Fabricated green building design method

Abstract

The invention relates to the technical field of computer aided design, in particular to a fabricated green building design method, which comprises the following steps of: creating three-dimensional geometric representation for fabricated components, setting physical attributes of selected materials of green building materials, and associating environmental product declaration data and unit construction cost data of the components; in the design process, a fabricated component is used as a core object, three-dimensional geometric representation is constructed, physical parameters of green building materials are embedded, environmental product declaration data and unit construction cost are combined, and structured component information with environmental and economic double attributes is formed; and thus, material traceability and resource evaluation collaboration of building design are realized. The component information serves as a basic unit in a three-dimensional space to be used for building a building model, a spatial topology is constructed according to the connection relation between the components, collaborative simulation analysis of overall energy consumption and cost is achieved through data embedded by the components, and the energy economic evaluation capacity of a design scheme is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and in particular to a method for designing assembled green buildings. Background Art

[0002] Computer-aided design refers to the technical means of using computer systems to design, model, analyze and optimize products.

[0003] Existing technologies often struggle to integrate design results with environmental data and monitoring information from the usage phase. This leads to a disconnect between building models and actual operational conditions, making it difficult to support subsequent energy consumption assessments, maintenance plan optimization, or fault prediction. For example, in the design of prefabricated components, relying solely on traditional modeling processes makes it difficult to effectively assess the impact of component material selection on carbon emissions or operating costs. This lack of data often leads to delayed structural diagnosis and maintenance responses, reducing the overall performance and sustainability of the building. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a prefabricated green building design method.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solution, a prefabricated green building design method, comprising the following steps:

[0006] Create a 3D geometric representation of the prefabricated component and set the physical properties of the green building materials selected, associate the component's environmental product declaration data with the unit construction cost data, and establish the environmental cost entry for the structural component;

[0007] Based on the structural component environmental cost item, the item is retrieved in three-dimensional space to perform unitized construction of the prefabricated building model and set the connection relationship between the components, and the geometric layout of the assembly unit is obtained. Based on the geometric layout of the assembly unit, the overall energy consumption simulation calculation of the building is performed according to the environmental data and cost data embedded in the item to obtain the comprehensive environmental cost value of the assembly;

[0008] Referring to the comprehensive environmental cost value of the assembly, a target component is selected in the digital twin base model for iterative adjustment of design parameters to generate key component adjustment parameters. Based on the key component adjustment parameters, expected maintenance cycle information is assigned to the component and identification of vulnerable parts is recorded to form optimized component life cycle characteristics;

[0009] Based on the optimized component life cycle characteristics, the input information type of fault diagnosis is defined for the key components of prefabricated green buildings, and component diagnosis access points are established. Based on the component diagnosis access points, the diagnostic information judgment rules are integrated and mapped and associated, and the building health status diagnostic index is output.

[0010] Preferably, the steps for obtaining the environmental cost items of the structural components are:

[0011] Retrieving component identification information of the prefabricated component, calling a standard geometric configuration template and a set of geometric boundary parameters corresponding to the component identification information, generating a three-dimensional geometric representation of the prefabricated component in three-dimensional modeling, and obtaining a three-dimensional geometric representation;

[0012] Based on the three-dimensional geometric representation, the material code of the green building material is called, the physical parameter list is matched through the material code, the density, thermal conductivity and heat capacity items in the physical parameter list are embedded in the three-dimensional geometric representation component attributes, and the environmental product declaration data and unit construction cost data corresponding to the material are loaded at the same time to generate a component modeling object with attribute information;

[0013] Based on the component modeling object with attribute information, the carbon emission factor, energy consumption value and pollutant emission value in the environmental product declaration data are extracted, and the component unit price, assembly labor cost and transportation cost in the unit construction cost data are combined. All extracted items are structured and integrated to generate structured component environmental cost items.

[0014] Preferably, the steps of obtaining the geometric layout of the assembly unit are:

[0015] In the three-dimensional space modeling, the component geometric representation parameters, physical property parameters and component number information in the structural component environmental cost item are called, the component placement order and spatial distribution order are set according to the component number information, and the initial layout of the component combination is generated;

[0016] Based on the initial layout of the component combination, the geometric boundary positions, connection node coordinates and installation direction information between adjacent components are extracted, contact determination and direction correction are performed on the physical contact surfaces between all components, connection relationships between components are set and node binding is completed, and a connection layout model of the assembly components is generated;

[0017] Based on the assembly component connection layout model, the geometric boundary coverage and spatial topological structure between components are analyzed, the spatial arrangement path is traced through the component connection sequence and a three-dimensional layout structure is formed to obtain the assembly unit geometric layout.

[0018] Preferably, the steps for obtaining the comprehensive value of the environmental cost of the assembly are:

[0019] Based on the geometric layout of the assembly unit, the spatial volume, unit energy consumption value, unit carbon emission value, unit construction cost value, life cycle maintenance frequency and life cycle number of each component are extracted to generate the basic parameter set required for the annualized cost of the component;

[0020] According to the basic parameter set required for the annualized cost of the component, the energy unit price and the carbon emission unit price are respectively called, and the unit energy consumption value and the unit carbon emission value are multiplied by the corresponding unit price to form a monetary expression. The unit construction cost and the carbon emission monetary value are annualized according to the life cycle years, and the annualized unit volume cost is multiplied by the component volume to form the annualized total cost set of the component;

[0021] Based on the annualized total cost set of the components, a comprehensive environmental cost value of the assembly is calculated.

[0022] Preferably, the steps for obtaining the key component adjustment parameters are:

[0023] Based on the comprehensive environmental cost value of the assembly, all components in the prefabricated building model are sorted from high to low according to the environmental cost ratio of the components, and components with an environmental cost ratio higher than a set threshold are selected as a candidate component set to generate a preliminary list of target components;

[0024] Based on the preliminary list of target components, the design parameter set of each component in the digital twin basic model is called, and the component size parameters, material parameters, and connection method parameters are extracted item by item. In combination with the stress state and position distribution in the application scenario, the adjustable design parameters of the individual components are determined to obtain the parameter set to be adjusted for the target component;

[0025] Based on the set of parameters to be adjusted for the target component, the adjustment cycle and simulation step are set, and each design parameter is recursively simulated according to the step size within the adjustable range. The design parameter combination that meets the conditions for reducing the comprehensive value of the environmental cost of the assembly is screened, and the adjustment parameters of the key components are generated.

[0026] Preferably, the steps for obtaining the optimized component lifecycle characteristics are:

[0027] Based on the component type, material properties, and connector information listed in the key component adjustment parameters, retrieve the failure timestamps within three years from all historical maintenance records by component number, calculate the time interval sequence between two adjacent failures of each component, and if the standard deviation of the sequence is less than 85% of the mean standard deviation of the period intervals of all components, mark the component as a stable failure component and generate a stable failure component number list;

[0028] Calculate the expected maintenance period of the components based on the stable fault component number list;

[0029] Based on the expected maintenance cycle, the component numbers whose expected maintenance cycle is lower than the lower limit of the component standard maintenance cycle are screened from the stable fault components, the component spatial position identification, material marking and connection node number information are extracted, and the optimized component life cycle characteristics are generated.

[0030] Preferably, the steps for obtaining the component diagnosis access point are:

[0031] Based on the optimized component life cycle characteristics, the monitoring requirements corresponding to different stages in the component performance degradation path are analyzed, and the diagnostic requirement parameters are matched according to the material aging state, electrical transmission stability and microcrack growth rate to generate a list of fault diagnosis input information types;

[0032] Based on the fault diagnosis input information type list, the sensor interface type, the electrical signal acquisition format and the data timing protocol standard are matched to generate a component diagnosis access point.

[0033] Preferably, the steps for obtaining the building health status diagnostic index are:

[0034] According to the component diagnosis access point, the preset component fault feature identification rules, trend change identification rules and cross-component abnormality collaborative judgment rules are called, and the change indicators in the current monitoring data items are matched one by one. The status classification and marking are combined with the historical data backtracking analysis results to generate a component health judgment result set;

[0035] Based on the component health judgment result set, all recorded status categories are associated and mapped, the distribution ratio of each status category in the spatial dimension and time series is counted, the component failure correlation density and the frequency of abnormal changes in the overall building status are extracted, and a building health status diagnostic index is formed.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are:

[0037] During the design process, this invention uses prefabricated components as the core object. By constructing a three-dimensional geometric representation and embedding the physical parameters of green building materials, combined with environmental product declaration data and unit construction costs, this system creates structured component information with both environmental and economic attributes, thereby achieving material traceability and resource evaluation in architectural design. Component information serves as the fundamental unit for building models in three-dimensional space, and spatial topology is constructed based on the connections between components. The embedded component data enables collaborative simulation and analysis of overall energy consumption and costs, enhancing the energy-economic assessment capabilities of design solutions. A parameter iteration mechanism is further introduced into the design process, selecting key components and adjusting their design parameters based on environmental cost results. This is supplemented by setting maintenance cycles and incorporating identification of vulnerable parts to construct a lifecycle description with time dimensions and degradation characteristics, enriching the ability to perceive component performance evolution in the information dimension. Based on lifecycle characteristics, the input information types required for diagnosis are defined, and a data acquisition path is established. Fault data is analyzed by combining specific signal types with historical characteristic changes, outputting numerical indicators for building health status monitoring, enabling continuous and digitally visualized structural diagnosis. Enhancing the design's ability to integrate environmental loads, maintenance behaviors, and risk responses is effective in improving green building assessment efficiency, information flow, and reliability prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] See also Figure 1 The present invention provides a technical solution, a prefabricated green building design method, comprising the following steps:

