A redevelopment space intelligent adaptation and modular component integration data system

Abstract

The application discloses an old space intelligent adaptation and modular part integration data system. The system comprises a space data acquisition and modeling module, a scanning target space to obtain a point cloud, a denoising, feature extraction and deviation analysis to generate a digital model; an elastic modulus grid generation module, an error probability model is constructed and the modulus grid is dynamically adjusted; a parameterized part configuration module, a part rule is stored, a combination scheme, a list and a drawing are automatically generated; a cutting path generation and control module, a cutting path is generated and cutting is controlled. The application can realize more than 85% adaptation of non-standard toilets in old space, cutting accuracy is ±1mm, effectively solves the problems of insufficient adaptation, low configuration efficiency and poor cutting accuracy of the prior art.

Description

Technical Field

[0001] This invention relates to the field of building decoration and renovation technology, specifically to a data system for intelligent adaptation and modular component integration in old space renovation. Background Technology

[0002] Currently, the demand for bathroom renovation in existing residential buildings is increasing. Traditional bathroom renovations, using a wet construction method, suffer from significant problems such as lengthy construction periods (averaging 7-10 days), large amounts of construction waste (≥1 ton per room), and a high incidence of common quality defects (accounting for over 30%), making it difficult to meet residents' actual needs for efficient, green, and minimally disruptive construction. Prefabricated construction technology, due to its dry construction methods, factory prefabrication, and on-site assembly, is considered an effective technical solution to these problems. However, the application of existing prefabricated technology to bathroom renovation in older buildings faces the following core bottlenecks:

[0003] Firstly, there is a lack of efficient space adaptation technology. Standard component systems are ill-equipped to address common issues in older buildings, such as non-standard room openings and depths (frequent deviations of ±50mm), complex pipeline misalignments, and poor surface flatness (errors of ±15mm). Existing technologies lack intelligent compensation mechanisms for spatial dimensional errors, and their flexible modular design is weak, making it difficult to achieve efficient adaptation to non-standard spaces.

[0004] Secondly, component configuration is inefficient. Current component selection and configuration mainly rely on manual experience and lack intelligent parametric configuration methods, making it difficult to quickly generate the optimal combination solution when faced with multiple constraints (structural safety, waterproof performance, construction feasibility, etc.).

[0005] Third, the on-site cutting accuracy and efficiency are insufficient. Current on-site cutting is mainly done manually, making it difficult to guarantee cutting accuracy, and the dust and noise generated during the cutting process seriously disturb residents' lives. Summary of the Invention

[0006] The purpose of this invention is to address the technical problems existing in the application of prefabricated decoration technology in old bathroom renovation, such as insufficient space adaptability, low component configuration efficiency, and poor on-site cutting accuracy, by providing a data system for intelligent space adaptation and modular component integration in old bathroom renovation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a data system for intelligent adaptation and modular component integration in urban renewal spaces, comprising:

[0008] The spatial data acquisition and modeling module is used to scan the target space to obtain point cloud data, and to perform noise reduction, feature extraction and spatial size deviation analysis on the point cloud data to generate a digital spatial model with error compensation information.

[0009] The elastic modular mesh generation module is used to construct a spatial error probability model based on the spatial size deviation distribution law in the digital spatial model, and dynamically adjust the mesh size and connection tolerance with basic modular units based on the spatial error probability model to generate an elastic modular mesh that adapts to the target modified space.

[0010] The parameterized component configuration module is used to store the topological relationships, interface constraints and combination rules of components, and automatically generate component combination schemes, bills of materials and prefabrication drawings based on the elastic modular grid and user requirements.

[0011] The cutting path generation and control module is used to generate a part cutting path according to the prefabricated processing drawings and control the cutting equipment to cut the part according to the cutting path.

[0012] As a further description of the above technical solution:

[0013] The spatial data acquisition and modeling module includes:

[0014] A point cloud denoising unit is used to remove noise from the point cloud data using statistical filtering and morphological processing methods.

[0015] The feature extraction unit is used to extract wall edge features and identify pipeline interfaces from the denoised point cloud data based on curvature calculation.

