A BIM-based viscous damping seismic optimization method and system for medical buildings

Abstract

The present invention relates to the field of computer-aided management technology and discloses a BIM-based viscous damping seismic optimization method and system for medical buildings. The method comprises: extracting attribute data of functional areas within the medical building based on the BIM model and determining the risk drift weight of each functional area; establishing a viscous damping life model based on the risk drift weight; establishing a multi-objective optimization function using the risk drift weight and the viscous damping life model to generate an optimal viscous damping configuration scheme; and collecting viscous damping status data in real time during the operation of the medical building, comparing it with a preset operation and maintenance strategy, and automatically adjusting parameters and updating the BIM model when the viscous damping status data deviates from a threshold. This method significantly enhances seismic performance and resource utilization efficiency, overcoming the shortcomings of the prior art, which are static design, passive maintenance, and lack of dynamic adaptability.

Description

Technical Field

[0001] The present invention relates to the field of computer-aided management technology, and in particular to a BIM-based viscous damping seismic optimization method and system for medical buildings. Background Art

[0002] With the increasing complexity of medical building functions and the improvement of safety standards, viscous dampers, as important passive control devices to improve the seismic performance of buildings, have been widely used in medical building structures. Existing technologies mainly determine the configuration parameters of viscous dampers based on structural static analysis and seismic response prediction during the design phase to improve the overall seismic performance. However, this type of static optimization method has obvious limitations: on the one hand, it lacks a response mechanism to the dynamic changes in risks during building operation, and cannot flexibly adjust the damper configuration based on the actual usage of functional areas, changes in personnel density, and equipment operating status. On the other hand, viscous dampers will experience performance degradation during long-term operation. Existing operation and maintenance methods mostly rely on regular inspections and empirical judgment, lacking real-time monitoring and intelligent adjustment methods, which can easily lead to the accumulation of equipment risks or waste of maintenance resources.

[0003] Furthermore, while BIM (Building Information Modeling) technology has been applied in building design and construction, the deep integration of BIM and intelligent algorithms in the dynamic optimization and operation and maintenance management of viscous damping systems is still in its exploratory stages. Existing BIM platforms primarily focus on static information management and lack the ability to connect with real-time data, making it difficult to support the dynamic configuration optimization and intelligent operation and maintenance requirements of viscous dampers throughout the building lifecycle. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the viscous damper configuration in existing medical buildings cannot dynamically respond to risk changes and life attenuation, and lacks intelligent optimization and BIM integrated management.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a BIM-based viscous damping seismic optimization method for medical buildings, comprising: extracting attribute data of functional areas within the medical building based on the BIM model, and determining the risk drift weight of each functional area;

[0007] Based on the risk drift weight, a viscous damping life model is established;

[0008] A multi-objective optimization function is established using the risk drift weight and viscous damping life model to generate the optimal viscous damping configuration scheme;

[0009] During the operation of the medical building, the status data of the viscous damping is collected in real time and compared with the preset operation and maintenance strategy. When the viscous damping status data deviates from the threshold, the parameters are automatically adjusted and updated to the BIM model.

[0010] As a preferred solution of the BIM-based viscous damping seismic optimization method for medical buildings described in the present invention, the attribute data of the functional areas within the medical building include personnel density, equipment load, regional functional requirements, and environmental factors;

[0011] Determining the risk drift weight includes reasoning on the attribute data of each functional area through a fuzzy logic algorithm to obtain the dynamic risk weight of each functional area; and using the hierarchical analysis method to perform a structured evaluation on the static attributes to obtain the static risk weight of each functional area; and weightedly fusing the dynamic risk weight with the static risk weight to form the risk drift weight.

[0012] As a preferred solution of the BIM-based viscous damping seismic optimization method for medical buildings described in the present invention, the establishment of a viscous damping life model includes establishing a basic life decay model based on the risk drift weight, the fatigue factor of the viscous damping material recorded in the BIM model, the structural force influence coefficient and the environmental parameters; and during the operation of the medical building, the basic life decay rate is dynamically corrected based on the risk drift weight and the viscous damping performance deviation value, and the viscous damping life decay rate is updated in real time.

