Existing building component reuse adaptation method and system based on multi-modal features

Abstract

The application relates to the technical field of building resource recycling, and discloses a method and system for recycling adaptation of existing building components based on multi-modal features. The method first extracts component attributes according to a component warehousing instruction, calls a collection module to collect multi-modal component data, constructs a digital twin and stores it in a component digital information database. Then, through a deep learning model, the digital twin is clustered according to a preset sustainable level to obtain a recycling cluster combination. Subsequently, the component digital information database is connected with a recycling scenario library, a recycling demand body in the scenario library is activated, and the corresponding sustainable level and component demand attributes are matched. Then, the corresponding cluster combination is called, the digital twin is matched, and a digital demand body is built. Finally, the digital matching degree of the digital demand body and the recycling demand body is calculated, and if the matching degree meets the standard, the digital demand body is output and the corresponding recycling component is called. The method improves the efficiency and scientificity of component recycling.

Description

Technical Field

[0001] This application relates to the technical field of building resource recycling, and in particular to a method and system for adapting and reusing existing building components based on multimodal characteristics. Background Technology

[0002] With the transformation and upgrading of the construction industry and the deepening of the green development concept, there is a need to vigorously develop prefabricated components and modular buildings. Prefabricated components and modular buildings adopt factory production and assembly construction mode, which can effectively avoid the quality fluctuation problems of traditional on-site casting construction, significantly improve the quality management level and overall technical level of buildings, and meet the industry development needs of energy conservation, environmental protection and efficient construction, and have become the mainstream development direction of the construction industry.

[0003] During the construction, renovation, and demolition of various construction projects, a large number of unused prefabricated components and scrap building components are inevitably generated. The total amount of these components is enormous, with a considerable proportion of them being idle steel and steel formwork in the infrastructure sector alone. Practical experience has shown that these components are not entirely useless. Most components, after professional testing, repair, and performance calibration, can have their original mechanical properties and functions restored and can be reused in various construction projects.

[0004] However, numerous prominent problems still exist in the current process of reusing existing building components, severely restricting the efficiency and quality of component reuse. The most critical issue lies in the limited dimension of component information. Existing adaptation methods rely solely on basic appearance data such as component dimensions and materials, failing to effectively integrate multimodal key information such as mechanical properties, damage status, service life, and maintenance records. This results in an incomplete and inaccurate assessment of the component's actual use value. Furthermore, adaptation decisions are highly subjective. Current adaptation processes primarily rely on the manual experience of technical personnel to determine the reuse scenarios and adaptation schemes for components, lacking a systematic adaptation basis. Summary of the Invention

[0005] To improve the efficiency and quality of reuse of existing building components, this application provides a method and system for adapting the reuse of existing building components based on multimodal features.

[0006] Firstly, this application provides a method for adapting and reusing existing building components based on multimodal features, employing the following technical solution:

[0007] A method for reusing and adapting existing building components based on multimodal features includes the following steps:

[0008] Based on the obtained component entry instruction, extract the component attributes corresponding to the component entry instruction, call the corresponding acquisition module based on the component attributes, collect multimodal component data corresponding to the component attributes, construct a digital twin of the reusable component based on the multimodal component data, and save the digital twin to the component digital information database.

[0009] The system calls a pre-defined deep learning model to analyze the component digital information database, and clusters the digital twins in the component digital information database based on a pre-defined sustainability level to obtain multiple reuse cluster combinations.

[0010] Obtain the preset reuse scenario interface, and establish a connection between the component digital information library and the reuse scenario library corresponding to the reuse scenario interface based on the reuse scenario interface; activate the reuse demand body in the reuse scenario library, and the reuse demand body matches the sustainability level and component demand attribute corresponding to the demand component from the component digital information library based on the built-in demand component.

[0011] Based on the sustainability level, the system requests the corresponding reuse cluster combination from the component digital information database, matches the digital twin corresponding to the component requirement attributes from the retrieved reuse cluster combination, and builds a digital requirement body from the matched digital twin.

[0012] The matching degree between the digital demand body and the reuse demand body is calculated as the digital matching degree. If the digital matching degree is greater than the preset reference matching degree, the digital demand body is output and the reuse component corresponding to the digital twin is retrieved.

[0013] By adopting the above technical solution, the component attributes corresponding to the component entry instructions are extracted, and multimodal component data is collected by the acquisition module. Based on the multimodal component data, a digital twin of the reusable component is constructed and saved to the component digital information database, realizing the comprehensive integration and centralized management of multi-dimensional component information. The component digital information database is analyzed by a deep learning model, and the digital twins are clustered according to the sustainability level to obtain reuse cluster combinations, which facilitates the rapid location of suitable components. A connection is established between the component digital information database and the reuse scenario database through the reuse scenario interface. After the reuse demand body is activated, the reuse demand body matches the corresponding sustainability level and component demand attributes based on the built-in demand components, realizing the dynamic adaptation of components and reuse scenarios. Based on the sustainability level, the corresponding reuse cluster combination is called, the corresponding digital twin is matched and a digital demand body is built, and the digital matching degree between the digital demand body and the reuse demand body is calculated. The digital demand body is output and the corresponding reusable component is called only when the digital matching degree exceeds the preset reference matching degree. This greatly improves the accuracy and efficiency of component matching, reduces the deviation caused by manual intervention, and improves the reuse efficiency and quality of existing building components.

[0014] Furthermore, the step of collecting multimodal component data corresponding to component attributes and constructing a digital twin of the reusable component based on the multimodal component data also includes the following sub-steps:

[0015] The three-dimensional scanning device is used to scan the reusable component to obtain three-dimensional data, the stress monitoring device is used to detect the reusable component to obtain stress data, and the material analysis device is used to analyze the reusable component to obtain material data. Multimodal construction data is constructed based on the three-dimensional data, stress data, and material data.

[0016] The system retrieves the original factory data corresponding to the reused component data from a pre-defined original component database. The original factory data includes original geometric features, original physical features, original material features, and rated time limit. The system calculates the three-dimensional loss degree based on the three-dimensional data and original geometric features, the stress loss degree based on the stress data and original physical features, and the material loss degree based on the material data and original material features. The system extracts the usage time of the reused component from the component attributes and calculates the time loss degree based on the usage time and rated time limit.

[0017] The component loss is calculated based on the three-dimensional loss, stress loss, material loss, and duration loss. If the component loss exceeds the preset loss threshold, a reuse loss warning is issued, and the multimodal construction data is marked as excessively lost.

[0018] By adopting the above technical solutions, the completeness of multimodal component data collection is improved, the component reuse screening criteria are adapted, and qualified components are accurately matched through loss warning and excessive loss marking, thereby reducing the use of unqualified components.

[0019] Furthermore, the step of calculating the component loss degree based on the three-dimensional loss degree, stress loss degree, material loss degree, and time loss degree also includes the following sub-steps:

[0020] The three-dimensional loss degree, stress loss degree, material loss degree, and duration loss degree are all percentage values, and the component loss degree is calculated using a weighted average algorithm; the three-dimensional loss degree, stress loss degree, material loss degree, and duration loss degree have a one-to-one corresponding three-dimensional weight, stress weight, material weight, and duration weight;

[0021] If the 3D loss is greater than the set 3D threshold, the 3D weights are adjusted according to the positive correlation between the 3D loss and the 3D threshold.

[0022] If the stress loss is greater than the set stress threshold, the three-dimensional weights are initialized, and then the stress weights are adjusted according to the positive correlation between the stress loss and the stress threshold.

[0023] If the material loss is greater than the set material threshold, the three-dimensional weight and stress weight are initialized, and the material weight is adjusted according to the positive correlation between the material loss and the material threshold.

[0024] If the duration loss is greater than the set duration threshold, the 3D weight, stress weight, and material weight are initialized, and the duration weight is adjusted according to the positive correlation between the duration loss and the duration threshold.

[0025] By adopting the above technical solution, the weights in the component loss calculation can be dynamically adjusted. The weighted average algorithm helps to maintain the objectivity of the component loss assessment, improve the accuracy of the component loss calculation, facilitate the reasonable matching and screening of reusable components, and adapt to the actual needs of component reuse.

[0026] Furthermore, the step of clustering digital twins in the component digital information database based on a preset sustainability level also includes the following sub-steps:

[0027] The process involves acquiring the sustainability level information, identifying sustainability classification requirements based on the sustainability level information, mapping the sustainability classification requirements to the multimodal construction data of the reusable components, and determining the sustainability level of the digital twin based on the correspondence between the multimodal construction data and the sustainability classification requirements. The lower the sustainability level, the lower the integrity of the digital twin during reuse; the higher the sustainability level, the higher the integrity of the digital twin during reuse.

[0028] Obtain the number of times the digital twin has been reused; if the number of reuses exceeds the preset number of repetitions, a multiple reuse prompt will be given, and the reuse ratio will be calculated based on the number of reuses and the number of repetitions. The sustainability level of the digital twin will be adjusted based on the negative correlation between the reuse ratio and the reuse ratio.

[0029] By adopting the above technical solutions, the adaptability between the sustainability level and the actual reuse status of the components is improved, and the sustainability level can be dynamically adjusted. This facilitates precise matching of the appropriate scenarios based on the integrity of the components and the number of reuses, which is conducive to improving the quality and efficiency of component reuse. Through multiple reuse prompts, the standardization of the component reuse process is improved.

[0030] Furthermore, the step of clustering digital twins in the component digital information database based on a preset sustainability level also includes the following sub-steps:

[0031] The digital twin obtains environmental data during its use from a preset environmental database; the environmental database contains environmental sensing data collected and generated by preset environmental sensors within a recorded time period, and the regional environmental data is obtained based on a preset filtering algorithm.

[0032] Based on a preset environmental reference template, standard environmental data and abnormal environmental data are separated from the environmental data. The intensity and duration of the standard environmental data are calculated as the standard environmental intensity and standard environmental duration, and the intensity and duration of the abnormal environmental data are calculated as the abnormal environmental intensity and abnormal environmental duration.

[0033] The total environmental duration is calculated based on the standard environmental duration and the abnormal environmental duration. Basic environmental data is obtained, and the basic environmental comparison value is calculated based on the total environmental duration and the basic environmental intensity.

[0034] The standard environment comparison value is calculated based on the standard environment duration and standard environment intensity; the abnormal environment comparison value is calculated based on the abnormal environment duration and abnormal environment intensity; and the comprehensive environment comparison value is calculated based on the standard environment comparison value and the abnormal environment comparison value.

[0035] The ratio of the comprehensive environmental comparison value to the basic environmental comparison value is called the environmental ratio.

[0036] The sustainability level of the digital twin is adjusted based on the negative correlation with the environmental ratio.

[0037] By adopting the above technical solutions, integrating environmental data and processing it precisely, the sustainability level is kept compatible with the actual environmental conditions of the components, and the sustainability level is dynamically adjusted based on environmental impact. By combining various environmental comparison values ​​to calculate environmental ratios and adjusting the level according to the environmental ratios, it is easier to match the component reuse scenarios more reasonably, improve the quality of component reuse, and at the same time maintain the correlation between environmental data processing and level adjustment, so that the sustainability level is more in line with the actual use value of reused components.

