its modules have mainly modern attributes.

In this study, functional types were combined with two levels of old and new space to define and classify module units, so as to meet the functional use needs of the crowd and realize the smooth transition of village space from traditional to modern. Figure 2 expresses the relationship between the abovementioned basic concepts.

2.2. Extraction and Connection of Modules

2.2.1. Determination of Extraction Scale of Modules

After determining the classification and attributes of module units, the modules needed to be extracted according to the scale hierarchy of the combined space. Previous studies have shown that setting three scale tiers of 100 m, 30 m, and 7 m is generally reasonable for quantifying village morphology [67]. Considering the overall modular scale of the village, this study selected the 30 m scale level as the basic scale of the module unit, and explained that these scale data were only used as a reference to consider the density of modules generated in the village. At the same time, the specific morphology of the module was considered: the module boundary was quantified using the aspect ratio (λ = b/a), where b is the long axis of the module unit body and a is the short axis of the module unit body. An aspect ratio of 1 is a square and 2 is a rectangle. Setting λ = 2 as the boundary, λ ≥ 2 is a flat-length module, λ < 1.5 is a square module, and 1.5 ≤ λ < 2 represents atypical flat-length and atypical square modules, with no obvious tendency [68]. In this study, based on the characteristics of the module unit with no tendency, we set module unit λ = 1.5 to facilitate rounding, i.e., b:a = 3:2, with the size of 30 × 20 m², in which the ratio of four types of functional spaces in the module was determined based on actual cases.

2.2.2. Extraction of Modules

When extracting the fixed-module units, it is necessary to adjust them according to the actual situation of the village, which is discussed later with the empirical evidence. For the nodes and basic module units, this study selected different case scenarios based on the three types of “continuation”, “integration”, and “reconstruction” to collect information. Specifically, (1) “Continuation” module: the spatial combinations that maintain traditional styles were selected from representative traditional villages as the case data; (2) “Integration” module: from the atypical traditional villages, the combination space optimized by the village background and the crowd demand was selected, which integrated the traditional space and the demand of modern life, as the case data; and (3) “Reconstruction” module: space combinations with new functions were selected from modern, new villages, which represented the reconstruction of modern village life and were used as the case data. Moreover, in order to comply with the definition of the combined space, there must be no less than two types of functional spaces in the selected scenario.

The extraction cases for the module unit of “continuation” were extracted from the first batch of villages in Huizhou that were selected to be on the list of Chinese traditional villages [69]. Specifically selected case villages were: Hong Village, Hongcun Town, Yi County; Xidi Village, Xidi Town, Yi County; and Nanping Village, Biyang Town, Yi County [70]. The selected villages are very representative traditional villages in Huizhou, whose spatial pattern has not changed much since their development, and the traditional space is basically preserved and intact, which is of high reference value.

Figure 3 presents the extraction process of the typical node module scenes and basic module scenes in traditional villages as a case of the “continuation” attribute. Based on the extraction results, it can be seen that the functional elements of nodes and basic modules in traditional villages are more obvious: nodes are mainly composed of commercial, recreational, and open activity spaces, while basic modules are mainly composed of residential and open activity spaces, and the proportion of public service spaces is lower in both.

In order to extract the module units of “integration”, this study selected 50 aboriginal villagers in Yuguang Village as survey samples, and obtained the results of villagers’ modernization and renewal intentions and functional space preferences for the village space through questionnaire surveys, random interviews, and spatial notation surveys, etc. Based on this, the traditional module units of the village were used as the basis framework for the modernization of the village space. Based on these results, the content data of the “integration” module are extracted by integrating modern functional requirements into the traditional module to realize the integration of traditional space and modern life. From the research results of Figure A1, it can be seen that the villagers have higher demands and preferences for commercial entertainment spaces in both the node module and the basic module.

Figure 4 presents the overall extraction and optimization process of the “integration” module: the node areas frequented by villagers in the research village are selected as the node modules in the simulation data, and the nodes of the residential areas in the eastern, middle, and western parts of the village are selected as the basic modules in the simulation data, so as to extract and analyze the composition of the module’s functional space. On this basis, the composition of the module was optimized by combining the demand tendencies of the villagers for the functional space.