[0013] As a preferred solution of the BIM-based viscous damping seismic optimization method for medical buildings described in the present invention, the multi-objective optimization function is to dynamically optimize the configuration parameters of the viscous damping in each functional area, with seismic safety as the core, while taking into account the lifespan and resource efficiency of the viscous damping;

[0014] A particle swarm optimization algorithm is used to solve a multi-objective optimization function to generate the optimal viscous damping configuration scheme;

[0015] The particle swarm optimization algorithm includes dynamically adjusting the inertia weight parameters of particles according to the risk weight and life decay rate calculated in real time, so that the particle search range adaptively converges or expands as the risk and life status changes.

[0016] As a preferred solution of the BIM-based viscous damping seismic optimization method for medical buildings described in the present invention, the particle swarm optimization algorithm further includes, after the particles update the configuration parameters, dynamically adjusting the particle positions based on a risk-driven dynamic optimization constraint mechanism to ensure that the particles always complete the search within the dynamic feasible domain;

[0017] The particles are evaluated by the fitness function, and the individual optimal solution and the global optimal solution are continuously updated iteratively. When the fitness meets the preset threshold or reaches the maximum number of iterations, the optimal viscous damping configuration scheme is output.

[0018] As a preferred solution of the BIM-based viscous damping seismic optimization method for medical buildings described in the present invention, the risk-driven dynamic optimization constraint mechanism includes dynamically generating real-time constraints using risk drift weights, calculating real-time constraint upper limits, and generating a safety threshold for structural response in real time. When the structural response corresponding to a particle exceeds the safety threshold, the particle position is scaled and corrected according to a preset ratio.

[0019] As a preferred solution of the BIM-based viscous damping seismic optimization method for medical buildings described in the present invention, the comparison with the preset operation and maintenance strategy includes dynamically setting performance thresholds for different areas based on the risk drift weights of each functional area; and calculating the health index of the viscous damping using the real-time status data of the viscous damping.

[0020] When it is detected that the health indicator deviates from the performance threshold, the system executes a differentiated adjustment strategy according to the degree of deviation, including automatically executing a linear correction strategy to make small optimization adjustments to the viscous damping configuration parameters when the degree of deviation is within the warning range;

[0021] When the degree of deviation exceeds the alarm threshold, the incremental particle swarm optimization algorithm is triggered to recalculate and generate the optimal viscous damping configuration parameters for the functional area based on the current risk status and the viscous damping life status.

[0022] A BIM-based viscous damping seismic optimization system for medical buildings, including:

[0023] The weight module extracts attribute data of functional areas in medical buildings based on the BIM model and determines the risk drift weight of each functional area;

[0024] Life module, which establishes a viscous damping life model based on risk drift weight;

[0025] The solution module uses the risk drift weight and viscous damping life model to establish a multi-objective optimization function and generate the optimal viscous damping configuration solution;

[0026] The adjustment module collects the status data of viscous damping in real time during the operation of the medical building, compares it with the preset operation and maintenance strategy, and automatically adjusts the parameters and updates it to the BIM model when the viscous damping status data deviates from the threshold.

[0027] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0028] A computer-readable storage medium stores a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0029] Beneficial effects of the present invention: The BIM-based viscous damping seismic optimization method for medical buildings provided by the present invention realizes differentiated safety management of the viscous damping configuration of medical buildings by extracting functional area attribute data based on the BIM model and dynamically calculating the risk drift weight; constructs a viscous damping life model in combination with risk weights, thereby improving the controllability and durability of the equipment throughout its life cycle; adopts multi-objective optimization function and particle swarm algorithm to dynamically adjust configuration parameters, significantly enhancing seismic performance and resource utilization efficiency; introduces risk-driven dynamic constraint mechanism and real-time operation and maintenance strategy to ensure automatic response to risk changes and equipment status deviations during building operation, quickly completes parameter optimization and BIM model synchronous update, and overall improves the seismic resilience, operation and maintenance intelligence level and management efficiency of medical buildings, overcoming the shortcomings of static design, passive maintenance and lack of dynamic adaptability in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 An overall flow chart of a BIM-based medical building viscous damping seismic optimization method provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0033] Example 1, reference Figure 1 , as one embodiment of the present invention, provides a BIM-based viscous damping seismic optimization method for medical buildings, comprising:

[0034] S1: Extract attribute data of functional areas in medical buildings based on the BIM model, determine the risk drift weight of each functional area, and establish a viscous damping life model.