[0016] The error analysis unit is used to compare the extracted features with the standard modulus to generate spatial size deviation distribution data.

[0017] The BIM reverse modeling unit is used to construct the digital spatial model based on the point cloud data and the spatial size deviation distribution data.

[0018] As a further description of the above technical solution:

[0019] The elastic modulus mesh generation module includes:

[0020] An error probability model construction unit is used to construct the spatial error probability model based on the spatial size deviation distribution data.

[0021] Modular mesh generation unit, which is used to generate an initial modular mesh with M=50 / 100mm as the basic modular unit;

[0022] An elastic adjustment unit is used to dynamically adjust the size and connection tolerance of each grid cell in the initial modular grid according to the output of the spatial error probability model, thereby forming the elastic modular grid.

[0023] As a further description of the above technical solution:

[0024] The parameterized component configuration module includes:

[0025] The parts library storage unit is used to store the parametric models of the chassis, wall panels, and roof functional units, as well as the topological relationships and interface constraint data between the functional units.

[0026] The rules engine unit stores the rules base for component assembly;

[0027] The intelligent configuration unit is used to automatically solve the optimal component combination scheme under multiple constraints based on the elastic modulus grid, user requirements and the rule base, and generate the corresponding bill of materials and prefabrication drawings.

[0028] As a further description of the above technical solution:

[0029] The cutting path generation and control module includes:

[0030] The visual positioning unit is used to acquire images of the parts to be cut and determine the cutting start position and cutting path based on image feature matching and three-dimensional spatial coordinate calculation.

[0031] Guide line generation unit, which is used to generate prefabricated optimized cutting guide lines with stress relief grooves on the surface of the part;

[0032] A cutting control unit is used to control the cutting device to perform cutting operations according to the cutting path.

[0033] As a further description of the above technical solution:

[0034] The cutting path generation and control module also includes a cutting simulation unit, which is used to perform virtual cutting simulation based on the cutting path before the cutting control unit performs the cutting operation, generate cutting effect preview data and output it to the display device.

[0035] As a further description of the above technical solution:

[0036] The system also includes:

[0037] The data closed-loop feedback module is used to feed back the actual size data of the cut parts to the spatial data acquisition and modeling module to update the spatial error probability model.

[0038] As a further description of the above technical solution:

[0039] The parameterized component configuration module also includes an interface constraint verification unit, which is used to verify the interface matching between functional units in the generated component assembly scheme, and triggers the elastic modulus mesh generation module to readjust the mesh parameters when the interfaces do not match.

[0040] As a further description of the above technical solution:

[0041] The system also includes:

[0042] The historical data storage and learning module is used to store spatial data, component configuration schemes and cutting parameters of historical renovation projects, and provides a data call interface for the parameterized component configuration module.

[0043] In summary, due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0044] 1. This invention constructs a spatial error probability model based on point cloud deviation analysis using an elastic modular mesh generation module. The mesh size and connection tolerance are dynamically adjusted using M=50 / 100mm as the basic modular unit. Compared to traditional rigid modular systems, this invention can flexibly adapt to non-standard spaces with bay / depth deviations of ±50mm and base surface flatness errors of ±15mm. The adaptation coverage rate for non-standard bathroom spaces in old building renovations can reach over 85%, effectively solving the adaptation problem between standard component systems and non-standard old building renovation spaces.

[0045] 2. Through the parameterized component configuration module, the topological relationships, interface constraints and combination rules of components are systematically stored and managed. Combined with the intelligent configuration engine, automatic solutions are achieved under multiple constraints. The optimal component combination scheme, bill of materials and prefabricated drawings can be automatically generated, which changes the inefficient mode of traditional selection based on manual experience.

[0046] 3. By combining machine vision positioning with cutting equipment, high-precision positioning and automated execution of the cutting path are achieved, with on-site cutting accuracy reaching ±1mm. Simultaneously, the prefabricated optimized cutting guide line design with stress relief grooves effectively prevents component deformation during cutting, and the cutting simulation unit allows for virtual cutting preview before execution, further reducing the risk of cutting errors.

