Intelligent Management System for the Entire Lifecycle of Prefabricated Buildings Based on BIM and IoT

Abstract

This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings, belonging to the field of intelligent management technology. The system includes: a stage data acquisition module for collecting building data at different lifecycle stages; a lifecycle impact module for constructing an IoT-BIM collaborative model, combining building data to generate specific digital information, and analyzing the impact of different lifecycle stages on building functions to obtain lifecycle impact; a hazard factor determination module for determining hazard factors at different lifecycle stages based on lifecycle impact and specific digital information, and constructing and determining hazard thresholds for different lifecycle stages; and a risk tracing module for determining whether the hazard factor exceeds the hazard threshold, and if so, generating a risk tracing scheme by associating specific digital information. This invention can improve management efficiency, enhance collaborative capabilities, improve the accuracy of risk assessment, and promote sustainable development.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and in particular to an intelligent management system for the entire life cycle of prefabricated buildings based on BIM and the Internet of Things. Background Technology

[0002] Currently, prefabricated buildings, as an important mode of industrialized construction, have their entire life cycle management covering stages such as design, production, construction, operation and maintenance to demolition, with frequent data interaction and diverse functional requirements in each stage.

[0003] In the construction process, the physical status data collected in real time by IoT devices and the digital information of BIM models are disconnected and not effectively linked or integrated. This results in data being scattered across different systems, making it difficult for managers to obtain comprehensive information across stages and dimensions through a unified platform, which affects the efficiency and accuracy of decision-making. Existing methods often rely on experience-based judgments or data from a single stage, without systematically analyzing the specific impact of each life cycle stage on building functions. Furthermore, they lack scientific risk quantification indicators and threshold systems, making it difficult to accurately identify potential risks and provide early warnings.

[0004] The real-time data collected by IoT devices is only used for local monitoring and is not deeply bound to the component attributes in the BIM model. At the same time, risk assessment is mostly based on static standards and does not consider the dynamic changes of functional objectives at different stages, resulting in delayed risk judgment and highly subjective threshold setting, which cannot meet the refined management and control requirements under the industrialized characteristics of prefabricated buildings.

[0005] The aforementioned problems severely restrict the level of intelligence in the full life cycle management of prefabricated buildings. There is an urgent need for a prefabricated building full life cycle intelligent management system based on BIM and the Internet of Things, which can achieve seamless connection of information at each stage and precise risk prevention and control through data association, functional impact analysis and scientific risk assessment. Summary of the Invention

[0006] This invention provides a prefabricated building lifecycle intelligent management system based on BIM and IoT to address the shortcomings of existing technologies, such as lack of collaborative management and inaccurate risk assessment.

[0007] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings, comprising:

[0008] The phase data acquisition module is used to divide prefabricated buildings into multiple life cycle phases according to the construction process and functions, and to collect building data for different life cycle phases.

[0009] The lifecycle impact module is used to build an IoT-BIM collaborative model, combine building data to generate unique digital information, and analyze the impact of different lifecycle stages on building functions to obtain lifecycle impact.

[0010] The hazard factor determination module is used to determine the hazard factor for different life cycle stages based on life cycle impact and specific digital information, and to construct and determine the hazard threshold for different life cycle stages.

[0011] The risk tracing module is used to determine whether the risk factor exceeds the risk threshold. If so, it generates a risk tracing solution by associating specific digital information.

[0012] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. The steps of dividing the stage data acquisition module into multiple lifecycle stages include:

[0013] Based on the process of prefabricated buildings from concept to demise, the entire life cycle is divided into several key stages, and the corresponding time boundaries and functional objectives that need to be guaranteed are clearly defined, and a mapping relationship table is established.

[0014] Based on the industrialized characteristics of prefabricated buildings and combined with multiple key links, the construction process is broken down into multiple independent stages.

[0015] For multiple independent stages, and in combination with the functional characteristics of prefabricated buildings, the functional objectives and corresponding technical requirements of each stage are determined.

[0016] This invention provides an intelligent management system for the entire lifecycle of prefabricated buildings based on BIM and IoT. The steps for constructing an IoT-BIM collaborative model for the lifecycle impact module include:

[0017] Based on user needs, define building functions, design standards, and construction cycles as requirement parameters, collect topographic data, surrounding environment data, meteorological data, and geological data as geographic information data, and determine the BIM model.

[0018] Register devices on the IoT platform and collect device data in real time to establish a mapping relationship with the corresponding components in the BIM model.