[0041] Create a 3D geometric representation of the prefabricated component and set the physical properties of the green building materials selected, associate the component's environmental product declaration data with the unit construction cost data, and establish the environmental cost entry for the structural component;

[0042] Based on the environmental cost entry of the structural component, the entry is retrieved in three-dimensional space to carry out unit construction of the prefabricated building model and set the connection relationship between the components, and the geometric layout of the assembly unit is obtained. Based on the geometric layout of the assembly unit, the overall energy consumption of the building is simulated according to the environmental data and cost data embedded in the entry to obtain the comprehensive environmental cost value of the assembly;

[0043] Referencing the comprehensive environmental cost value of the assembly, target components are selected in the digital twin base model for iterative adjustment of design parameters. Key component adjustment parameters are generated. Based on these key component adjustment parameters, expected maintenance cycle information is assigned to the component and identification of vulnerable parts is recorded, forming optimized component lifecycle characteristics.

[0044] Based on the optimized component life cycle characteristics, the input information type of fault diagnosis is defined for the key components of prefabricated green buildings, and component diagnosis access points are established. Based on the component diagnosis access points, the diagnostic information judgment rules are integrated and mapped and associated, and the building health status diagnostic index is output.

[0045] The steps for obtaining the environmental cost items of structural components are:

[0046] Retrieving component identification information of the prefabricated component, calling a standard geometric configuration template and a set of geometric boundary parameters corresponding to the component identification information, generating a three-dimensional geometric representation of the prefabricated component in three-dimensional modeling, and obtaining a three-dimensional geometric representation;

[0047] Based on the 3D geometric representation, the material code of the green building material is called, and the physical parameter list is matched through the material code. The density, thermal conductivity and heat capacity items in the physical parameter list are embedded in the 3D geometric representation component attributes. At the same time, the environmental product declaration data and unit construction cost data corresponding to the material are loaded to generate a component modeling object with attribute information;

[0048] Based on the component modeling object with attribute information, the carbon emission factor, energy consumption value and pollutant emission value in the environmental product declaration data are extracted, and the component unit price, assembly labor cost and transportation cost in the unit construction cost data are combined. All extracted items are structured and integrated to generate structured component environmental cost items.

[0049] Specifically, the component identification information of the prefabricated component is retrieved. For example, a prefabricated wall panel component is coded as "PW-001-TypeA-L3000-W150-H2800", where "PW" represents prefabricated wall panel, "001" is the unique serial number within the project, and "TypeA" refers to a specific construction type or atlas reference.

[0050] "L3000-W150-H2800" directly gives the size information of 3000 mm in length, 150 mm in width and 2800 mm in height. Alternatively, the component identification information can be a simpler unique identification code such as "CompID-789012", which is associated with its type, standard drawing set number and detailed size parameters in the project component database. The system then accesses a pre-built and maintained standard geometric configuration template library based on the component type and specification information explicitly or implicitly included in the component identification information. This template library includes component prototypes commonly used by enterprises or industry standards, such as beams, columns, plates, walls and other types of parametric model files. These templates are stored in STEP or IFC format to ensure the universality and parametric driving capabilities of the model. For "PW-001-TypeA", the system will select the corresponding "TypeA" wall panel parametric template and select it from the component A set of geometric boundary parameters, i.e., a length of 3000 mm, a width of 150 mm, a height of 2800 mm, and possible reserved opening positions and size information, are extracted from the identification information or associated database. Subsequently, in a 3D modeling software environment (such as the Autodesk Revit API interface or the Bentley MicroStation development environment), this template is called programmatically, and the extracted geometric boundary parameter values are assigned to the corresponding parameters of the template. For example, the "length", "width", and "height" attributes of the template instance are set through the API function, thereby driving the template to generate component instances with specific sizes and shapes. This process may also include automatically generating detailed component structures according to the rules defined by the template, such as chamfers and placeholders for embedded parts, which are ultimately concretized into an accurate and operational component geometric model in the 3D digital space to obtain a 3D geometric representation of the prefabricated component.

[0051] Based on the three-dimensional geometric representation, that is, the digital component model containing precise geometric contours and dimensions generated in the previous step, the system further retrieves the material code of the preset green building material from the component basic information associated with the three-dimensional geometric representation. For example, for a specific precast concrete wall panel component, its material code may be set to "RC-C30-FA20-RG15", which represents the use of C30 strength grade concrete, in which the fly ash (FA) content is 20% and the recycled coarse aggregate (RG) replacement rate is 15%. The system uses this material code to match and search in the pre-established green building material physical parameter database, which contains the physical performance indicators of various building materials. After a successful match, the system automatically extracts the core thermal performance data from the physical parameter list corresponding to the "RC-C30-FA20-RG15" code, including the density of the material, such as 2450kg / m 3The material's thermal conductivity, for example, 1.65 W / (m·K), and its specific heat capacity, for example, 980 J / (kg·K), are extracted physical parameter values that are programmatically embedded into the component attribute set represented by the three-dimensional geometry, becoming essential technical parameters for the digital component. Simultaneously, the system also queries the associated Environmental Product Declaration database and unit construction cost database in parallel based on the same material code, "RC-C30-FA20-RG15." The Environmental Product Declaration database stores reports issued by certification bodies or provided by manufacturers, including the environmental impact potential of materials at various stages of their life cycle. The unit construction cost database contains cost composition information such as the material's purchase price, processing costs, and transportation costs. This information is typically sourced from historical corporate project data, market inquiries, or professional engineering cost information services. The system loads key environmental impact data corresponding to the material, such as carbon emissions per unit volume and total lifecycle energy consumption, and loads the corresponding unit volume or unit mass construction cost data from the cost database. Ultimately, all this information is integrated to generate a component modeling object with attribute information.

[0052] Based on the component modeling object with attribute information, which not only contains the component's three-dimensional geometry but also embeds the physical properties of its selected materials, Environmental Product Declaration (EPD) data, and unit construction cost data, the system begins to deeply extract and structure data integration. First, for environmental impact data, the system accurately extracts specific indicators that are critical to subsequent environmental performance evaluation from the loaded EPD data, including but not limited to the global warming potential value (i.e., carbon emission factor) per unit product, for example, recorded as 150.5kgCO2eq / m 3 The total primary energy consumption (energy consumption value) during the life cycle is recorded as 850MJ / m 3 , and specific types of pollutant emission values, such as acidification potential or eutrophication potential, if the EPD data provide such subdivisions, they will be extracted together, for example, volatile organic compound (VOC) emissions of 0.05g / m 3 Next, the system turns to cost data, decomposing and extracting various cost components from the loaded unit construction cost data, including the unit volume or unit mass procurement cost of the component material itself (component unit price). For example, the cost of concrete material is 600 yuan / m 3 The labor cost required for the prefabrication or on-site assembly of components may be calculated based on the type of work and working hours and then allocated to each component. For example, the labor cost for assembling prefabricated wall panels is 80 yuan / m 3 , and the unit transportation cost of transporting the components from the production site to the construction site. This cost is usually related to the transportation distance and component characteristics. For example, the transportation cost is 45 yuan / m 3After completing the extraction of environmental data and cost data respectively, the system will integrate all these extracted items, namely carbon emission factors, energy consumption values, pollutant emission values, component unit prices, assembly labor costs, and transportation costs, into a unified structured integration process. This means that data from different sources and dimensions will be organized and stored in a predefined standardized data format. For example, a data record containing fields such as component ID, carbon emission factors, energy consumption values, pollutant A emission values, component material unit prices, assembly labor costs, and transportation costs will be created to ensure data consistency, traceability, and ease of use, and ultimately generate structured component environmental cost entries.

[0053] The steps to obtain the geometric layout of the assembly unit are:

[0054] In the three-dimensional space modeling, the component geometric representation parameters, physical property parameters and component number information in the structural component environmental cost item are called, and the component placement order and spatial distribution order are set according to the component number information to generate the initial layout of the component combination;

[0055] Based on the initial layout of the component assembly, the geometric boundary positions, connection node coordinates and installation direction information between adjacent components are extracted. Contact determination and direction correction are performed on the physical contact surfaces between all components. The connection relationship between components is set and node binding is completed to generate the assembly component connection layout model.

[0056] Based on the assembly component connection layout model, the geometric boundary coverage and spatial topological structure between components are analyzed. The spatial arrangement path is traced through the component connection sequence to form a three-dimensional layout structure and obtain the assembly unit geometric layout.