[0047] 4. The actual size data of the cut parts is fed back to the spatial error probability model through the data closed-loop feedback module, so as to realize the iterative update of the model; the historical data storage and learning module accumulates transformation project data for the system, continuously optimizes the solution efficiency of the intelligent configuration algorithm, and enables the system to have self-learning and continuous evolution capabilities.

[0048] 5. By combining factory prefabrication with high-precision automated on-site cutting, the amount of on-site wet work is greatly reduced, construction noise and dust emissions are reduced, and construction waste is reduced, which meets the requirements of green and low-carbon construction. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a system module diagram of a data system for intelligent adaptation and modular component integration in urban renewal spaces.

[0051] Figure 2 This is a structural block diagram of the spatial data acquisition and modeling module in a smart adaptation and modular component integration data system for urban renewal spaces.

[0052] Figure 3 This is a structural block diagram of the elastic modular grid generation module in a smart adaptation and modular component integration data system for urban renewal spaces.

[0053] Figure 4 This is a structural block diagram of a parameterized component configuration module in a smart adaptation and modular component integration data system for urban renewal spaces.

[0054] Figure 5 This is a structural block diagram of the cutting path generation and control module in a smart adaptation and modular component integration data system for urban renewal spaces. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0057] It should be noted that similar labels 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.

[0058] In the description of the embodiments of the present invention, it should be noted that the terms "upper" and "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0059] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection, an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0060] Example 1:

[0061] Please see Figure 1-5 The present invention provides a technical solution: a data system for intelligent adaptation and modular component integration in urban renewal spaces, including a spatial data acquisition and modeling module, an elastic modular mesh generation module, a parameterized component configuration module, and a cutting path generation and control module.

[0062] The output of the spatial data acquisition and modeling module is connected to the input of the elastic modulus grid generation module, the output of the elastic modulus grid generation module is connected to the input of the parametric component configuration module, and the output of the parametric component configuration module is connected to the input of the cutting path generation and control module.

[0063] The system's workflow is as follows:

[0064] 1. First, the spatial data acquisition and modeling module performs 3D laser scanning on the target space to be transformed (such as the bathroom of an existing residential building) to obtain high-precision point cloud data. Then, it performs noise reduction, feature extraction and spatial size deviation analysis on the point cloud data to generate a digital spatial model with error compensation information.

[0065] In this embodiment, the spatial data acquisition and modeling module includes a point cloud denoising unit, a feature extraction unit, an error analysis unit, and a BIM reverse modeling unit.

[0066] Point Cloud Denoising Unit: A 3D laser scanner is used to scan the target toilet to acquire raw point cloud data. The point cloud denoising unit uses statistical filtering and morphological processing methods to remove noise from the point cloud data. Specifically, the statistical filtering method calculates the distance distribution between each point and other points in its neighborhood, identifying points whose distance exceeds a certain threshold (e.g., twice the standard deviation) as outliers and removing them; the morphological processing method removes environmental noise through opening and closing operations while retaining effective structural information.

[0067] Feature extraction unit: Based on curvature calculation, it extracts wall edge features and identifies pipeline interfaces from the denoised point cloud data. Specifically, it calculates the curvature value of each point in the point cloud, identifies areas with drastic curvature changes as edge feature points, and forms complete wall edge lines through an edge connection algorithm; at the same time, it identifies the location of water supply and drainage pipeline interfaces based on the geometric features of the pipelines (circular cross-section, specific radius range, etc.).

[0068] Error Analysis Unit: This unit compares extracted features such as wall edges and pipe interfaces with the standard module, calculates the deviation values ​​in each dimensional direction, and generates spatial dimensional deviation distribution data. Deviation analysis includes deviations in the bay opening direction, depth direction, net height direction, and wall flatness.

[0069] BIM Reverse Modeling Unit: Constructs a digital spatial model based on point cloud data and spatial dimension deviation distribution data. Specifically, using point cloud data as the geometric reference, deviation distribution data is overlaid as error compensation information to generate a BIM reverse model containing measured geometric information and error compensation information.