[0035] Based on the BIM model, the medical building is divided into functional areas and the basic attributes of each area are defined. Functional areas include: ICU (Intensive Care Unit), operating room, inpatient ward, public areas (such as corridors and reception halls), and equipment areas.

[0036] The attribute data of each functional area is automatically extracted through the BIM platform and recorded in the database. This step provides basic data support for subsequent risk assessment and optimization.

[0037] The attribute data of functional areas include personnel density, equipment load, regional functional requirements and environmental factors.

[0038] Through the fuzzy logic reasoning mechanism driven by multi-dimensional attribute data, a comprehensive assessment of the personnel density, equipment load, functional requirements and environmental factors of the functional areas was carried out. The analytic hierarchy process (AHP) was introduced to carry out hierarchical correction of the strategic importance of the functional areas, realizing differentiated adjustment of risk weights. Through the linkage mechanism of fuzzy logic algorithm and AHP, a dynamic drift model of risk weights was established and converted into risk weight coefficients, so that the risk assessment results can be automatically adjusted as key indicators change during the operation of the building.

[0039] Based on the functional area's attribute parameters such as personnel density, equipment load, structural importance, and environmental impact, and combined with the weight relationship of each parameter's impact on seismic risk, a calculation model for the functional area's initial risk index is constructed. The formula is expressed as follows:

[0040] ;

[0041] in, Indicates the Population density in functional areas, Indicates the Equipment load of functional area; Indicates the Structural importance coefficient of functional area; Indicates the Environmental impact factors of functional areas; 、 、 、 is the corresponding weight coefficient, which is set according to the medical building design specifications and empirical data to ensure that the impact of each attribute on the risk index is reasonably reflected; Indicates the Initial risk indicators for functional areas.

[0042] During the use of medical buildings, fuzzy logic algorithms are introduced to construct dynamic adjustment factors based on the attribute data of real-time functional areas. , to achieve dynamic drift of risk weights. The dynamic risk weight calculation formula is:

[0043] ;

[0044] in, Represents a fuzzy membership function that automatically adjusts the risk level based on real-time input parameters; Indicates the Dynamic risk weights for functional areas.

[0045] Through fuzzy logic algorithms, intelligent reasoning of real-time status can be performed to dynamically reflect the actual seismic resistance needs of each functional area under different operating conditions, thereby improving the flexibility and accuracy of risk management.

[0046] The risk assessment of medical building functional areas is divided into three layers: target layer: functional area risk level determination; criterion layer: main factors affecting risk, including: personnel density , equipment load , structural importance and environmental impact ; Plan layer: specific functional areas.

[0047] Based on the four factors of the criterion layer and expert experience, a pairwise comparison judgment matrix is ​​constructed. , assuming that the personnel density is twice as important as the equipment load, the equipment load is as important as the structure, and the environmental impact is relatively weak, we can get:

[0048] ;

[0049] Among them, the matrix It reflects the relative importance of each factor to the risk level and follows the Saaty standard ratio scale.

[0050] By normalization and eigenvalue method, the weight vector of the judgment matrix is ​​calculated , according to the attribute values ​​of each functional area, use the weighted summation method to calculate the functional area The static risk weight of

[0051] ;

[0052] in, represents the population density weight, Indicates the equipment load weight, represents the structural importance weight, represents the environmental impact weight; Indicates the Static risk weights for functional areas.

[0053] In order to further enhance the structured decision-making ability of risk assessment, dynamic risk weights Static risk weights calculated based on the analytic hierarchy process (AHP) Perform weighted fusion to form risk drift weights :

[0054] ;

[0055] in, , set the balance between dynamic and static weights according to actual needs.

[0056] Based on the viscous damping material properties, installation structure information, and environmental parameters recorded in the BIM platform, a basic life attenuation model of viscous damping is established. The formula is:

[0057] ;

[0058] in, is the fatigue factor of the viscous damping material; is the structural force influence coefficient; Environmental impact factors; 、 、 is the weight coefficient, which is determined based on engineering experience and historical data.