[0038] Furthermore, the method also includes the following steps:

[0039] The digital twin obtains environmental data during its use from a preset environmental database; the environmental database contains environmental sensing data collected and generated by preset environmental sensors within a recorded time period, and the regional environmental data is obtained based on a preset filtering algorithm.

[0040] Based on a preset environmental reference template, standard environmental data and abnormal environmental data are separated from the environmental data. The intensity and duration of the standard environmental data are calculated as the standard environmental intensity and standard environmental duration, and the intensity and duration of the abnormal environmental data are calculated as the abnormal environmental intensity and abnormal environmental duration.

[0041] The total environmental duration is calculated based on the standard environmental duration and the abnormal environmental duration. Basic environmental data is obtained, and the basic environmental comparison value is calculated based on the total environmental duration and the basic environmental intensity.

[0042] The standard environment comparison value is calculated based on the standard environment duration and standard environment intensity; the abnormal environment comparison value is calculated based on the abnormal environment duration and abnormal environment intensity; and the comprehensive environment comparison value is calculated based on the standard environment comparison value and the abnormal environment comparison value.

[0043] The ratio of the comprehensive environmental comparison value to the basic environmental comparison value is called the environmental ratio.

[0044] If a reuse loss warning has been issued, the loss threshold will be adjusted based on the negative correlation with the environmental ratio.

[0045] If the prompt has been used multiple times, the number of repetitions will be adjusted according to the negative correlation between the environmental ratio and the prompt.

[0046] By adopting the above technical solution, the environmental ratio is obtained by processing environmental data through the system. Combined with the actual situation of reuse loss warning and multiple reuse prompt, the loss threshold and the number of repetitions can be dynamically adjusted to maintain the adaptability of parameter settings to the environmental impact of components and actual usage status. This facilitates a more reasonable matching of the screening and classification requirements for component reuse.

[0047] Furthermore, the step of reusing the demand body to match the sustainability level and component demand attributes corresponding to the demand components from the component digital information database based on the built-in demand components also includes the following sub-steps:

[0048] Obtain the component name, component graphic, and component material of the required component;

[0049] Multiple digital twins are matched from the component digital information database based on the component name as the first temporary set; multiple digital twins are matched from the component digital information database based on the component graphic as the second temporary set; multiple digital twins are matched from the component digital information database based on the component material as the third temporary set; the set of the first temporary set, the second temporary set, and the third temporary set is taken as the component temporary set;

[0050] Based on the component name, component graphic, and component material composition requirements, multiple digital twins that meet the preset requirements strategy are matched from the temporary set of components according to the requirements attributes. The number of multiple matched digital twins is less than the preset requirement matching number.

[0051] The average sustainability level of multiple digital twins is calculated as the sustainability level average, and the sustainability level corresponding to the demand component is selected based on the average sustainability level; or, the sustainability level corresponding to the demand component includes the sustainability levels of multiple digital twins.

[0052] The sustainability levels corresponding to the demand components include the demand attributes of multiple digital twins.

[0053] By adopting the above technical solution, a subset of digital twins is first selected, and then the range of selectable digital twins is expanded based on the attributes of the subset of digital twins. Finally, the most suitable digital twin is selected. A temporary set of components is constructed through multi-dimensional screening, and further screening is performed in conjunction with the requirements attributes to maintain the compatibility between sustainability level, requirements attributes, and required components. This facilitates a more comprehensive selection of digital twins that meet the requirements, improves the quality of component reuse, and helps maintain the correlation between the matching process and the requirements attributes, thereby achieving a reasonable selection of sustainability level.

[0054] Furthermore, the step of calculating the matching degree between digital demand and reuse demand also includes the following sub-steps:

[0055] The three-dimensional graphic of the reuse demand object is used as the first graphic, and the three-dimensional graphic of the digital demand object is used as the second graphic. The degree of accommodation of the second graphic with respect to the first graphic is calculated. The degree of accommodation is inversely correlated with the degree of interference between the first graphic and the second graphic.

[0056] The first graphic is placed in the second graphic to obtain its location. After placing the first graphic in the location, the simulation pass rate is calculated based on the preset BIM model. The simulation pass rate is inversely correlated with the number of error messages and warning prompts output by the BIM model.

[0057] The matching degree is calculated based on the capacity and the degree of simulation.

[0058] By adopting the above technical solution, and combining the 3D graphic accommodation degree with the BIM model simulation degree to calculate the matching degree, the matching result is kept consistent with the actual adaptation state of the component, which is conducive to accurate matching and adaptation of the component.

[0059] Furthermore, the method also includes the following steps:

[0060] The sustainability level of the digital twin corresponding to the digital demand body is obtained as the demand sustainability level. The average value is calculated based on the demand sustainability level as the demand level average value. The first ratio is calculated based on the demand level average value and the preset first demand reference value. The reference matching degree is adjusted based on the negative correlation of the first ratio.

[0061] Alternatively, the sustainability level of the digital twin corresponding to the digital demand entity can be obtained as the demand sustainability level. The sum of the demand sustainability level is calculated as the demand level sum value. The second ratio is calculated based on the demand level sum value and the preset second demand reference value. The reference matching degree is adjusted based on the negative correlation of the second ratio.

[0062] By adopting the above technical solution, the corresponding ratio can be obtained by calculating the average or sum of the demand duration levels, thereby achieving dynamic adjustment of the reference matching degree and maintaining the adaptability of the reference matching degree to the component demand duration level. This helps the component matching standard to fit actual needs and improves the matching quality between digital demand bodies and reuse demand bodies.

[0063] Secondly, this application provides an adaptation system for the reuse of existing building components based on multimodal features, employing the following technical solution:

[0064] A multimodal feature-based adaptation system for the reuse of existing building components includes a processor that executes the steps of the multimodal feature-based adaptation method for the reuse of existing building components as described in any of the preceding claims. Attached Figure Description

[0065] Figure 1 This is a step-by-step diagram of a method for reusing and adapting existing building components based on multimodal features.

[0066] Figure 2 It involves collecting multimodal component data corresponding to component attributes and constructing a digital twin of the reusable component based on the multimodal component data. Detailed Implementation

[0067] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0068] This application discloses a multimodal feature-based method for adapting and reusing existing building components. This method is applied in the field of building resource recycling technology and is suitable for adapting and reusing surplus prefabricated components and waste building components generated during construction, renovation, and demolition processes. It can achieve intelligent and precise matching of various existing building components, such as trusses, structural columns, crane beams, and enclosure components, with multiple reuse scenarios including structural construction, landscape creation, and interior decoration. This effectively solves problems such as single information dimension, subjective adaptation decisions, and low matching efficiency in traditional component reuse processes, significantly improving the efficiency and quality of existing building component reuse. (Refer to...) Figure 1 The following detailed explanation of the method is based on specific implementation steps. The main body of this method is the existing building component reuse adaptation decision system based on multimodal features. The system is equipped with hardware and software carriers such as data acquisition module, deep learning model, component digital information database, and reuse scenario database.

[0069] Based on the acquired component entry command, the component attributes corresponding to the command are extracted. The corresponding data acquisition module is then invoked based on these attributes to collect multimodal component data. A digital twin of the reusable component is constructed from the multimodal component data and saved to the component digital information database. The component entry command is triggered by staff entering basic component information. Component attributes include component type, component source, initial specifications, and usage status. Component types are categorized as steel structure components, concrete components, and enclosure components. Component sources include leftover precast components from construction and components from demolished buildings. The system automatically matches and calls the corresponding professional acquisition modules based on the extracted component attributes. For example, it calls the stress detection module for steel structure components, the material analysis module for concrete components, and the 3D scanning module for all types of components. The acquisition modules use hardware such as 3D scanning equipment, stress detection equipment, and material analysis equipment to acquire multimodal component data, including geometric features, physical features, and material features. Geometric features include data on size, shape, 3D contour, and structural construction. Physical features include data on mechanical properties, stress state, damage degree, and deformation. Material features include data on composition, aging state, material purity, and durability. Relying on a digital twin modeling engine, the acquired multimodal component data is fused and modeled to construct a digital twin that can map the actual physical state and performance parameters of the component in real time. This digital twin completely replicates the multidimensional features and actual state of the component. Subsequently, the constructed digital twin is indexed according to component type and modal features and saved in a pre-made component digital information database, realizing the comprehensive integration and centralized management of multidimensional component information.

[0070] This application utilizes a pre-defined deep learning model to analyze a digital information database of building components. Based on a pre-defined sustainability level, the digital twins in the database are clustered to obtain multiple reuse cluster combinations. The pre-defined deep learning model is a clustering analysis model trained on a large number of multimodal building component data samples. It possesses the capabilities of component modal feature extraction, feature similarity analysis, and clustering, accurately identifying the core feature parameters of each digital twin. The sustainability level is pre-set according to industry standards for building component reuse and construction safety requirements. The classification is based on the component's performance retention, structural integrity, and actual reuse value; a higher level indicates greater reuse value and a wider range of applicable scenarios. The deep learning model performs comprehensive feature extraction and analysis on all digital twins in the component digital information database. It compares and matches the core modal features of each digital twin with the feature thresholds of the sustainability level, grouping digital twins with similar features and reuse value at the same level into one category. This results in multiple reuse cluster combinations, each uniquely corresponding to a sustainability level. This clustering process establishes a hierarchical component classification system, which provides a basis for classification and retrieval for subsequent component adaptation, facilitates the rapid location of components that meet the needs of the scenario, and greatly improves the efficiency of subsequent adaptation work.

[0071] The system acquires a preset reuse scenario interface and establishes a connection between the component digital information database and the corresponding reuse scenario database based on this interface. It then activates the reuse demand entity in the reuse scenario database, which matches the sustainability level and component demand attributes corresponding to the built-in demand components from the component digital information database. The reuse scenario interface serves as a bidirectional data interaction interface between the component digital information database and the reuse scenario database, possessing core functions such as data transmission, feature mapping, and inter-database linkage. It is the dedicated carrier for achieving information exchange between the two databases. The reuse scenario database stores various targeted digital models of reuse scenarios. All scenarios are redesigned based on the actual characteristics of existing building components, comprehensively covering multiple types of reuse needs in building engineering, including structural construction, landscape creation, and interior decoration. Each reuse scenario corresponds to a digital reuse demand entity. The reuse demand entity is the digital demand carrier of the reuse scenario, pre-stored with all information on the demand components required to complete the scenario's construction, including component type, performance requirements, specifications, and quantity requirements. After activating a reuse requirement that matches the actual engineering needs, the requirement uses a built-in feature matching algorithm to accurately compare the feature information of the required components with the modal features of the digital twins of each component in the component digital information database. It then matches the sustainability level and component requirement attributes that are compatible with the required components from the component digital information database. The component requirement attributes are the core adaptation indicators of the required components, including size range, mechanical performance threshold, material requirements, structural integrity requirements, etc. This process realizes the dynamic adaptation of components to reuse scenarios, making the component adaptation process closely aligned with the construction needs of the actual project.