[0070] 2. Secondly, the elastic modular mesh generation module constructs a spatial error probability model based on the spatial size deviation distribution law in the digital spatial model, and dynamically adjusts the mesh size and connection tolerance with basic modular units based on the model to generate an elastic modular mesh that adapts to the target modified space.

[0071] In this embodiment, the elastic modular mesh generation module includes an error probability model construction unit, a modular mesh generation unit, and an elastic adjustment unit.

[0072] Error Probability Model Construction Unit: This unit constructs a spatial error probability model based on spatial dimensional deviation distribution data. Specifically, it performs statistical analysis on spatial dimensional deviation data from a large number of historical renovation projects, fits the probability distribution function (such as a normal distribution) of deviations in each dimensional direction, and establishes a spatial error probability model. This model can quantitatively describe the probability distribution of each dimensional deviation occurring in a specific renovated space.

[0073] Modular mesh generation unit: An initial modular mesh is generated using M=50 / 100mm as the basic modular unit. Specifically, an initial mesh system is generated in the three-dimensional space of the digital spatial model, with 50mm as the smallest modular unit and 100mm as the basic modular unit. Each mesh unit is divided according to the standard modular size.

[0074] The flexible adjustment unit dynamically adjusts the size and connection tolerance of each grid cell in the initial modular grid based on the output of the spatial error probability model, forming a flexible modular grid. Specifically, for areas with large deviations (such as areas with tilted walls or offset pipelines), the flexible adjustment unit increases the size adjustment range and connection tolerance range of the grid cells in that area; for areas with small deviations, it maintains the standard modular size. Through this dynamic adjustment mechanism, the modular grid can flexibly adapt to the actual size of non-standard spaces. The flexible adjustment unit has self-learning capabilities and can continuously optimize the adjustment strategy based on historical data.

[0075] 3. Next, the parametric component configuration module automatically generates component assembly schemes, bills of materials, and prefabrication drawings by calling the stored component topology relationships, interface constraints, and combination rules based on the flexible modular grid and user requirements.

[0076] In this embodiment, the parameterized component configuration module includes a component library storage unit, a rule engine unit, and an intelligent configuration unit.

[0077] The component library storage unit stores the parametric models of the chassis, wall panels, and roof functional units, as well as the topological relationships and interface constraint data between these units. Specifically, the parametric models of each functional unit include geometric dimensions (length, width, thickness), material parameters, and connection method parameters; the topological relationships define the spatial relationships and connection order between the functional units; and the interface constraint data defines the fit tolerances, degree-of-freedom restrictions, and sealing methods between the functional units.

[0078] Rule Engine Unit: Stores a rule base for component assemblies. This rule base includes structural safety rules (such as load-bearing capacity requirements), waterproofing performance rules (such as waterproofing layer continuity and overlap requirements), and construction feasibility rules (such as installation sequence and operating space requirements). The rule engine unit uses a rule-based knowledge representation method to encode and store each rule in "IF-THEN" format.

[0079] The intelligent configuration unit automatically solves for the optimal component combination scheme under multiple constraints based on the flexible modular grid, user requirements, and rule base, and generates the corresponding bill of materials and prefabricated drawings. Specifically, the intelligent configuration unit takes the spatial geometric information provided by the flexible modular grid as input, combines it with user functional requirements (such as sanitary ware configuration, storage needs, style preferences, etc.), and automatically solves for the optimal component combination scheme that satisfies all constraints under multiple constraints provided by the rule engine unit, using constraint solving algorithms (such as backtracking-based constraint satisfaction algorithms or heuristic search algorithms), and outputs a detailed bill of materials and prefabricated drawings that can be processed.

[0080] 4. Finally, the cutting path generation and control module generates the part cutting path according to the prefabricated processing drawings and controls the cutting equipment to cut the part according to the cutting path.

[0081] In this embodiment, the cutting path generation and control module includes a visual positioning unit, a guide line generation unit, a cutting control unit, and a cutting simulation unit.

