A method and system for ventilation optimization for overhead layer basements

Abstract

The application relates to the technical field of building ventilation, in particular to a ventilation optimization method and system for an overhead layer basement. The method comprises the following steps: collecting basement space structure data and load-bearing member distribution information, constructing a three-dimensional physical model, and performing load-bearing interference checking on the opening position to obtain a safe ventilation opening candidate set; dividing a functional area according to a space layout, planning a mobile drying fan operation station and a traversal path, and collecting multiple groups of measured data; calibrating a boundary condition of a digital twin simulation model based on the measured data; performing global permutation and combination simulation on the ventilation opening position and the fan station as bivariate; constructing a multi-objective optimization model considering ventilation effect, humidity uniformity and energy consumption, and screening an optimal coupling scheme; and outputting a standardized construction design and realizing dynamic regulation and control. The method realizes coupling optimization of the ventilation opening position and the fan station, and improves the ventilation and dehumidification efficiency and effect of the overhead layer basement.

Description

Technical Field

[0001] This invention relates to the field of building wall construction technology, specifically to a ventilation optimization method and system for elevated basements. Background Technology

[0002] With the acceleration of urbanization and the continuous development of building technology, the scale of modern residential communities is expanding, and the utilization rate of underground space in buildings is significantly improving. As an important component of residential buildings, the ventilation and dehumidification effects of the basement directly affect the overall living comfort, structural durability, and service life of the building.

[0003] Existing technologies include methods to achieve indoor and outdoor air convection and ventilation by opening ventilation holes in the basement enclosure structure, such as patent CN202111184737.X "An Indoor and Outdoor Ventilation Control System and Control Method", which discloses a technical solution of opening ventilation holes in the elevated floor and the underground enclosure structure, and using the holes and fans to construct an indoor and outdoor connected ventilation system; there are also technologies that use dehumidifying fans for moisture control in underground spaces, such as patent CN201910455851.8 "A Passive Multi-Interval Anti-Condensation Control System and Control Method", which focuses on multi-zone condensation prevention and control in enclosed spaces, and uses fans to provide targeted ventilation and dehumidification in areas with high condensation incidence.

[0004] Although installing ventilation openings and dehumidifying fans have become standard techniques in basement ventilation and dehumidification, current basement ventilation designs in elevated floors remain relatively rudimentary. On the one hand, it's difficult to implement precise, zoned dehumidification control based on the varying humidity distribution in different areas of the basement, easily leading to insufficient dehumidification in certain areas or overall energy waste. On the other hand, the methods for evaluating and verifying ventilation effectiveness are lagging, relying mainly on on-site measurements after project completion. There's a lack of predictive simulations, scheme comparisons, and early optimization capabilities during the design phase. This makes it impossible to identify potential defects such as unreasonable ventilation paths, improper opening locations, uneven airflow distribution, and insufficient dehumidification coverage before construction, resulting in difficulties in effectively optimizing the design in the early stages and significant challenges and costs for later rectification. Furthermore, the lack of dynamic control capabilities prevents real-time adjustments to operating strategies based on actual environmental parameters, easily leading to long-term dampness and mold growth in continuously humid environments, directly impacting residents' living experience and the quality of the building environment. These problems collectively contribute to the current predicament of low efficiency in elevated floor basement ventilation and dehumidification design, poor system operation, and high maintenance costs. Summary of the Invention

[0005] In view of this, the problem to be solved by the present invention is to provide a ventilation optimization method and system for elevated basement floors.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A ventilation optimization method for a basement with an open floor plan includes the following steps: S1. Collect the full-area basic feature data of the elevated floor and basement, and construct a three-dimensional physical model; based on the building safety constraints, perform interference verification on the preset candidate opening positions, eliminate opening points that violate the building safety constraints, and form a candidate set of ventilation openings; S2. Based on the distribution characteristics of the ventilation opening candidate set, the basement is divided into areas according to the airflow radiation range of each ventilation opening; with the airflow direction of each ventilation opening as a constraint, the set of working stations and the sequence of traversal paths of the mobile drying fan are planned so that the working path of the fan fits the airflow diffusion range of the ventilation opening, and each fan is controlled to carry out working condition tests in sequence according to the preset station, and multiple sets of actual ventilation flow and ambient humidity measurement data of the mobile drying fan are collected. S3. Based on the three-dimensional physical model and combined with the measured data, the boundary conditions are calibrated, and a digital twin simulation model that replicates the fluid-wet coupling evolution of the basement is constructed. S4. Conduct full-domain permutation and combination simulation calculations to obtain ventilation and dehumidification performance parameters under the combination of ventilation openings and fan positions at different locations in batches; S5. Construct a multi-objective optimization model that takes into account airflow organization effect evaluation index, humidity uniformity index and energy consumption index, perform quantitative scoring and ranking, and screen out the coupling scheme of ventilation opening and fan station, including the optimal primary scheme and the secondary backup scheme. S6. Based on the optimal primary scheme, output construction design guidance, and after it is put into operation, determine the humidity distribution deviation by combining the real-time collected environmental data; use the preset sorted backup scheme positions as the adjustment benchmark, dynamically correct the fan operating parameters and sequentially switch the work stop position.

[0007] In S1, the global basic feature data includes building space geometric data, structural load-bearing layout data, and indoor environmental base parameters; Import the above data into the simulation platform to construct a digital twin model of the basement; calculate the ventilation efficiency of each candidate ventilation opening based on the digital twin model of the basement; when the ventilation efficiency is lower than the preset efficiency threshold, or the minimum distance between the opening position and the load-bearing component is less than the safety distance threshold, the candidate ventilation opening is eliminated.

