BIM-based building intelligent design optimization system and method
By combining the BIM system with multi-source data analysis and dynamically optimizing pile foundation design, the problem of insufficient adaptability to geological environments in traditional intelligent building design is solved, achieving high-precision, low-cost building design optimization.
Patent Information
- Application Number
- CN202510930000.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-07
- Publication Date
- 2025-10-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing intelligent building design systems lack the ability to conduct in-depth analysis and dynamic response to the geological and environmental adaptability of construction sites, resulting in increased construction difficulty, waste of resources, and safety hazards. In particular, there is a lack of intelligent optimization mechanisms in pile foundation design.
Through the BIM-based building intelligent design optimization system, comprehensive multi-source maps are generated using data sources such as elevation grid surfaces, structured parameter tables, and water level contour maps. Combined with drone lidar and drilling exploration reports, slope values and bearing capacity data are calculated, and pile types, pile lengths, and pile diameters are dynamically matched. The dynamic feature-driven method is used to encapsulate BIM data and feature check codes are used to detect abnormal data to generate high-precision BIM models.
It improves the adaptability and accuracy of the design scheme to the natural environment, reduces construction risks and subsequent maintenance costs, improves foundation stability and safety, realizes the intelligence and efficiency of the design, and ensures the accuracy and completeness of the BIM model.
Smart Images
Figure CN120764033A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of intelligence, in particular to a building intelligent design optimization system and method based on BIM. BACKGROUND
[0002] With the rapid development of informationization, digitization and intelligent technology in the construction industry, traditional two-dimensional drawings and linear construction methods have been difficult to meet the needs of modern buildings in terms of complexity, multi-specialty collaboration and fine management. As a digital representation means for the whole process of integrated design, construction and operation, building information modeling (BIM) is becoming one of the key technologies to promote the intelligent transformation of the construction industry.
[0003] However, the existing traditional building intelligent design system generally lacks in-depth analysis and dynamic response capability for the adaptability of building site geology and environment. In the design process, it relies on drawings and standard models, ignoring the comprehensive evaluation of key natural conditions such as terrain slope value, soil bearing capacity data and underground water level, resulting in a disconnection between building design and actual site conditions, which not only increases the risk of construction difficulty, but also easily causes structural hidden dangers such as settlement and water seepage in the later period.
[0004] In the design of pile foundation, the existing method mostly uses static experience matching or artificial parameter setting, lacking the mechanism of intelligent optimization combined with actual site geological data. This not only may cause redundancy in pile length and diameter setting, resource waste, but also easily causes problems such as insufficient foundation stability and decreased building safety due to unreasonable design. Overall, the traditional system has obvious shortcomings in the intelligent design of foundation driven by site conditions.
[0005] In view of this, the present application provides a building intelligent design optimization system and method based on BIM to solve the above problems. SUMMARY
[0006] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme, a building intelligent design optimization system based on BIM, comprising:
[0007] The land adaptability evaluation module: through the elevation grid surface, the structured parameter table, the water level contour map and the BIM construction layer, the coordinate system is unified, and the comprehensive multi-source map is obtained; the comprehensive multi-source map is divided into L grid units, the slope value of the grid node in the grid unit is calculated by using the adjacent elevation difference gradient method; and the comprehensive building suitability value of the grid unit is calculated; the grid unit is divided into suitable units and non-suitable units;
[0008] The intelligent pile foundation optimization module: for suitable units, generate intelligent pile foundation data; wherein, obtain the bearing capacity data, slope value and water level depth value of each grid node in the suitable unit; match the water level depth value with the preset pile foundation rule library to obtain the pile type, foundation pile length and foundation pile diameter; use the bearing capacity data and slope value to optimize the foundation pile length and foundation pile diameter; calculate the single pile bearing capacity data according to the optimized foundation pile diameter, and calculate the pile number using the single pile bearing capacity data;
[0009] The BIM data integration module: set up static interface, dynamic interface and environment interface, use dynamic feature driven method to encapsulate the data in dynamic interface and environment interface into BIM data, and use feature check code comparison method to test whether the encapsulated BIM data is abnormal, and discard the abnormal data;
[0010] The BIM model performance optimization module: for the encapsulated BIM data, generate BIM model, obtain the attribute completeness rate, attribute accuracy rate and attribute coverage rate of the model, and calculate the performance index of the BIM model component attribute, according to the performance index of the BIM model, evaluate the performance level of the BIM model, the performance level includes high adaptive level and medium adaptive level; for the medium adaptive level, supplement the component attribute, and reevaluate the performance level of the BIM model.