For the extraction cases of the “reconstruction” module units, the new functional space scenarios were mainly selected from the typical cases of modern, new, rural areas. Specifically selected new rural cases were: Dongziguan Village, Mugou Village New Rural Community, and Wufang Village, a “model village for rural revitalization”. Among them, Dongziguan Village is a modernization based on the ancient village, which has not detached from the core relationship of the spatial development of rural agglomerations [71]. The construction of the new village in Mugou Village has inherited the characteristics of traditional settlements in terms of landform, space, spatial layout, and scale [72]. Wufang Village is committed to developing its unique vernacular characteristics while maintaining the existing space [73]. This study selected the node at the entrance and the basic residential area of the villages as the modular extraction, which has a rich type of functional spatial composition and is a representative display of modernized rural space. Figure 5 shows the overall extraction process of the “reconstruction” module.

2.2.3. Extraction of Modules Connection Rules

As a combination unit of multi-factor functional spaces, the connecting surface of the module unit consists of the boundaries of various types of functional space. Therefore, when exploring the connection rules between module units, the articulation mechanism between different functional spaces must be considered. This study mainly summarizes and creates from two aspects: firstly, it is summarized according to the actual situation of the representative traditional villages, which have been renewed, perfected, and well-developed at present; secondly, it is created according to the actual needs of the residents of the selected villages.

When considering the connection rules in combination with the background situation of the village, since there is no combination of functional space in the fixed module, it is not discussed here. On this basis, there are three types of connections: node modules connecting node modules, node modules connecting basic modules, and basic modules connecting basic modules. The previously mentioned universal connection rules representing the combination of functional spaces among traditional villages were extracted and summarized as the basic connection rules. Figure 6 presents the extraction process of the module connection rules.

After evaluating the statistics and analysis, it was found that due to the efficient use of village space, it is rare for different types of public service spaces to be arranged next to each other, and the proportion of their allocation is also low; public service spaces and commercial entertainment spaces tend to exist in a certain range with only one of them. At the same time, the results of the questionnaire survey and random interviews with the villagers show that the crowd needs more commercial entertainment space than other types of space.

2.3. Self-Generating Arithmetic Rules for Modules

In this study, the generation probability of the intelligent algorithm is mainly based on the density of projection points in the two-dimensional plane or three-dimensional space as a visual expression. Specifically, in the two-dimensional plane, we can set a specific “line” as the trend area of the projection point set. In three-dimensional space, on the one hand, a “box space” can be established as a “point” on the regular base map, and it is stipulated that no module generation can be carried out in this space. On the other hand, by controlling the bulge of the node area of the base image, the projection points in the space can be more concentrated and denser on the “surface” of the raised area, so that the generation probability is higher than that of other planar areas. Based on the above self-generated probability rules, this study proposed the corresponding self-generated probability rules for “point”, “line”, and “surface” by combining the three different functional types of the module unit body: fixed, node, and basic. Specifically: (1) The fixed module is a node in the village that cannot be moved or updated, and its base map generation rule is within the “point area”; (2) the basic module is the existence that is generally visible in the village and forms the base of the village map with the road space, and its rules for generating the base map can be associated with the “line area” of streets, lanes, and roads; and (3) the node module is the node above the base map of the village, and its map generation rules are combined with the “surface area” of the village.

The fixed-module unit can be directly projected onto the base map of rule generation according to the village survey situation, and it exists as a fixed “box space” unit to be avoided, that is, the generation rules for the “point” of the fixed-module unit in the village.

The basic module unit, as the daily living space of villagers, is mainly connected into a whole through the internal road network of the village, forming the spatial structure of the village. Therefore, the generation rule of the basic module unit body in this study starts from the “line” and follows the following steps: (1) Construction of the basic base map area: Firstly, the boundary of the village was determined to form the basic base map area (for the convenience of the intelligent algorithm operation, this study ignored the difference in terrain height). According to the development needs of the village edge, the scope of the base map was appropriately adjusted. (2) Construct the “line” to generate the regular area: a. Extract the complete road system of the village; b. Analyze and simplify the road system, focusing on the main and secondary roads closely related to the living space, so as to simplify the road system and improve the efficiency of intelligent algorithm processing; and c. Adjust the simplified road system in combination with the village development planning to achieve overall optimization and development. (3) The contents of the above two steps were combined to establish the generation rules for the “line” of the basic module unit of the village.