[0019] A redundancy removal algorithm is used to calculate the redundancy rate of the mapping relationship and remove redundant data to obtain clean data. The formula is expressed as:

[0020]

[0021] In the formula, It's about cleaning data. It is the original data in the mapping relationship. It is data Redundancy rate, It is the redundancy threshold.

[0022] Data fusion rules are determined based on the semantics and timestamps of the cleaned data, and a linear weighted fusion algorithm is used to fuse equipment data and BIM model data.

[0023] The merged data is then updated into the BIM model to obtain the IoT-BIM collaborative model.

[0024] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. The steps for the lifecycle impact module to generate exclusive digital information include:

[0025] Based on the design drawings of prefabricated buildings, the building data is input into the IoT-BIM collaborative model.

[0026] For different lifecycle stages, real-time state data that changes over time is collected as dynamic process data.

[0027] Dynamic process data is associated with components in the IoT-BIM collaborative model through unique component IDs.

[0028] The dynamic process data of the same component at different life cycle stages are sorted by time axis to form a data timeline for time correlation.

[0029] Dynamic process data is categorized into quality, safety, and energy consumption, and then bound to the corresponding attribute columns of the IoT-BIM collaborative model to obtain exclusive digital information.

[0030] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. The steps for the lifecycle impact module to obtain the lifecycle impact include:

[0031] Based on building function, structural safety, durability and reliability, ease of use, energy saving and low carbon emissions, and resource regeneration are used as functional evaluation dimensions.

[0032] We analyzed the direct impact paths of different lifecycle stages on functional evaluation dimensions and established a matrix of functional impact relationships at each stage.

[0033] Based on the phased functional impact relationship matrix, key impact indicators that directly reflect the degree of functional impairment are extracted from building data, and corresponding impact rules are formulated for the functional evaluation dimensions.

[0034] Based on the corresponding impact rules and building data, the comprehensive impact value for each life cycle stage is calculated.

[0035] Based on the magnitude of the comprehensive impact value, the impacts at each stage of the life cycle are classified into different levels, and the life cycle impact is obtained by combining the impact path analysis.

[0036] This invention provides an intelligent management system for the entire lifecycle of prefabricated buildings based on BIM and the Internet of Things, including a risk factor determination module:

[0037] The risk indicator screening unit is used to extract risk elements at different life cycle stages from life cycle impacts and proprietary digital information.

[0038] The hazard coefficient calculation unit is used to determine the index weights of different risk factors by using the coefficient of variation method and combining the importance of building functions, and to calculate the hazard coefficients at different life cycle stages.

[0039] The hazard threshold determination unit is used to set hazard thresholds for key risk indicators at different life cycle stages, based on historical data from similar prefabricated building projects.

[0040] This invention provides an intelligent management system for the entire lifecycle of prefabricated buildings based on BIM and the Internet of Things. The steps for the risk indicator screening unit to extract risk factors include:

[0041] Guided by the functional objectives at different lifecycle stages, identify the risk areas that need to be addressed at each stage.

[0042] From the life cycle impact, we extract potential risk factors that negatively affect functionality at different life cycle stages.

[0043] Data associated with potential risk factors are selected from proprietary digital information.

[0044] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. The steps for calculating the risk factor in the risk factor calculation unit include:

[0045] The risk factors are standardized, and the ratio of the mean to the standard deviation is used as the coefficient of variation.

[0046] The building functions associated with different life cycle stages are scored and normalized to obtain the functional importance weights. The functional association weights are then allocated to the risk factors according to the relationship between the risk factors and the functions.

[0047] The index weights are calculated based on the coefficient of variation and functional association weights.

[0048] The risk data at different lifecycle stages is extracted from the proprietary digital information and standardized to obtain the target quantitative value. The risk coefficient is then obtained by combining the indicator weights.

[0049] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. The steps for setting hazard thresholds in the hazard threshold determination unit include:

[0050] Data screening criteria were determined based on project type, data completeness, time range, and sample size, and historical data of the same type were collected.

[0051] By analyzing historical data containing risk factors using statistical analysis tools, we can obtain data distribution characteristics, and then match the historical data with risk event records to obtain risk correlation analysis.

[0052] Based on data distribution characteristics and risk correlation analysis, hazard thresholds are established for different life cycle stages.

[0053] This invention provides an intelligent management system for the entire lifecycle of prefabricated buildings based on BIM and the Internet of Things. The steps for the risk tracing module to obtain a risk tracing solution include:

[0054] Based on different life cycle stages, the correspondence between risk factors and risk thresholds is compiled, and the comparison frequency is set according to the work rhythm.