[0072] Based on the sustainability level, the system requests the corresponding reuse cluster combination from the component digital information database. From this database, it matches a digital twin that corresponds to the component's requirement attributes. The matched digital twins are then used to construct a digital requirement entity. Using the sustainability level obtained in the previous steps as the search criteria, a targeted retrieval request is initiated to the component digital information database. Based on a pre-established search index, the database quickly locates and returns the reuse cluster combination corresponding to that sustainability level. This targeted retrieval replaces the indiscriminate full-database search, effectively reducing the search scope and improving component retrieval efficiency. Within the retrieved reuse cluster combination, a second, precise screening is performed according to the core adaptation indicators of the component's requirement attributes. Digital twins that do not meet the indicator requirements are eliminated, leaving only those that perfectly match the component's requirement attributes. Subsequently, relying on the building digital modeling platform, all successfully matched digital twins are digitally spliced ​​and built according to the scenario design requirements, spatial layout planning, and component combination methods of the reuse demand body, forming a digital demand body corresponding to the reuse demand body. This digital demand body is a digital implementation plan after the reuse components are adapted to the target scenario. It can completely restore the application form, spatial layout, and combination relationship of the adapted components in the target scenario, providing a visual digital carrier for subsequent matching degree verification.

[0073] The matching degree between the digital demand body and the reuse demand body is calculated as the digital matching degree. If the digital matching degree is greater than the preset reference matching degree, the digital demand body is output and the corresponding reused component of the digital twin is retrieved. A weighted comprehensive matching algorithm is used to calculate the digital matching degree, comprehensively considering multiple dimensions such as component specification adaptability, performance fit, spatial layout fit, and assembly fit. Each dimension of adaptability is assigned a weight value that conforms to the actual requirements of the project. The weighted calculation yields a digital matching degree that reflects the overall fit between the digital demand body and the reuse demand body. The higher the value, the better the fit between the two. The preset reference matching degree is a threshold pre-set according to building construction specifications, component reuse safety standards, and industry technical requirements. It is the minimum standard for judging whether the component adaptability is qualified. The calculated digital matching degree is compared with the reference matching degree. If the digital matching degree is greater than the preset reference matching degree, it means that the adapted component fully meets the construction requirements and safe reuse standards of the target scenario. At this time, the completed digital requirement body is automatically output. This digital requirement body is a visualized component reuse adaptation solution, which includes core information such as component usage list, spatial layout diagram, and component modification suggestions. At the same time, based on the associated storage information of each digital twin in the digital requirement body, a retrieval command is sent to the actual storage end of the component to accurately retrieve the corresponding reused component for use in engineering construction. If the digital matching degree does not reach the preset reference matching degree, the previous step is returned, and secondary component matching and digital requirement body construction are carried out in the reuse cluster combination until a digital requirement body that meets the matching degree requirements is obtained, ensuring that the finally retrieved actual reused component is highly adapted to the requirements of the target scenario.

[0074] Reference Figure 2 The core steps of collecting multimodal component data corresponding to component attributes and constructing a digital twin of the reusable component based on the multimodal component data also include the following steps:

[0075] Based on the extracted component attributes, matching professional testing and data acquisition equipment is invoked to collect feature data from the reused components in a comprehensive and thorough manner. The collection objects, content, and data output standards of various equipment are precisely adapted to the component type. The specific data acquisition process is as follows:

[0076] 3D data acquisition: Using 3D scanning equipment such as laser 3D scanners and photogrammetry instruments, the overall outline and local details of the reusable components are scanned in full size. The acquired 3D point cloud data includes information such as the actual size of the components, their outline, structural features, surface deformation areas, missing parts of local components, and joint gaps. This data directly maps the geometric features of the components, providing a precise basis for subsequent geometric loss analysis.

[0077] Stress data acquisition: Stress monitoring equipment such as static stress strain gauges and dynamic mechanical testing instruments are used to conduct mechanical performance tests on key stress-bearing parts, stress concentration areas, and the overall structure of the components. For steel structure components, the focus is on welds and bolt connections, and for concrete components, the focus is on reinforcing bars and the concrete body. Stress data is collected that includes information such as the actual compressive strength, tensile strength, shear strength, stress distribution, degree of structural damage, and deformation coefficient of the components. This data fully reflects the physical characteristics of the components.

[0078] Material data acquisition: Material analysis equipment such as X-ray fluorescence material analyzer, aging degree detector, and ultrasonic flaw detector are used to conduct a comprehensive analysis of the material composition, aging state, and internal defects of the components. Material data including the actual composition of the components, material aging coefficient, surface oxide layer thickness, material differences due to local repair and replacement, and internal micro-cracks are collected. This data accurately reflects the material characteristics of the components.

[0079] After completing the above three types of data collection, the data cleaning module removes redundant, abnormal, and error-related data. Then, the data structuring and integration module classifies, associates, and stores the three-dimensional data, stress data, and material data according to four levels of dimensions: "unique component identifier - feature dimension - data index - collection location." This constructs complete, standardized, and traceable multimodal component data, achieving full coverage of the three core characteristics of the component: geometry, physics, and materials. This provides basic data support for subsequent loss assessment and digital twin construction.

[0080] A pre-built database of original components is provided, which includes the original factory standard parameters of mainstream types of components in the construction industry, covering all common categories such as steel structures, concrete components, enclosure components, and prefabricated modular components. The original factory data of each type of component includes four core dimensions: original geometric characteristics, original physical characteristics, original material characteristics, and design rated time limit. The data are all derived from the factory inspection reports of component manufacturers, and are authoritative and standardized.

[0081] Benchmark data matching and retrieval: The completed multimodal component data is accurately matched with the original component database. Using the component's model, specifications, production batch, and manufacturer as matching keywords, the original factory data that completely corresponds to the reused component is retrieved as the benchmark reference for calculating the component's loss rate.

[0082] Standardized calculation of loss metrics across various dimensions: Based on the original factory data, a standardized algorithm is used to calculate the loss rate in four dimensions: component geometry, physical properties, materials, and service life. All loss rates are expressed as percentage values; higher values ​​indicate greater loss in the corresponding dimension. The specific calculation method is as follows:

[0083] 3D Loss: Using a 3D feature similarity algorithm, the actual collected 3D point cloud data is compared with the standard dimensions, standard contours, and standard structures in the original geometric features at the pixel level. The similarity value between the two is calculated, and the result is obtained by formulating 3D loss = (1 - 3D feature similarity) × 100%. This index directly reflects the geometric loss state of the component, such as geometric deformation, structural loss, local damage, and joint deformation. The higher the similarity, the smaller the 3D loss.

[0084] Stress loss degree: The core mechanical indicators (compressive, tensile, and shear strength) in the actual collected stress data are compared with the standard mechanical thresholds in the original physical characteristics to calculate the attenuation ratio of mechanical properties. The result is obtained by the formula Stress loss degree = (1 - average actual mechanical properties / average original standard mechanical properties) × 100%. This indicator reflects the loss state of the component's mechanical properties, the decrease in the structural load-bearing capacity, and the uneven distribution of stress at the physical level.

[0085] Material loss degree: The actual collected material data is compared with the composition standard and material performance index in the original material characteristics. Taking into account factors such as composition deviation, aging coefficient, oxidation degree, and differences in repair and replacement materials, the material deterioration ratio is obtained by weighted calculation method, that is, material loss degree. This index reflects the material loss state of the component, such as changes in material composition, aging and oxidation, local material replacement, and internal micro-defects.

[0086] Duration loss: Extract the actual usage time (accurate to the month) of the reused component from the component attributes, calculate the ratio of the actual usage time to the design rated time limit in the original factory data, and obtain the result by formula Duration loss = (actual usage time / design rated time limit) × 100%. The shorter the usage time, the smaller the duration loss. This indicator reflects the natural wear and tear of the component in terms of its service life.

[0087] A weighted average algorithm is used to comprehensively calculate the above-mentioned three-dimensional loss degree, stress loss degree, material loss degree, and time loss degree to obtain the component loss degree that reflects the overall loss state of the component. This calculation process fully considers the core needs of reuse of different types of components and realizes differentiated and precise setting of weights. The specific steps are as follows:

[0088] Preset and Adaptation of Weighting Coefficients: Basic weighting coefficients are preset for the four dimensions of loss, with a base value of 0.25 for 3D weighting, 0.35 for stress weighting, 0.25 for material weighting, and 0.15 for duration weighting. It also supports dynamic adjustment of weights based on component type and subsequent reuse requirements. For example, structural components (such as trusses and structural columns) require an increased stress weighting to 0.45, enclosure components (such as walls and window frames) require an increased material weighting to 0.35, and landscape decoration components require an increased 3D weighting to 0.35, ensuring that the loss calculation results closely match the actual reuse requirements of the components.

[0089] Comprehensive calculation of component loss degree: The comprehensive result is obtained by using the formula Component loss degree = 3D loss degree × 3D weight + stress loss degree × stress weight + material loss degree × material weight + duration loss degree × duration weight. The calculation logic follows the principle that "the higher the similarity of each dimension and the shorter the actual usage time, the smaller the corresponding loss degree, the smaller the final component loss degree, and the higher the component reuse value."

[0090] Loss Threshold Comparison and Early Warning Marking: Based on building component reuse safety standards, industry construction specifications, and component types, differentiated preset loss thresholds are established. For example, the loss threshold for structural components is set at 30%, for landscape decoration components at 50%, and for enclosure components at 40%. This threshold represents the maximum loss limit for a component to enter the subsequent reuse adaptation process. The calculated component loss degree is then compared with the corresponding preset loss threshold in real time.

[0091] If the component loss degree is less than or equal to the loss threshold, it means that the overall loss of the component is within an acceptable range. The multimodal component data is then pushed to the next stage to build a digital twin of the component.

[0092] If the component loss degree is greater than the loss threshold, it means that the overall loss of the component exceeds the reuse safety and adaptability standards. The reuse loss warning prompt will be triggered immediately. A visual warning pop-up window will appear on the operation interface, clearly marking the unique identifier of the component, the specific dimension of the loss exceeding the threshold, the loss degree value of each dimension and the overall component loss degree value. At the same time, an SMS / back-end reminder will be sent to the management personnel.

[0093] For components that trigger loss warnings, their corresponding multimodal component data are marked with an exclusive excessive loss label. The label information includes "excessive loss - exceeding threshold dimension - loss degree value". This label is synchronously associated with the entire lifecycle of the component data. Even if the digital twin construction stage is entered later, the label will be permanently retained, realizing accurate and traceable identification of unqualified components.

[0094] The sub-steps for calculating the component loss based on the three-dimensional loss degree, stress loss degree, material loss degree, and time loss degree also include the following sub-steps:

[0095] In this step, the 3D loss degree, stress loss degree, material loss degree, and duration loss degree are all percentage values ​​obtained through standardized calculations. The core uses a weighted average algorithm to calculate the overall loss degree of the component. The basic calculation logic is: Component loss degree = 3D loss degree × 3D weight + stress loss degree × stress weight + material loss degree × material weight + duration loss degree × duration weight. Among them, the 3D weight, stress weight, material weight, and duration weight are weight coefficients that correspond one-to-one with the four major loss degrees. Basic initial values ​​are set for each weight in advance. The basic initial values ​​are set according to the general industry standards for the reuse of building components, and the sum of the weight coefficients is always 1. For example, the default basic value is 0.25 for the 3D weight, 0.35 for the stress weight, 0.25 for the material weight, and 0.15 for the duration weight. The initial values ​​can be fine-tuned according to the major component categories to ensure the objectivity of the basic calculation.