The node module unit, as an entertainment and leisure space, should be considered from the perspective of the whole village to generate the regular base map, balance the relationship between the local node and the whole village, and ensure that the node can serve the needs of the whole village. The specific steps were: (1) Build the base map area: the same method as the basic module. (2) Construction of the node generation area: multiple types of node generation areas were delimited on the base map and superimposed as the final field node range map, including: a. The village function space distribution map was used to determine the existing public space nodes; b. Draw the building kernel density map to identify the core nodes of village settlements; c. Determine the activity demand nodes of the crowd through questionnaire surveys and on-site investigations; and d. Adjust the prospective development nodes according to the development plan of villages. (3) The contents of the above two steps were combined to establish the generation rules for the “surface” of the node module unit of the village.

In summary, after dividing the point, line, and surface rule areas of different functional modules, the algorithmic rules of module self-generation were established and presented in the form of graphics, so as to realize the targeted intelligent generation of module units in the village.

2.4. Evaluation of the Results of the Self-Generation of Modules

After determining the generation rules of module units, a self-generation simulation can be carried out to optimize the existing spatial layout of the village. Since the self-generation simulation of the module unit body was optimized and improved based on the current spatial texture of the village, its self-generation mechanism matched the current spatial mechanism of the village, and there was a certain degree of similarity between the two, the simulation results can be evaluated using the fractal theory. Fractal, proposed by Mandelbrot, refers to the shape of the part that is similar to the whole, and its characteristics are quantified through the fractal dimension, which reflects the unevenness and complexity of the object. The fractal dimension allows the research to visualize the presentation and quantitative analysis with the help of certain precise data [74].

In fractal geometry, the counting box dimension is used to measure the complexity of a collection of fractals. In order to determine the counting box dimension of a certain fractal S, it is envisioned that the fractal is placed on an ever-refined grid, the number of grid cells required to cover the fractal is counted, and the counting box dimension is calculated by observing the pattern of change with the grid refinement [75]. Specifically, when the grid edge length is ε, the space is divided into N cells, the counting box dimension is calculated as:

$${dim}_{box}\left(S\right):=\underset{\epsilon \to 0}{\lim }{\displaystyle \frac{logN\left(\epsilon \right)}{\mathrm{l}\mathrm{o}\mathrm{g}\left(1/\epsilon \right)}}$$

Therefore, this study adopted the calculation method of dimensionality of counting box, through the calculation of the dimensionality of village background and graphical spatial texture of the simulation results, to carry out scientific quantitative research on the diverse generation of the simulation results of intelligent algorithms, and to analyze and screen out the self-generation of the simulation results of module units applicable to the actual village.

3. A Case Study

In the whole simulation process, this study utilized the Wasp plug-in in Grasshopper to take Yuguang Village, an atypical traditional village in Huizhou, as a specific object, selected different functional types and new and old attributes of the module units to be combined, established the generation rule base map for the background situation of Yuguang Village, and simulated the process on this basis, obtaining the simulation results of the combination of the five types for the discussion of applicability. Based on the fractal theory, multiple simulation results among the combination types were calculated and evaluated to provide an optimal solution for the spatial renewal of Yuguang Village.

3.1. Basic Information of Yuguang Village

Yuguang Village is located in Huizhou, China, and the overall layout is relatively centralized and aggregated. Figure 7 shows the functional distribution of Yuguang Village: almost all of the ancient ancestral halls have been demolished and converted into residential houses; most of the historical monuments are located in the western part of the village and scattered; and the residential buildings of the villagers are mainly concentrated in the eastern and the central parts of the village. Public service space is scarce, with only public toilets, garbage collection points, express delivery stations, and small supermarkets. There are two open-air squares: the northern one is larger in size and is also used as a parking lot, while the southern one is now abandoned with debris piled up. There is plenty of room for development around the edge of the village. In the village, the main roads are located in the northern part, which do not pass through the village; inside the village, there are slate alleys, and the main alleys bifurcates from east to west and from north to south. In general, the overall living space of the village is discontinuous, mixing the old and the new; the public service space is detached from the daily activities of the villagers and is insufficient; the recreational space is confined to the residential area and insufficient; and the open space is singular and ambiguously positioned, and is not practical.