[0055] Identify the risk factors exceeding the danger threshold, analyze the root causes of the risks, and formulate a risk tracing plan that includes a risk overview, causal analysis, corrective measures, responsible parties, and implementation details.

[0056] This invention provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. Through phased data acquisition and lifecycle impact modules, it achieves comprehensive data collection and analysis throughout the building's entire lifecycle, improving management efficiency and the scientific basis of decision-making. The construction of an IoT-BIM collaborative model enables deep integration of building data and IoT device data, enhancing collaboration among stakeholders and reducing information silos. By calculating hazard coefficients and setting hazard thresholds, the accuracy and objectivity of risk assessment are improved, providing a reliable basis for risk management. Furthermore, the analysis of the root causes of risks and the generation of detailed risk tracing schemes improve the efficiency and accuracy of risk tracing, facilitating timely corrective measures and reducing risk losses. Analyzing the impact of different lifecycle stages on building functions helps optimize building design and construction schemes, improve building sustainability, and reduce resource waste and environmental pollution. Finally, it enhances the safety and comfort of building use, improves user experience, and strengthens the social and economic benefits of the building. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0058] Figure 1This is one of the flowcharts of an intelligent management system for the entire life cycle of prefabricated buildings based on BIM and the Internet of Things provided in this embodiment of the invention.

[0059] Figure 2 This is the second flowchart of an intelligent management system for the entire life cycle of prefabricated buildings based on BIM and the Internet of Things, provided in an embodiment of the present invention.

[0060] Figure 3 This is the third flowchart of an intelligent management system for the entire life cycle of prefabricated buildings based on BIM and the Internet of Things, provided in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] The following is combined Figures 1-3 This invention describes an intelligent management system for the entire lifecycle of prefabricated buildings based on BIM and the Internet of Things.

[0063] like Figure 1 As shown in the figure, an embodiment of the present invention provides an intelligent management system for the entire life cycle of prefabricated buildings based on BIM and the Internet of Things, comprising:

[0064] The phase data acquisition module is used to divide prefabricated buildings into multiple life cycle phases according to the construction process and functions, and to collect building data for different life cycle phases.

[0065] The steps involved in dividing the phased data acquisition module into multiple lifecycle phases include:

[0066] Based on the process of prefabricated buildings from concept to demise, the entire life cycle is divided into several key stages, and the corresponding time boundaries and functional objectives that need to be guaranteed are clearly defined, and a mapping relationship table is established.

[0067] Based on the industrialized characteristics of prefabricated buildings and combined with multiple key links, the construction process is broken down into multiple independent stages.

[0068] For multiple independent stages, and in combination with the functional characteristics of prefabricated buildings, the functional objectives and corresponding technical requirements of each stage are determined.

[0069] The life cycle phases can include the design phase, production phase, construction phase, operation and maintenance phase, and demolition and recycling phase.

[0070] Design phase:

[0071] Time boundary: After project approval → before confirmation of prefabricated component production drawings.

[0072] Core tasks: Complete architectural design, component breakdown design, multi-disciplinary collaboration, and feasibility verification for production and construction.

[0073] Key points of functional control: Component disassembly must comply with the "Technical Standard for Prefabricated Concrete Buildings".

[0074] Functional control focus 2: Integrated design of pipelines and components, such as pre-embedded water and electricity pipelines in prefabricated wall panels, to reduce the need for later wall chiseling and maintenance.

[0075] Functional control focus 3: The insulation layer is integrated with the prefabricated components, and the thickness of the insulation layer meets local energy-saving standards.

[0076] Production stage:

[0077] Time boundary: After the component production drawings are confirmed → before all prefabricated components leave the factory and are transported to the construction site.

[0078] Core tasks: Prefabricate components according to design drawings, conduct quality inspection, and assign digital codes.

[0079] Functional control focus 1: Concrete strength must reach the design value, and the thickness deviation of the steel reinforcement protective layer should be ≤ ±5mm to avoid accelerated carbonation.

[0080] Functional control focus 2: The positional deviation of embedded parts should be ≤ ±3mm to avoid connection failure during construction.

[0081] Construction phase:

[0082] Time boundary: After the first batch of components arrives on site and passes inspection → before the building is completed, passes inspection, and is put into use.

[0083] Core tasks: component hoisting and installation, joint grouting / connection, on-site pipeline laying, and final acceptance.

[0084] Functional control focus 1: Verticality deviation of component hoisting ≤ 5mm, grouting density of joints ≥ 95%.

[0085] Functional control focus 2: Door and window opening size deviation ≤ ±3mm, to ensure sealing during subsequent door and window installation.