[0082] The visual positioning unit acquires images of the part to be cut using an industrial camera, and determines the cutting start position and cutting path based on image feature matching and 3D spatial coordinate calculation. Specifically, the industrial camera acquires multi-angle images of the part placed on the cutting table, extracts feature points on the part surface (such as pre-made positioning marks or the part's own geometric features) through image processing algorithms, matches and calculates the 2D image features with the 3D spatial coordinates to obtain the precise pose of the part in the cutting table coordinate system, and then determines the cutting start position and cutting path.

[0083] Guide line generation unit: Generates prefabricated optimized cutting guide lines with stress relief grooves on the surface of the component. Specifically, according to the cutting requirements in the prefabrication drawings, cutting guide lines are generated on the surface of the component; at the same time, stress relief grooves are preset at key positions of the cutting guide lines (such as corners and stress concentration areas) to release internal stress in the component during the cutting process and prevent cutting deformation.

[0084] Cutting Control Unit: Controls the cutting equipment to perform cutting operations according to the cutting path. Specifically, the cutting control unit converts the cutting start position and cutting path determined by the vision positioning unit into motion control commands for the five-axis linkage cutting equipment, driving the high-rigidity guide rail and servo system to perform automated cutting. The five-axis linkage cutting equipment can move collaboratively in five degrees of freedom, achieving precise following of complex cutting paths, with a cutting accuracy of ±1mm.

[0085] Cutting Simulation Unit: Before the cutting control unit executes the cutting operation, it performs a virtual cutting simulation based on the cutting path, generates cutting effect preview data, and outputs it to the display device. Specifically, the cutting simulation unit simulates the entire cutting process in a virtual environment, including the feed path of the cutting tool, the distribution of cutting force, and the stress response of the part, generating a cutting effect preview for operator confirmation. If the simulation results show cutting conflicts or quality problems, the cutting parameters can be adjusted in time to avoid material waste in actual cutting.

[0086] Example 2:

[0087] Please see Figure 1 The figure shows a data system for intelligent adaptation and modular component integration of old space renovation provided by Embodiment 2 of the present invention. Based on the above embodiments, the following technical solutions are further improved: The system of the present invention also includes a data closed-loop feedback module and a historical data storage and learning module.

[0088] The data closed-loop feedback module feeds back the actual dimensions of the cut parts to the spatial data acquisition and modeling module to update the spatial error probability model. Specifically, after the parts are cut, the actual dimensions of the cut parts are acquired using measuring equipment and compared with the theoretical dimensions in the prefabrication drawings to calculate the cutting error. This cutting error data is fed back to the error probability model construction unit to update the parameters of the spatial error probability model, enabling the model to have higher prediction accuracy in subsequent projects.

[0089] The historical data storage and learning module stores spatial data, component configuration schemes, and cutting parameters from historical renovation projects, and provides a data access interface for the parameterized component configuration module. Specifically, this module accumulates a large amount of complete data from renovation projects, including spatial scan data, flexible modular grid parameters, component configuration schemes, cutting parameters, and actual installation effects. The intelligent configuration unit can use historical data to perform case-based reasoning, optimize solution strategies, and improve the rationality and efficiency of configuration schemes.

[0090] Example 3:

[0091] Please see Figure 4 The figure shows a smart adaptation and modular component integration data system for old space renovation provided by Embodiment 3 of the present invention. Based on the above embodiments, the following technical solutions are further improved: the parameterized component configuration module also includes an interface constraint verification unit.

[0092] Interface constraint verification unit: Verifies the interface compatibility between functional units in the generated component assembly scheme, and triggers the elastic modulus mesh generation module to readjust mesh parameters when interfaces do not match.

[0093] Specifically, after the intelligent configuration unit generates the component assembly scheme, the interface constraint verification unit checks the interface fit between each functional unit, including dimensional fit (such as the insertion gap between the wall panel and the chassis), positional fit (such as the alignment of the wall panels), and sealing fit (such as the compression of the waterproof sealing strip). If any interface is found to be inconsistent with the preset constraints, the interface constraint verification unit feeds back the mismatch information to the elastic modulus mesh generation module, triggering the elastic adjustment unit to readjust the mesh parameters of the relevant area until all interface constraints are met. Through this verification and feedback mechanism, the final component assembly scheme is ensured to have good assemblability and sealing performance during the assembly stage.