3.2. Simulation of Intelligent Grouping of Modules in Yuguang Village

3.2.1. Modules Selection

Firstly, according to the distribution of Yuguang Village’s historical relics, the location of the fixed-module unit was extracted. Secondly, based on the current situation of Yuguang Village and the needs of villagers, the nodes and basic modules needed for the simulation were selected from the modules extracted from the above three cases of “continuation”, “integration”, and “reconstruction”. According to the survey results of the villagers’ demands, it can be seen that villagers have the highest demand for recreational activity centers, followed by the demand for public service centers. Therefore, according to the proportion of various functional spaces in the above two types of attribute module cases, the node unit case in the “continuation” module selected Xidi Village, and the basic unit case selected Hong Village. The node and basic module unit in the “reconstruction” module selected Dongziguan Village. The “integration” attribute module unit was optimized by combining the actual situation of Yuguang Village with the actual needs of the villagers and is presented above.

3.2.2. Establishment of the Modules’ Generation Rules

The first level is to organize the architectural texture of the village through the existing environment of Yuguang Village, and to connect the top corners of the outermost buildings of the village to form the preliminary village boundary. This is combined with the village edge space to improve the village boundary and finally form the overall scope of the base map of the unit. At the second level, three rules of “point”, “surface”, and “line” are formed on the base map for the attributes of fixed, node, and basic module, respectively.

Figure 8 shows the steps of establishing the “point” rule drawings of Yuguang Village. First of all, the fixed-module-unit “point” rule is based on the overall functional distribution of Yuguang Village, extracting the location of historical monuments in pieces, and at the same time, according to the future planning and development map of Yuguang Village for the land use of cultural relics and monuments, adjusting the scope of the box selection of historical monuments. Finally, the “box” space is generated on the rule base map as a fixed-module-unit “point” base map rule.

Figure 9 shows the steps of establishing the “line” rule drawings of Yuguang Village. First, the overall road system is extracted based on the village map base relationship, including the main and secondary roads as well as the alleys and paths in the village. On this basis, according to the distribution area of residential and living function space in the village, the main road branches that the villagers walk on every day were extracted, and the fragmented and small lines were sieved out to ensure the smoothness and clarity of the overall lines. Finally, according to the village development plan for the villagers’ residential land area, the main body retained the roads used daily, redundant roads for other functional purposes were removed, and the “line” base map rules of the basic module were finally formed.

Figure 10 shows the steps of establishing the “surface” rule drawings of Yuguang Village. The “surface” rule of the node module unit was synthesized by four aspects of the nodes. The first task was the node that summarized the natural historical environment of the village. For the village settlement core density analysis, we had to understand the village settlement formation process to determine the high-density settlement center node of the village. The second task was to understand the nodes of the current functional distribution of the village. Through the analysis of the functional distribution of the village, as well as field visits and the research, the village ’s northern and southern open activity square nodes were determined. The third task was to summarize the nodes where the villagers stay, daily. Through the questionnaire surveys and random interviews with the villagers, it is known that the villagers in Yuguang Village often gather and play after meals in some streets, alleys, or small space nodes in front of and behind their houses. In addition, the only existing large village square (north of the village) is also a node where villagers often go. The fourth task was to extract the nodes of entertainment for the planned development of the village. According to the division of commercial, cultural, sports, and technological land in the plan, the activity nodes in the northeast and north of the village were identified. The above four aspects of the nodes were summarized into the final-node-module “surface” base map rule area.

3.2.3. Simulation of Module Grouping

In order to ensure the clarity and diversity of the simulation results and reduce simulation errors, two case module units were selected to participate in each intelligent fabric simulation, that is, the fixed-module unit remained unchanged, and the node and basic module selected one case module from the three types of attributes of “continuation”, “integration”, and “reconstruction”. Among them, in order to ensure the smooth transition of the new and old village combination space, in the combination of two case modules, the module with the “integration” attribute participated in each simulation, that is, there were five types of simulation combinations: The node “integration” module and the basic “continuation” module, the node “integration” module and the basic “integration” module, the node “integration” module and the basic “reconstruction” module, the node “continuation” module and the basic “integration” module, and the node “reconstruction” module and the basic “integration” module.

The simulation base map in Figure 11 was generated according to the rules of “point”, “line”, and “surface”. Among them, the probability of the overall village module is indicated by the density of the projected points, such as the node areas highlighted in the figure, the “line” extracted from the study, and the edge areas of the village development, where the projected points are denser. The fixed-module “box space” area is interspersed with the probabilistic base map and space.