[0008] In S2, based on the location of the partition walls and the regional dividing lines formed by connecting the center points of the column grid, the building functional zones of the basement are delineated; then, based on the building functional zones, combined with the distribution characteristics of the ventilation opening candidate set and the airflow radiation range of each ventilation opening, ventilation control zones are delineated; no less than two fan circulation nodes are set on the dividing lines between adjacent building functional zones.

[0009] In S2, a full-coverage traversal algorithm is used to optimize the fan docking locations. This includes: using the fan flow nodes on the building's functional zoning lines as reference anchor points, calculating the airflow coverage of each preset station, detecting the ventilation blind zone distance between adjacent stations, and if the blind zone is greater than the preset maximum allowable blind zone threshold, adding docking points between adjacent stations; if the blind zone is less than the preset minimum reasonable blind zone threshold, eliminating redundant docking points. After the station adjustments are completed, all docking stations are connected and sorted to generate continuous traversal paths, ensuring that the overall ventilation blind zone is stably controlled within the preset reasonable blind zone threshold range.

[0010] In S3, when calibrating the model boundary conditions, the measured airflow and humidity data collected after the station location was optimized by the full-coverage traversal algorithm in S2 are used as the benchmark values ​​to iteratively correct the initial boundary conditions of the digital twin simulation model. This includes: synchronously replicating the on-site fan layout to generate simulation calculation field data, comparing the collected measured field data with the simulation calculation field data for deviation, and then correcting the ventilation resistance parameters and initial environmental parameters corresponding to the internal walls and column grid of the model based on the deviation feedback. The comparison and parameter correction process is executed iteratively until the deviation is less than the preset convergence threshold, thereby achieving the fitting of the simulation environment field with the real physical environment of the basement.

[0011] In S4, compliant ventilation openings within the same ventilation control area are directionally bound and paired with the corresponding planned fan operation positions within the ventilation control area, achieving a full combination of openings and fan configurations within the ventilation control area. The pairing process retains the relative positions and traversal order of the original docking points of each group of fans. Based on the digital twin simulation model with boundary calibration completed in S3, the spatial layout conditions of each pairing combination are loaded sequentially, synchronously restoring the ventilation conditions of the currently paired compliant ventilation openings and the arrangement of the predetermined docking points of the mobile drying fans. The ventilation and dehumidification performance parameters corresponding to each coupled condition are deduced group by group, and all feasible ventilation configuration schemes are fully traversed. The ventilation and dehumidification performance parameters include three basic evaluation parameters: airflow organization, humidity uniformity, and energy consumption.

[0012] In S5, when constructing a multi-objective optimization model, the airflow organization effect evaluation index, humidity uniformity index, and energy consumption index are incorporated into the same weight allocation framework, so that the weight ratio of each index forms a balance relationship. The analytic hierarchy process is adopted to allocate differentiated weights to each evaluation index according to the differences in the actual environmental conditions of each ventilation control zone.

[0013] The ventilation and dehumidification performance parameters corresponding to each evaluation index are processed to unify the dimensions to obtain a single standardized score. The weighted summation method is used to calculate the comprehensive score of each coupling scheme by successively weighting and superimposing the single standardized score with the corresponding weight. The comprehensive score is used as the basis for scheme selection. The coupling scheme with the best comprehensive score is selected as the optimal primary scheme, and multiple backup schemes are generated in order of score ranking.

[0014] In S6, real-time measured environmental humidity data of each predetermined monitoring point in the basement are collected, and the deviation between the current humidity distribution and the preset target humidity distribution is calculated. The average deviation of all monitoring points in the same ventilation control area is calculated to quantify the degree of humidity deviation in the area. When the degree of humidity deviation in the area exceeds the preset allowable deviation range, the operating parameters of the mobile drying fan are adjusted according to the degree of deviation. The mobile drying fan is switched to the predetermined work stop position step by step according to the preset priority of the backup plan.

[0015] A ventilation and dehumidification optimization system for elevated basements, employing a ventilation optimization method for elevated basements, the system comprising: The modeling and verification module is used to collect the full-domain foundation feature data of the basement of the elevated floor, construct a three-dimensional physical model, and perform load-bearing interference verification on the candidate opening positions, and output a set of candidate safe ventilation openings. The regional planning module is used to divide the building into functional zones based on the basement spatial layout, and to divide the ventilation control zones, and to plan the working positions and traversal paths of the mobile drying fans. The simulation and testing module is used to control the mobile drying fan to carry out working condition tests and collect measured data at various work stations, and to complete the boundary condition calibration based on the three-dimensional physical model and measured data. The combined optimization module is used to carry out global permutation and combination simulation with ventilation opening position and fan station position as bivariate parameters, construct a multi-objective optimization model, quantify and score each scheme, and select the optimal coupling scheme. The dynamic control module is used to adaptively adjust the operating parameters of the mobile drying fan and switch the work stop position based on real-time environmental data after the system is running.