[0057] Specifically, in three-dimensional space modeling, the system first calls the structural component environmental cost entries generated in the previous step. These entries are created independently for each prefabricated component and contain the unique component number information of the component, such as "CW-01-FL01-N-A03" represents the composite wall numbered A03 on the first floor facing north, as well as detailed component geometric representation parameters, such as the wall length of 3200 mm, height of 2800 mm, and thickness of 200 mm. It may also include the location and size of reserved door and window openings. At the same time, the entry also includes physical property parameters related to the component, such as density, thermal conductivity, etc. Although these physical property parameters are not directly involved in positioning in the initial layout stage, they are part of the complete component information. The system is based on a predefined assembly sequence list or construction logic extracted from the building information model (BIM), which lists in detail each component number and its target absolute position in the overall three-dimensional space. Coordinates (e.g., X=15000, Y=12000, Z=3000, in millimeters) and reference orientation (e.g., rotate 90 degrees around the Z axis), or the component number information itself may have embedded relative positioning logic such as floor and grid position. The system reads the component number information one by one and assigns these spatial positioning instructions to its associated component geometric representation parameters (i.e., 3D model or its generation parameters). For example, by calling the API function of the 3D modeling software, the geometric model instance of the component "CW-01-FL01-N-A03" is placed at the specified coordinate point (15000, 12000, 3000) and the specified rotation transformation is applied to ensure that its position and orientation in the virtual construction environment conform to the design intent. This process loads and places all the components to be built into the 3D scene in sequence, without considering the precise connection between them, and only focusing on their approximate spatial position and design order, thereby generating an initial layout of the component combination.

[0058] Based on the initial layout of the component combination, in which each prefabricated component has been initially placed in the three-dimensional scene according to the preset spatial coordinates and directions, the system then extracts and verifies the detailed interface information of the adjacent component pairs that are scheduled to be connected. This includes reading the specific areas for connection on their predefined geometric boundaries (such as beam ends, column node plates, and wall panel edges) from the structural component environmental cost items of each component or its associated detailed design library, extracting the precise three-dimensional coordinates of these connection interfaces and the specific coordinates of the connection nodes. For example, the center coordinates of the embedded bolt group at the end of a beam are (Xb, Yb, Zb) and their The normal vector of the flange and the center coordinates of the corresponding bolt hole on the column bracket connected to it are (Xc, Yc, Zc) and the normal vector of the contact surface. At the same time, the preset installation direction information of the component is extracted, such as the requirement that the flange of the beam must be kept horizontal. Subsequently, the system starts the physical contact surface analysis program for all planned connected component pairs. The program first performs Boolean operations or boundary representation (B-Rep) analysis on the solid models of the components to identify whether there is geometric interference or improper gap between the components. Specifically, if the volume intersection of the two component solid models is greater than a preset collision volume threshold, such as 1.0 cm 3 , a collision is determined to have occurred. The collision volume threshold is set based on the manufacturing tolerances of the components and the minimum adjustment space allowed for on-site installation. For example, for components with a manufacturing tolerance of ±2mm, the slight overlap that may occur when installed in the ideal position should not be considered a serious collision. Therefore, the threshold is set to a small value that can distinguish between design errors and acceptable tolerances. If a collision is determined to have occurred, or the relative position of the connection nodes or the installation orientation of the components do not meet the design requirements (for example, the normal vector deviation is greater than 0.5°), the system will perform orientation correction and position fine-tuning. For example, by minimizing the distance between the connection nodes or aligning key geometric features (such as axes and faces), the six-degree-of-freedom parameters (position and orientation) of one or both components are automatically adjusted until the connection conditions are met and the collision is eliminated. After the correction is completed, the system formally sets the connection relationship between the components at the data level. For example, it records that beam "B001" is connected to column "C005" through its "end node A" and may specify a connection type such as "high-strength bolt connection" to complete the node binding and generate the assembly component connection layout model.

[0059] Based on the assembly component connection layout model, which already contains the precise spatial position, posture and bound connection relationship information of all components, the system further performs in-depth analysis of the spatial interaction and overall structure between components. First, it calculates and analyzes the geometric boundary coverage between components by querying the geometric data of each component and the connection information of adjacent components. For example, for the connection between wall panels and floor slabs, the system calculates the coverage area of the actual contact between the top surface of the wall panel and the bottom surface of the floor slab or within the allowable construction gap, as well as any non-contact but related boundary features, such as the continuity or disconnection of the insulation layer and the waterproof layer. At the same time, the system constructs and analyzes the spatial topology of the entire assembly, which involves treating each component as a node and the physical connections between them (nodes bound by previous steps) as edges, thereby forming a graph network representing the connectivity of the building structure. This network clearly defines the structure. The adjacency relationship, support relationship and potential load transfer path between parts. For example, the system can identify that a main beam directly supports three secondary beams and transfers the load to the foundation through the columns at both ends. Subsequently, the system uses the established connection relationship between components and the component installation sequence (if defined) to start from one or more starting components (such as foundation components or core tube components), and gradually traverse the entire assembly by tracing these connection sequences and spatial adjacency relationships. This tracing process can identify the hierarchical relationship, dependency relationship and actual arrangement path of components in space. For example, starting from the foundation, the connection of columns, beams and plates is traced upward to form a bottom-up assembly path. This process summarizes the precise position, direction, connection method and spatial relationship of all components, and finally integrates this information to form a complete and detailed three-dimensional layout structure to obtain the assembly unit geometric layout.

[0060] The steps to obtain the comprehensive value of the environmental cost of an assembly are:

[0061] Based on the geometric layout of the assembly unit, the spatial volume, unit energy consumption value, unit carbon emission value, unit construction cost value, life cycle maintenance frequency and life cycle years of each component are extracted to generate the basic parameter set required for the annualized cost of the component;

[0062] According to the basic parameter set required for the annualized component cost, the energy unit price and carbon emission unit price are respectively called, and the unit energy consumption value and unit carbon emission value are multiplied by the corresponding unit price to form a monetary expression. The unit construction cost and carbon emission monetary value are annualized according to the life cycle years, and the annualized unit volume cost is multiplied by the component volume to form the annualized total cost set of the component;

[0063] Based on the annualized total cost set of components, the comprehensive environmental cost value of the assembly is calculated using the following formula:

[0064]

[0065] Among them, E is the comprehensive value of the environmental cost of the assembly, the unit is yuan / (m 3 ·year), Q k is the unit energy consumption value of the kth component (MJ / m 3 ), L k is the life cycle maintenance frequency of the kth component (times / year), q is the energy price (yuan / MJ), M k is the unit construction cost of the kth component (yuan / m 3 ), D k is the unit carbon emission value of the kth component (kgCO2 / m 3 ), d is the unit price of carbon emissions (yuan / kgCO2), T k is the life cycle of the kth component (years), V k is the spatial volume of the kth component (m 3 ), z is the total number of components.

[0066] Specifically, based on the geometric layout of the assembly unit, which is a digital expression of the precise three-dimensional models of all prefabricated components and their spatial positions and connection relationships generated in the previous step, the system starts the parameter extraction program for each independent component in this layout. First, for the spatial volume of the component, the system directly queries or calculates it from its three-dimensional geometric model. For example, a concrete wall panel component with an ID of "ConcreteWall-001" has a clear boundary in its three-dimensional model in the geometric layout of the assembly unit. Its actual occupied space volume is obtained through geometric operations (for example, integration of the closed body or calculation based on the parametric formula of its standard geometric shape), which is recorded as V k , for example 3.5m 3 Then, based on the unique identification information of the component, the system back-searches the environmental cost item database of the structural component established at the beginning of the project, and extracts the original certified data of the energy consumption per unit volume of the component that has been determined in the design and material selection stage. This is the unit energy consumption value Q k , for example 250MJ / m 3 Similarly, the original certified data of carbon emissions per unit volume of material is extracted from this entry, that is, the unit carbon emission value D k , for example 180kgCO2 / m 3 , and the basic data of the construction cost per unit volume of materials, including material costs, prefabrication processing fees, etc., which is the unit construction cost value M k , for example 750 yuan / m 3 , for the component life cycle maintenance frequency L k and life cycle T kSince this information may not fully cover the detailed settings of the operation and maintenance phase in the early entries, the system will query a preset "component life cycle characteristic reference database", which provides recommended maintenance frequency and expected service life based on component type, material properties and typical application environment (for example, concrete wall panels in indoor dry environment). For example, for "ConcreteWall-001", its life cycle maintenance frequency L k It may be 0.05 times per year (i.e., a major maintenance is performed every 20 years), and the life cycle is T k It may be 50 years. All these extracted parameters (spatial volume, unit energy consumption value, unit carbon emission value, unit construction cost value, life cycle maintenance frequency, and life cycle years) are integrated to generate the basic parameter set required for the annualized cost of each component.