The five types of simulation combinations are presented and discussed in three representative simulation results that are most in line with the village background, and the distribution of four types of functional spaces was extracted separately for each simulation result to meet different functional needs. The simulation not only considered the renewal of the village within the existing scope, but also integrated the development of the peripheral space of the village. Figure 12 presents the results of the five types of simulation.

(1)

Simulation combination type of the node “integration” module and basic “continuation” module

This type is suitable for village areas where the public buildings are old or well-preserved and not suitable for large-scale renewal, and the neighboring residential buildings have a traditional historical space, which is regarded as the embodiment of the overall space of the village and other similar places. The main renewal action in this kind of area is the renewal and functional replacement of small public spaces to serve the villagers. By overlaying the current status of the area with the results of intelligent organization, the possible distribution of the corresponding space nodes of functional space types, such as commercial and recreational space, is screened in the simulation results, and the renewal and replacement of the current public space is considered.

(2)

Simulation combination type of the node “integration” module and basic “integration” module

This type is suitable for the village area where the current integrity and spatial manifestation of the public building space and residential building space are relatively good, and part of the space needs to be slowly renewed with the insertion of some functional spaces in accordance with the needs of the crowd, and other similar places. The main renewal action of this kind of area is the insertion of the functional spaces demanded by the villagers in the overall area, such as the insertion of some recreational spaces; the overall traditional space remains unchanged, mainly for the insertion of modern life needs in the form of acupuncture. The functional type of the current regional demand should be determined according to the intelligent group structure simulation results, followed by the corresponding combination of functional space types at the distribution points, and the corresponding functional insertions.

(3)

Simulation combination type of the node “integration” module and basic “reconstruction” module

This type is suitable for the village area where the public node space is mainly traditional space, while the residential building style leans toward modernity, and the collision of old and new styles is more obvious. The main renewal action in this type of area is to optimize the spatial combination of modern residential buildings in the living area and to realize the transition between the old and new styles by modernizing the edge space in the public activity area. The simulation results of the intelligent group structure provide diversified spatial combinations of modern houses as well as relevant modernized spatial locations, which can quickly realize the integration of modern styles into traditional spaces.

(4)

Simulation combination type of the node “continuation” module and basic “integration” module

This type is applicable to the village area where there are traditional buildings in the public activity space, as the continuation of the traditional space of the village, and the adjacent residential space is connected with it to maintain the overall village space, and it is not suitable for large-scale reconstruction or other similar activities. The main renewal action of this kind of area is to optimize the living experience of the living space and to introduce modern functional space in some aberrant space appropriately. Intelligent module generation simulation realizes the diversified composition of a renewable deformed space and traditional functional space, and provides the type of optimized combination and the regional distribution of the corresponding functional space as a reference.

(5)

Simulation combination type of the node “reconstruction” module and basic “integration” module

This type is applicable to the village area where the public activity space needs to be renewed and reconstructed urgently, and the adjacent living space is improved with a modern reconstructed functional space at the same time. The main renewal action in this type of area is to reconstruct the public activity space of the village and consider diversified spatial combinations, and at the same time, it can be interactively integrated with the original living space. Intelligent module generation simulation not only quickly generates spatial combinations to help create modern reconstructed nodal spaces, but also connects the existing spatial combinations to realize the integration of the two.

In general, the intelligent fabric generation of modules not only helps villages achieve rapid and diverse spatial combinations, but also comprehensively considers the layout of different functional spaces. According to the type and style of the village’s public or residential area, this method carries out a variety of combination renewals of “continuation”, “integration”, and “reconstruction”, which provide a valuable reference for the renewal of village space. In addition, its high efficiency also improves the fault tolerance of the design scheme.

3.3. Evaluation of the Results of the Intelligent Organization of Modules

This study mainly carried out the methodological test with the help of Yuguang Village and produced five types of combination simulation results. However, for the future renewal and development of Yuguang Village, the selection of combination types and the screening of simulation results should still consider the relevant practical factors. From the spatial structure and function distribution of Yuguang Village, it can be seen that the number of historical relics is small and scattered, but the degree of preservation is relatively complete and representative. The residential buildings in the village are of a certain age, an overall tendency to have a modern style, and the quality of life of the villagers is poor. In general, the proportion of modern-style buildings in the village is slightly higher than that of traditional space. Based on this, the node module of Yuguang Village is more suitable for the “integration” of old and new attributes, helping to promote the integration of architectural spaces, such as historical sites and modern human habitation facilities. The basic module is more suitable for the new and old attributes of the “reconstruction” and places them into the diverse space of the new countryside to improve the comfort of villagers. In summary, Yuguang Village is more suitable for the simulation combination type of the node “integration” module and basic “reconstruction” module.