[0016] The advantages and positive effects of this invention are: By collecting measured data on ventilation flow and ambient humidity based on the site's inherent air intake environment and comparing them item by item with simulation output data under the same operating conditions, the simulation boundary parameters are adjusted according to the data differences. Then, numerical simulation is carried out based on a three-dimensional physical model to generate simulation calculation field data for the corresponding fan layout conditions. The deviation between measured and simulated data is used to reverse-correct the ventilation resistance parameters of the internal walls and column grid of the model and the initial environmental parameters, and this process is repeated multiple times. Based on the distribution of ventilation openings and airflow radiation characteristics, the area is divided and the working positions and traversal paths of mobile drying fans are planned. Ventilation is carried out based on numerical simulation. Performance calculations for various combinations of openings and fan locations were performed. A multi-objective optimization model was used to quantitatively screen coupled configurations that included the optimal primary and backup schemes, based on comprehensive airflow organization effect evaluation indicators, humidity uniformity indicators, and energy consumption indicators. After the project was put into operation, humidity distribution deviations were determined based on real-time environmental data, and fan operating parameters were dynamically adjusted and operating locations were switched as needed. Full-process numerical simulation replaced empirical estimation, reducing the deviation between the digital twin simulation model and the actual on-site working conditions, reducing blind spots in basement ventilation and dehumidification, and improving the design rationality and adaptive control level of the opening and fan matching schemes. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] In the attached diagram: Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the three-dimensional physical model construction and safe opening screening process in this invention; Figure 3 This is a schematic diagram of the process for establishing the digital twin simulation model and calibrating boundary conditions in this invention; Figure 4 This is a schematic diagram of the optimization and screening process for the coupling of ventilation opening location and fan station location in this invention; Figure 5 This is a schematic diagram of the intelligent dynamic control mechanism in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] In modern residential construction, the basement level, as a crucial component of the building, directly impacts the overall building's living comfort and lifespan. However, current basement ventilation design relies heavily on manual experience to determine ventilation opening locations and uses fixed ventilation equipment for dehumidification. This approach lacks theoretical basis and fails to fully consider structural load-bearing constraints and airflow distribution characteristics, leading to inappropriate opening locations and affecting overall ventilation efficiency. Furthermore, the selection of ventilation equipment is often limited, typically using fixed units that cannot address humidity differences in different areas of the basement, resulting in energy waste or insufficient dehumidification. In addition, existing ventilation design methods lack pre-construction predictive simulation tools, making it difficult to identify and optimize potential problems during the design phase. Moreover, the ventilation system lacks dynamic control capabilities, failing to adjust its operation strategy in real-time based on actual environmental parameters.

[0023] In modern residential construction, the basement level, as a crucial component of the building, directly impacts the overall building's living comfort and lifespan. However, current basement ventilation design relies heavily on manual experience to determine ventilation opening locations and uses fixed ventilation equipment for dehumidification. This approach lacks theoretical basis and fails to fully consider structural load-bearing constraints and airflow distribution characteristics, leading to inappropriate opening locations and affecting overall ventilation efficiency. Furthermore, the selection of ventilation equipment is often limited, typically using fixed units that cannot address humidity differences in different areas of the basement, resulting in energy waste or insufficient dehumidification. In addition, existing ventilation design methods lack pre-construction predictive simulation tools, making it difficult to identify and optimize potential problems during the design phase. Moreover, the ventilation system lacks dynamic control capabilities, failing to adjust its operation strategy in real-time based on actual environmental parameters.

[0024] Please also refer to the following: Figures 1 to 5 This invention provides a ventilation design method for a basement with an elevated floor. The method in this embodiment includes the following steps: First, spatial structural data, load-bearing component distribution information, and environmental monitoring parameters of the basement were collected to construct a three-dimensional physical model. Based on airflow principles and structural safety constraints, interference verification was performed on preset candidate opening locations, eliminating openings with load-bearing interference to obtain a set of safe ventilation openings. Then, functional areas were divided according to the internal spatial layout characteristics of the basement, and the set of working stations and traversal path sequences for mobile drying fans were planned to form a full-coverage dynamic dehumidification layout. Each fan was controlled to conduct operating condition tests sequentially at its preset station, collecting multiple sets of actual ventilation flow and ambient humidity measurement data. Finally, based on the three-dimensional physical model and the measured data, boundary conditions were calibrated to establish a digital twin simulation model that can realistically replicate the airflow field distribution and humidity field evolution within the basement. Then, using the ventilation opening location and fan location as dual-variable parameters, a full-domain permutation and combination simulation calculation was conducted to obtain ventilation and dehumidification performance parameters under different opening-fan combination schemes in batches. A multi-objective optimization model was constructed that takes into account ventilation effect evaluation index, humidity uniformity index, and energy consumption index. All simulation schemes were quantitatively scored and ranked to select the optimal ventilation opening and fan location coupling scheme. Finally, based on the optimal coupling scheme, a standardized construction design scheme was output. After the system was put into operation, the fan operating parameters were adaptively adjusted in combination with real-time collected environmental data to achieve dynamic control.

[0025] Specifically, it includes the following steps: S1. Collect the full-area basic feature data of the elevated floor and basement, and construct a three-dimensional physical model; based on the building safety constraints, perform interference verification on the preset candidate opening positions, eliminate opening points that violate the building safety constraints, and form a candidate set of ventilation openings; During data acquisition, the overall basic feature data includes basement floor height, plan dimensions, partition wall distribution, and ventilation shaft location information; the load-bearing component distribution information includes shear wall location, column grid layout, and beam orientation data; the environmental monitoring parameters include initial relative humidity, temperature, and air velocity values ​​for different areas. The data acquisition module uses a 3D laser scanner to accurately scan the interior space of the basement, acquiring spatial structural data such as floor height and plan dimensions; it exports the partition wall distribution and ventilation shaft location information of the basement through the Building Information Modeling (BIM) system; it locates load-bearing components using an ultrasonic thickness gauge or rebar detector, acquiring shear wall location, column grid layout, and beam orientation data; the environmental monitoring parameters are collected in real time by temperature and humidity sensors and wind speed sensors distributed in various areas of the basement, obtaining initial relative humidity, temperature, and air velocity values ​​for different areas, which serve as input data for subsequent construction of the 3D physical model.