[0067] According to the basic parameter set required for the annual cost of the component, the parameter set collects the spatial volume V for each component. k , unit energy consumption value Q k , unit carbon emission value D k , unit construction cost value M k , life cycle maintenance frequency L k and life cycle T k The system first calls the external economic parameter module to obtain the current energy unit price and carbon emission unit price. The energy unit price q refers to the market price of unit energy or the project-specific energy cost. For example, it can be obtained from the industrial electricity price or comprehensive energy cost report published by the local energy supplier, with a value of 0.15 yuan / MJ. The carbon emission unit price d refers to the market transaction price of unit carbon dioxide emissions. For example, the average price of the regional carbon emission rights trading market is 0.08 yuan / kgCO2. After obtaining these unit prices, the system calculates the unit energy consumption value Q of each component. k Multiplying the corresponding energy unit price q, we can get the annualized operating energy consumption cost per unit volume of the component, and calculate the unit carbon emission value D k Multiplying the corresponding carbon emission unit price d, we can get the monetary expression of the carbon emission environmental cost implied by the unit volume material of the component. Then, the system converts the unit construction cost M of the component into k The monetary expression of its carbon emissions (i.e. d·D k ) to obtain the sum of the initial materialized cost and the environmental impact cost, and divide this sum by the life cycle of the component T k , thus realizing the annualization of this part of the cost, and obtaining the annualized unit volume construction cost and carbon emission cost. Finally, the system combines this annualized unit volume cost with the spatial volume of the component itself V kMultiply them to get the annualized cost of the component due to initial investment and carbon emissions. At the same time, the annualized operating energy consumption cost per unit volume (i.e. q·Q k ) and the component life cycle maintenance frequency L k and component volume V k Multiplying these two costs together gives the annualized operating cost of the component due to use and maintenance. These two parts of the annualized cost (annualized initial investment and carbon emission cost, and annualized operation and maintenance cost) together constitute the annualized total cost of the component. The system completes this calculation for all components to form a set of annualized total component costs.

[0068] formula: The benefit of the formula is that it comprehensively evaluates the environmental impact and economic cost of prefabricated buildings in a unified framework, and annualizes them and converts them into unit volume, thus providing a standardized measurement indicator. Specifically, the formula is calculated by the first term q·Q k ·L k ·V k The operating costs of components due to energy consumption and maintenance during their life cycle were calculated, and the environmental load of energy consumption was reflected in an economic form and associated with the frequency of maintenance. The initial construction costs of each component and its implicit carbon emission environmental costs were calculated, and these one-time investments were amortized over the entire life cycle, achieving annualized costs. The introduction of a carbon emission unit price d allowed environmental externalities to be internalized as cost considerations. Ultimately, by summing these annualized costs of all components and dividing them by the total building volume, the annual comprehensive environmental and economic costs per unit building volume were obtained. This design allows building schemes of different sizes and component compositions to be compared on a relatively fair basis, helping designers identify components with high environmental or economic costs, allowing for targeted optimization and promoting green and low-carbon design.

[0069] The steps for obtaining the parameter q are as follows: the energy unit price q represents the cost per unit of energy. For example, when querying the average industrial electricity price in the most recent quarter, or the energy procurement contract price for a specific project, the specific value needs to be adapted to the region and time. For example, if the average comprehensive energy price in a certain region in the previous year was 0.15 yuan / MJ, then q = 0.15 yuan / MJ.

[0070] Parameter Q k The steps to obtain the k-th component unit energy consumption value Q k Represents the total energy consumed in producing and transporting the component per unit volume. For example, for a specific precast concrete wall panel (component 1), the EPD-certified comprehensive energy consumption per unit volume is 250 MJ / m 3 , then Q1=250MJ / m 3 .

[0071] Parameter L k The steps to obtain are: the life cycle maintenance frequency L of the kth component k It represents the average number of times per year that the component requires maintenance during its life cycle. This data is usually determined based on the durability of the material, component type, operating environment, and industry maintenance standards. It can be obtained from building maintenance manuals, professional engineering guides, or product technical specifications provided by the manufacturer. For example, for the precast concrete wall panel (component 1) mentioned above, if its design requires surface renovation and inspection every 20 years, its average annual maintenance frequency is L1 = 1 / 20 = 0.05 times / year.

[0072] Parameter V k The steps for obtaining the k-th component are: the spatial volume V k Refers to the actual physical volume occupied by the component in the 3D model. This data is directly extracted from the 3D CAD or BIM model of the assembly unit geometry. For example, the geometric model of component 1 (precast concrete wall panel) is automatically calculated by the software and its volume is 3.5m 3 , then V1=3.5m 3 .

[0073] Parameter M k The steps to obtain are: the unit construction cost M of the kth component k Refers to the direct economic cost required to manufacture and install the component per unit volume, including material costs, processing costs, transportation costs, and installation labor costs. This data is calculated and recorded based on supplier quotations, market price information, or the company's cost database when establishing the environmental cost items for structural components at the beginning of the project. For example, the comprehensive unit construction cost of component 1 (precast concrete wall panel) is 750 yuan / m 3 , then M1=750 yuan / m 3 .

[0074] The steps for obtaining parameter d are as follows: the carbon emission unit price d represents the economic cost of unit mass carbon dioxide emissions, which usually refers to the national or regional carbon emission trading market price, or the carbon tax or carbon cost reference value set to guide emission reduction. For example, if it is 80 yuan / ton CO2, that is, 0.08 yuan / kgCO2, then d = 0.08 yuan / kgCO2.

[0075] Parameter D k The steps to obtain the unit carbon emission value D of the kth component are: k Indicates the carbon dioxide emissions generated by the production and transportation of the component per unit volume. This data also comes from the EPD certification provided by the component supplier or the industry database and is recorded in the environmental cost item of the structural component. For example, the EPD certified carbon emissions per unit volume of component 1 (precast concrete wall panel) is 180kgCO2 / m 3, then D1=180kgCO2 / m 3 .

[0076] Parameter T k The steps to obtain are: the life cycle of the kth component T k It refers to the number of years from the time a component is installed and used until it is expected to be scrapped or require major replacement. This data is determined based on building design specifications, material durability standards and the service life promised by the manufacturer. For example, for component 1 (precast concrete wall panel), its design service life is usually 50 years, so T1 = 50 years.

[0077] The steps for obtaining the parameter z are as follows: the total number of components z refers to the total number of prefabricated components involved in the calculation of the comprehensive environmental cost value of this assembly. This value is obtained by counting the number of independent component instances contained in the geometric layout of the assembly unit. For example, if a small assembly unit contains 2 components, then z = 2.

[0078] Calculation process: Take an assembly with two components (z = 2) as an example. Component 1 (precast concrete wall panel): q = 0.15 yuan / MJ, Q1 = 250MJ / m 3 , L1=0.05 times / year, V1=3.5m 3 , M1=750 yuan / m 3 , d=0.08 yuan / kgCO2, D1=180kgC, O2 / m 3 , T1 = 50 years;

[0079] Component 2 (prefabricated steel beam): Q2 = 80 MJ / m 3 (Energy consumption for steel production is relatively low, but this refers to the component level, taking into account processing, etc.), L2 = 0.02 times / year (for example, a major anti-corrosion treatment is carried out every 50 years, etc.),

[0080] V2=0.8m 3 , M2 = 4500 yuan / m 3 (Steel cost is high), D2=600kgCO2 / m 3 (Steel production has higher carbon emissions), T2 = 50 years;

[0081] Calculate the annualized cost contribution of component 1: Operating cost portion (component 1): q·Q1·L1·V1=0.15·250·0.05·3.5=1.875·3.5=6.5625 yuan / year, Annualized initial cost portion (component 1): Yuan / year, total annual cost of component 1:

[0082] 6.5625+53.508=60.0705 yuan / year;

[0083] Calculate the annualized cost contribution of component 2: Operating cost part (component 2): q·Q2·L2·V2=0.15·80·0.02·0.8=0.24·0.8=0.192 yuan / year, annualized initial cost part (component 2): Yuan / year, total annualized cost of component 2: 0.192 + 72.768 = 72.96 Yuan / year;

[0084] Calculate the comprehensive environmental cost value E of the assembly: Numerator (total annualized cost): Yuan / year, denominator (total volume): Yuan / (m 3 ·Year)

[0085] The results show that the annualized environmental cost of the assembly is 30.94 yuan / (m 3 ·year). This value represents the comprehensive economic expenditure borne by each cubic meter of building volume, averaged annually throughout the building's life cycle, including initial construction costs, operating energy costs, maintenance costs, and carbon emission environmental costs. This value is the final output of the current step, "Obtaining the Comprehensive Environmental Cost Value of the Assembly." A lower E value generally means that the assembly design scheme has better economy and environmental friendliness throughout its life cycle, while a higher E value may indicate that the design scheme has room for optimization in terms of material selection, component design, or energy efficiency.

[0086] The steps for obtaining the key component adjustment parameters are as follows:

[0087] Based on the comprehensive value of the environmental cost of the assembly, all components in the prefabricated building model are sorted from high to low according to the environmental cost ratio of the component, and components with an environmental cost ratio higher than the set threshold are selected as the candidate component set to generate a preliminary list of target components;

[0088] Based on the preliminary list of target components, the design parameter set of each component in the digital twin basic model is called, and the component size parameters, material parameters, and connection method parameters are extracted item by item. Combined with the stress state and position distribution in the application scenario, the adjustable design parameters of each component are determined to obtain the parameter set to be adjusted for the target component;

[0089] Based on the set of parameters to be adjusted for the target component, the adjustment cycle and simulation step are set, and each design parameter is recursively simulated according to the step size within the adjustable range. The design parameter combination that meets the conditions for reducing the comprehensive environmental cost value of the assembly is screened, and the adjustment parameters of the key components are generated.