[0086] Operation and maintenance phase:

[0087] Time boundary: After the building is put into use → before the building reaches its designed service life or before demolition is determined.

[0088] Core tasks: structural health monitoring, facility maintenance, energy consumption management, and safety hazard investigation.

[0089] Functional control focus 1: Inspect the width of cracks in precast components annually.

[0090] Functional control focus 2: Monitor air conditioning / lighting energy consumption.

[0091] Demolition / Recycling Phase:

[0092] Time boundary: After the building is decommissioned and demolition is approved → after all components / materials are classified, recycled or disposed of in compliance with regulations.

[0093] Core tasks: Safely dismantle components, classify and screen reusable / renewable materials, and environmentally friendly process construction waste.

[0094] Functional control focus 1: Resource recycling, including the reuse rate of steel structure components and the conversion rate of recycled aggregates in concrete components.

[0095] Functional control focus 2: Environmental safety, dust concentration during demolition.

[0096] The lifecycle impact module is used to build an IoT-BIM collaborative model, combine building data to generate unique digital information, and analyze the impact of different lifecycle stages on building functions to obtain lifecycle impact.

[0097] The steps involved in building an IoT-BIM collaborative model for the lifecycle impact module include:

[0098] Based on user needs, define building functions, design standards, and construction cycles as requirement parameters, collect topographic data, surrounding environment data, meteorological data, and geological data as geographic information data, and determine the BIM model.

[0099] Register devices on the IoT platform and collect device data in real time to establish a mapping relationship with the corresponding components in the BIM model. The formula is expressed as:

[0100]

[0101] In the formula, It's a mapping relationship. It is a component in the model. It is an Internet of Things (IoT) device.

[0102] A redundancy removal algorithm is used to calculate the redundancy rate of the mapping relationship and remove redundant data to obtain clean data. The formula is expressed as:

[0103]

[0104] In the formula, It's about cleaning data. It is the original data in the mapping relationship. It is data Redundancy rate, It is the redundancy threshold.

[0105] Data fusion rules are determined based on the semantics and timestamps of the cleaned data, and a linear weighted fusion algorithm is used to fuse equipment data and BIM model data. The formula is expressed as follows:

[0106]

[0107] In the formula, It is the merged data. It is the weight of the BIM model data. It is the weight of the device data. It's equipment data. It is BIM model data.

[0108] The merged data is then updated into the BIM model to obtain the IoT-BIM collaborative model, expressed by the formula:

[0109]

[0110] In the formula, yes A real-time IoT-BIM collaborative model It is the IoT-BIM collaborative model from the previous moment. This is the currently merged data.

[0111] The steps involved in generating unique digital information for the lifecycle impact module include:

[0112] Based on the design drawings of prefabricated buildings, the building data is input into the IoT-BIM collaborative model.

[0113] Building data may include: basic building information: project name, address, building area, structure type, prefabrication rate, and design service life.

[0114] Component list information: all prefabricated components, dimensions, weight, manufacturer, and design load-bearing capacity.

[0115] Spatial location information: the three-dimensional coordinates of each component in the building and its connection relationship with other components.

[0116] For different lifecycle stages, real-time state data that changes over time is collected as dynamic process data.

[0117] Dynamic process data may include:

[0118] Design phase: Collect "professional collision detection records" and "design change records" through the IoT-BIM collaborative model.

[0119] Production stage: Deploy concrete strength sensors and rebar positioning scanners on the production line to collect data on curing temperature, pouring time, rebar cover thickness deviation, and component flatness. Record this data into the component's production batch number and manufacturing date.

[0120] During the construction phase: Verticality deviation and elevation error are collected using a laser positioning device, and grout density and rebar connection strength are collected using an ultrasonic testing device.

[0121] Operation and maintenance phase: Strain gauges and crack sensors are deployed at beam-column joints and exterior wall panels to collect data on crack width and displacement changes. Monthly electricity and water consumption are collected via smart meters / water meters.

[0122] Demolition phase: "Component damage data" are collected through visual recognition equipment, and "environmental protection data" are collected through environmental monitoring instruments.

[0123] Dynamic process data is associated with components in the IoT-BIM collaborative model through unique component IDs.

[0124] The dynamic process data of the same component at different life cycle stages are sorted by time axis to form a data timeline for time correlation.

[0125] Dynamic process data is categorized into quality, safety, and energy consumption, and then bound to the corresponding attribute columns of the IoT-BIM collaborative model to obtain exclusive digital information.

[0126] like Figure 2 As shown, the steps for the lifecycle impact module to obtain lifecycle impact include:

[0127] Based on building function, structural safety, durability and reliability, ease of use, energy saving and low carbon emissions, and resource regeneration are used as functional evaluation dimensions.