[0026] Based on the collected data, the 3D modeling module constructs a 3D physical model to represent the spatial form of the basement, the layout of load-bearing components, and the location of ventilation shafts. After constructing the 3D physical model, the system performs interference checks on preset candidate opening locations based on airflow principles and structural safety constraints, eliminating opening points with load-bearing interference to obtain a set of candidate safe ventilation openings. During the selection of candidate opening locations, the ventilation efficiency coefficient of each candidate location is calculated according to airflow principles. First, the instantaneous airflow of the opening is solved using the thin-walled orifice outflow formula, as shown in the following formula:

[0027] in, The instantaneous air volume at the opening. For the flow coefficient of thin-walled orifices, the commonly used empirical value for conventional ventilation holes in the building envelope is taken. ; Pre-design the cross-sectional area for candidate openings. The static pressure difference between indoor and outdoor air was measured on-site. The density of air at room temperature is taken as... , Multiply by a conversion factor of 3600 to obtain the estimated hourly air volume. Then, combine this with the theoretical ventilation air volume for each zone to calculate the opening ventilation efficiency coefficient. The calculation formula is as follows:

[0028] in, This refers to the ventilation efficiency coefficient. To estimate the effective actual ventilation volume flow rate for the opening, The theoretical required ventilation volumetric flow rate for the zone to which the opening belongs is calculated based on the air change rate for basements specified in current HVAC codes. The calculation formula is as follows:

[0029] The minimum air exchange rate required by the regulations for basements is determined according to the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings". The building area of ​​the ventilation zone to which the opening belongs. This refers to the actual floor height of the basement. when If the efficiency is below the preset threshold, or the minimum distance between the opening and the load-bearing component is less than the safety clearance threshold, the candidate opening location is eliminated. The minimum distance between the opening and the load-bearing component is calculated by spatial calculation using the coordinates of the opening and the load-bearing component extracted from the three-dimensional physical model. The safety clearance threshold is determined with reference to the basement building structure design code. The condition is determined by comparing the numerical values. Through the screening mechanism based on airflow principles and structural safety constraints, it is ensured that the ventilation opening location meets both airflow distribution requirements and structural safety requirements, avoiding the design problems caused by determining the opening location solely based on manual experience in the existing technology.

[0030] S2. Based on the distribution characteristics of the ventilation opening candidate set, the basement is divided into zones according to the airflow radiation range of each ventilation opening; with the airflow direction of each ventilation opening as a constraint, the set of working stations and the sequence of traversal paths of the mobile drying fan are planned so that the working path of the fan fits the airflow diffusion range of the ventilation opening, and each fan is controlled to carry out working condition tests in sequence according to the preset station, and multiple sets of actual ventilation flow and ambient humidity measurement data of the mobile drying fan are collected.

[0031] In step S2, functional areas are divided based on the spatial layout characteristics of the basement, and the set of working stations and traversal path sequences for the mobile drying fans are planned to form a full-coverage dynamic dehumidification layout. When dividing functional areas, the following rules are used to determine area boundaries: independent functional zones are divided based on the location of partition walls, and area dividing lines are formed by connecting the center points of the column grid. At least two fan flow nodes are set on the dividing lines between adjacent zones. Based on the building's functional zoning, ventilation control zones are defined by combining the location of openings and the airflow radiation range. For example, for a basement with multiple independent functional zones, such as parking areas, equipment rooms, and storage rooms, the system divides the basement into several independent functional zones based on the location of partition walls, and fan flow nodes are set on the dividing lines between adjacent zones to ensure smooth fan flow between different zones. When planning the fan traversal path, a full-coverage traversal algorithm is used to ensure that the path coverage blind zone between adjacent stations is less than a preset blind zone threshold. The specific implementation process is as follows: using the fan flow nodes on the building's functional zoning lines as reference anchor points, the airflow coverage range of each preset station is calculated, and the ventilation blind zone distance between adjacent stations is detected. If the blind zone is greater than the preset maximum allowable blind zone threshold, additional stopping points are added between adjacent stations; if the blind zone is less than the preset minimum reasonable blind zone threshold, redundant stopping points are removed. After the station adjustments are completed, all stopping points are connected and sorted to generate a continuous traversal path. The optimal traversal path is calculated using the full-coverage traversal algorithm to ensure that the fan can cover every corner of the basement during movement, avoiding ventilation and dehumidification blind zones.

[0032] After completing the functional area division and path planning, the operating condition test module controls each mobile drying fan to conduct operating condition tests sequentially according to preset stations, collecting multiple sets of actual measured data on ventilation flow and ambient humidity. Specifically, the mobile drying fans are tested under no-load conditions at preset stopping points. Although no formal ventilation openings are opened at this time, outdoor natural air intake is used as a unified benchmark to collect measured air volume and humidity. The simulation terminal simultaneously replicates the same initial environment without openings and with natural air intake, and performs data benchmarking under the same preconditions to avoid data distortion caused by missing openings. The mobile drying fans arrive at each operating station sequentially according to the planned traversal path, and conduct timed operating condition tests at each station, such as running at each station for 10 minutes, collecting measured data such as ventilation flow and ambient humidity at that station. These measured data include, but are not limited to: air intake volume, air output volume, relative humidity values, temperature values, and air velocity data at each monitoring point. By conducting operating condition tests at multiple stations and collecting multiple sets of measured data, a practical basis is provided for the subsequent calibration of the digital twin simulation model.