[0094] In summary, the intelligent adaptation and modular component integration data system for urban renewal spaces provided by this invention can be widely applied to prefabricated decoration projects in existing residential bathroom renovations, kitchen renovations, and other interior spaces. Through the system integration of technologies such as 3D laser scanning, BIM reverse modeling, flexible modular mesh, parametric component configuration, machine vision positioning, and automated cutting, this system achieves efficient adaptation of non-standard spaces in urban renewal projects and high-precision component molding, demonstrating significant value for widespread application.

[0095] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A data system for intelligent adaptation and modular component integration in urban renewal spaces, characterized in that, include: The spatial data acquisition and modeling module is used to scan the target space to obtain point cloud data, and to perform noise reduction, feature extraction and spatial size deviation analysis on the point cloud data to generate a digital spatial model with error compensation information. The elastic modular mesh generation module is used to construct a spatial error probability model based on the spatial size deviation distribution law in the digital spatial model, and dynamically adjust the mesh size and connection tolerance with basic modular units based on the spatial error probability model to generate an elastic modular mesh that adapts to the target modified space. The parameterized component configuration module is used to store the topological relationships, interface constraints and combination rules of components, and automatically generate component combination schemes, bills of materials and prefabrication drawings based on the elastic modular grid and user requirements. The cutting path generation and control module is used to generate a part cutting path according to the prefabricated processing drawings and control the cutting equipment to cut the part according to the cutting path.

2. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, The spatial data acquisition and modeling module includes: A point cloud denoising unit is used to remove noise from the point cloud data using statistical filtering and morphological processing methods. The feature extraction unit is used to extract wall edge features and identify pipeline interfaces from the denoised point cloud data based on curvature calculation. The error analysis unit is used to compare the extracted features with the standard modulus to generate spatial size deviation distribution data. The BIM reverse modeling unit is used to construct the digital spatial model based on the point cloud data and the spatial size deviation distribution data.

3. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, The elastic modulus mesh generation module includes: An error probability model construction unit is used to construct the spatial error probability model based on the spatial size deviation distribution data. Modular mesh generation unit, which is used to generate an initial modular mesh with M=50 / 100mm as the basic modular unit; An elastic adjustment unit is used to dynamically adjust the size and connection tolerance of each grid cell in the initial modular grid according to the output of the spatial error probability model, thereby forming the elastic modular grid.

4. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, The parameterized component configuration module includes: The parts library storage unit is used to store parametric models of functional units including chassis, wall panels, and roof panels, as well as topological relationships and interface constraint data between each functional unit. The rules engine unit stores the rules base for component assembly; The intelligent configuration unit is used to automatically solve the optimal component combination scheme under multiple constraints based on the elastic modulus grid, user requirements and the rule base, and generate the corresponding bill of materials and prefabrication drawings.

5. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, The cutting path generation and control module includes: The visual positioning unit is used to acquire images of the parts to be cut and determine the cutting start position and cutting path based on image feature matching and three-dimensional spatial coordinate calculation. Guide line generation unit, which is used to generate prefabricated optimized cutting guide lines with stress relief grooves on the surface of the part; A cutting control unit is used to control the cutting device to perform cutting operations according to the cutting path.

6. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 5, characterized in that, The cutting path generation and control module also includes a cutting simulation unit, which is used to perform virtual cutting simulation based on the cutting path before the cutting control unit performs the cutting operation, generate cutting effect preview data and output it to the display device.

7. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, Also includes: The data closed-loop feedback module is used to feed back the actual size data of the cut parts to the spatial data acquisition and modeling module to update the spatial error probability model.

8. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, The parameterized component configuration module also includes an interface constraint verification unit, which is used to verify the interface matching between functional units in the generated component assembly scheme, and triggers the elastic modulus mesh generation module to readjust the mesh parameters when the interfaces do not match.

9. The intelligent adaptation and modular component integration data system for urban renewal spaces according to claim 1, characterized in that, Also includes: The historical data storage and learning module is used to store spatial data, component configuration schemes and cutting parameters of historical renovation projects, and provides a data call interface for the parameterized component configuration module.

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