[0096] Meanwhile, specific thresholds are preset for the four dimensions of loss: three-dimensional threshold, stress threshold, material threshold, and duration threshold. Each threshold is set differently according to the component loss safety standard and reuse adaptation requirements of the corresponding dimension. For example, the stress threshold for structural components is set to 20%, and the three-dimensional threshold is set to 25%. The three-dimensional threshold for landscape decoration components is set to 30%, and the material threshold is set to 35%. All thresholds are critical values ​​for determining whether there is significant loss in the corresponding dimension. When the loss of a certain dimension exceeds its specific threshold, it means that the dimension is the core loss dimension of the component. The influence of the dimension on the overall loss needs to be amplified by weight adjustment to accurately reflect the actual core loss status of the component.

[0097] The loss in the four dimensions and their corresponding thresholds are judged and adjusted sequentially in the following order: 3D loss → stress loss → material loss → duration loss. Each subsequent dimension's adjustment is based on the judgment result of the previous dimension. The core adjustment rule is that if a threshold is exceeded, the pre-set weight is initialized and the weight of this dimension is adjusted positively. The specific implementation process is as follows:

[0098] Adjustment of 3D loss function and 3D weights:

[0099] The calculated 3D loss degree is compared with the preset 3D threshold. If the 3D loss degree is less than or equal to the 3D threshold, it means that there is no significant loss in the geometric dimension of the component, and the 3D weight remains unchanged from its initial value. If the 3D loss degree is greater than the 3D threshold, it means that there is significant loss in the geometric dimension of the component (such as severe deformation, structural loss, etc.). In order to highlight the impact of this core loss dimension, the 3D weight is positively adjusted according to the ratio of the 3D loss degree to the 3D threshold. That is, the larger the ratio, the higher the value of the 3D weight after adjustment. After adjustment, the 3D weight still meets the value range requirements of the weight coefficient. The weights of other dimensions (stress, material, duration) remain unchanged from their initial values, and the sum of the four weight coefficients after adjustment is still 1.

[0100] Adjustment of stress loss degree and stress weight:

[0101] After the three-dimensional weights are adjusted, the stress loss degree is compared with the preset stress threshold. If the stress loss degree is less than or equal to the stress threshold, it means that there is no significant loss in the physical and mechanical dimensions of the component, and the stress weight remains unchanged at its current value (the initial value or the corresponding value after the three-dimensional weight adjustment). If the stress loss degree is greater than the stress threshold, it means that there is significant loss in the mechanical properties of the component (such as a significant decrease in strength, abnormal stress distribution, etc.). This dimension is the core loss dimension of the component, and the mechanical loss has a higher priority on the safety of the component's reuse than the geometric loss. Therefore, the three-dimensional weights are initialized first (restored to their initial values), and then the stress weights are positively correlated with the ratio of the stress loss degree to the stress threshold. The larger the ratio, the higher the value of the adjusted stress weight. The material and duration weights remain unchanged at their current values, and the sum of the four weight coefficients after adjustment is still 1.

[0102] Adjustment of material loss and material weight:

[0103] After adjusting the stress weight, the material loss degree is compared with the preset material threshold. If the material loss degree is less than or equal to the material threshold, it means that there is no significant loss in the component material dimension, and the material weight remains unchanged. If the material loss degree is greater than the material threshold, it means that there is significant loss in the component material level (such as severe oxidation, compositional degradation, internal defects, etc.). This dimension is the core loss dimension of the component, and the material loss is the essential loss of the component, which has a higher priority than geometric and mechanical loss. Therefore, the three-dimensional weight and stress weight are initialized first (both restored to their original values). Then, the material weight is positively adjusted according to the ratio of the material loss degree to the material threshold. The larger the ratio, the higher the adjusted material weight value. The duration weight remains unchanged. After adjustment, the sum of the four weight coefficients is still 1.

[0104] Adjustment of duration loss and duration weight:

[0105] After adjusting the material weight, the time loss is compared with the preset time threshold. If the time loss is less than or equal to the time threshold, it means that there is no significant natural wear and tear in the service life dimension of the component, and the time weight remains unchanged. If the time loss is greater than the time threshold, it means that the component has approached or exceeded the design rated time limit, and the natural wear and tear has reached a significant level. This dimension is the core wear and tear dimension of the component, and the impact of age wear and tear has a higher priority than geometric, mechanical, and material wear and tear. Therefore, the three-dimensional weight, stress weight, and material weight are initialized first (all restored to their respective basic initial values). Then, the time weight is positively adjusted according to the ratio of the time loss to the time threshold. The larger the ratio, the higher the value of the adjusted time weight. After adjustment, the sum of the four weight coefficients is still 1.

[0106] In the above weight adjustment process, the specific calculation method for positive correlation adjustment can adopt a linear positive correlation algorithm. For example, the adjusted weight = basic initial value + (loss degree / threshold - 1) × adjustment coefficient, where the adjustment coefficient is a preset fixed value, and ensures that the weight of a single dimension does not exceed 0.6 and the sum of all weights is always 1, so as to avoid the imbalance of the comprehensive loss degree assessment caused by the excessive weight of a single dimension; while the weight initialization is to restore the corresponding dimension weight to the preset basic initial value, so that the calculation result of the comprehensive loss degree is more in line with the actual state of the component.

[0107] After completing the weight determination and dynamic adjustment of all dimensions, the adjusted final weight coefficients are substituted into the weighted average algorithm formula to calculate the overall loss of the component. This result retains the objective quantitative characteristics of the weighted average algorithm and amplifies the impact of the core loss dimension through dynamic weight adjustment.

[0108] The core steps of clustering digital twins in the component digital information database based on the preset sustainability level also include the following steps:

[0109] A hierarchical sustainability rating system was pre-established based on industry standards, safety requirements, and reuse value gradients for building component reuse. This system is divided into multiple levels (e.g., S / A / B / C / D levels) according to the integrity of component reuse, from high to low. The higher the sustainability level, the higher the structural integrity and performance retention of the actual component corresponding to the digital twin when reused, and the more advanced the reuse scenarios and the higher the performance requirements of the component. The lower the sustainability level, the lower the integrity of component reuse, and the more suitable it is for reuse scenarios with lower performance and integrity requirements. Each level corresponds to clear and quantifiable sustainability classification requirements, which are directly related to the multimodal component data and cover core quantitative indicators such as three-dimensional loss degree, stress loss degree, material loss degree, time loss degree, and overall component loss degree. Different levels set different indicator thresholds for classification requirements. For example, the S-level sustainability classification requirements are overall component loss degree ≤10% and stress loss degree ≤5%, while the A-level requirements are overall loss degree ≤20% and stress loss degree ≤10%, ensuring the quantification and measurability of the classification requirements.

[0110] The system retrieves complete sustainability level information from a pre-defined rule base, including level hierarchy, core definitions of each level, and corresponding sustainability classification requirements. Then, using a feature recognition algorithm, it breaks down the sustainability classification requirements of each level into quantifiable and comparable indicators, establishing a one-to-one mapping with the multimodal component data of reusable components. This means precisely mapping indicators such as loss thresholds and performance retention thresholds in the classification requirements to specific data items such as three-dimensional loss, stress loss, and overall loss in the multimodal component data. Finally, it performs a full-item quantitative comparison between the multimodal component data associated with the digital twin and the sustainability classification requirements of each level to determine whether the component data meets all classification requirements of the corresponding level. The highest level that fully meets all requirements is used as the basic sustainability level of the digital twin, achieving a precise match between the sustainability level and the actual wear and tear and reuse value of the component.

[0111] A full lifecycle reuse information recording module is set up for the digital twin of the components. This module records in real time the core information of the actual component corresponding to the digital twin, such as the number of times it is reused, the scenario of each reuse, the usage duration, and the modification process. The reuse count is a cumulative number of reuses. The initial reuse count of a non-reusable component is 0. The count is automatically incremented by 1 for each compliant reuse process, ensuring the accuracy and traceability of the reuse count record. At the same time, a repetition threshold is preset according to the component type. This threshold is the reasonable upper limit of the cumulative reuse count of the component, which is determined by the material characteristics of the building component, the structural fatigue strength, and industry reuse standards. For example, the repetition threshold is set to 5 times for steel structure components, 2 times for concrete components, and 3 times for lightweight landscape decoration components. The repetition threshold is set differently for different types of components to meet the durability requirements of actual reuse of components.

[0112] Reuse count acquisition and comparison: The cumulative reuse count of the digital twin to be clustered is accurately obtained from the full life cycle reuse information recording module, and the count is compared with the preset repetition count threshold of the corresponding component type in real time;

[0113] Normal state handling: If the number of times the digital twin is reused is less than or equal to the preset repetition threshold, it means that the component is still within the reasonable number of reuses, its basic sustainability level remains unchanged, and no adjustment is required;

[0114] Handling of exceeding reuse limits: If the number of reuses of the digital twin exceeds the preset reuse threshold, it indicates that the component has exceeded the reasonable reuse limit, posing a risk of structural fatigue and accelerated performance degradation. In this case, two operations are performed:

[0115] Trigger multiple reuse prompt: Immediately pop up a visual multiple reuse prompt pop-up window on the operation interface, and send background reminders and SMS notifications to managers and component reuse docking personnel. The prompt content includes information such as the component's unique identifier, component type, cumulative reuse count, preset repeat count threshold, and risk warning of exceeding the number of times. It clearly informs that the component has exceeded the reasonable reuse count and needs to undergo a second performance test and evaluation, standardizes the component reuse process, and avoids the safety hazards caused by blindly reusing the component beyond the number of times.

[0116] Dynamic adjustment of sustainability level: First, the reuse ratio is calculated according to the formula Reuse Ratio = Actual Cumulative Reuse Times / Preset Repetition Threshold. Then, the basic sustainability level of the digital twin is negatively adjusted based on this ratio. That is, the larger the reuse ratio, the more levels the sustainability level of the component is downgraded. The adjustment rule is a preset hierarchical downgrade standard. For example, when the reuse ratio is 1.2-1.5, the basic sustainability level is downgraded by 1 level; when the ratio is 1.5-2.0, it is downgraded by 2 levels; when the ratio is >2.0, it is downgraded by 3 levels and marked as "only suitable for simple landscape scenarios". This ensures that the downgrade magnitude matches the degree of overuse and accurately reflects the impact of overuse on the reuse value of the component.

[0117] After completing the basic sustainability level determination and dynamic adjustment based on the number of reuses, the final sustainability level is the sole basis for the digital twin to participate in clustering. The final sustainability level is permanently associated with the digital twin and synchronously updated to the retrieval index of the component digital information database, providing an accurate and relevant basis for the actual state of the component for subsequent clustering analysis.