[0090] Specifically, based on the previously calculated comprehensive value E of the assembly environmental cost, the system first needs to identify the components that contribute most to the comprehensive value in order to perform targeted optimization. The specific implementation process is: for all z components in the prefabricated building model, calculate the annualized total cost of each component k, that is, This value is obtained when calculating the numerator of the E value, and then the annualized total cost of the entire assembly is calculated. Next, calculate the environmental cost percentage P of each component k =(C k / C total )·100%, the system will divide all components into groups according to their environmental cost ratio P k Sort from high to low, and select components whose environmental cost ratio is higher than a detailed "key component screening threshold" as candidates. The setting method of the "key component screening threshold" is as follows: First, statistically obtain a numerical list of the environmental cost ratios of all components. For example, for an assembly containing 10 components, the ratio list may be [25%, 18%, 12%, 10%, 8%, 7%, 6%, 5%, 5%, 4%]. Then, set this threshold to 10%. The basis for determining this 10% is: through analysis of multiple prefabricated green building projects of similar scale and type in the past, it was found that in these projects, the components with an environmental cost ratio of more than 10% for a single component usually accounted for 20% to 30% of the total number of components, but their The sum of the costs can reach 60% to 70% of the overall optimization potential, so 10% is used as an empirical balance point that can effectively screen out components with significantly optimized space. For example, based on historical data analysis, a project concluded that taking components with environmental costs exceeding 10% as optimization targets can reduce the overall environmental cost by 5% to 8% on average. The additional optimization benefits brought about by further lowering the threshold grow slowly, but the analysis workload increases significantly. Therefore, 10% is selected as the screening criterion. In this example, the environmental costs of three components accounting for 25%, 18% and 12% are all higher than 10%, so these components are selected into the candidate component set. Finally, the identification information of these selected components is summarized to generate a preliminary list of target components.

[0091] Based on the preliminary list of target components, which clearly identifies key components with a high environmental cost ratio and therefore have priority optimization needs, the system calls the associated digital twin basic model for each target component in the list. The model is a dynamic digital expression that contains the entire life cycle information of the component (including geometry, physics, rules, documents, etc.). The system extracts the complete design parameter set of each target component from the digital twin basic model. These parameter sets are pre-defined in the component family library or template file. For example, for a target component "prefabricated steel beam B-01", its design parameter set may include :Component size parameters, such as beam section type (e.g. H-beam, I-beam), section height (e.g. 300 mm), flange width (e.g. 150 mm), web thickness (e.g. 8 mm), flange thickness (e.g. 12 mm), beam length (e.g. 6000 mm), material parameters, such as steel grade (e.g. Q355B, Q235B), yield strength, tensile strength, elastic modulus, steel density, recycled material content percentage (e.g. 0% to 50%), and connection method parameters, such as end connection type (e.g. bolted connection, bolt connection), bolt specifications and quantity (e.g. M20 high-strength bolts), 8), weld type and length (e.g. fillet weld, length 100 mm). After the system extracts these parameters one by one, not all parameters are suitable for adjustment. The system will judge its adjustability based on the specific stress state and spatial position distribution of the component in the actual application scenario. The stress state data is retrieved from the structural analysis report associated with the digital twin basic model. For example, the current maximum stress of "prefabricated steel beam B-01" is 60% of the design stress, then there is a certain amount of room for downward adjustment of its cross-sectional size. The position distribution takes into account its function and importance in the building. For example, the beam is located above the non-load-bearing interior partition wall, and the deformation If the requirements are not high, the material grade or cross-sectional size will have greater adjustability. On the contrary, if it is the main load-bearing beam and is located at the center of the span, the adjustment range will be limited. Through the above analysis, the system screens out the design parameters that do have adjustment potential and their allowable adjustment range (for example, the adjustable range of the cross-sectional height is 280 mm to 320 mm, and the steel grade can be Q355B or Q345B) under the premise of meeting structural safety, functional requirements and relevant specifications (for example, the requirements for the minimum size and structure of components in the national standard GB50017-2017 "Steel Structure Design Standard"), and obtains the parameter set to be adjusted for the target component.

[0092] Based on the parameter set to be adjusted for the target component, the parameter set lists the design parameters that can be adjusted and their respective adjustment ranges or optional value lists for each selected key component. The system then sets the adjustment strategy for the parameter optimization process, including setting the upper limit of the number of adjustment iterations and the simulation step size of each parameter. The upper limit of the number of adjustment iterations is set according to the computing resources and optimization time budget, for example, it is set to 100 iterations. The setting of the simulation step size depends on the parameter type: for continuous size parameters, such as the adjustable range of the beam section height from 280 mm to 320 mm, if its manufacturing accuracy and standard modulus allow, the step size can be set to 10 mm, and the simulation will test 280 mm, 290 mm, 300 mm, and 310 mm. , 320 mm, these discrete values, for discrete material parameters, such as steel grades can be selected Q355B or Q345B, the step size is to select these options one by one, for connection parameters, such as the number of bolts can be adjusted from 6 to 10, the step size can be set to 2, the system adopts a systematic parameter combination traversal method, for example, for multi-parameter adjustment of a single component, the orthogonal experimental design method can be used to reduce the number of simulations, or the importance of parameters can be sorted and adjusted step by step. In each iteration, the system selects one or a group of new design parameter combinations to apply to the target component, and updates the corresponding properties of the component in the digital twin model, which will directly affect the environmental impact data of the component (such as the new material usage resulting in M k , Q k 、D k change) and volume V k Then, the system uses the previously established calculation logic to recalculate the comprehensive environmental cost value E of the entire assembly. new , that is, using a new data set containing the adjusted component parameters, substitute into the formula Perform the operation and obtain E new Compared with the comprehensive value of environmental cost before adjustment E original Or the optimal value E obtained in the previous iteration best Compare and filter out all the new <E best The design parameter combination, and the parameter combination that meets this condition and its corresponding E new The value is recorded. If the E of this iteration new Better, then update E best =E new After a preset number of iterations or when the E-value no longer decreases significantly (for example, the E-value decreases by less than 0.1% after 5 consecutive iterations), the system selects the set of design parameter combinations that minimizes the E-value from all recorded valid parameter combinations, and finally summarizes these selected parameters and their corresponding components to generate key component adjustment parameters.

[0093] The steps to obtain the optimized component lifecycle characteristics are:

[0094] Based on the component type, material properties, and connector information listed in the key component adjustment parameters, retrieve the failure timestamps within three years from all historical maintenance records by component number. Calculate the time interval sequence between two consecutive failures for each component. If the standard deviation of this sequence is less than 85% of the mean standard deviation of the period intervals of all components, mark the component as a stable failure component and generate a stable failure component number list.

[0095] Based on the stable fault part number list, calculate the expected maintenance period of the component using the following formula:

[0096]

[0097] Among them, QT m is the expected maintenance period of the component (unit: month), r is the total number of stable fault components, H ref is the cumulative operating life reference value of the standard component (unit: hours), H c is the actual cumulative operating time of the cth component (unit: hour), δ c is the ratio of the average operation interruption time caused by a single failure of the cth component to the total operation time of the evaluation period (dimensionless), f c is the average monthly failure frequency of the cth component (unit: times / month), f ref is the reference monthly failure frequency (unit: times / month), 720 is the unit conversion constant (hours per month);

[0098] Based on the expected maintenance cycle, the component numbers whose expected maintenance cycle is lower than the lower limit of the component standard maintenance cycle are screened from stable fault components. The component spatial position identification, material marking and connection node number information are extracted to generate optimized component life cycle characteristics.

[0099] Specifically, based on the component types (e.g., specific precast concrete beams, steel structure nodes, or integrated equipment units such as the air handling unit AHU-01), material properties (e.g., concrete strength grade C30, steel grade Q355B), and connector information (e.g., high-strength bolts M20 grade 8.8) listed in the key component adjustment parameters, the system first retrieves all the relevant information related to these key components and their identifiable, independent functional components (e.g., the fan motor "FanMotor-A", heating coil "HeatingCoil-B", and sensor "TempSensor-C" in AHU-01). Historical maintenance records, filter out all failure events in the past three years (i.e., 36 months), and extract the associated part number and the failure occurrence timestamp accurate to the hour for each failure event. For example, the failure timestamps recorded for part "FanMotor-A" in the past three years may be "2022-01-10 08:00:00", "2022-07-15 14:00:00", "2023-01-2010:00:00", "2023-08-01 16:00:00", "2024-02-10 09:00:00". The system then generates a fault time for each part that has experienced at least two failures during the evaluation period. , calculate the time intervals between two adjacent faults (units are uniformly converted to hours) to form a time interval sequence. For "FanMotor-A", its time interval (hour) sequence is [t2-t1, t3-t2, t4-t3, t5-t4]. For example, the sequence [4470, 4512, 4634, 4608] hours is obtained. Subsequently, the system calculates the arithmetic mean and standard deviation of this time interval sequence, and calculates an overall mean for the "standard deviation of the fault time interval sequence" of all analyzed components, that is, the "mean of the standard deviation of the periodic intervals of all components". Then, the system sets a screening condition: if the fault time interval sequence of a component is If the standard deviation of the series is lower than 85% of the previously calculated "mean standard deviation of all component cycle intervals," the component is preliminarily determined to have a relatively stable failure mode. This 85% threshold is set based on statistical analysis of a large amount of equipment maintenance data. It aims to screen out components whose failure interval dispersion is significantly lower than the overall average dispersion (i.e., variability is at least 15% lower). The failure mode of such components is more likely to be caused by cumulative damage such as wear and fatigue, rather than purely random impact failures. Their failures are relatively predictable. For example, if the "mean standard deviation of all component cycle intervals" is 500 hours, the screening threshold is 500 × 0.85 = 425 hours. If the standard deviation of the time interval sequence [4470, 4512, 4634, 4608] for "FanMotor-A" is calculated to be 210 hours, and since 210 < 425, "FanMotor-A" is marked as a stable fault component, the system collects the unique numbers of all components that meet this condition and their corresponding component information to generate a stable fault component number list.