[0011] Further, the specific way of calculating the comprehensive building suitability value of the grid unit includes:
[0012] Use the unmanned aerial vehicle to carry the laser radar to scan the building site, obtain the original point cloud data of the land, and convert the original point cloud data of the land into an elevation grid surface with geographic coordinates;
[0013] Obtain N drilling exploration reports of the built site, extract soil layering parameters from the drilling exploration report, the soil layering parameters include bearing capacity data and permeability coefficient, and generate a structured parameter table based on the bearing capacity data and permeability coefficient;
[0014] Obtain regional groundwater monitoring data, monitor water level depth, and generate a water level contour map using Kriging interpolation method;
[0015] Import CAD drawings or SHP files in municipal archives, and convert them into BIM construction layers using BIM software;
[0016] Coordinate system of the elevation grid surface, the structured parameter table, the water level contour map and the BIM construction layer is unified, and a comprehensive multi-source map is obtained;
[0017] For the comprehensive multi-source map, divide the comprehensive multi-source map into L o*o grid units, calculate the slope value of the grid nodes in the grid unit using the adjacent elevation difference gradient method, and generate a disaster risk area according to the slope value, the disaster risk area includes landslide high risk area, landslide potential risk area and safe area;
[0018] The comprehensive building suitability value of each grid cell is calculated based on the multi-directional data and slope values in the comprehensive multi-source map; and the comprehensive building suitability value is compared with the theoretical building suitability value. When the comprehensive building suitability value is greater than the theoretical building suitability value, the grid cell is defined as an unsuitable cell; when the comprehensive building suitability value is less than or equal to the theoretical building suitability value, the grid cell is defined as a suitable cell.
[0019] Furthermore, the specific method of using the adjacent elevation difference gradient method to calculate the slope value of the grid node in the grid unit includes:
[0020] Calculate the horizontal elevation difference and the vertical elevation difference between the elevation values of the grid node and the adjacent grid nodes in the corresponding grid unit; calculate the comprehensive elevation difference based on the horizontal elevation difference and the vertical elevation difference;
[0021] When the comprehensive elevation difference is less than the standard elevation difference, the grid cell is expanded to P*P to obtain a changed grid cell; when the comprehensive elevation difference is greater than the standard elevation difference, the grid cell is reduced to q*q to obtain a changed grid cell;
[0022] For the changed grid cell corresponding to the grid node, the comprehensive horizontal elevation difference and the comprehensive vertical elevation difference between the grid node and the adjacent grid nodes in the changed grid cell are calculated; the slope value of the grid node is calculated by the comprehensive horizontal elevation difference, the comprehensive vertical elevation difference and the distance between the grid nodes.
[0023] Furthermore, the specific method of generating intelligent pile foundation data for suitable units includes:
[0024] For suitable units, obtain the bearing capacity data, slope value and water level depth value of each grid node in the suitable unit;
[0025] Preset pile foundation rule library to match pile type, pile length and pile diameter according to water level depth value;
[0026] For the foundation pile length and foundation pile diameter, the bearing capacity data and slope value are used to optimize the foundation pile length and foundation pile diameter to obtain the optimized foundation pile length and optimized foundation pile diameter;
[0027] The single pile bearing capacity data is calculated based on the optimized foundation pile diameter, and the number of piles is calculated using the total building load, single pile bearing capacity data and safety factor.
[0028] Furthermore, the static interface, dynamic interface and environment interface are set up, the data in the dynamic interface and environment interface are encapsulated into BIM data using a dynamic feature-driven method, and a feature check code comparison method is used to check whether the encapsulated BIM data is abnormal. The specific method of discarding abnormal BIM data includes:
[0029] The multi-source data access stage includes static interfaces, dynamic interfaces, and environmental data integration interfaces;
[0030] Import IFC files, CAD drawings and equipment parameter tables into the static interface;
[0031] Import the equipment's operating status data and smart pile foundation data into the dynamic interface;
[0032] Import the building's internal and external environmental data into the environmental interface;
[0033] Based on the equipment's operating status data, intelligent pile foundation data, and building internal and external environment data, the dynamic feature-driven method is used to encapsulate them into BIM data;
[0034] The characteristic check code comparison method is used to check the benchmark quantum check code of the encapsulated BIM data, and the abnormal BIM data is discarded.