[0059] Based on risk drift weight and viscous damping operating state deviation , dynamically adjust the basic life decay rate to form a real-time updated life decay rate , and its calculation formula is:

[0060] ;

[0061] in, is the risk-sensitive adjustment coefficient, which is used to reflect the impact of risk changes on the lifespan decay rate; is the performance deviation adjustment coefficient, which reflects the dynamic correction of the life prediction due to the actual degradation of viscous damping; Indicates the deviation rate between the current performance of viscous damping and the standard performance, calculated based on the real-time monitoring data of the sensor; Represents the initial life decay rate.

[0062] To address the technical challenges of static, lagging, and lack of dynamic adaptability in risk assessment and lifespan management of viscous damping configurations in medical buildings, a proposed solution introduces a fuzzy logic reasoning mechanism driven by multidimensional attribute data, combined with the Analytic Hierarchy Process (AHP) to dynamically adjust the strategic importance of functional areas, significantly improving the flexibility and accuracy of risk assessment. Unlike existing technologies that rely solely on fixed parameters or a single assessment model, this solution constructs a dynamic risk drift weighting model that intelligently adjusts the risk level of each functional area based on real-time changes in occupant density, equipment load, structural importance, and environmental factors, ensuring that the seismic requirements of key areas remain dynamically controllable during the operation of medical buildings. Furthermore, by integrating static risk weights with dynamic adjustment factors, a structured and evolvable risk management system is formed, addressing the technical flaw of traditional risk assessments that cannot respond to changes in building usage and improving the scientific and real-time nature of overall seismic configuration decision-making.

[0063] Furthermore, in terms of viscous damping life management, the adopted technical solution is based on the component information and sensor monitoring data of the BIM platform, and a dynamic life decay model is established that integrates risk factors and operating status deviations. Different from the static life prediction method used in existing technologies, this model can reflect the actual degradation trend of viscous damping under different risk environments and stress conditions in real time by introducing a risk-sensitive adjustment coefficient and a performance deviation adjustment mechanism, thereby realizing dynamic correction and precise control of life prediction. This technical improvement effectively avoids the misjudgment problem caused by ignoring risk fluctuations and equipment status changes in traditional life management, improves the long-term reliability and operation and maintenance efficiency of the viscous damping configuration, and ensures a balance between safety and durability throughout the life cycle of the building.

[0064] S2: A multi-objective optimization function is established using the risk drift weight and the viscous damping life model to generate the optimal viscous damping configuration scheme.

[0065] The risk drift weight and viscous damping life model are used to establish a system with seismic safety as the core goal. Through a dynamic control mechanism, the configuration parameters of viscous damping in each functional area are dynamically optimized while taking into account equipment life and resource efficiency. The multi-objective optimization function is Represents functional area The viscous damping parameter combination (including damping coefficient, installation position, etc.).

[0066] The multi-objective optimization function formula is:

[0067] ;

[0068] in, Indicates configuration parameters Structural response under

[0069] Represents the optimization goal, find the one that minimizes the multi-objective optimization function , that is, the optimal viscous damping configuration scheme. Indicates the number of functional areas; Indicates configuration parameters Next, the functional area Structural response; Indicates configuration parameters and time Next, the functional area The viscous damping life decay rate; represents the lifespan control coefficient.

[0070] Initialize particles , each particle represents a viscous damping configuration parameter solution ; Randomly generate initial position and speed ; Record particles The historical optimal solution and the global optimal solution of particle swarm .

[0071] In the In the iteration, the particle updates its velocity and position according to the following formula:

[0072]

[0073]

[0074] in, represents the learning factor; Represents a random number to enhance search diversity. Represents particles In the The speed of iterations.

[0075] Represents functional area The corresponding particle The adaptive inertia weight of the functional area risk state and equipment life pressure of the iteration is as follows:

[0076] ;

[0077] in, 、 Represents the control weight coefficient of risk and life span, satisfying , flexibly set priorities according to medical building operation and maintenance strategies; Represents functional area Real-time risk drift weight; Indicates the iteration round. 、 Indicates the maximum and minimum values ​​of the inertia weight. Indicates the maximum reference value of the risk drift weight; Indicates the maximum reference value of the life decay rate.

[0078] After each update, calculate the current position of the particle Fitness under multi-objective optimization function:

[0079] ;

[0080] renew and .