[0128] We analyzed the direct impact paths of different lifecycle stages on functional evaluation dimensions and established a matrix of functional impact relationships at each stage.

[0129] Design phase: Impact on functionality: structural safety, ease of use, energy saving and low carbon emissions.

[0130] Impact path: such as "components are broken down into too many parts → number of nodes increases → connection strength decreases → structural safety risk increases".

[0131] Production stage: Impact on functionality: structural safety, durability and reliability.

[0132] Impact path: such as "insufficient curing temperature → low concrete strength → reduced load-bearing capacity of components → impact on structural safety".

[0133] Construction phase: Impact on functionality: structural safety, ease of use, durability and reliability.

[0134] Impact path: such as "excessive deviation in verticality during hoisting → eccentric stress on components → cracking after long-term use → both structural safety and durability are affected".

[0135] Operation and maintenance phase: Impact on functions: structural safety, durability and reliability, energy saving and low carbon emissions.

[0136] Impact path: such as "failure to repair cracks in time → rainwater seepage → steel reinforcement corrosion → deterioration of structural safety and durability".

[0137] Dismantling and recycling phase: Impact on functions: resource regeneration, environmental safety.

[0138] Impact path: such as "indiscriminate crushing and demolition → damage to reusable components → decline in resource regeneration rate".

[0139] Based on the phased functional impact relationship matrix, key impact indicators that directly reflect the degree of functional impairment are extracted from building data, and corresponding impact rules are formulated for the functional evaluation dimensions.

[0140] Based on the corresponding impact rules and building data, the comprehensive impact value for each life cycle stage is calculated.

[0141] The steps for calculating the overall impact value may include:

[0142] Calculate the impact value of a single function: For example, during the construction phase, "the non-compliance rate of grouting density at nodes is 20%". According to the rule "for every 10% increase in the non-compliance rate, the impact value of durability and reliability functions increases by 0.15", then the impact value of this indicator on "durability and reliability single function = 20% / 10% × 0.15 = 0.3".

[0143] Calculate the overall impact value: For each stage, assign weights according to the importance of the function (e.g., structural safety weight 0.4, durability and reliability weight 0.3, ease of use weight 0.15, energy saving and low carbon weight 0.1, resource regeneration weight 0.05), and sum the individual impact values ​​of each function by weight: Overall impact value of a certain stage = (structural safety impact value × 0.4) + (durability and reliability impact value × 0.3) + (ease of use impact value × 0.15) + (energy saving and low carbon impact value × 0.1) + (resource regeneration impact value × 0.05).

[0144] Based on the magnitude of the comprehensive impact value, the impacts at each stage of the life cycle are classified into different levels, and the life cycle impact is obtained by combining the impact path analysis.

[0145] The grading criteria can be as follows:

[0146] High impact: Overall impact value > 0.6 → Immediate intervention measures are required, such as rework or reinforcement.

[0147] Medium impact: 0.3 ≤ comprehensive impact value ≤ 0.6 → an improvement plan needs to be developed.

[0148] Low impact: Overall impact value < 0.3 → Existing management model can be maintained, with continuous monitoring.

[0149] Lifecycle impacts can include the distribution of impact levels across different lifecycle stages.

[0150] Key influencing factors can be traced back to the production stage, where a high-impact factor stems from the fact that only 82% of concrete strength tests passed.

[0151] The functional impact trend prediction can include the fact that if cracks are not treated during the operation and maintenance phase, the structural safety impact value will rise to 0.7 after 1 year.

[0152] Targeted improvement suggestions could include increasing the frequency of ultrasonic testing for joint grouting during the construction phase.

[0153] The hazard factor determination module is used to determine the hazard factor for different life cycle stages based on life cycle impact and specific digital information, and to construct and determine the hazard threshold for different life cycle stages.

[0154] The risk factor determination module includes:

[0155] The risk indicator screening unit is used to extract risk elements at different life cycle stages from life cycle impacts and proprietary digital information.

[0156] The steps for extracting risk factors using the risk indicator screening unit include:

[0157] Guided by the functional objectives at different lifecycle stages, identify the risk areas that need to be addressed at each stage.

[0158] Design phase: The core functions are "ensuring structural safety, ease of use, and feasibility of production and construction", and the risk factor extraction focuses on "component disassembly compliance, professional collaboration integrity, and design and production and construction matching".

[0159] Production stage: The core function is to "ensure the quality and durability of components, the stability of parameters, and traceability", and the risk factor extraction focuses on "component quality defects, production parameter fluctuations, and the integrity of digital coding".