[0035] Furthermore, the specific method of encapsulating the BIM data using the dynamic feature-driven method includes:
[0036] Each data point in each group of equipment operating status data, smart pile foundation data, and building internal and external environment data is defined as a characteristic data point; the local fluctuation rate within Fd timestamps before the characteristic data point is calculated; and the stability data of the characteristic data point is calculated based on the local fluctuation rate;
[0037] Add a small noise to each feature data point to obtain a feature data noise point; calculate the disturbance rate of the data point based on the ratio of the difference between the feature data noise point and the feature data point and the small noise; calculate the feature data disturbance point of the previous timestamp and the next timestamp of the feature data point, and obtain the disturbance rate of the feature data point of the previous timestamp and the next timestamp; calculate the sensitivity data of the feature data point of the current timestamp based on the average disturbance rate of the feature data points of the previous timestamp, the next timestamp and the current timestamp;
[0038] For the stability data and sensitivity data of the characteristic data points, for example, the importance data value of the characteristic data points is calculated using a weighted average method; the importance data value of each characteristic data point is arranged in descending order to obtain a descending sequence;
[0039] Define the first 50% of the descending sequence as key parameters, the 50% to 80% of the descending sequence as non-key parameters, and the last 20% of the descending sequence as unstable parameters;
[0040] Encapsulate key parameters and non-key parameters into BIM data.
[0041] Furthermore, the specific method of using the feature check code comparison method to check the reference quantum check code of the encapsulated BIM data and discarding abnormal BIM data includes:
[0042] The weights of key parameters in BIM data are set as Qw, and non-key parameters are set as Qe; a reference quantum check code is calculated based on the key parameter weights, key parameter feature data points, non-key parameter weights, and non-key feature data points;
[0043] For the encapsulated BIM data, the reference quantum check code of the encapsulated BIM data is calculated, and the encapsulated reference quantum check code is matched with the reference quantum check code of the BIM data. If the match is inconsistent, it means that the encapsulated BIM data is abnormal, and the encapsulated BIM data is discarded.
[0044] Furthermore, the efficiency index of the BIM model component attributes is calculated, and the performance level of the BIM model is evaluated based on the efficiency index of the BIM model. The performance level includes a high adaptability level and a medium adaptability level. The specific method includes:
[0045] Input CAD drawings and packaged BIM data into BIM software to build a BIM model;
[0046] Traverse the components of the BIM model and obtain the attributes of the components of the BIM model. If the attribute is null or an empty string, it is considered missing. Calculate the attribute completeness rate of the BIM model;
[0047] Compare the attributes in the BIM model components with the standard attributes to check whether the attributes are normal and calculate the attribute accuracy of the BIM model components;
[0048] Count the total types of component attributes in the BIM model and compare them with the total types of component attributes in the standard BIM model to calculate the BIM model attribute coverage;
[0049] The performance index of component attributes in the BIM model is calculated based on the attribute completeness rate, attribute accuracy rate and attribute coverage rate of the BIM model. If the attribute performance index is greater than the benchmark value, it means that the BIM model has a high adaptability level; if the attribute performance index is less than or equal to the benchmark value, it means that the BIM model has a medium adaptability level.
[0050] Furthermore, for the medium adaptability level, the specific methods of supplementing component attributes and re-evaluating the performance level of the BIM model include:
[0051] For high adaptation levels, use it directly;
[0052] For the medium adaptability level, standard attributes are used to supplement the attributes that are null or empty strings. For the supplemented attributes, the attribute completeness rate, attribute accuracy rate and attribute coverage rate are calculated innovatively to calculate the attribute performance index. The performance level of the BIM model is evaluated based on the attribute performance index. If the performance level is high adaptability, it is used directly. If the performance level is medium adaptability, it is re-evaluated until the evaluation result is high adaptability.