[0033] The mobile drying fan sequentially arrives at each stop along the planned continuous traversal path to conduct no-load operation tests. Based on the original natural air infiltration environment of the site, measured data of ventilation flow and ambient humidity are collected at each point. Simultaneously, the simulation terminal sets up the original environmental conditions consistent with the site, and compares the measured data on site with the output data of the simulation under the same operating conditions item by item. When the measured air volume deviates from the simulation value, the natural air leakage boundary coefficient of the model is finely adjusted accordingly. When the measured humidity deviates, the initial temperature and humidity boundary parameters of the model environment are corrected. The simulation boundary parameters are adjusted by using single-direction parameters to reduce the initial simulation error.

[0034] S3. Based on the three-dimensional physical model and combined with the measured data, the boundary conditions are calibrated, and a digital twin simulation model that replicates the fluid-wet coupling evolution of the basement is constructed. When calibrating the boundary conditions in S3, the following calibration strategy is adopted: using the measured data collected in step S2 as the reference value, the initial boundary conditions of the digital twin simulation model are iteratively corrected so that the deviation between the simulated airflow velocity field and humidity field and the measured value is less than the preset convergence threshold. The digital twin simulation model is established using the computational fluid dynamics (CFD) method, and its initial boundary conditions include the location and area of ​​the air inlet, the location and area of ​​the air outlet, the wall boundary conditions, and the initial temperature and humidity distribution.

[0035] Based on the initial boundary parameters obtained from the previous parameter adjustments, this step replicates the on-site mobile drying fan layout to generate simulation calculation field data. The collected measured field data is compared with the simulation calculation field data to identify the deviation. Based on the deviation feedback, the ventilation resistance parameters corresponding to the internal walls and column grid of the model are continuously corrected in reverse to match the initial environmental parameters. The comparison and parameter correction process is executed iteratively for multiple rounds until the overall deviation is less than the preset convergence threshold, ultimately achieving a high-precision fit between the simulated environmental field and the real physical environment of the basement.

[0036] Using measured data as a benchmark, the initial boundary conditions are continuously adjusted through an iterative algorithm, gradually narrowing the deviations between the simulated airflow velocity and humidity fields and the measured values ​​to within a preset convergence threshold. When the deviation is less than the preset convergence threshold, calibration is complete. A digital twin simulation model is then used to replicate the dynamic process of airflow distribution and humidity field evolution within the basement. This solves the technical problem of lacking pre-construction predictive simulation methods in existing technologies, enabling the identification and optimization of potential problems during the design phase.

[0037] S4. Conduct full-domain permutation and combination simulation calculations to obtain ventilation and dehumidification performance parameters under the combination of ventilation openings and fan positions at different locations in batches; In step S4, a global permutation and combination simulation is conducted using ventilation opening locations and fan station locations as bivariate parameters to obtain ventilation and dehumidification performance parameters under different opening-fan combination schemes in batches. During the permutation and combination simulation, the values ​​for ventilation opening locations are all elements in the safe ventilation opening candidate set obtained in step S1, and the values ​​for fan station locations are all elements in the working station location set determined in step S2. The total number of simulation schemes is calculated using the formula N = M × K, where M is the number of elements in the safe ventilation opening candidate set and K is the number of elements in the working station location set. For example, when the safe ventilation opening candidate set contains 12 candidate opening locations and the working station location set contains 8 candidate station locations, the total number of simulation schemes is 96. Simulation calculations are performed for each combination scheme, outputting the ventilation and dehumidification performance parameters for each scheme, including but not limited to: wind speed distribution, humidity distribution, ventilation efficiency, and energy consumption indicators at each monitoring point. This provides data support for subsequent multi-objective optimization models.

[0038] S5. Construct a multi-objective optimization model that takes into account airflow organization effect evaluation index, humidity uniformity index and energy consumption index, perform quantitative scoring and ranking, and screen out the coupling scheme of ventilation opening and fan station, including the optimal primary scheme and the secondary backup scheme.

[0039] In step S5, when constructing the multi-objective optimization model, the weight coefficients of each objective are determined using the analytic hierarchy process (AHP), and the weight coefficients of the ventilation effect evaluation indicators are also determined. The value ranges from 0.3 to 0.4, representing the weighting coefficient of the humidity uniformity index. The value ranges from 0.4 to 0.5, representing the weighting coefficient of the energy consumption index. The value ranges from 0.2 to 0.3, and satisfies... + + =1. The ventilation effect evaluation index reflects the degree of air circulation inside the basement. It is calculated as the weighted average of the differences between the wind speed at each monitoring point and the target wind speed. The wind speed at the monitoring points is measured on-site by wind speed sensors deployed within the ventilation control area. The target wind speed is taken as the general benchmark value of 0.15 m / s according to the underground parking garage HVAC design code. The weight of each monitoring point is allocated according to the area proportion of its respective zone. The humidity uniformity index reflects the uniformity of humidity distribution inside the basement. It is calculated as the standard deviation of the difference between the humidity at each monitoring point and the average humidity. The energy consumption index reflects the energy consumption required for fan operation. It is calculated as the sum of the products of the fan power and operating time at each station. When conducting quantitative scoring, a weighted summation method is used to calculate the comprehensive score of each scheme.

[0040] in, To normalize the score for ventilation effectiveness, For the normalized score of humidity uniformity, Energy consumption normalization score; select The scheme with the highest value is selected as the optimal coupling scheme. All remaining schemes are sorted in descending order of comprehensive score and designated as first-level backup schemes, second-level backup schemes, etc. Each level of backup scheme also corresponds to a dedicated set of ventilation opening combinations and mobile drying fan docking station combinations. All parameters of the primary and backup schemes are archived in a unified manner as alternative benchmarks for later dynamic control.