[0160] Construction phase: The core functions are "ensuring installation accuracy, node reliability, and construction safety", and the risk factor extraction focuses on "installation accuracy deviation, node connection quality, and on-site safety violations".

[0161] Operation and maintenance phase: The core functions are "maintaining structural health, normal facility operation, and controllable energy consumption", and the risk factor extraction focuses on "structural degradation, facility failure, and excessive energy consumption".

[0162] Dismantling / Recycling Phase: The core function is to "achieve component recyclability, environmental compliance, and efficiency standards", and the risk factor extraction focuses on "component damage, environmental violations, and lagging recycling efficiency".

[0163] From the life cycle impact, we extract potential risk factors that negatively affect functionality at different life cycle stages.

[0164] If the lifecycle impact during the design phase shows "overly detailed component breakdown leads to an increase in the number of nodes and a decrease in connection strength", then "insufficient compliance of component breakdown" is initially identified as a potential risk factor.

[0165] If the life cycle impact during the production stage shows that "insufficient concrete curing temperature leads to substandard strength, affecting component durability", then "fluctuation of production parameters" is preliminarily identified as a potential risk factor.

[0166] If the life cycle impact during the construction phase shows that "inadequate grouting at nodes leads to rainwater infiltration and causes component corrosion", then "defects in node connection quality" are preliminarily identified as a potential risk factor.

[0167] Data associated with potential risk factors are selected from proprietary digital information.

[0168] Risk factors may include: Design phase: compliance deviation rate of component breakdown, omission rate of professional collaboration, and mismatch rate between production and construction.

[0169] Production stage: Extract component quality defect rate, production parameter fluctuation, and digital code missing rate.

[0170] During the construction phase: extract the rate of installation accuracy exceeding the standard, the rate of defects in node quality, and the frequency of safety violations.

[0171] Operation and maintenance phase: Extract structural degradation rate, facility failure frequency, and energy consumption exceedance rate.

[0172] Demolition phase: Extraction and recycling damage rate of components, number of environmental violations, and recycling efficiency failure rate.

[0173] The hazard coefficient calculation unit is used to determine the index weights of different risk factors by using the coefficient of variation method and combining the importance of building functions, and to calculate the hazard coefficients at different life cycle stages.

[0174] like Figure 3 As shown, the steps for calculating the hazard factor in the hazard factor calculation unit include:

[0175] The risk factors are standardized, and the ratio of the mean to the standard deviation is used as the coefficient of variation.

[0176] The building functions associated with different life cycle stages are scored and normalized to obtain the functional importance weights. The functional association weights are then allocated to the risk factors according to the relationship between the risk factors and the functions.

[0177] The indicator weights are calculated based on the coefficient of variation and functional association weights, expressed by the following formula:

[0178]

[0179] In the formula, It is the indicator weight. It is the serial number of the risk element. It is a combination of risk factors.

[0180] in,

[0181] In the formula, These are the base weights after normalizing the coefficient of variation. It is a functional association weight. It is the first Adjusted weights for each risk factor.

[0182] in,

[0183] In the formula, It is the coefficient of variation.

[0184] Actual risk data at different lifecycle stages is extracted from proprietary digital information, standardized to obtain target quantified values, and combined with indicator weights to obtain the risk coefficient. The formula is expressed as follows:

[0185]

[0186] In the formula, It is the risk factor. It is the target quantification value.

[0187] The hazard threshold determination unit is used to set hazard thresholds for key risk indicators at different life cycle stages, based on historical data from similar prefabricated building projects.

[0188] The steps for setting the hazard threshold in the hazard threshold determination unit include:

[0189] Data screening criteria were determined based on project type, data completeness, time range, and sample size, and historical data of the same type were collected.

[0190] By analyzing historical data containing risk factors using statistical analysis tools, we can obtain data distribution characteristics, and then match the historical data with risk event records to obtain risk correlation analysis.

[0191] Based on data distribution characteristics and risk correlation analysis, hazard thresholds are established for different life cycle stages.

[0192] Threshold division principles may include:

[0193] Low-risk threshold: This corresponds to the range of indicators where "no risk events occur," ensuring that more than 90% of projects fall within this range and are therefore risk-free.

[0194] Medium risk threshold: This corresponds to the range of indicators where "minor risk events occur," and projects within this range need to activate risk warnings.

[0195] High-risk threshold: The indicator range corresponding to the occurrence of "serious risk events". The "risk event trigger threshold" is the threshold value within which projects within this range must be immediately suspended for rectification.