[0100] formula: The benefit of the formula is that it provides a method to dynamically evaluate the expected maintenance cycle of components by combining the historical performance of the components, the impact of failures and industry benchmarks. This method aims to go beyond the traditional maintenance plan based on fixed time and achieve more targeted predictive maintenance. The formula comprehensively considers the ratio of the actual operating time of the component to the reference life. The ratio of actual fault frequency to reference fault frequency To quantify the “health” or “failure acceleration” of the component, and smooth the acceleration effect through a logarithmic function, the parameter δ c The severity of a single fault's impact on component operation is introduced, so that components with more severe fault consequences receive shorter expected maintenance cycles. Ultimately, by averaging the adjusted "useful life" of all stable fault components within a component and converting it into months, data support is provided for component-level maintenance decisions, helping to optimize resource allocation and improve overall system reliability and cost-effectiveness.

[0101] The steps for obtaining the parameter r are as follows: The total number of stable faulty components, r, refers to the number of components contained within a specific component that were successfully identified and included in the "Stable Faulty Component Number List" in the previous step when analyzing the component. This value is directly obtained by querying the list and counting the number of stable faulty components belonging to the target component. For example, when calculating the expected maintenance cycle for component "AHU-01" (air handling unit), if its list contains the three stable faulty components "FanMotor-A", "DamperActuator-X", and "ControlValve-Z", then r = 3.

[0102] Parameter H ref The steps to obtain the cumulative operating life reference value H of the standard component are: refThis value represents the mean time to failure (MTTF) or designed service life in hours for standard components of the same type under typical operating conditions, as declared by the manufacturer or recognized by the industry. This data is obtained from the equipment's technical specifications, industry reliability databases, or statistical analysis of a large number of historical data on similar components. When selecting, ensure that the benchmark value matches the functional type and design standard of the component being evaluated. For example, for the component "FanMotor-A" (fan motor), if it is a general-purpose three-phase asynchronous motor with medium power (e.g., 5-15kW), industry statistics show that its design life or MTTF is usually between 20,000 and 40,000 hours. A typical value, H, is selected here. ref =

[0103] 20,000 hours.

[0104] Parameter H c The steps for obtaining the cth stable fault component are: the actual cumulative running time H c Refers to the period from the first use of the component to the current data analysis cutoff point. For example, for component "FanMotor-A", by checking its BMS operation record since installation, it is confirmed that it has accumulated 15,000 effective operation hours, then H c =15000 hours.

[0105] Parameter δ c The steps to obtain δ are: the ratio of the average operation interruption time caused by a single failure of the cth stable fault component to the total planned or actual operation time of the component during the evaluation period c For example, during the three-year evaluation period, the total planned operating time of component "FanMotor-A" is approximately 3 years × 3000 hours / year = 9000 hours (e.g., non-continuous operation). The average system downtime or component downtime caused by a single fault repair is 200 hours (including the entire process of fault diagnosis, spare parts waiting, repair operation, test verification, etc.). Then, the calculation If the component has only been running for H hours due to failure or maintenance within three years, c,eval = 5000 hours, and the fault is serious, with an average interruption of 200 hours per single fault, then δ c = 200 hours / 5000 hours = 0.04. In this embodiment, the average repair and equivalent interruption time of the component "FanMotor-A" is 1000 hours, and its total effective operating time in the evaluation period is 10000 hours, then δ c =1000 hours / 10000 hours=0.1.

[0106] Parameter f c The steps to obtain the value are: the average monthly failure number f of the cth stable fault component cIt refers to the total number of actual failures of the component during the evaluation period (for example, three years, or 36 months) divided by the total number of months in the evaluation period. For example, if component "FanMotor-A" has had 6 recordable failures in the past three years, then its average monthly failure number f is c =6 times / 36 months=0.1667 times / month.

[0107] Parameter f ref The steps to obtain the reference monthly fault frequency f ref It refers to the average number of expected monthly failures of the same type of standard components under industry statistics or reference conditions provided by the manufacturer. This data can be obtained from equipment reliability manuals, statistical reports published by industry associations, or analysis based on a large amount of historical data of similar equipment. For example, for motors in the "FanMotor-A" category, the average monthly failure frequency is 0.02 times / month (i.e., a failure occurs once every 50 months on average), then f ref =0.02 times / month.

[0108] The steps for obtaining parameter 720 are as follows: The unit conversion constant 720 is used to convert the cycle value calculated with hours as the basic unit into the expected maintenance cycle with "months" as the unit. This is a fixed conversion factor.

[0109] Calculation process: Take the component "AHU-01" as an example, for example, it contains r = 1 stable fault component "FanMotor-A". The parameter values are as follows: r = 1H ref =20000 hours, H c =15000 hours (for FanMotor-A), δ c =0.1 (for FanMotor-A, it means the fault has a greater impact), f c = 0.1667 times / month (for FanMotor-A), f ref =0.02 times / month;

[0110] Calculate the part between brackets First calculate the ratio: Then calculate the logarithmic term inside (let this be X factor ): Calculate the natural logarithm: ln(X factor )=ln(7.25125)≈1.9812, calculate the denominator: δ c ·ln(X factor )=0.1·1.9812=0.19812, calculate the value in the brackets (this is the adjusted effective life H of a single component c eff,c ): Hour;

[0111] Calculating QT m : Since r = 1, the sum is the H of a single component eff,c . moon

[0112] The results indicate that the expected maintenance period for the AHU-01 component, calculated based on the characteristics of its stable fault component, FanMotor-A, and selected parameters, is approximately 140.2 months (approximately 11 years and 8 months). This value represents the approximate time span during which the component will continue to operate until the next planned maintenance is required, based on the component's current operating status, fault history, and the severity of the fault, in conjunction with the performance of standard components.

[0113] Based on the expected maintenance period QT calculated for each component (e.g., component "AHU-01") containing a stable fault component m (For example, about 140.2 months), the system then compares these calculated periods with the corresponding entries in a preset "component standard maintenance period lower limit database", which sets a minimum acceptable maintenance interval based on safety, regulations or basic performance guarantees for different types of components or their internal key components (for example, "FanMotor-A" belongs to the "medium-sized fan motor" category). The setting of its lower limit is mainly based on three aspects: First, the statutory minimum inspection or maintenance cycle for specific equipment (such as pressure vessels, elevators, and key components in fire protection facilities) in national or industry mandatory specifications. For example, a certain type of safety valve may be required to be calibrated at least once every 12 months, and its lower limit is 12 months; second, the shortest preventive maintenance recommended by the equipment manufacturer for this model component in its official maintenance manual or technical guide. The interval is usually based on a large amount of test data and early failure mode analysis. For example, the lower limit of the bearing grease replenishment cycle of a certain brand of precision water pump may be 6 months; the third is the internal management maintenance requirement bottom line set by the operation and maintenance management party in combination with the long-term operation and maintenance experience and risk tolerance assessment of this building or similar projects. For example, for key air-conditioning units that protect the core production or operation area environment, even if the manufacturer recommends 12 months, the company may set an 8-month maintenance cycle lower limit based on its importance and historical failure impact to ensure higher system reliability. The system retrieves the corresponding "component standard maintenance cycle lower limit" value from the database according to the component type or part model. For example, if the standard maintenance cycle lower limit of the "medium-sized fan motor" category to which "FanMotor-A" belongs is set to 24 months, the calculated QT m= 140.2 months compared with this 24 months. Since 140.2>24, the currently calculated expected maintenance period of the "FanMotor-A" component of this assembly is not lower than its standard lower limit. If the QT calculated for another component "PumpSeal-X" is m If the expected maintenance period is 1.8 months and the lower limit of its standard maintenance period is 3 months, then because 1.8<3, the "PumpSeal-X" component will be screened out. For all these screened out components with expected maintenance periods lower than the lower limit of their corresponding standard maintenance periods, the system further extracts their detailed positioning and attribute information from the digital twin basic model or asset information management system, including the three-dimensional spatial position identification of the component within its component or the overall structure of the building (for example, building coordinates X, Y, Z, the system pipeline number, the equipment room name), the core material mark of the main functional part of the component (for example, bearing steel GCr15, sealing nitrile rubber NBR), and the connection node number information for the physical connection or functional interaction between the component and other systems or components (for example, the coupling model of the motor output shaft connection, the lubricating oil injection port number). This information is integrated to record the current status and key attributes of each screened out component, and generate optimized component life cycle characteristics.