[0118] The core steps of clustering digital twins in the component digital information database based on the preset sustainability level also include the following steps:

[0119] An environmental database is pre-built, serving as a dedicated storage and management platform for component usage environmental data. The data source is pre-installed environmental sensors deployed in various building scenarios. These sensors collect real-time environmental parameters related to component performance degradation, such as temperature and humidity, pH levels, corrosive gas concentrations, UV intensity, and vibration frequency. All sensor data includes traceability information such as collection time, collection area, and the corresponding component's unique identifier. The raw sensor data is first processed using pre-defined filtering algorithms (such as mean filtering and median filtering) to remove abnormal fluctuations and collection errors, resulting in accurate regional environmental data. This data is then categorized and integrated according to the component's unique identifier, forming complete environmental data for each component during its usage period, which is then stored in the environmental database.

[0120] When determining the sustainability level of a digital twin, a targeted search is performed on the environmental database based on the unique identifier of the component associated with the digital twin to accurately obtain all environmental data of the component during its actual use. At the same time, the obtained environmental data is filtered by time dimension to remove environmental data from non-actual use stages such as component maintenance period and idle period, and only environmental data from the stage when the component participates in the service of the building structure and actually performs its function is retained. This ensures that the environmental data for subsequent analysis is valid and real use environment data, and that the environmental impact assessment is highly consistent with the actual service status of the component.

[0121] Environmental reference templates are pre-defined based on the material characteristics and structural tolerance of different types of building components. These templates clearly define the standard environmental ranges for various components, i.e., the range of environmental parameters within which the component's performance does not significantly degrade and it can be used stably for a long time. They also define the criteria for judging abnormal environments, i.e., the thresholds of environmental parameters that would lead to accelerated aging and performance degradation of the components. For example, for steel structure components, an air humidity ≤60% and no corrosive gases are defined as a standard environment, while humidity >80% or corrosive gas concentration >0.05% are defined as an abnormal environment. For concrete components, a pH of 7.0-8.0 is defined as a standard environment, while pH <5.5 or >9.5 is defined as an abnormal environment. The environmental reference templates for different components are set differently to match the actual environmental tolerance characteristics of the components.

[0122] Environmental data splitting: The acquired valid environmental data is compared with the preset environmental reference template parameter by parameter. According to whether it meets the standard environmental range, the environmental data is accurately split into standard environmental data and abnormal environmental data. The standard environmental data is the environmental parameter record of the component under normal tolerance environment, and the abnormal environmental data is the environmental parameter record of the component under harsh environment of accelerated wear.

[0123] Environmental intensity and duration calculation: The two types of data after splitting are quantitatively analyzed separately. According to the preset environmental intensity quantification rules in the environmental reference template, environmental parameters such as temperature, humidity, and corrosiveness concentration are converted into quantifiable environmental intensity values ​​(range 0-1, the higher the value, the greater the impact of the environment on the component). The standard environmental intensity value is concentrated in the range of 0-0.3, and the abnormal environmental intensity value is concentrated in the range of 0.3-1. Then, the two types of data are statistically analyzed in terms of time dimension to calculate the actual cumulative service time of the component in the standard environment and the abnormal environment, which are recorded as standard environment duration and abnormal environment duration (unit: hours), respectively. The sum of the two durations is the total environmental duration. This total environmental duration is the actual cumulative time of the component in the service environment, which can be shorter than the overall service time of the component, because the time of the component maintenance period, idle period and other non-service stages are not included.

[0124] By calculating the basic environmental comparison value, standard / abnormal environmental comparison value, and comprehensive environmental comparison value in a stratified manner, an environmental ratio is finally obtained that can quantitatively reflect the degree of deviation between the actual use environment of the component and the standard environment. All comparison values ​​and environmental ratios are calculated based on quantified environmental intensity and duration indicators to ensure the objectivity and accuracy of the environmental impact assessment. The specific calculation process is as follows:

[0125] Calculate the basic environmental comparison value: retrieve the basic environmental data of the corresponding component from the environmental reference template. This data includes the basic environmental strength of the component under the standard service environment, that is, the benchmark strength value of the standard environment, which is a fixed constant. Then, calculate the result according to the formula: Basic environmental comparison value = Total environmental duration × Basic environmental strength. This index is the benchmark value of the environmental impact of the component under the ideal standard environment, and serves as the reference for subsequent environmental deviation analysis.

[0126] Calculate the standard / abnormal environment comparison value: Quantify the impact of standard and abnormal environments respectively. The standard environment comparison value = standard environment duration × average standard environment intensity and the abnormal environment comparison value = abnormal environment duration × average abnormal environment intensity are calculated to obtain two types of results. The average intensity of standard / abnormal environments is the arithmetic mean of all intensity values ​​in the corresponding environmental data. This index reflects the actual cumulative impact of standard and abnormal environments on the components respectively.

[0127] Calculate the comprehensive environmental comparison value: Based on the weights of the impact of standard environment and abnormal environment on component wear, the comparison values ​​of the two types of environments are weighted and summed to obtain the comprehensive environmental comparison value. The formula is: Comprehensive environmental comparison value = Standard environment comparison value × Standard weight + Abnormal environment comparison value × Abnormal weight. The abnormal weight (e.g., 0.7) is much higher than the standard weight (e.g., 0.3), highlighting the accelerated wear effect of abnormal environment on component. The weight values ​​are preset according to the material characteristics of the component and the sum is 1.

[0128] Calculate the final environmental ratio: The result is obtained by the formula Environmental Ratio = Comprehensive Environmental Comparison Value / Basic Environmental Comparison Value. This index is the ratio of the cumulative impact of the actual use environment of the component to the impact of the ideal standard environment. The larger the environmental ratio, the more severe the actual use environment of the component, the more significant the accelerated wear and tear effect of the environment on the component, and the lower the actual reuse value of the component. If the environmental ratio = 1, it means that the use environment of the component is close to the ideal standard environment, and the environment has no significant accelerated wear and tear effect on the component.

[0129] The calculated environmental ratio is used as a quantitative indicator of environmental impact. The sustainability level of the digital twin (the level after the basic level determination and reuse frequency adjustment have been completed) is negatively correlated and dynamically adjusted. The core adjustment principle is that the larger the environmental ratio, the greater the reduction in sustainability level, ensuring that the adjustment range matches the severity of environmental degradation.

[0130] A pre-set grading rule for sustainability assessment is established. This rule sets a corresponding downward adjustment level for the sustainability level based on the environmental ratio range, with a maximum downward adjustment of three levels. This prevents imbalances in the assessment caused by a single environmental factor. For example, for steel structure components, the sustainability level is lowered by one level when the environmental ratio is 1.0-1.5; by two levels when it is 1.5-2.0; and by three levels when it is >2.0. When the environmental ratio is ≤1.0, it indicates that the component is used in a standard or excellent environment with no accelerated degradation, and the sustainability level remains unchanged. Furthermore, the adjusted level must not be lower than the preset minimum sustainability level. If the adjusted level is lower than the minimum value, it is directly set as the minimum sustainability level to ensure the rationality of the grading system.

[0131] After the environmental ratio is adjusted to the corresponding level, the result is used as the final sustainability level of the digital twin and updated synchronously in the component digital information database. It is then permanently associated with the digital twin and serves as the sole level basis for subsequent cluster analysis. At the same time, all information such as environmental data, environmental ratio, and level adjustment records are archived in the component's full life cycle information to achieve traceability of environmental impact assessment.

[0132] The existing building component reuse and adaptation method based on multimodal features in this application also includes the following steps:

[0133] Based on the unique component identifier associated with the digital twin of the parameter to be adjusted, the system retrieves and obtains full environmental data of the component during its actual use from a pre-set environmental database. The data source for the environmental database consists of pre-set environmental sensors deployed in various building usage scenarios. These sensors can collect environmental parameters directly related to component performance degradation, such as temperature and humidity, pH, corrosive gas concentration, ultraviolet radiation intensity, lightning strike frequency, and rainfall intensity. All raw environmental sensor data includes traceable information such as collection time, collection area, and component association identifier. The raw sensor data is pre-processed using pre-set filtering algorithms (such as mean filtering and median filtering) to remove abnormal data caused by collection errors and signal fluctuations, resulting in accurate regional environmental data. This data is then categorized and integrated according to component identifiers before being stored in the environmental database, ensuring the authenticity and validity of the data.

[0134] At the same time, the acquired environmental data is effectively filtered in terms of time dimension, eliminating data from the component maintenance period, idle period, and non-use stage data that has not been put into actual service, and retaining only the environmental data of the stage when the component participates in the service of the building structure and actually performs its use function, so as to provide an effective data foundation for subsequent environmental quantitative analysis.

[0135] Specific environmental reference templates have been pre-established based on the material characteristics and structural tolerance of different types of building components. These templates clearly define the standard environmental ranges (environmental parameter ranges within which component performance does not significantly degrade and can be stably used for a long period), abnormal environment judgment criteria (environmental parameter thresholds that would lead to accelerated aging and performance degradation of components), and environmental intensity quantification rules. Abnormal environments include environmental types that can cause significant damage to components, such as thunderstorms, lightning strikes, high-concentration corrosion, and extreme temperature and humidity. Furthermore, the environmental intensity value is positively correlated with the degree of environmental abnormality; the higher the environmental intensity value, the more abnormal the environment, and the more significant the accelerated damage to the component.

[0136] Environmental data classification and splitting: The filtered valid environmental data is compared with the environmental reference template of the corresponding component by parameter and time period. According to whether it meets the standard environmental range, the environmental data is accurately split into standard environmental data and abnormal environmental data, which correspond to the service records of the component in normal tolerance environment and harsh accelerated wear environment, respectively.

[0137] Environmental intensity and duration quantification: Based on the environmental intensity quantification rules in the environmental reference template, non-quantifiable environmental parameters such as temperature and humidity, lightning strike frequency, and corrosion concentration are converted into calculable standard environmental intensity values ​​and abnormal environmental intensity values, ranging from 0 to 1. The higher the value, the greater the impact of the environment on the component. At the same time, the two types of data are cumulatively statistically analyzed in terms of time dimension to calculate the actual cumulative service time of the component in the standard environment and abnormal environment, which are recorded as standard environment duration and abnormal environment duration (unit: hours), respectively. The sum of the two durations is the total environmental duration. This total environmental duration may be shorter than the overall service life of the component, because the non-service stage time such as the component maintenance period is not included in the statistics.

[0138] By calculating the basic environmental comparison value, the standard / abnormal environmental comparison value, and the comprehensive environmental comparison value in a stratified manner, an environmental ratio is finally obtained that can quantitatively reflect the degree of deviation between the actual use environment of the component and the ideal standard environment. All calculation processes are based on quantified environmental intensity and duration indicators to ensure the objectivity and accuracy of the environmental impact assessment. The specific calculation logic is as follows:

[0139] Calculate the basic environmental comparison value: retrieve the basic environmental data of the corresponding component from the environmental reference template. This data includes the basic environmental strength of the component under ideal standard environment (a fixed constant determined by the component material characteristics and industry standards). The basic environmental comparison value is calculated using the formula: Basic Environmental Comparison Value = Total Environmental Duration × Basic Environmental Strength. This index is the ideal environmental impact benchmark value of the component under no abnormal environmental influence, and serves as a reference for subsequent actual environmental deviation analysis.