[0196] The risk tracing module is used to determine whether the risk factor exceeds the risk threshold. If so, it generates a risk tracing solution by associating specific digital information.

[0197] The steps by which the risk tracing module obtains the risk tracing solution include:

[0198] Based on different life cycle stages, the correspondence between risk factors and risk thresholds is compiled, and the comparison frequency is set according to the work rhythm.

[0199] Identify the risk factors exceeding the danger threshold, analyze the root causes of the risks, and formulate a risk tracing plan that includes a risk overview, causal analysis, corrective measures, responsible parties, and implementation details.

[0200] The risk tracing plan may include: Risk overview: Phase: Construction phase.

[0201] Risk factor: 0.65 (0.05 above the limit, high risk).

[0202] Key factors exceeding limits: installation accuracy exceeding the standard rate and node quality defect rate.

[0203] Cause analysis: Installation accuracy exceeded the standard: uncalibrated hoisting equipment, insufficient personnel training, and the influence of strong winds.

[0204] Node quality defects: Grouting personnel did not vibrate according to specifications; the flowability of grout batch HG-20241001 was poor.

[0205] Corrective measures: Equipment: Immediately stop using the JL-08 hoisting equipment and contact a third party for calibration.

[0206] Personnel: Organize retraining for all installation and grouting personnel.

[0207] Materials: The use of grouting material batch HG-20241001 is suspended, and materials from the same batch are sampled for testing.

[0208] Environment: Establish a control rule of "suspending installation when wind speed is greater than level 5" and monitor the weather in real time.

[0209] Responsible party: Equipment calibration: Equipment administrator of the Engineering Department.

[0210] Personnel training: Training specialist in the security department.

[0211] Material sampling inspection: Quality inspector of the Materials Department.

[0212] Completion timeframe: Emergency rectification may include equipment calibration, material sampling inspection, etc.

[0213] Long-term rectification includes personnel training and environmental management.

[0214] This embodiment provides a BIM and IoT-based intelligent management system for the entire lifecycle of prefabricated buildings. By dividing the entire lifecycle of prefabricated buildings into stages and collecting data, it can comprehensively and accurately grasp the status and functional objectives of buildings at different stages, achieving precise management of the entire building lifecycle and improving the overall quality and performance of buildings. Constructing an IoT-BIM collaborative model effectively integrates IoT device data with BIM model data, eliminating data silos, improving data utilization efficiency, and providing more comprehensive and accurate information support for building management. Generating unique digital information and analyzing lifecycle impacts allows for a deeper understanding of the impact of different lifecycle stages on building functions, timely detection of potential functional impairment problems, and proactive prevention and resolution measures to ensure the normal functioning of buildings. By determining risk factors and risk thresholds, it can accurately assess the degree of danger at different lifecycle stages, providing a scientific basis for building safety management, enabling timely measures to reduce risks, and ensuring the safe use of buildings. When risks occur, it can quickly locate the root cause of the risk and provide a detailed risk tracing plan, helping to solve problems in a timely manner, reduce losses caused by risks, and improve the efficiency and reliability of building management.