[0053] A BIM-based building intelligent design optimization method, which is implemented based on the BIM-based building intelligent design optimization system, includes:
[0054] Step SS1: coordinate system 1 is performed using the elevation grid surface, structured parameter table, water level contour map, and BIM construction layer to obtain a comprehensive multi-source map; the comprehensive multi-source map is divided into L grid cells, and the slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method; the comprehensive building suitability value of the grid cell is calculated; and the grid cell is divided into suitable cells and unsuitable cells;
[0055] Step SS2: Generate intelligent pile foundation data for suitable units; obtain bearing capacity data, slope value, and water level depth value of each grid node in the suitable unit; match the water level depth value with a preset pile foundation rule library to determine the pile type, foundation pile length, and foundation pile diameter; optimize the foundation pile length and foundation pile diameter using the bearing capacity data and slope value; calculate the single pile bearing capacity data based on the optimized foundation pile diameter, and calculate the number of piles using the single pile bearing capacity data;
[0056] Step SS3: Set up static interfaces, dynamic interfaces, and environment interfaces, encapsulate the data in the dynamic interfaces and environment interfaces into BIM data using a dynamic feature-driven method, and use a feature check code comparison method to check whether the encapsulated BIM data is abnormal. If abnormal, discard it.
[0057] Step SS4: Generate a BIM model for the encapsulated BIM data, obtain the model's attribute completeness, attribute accuracy, and attribute coverage, and calculate the performance index of the BIM model component attributes. Based on the BIM model's performance index, evaluate the BIM model's performance level, which includes a high adaptability level and a medium adaptability level. For the medium adaptability level, supplement the component attributes and re-evaluate the BIM model's performance level.
[0058] The technical effects and advantages of the BIM-based building intelligent design optimization system and method of the present invention are as follows:
[0059] This method integrates multiple data sources, such as elevation grids, structured soil parameters, and water levels, and combines them with BIM to construct layers, achieving comprehensive digital representation and analysis of construction sites. It also uses data such as drone lidar point clouds and borehole exploration reports to accurately calculate slope values, bearing capacity data, and water level distribution, and scientifically divide suitable and unsuitable units. This significantly improves the adaptability and accuracy of design solutions to the natural environment, reducing construction risks and subsequent maintenance costs.
[0060] For suitable units, the pile type, length, and diameter are dynamically matched based on bearing capacity data, slope values, and water level depth values. By optimizing foundation pile parameters, the conservative and wasteful nature of traditional empirical design is avoided. The bearing capacity of individual piles and the number of piles are calculated based on the optimized foundation pile diameter, achieving intelligent and efficient design, improving foundation stability and safety, while reducing material and construction costs.
[0061] Integrate multi-source data through static interfaces, dynamic interfaces, and environmental interfaces, and encapsulate them into BIM data using a dynamic feature-driven approach. Combined with feature checksum comparison methods, abnormal data is automatically detected and discarded to ensure the accuracy and integrity of BIM data and enhance the reliability of subsequent design, analysis, and management work.
[0062] A model is constructed based on the encapsulated BIM data, and the performance index is calculated using indicators such as attribute completeness, accuracy, and coverage to achieve a scientific assessment of the BIM model's performance level. For models with a medium adaptability level, missing attributes are automatically supplemented, and repeated assessments are performed until a high adaptability level is reached, ensuring the high quality and applicability of the final model to support subsequent intelligent design and construction applications.
[0063] Extract key data from comprehensive multi-source maps, effectively integrate complex terrain, geological and environmental information, automatically generate comprehensive building suitability values, realize intelligent and refined evaluation of construction sites, and greatly improve the scientific rationality of design solutions. BRIEF DESCRIPTION OF THE DRAWINGS
[0064] Figure 1 This is a schematic diagram of a BIM-based building intelligent design optimization system of the present invention;
[0065] Figure 2 This is a schematic diagram of the intelligent pile foundation optimization module of the present invention;
[0066] Figure 3 This is a schematic diagram of a BIM-based building intelligent design optimization method of the present invention. DETAILED DESCRIPTION
[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0068] Example 1
[0069] See also Figure 1 and Figure 2 As shown, this embodiment provides a BIM-based building intelligent design optimization system, including:
[0070] Land adaptability assessment module: The elevation grid surface, structured parameter table, water level contour map, and BIM construction layer are used to establish a coordinate system to obtain a comprehensive multi-source map. The comprehensive multi-source map is divided into L grid cells, and the slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method. The comprehensive building suitability value of the grid cell is also calculated, and the grid cells are divided into suitable cells and unsuitable cells.