[0041] For a specific scheme, the normalized score for ventilation effect is 0.85, the normalized score for humidity uniformity is 0.78, and the normalized score for energy consumption is 0.92, with weighting coefficients... , , With values ​​of 0.35, 0.45, and 0.20 respectively, the overall score is 0.8325. All simulation schemes are ranked according to their overall scores, and the scheme with the highest score is selected as the optimal coupling scheme for ventilation opening and fan location. Through a multi-objective optimization method, the synergistic optimization of ventilation effect, humidity uniformity, and energy consumption indicators is achieved, overcoming the problem of existing technologies that only consider ventilation effect or energy consumption.

[0042] S6. Based on the optimal primary scheme, output construction design guidance, and after it is put into operation, determine the humidity distribution deviation by combining the real-time collected environmental data; use the preset sorted backup scheme positions as the adjustment benchmark, dynamically correct the fan operating parameters and sequentially switch the work stop position.

[0043] In step S6, during adaptive adjustment, real-time measured environmental humidity data from designated monitoring points in the basement are collected. The average deviation of all monitoring points within the same ventilation control area is calculated to quantify the humidity deviation level of the area. When the humidity deviation level exceeds the preset allowable deviation range, the fan airflow adjustment coefficient is determined by referring to a table based on the deviation value, and the operating parameters of the mobile drying fan are updated according to the adjustment coefficient. If the humidity deviation of the area still exceeds the standard after adjusting only the fan operating parameters, and the overall dehumidification effect of the main solution does not meet the design requirements, the backup solutions are sequentially switched to the fan docking station combination corresponding to the next level of backup solutions according to the pre-arranged backup solution from high to low score. The opening-fan matching arrangement of the backup solution is adopted to continue operation. This process is repeated sequentially until the humidity of the area is controlled within the qualified range. The adjustment coefficient comparison table is generated based on multiple sets of previous CFD simulation pre-test calibrations. Different humidity deviation conditions are set in advance in the digital twin simulation model. The fan air volume ratio required to reach the target humidity under each deviation is calculated by simulation. The test data are summarized to form a fixed comparison table. The larger the deviation, the higher the value of the air volume adjustment coefficient.

[0044] By deploying multiple temperature and humidity sensors in the basement, humidity data at each monitoring point is collected in real time, and the deviation between the current humidity distribution and the target humidity distribution is calculated. When the deviation exceeds the allowable range, the airflow adjustment coefficient of the fan is determined according to a preset adjustment coefficient table. This adjustment coefficient is directly proportional to the deviation value. The operating parameters of the mobile drying fan, including fan speed, airflow, and running time, are updated based on the adjustment coefficient, thereby achieving adaptive adjustment of the fan's operating parameters. This intelligent dynamic control mechanism solves the technical problem of the lack of dynamic control capability in existing ventilation systems. It can adjust the operating strategy in real time according to seasonal changes and actual environmental parameters, effectively solving the problem of basement dampness, especially in the humid climate of southern regions.

[0045] The method of the present invention will be described in detail below through a specific application example: The basement of a residential complex has a rectangular floor plan, measuring 50m east-west and 30m north-south, with a floor height of 3.2m. It features two rows of reinforced concrete columns spaced 8m apart. The basement is divided into three functional zones—parking, equipment rooms, and storage—by partition walls. A data acquisition module collects spatial structure data, load-bearing component distribution information, and environmental monitoring parameters to obtain 3D point cloud data and temperature and humidity distribution data. Then, a 3D modeling module constructs a 3D physical model based on the collected data and performs interference checks on 20 pre-set candidate opening locations. Based on airflow principles, the ventilation efficiency coefficient of each candidate location is calculated, and those locations that are insufficiently far from load-bearing components and have ventilation efficiency coefficients below a threshold are eliminated. Finally, a candidate set of 12 safe ventilation opening locations is obtained.

[0046] Based on the internal spatial layout characteristics of the basement, functional areas were divided into three independent functional zones. Three fan circulation nodes were set on the dividing lines between adjacent zones, for a total of nine circulation nodes. When planning the fan traversal path, a full-coverage traversal algorithm was used to calculate the optimal traversal path, resulting in a set of six workstations. Then, the operating condition testing module controlled the mobile drying fan to conduct operating condition tests sequentially at the six preset workstations. Each workstation ran for 15 minutes, and measured data on ventilation flow and ambient humidity were collected at each workstation. The measured data showed that the average humidity in the parking area was 56%, the average humidity in the equipment room area was 52%, and the average humidity in the storage room area was 61%, indicating significant humidity differences between the areas.

[0047] Based on a 3D physical model and measured data, a digital twin simulation model was established and boundary conditions were calibrated. Then, using 12 safe ventilation opening locations and 6 fan locations as bivariate parameters, simulation calculations were performed for 72 combined schemes, obtaining the ventilation and dehumidification performance parameters of each scheme in batches. Subsequently, a multi-objective optimization model was constructed, and the weighting coefficients were determined using the analytic hierarchy process (AHP). =0.35、 =0.45、 =0.20, and 72 schemes were quantitatively scored and ranked. The optimal coupling scheme with the highest comprehensive score was selected. This scheme adopts a combination of 4 ventilation opening positions and 3 fan positions.