In the evaluation of the three simulation results among these types of simulation combinations, this study used the counting box dimension to intelligently compute the fractal dimension of the generating planes and selected the generated result. Figure 13 shows the fractal dimension of original villages that converge to the optimal solution by analyzing their fractal characteristics. This study further utilized the computer programming technology based on Python 3.10 language to perform the automated evaluation of the intelligent grouping results. According to the current overall spatial scale of Yuguang Village, the unit scale level of 7 m was extracted from the previously mentioned 100 m, 30 m, and 7 m as the minimum scale of the box dimension, and the scales were expanded to 56 m, 28 m, 14 m, and 7 m. First, the fractal box dimensions of the background plane of Yuguang Village were computed to be 1.631, 1.596, and 1.525, with the fluctuation degree as follows: 0.035–0.071. Figure 13 represents the calculation process of the current situation of Yuguang Village. It can be seen that the overall spatial morphology of Yuguang Village has a similarity in the fractal dimensions at each fractal level, indicating that the overall spatial texture of the village is relatively flat, with high continuity in complexity, a well-organized spatial structure, and obvious fractal characteristics. Meanwhile, for the three simulation results among the combination types, the calculation of the dimensions of each level of the fractal counting box was carried out, numbered as ① ② ③: ①: 1.592, 1.679, 1.718; ②: 1.672, 1.623, 1.628; and ③: 1.674, 1.667, 1.718. Figure 14 shows the calculation process of the simulation results. The fractal counting box dimensions of each level of number, ①, ②, ③, are relatively close to each other, in which the fluctuation of ① is in the range of 0.039–0.087; the fluctuation of ② is in the range of 0.005–0.049; and the fluctuation of ③ is in the range of 0.007–0.051. Figure 15 represents the data fluctuation for the current situation of Yuguang Village and the simulation results. Through the calculation of the fractal counting box dimensions of the three types of simulation results, it can be seen that, on the whole, the fractal counting box dimensions of each simulation result have high similarity between the various levels, and the continuity of the complexity level of each level is better, with all of them generally having a high fractal characteristic. Among them, the fractal dimension of No. ① is closer to the overall fractal dimension of Yuguang Village, which continues the overall spatial pattern law of Yuguang Village to a certain extent and has a high degree of similarity. Therefore, number ① can be chosen as the update scheme of the convergent optimal solution for the simulation of Yuguang Village.

4. Discussion

Through the above overall method framework, this paper carried out in-depth research on three key points: (1) to realize the smooth transition of the overall spatial space of atypical traditional villages from traditional to modern by establishing the module-unit combination atlas data of three types of new and old attributes of “continuation”, “integration”, and “reconstruction”. By splitting the modules of different function types, the connection rules between the components of the module combination space are studied, and this is extended to the connection between the modules of “continuation”, “integration”, and “reconstruction”. Based on this, this research combines intelligent algorithms to generate the diversification of existence, which promotes a wide and flexible interaction between old-and-new-attribute modules, and then helps the smooth transition of the overall spatial space from traditional to modern. (2) Through the establishment of the three functional types of module unit generation rules of fixed, node, and basic, the targeted combination operation mechanism of the digital intelligent algorithm and atypical traditional village spatial update is discussed. By dismantling the key generation operation conditions of the intelligent algorithm, the program steps of combining the probability map generated by the intelligent algorithm with the village background and future development map were found and established. In this process, a generation probability map of “point”, “line”, and “surface” suitable for different functional types of module units is formed, which is the targeted combination operation mechanism of the digital intelligent algorithm and atypical traditional village spatial update. (3) By combining and classifying modules of different functional types and old and new attributes, the renewal and protection strategies suitable for different types of whole village space or differently themed village areas are finally explored. According to the preference of the node and the basic module unit for the three attributes of “continuation”, “integration”, and “reconstruction”, the diversified combination and simulation of the module unit are carried out, and the scheme toward achieving the optimal solution is screened out as the basis for the update and protection strategy. It is a renewal and protection strategy applicable to different types of whole village space or different types of village areas.