[0071] Intelligent pile foundation optimization module: Generates intelligent pile foundation data for suitable units. This module obtains the bearing capacity data, slope value, and water level depth value for each grid node in the suitable unit. The water level depth value is matched with a preset pile foundation rule library to determine the pile type, pile length, and pile diameter. The bearing capacity data and slope value are used to optimize the pile length and diameter. The bearing capacity data of a single pile is calculated based on the optimized pile diameter, and the number of piles is calculated using the single pile bearing capacity data.
[0072] BIM data integration module: Set up static interfaces, dynamic interfaces, and environment interfaces, encapsulate the data in the dynamic interfaces and environment interfaces into BIM data using the dynamic feature-driven method, and use the feature check code comparison method to check whether the encapsulated BIM data is abnormal. If abnormal, discard it;
[0073] BIM model performance optimization module: Generates a BIM model for the encapsulated BIM data, obtains the model's attribute completeness, attribute accuracy, and attribute coverage, and calculates the performance index of the BIM model component attributes. Based on the BIM model's performance index, the module evaluates the BIM model's performance level, which includes high and medium adaptability levels. For the medium adaptability level, the module supplements the component attributes and re-evaluates the BIM model's performance level.
[0074] The traditional building intelligence module of BIM lacks a land adaptability module, which means that the geological, climatic, and environmental factors of the land cannot be fully evaluated and integrated during the building design process, leading to a mismatch between building design and site conditions; the lack of analysis of soil bearing capacity, groundwater level, and climate may result in unreasonable foundation design, unanticipated disaster risks, low building energy efficiency, and potential safety hazards during later operation; overall, BIM systems that do not consider land adaptability tend to increase construction costs, maintenance fees, and project risks, affecting the long-term sustainability and performance of buildings;
[0075] The specific way to calculate the comprehensive building suitability value of the grid cell includes:
[0076] Using a drone to carry a laser radar to scan the building site, obtaining the original point cloud data of the land, and converting the original point cloud data of the land into an elevation grid surface with geographic coordinates;
[0077] Obtaining N building site drilling exploration reports, extracting soil layering parameters from the drilling exploration reports, the soil layering parameters including bearing capacity data and permeability coefficient, and generating a structured parameter table based on the bearing capacity data and permeability coefficient;
[0078] The building site drilling exploration report is obtained by laying out multiple geological drill holes in the building site, combined with in-situ testing and indoor geotechnical testing; specifically, first, use an engineering geological drilling machine to drill the building site, extract soil samples at different depths, and conduct standard penetration tests (SPT) to evaluate soil compaction; then, send the disturbed and undisturbed samples collected to a geotechnical laboratory to conduct physical and mechanical property tests to obtain bearing capacity data and permeability coefficients; finally, integrate the data from each drill hole to form a complete geological survey report, which is the drilling exploration report;
[0079] Obtain regional groundwater monitoring data, monitor water level depth, and use Kriging interpolation method to generate water level contour map; the groundwater monitoring data is obtained by laying out groundwater observation wells in the building area, combined with automatic water level monitoring devices or manual observation recording methods; in the specific operation, multiple monitoring wells are set up in the monitoring area, and water level meters or data recorders are installed in each well to record the dynamic changes of groundwater level for a long time; the obtained water level data is processed by data aggregation and spatial interpolation algorithm (such as Kriging interpolation method) to form the groundwater level contour map of the area, thereby providing a basis for hydrogeological analysis of the building site;
[0080] Import CAD drawings or SHP files in municipal archives, and convert them into BIM construction layers using BIM software;
[0081] For example, using the control point correction method, the elevation grid surface, the structured parameter table, the water level contour map, and the BIM construction layer are coordinated in the spatial position to obtain the integrated multi-source map;
[0082] For the integrated multi-source map, the integrated multi-source map is divided into L o*o grid cells, the slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method, and the disaster risk area is generated according to the slope value. The disaster risk area includes the landslide high-risk area, the landslide potential risk area, and the safe area. When the slope value is greater than Pp1, it indicates that this coordinate system is a landslide high-risk area. When the slope value is between Pp1 and Pp2, it indicates that this coordinate system is a landslide potential risk area. When the slope value is less than Pp2, it indicates that this coordinate is a safe area.