[0048] Based on the optimal coupling scheme, a standardized construction design plan is output, clearly defining the specific location, size, and construction process of ventilation openings, the selection, quantity, and layout of mobile drying fans, and the routing and connection method of the duct system. After the system is put into operation, it adaptively adjusts the fan operating parameters based on real-time collected environmental data. When high humidity is detected in a certain area, the system automatically increases the airflow output of fans in that area; when the overall humidity is detected to be relatively uniform, the system automatically reduces the fan airflow to save energy. This dynamic control mechanism ensures that the humidity inside the basement is always maintained within a comfortable range, effectively solving the problem of bacteria and mold growth in basements under the humid climate of southern China, improving the living experience of upstairs residents and the overall environmental quality of the community.

[0049] This invention also provides a digital twin-based optimization system for ventilation and dehumidification of elevated basements. This system applies the aforementioned digital twin-based optimization method for ventilation and dehumidification of elevated basements and includes: a data acquisition module, a 3D modeling module, a region division and path planning module, a working condition testing module, a digital twin simulation module, a combined simulation module, an optimization screening module, a construction design output module, and a dynamic control module. The data acquisition module collects spatial structural data, load-bearing component distribution information, and environmental monitoring parameters of the elevated basement. This module can achieve data acquisition through devices such as a 3D laser scanner, a Building Information Modeling (BIM) system, and distributed temperature and humidity sensors. The 3D modeling module constructs a 3D physical model based on the data acquired by the data acquisition module, performs load-bearing interference verification on candidate opening locations, and outputs a set of safe ventilation opening candidates. This module uses 3D modeling software to construct the physical model and perform interference verification on the opening locations. The region division and path planning module divides functional areas according to the basement spatial layout and plans the operating positions and traversal paths of mobile drying fans. This module uses a region division algorithm and a full-coverage traversal algorithm to achieve functional area division and path planning. The operating condition test module is used to control the fan to carry out operating condition tests at various stations and collect measured data. This module forms a closed-loop test system with the mobile drying fan through a programmable logic controller (PLC) to realize automated control and data acquisition of operating condition tests.

[0050] The digital twin simulation module is used to establish a digital twin simulation model by combining the 3D physical model built by the 3D modeling module with the measured data collected by the working condition testing module to complete the boundary condition calibration. This module uses computational fluid dynamics (CFD) software and humidity field simulation software to simulate and calculate the airflow and humidity fields. The combined simulation module is used to conduct full-domain permutation and combination simulations with ventilation opening positions and fan locations as bivariate parameters, outputting the performance parameters of each scheme. This module uses batch processing simulation scripts to automate the simulation calculations. The optimization and selection module is used to build a multi-objective optimization model, quantitatively score all schemes output by the combined simulation module, and select the optimal coupled scheme. This module uses a multi-objective optimization algorithm to achieve intelligent scheme selection. The construction design output module is used to output standardized construction design schemes based on the optimal coupled schemes. This module uses computer-aided design (CAD) software and building information modeling (BIM) technology to automatically generate construction drawings. The dynamic control module is used to adaptively adjust the fan operating parameters based on real-time environmental data after the system is put into operation, achieving dynamic control. This module uses adaptive control algorithms and Internet of Things (IoT) technology to achieve real-time adjustment of fan operating parameters.

[0051] Through the aforementioned system, the data acquisition module can collect various basic data of the basement completely and accurately, providing data support for subsequent analysis and optimization. The 3D modeling module can construct an accurate 3D physical model and select reasonable ventilation opening locations based on airflow principles and structural safety constraints. The area division and path planning module can scientifically divide functional areas according to the spatial layout of the basement and plan the optimal fan traversal path. The operating condition testing module can automatically conduct operating condition tests and collect real measured data. The digital twin simulation module can establish a high-precision simulation model to accurately predict the airflow and humidity fields in the basement. The combined simulation module can batch calculate the ventilation and dehumidification performance parameters of different schemes, providing a data foundation for optimization and selection. The optimization and selection module can select the optimal coupling scheme of ventilation openings and fan locations through multi-objective optimization algorithms. The construction design output module can output standardized construction design schemes to guide on-site construction. The dynamic control module can adaptively adjust fan operating parameters according to real-time environmental data to achieve intelligent dynamic control.

[0052] Through the collaborative work between modules, the entire system forms a complete closed-loop control system from data acquisition to scheme optimization to dynamic control, which solves the technical problems of lack of theoretical basis and inability to dynamically control ventilation design in existing technologies, and improves the ventilation and dehumidification efficiency of the basement of the elevated floor.

[0053] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

[0054] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of this patent.