[0140] Calculate the standard / abnormal environment comparison value: Quantify the actual cumulative impact of standard and abnormal environments on components. The standard environment comparison value = standard environment duration × average standard environment intensity and the abnormal environment comparison value = abnormal environment duration × average abnormal environment intensity are used to calculate two types of results. The average intensity of standard / abnormal environments is the arithmetic mean of all intensity values ​​in the corresponding environmental data. This indicator directly reflects the degree of impact of different environments on the actual wear and tear of components.

[0141] Calculate the comprehensive environmental comparison value: Considering that the accelerated wear of components by abnormal environments is much greater than that of standard environments, a differential weight is preset for the comparison values ​​of the two types of environments. The weight of abnormal environment is greater than that of standard environment, and the sum of the weights is 1. For example, the weight of standard environment is 0.2 and the weight of abnormal environment is 0.8. The comprehensive environmental comparison value is calculated by the formula: Comprehensive environmental comparison value = Standard environment comparison value × Standard weight + Abnormal environment comparison value × Abnormal weight, highlighting the core wear impact of abnormal environment on components.

[0142] Calculate the final environmental ratio: The result is obtained by the formula Environmental Ratio = Comprehensive Environmental Comparison Value / Basic Environmental Comparison Value. This index is the ratio of the cumulative impact of the actual service environment of the component to the impact of the ideal standard environment. The larger the environmental ratio, the more severe the actual service environment of the component, the more significant the accelerated wear effect of the environment on the component, and the higher the actual performance degradation of the component. If the environmental ratio = 1, it means that the service environment of the component is close to the ideal standard environment, and there is no significant accelerated wear.

[0143] The environmental ratio calculated above is used as the basis for quantifying environmental impact. Combined with the actual situation of whether the component triggered reuse loss warnings or multiple reuse warnings during the initial warehousing inspection and grade determination stages, the loss threshold and number of repetitions are dynamically adjusted in a targeted negative correlation. That is, the larger the environmental ratio, the more severe the accelerated environmental damage to the component, and the greater the reduction in the corresponding threshold and number of repetitions. For components that did not trigger relevant warnings, their loss threshold and number of repetitions remain unchanged from the initial preset values. The specific adjustment rules are as follows:

[0144] Loss threshold adjustment when triggering reuse loss warning:

[0145] If a component triggers a reuse loss warning during the early stages of multimodal data acquisition and component loss calculation because its loss exceeds the initial preset loss threshold, it indicates that the component already has significant self-loss. In this case, the component's specific loss threshold is negatively adjusted based on the environmental ratio. The core logic of the adjustment is: the larger the environmental ratio, the more severe the accelerated environmental loss of the component, and the lower its actual tolerable loss upper limit; therefore, the loss threshold is lowered accordingly. After adjustment, the loss threshold should not be lower than the preset minimum loss threshold lower limit to avoid the component being directly judged as unusable due to over-adjustment. For example, if the initial loss threshold of a steel structure component is 30%, and the environmental ratio is 1.5, its loss threshold is lowered to 25%; if the environmental ratio is 2.0, it is lowered to 20%, so that the loss threshold is adapted to the dual state of the component's self-loss and environmental loss.

[0146] Adjustment of the number of repetitions when triggering multiple use prompts:

[0147] If a component triggers a multiple-use warning during the initial sustainability assessment phase because its cumulative reuse count exceeds the initial preset number of repetitions, it indicates that the component has exceeded the normal reasonable number of reuses and poses a risk of structural fatigue and continuous performance degradation. In this case, the component's specific repetition count is negatively adjusted based on the environmental ratio. The core logic of the adjustment is: the larger the environmental ratio, the more severe the accelerated environmental wear and tear on the component during multiple reuses, and the fewer cumulative reuses it can withstand. Therefore, the repetition count is reduced accordingly. After adjustment, the repetition count is not less than 1 time, ensuring that reasonable reuse space is still reserved for the component. For example, if a concrete component's initial repetition count is 2 times, and the environmental ratio is 1.3, its repetition count is reduced to 1 time; if the environmental ratio is >2.0, the minimum repetition count of 1 time is maintained, ensuring that the repetition count fully reflects the component's reuse count and the actual state of environmental impact.

[0148] After adjustment, the updated loss threshold and repetition count are uniquely associated with the component's digital twin and synchronized to the full lifecycle information of the component's digital information database. This serves as the exclusive judgment parameter for the component's subsequent reuse screening, level determination, and scenario matching. All adjustment records, environmental data, and calculation processes are archived to achieve traceability of parameter adjustment.

[0149] The core steps of the above-mentioned reuse demand entity matching the sustainability level and component demand attributes corresponding to the demand components from the component digital information database based on the built-in demand components also include the following steps:

[0150] The reuse demand body is a digital demand model corresponding to the reuse scenario in the reuse scenario library. It pre-stores the core feature information of the required components needed to complete the construction of the scenario. From the built-in data of the reuse demand body, three core quantifiable features of the required components are accurately extracted: component name, component image, and component material. The component name is a standardized architectural component designation, such as truss, structural column, crane beam, window frame, red brick, etc., serving as the basic basis for component type identification. The component image is a 3D outline, 2D dimension, or structural construction image of the required component, containing geometric feature information such as the component's shape, size, and structural layout, serving as the core basis for shape matching. The component material is the material type and core material parameters of the required component, such as Q235 steel structure, C30 concrete, clay red brick, aluminum alloy, etc., serving as the core basis for material property matching. These three core features form the basic identification dimensions of the required components, providing a clear matching basis for subsequent multi-dimensional initial screening.

[0151] Using the extracted component name, component graphic, and component material as independent matching dimensions, feature matching is performed on all digital twins in the component digital information database to obtain temporary matching sets for the three dimensions. Then, a temporary set of components is constructed by taking the intersection of sets, thereby achieving multi-dimensional initial screening of digital twins and accurately narrowing the matching range. The specific implementation process is as follows:

[0152] Name dimension matching: The component name of the required component is accurately compared with the standardized component name of the digital twin in the component digital information database. All digital twins with the same name are matched to form the first temporary set. This set realizes the basic screening of component type and removes digital twins that do not match the required component type.

[0153] Graphical dimension matching: Using a graphic feature similarity algorithm, the component graphics of the required component are compared with the three-dimensional geometric graphics and structural construction graphics associated with the digital twin at the pixel level in terms of outline, size and structure. The graphic feature similarity is calculated, and all digital twins with similarity reaching the preset graphic threshold are matched to form a second temporary set. This set realizes the screening of component geometric features and eliminates digital twins with excessive deviations from the shape and size of the required component.

[0154] Material dimension matching: The component material and core material parameters of the required component are compared with the material feature data of the digital twin. All digital twins with the same material type and material parameters within the preset deviation range are matched to form a third temporary set. This set enables the screening of component material characteristics and eliminates digital twins that do not match the material of the required component.

[0155] Constructing a temporary set: Perform set intersection processing on the first, second, and third temporary sets to extract the digital twins commonly contained in the three sets, thus constructing a component temporary set. This set is a collection of digital twins that simultaneously meet the three basic requirements of component name, shape, and material. This multi-dimensional initial screening significantly narrows down the scope of subsequent precise matching while ensuring the basic adaptability of the matching results.

[0156] The three core features of the required components—component name, component graphics, and component material—are structurally integrated to form the basic requirement attributes of the required components. These attributes serve as the core basis for subsequent precise screening. Simultaneously, a requirement strategy is pre-set. This strategy is a quantitative matching rule based on the requirement attributes. Its core is to quantitatively calculate and sort the digital twins in the temporary set of components according to their similarity or requirement degree with the requirement attributes. The similarity calculation targets comparable features such as component graphics and material parameters, while the requirement degree calculation targets hard indicators such as component performance and dimensions. The specific screening process is as follows:

[0157] Quantitative matching degree calculation: Based on the preset demand strategy, for each digital twin in the temporary set of components, calculate the similarity or requirement degree between it and the basic demand attributes of the demand component to obtain the quantitative matching value of each digital twin. The higher the matching value, the better the adaptability of the digital twin to the demand component.

[0158] Sort and filter digital twins: Sort the digital twins in the temporary component set from highest to lowest according to their quantitative matching values. Select the top N digital twins as the precise re-screening results, and ensure that the number of selected digital twins is strictly less than the preset requirement matching number. The preset requirement matching number is the maximum number of selectable digital twins set according to the component usage requirements of the reuse scenario. By selecting the top N digital twins with a number less than this value, the re-screening results are guaranteed to be the digital twins in the temporary component set with the best adaptability to the requirement attributes, achieving an upgrade from "basic adaptation" to "precise adaptation" in the filtering process.

[0159] Based on multiple digital twins with optimal fit obtained through precise re-screening, the sustainability level corresponding to the required components is scientifically determined through two methods: selecting the average value or retaining all of them. This ensures that the sustainability level highly matches the actual fit requirements of the required components. The specific selection rules are as follows:

[0160] Average value selection method: Extract the sustainability level of all digital twins after precise screening, convert each level into a calculable value according to the preset level quantification rules, such as S level is 5, A level is 4, B level is 3, and so on. Calculate the arithmetic mean of all values ​​to obtain the average sustainability level. Then, based on the average sustainability level, match the corresponding standardized sustainability level and use this level as the sustainability level corresponding to the required component. This method is suitable for reuse scenarios where there are uniform requirements for component levels, achieving standardized adaptation of levels.

[0161] Full retention method: The sustainability level of all digital twins after precise screening is fully retained, and multiple sustainability levels are used together as the sustainability level corresponding to the required components. This method is suitable for reuse scenarios where there are no uniform requirements for component levels and multiple levels of components can be used in combination, so as to achieve diversified adaptation of levels.

[0162] The two selection methods can be automatically selected based on the scenario requirements of the reuse demand entity, or manually set by staff according to actual engineering requirements, ensuring the flexibility and adaptability of sustainability level selection.

[0163] After precise screening, the core characteristic attributes of all digital twins are integrated to form component requirement attributes corresponding to the required components. These component requirement attributes are not characteristic attributes of a single digital twin, but rather a set of characteristic attributes of multiple optimally matched digital twins, covering multi-dimensional information such as the component's geometric features, material features, physical features, and sustainability level features. Simultaneously, feature extraction and summarization are performed on the integrated component requirement attributes, retaining the common features of all digital twins and annotating differentiated features. This ensures that the component requirement attributes reflect both the basic adaptation requirements of the required components and the characteristic differences of the actual available digital twins, providing a comprehensive and accurate attribute basis for subsequent matching of digital twins from the corresponding sustainability level.

[0164] The core steps in calculating the matching degree between digital demand and reuse demand also include the following steps:

[0165] From the digital models of the reuse demand body and the digital demand body, full-size 3D graphics of both are extracted. The 3D graphics of the reuse demand body are defined as the first graphics, which represent the ideal 3D space and structural requirements of the component combination for the target reuse scenario and serve as the benchmark for matching degree calculation. The 3D graphics of the digital demand body are defined as the second graphics, which represent the 3D space and structural form formed after the actual matching building component digital twins are combined and serve as the object to be tested for matching degree calculation.