[0215] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A BIM and Internet of Things based intelligent management system for the whole life cycle of fabricated buildings, characterized in that, include: The phase data acquisition module is used to divide prefabricated buildings into multiple life cycle phases according to the construction process and function, and to collect building data for different life cycle phases. The life cycle impact module is used to construct an IoT-BIM collaborative model, generate exclusive digital information by combining the building data, and analyze the impact of different life cycle stages on building functions to obtain the life cycle impact. The steps for the lifecycle impact module to construct the IoT-BIM collaborative model include: Based on user needs, define building functions, design standards, and construction cycles as requirement parameters, collect topographic data, surrounding environment data, meteorological data, and geological data as geographic information data, and determine the BIM model; Register devices on the Internet of Things platform and collect device data in real time to establish a mapping relationship with the corresponding components in the BIM model; Using a redundancy removal algorithm, the redundancy rate of the mapping relationship is calculated, and redundant data is removed to obtain clean data. The formula is expressed as: In the formula, It's about cleaning data. It is the original data in the mapping relationship. It is data Redundancy rate, It is the redundancy rate threshold; Based on the semantics and timestamps of the cleaning data, data fusion rules are determined, and a linear weighted fusion algorithm is used to fuse the equipment data and BIM model data. The merged data is then updated into the BIM model to obtain the IoT-BIM collaborative model; The steps for generating the specific digital information include: Based on the design drawings of prefabricated buildings, the building data is input into the IoT-BIM collaborative model; For different lifecycle stages, real-time state data that changes over time is collected as dynamic process data; The dynamic process data is associated with the components in the IoT-BIM collaborative model using a unique component ID; The dynamic process data of the same component at different life stages are sorted by time axis to form a data timeline for time correlation; The dynamic process data is categorized into quality, safety, and energy consumption, and then bound to the corresponding attribute columns of the IoT-BIM collaborative model to obtain the exclusive digital information. The risk factor determination module is used to determine the risk factor of different life cycle stages based on the life cycle impact and the specific digital information, and to construct and determine the risk threshold of different life cycle stages. The risk tracing module is used to determine whether the risk factor exceeds the risk threshold. If so, it generates a risk tracing scheme by associating the unique digital information. 2.The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 1, characterized in that, The steps for dividing the data acquisition module into multiple lifecycle stages include: Based on the process of prefabricated buildings from concept to extinction, the entire life cycle is divided into multiple key stages, and the corresponding time boundaries and functional objectives to be guaranteed are clearly defined, and a mapping relationship table is established. Based on the industrial characteristics of prefabricated buildings and combined with multiple key links, the construction process is broken down into multiple independent stages. For multiple independent stages, and in combination with the functional characteristics of prefabricated buildings, the functional objectives and corresponding technical requirements of each stage are determined. 3.The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 1, characterized in that, The steps by which the life cycle impact module obtains the life cycle impact include: Based on the aforementioned building functions, structural safety, durability and reliability, ease of use, energy saving and low carbon emissions, and resource regeneration are used as functional evaluation dimensions. Analyze the direct impact paths of different lifecycle stages on the functional evaluation dimensions and establish a stage-function impact relationship matrix; Based on the stage function impact relationship matrix, key impact indicators that directly reflect the degree of functional impairment are extracted from the building data, and corresponding impact rules are formulated for the functional evaluation dimensions. Based on the corresponding impact rules and the building data, calculate the comprehensive impact value for each life cycle stage; Based on the magnitude of the comprehensive impact value, the impact on each stage of the life cycle is divided into different levels, and the life cycle impact is obtained by combining the impact path analysis.

4. The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 1, characterized in that, The risk factor determination module includes: The risk indicator screening unit is used to extract risk elements for different life cycle stages from the life cycle impact and the specific digital information. The hazard coefficient calculation unit is used to determine the index weights of different risk factors by using the coefficient of variation method and combining the importance of building functions, and to calculate the hazard coefficients at different life cycle stages. The hazard threshold determination unit is used to set the hazard threshold for key risk indicators at different life cycle stages, based on historical data from similar prefabricated building projects.

5. The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 4, characterized in that, The steps for the risk indicator screening unit to extract the risk factors include: Guided by the functional objectives at different lifecycle stages, identify the risk areas that need to be addressed at each stage; From the aforementioned life cycle impacts, potential risk factors that negatively affect functionality at different life cycle stages are extracted; Data associated with the potential risk factors are selected from the proprietary digital information and used as the risk factors. 6.The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 4, characterized in that, The steps for the hazard factor calculation unit to calculate the hazard factor include: The risk factors are standardized, and the ratio of the mean to the standard deviation is used as the coefficient of variation. The building functions associated with different life cycle stages are scored and normalized to obtain the functional importance weights. The functional association weights are then assigned to the risk factors according to their relationship with the functions. The index weight is calculated based on the coefficient of variation and the functional association weight. The risk data for different lifecycle stages is extracted from the proprietary digital information and standardized to obtain the target quantitative value. The risk coefficient is then obtained by combining the indicator weights. 7.The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 4, characterized in that, The step of setting the danger threshold by the danger threshold determination unit includes: Data screening criteria were determined based on project type, data dimension completeness, time range, and sample size, and historical data of the same type were collected. By analyzing historical data containing risk factors using statistical analysis tools, we can obtain data distribution characteristics, and then match the historical data with risk event records to obtain risk correlation analysis. Based on the data distribution characteristics and the risk correlation analysis, the danger thresholds for different life cycle stages are determined. 8.The BIM and Internet of Things based fabricated building whole life cycle intelligent management system according to claim 1, characterized in that, The steps by which the risk tracing module obtains the risk tracing scheme include: Based on different life cycle stages, the correspondence between the risk coefficient and the risk threshold is organized, and the comparison frequency is set according to the work rhythm; The risk coefficient exceeding the aforementioned danger threshold is located, the root cause of the risk is analyzed, and a risk tracing plan is formulated, which includes a risk overview, causal analysis, corrective measures, responsible parties, and completion implementation.

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