[0081] In each round of particle swarm iteration, a risk-driven dynamic optimization constraint adaptation mechanism is introduced to dynamically generate real-time constraints using risk drift weights and calculate the upper limit of the real-time constraints:

[0082] ;

[0083] in, Represents functional area Upper limit of initial structural response; is the dynamic constraint sensitivity coefficient; Indicates the risk weight reference benchmark value.

[0084] like, Scale proportionally , and then immediately recalculate the fitness:

[0085] ;

[0086] like , Then update all particles .

[0087] When the fitness is less than the fitness threshold, the iteration stops; or when the maximum number of iterations is reached, the iteration stops and the current iteration step is output. Optimal viscous damping configuration scheme .

[0088] The optimal viscous damping configuration scheme for medical buildings under the current risk-life status is directly written back to BIM and issued for implementation.

[0089] To address the issues of static parameter settings and the inability to respond to changes in risk and equipment lifespan in existing viscous damping configuration methods in real time, a dynamic multi-objective optimization mechanism combining risk drift weights and a lifespan model was proposed. By constructing an optimization function with comprehensive objectives for seismic safety, equipment lifespan, and resource efficiency, configuration parameters can be dynamically adjusted within different functional areas based on the real-time risk level and damper operating status, significantly improving the flexibility and accuracy of the configuration strategy. Furthermore, an adaptive inertia weight control method was introduced during the optimization process, enabling the particle swarm algorithm to intelligently adjust the search depth and convergence speed based on risk and lifespan pressures, avoiding the local optimum and computational inefficiency often encountered by traditional optimization algorithms in complex environments. Furthermore, through a risk-driven dynamic constraint adaptation mechanism, a safety boundary for the structural response is generated in real time, and particle positions are proportionally corrected to ensure that the optimization process remains within a safe and controllable range, enhancing seismic protection capabilities in high-risk environments.

[0090] Furthermore, the above-mentioned improvement measures effectively break through the technical bottleneck of the existing technology that the viscous damping configuration scheme cannot dynamically adapt to changes in the building's operating status, and realize intelligent optimization based on real-time risk perception and life management. Through the synergistic effect of dynamic multi-objective optimization functions and particle swarm adaptive control, the configuration parameters can extend the service life of equipment, reduce unnecessary maintenance frequency, and reduce operation and maintenance costs while ensuring structural safety. At the same time, combined with the dynamic constraint mechanism, it ensures that the configuration range is automatically tightened when the risk increases, strengthens safety redundancy, and releases optimization space when the risk decreases, thereby improving resource allocation efficiency. Finally, the optimization results are directly synchronized to the BIM model, realizing closed-loop management from data collection, intelligent optimization to building information model updates, enhancing the stability, reliability and intelligence level of the viscous damping system throughout the life cycle of medical buildings, and having good engineering application value and promotion prospects.

[0091] S3: During the operation of the medical building, the viscous damping status data is collected in real time and compared with the preset operation and maintenance strategy. When the viscous damping status data deviates from the threshold, the parameters are automatically adjusted and updated to the BIM model.

[0092] Multiple sensors are deployed on viscous dampers within medical buildings to collect critical operational data, including damping force, displacement amplitude, and ambient temperature and humidity. These sensors are connected to an edge computing gateway via wired or wireless connections for real-time data aggregation and preliminary preprocessing, including noise filtering and health indicator extraction. The processed monitoring data is uploaded to a cloud-based operations and maintenance platform via a transmission protocol and synchronously mapped to the corresponding component attributes in the BIM model.

[0093] In the operation and maintenance platform, based on the risk drift weight of each functional area, the performance thresholds of the dynamic operation and maintenance strategies for different areas are preset. The formula is expressed as:

[0094] ;

[0095] in, is the initial performance threshold, Represents the risk adjustment factor, ensuring that performance requirements are automatically tightened when risks increase. Functional Area At the moment performance thresholds below.

[0096] When the system receives real-time data, it calculates the health indicator deviation rate :

[0097] ;

[0098] when When , it is in the warning state, and a fast linear correction is performed, and the viscous damping parameters are fine-tuned to quickly complete a small optimization. When , the alarm state triggers the incremental particle swarm optimization, and the optimal damping parameters of the functional area are recalculated based on the current risk and life state.