[0166] The core of this step is calculating the degree to which the second graphic accommodates the first graphic. This indicator characterizes the extent to which the first graphic can be placed within the second graphic without interference, and the degree of accommodation is strictly inversely correlated with the degree of interference between the first and second graphics; that is, the lower the degree of interference, the greater the space for the first graphic to be placed without interference within the second graphic, and the higher the degree of accommodation. If the first and second graphics have no interference whatsoever, the degree of accommodation is 100%; if they completely interfere, the degree of accommodation is 0%. The specific calculation process is as follows:

[0167] The first and second graphics are imported into the 3D spatial comparison engine, and the spatial coordinates of the two graphics are aligned. The core spatial reference of the reuse scene is used as the origin to unify the 3D coordinate system of the two graphics and ensure the accuracy of the comparison.

[0168] Using a 3D spatial collision detection algorithm, the two aligned graphics are subjected to full-dimensional collision detection to identify the spatial interference region between the first and second graphics. The core parameters such as the volume, area, and spatial position of the interference region are calculated, and the degree of interference is quantified based on these parameters and expressed as a percentage. The higher the value, the more severe the interference.

[0169] The final tolerance level is calculated according to the preset formula: Tolerance Level = 100% - Interference Level × Weighting Coefficient. The weighting coefficient is a fixed value preset according to the scene type (value 0-1), which is used to correct the influence weight of the interference level on the tolerance level under different scenes. The calculated tolerance level result is presented in the form of a percentage, with a value range of 0%-100%, which is convenient for subsequent unified weighted calculation with the simulation pass rate.

[0170] Based on the accommodability level, the simulation pass rate is further calculated from the perspectives of spatial adaptation and construction feasibility. This index represents the degree to which the actual matched component combination (second graphic) meets the building construction specifications and structural safety requirements after being optimally positioned in the target scene (first graphic). Furthermore, the simulation pass rate is strictly inversely correlated with the number of errors and warnings output by the BIM model; that is, the fewer the errors and warnings output by the BIM model, the more the spatial layout and structural matching of the component combination conforms to the actual engineering requirements, and the higher the simulation pass rate. If there are no errors or warnings, the simulation pass rate is 100%; if the number of errors and warnings exceeds a preset threshold, the simulation pass rate is 0%. The specific implementation process is as follows:

[0171] Matching the optimal accommodation position: Based on the above three-dimensional spatial collision detection results, the optimal spatial position that can completely accommodate the first graphic is selected from the non-interference area of ​​the second graphic. This position must meet the spatial layout requirements of the target scene and the stress requirements of the components, and have the smallest deviation from the reference coordinates of the first graphic, so as to ensure that it is the most reasonable component placement and combination position in actual engineering construction.

[0172] BIM Model Import and Simulation Deployment: The first and second graphics after alignment with coordinates, as well as the optimal accommodation location information obtained by matching, are synchronously imported into the preset BIM model. This BIM model is a professional building information model equipped with building construction specifications, structural safety standards, and component installation process requirements, covering core functions such as structural mechanics verification, spatial layout compliance detection, and construction procedure feasibility verification.

[0173] Simulation testing and result statistics: The BIM model is deployed from the first drawing to the second drawing based on the optimal accommodation position according to the actual construction logic of the building project. The entire process is digitally simulated and tested. At the same time, the error information (such as structural stress failure, spatial layout violation of construction specifications, insufficient component installation space and other hard violation information) and early warning (such as component gap too small, uneven stress distribution and other potential risk information) output during the simulation are recorded in real time. The number of errors and early warnings is also statistically analyzed.

[0174] Simulation pass rate quantification: Based on preset quantification rules and combined with the statistical number of errors and warnings, the simulation pass rate is calculated. Specifically, the preset error and warning threshold is used. If the statistical number is less than or equal to the threshold, the simulation pass rate is calculated using the formula: Simulation pass rate = 100% - (Actual statistical number / Error and warning threshold) × 100%. If the statistical number is greater than the threshold, the simulation pass rate is directly recorded as 0%. The result is also presented as a percentage, with a value range of 0%-100%.

[0175] Using the calculated capacity and simulation pass rate as core calculation indicators, a weighted average algorithm is employed to calculate the final digital matching degree between the digital demand body and the reuse demand body. This fully considers the spatial adaptability and engineering feasibility of component combinations, ensuring that the matching degree result comprehensively reflects the actual adaptability of the two. The specific calculation rules are as follows:

[0176] Preset differentiated weighting coefficients: Based on the type of reuse scenario, preset differentiated weighting coefficients are used for the capacity and simulation passability, and the sum of the two weighting coefficients is 1. Specifically, for scenarios with higher requirements for spatial layout, such as landscape creation and interior decoration, the weighting coefficient for capacity is increased (e.g., capacity weight 0.6, simulation passability weight 0.4); for scenarios with higher requirements for construction specifications and structural safety, such as structural construction and engineering construction, the weighting coefficient for simulation passability is increased (e.g., capacity weight 0.3, simulation passability weight 0.7), to achieve precise matching of weights with the actual needs of the scenario;

[0177] Weighted calculation of final matching degree: The final result is obtained by calculating the unified formula: Digital matching degree = accommodation degree × accommodation weight coefficient + simulation pass degree × simulation weight coefficient. The result is presented as a percentage, with a value range of 0%-100%. The higher the value, the better the adaptability between the digital demand body and the reuse demand body, and the higher the spatial fit and engineering feasibility of the actual component combination applied to the target reuse scenario.

[0178] Result verification and storage: The calculated numerical matching degree is verified for reasonableness to ensure that the result is within the effective range of 0%-100%. At the same time, all calculated data such as the degree of containment, the degree of simulation pass, the weight coefficient, and the final numerical matching degree are associated and stored with the corresponding numerical demand body and reuse demand body to achieve traceability of the matching degree calculation process.

[0179] After the final digital matching degree calculation is completed, the result can be compared with the preset reference matching degree to determine whether to output the digital requirement body and retrieve the corresponding reuse component.

[0180] The existing building component reuse and adaptation method based on multimodal features in this application also includes the following steps:

[0181] After the digital demand entity is built, the sustainability level corresponding to all digital twins in the digital demand entity is accurately extracted from the component digital information database, and this type of sustainability level is uniformly defined as the demand sustainability level. The demand sustainability level directly reflects the actual reuse value, structural integrity, and performance retention of all reusable components in the digital demand entity, and is the core quantitative basis for subsequent dynamic adjustment of reference matching degree. All extracted demand sustainability levels will be standardized and organized, and the levels in text / level symbol form (such as S / A / B / C level, 1 / 2 / 3 / 4 level) will be converted into a calculable numerical form according to the preset level quantification rules to ensure the accuracy of subsequent numerical calculations. For example, S level is set to 5, A level to 4, B level to 3, C level to 2, and D level to 1. After the level quantification conversion is completed, a demand sustainability level numerical set is formed, which provides a data basis for the subsequent calculation of average value and sum value.

[0182] To adjust the reference matching degree, two optional calculation methods are provided: average calculation and sum calculation. Staff can manually select the method based on the actual needs of the reuse scenario, or the calculation method can be automatically matched based on the scenario type (such as structural construction, landscape creation, or interior decoration). Both methods use quantitative calculations to obtain a ratio reflecting the overall level of demand sustainability. The specific implementation process is as follows:

[0183] Average calculation method: Obtain the first ratio:

[0184] If the average calculation method is chosen, the arithmetic mean of the compiled set of demand sustainability level values ​​is calculated using the formula: Demand Level Average = Sum of all demand sustainability level values ​​ / Number of demand sustainability level values. This yields the average demand level, reflecting the overall average level of component sustainability within the digital demand body. Simultaneously, a first demand reference value matching the component type and scenario requirements is retrieved from a pre-set level parameter library. This value represents the ideal average reference value for component sustainability in the corresponding scenario, determined by building industry reuse standards and scenario performance requirements, and using the same quantitative standards as the demand sustainability levels. Subsequently, a first ratio is calculated using the formula: First Ratio = Average Demand Level / First Demand Reference Value. This ratio directly reflects the deviation between the average level of component sustainability in the digital demand body and the ideal reference level. The smaller the first ratio, the lower the overall sustainability level of the components, and the worse their actual reuse value.

[0185] Sum calculation method: Obtain the second ratio:

[0186] If the summation calculation method is chosen, the collected set of demand sustainability level values ​​is summed. The sum of demand levels, calculated using the formula: Demand Level Sum = Sum of All Demand Sustainability Level Values, yields a demand level sum that reflects the overall sustainability level of components within the digital demand entity. This value is related to the number of components and the level of each individual component; the more components and the higher their levels, the larger the demand level sum. Simultaneously, a second demand reference value is retrieved from a pre-set level parameter library. This value represents the ideal sum of component sustainability levels for the corresponding scenario, determined by the required number of components and the ideal level, using the same quantitative standard as the demand sustainability level. Subsequently, a second ratio is calculated using the formula: Second Ratio = Demand Level Sum / Second Demand Reference Value. This ratio directly reflects the deviation between the overall sustainability level of components in the digital demand entity and the ideal reference level. The smaller the second ratio, the lower the overall sustainability level of the components, and the worse their actual reuse value.

[0187] The first and second ratios obtained by the two calculation methods mentioned above are both dimensionless quantitative indicators, with a value range of 0 to +∞. The two methods are mutually exclusive, and only one is selected for execution to ensure the uniqueness of the ratio calculation and the accuracy of the reference matching degree adjustment.

[0188] The calculated first or second ratio is used as the adjustment basis to dynamically adjust the preset reference matching degree in a negative correlation. The core adjustment logic is as follows: the smaller the ratio, the lower the overall sustainability level of the components in the digital demand body, and the worse the actual reuse value. Therefore, the matching judgment standard is appropriately lowered, i.e., the reference matching degree is reduced. The larger the ratio, the higher the overall sustainability level of the components, and the better the actual reuse value. Therefore, the matching judgment standard is maintained or slightly increased to ensure matching quality. If the ratio is equal to 1, it means that the sustainability level of the components is consistent with the ideal reference level, and the reference matching degree remains unchanged at the initial preset value.

[0189] During the specific adjustment process, a pre-set negative correlation adjustment rule between grade and matching degree is implemented. This rule sets the adjustment ratio and lower limit of the reference matching degree according to the numerical range of the ratio, ensuring that the adjusted reference matching degree not only conforms to the actual grade of the component but also does not fall below the safety baseline for the reuse of the building project. The specific implementation requirements are as follows:

[0190] Quantitative adjustment of the ratio: Set a fixed adjustment coefficient (value 0-1), and calculate the adjusted reference matching degree by formula: reference matching degree adjustment value = initial reference matching degree × (1 - (1 - ratio) × adjustment coefficient), so as to achieve smooth and quantitative adjustment of the reference matching degree; if the ratio > 1, the adjusted reference matching degree shall not exceed 1.2 times the initial reference matching degree, so as to avoid qualified components being mistakenly screened due to excessively high judgment standards;

[0191] Set adjustment lower limit: Preset the minimum threshold of reference matching degree for various scenarios, such as 70% for structural construction scenario and 50% for landscape creation scenario. If the reference matching degree is lower than the minimum threshold after negative correlation adjustment, the reference matching degree will be directly set to the corresponding minimum threshold to ensure that even if the sustainability level of the component is low, its matching judgment still meets the basic safety and adaptability requirements of building reuse.