At the same time, in the practical application process of the village spatial renewal method, it is necessary to comprehensively consider the challenges created by multiple influencing factors, such as social and economic conditions, construction technical conditions, and policy guidance. Social and economic factors play a crucial role in the development process of villages, which not only directly affect the improvement of local infrastructure, but also determine the development mode of the tourism business and cultural inheritance to a certain extent [76]. These factors jointly determine the quality of life of local residents and provide potential space and directions for future sustainable development [77]. Therefore, this study closely combines the local government’s planning objectives for village development and the future economic development model, and divides the module components into four types of functional spaces, which helps designers to comprehensively weigh the proportional allocation of various functional elements in the attribute module for the transition to a modern style, so as to ensure the high applicability of the village space renewal method and reduce the design cost of space renewal. It can also maximize the consistency between the methods and sustainable development goals [16], and finally realize the coordination and unification of economic benefits, social benefits, and environmental benefits [78]. In addition, modern construction technology provides a more efficient and sustainable means for the repair and transformation of atypical traditional villages, but excessive modernization may damage their authenticity [79]. Spatial renewal requires the transition of traditional processes, and the combination of modern technology and traditional technology is more applicable to the spatial renewal design of atypical traditional villages, which also corresponds to the division of the research team for the transition of the new and old attributes of the three types of modules. As shown in Figure 16, in the process of practical application to village spatial renewal design, the application of the simulation results has the following steps: (1) Firstly, after determining the related types and connection and generation rules of the relevant modules, the appropriate module combination type is selected based on the distribution characteristics of traditional and modern architectural styles in the village and the future planning direction of the village under the influence of social and economic policies. (2) Secondly, the selected combination module type is used to carry out the self-generated simulation, and the scheme that is the most consistent with the existing texture of the village, the development goal, and feasible construction is selected from a variety of possible results. (3) On this basis, a further analysis of the simulation results is conducted. The results of the simulation are divided into two levels: a. The overall layout of the combination module: the key area of the dense generation of modules is identified, which is regarded as the core concern area of village spatial renewal. b. For the distribution of the four functional spaces: the simulation results are superimposed with the actual spatial structure of the village, and the local population demand and choice direction as well as the policy planning and development direction are investigated to define the area, and the corresponding functional space is placed in the area or the abandoned space is replaced. In summary, the methodological framework not only exhibits a high degree of flexibility and efficiency, but also goes beyond the basic need to deal with repetitive design tasks.

It is worth mentioning that the method framework proposed in this study not only has a significant effect on the spatial renewal of atypical traditional villages, but also shows a high degree of universality, sustainability, and scalability. First of all, whether for traditional villages, atypical traditional villages, or villages on the edge of the city, although these different types of villages are different in terms of their cultural background and environmental characteristics, and there are certain commonalities in their spatial structure. In the spatial update of other types of villages, by clarifying the urgent problems of the villages and the needs of people, and drawing on the spatial combination module of excellent cases as the basic data, the module generation rules can be established based on the spatial structure and texture conditions of villages, and the design scheme for reference can be obtained by calculation. Digital technology and artificial intelligence-based design can effectively deal with systemic problems, such as the introduction of modern infrastructure, the continuation of spatial texture, and the protection of historical and cultural features. By inputting the original texture scale, spatial layout, and cultural characteristics of traditional villages as the conditions or data into the operation model, digital generation technology can more accurately output the design scheme that conforms to the original village characteristics and spatial scale, so as to achieve the goal of “repairing the old as the old” and maintain the unique cultural value of the village. Secondly, the spatial module mechanism used by digital technology has a high degree of free division and function replacement ability, which ensures the sustainability and accommodation of spatial development; reduces the interference in the villagers’ lives, while maintaining the original social network and social vitality; and promotes sustainable development [80]. At the same time, by adjusting the generation density of modules in the program and resetting the expansion gap, this method can flexibly adapt to the changes in future environmental and social needs and improve the comprehensive utilization efficiency of spatial resources [81]. Finally, the digital method shows significant benefits in coping with extreme natural disasters and promoting the sustainable development of traditional villages [82]. By inputting relevant conditions such as village characteristics and crowd needs, this method can help designers efficiently process a large amount of data, respond to and solve complex spatial layout problems, and help quickly deal with post-disaster reconstruction problems [83]. This method not only improves the comprehensive management level, but also ensures the organic combination of cultural heritage protection and modernization construction, realizes the comprehensive coverage from post-disaster emergency response to long-term development planning, and effectively promotes the sustainable development of traditional villages [84]. In a word, this method framework not only provides a systematic and scientific solution for different types of village spatial renewal projects, but also fully considers the future diversified development needs and the realization of sustainability goals. By integrating spatial resources, optimizing spatial functions, and innovating technology applications, it ensures the coordinated development of spatial renewal, cultural inheritance, and economic vitality in the process of village development, and promotes the organic combination of short-term benefits and long-term development.