Claims

1. A ventilation optimization method for basements with elevated floors, characterized in that, Includes the following steps: S1. Collect the full-area basic feature data of the elevated floor and basement to establish a three-dimensional physical model, and filter candidate openings under building safety constraints to generate a candidate set of ventilation openings; S2. The basement is divided into zones based on the distribution of ventilation opening candidate locations and airflow radiation characteristics. The working positions and traversal paths of the mobile drying fan are planned based on the airflow direction constraints of the ventilation openings. The fan is controlled to carry out working condition tests in sequence according to the preset positions, and the corresponding measured data of ventilation flow and ambient humidity are collected. S3. Based on the three-dimensional physical model and combined with the measured data, the boundary conditions are calibrated, and a digital twin simulation model that replicates the wet-flow coupling evolution of the basement is constructed. S4. Conduct full-domain permutation and combination simulation calculations to obtain ventilation and dehumidification performance parameters under the combination of ventilation openings and fan positions at different locations in batches; S5. Based on multiple evaluation indicators, a multi-objective optimization model is built, and after quantitative comparison, the opening and wind turbine location coupling scheme, which includes the main scheme and the backup scheme, is determined. S6. Real-time collected environmental data is used to identify humidity distribution deviations. Based on the preset sorted backup plan positions, the fan operating parameters are dynamically corrected and the work stop positions are switched in sequence. A full-coverage traversal algorithm is used to optimize the fan docking locations. Using the fan flow nodes on the building functional zoning lines as reference anchor points, the airflow coverage of each preset station is calculated, and the ventilation blind zone distance between adjacent stations is detected. If the blind zone is greater than the preset maximum allowable blind zone threshold, additional docking points are added between adjacent stations. If the blind zone is less than the preset minimum reasonable blind zone threshold, redundant docking points are removed. After the station adjustments are completed, all docking stations are connected and sorted to generate continuous traversal paths, so that the ventilation blind zone of the entire area is stably controlled within the preset reasonable blind zone threshold range. The compliant ventilation openings within the same ventilation control area are directionally bound and paired with the corresponding planned fan operation positions within the ventilation control area to achieve a full combination of openings and fan configurations within the ventilation control area. The pairing process retains the relative positions and traversal order of the original docking points of each group of fans. Through a digital twin simulation model with completed boundary calibration, the spatial layout conditions of each pairing combination are loaded sequentially, synchronously restoring the ventilation conditions of the currently paired compliant ventilation openings and the arrangement of the predetermined docking points of the mobile drying fans. The ventilation and dehumidification performance parameters corresponding to each coupled condition are deduced group by group, and all feasible ventilation configuration schemes are fully traversed. The ventilation and dehumidification performance parameters include three basic evaluation parameters: airflow organization, humidity uniformity, and energy consumption.

2. The ventilation optimization method for a basement with an elevated floor according to claim 1, characterized in that, Based on the location of the partition walls and the regional dividing lines formed by connecting the center points of the column grid, the building functional zones of the basement are delineated; then, based on the building functional zones, the ventilation control zones are delineated by combining the distribution characteristics of the ventilation opening candidate set and the airflow radiation range of each ventilation opening; at least two fan circulation nodes are set on the dividing lines between adjacent building functional zones.

3. The ventilation optimization method for a basement with an elevated floor according to claim 1, characterized in that, The mobile drying fan completes the working condition test at each stop along the generated continuous traversal path. Based on the on-site air intake environment, the measured data of ventilation flow and ambient humidity are collected. The measured data are compared with the simulation output data item by item, and the simulation boundary parameters are adjusted according to the data difference.

4. The ventilation optimization method for a basement with an elevated floor according to claim 3, characterized in that, When calibrating the model boundary conditions, the on-site fan layout is replicated to generate simulation calculation field data. The collected measured field data is compared with the simulation calculation field data to identify the deviation. Then, based on the deviation feedback, the ventilation resistance parameters of the internal walls and column grid and the initial environmental parameters are corrected in reverse. The comparison and parameter correction process is repeated until the deviation is less than the preset convergence threshold, so as to achieve the fitting of the simulation environment field with the real physical environment of the basement.

5. A ventilation optimization method for a basement with an elevated floor according to claim 1, characterized in that, When constructing a multi-objective optimization model, the airflow organization effect evaluation index, humidity uniformity index, and energy consumption index are incorporated into the same weight allocation framework, so that the weight ratio of each index forms a balance relationship. The analytic hierarchy process is adopted to allocate differentiated weights to each evaluation index according to the differences in the actual environmental conditions of each ventilation control zone.

6. A ventilation optimization method for a basement with an elevated floor, as described in claim 5, characterized in that, The ventilation and dehumidification performance parameters corresponding to each evaluation index are processed to unify the dimensions to obtain the standardized scores of each item; the standardized scores of each item are then weighted and superimposed with their corresponding weights to obtain the comprehensive scores of each coupling scheme. The overall score is used as the basis for scheme selection. The coupling scheme with the best overall score is selected as the optimal primary scheme, and multiple backup schemes are generated in order of score ranking.

7. A ventilation optimization method for a basement with an elevated floor, as described in claim 6, characterized in that, Real-time environmental humidity data is collected from designated monitoring points in the basement. The deviation between the current humidity distribution and the preset target humidity distribution is calculated. The average deviation of all monitoring points in the same ventilation control area is calculated to quantify the degree of humidity deviation in the area. When the degree of humidity deviation in the area exceeds the preset allowable deviation range, the operating parameters of the mobile drying fan are adjusted according to the degree of deviation. The mobile drying fan is switched to the designated work stop position step by step according to the preset priority of the backup plan.

8. A ventilation optimization system for elevated basement floors, employing a ventilation optimization method for elevated basement floors as described in any one of claims 1 to 7, characterized in that, include: The modeling and verification module is used to collect the full-domain foundation feature data of the basement of the elevated floor, construct a three-dimensional physical model, and perform load-bearing interference verification on the candidate opening positions, and output a set of candidate safe ventilation openings. The regional planning module is used to divide the building into functional zones based on the basement spatial layout, and to divide the ventilation control zones, and to plan the working positions and traversal paths of the mobile drying fans. The simulation and testing module is used to control the mobile drying fan to carry out working condition tests and collect measured data at various work stations, and to complete the boundary condition calibration based on the three-dimensional physical model and measured data. The combined optimization module is used to carry out global permutation and combination simulation with ventilation opening position and fan station position as bivariate parameters, construct a multi-objective optimization model, quantify and score each scheme, and select the optimal coupling scheme. The dynamic control module is used to adaptively adjust the operating parameters of the mobile drying fan and switch the work stop position based on real-time environmental data after the system is running.

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