[0192] Results Synchronization and Storage: The adjusted final reference matching degree is used as the exclusive reference matching degree for the matching judgment between the digital demand body and the reuse demand body. It is synchronized to the matching degree comparison module. At the same time, information such as demand persistence level, calculated average / sum value, quantification ratio, adjustment process and results are associated and stored with the digital demand body to achieve full traceability of the reference matching degree adjustment process.

[0193] After the dynamic adjustment of the reference matching degree is completed, the digital matching degree of the digital demand body and the reuse demand body is compared with the dedicated reference matching degree to determine whether to output the digital demand body and retrieve the corresponding reuse component.

[0194] This application also discloses an existing building component reuse adaptation system based on multimodal features, including a processor, wherein the processor executes the steps of the existing building component reuse adaptation method based on multimodal features as described in any of the above embodiments.

Claims

1. A method for adapting and reusing existing building components based on multimodal features, characterized in that, Includes the following steps: Based on the obtained component entry instruction, extract the component attributes corresponding to the component entry instruction, call the corresponding acquisition module based on the component attributes, collect multimodal component data corresponding to the component attributes, construct a digital twin of the reusable component based on the multimodal component data, and save the digital twin to the component digital information database. The system calls a pre-defined deep learning model to analyze the component digital information database, and clusters the digital twins in the component digital information database based on a pre-defined sustainability level to obtain multiple reuse cluster combinations. Obtain the preset reuse scenario interface, and establish a connection between the component digital information library and the reuse scenario library corresponding to the reuse scenario interface based on the reuse scenario interface; activate the reuse demand body in the reuse scenario library, and the reuse demand body matches the sustainability level and component demand attribute corresponding to the demand component from the component digital information library based on the built-in demand component. Based on the sustainability level, the system requests the corresponding reuse cluster combination from the component digital information database, matches the digital twin corresponding to the component requirement attributes from the retrieved reuse cluster combination, and builds a digital requirement body from the matched digital twin. The matching degree between the digital demand body and the reuse demand body is calculated as the digital matching degree. If the digital matching degree is greater than the preset reference matching degree, the digital demand body is output and the reuse component corresponding to the digital twin is retrieved.

2. The method for adapting and reusing existing building components based on multimodal features according to claim 1, characterized in that, The step of collecting multimodal component data corresponding to component attributes and constructing a digital twin of the reusable component based on the multimodal component data also includes the following sub-steps: The three-dimensional scanning device is used to scan the reusable component to obtain three-dimensional data, the stress monitoring device is used to detect the reusable component to obtain stress data, and the material analysis device is used to analyze the reusable component to obtain material data. Multimodal construction data is constructed based on the three-dimensional data, stress data, and material data. The system retrieves the original factory data corresponding to the reused component data from a pre-defined original component database. The original factory data includes original geometric features, original physical features, original material features, and rated time limit. The system calculates the three-dimensional loss degree based on the three-dimensional data and original geometric features, the stress loss degree based on the stress data and original physical features, and the material loss degree based on the material data and original material features. The system extracts the usage time of the reused component from the component attributes and calculates the time loss degree based on the usage time and rated time limit. The component loss is calculated based on the three-dimensional loss, stress loss, material loss, and duration loss. If the component loss exceeds the preset loss threshold, a reuse loss warning is issued, and the multimodal construction data is marked as excessively lost.

3. The method for adapting and reusing existing building components based on multimodal features according to claim 2, characterized in that, The step of calculating the component loss based on the three-dimensional loss degree, stress loss degree, material loss degree, and time loss degree also includes the following sub-steps: The three-dimensional loss degree, stress loss degree, material loss degree, and duration loss degree are all percentage values, and the component loss degree is calculated using a weighted average algorithm; the three-dimensional loss degree, stress loss degree, material loss degree, and duration loss degree have a one-to-one corresponding three-dimensional weight, stress weight, material weight, and duration weight; If the 3D loss is greater than the set 3D threshold, the 3D weights are adjusted according to the positive correlation between the 3D loss and the 3D threshold. If the stress loss is greater than the set stress threshold, the three-dimensional weights are initialized, and then the stress weights are adjusted according to the positive correlation between the stress loss and the stress threshold. If the material loss is greater than the set material threshold, the three-dimensional weight and stress weight are initialized, and the material weight is adjusted according to the positive correlation between the material loss and the material threshold. If the duration loss is greater than the set duration threshold, the 3D weight, stress weight, and material weight are initialized, and the duration weight is adjusted according to the positive correlation between the duration loss and the duration threshold.

4. The method for adapting and reusing existing building components based on multimodal features according to claim 1, characterized in that, The step of clustering digital twins in the component digital information database based on a preset sustainability level also includes the following sub-steps: The process involves acquiring the sustainability level information, identifying sustainability classification requirements based on the sustainability level information, mapping the sustainability classification requirements to the multimodal construction data of the reusable components, and determining the sustainability level of the digital twin based on the correspondence between the multimodal construction data and the sustainability classification requirements. The lower the sustainability level, the lower the integrity of the digital twin during reuse; the higher the sustainability level, the higher the integrity of the digital twin during reuse. Obtain the number of times the digital twin has been reused; if the number of reuses exceeds the preset number of repetitions, a multiple reuse prompt will be given, and the reuse ratio will be calculated based on the number of reuses and the number of repetitions. The sustainability level of the digital twin will be adjusted based on the negative correlation between the reuse ratio and the reuse ratio.

5. The method for adapting and reusing existing building components based on multimodal features according to claim 4, characterized in that, The step of clustering digital twins in the component digital information database based on a preset sustainability level also includes the following sub-steps: The digital twin obtains environmental data during its use from a preset environmental database; the environmental database contains environmental sensing data collected and generated by preset environmental sensors within a recorded time period, and the regional environmental data is obtained based on a preset filtering algorithm. Based on a preset environmental reference template, standard environmental data and abnormal environmental data are separated from the environmental data. The intensity and duration of the standard environmental data are calculated as the standard environmental intensity and standard environmental duration, and the intensity and duration of the abnormal environmental data are calculated as the abnormal environmental intensity and abnormal environmental duration. The total environmental duration is calculated based on the standard environmental duration and the abnormal environmental duration. Basic environmental data is obtained, and the basic environmental comparison value is calculated based on the total environmental duration and the basic environmental intensity. The standard environment comparison value is calculated based on the standard environment duration and standard environment intensity; the abnormal environment comparison value is calculated based on the abnormal environment duration and abnormal environment intensity; and the comprehensive environment comparison value is calculated based on the standard environment comparison value and the abnormal environment comparison value. The ratio of the comprehensive environmental comparison value to the basic environmental comparison value is called the environmental ratio. The sustainability level of the digital twin is adjusted based on the negative correlation with the environmental ratio.

6. The method for reusing and adapting existing building components based on multimodal features according to claim 2 or 4, characterized in that, The method also includes the following steps: The digital twin obtains environmental data during its use from a preset environmental database; the environmental database contains environmental sensing data collected and generated by preset environmental sensors within a recorded time period, and the regional environmental data is obtained based on a preset filtering algorithm. Based on a preset environmental reference template, standard environmental data and abnormal environmental data are separated from the environmental data. The intensity and duration of the standard environmental data are calculated as the standard environmental intensity and standard environmental duration, and the intensity and duration of the abnormal environmental data are calculated as the abnormal environmental intensity and abnormal environmental duration. The total environmental duration is calculated based on the standard environmental duration and the abnormal environmental duration. Basic environmental data is obtained, and the basic environmental comparison value is calculated based on the total environmental duration and the basic environmental intensity. The standard environment comparison value is calculated based on the standard environment duration and standard environment intensity; the abnormal environment comparison value is calculated based on the abnormal environment duration and abnormal environment intensity; and the comprehensive environment comparison value is calculated based on the standard environment comparison value and the abnormal environment comparison value. The ratio of the comprehensive environmental comparison value to the basic environmental comparison value is called the environmental ratio. If a reuse loss warning has been issued, the loss threshold will be adjusted based on the negative correlation with the environmental ratio. If the prompt has been used multiple times, the number of repetitions will be adjusted according to the negative correlation between the environmental ratio and the prompt.

7. The method for adapting and reusing existing building components based on multimodal features according to claim 1, characterized in that, The step of reusing the demand body to match the sustainability level and component demand attributes corresponding to the demand components from the component digital information database based on the built-in demand components also includes the following sub-steps: Obtain the component name, component graphic, and component material of the required component; Multiple digital twins are matched from the component digital information database based on the component name as the first temporary set; multiple digital twins are matched from the component digital information database based on the component graphic as the second temporary set; multiple digital twins are matched from the component digital information database based on the component material as the third temporary set; the set of the first temporary set, the second temporary set, and the third temporary set is taken as the component temporary set; Based on the component name, component graphic, and component material composition requirements, multiple digital twins that meet the preset requirements strategy are matched from the temporary set of components according to the requirements attributes. The number of multiple matched digital twins is less than the preset requirement matching number. The average sustainability level of multiple digital twins is calculated as the sustainability level average, and the sustainability level corresponding to the demand component is selected based on the average sustainability level; or, the sustainability level corresponding to the demand component includes the sustainability levels of multiple digital twins. The sustainability levels corresponding to the demand components include the demand attributes of multiple digital twins.

8. The method for adapting and reusing existing building components based on multimodal features according to claim 1, characterized in that, The step of calculating the matching degree between digital demand and reuse demand also includes the following sub-steps: The three-dimensional graphic of the reuse demand object is used as the first graphic, and the three-dimensional graphic of the digital demand object is used as the second graphic. The degree of accommodation of the second graphic with respect to the first graphic is calculated. The degree of accommodation is inversely correlated with the degree of interference between the first graphic and the second graphic. The first graphic is placed in the second graphic to obtain its location. After placing the first graphic in the location, the simulation pass rate is calculated based on the preset BIM model. The simulation pass rate is inversely correlated with the number of error messages and warning prompts output by the BIM model. The matching degree is calculated based on the capacity and the degree of simulation.

9. The method for adapting and reusing existing building components based on multimodal features according to claim 8, characterized in that, The method also includes the following steps: The sustainability level of the digital twin corresponding to the digital demand body is obtained as the demand sustainability level. The average value is calculated based on the demand sustainability level as the demand level average value. The first ratio is calculated based on the demand level average value and the preset first demand reference value. The reference matching degree is adjusted based on the negative correlation of the first ratio. Alternatively, the sustainability level of the digital twin corresponding to the digital demand entity can be obtained as the demand sustainability level. The sum of the demand sustainability level is calculated as the demand level sum value. The second ratio is calculated based on the demand level sum value and the preset second demand reference value. The reference matching degree is adjusted based on the negative correlation of the second ratio.

10. A reuse adaptation system for existing building components based on multimodal features, characterized in that, Includes a processor, wherein the steps of the existing building component reuse adaptation method based on multimodal features as described in any one of claims 1-9 are executed.

Images