5. Conclusions

Against the background of accelerated modernization, many atypical traditional villages in the Huizhou region of China are facing a large number of spatial renewal needs. These villages not only contain a rich historical and cultural heritage, but also play a crucial role in promoting the sustainable development of the local economy and enhancing the quality of life of the residents. In view of this, the research and development of efficient and locally relevant spatial regeneration strategies have become core issues of great concern to academics and the community [85,86,87]. Especially noteworthy is that, in the current era of the continuous advancement of intelligent technology, supporting the modernization and transformation of traditional villages through the introduction of digital technology not only provides a new way of thinking to address the abovementioned challenges, but also constitutes a highly promising direction in the current research field [88].

In view of the abovementioned background, this study aims to construct an algorithmic generation model that better fits the actual situation of atypical traditional villages by combining the two dimensions of the current situation of villages and the needs of the residents. Specifically, first, this paper constructs a series of spatial module units covering different functional types and old and new attributes through an in-depth analysis of the existing traditional villages and the future needs of their residents. The results can be used in the future functional distribution planning of atypical traditional villages and the continuation of spatial renewal space, which provides a diversified design reference basis for the spatial renewal of traditional villages in Huizhou. Secondly, with the help of digital intelligent algorithms to diversify the combination of village spatial module units, we can explore the intelligent renewal of the overall spatial continuity of atypical traditional villages and the combination of old and new spaces in the future, to expand the prospect of digital village construction, and to innovate a the research path to assist village spatial renewal through the intelligent grouping of diversified spatial modules, so as to promote the construction and development of atypical traditional villages. Thirdly, from the technical level, this study constructed a set of technical systems and methodological frameworks for village spatial renewal. To a certain extent, this system provides a novel methodological perspective for the diversified and integrated application of intelligent algorithms in atypical and other types of village spaces, with the characteristics and advantages of high efficiency, intelligence, and diversity. As a result, it not only promotes and expands the research in the field of intelligent village space renewal, but also points out the direction for the combination of design research and intelligent algorithms.

In summary, the methodological framework proposed in this study not only pro-vides powerful design guidance for the spatial renewal of atypical traditional villages and other types of villages, but also significantly improves the design efficiency of spatial re-newel schemes and provides designers with a wider range of choices. However, this study also has certain limitations and potential for development:

(1)

The case sources of the three new–old-attribute module units exhibit significant cultural limitations. The current module unit database is primarily based on Huizhou village cases, whose architectural forms and spatial organization demonstrate strong regional specificity, making direct application to village renewal in other cultural contexts difficult. Additionally, the extraction of module patterns relies heavily on manual identification and classification, which not only carries the risk of subjective bias but also faces practical constraints, such as high labor input and time costs. Future research should focus on expanding a cross-cultural module database by comparing cases from multiple regions to establish a multi-level module system. Moreover, deep learning-based image recognition technology should be introduced to develop a semi-supervised learning framework for automatic spatial feature extraction, significantly improving module extraction efficiency and reducing application costs.

(2)

While the self-generation rules of the modules in this study comprehensively consider multiple factors, they lack detailed multi-dimensional classification. Future research should further expand and refine the foundational rules for module self-generation by addressing multi-dimensional influencing factors. For example, integrating real-time GIS data to capture dynamic information, such as spatial usage density and pedestrian hotspots, could optimize module adaptability. Alternatively, a participatory platform could be established to form a “demand–update–evaluation” system through resident voting and behavioral trajectory analysis, exploring user-centric priorities and additional possibilities in spatial renewal to enhance the relevance and accuracy of the simulation results.

(3)

The application of digital technology in this study is confined to “modular” spaces, lacking consideration for the historical and cultural characteristics of villages and obscuring the spatial texture of village architecture. Future research should further diversify the data sources of module units and integrate related generative rules to better incorporate village historical and cultural features, thereby improving the overall outcomes.