[0099] After parameter adjustments are completed, the system writes the updated results to the attribute information of the corresponding viscous damping component through the BIM platform interface, achieving real-time synchronization with the building information model. Simultaneously, the operation and maintenance system generates an adjustment log, recording the triggering cause, adjustment process, and effective parameters, facilitating subsequent operation and maintenance traceability and management. Based on continuously collected operational data, the system dynamically adjusts the life prediction model and operation and maintenance strategy, forming a closed-loop optimization mechanism to improve the accuracy and response speed of subsequent risk assessment and parameter adjustments.

[0100] In response to the dynamic management needs of viscous dampers during the operation of medical buildings, existing technologies generally have fixed operation and maintenance strategies, static threshold settings, and an inability to effectively respond to changes in risks and fluctuations in equipment status. By introducing a dynamic threshold adjustment mechanism based on risk drift weights, automatic regulation of performance thresholds that change in real time with the risk level of functional areas is achieved, avoiding the situation where traditional fixed thresholds are insufficiently responsive in high-risk environments or waste resources in low-risk conditions. At the same time, combined with a graded judgment system for health indicator deviation rates, the operation and maintenance response strategies corresponding to different degrees of deviation are distinguished. For the first time, a linkage mechanism of rapid linear correction and intelligent incremental optimization is implemented in viscous damping management, improving the flexibility and accuracy of parameter adjustment.

[0101] By deeply integrating real-time monitoring data, dynamic threshold control, and optimization algorithms, this solution effectively establishes a closed loop of intelligent operation and maintenance for the entire life cycle of viscous dampers. Compared with the existing methods that rely on manual inspections or periodic maintenance, it can quickly respond to the initial abnormality of equipment status, reducing the risk of failure and maintenance costs. At the same time, parameter adjustment results are synchronized in real time through the BIM model to ensure the dynamic consistency and visual management capabilities of building information, facilitating subsequent operation and maintenance traceability and strategy optimization. Continuous data collection and model self-correction mechanisms further improve the accuracy of risk judgment and the system's adaptability, significantly enhancing the seismic safety and operation and maintenance efficiency of medical buildings in complex operating environments.

[0102] Example 2, an embodiment of the present invention, provides a BIM-based viscous damping seismic optimization system for medical buildings, including:

[0103] The weight module extracts the attribute data of functional areas in medical buildings based on the BIM model and determines the risk drift weight of each functional area.

[0104] The life module establishes a viscous damping life model based on risk drift weights.

[0105] The solution module uses the risk drift weight and viscous damping life model to establish a multi-objective optimization function and generate the optimal viscous damping configuration solution.

[0106] The adjustment module collects the status data of viscous damping in real time during the operation of the medical building, compares it with the preset operation and maintenance strategy, and automatically adjusts the parameters and updates it to the BIM model when the viscous damping status data deviates from the threshold.

[0107] Example 3, an embodiment of the present invention, is different from the previous two embodiments in that:

[0108] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0109] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0110] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0111] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0112] Example 4 is an embodiment of the present invention, which provides a BIM-based viscous damping seismic optimization method and system for medical buildings. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0113] A medical building has five functional areas: ICU (Intensive Care Unit), operating room, inpatient ward, corridor, and equipment area. First, a building BIM model was created using Revit software. The following attributes were imported into each area: ICU density of 2.40 people / m2, equipment load of 12.50 kW, structural importance coefficient of 1.00, and environmental impact factor of 0.85; operating room density of 1.80 people / m2, 9.20 kW, 0.90, and 0.80; ward density of 1.20 people / m2, 4.50 kW, 0.60, and 0.70; corridor density of 0.60 people / m2, 2.00 kW, 0.40, and 0.60; and equipment area density of 0.50 people / m2, 15.00 kW, 0.80, and 0.90. Fuzzy logic and the analytic hierarchy process (AHP) were used to calculate dynamic and static risk weights, respectively, and then weighted and integrated in a ratio of 0.7:0.3 to obtain the risk drift weight for each area.

[0114] Based on the aforementioned risk weights and the damper material fatigue factor of 0.02, force influence coefficients, and environmental parameters extracted from the BIM model, a basic life decay model was established. Combined with performance deviations extracted from real-time monitoring, the life decay rates were updated using a dynamic correction formula. The initial life decay rates were calculated to be 0.030 for the ICU, 0.028 for the operating room, 0.020 for the ward, 0.015 for the corridor, and 0.025 for the equipment area.