[0114] The steps to obtain the component diagnostic access point are:

[0115] Based on the optimized component life cycle characteristics, the corresponding monitoring requirements at different stages of the component performance degradation path are analyzed. The diagnostic requirement parameters are matched according to the material aging state, electrical transmission stability, and microcrack growth rate, and a list of fault diagnosis input information types is generated.

[0116] Based on the list of fault diagnosis input information types, the sensor interface type, electrical signal acquisition format and data timing protocol standard are matched to generate component diagnosis access points.

[0117] Specifically, based on the optimized component life cycle characteristics, which indicate which components and their internal parts need priority attention due to their expected short maintenance cycles, the system starts the performance degradation analysis and monitoring requirements definition process for the components included in these characteristics (for example, a critical reinforced concrete beam or a frequently started and stopped water pump unit). First, the system accesses a pre-built "component performance degradation knowledge base", which is compiled and maintained based on industry standards, material science research, equipment failure mode and effect analysis (FMEA) results and accumulated historical operation and maintenance experience, and describes different Typical performance degradation paths and their main stages of various types of components (such as concrete structures, steel structures, rotating equipment, and electrical lines) under different environmental and load conditions. For example, for a reinforced concrete beam, its degradation path includes: in the early stage (0-5 years), it is mainly normal creep and shrinkage of concrete, and the monitoring needs focus on the benchmark measurement of structural displacement; in the middle stage (5-20 years), steel bars may begin to rust due to carbonization or chloride ion corrosion, and the monitoring needs shift to the electrochemical parameters of steel bars (such as half-cell potential) and concrete resistivity; in the late stage (more than 20 years), rust expansion cracks and concrete cracking may occur. Concrete spalling monitoring requirements include crack width, acoustic emission signals, and changes in structural vibration characteristics. For each component listed in the "Optimize Component Lifecycle Characteristics", the system analyzes its material properties (for example, C30 concrete, HRB400 steel bars) and connection information (for example, high-strength bolt model), matches the most relevant degradation path from the knowledge base, and targets different stages in the path according to preset focus points, such as material aging status (for example, concrete carbonization depth, steel corrosion level), electrical transmission stability (for example, cable insulation resistance changes, motor winding temperature anomalies), and The microcrack growth rate (for example, the crack width growth rate of the concrete structure surface, the acoustic emission signal intensity of the fatigue crack growth of the metal component) is searched and matched with the corresponding diagnostic requirement parameters from the knowledge base. These parameters are quantifiable indicators that can characterize specific degradation states. For example, for the carbonization stage of concrete, the diagnostic requirement parameter is the "carbonization depth value of the concrete surface". For the aging of the motor winding, the parameters are the "winding insulation resistance value" and "three-phase current imbalance". Finally, all the identified parameters and their associated components, degradation stages and other information are summarized to generate a list of fault diagnosis input information types.

[0118] Based on the list of fault diagnosis input information types, which provides clear monitoring targets for each component or part that needs to be monitored and its performance degradation parameters of concern (for example, the "crack width" of "concrete beam BL-01" and the "bearing vibration acceleration" of "water pump P-102A"), the system then matches each item with the technical specifications required for monitoring. The first step is to match the sensor interface type. The system queries a preset "sensor and data acquisition equipment specification database", which includes the models, measurable parameters, measurement range, accuracy, and output signal types of various commonly used industrial sensors (for example, 4-20mA current loop, 0-5V voltage, RS485 digital signal, I Based on the parameter type and component characteristics (such as installation space and environmental conditions) in the "Fault Diagnosis Input Information Type List", the system selects the appropriate sensor type and model. For example, monitoring "crack width" may match a resistance strain crack meter (output resistance changes, which requires a bridge to convert into a voltage signal) or a fiber Bragg grating sensor (output wavelength changes, requiring a specific demodulator), while monitoring "bearing vibration acceleration" may match a piezoelectric accelerometer (IEPE interface, output voltage signal). When selecting, it is necessary to ensure that the sensor's range covers the expected parameter change range. For example, if the expected crack width is The range of the crack meter should be greater than or equal to 2 mm, and its accuracy should be better than 0.01 mm. If the sensor output is an analog electrical signal, it is necessary to further match the acquisition format of the electrical signal, including determining the parameters of the analog input module of the required data acquisition card (DAQ) or programmable logic controller (PLC), such as sampling resolution (for example, a 24-bit ADC is used for weak strain signals and a 16-bit ADC is used for general temperature signals), and sampling frequency (for example, a few times a day is sufficient for slowly changing crack growth, while a sampling frequency of several kilohertz is required for high-frequency vibration signals. For example, bearing vibration monitoring requires a sampling frequency of no less than 2.56 times the bearing characteristic frequency). times), finally, the system matches the data transmission timing protocol standard, and selects the appropriate communication protocol from the supported protocol library (such as ModbusRTU / TCP, OPCUA, MQTT, BACnet / IP) based on the on-site network conditions, data real-time requirements and system integration requirements. For example, for sensor nodes that are widely distributed and have a small amount of data, ModbusRTU can be used for transmission through the RS485 bus. For monitoring points that require high real-time performance or large data volume transmission, Ethernet-based OPCUA or ModbusTCP protocols can be used. Through the above matching process, specific sensing, acquisition and communication solutions are determined for each diagnostic requirement parameter, and component diagnostic access points are generated.

[0119] The steps to obtain the building health status diagnostic index are:

[0120] Based on the component diagnosis access point, the preset component fault feature identification rules, trend change identification rules, and cross-component abnormality collaborative judgment rules are called to match the change indicators in the current monitoring data items one by one. Combined with the historical data backtracking analysis results, the status classification and marking are performed to generate a component health judgment result set;

[0121] Based on the component health judgment result set, all recorded status categories are associated and mapped, and the distribution ratio of each status category in the spatial dimension and time series is counted. The component failure correlation density and the frequency of abnormal changes in the overall building status are extracted to form a building health status diagnostic index.

[0122] Specifically, based on the real-time and recent historical monitoring data streams obtained by the component diagnostic access point, the system calls a preset rule set stored in the "building diagnostic knowledge base". This knowledge base is established and continuously updated based on the equipment operation manual, industry maintenance standards (for example, ASHRAE Guideline 36 for HVAC systems), the operation and maintenance experience of senior engineers, and statistical analysis of historical failure cases. It contains three types of core diagnostic rules: First, component fault feature recognition rules. This type of rule sets threshold logic for a specific parameter combination of a single monitoring point or a single component. For example, for the outlet pressure monitoring point of water pump P-101, a rule may be "If the outlet pressure value is lower than the design working pressure lower limit ( For example, the design value is 0.5MPa, and the lower limit is set to 80% of 0.5MPa, that is, 0.4MPa. This 80% coefficient is determined based on the water pump performance curve and the system's minimum demand pressure to ensure that problems can be discovered in a timely manner without being too sensitive to cause false alarms) for more than 2 minutes (this duration is set to 3 times the normal fluctuation time of the system. For example, if the system pressure fluctuation cycle is 40 seconds, then 2 minutes is 3 cycles), it is marked as 'abnormally low outlet pressure'". The second is the trend change identification rule. This type of rule analyzes the change trend of the parameter over a period of time. For example, for the compressor bearing temperature of the chiller CH-02, the rule can be "If the growth rate of the sliding average value of the bearing temperature (window is 6 hours) is maintained for 72 consecutive hours If the temperature rise continues to be greater than 0.2 degrees Celsius per hour (this rate threshold is set according to the early warning guidelines provided by the manufacturer of this model compressor, indicating abnormal temperature rise), and the current temperature has exceeded 65 degrees Celsius (this temperature threshold is 85% of the safe operating upper limit of bearing materials and lubricants), it will be marked as 'bearing temperature deterioration trend'". Finally, there are cross-component abnormality collaborative judgment rules. Such rules associate the monitoring data of multiple components or parts. For example, "If the return air CO2 concentration of air handling unit AHU-03 is higher than 1200ppm (this is 1.1 times the recommended upper limit for specific spaces in the indoor air quality standard ASHRAE62.1, and the 1.1 times factor is a warning buffer) and the feedback signal of the opening degree of its associated fresh air valve is lower than 10% (this is If the valve opening percentage is less than 90% of the lower limit of the designed minimum fresh air volume, it is marked as a 'coordinated anomaly of insufficient fresh air supply and excessive CO2 concentration'. The system matches each monitoring data item currently obtained through the component diagnostic access point (such as pressure, temperature, current frequency, and valve opening percentage) with the applicable rules in these three rule bases. Combined with retrospective analysis of the component or part's historical monitoring data (for example, comparing whether the current anomaly pattern is similar to the pattern before the historical fault), the current component's operating status is classified and marked as "normal," "concern," "warning," or "critical." These markings are determined based on the severity predefined in the rule or the combination logic of multiple rules to generate a component health judgment result set.

[0123] Based on the component health judgment result set, which contains the health status classification marks of each component or its sub-components at a specific time point (for example, the status of "water pump P-101" is "warning", and the status of "chiller CH-02" is "concern"), the system first associates and maps all recorded status categories ("normal", "concern", "warning", "serious"), and associates each component with a status mark with its physical space location in the building (for example, the floor, area, room number, which is obtained from the building information model BIM or equipment asset management database) and the functional system to which it belongs (for example, HVAC system, water supply and drainage system, power supply and distribution system, which is also obtained from BIM or equipment asset management database). The system performs statistical analysis and calculates the distribution ratio of each status category in different dimensions: in the spatial dimension, for example, the percentage of components in the "warning" state in the "HVAC system" accounts for the total number of components in the system, or the percentage of equipment in the "severe" state in the "third floor of the east wing of the building" area accounts for the total number of equipment in the area. If the "HVAC system" has 50 main components and 5 of them are in the "warning" state, the warning ratio is 5 / 50×100%=10%. In the time series dimension, for example, the daily average number of components in the "concern" state in the entire building in the past week, or the percentage of specific key components (such as the main transformer) in the "non-positive" state is calculated. Then, the system further extracts two core indicators: one is the component failure correlation density. This indicator first needs to define "failure correlation". For example, if two components are in the same functional subsystem and the failure of one component (status is "serious") has a probability of more than 60% (this probability threshold is set according to risk assessment and operation and maintenance experience) of causing another component to enter the "warning" or "serious" state within 24 hours according to the preset fault propagation model (the model is based on the system topology and component dependencies), then the two components are considered to be "failure-related". The failure correlation density is calculated as the number of components in a specific spatial area or functional system that are in the "warning" or "serious" state and have the above-mentioned "failure correlation" with each other. The first is the ratio of the number of component pairs to the total number of components in the area or system. For example, if there are 20 components in an area and 3 pairs (i.e., a part of 6 components) are found to be in a "critical" state and are interrelated, the density can be expressed as 3 / (20×19 / 2) (the total number of possible pairs) or more directly as the proportion of the number of affected interrelated components, such as 6 / 20. The second is the frequency of abnormal changes in the overall state of the building. This indicator first defines the "abnormal change in the overall state of the building" event. For example, when the number of newly added "critical" components in a day exceeds 3 (this number threshold is set according to the building scale and daily maintenance capacity, and exceeding this value indicates a concentrated outbreak of abnormalities), or the "failure correlation density" of any key functional system exceeds the preset system risk threshold (e.g., 0.15), it is recorded as a "building overall status change" event. The change frequency is the number of such "building overall status change" events that occur within a unit of time (for example, weekly or monthly). For example, if there were two such events last month, the monthly change frequency is 2 times / month. By integrating these statistical proportions, correlation density, and change frequency data, a building health status diagnostic index is formed.

[0124] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for designing an assembled green building, characterized in that: The following steps are involved: Create a 3D geometric representation of the prefabricated component and set the physical properties of the green building materials selected, associate the component's environmental product declaration data with the unit construction cost data, and establish the environmental cost entry for the structural component; Based on the structural component environmental cost item, the item is retrieved in three-dimensional space to perform unitized construction of the prefabricated building model and set the connection relationship between the components, and the geometric layout of the assembly unit is obtained. Based on the geometric layout of the assembly unit, the overall energy consumption simulation calculation of the building is performed according to the environmental data and cost data embedded in the item to obtain the comprehensive environmental cost value of the assembly; Referring to the comprehensive environmental cost value of the assembly, a target component is selected in the digital twin base model for iterative adjustment of design parameters to generate key component adjustment parameters. Based on the key component adjustment parameters, expected maintenance cycle information is assigned to the component and identification of vulnerable parts is recorded to form optimized component life cycle characteristics; Based on the optimized component life cycle characteristics, the input information type of fault diagnosis is defined for the key components of prefabricated green buildings, and component diagnosis access points are established. Based on the component diagnosis access points, the diagnostic information judgment rules are integrated and mapped and associated, and the building health status diagnostic index is output.

2. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the environmental cost items of the structural components are: Retrieving component identification information of the prefabricated component, calling a standard geometric configuration template and a set of geometric boundary parameters corresponding to the component identification information, generating a three-dimensional geometric representation of the prefabricated component in three-dimensional modeling, and obtaining a three-dimensional geometric representation; Based on the three-dimensional geometric representation, the material code of the green building material is called, the physical parameter list is matched through the material code, the density, thermal conductivity and heat capacity items in the physical parameter list are embedded in the three-dimensional geometric representation component attributes, and the environmental product declaration data and unit construction cost data corresponding to the material are loaded at the same time to generate a component modeling object with attribute information; Based on the component modeling object with attribute information, the carbon emission factor, energy consumption value and pollutant emission value in the environmental product declaration data are extracted, and the component unit price, assembly labor cost and transportation cost in the unit construction cost data are combined. All extracted items are structured and integrated to generate structured component environmental cost items.

3. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the geometric layout of the assembly unit are: In the three-dimensional space modeling, the component geometric representation parameters, physical property parameters and component number information in the structural component environmental cost item are called, the component placement order and spatial distribution order are set according to the component number information, and the initial layout of the component combination is generated; Based on the initial layout of the component combination, the geometric boundary positions, connection node coordinates and installation direction information between adjacent components are extracted, contact determination and direction correction are performed on the physical contact surfaces between all components, connection relationships between components are set and node binding is completed, and a connection layout model of the assembly components is generated; Based on the assembly component connection layout model, the geometric boundary coverage and spatial topological structure between components are analyzed, the spatial arrangement path is traced through the component connection sequence and a three-dimensional layout structure is formed to obtain the assembly unit geometric layout.

4. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the comprehensive value of the environmental cost of the assembly are: Based on the geometric layout of the assembly unit, the spatial volume, unit energy consumption value, unit carbon emission value, unit construction cost value, life cycle maintenance frequency and life cycle number of each component are extracted to generate the basic parameter set required for the annualized cost of the component; According to the basic parameter set required for the annualized cost of the component, the energy unit price and the carbon emission unit price are respectively called, and the unit energy consumption value and the unit carbon emission value are multiplied by the corresponding unit price to form a monetary expression. The unit construction cost and the carbon emission monetary value are annualized according to the life cycle years, and the annualized unit volume cost is multiplied by the component volume to form the annualized total cost set of the component; Based on the annualized total cost set of the components, a comprehensive environmental cost value of the assembly is calculated.

5. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the key component adjustment parameters are as follows: Based on the comprehensive environmental cost value of the assembly, all components in the prefabricated building model are sorted from high to low according to the environmental cost ratio of the components, and components with an environmental cost ratio higher than a set threshold are selected as a candidate component set to generate a preliminary list of target components; Based on the preliminary list of target components, the design parameter set of each component in the digital twin basic model is called, and the component size parameters, material parameters, and connection method parameters are extracted item by item. In combination with the stress state and position distribution in the application scenario, the adjustable design parameters of the individual components are determined to obtain the parameter set to be adjusted for the target component; Based on the set of parameters to be adjusted for the target component, the adjustment cycle and simulation step are set, and each design parameter is recursively simulated according to the step size within the adjustable range. The design parameter combination that meets the conditions for reducing the comprehensive value of the environmental cost of the assembly is screened, and the adjustment parameters of the key components are generated.

6. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the optimized component life cycle characteristics are as follows: Based on the component type, material properties, and connector information listed in the key component adjustment parameters, retrieve the failure timestamps within three years from all historical maintenance records by component number, calculate the time interval sequence between two adjacent failures of each component, and if the standard deviation of the sequence is less than 85% of the mean standard deviation of the period intervals of all components, mark the component as a stable failure component and generate a stable failure component number list; Calculate the expected maintenance period of the components based on the stable fault component number list; Based on the expected maintenance cycle, the component numbers whose expected maintenance cycle is lower than the lower limit of the component standard maintenance cycle are screened from the stable fault components, the component spatial position identification, material marking and connection node number information are extracted, and the optimized component life cycle characteristics are generated.

7. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the component diagnosis access point are: Based on the optimized component life cycle characteristics, the monitoring requirements corresponding to different stages in the component performance degradation path are analyzed, and the diagnostic requirement parameters are matched according to the material aging state, electrical transmission stability and microcrack growth rate to generate a list of fault diagnosis input information types; Based on the fault diagnosis input information type list, the sensor interface type, the electrical signal acquisition format and the data timing protocol standard are matched to generate a component diagnosis access point.

8. The prefabricated green building design method according to claim 1, characterized in that: The steps for obtaining the building health status diagnostic index are as follows: According to the component diagnosis access point, the preset component fault feature identification rules, trend change identification rules and cross-component abnormality collaborative judgment rules are called, and the change indicators in the current monitoring data items are matched one by one. The status classification and marking are combined with the historical data backtracking analysis results to generate a component health judgment result set; Based on the component health judgment result set, all recorded status categories are associated and mapped, the distribution ratio of each status category in the spatial dimension and time series is counted, the component failure correlation density and the frequency of abnormal changes in the overall building status are extracted, and a building health status diagnostic index is formed.

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