[0115] Subsequently, a multi-objective optimization function was constructed, balancing minimization of structural seismic response, extended damper life, and efficient operation and maintenance resources. A modified particle swarm optimization (PSO) algorithm was employed to solve the problem. During the PSO iteration, the inertia weights were adaptively adjusted based on real-time risk weights and life decay rates. After each position update, the excess solution was scaled back to the feasible region using a risk-driven dynamic constraint mechanism. To validate the results, a maximum number of iterations was set to 100, with a convergence threshold of 0.001.

[0116] During the building's operational phase, each damper was equipped with displacement and temperature and humidity sensors with a sampling frequency of 50Hz. This data was uploaded to an edge computing node via LoRaWAN, where Kalman filtering and wavelet decomposition were performed to extract health indicators (damping ratio and energy dissipation rate). The operations and maintenance platform dynamically generated performance thresholds based on the risk weights of each zone and calculated the health indicator deviation rate in real time. If the deviation rate was within the 5%–15% range, a linear correction was performed. If the deviation rate exceeded 15%, an incremental PSO was initiated, and the parameters were updated after 20 iterations. All adjustment results were synchronized back to the BIM model via the REST interface, with the current status indicated in red, orange, and green.

[0117] Risk drift weights: ICU 0.85, operating room 0.78, ward 0.52, corridor 0.35, equipment area 0.65. Initial life decay rate: ICU 0.030±0.00, operating room 0.028±0.00, ward 0.020±0.00, corridor 0.015±0.00, equipment area 0.025±0.00.

[0118] Structural responses before and after optimization (maximum inter-story displacement, unit: mm): ICU decreased from 12.35 to 8.47; operating room decreased from 10.20 to 7.15; ward decreased from 8.80 to 6.22; corridor decreased from 5.60 to 4.13; equipment area decreased from 9.45 to 6.95.

[0119] Life expectancy prediction before and after optimization (years): ICU increased from 8.20 to 9.45; operating room increased from 8.50 to 9.60; ward increased from 10.00 to 10.85; corridor increased from 12.50 to 13.40; equipment area increased from 9.00 to 10.10.

[0120] Optimization calculation time: The average time for a single global optimization is 125.40 seconds, and the average time for incremental optimization is 18.75 seconds. Compared with the traditional static PSO, the calculation amount is reduced by about 32.50%.

[0121] The above experiments demonstrate that, by comparing traditional static optimization methods with the dynamic risk-lifetime linkage optimization described in this embodiment, the seismic performance of medical building structures can be significantly improved and the service life of viscous dampers can be extended. First, from the perspective of structural response, the maximum interstory drift in high-risk areas such as the ICU was significantly reduced from 12.35 mm to 8.47 mm, a decrease of 31.42%. The reductions in operating rooms and equipment areas were 29.90% and 26.50%, respectively. This demonstrates that the risk-driven dynamic constraint scaling mechanism prioritizes vibration reduction at key nodes in high-risk areas. On the other hand, low-risk areas such as corridors saw a 26.30% reduction in response, freeing up optimization resources while ensuring performance, demonstrating the flexibility and efficiency of dynamic adjustment. Second, from the perspective of lifespan prediction, the lifespan of the high-risk ICU increased from 8.20 years to 9.45 years, a 15.24% increase; the operating room increased by 12.94%, with an overall average increase of approximately 9.17%. This demonstrates that incorporating dynamic correction of the lifespan decay rate into the objective function effectively delays device performance degradation, reducing maintenance frequency and costs. Thirdly, the incremental optimization mechanism reduces the average recalculation time upon alarm triggering to 18.75 seconds, which is 32.50% less than the overall verification optimization time of 125.40 seconds, proving that the adoption of a local iteration strategy can significantly shorten the operation and maintenance response cycle while ensuring accuracy. Finally, through real-time updates and visualization of the BIM model, the maintenance team can intuitively obtain the damper status and adjustment history, improving management transparency and decision-making efficiency. In summary, the embodiment overcomes the shortcomings of static optimization and offline operation and maintenance of existing technologies through the deep integration of risk perception, life management and intelligent algorithms, demonstrating significant innovation and practical value.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A BIM-based viscous damping seismic optimization method for medical buildings, characterized by: include: Extract attribute data of functional areas in medical buildings based on BIM models and determine the risk drift weight of each functional area; Based on the risk drift weight, a viscous damping life model is established; A multi-objective optimization function is established using the risk drift weight and viscous damping life model to generate the optimal viscous damping configuration scheme; During the operation of the medical building, the viscous damping status data is collected in real time and compared with the preset operation and maintenance strategy. When the viscous damping status data deviates from the threshold, the parameters are automatically adjusted and updated to the BIM model. Determining the risk drift weight includes reasoning the attribute data of each functional area through a fuzzy logic algorithm to obtain a dynamic risk weight of each functional area; The analytic hierarchy process is used to conduct a structured evaluation of static attributes and obtain the static risk weight of each functional area; The dynamic risk weight and the static risk weight are weighted and integrated to form the risk drift weight; The establishing of the viscous damping life model includes establishing a basic life decay model based on the risk drift weight, the fatigue factor of the viscous damping material recorded in the BIM model, the structural stress influence coefficient and the environmental parameters; and dynamically correcting the basic life decay rate based on the risk drift weight and the viscous damping performance deviation value during the operation of the medical building, thereby updating the viscous damping life decay rate in real time; The multi-objective optimization function is to dynamically optimize the configuration parameters of the viscous damping in each functional area, with seismic safety as the core, while taking into account the life and resource efficiency of the viscous damping; A particle swarm optimization algorithm is used to solve a multi-objective optimization function to generate the optimal viscous damping configuration scheme; The particle swarm optimization algorithm includes dynamically adjusting the inertia weight parameters of particles according to the risk weight and life decay rate calculated in real time, so that the particle search range adaptively converges or expands as the risk and life status changes.

2. The BIM-based viscous damping seismic optimization method for medical buildings according to claim 1, characterized in that: The attribute data of the functional areas in the medical building include personnel density, equipment load, regional functional requirements and environmental factors.

3. The BIM-based viscous damping seismic optimization method for medical buildings according to claim 2, characterized in that: The particle swarm optimization algorithm further includes, after the particles update the configuration parameters, dynamically adjusting the particle positions based on a risk-driven dynamic optimization constraint mechanism to ensure that the particles always complete the search within the dynamic feasible domain; The particles are evaluated by the fitness function, and the individual optimal solution and the global optimal solution are continuously updated iteratively. When the fitness meets the preset threshold or reaches the maximum number of iterations, the optimal viscous damping configuration scheme is output.

4. The BIM-based viscous damping seismic optimization method for medical buildings according to claim 3, characterized in that: The risk-driven dynamic optimization constraint mechanism includes dynamically generating real-time constraints using risk drift weights, calculating real-time constraint upper limits, and generating a safety threshold for structural responses in real time. When the structural response corresponding to a particle exceeds the safety threshold, the particle position is scaled and corrected according to a preset ratio.

5. The BIM-based viscous damping seismic optimization method for medical buildings according to claim 4, characterized in that: The comparison with the preset operation and maintenance strategy includes dynamically setting performance thresholds for different areas based on the risk drift weights of each functional area; calculating the health index of the viscous damper using the real-time status data of the viscous damper; When it is detected that the health indicator deviates from the performance threshold, the system executes a differentiated adjustment strategy according to the degree of deviation, including automatically executing a linear correction strategy to make small optimization adjustments to the viscous damping configuration parameters when the degree of deviation is within the warning range; When the degree of deviation exceeds the alarm threshold, the incremental particle swarm optimization algorithm is triggered to recalculate and generate the optimal viscous damping configuration parameters for the functional area based on the current risk status and the viscous damping life status.

6. A BIM-based viscous damping seismic optimization system for medical buildings using the method according to any one of claims 1 to 5, characterized in that: The weight module extracts attribute data of functional areas in medical buildings based on the BIM model and determines the risk drift weight of each functional area; Life module, which establishes a viscous damping life model based on risk drift weight; The solution module uses the risk drift weight and viscous damping life model to establish a multi-objective optimization function and generate the optimal viscous damping configuration solution; The adjustment module collects the status data of viscous damping in real time during the operation of the medical building, compares it with the preset operation and maintenance strategy, and automatically adjusts the parameters and updates it to the BIM model when the viscous damping status data deviates from the threshold.

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