[0083] Since the integrated multi-source map is generated from the elevation grid surface, the structured parameter table, the water level contour, and the BIM construction layer, the multi-orientation data of the grid node in the grid cell is extracted from the integrated multi-source map, such as the bearing capacity data, the permeability coefficient, the water level depth value, the elevation value, the building height, and the total building load (the total building load is obtained by querying the building structure load specification according to the area, height, and number of floors of the building). The slope value is calculated to obtain the comprehensive value of the grid node. For each grid node in the grid cell, the weighted average method is used to calculate the comprehensive building suitability value of the grid cell. The comprehensive building suitability value of each grid cell is calculated. The comprehensive building suitability value is compared with the theoretical building suitability value (the theoretical building suitability value is obtained by empirical method). When the comprehensive building suitability value is greater than the theoretical building suitability value, it is divided into non-suitable unit. When the comprehensive building suitability value is less than or equal to the theoretical building suitability value, it is divided into suitable unit. The grid cell is divided into suitable unit and non-suitable unit. The building is carried out for the suitable unit, and the non-suitable unit can be set as green or playground;
[0084] For the elevation grid surface, the structured parameter table, the water level contour map, and the BIM construction layer, a unified coordinate system of spatial, geological, hydrological, and building multi-dimensional information is formed. The information island problem is eliminated, and the accuracy and reliability of the evaluation result are improved. The control point correction method is used to unify the coordinate system, which solves the spatial position deviation problem of different data sources, ensures data consistency, and improves the reliability of subsequent analysis;
[0085] Deep learning models (such as convolutional neural networks) are introduced to automatically extract key features (such as elevation, soil bearing capacity, water level, etc.) from the integrated multi-source map, and to calculate the comprehensive building suitability value in combination with the slope value. Traditional methods rely on manual experience or simple statistical models (such as linear regression), which are difficult to capture complex spatial correlations and nonlinear features;
[0086] The advantage of the land adaptability module is that it can provide scientific and accurate decision-making support for building design by comprehensively analyzing multi-dimensional data such as land geology, climate, and hydrology. It integrates multi-source information such as drone lidar scanning, geological drilling exploration reports, and groundwater monitoring data, combined with advanced intelligent algorithms such as deep learning and kriging interpolation methods, to evaluate the land's carrying capacity, disaster risk, and hydrogeological conditions, thereby ensuring a high degree of match between building design and site conditions. The module can effectively identify suitable and unsuitable units, optimize foundation design, avoid potential natural disaster risks, improve the safety, sustainability, and operational efficiency of buildings, reduce construction and operation and maintenance costs, and promote the development of green buildings.
[0087] The specific methods of using the adjacent elevation difference gradient method to calculate the slope value of the grid node in the grid unit include:
[0088] Calculate the horizontal elevation difference and the vertical elevation difference between the elevation values of the grid node and the adjacent grid nodes in the corresponding grid unit; calculate the comprehensive elevation difference based on the horizontal elevation difference and the vertical elevation difference;
[0089] When the comprehensive elevation difference is less than the standard elevation difference, the grid cell is expanded to P*P to obtain a changed grid cell; when the comprehensive elevation difference is greater than the standard elevation difference, the grid cell is reduced to q*q to obtain a changed grid cell;
[0090] For the changed grid cell corresponding to the grid node, the comprehensive horizontal elevation difference and the comprehensive vertical elevation difference between the grid node and the adjacent grid nodes in the changed grid cell are calculated; the slope value of the grid node is calculated by the comprehensive horizontal elevation difference, the comprehensive vertical elevation difference and the distance between the grid nodes. The formula is: ,in, is the slope value of the grid node, is the comprehensive longitudinal elevation difference, is the comprehensive horizontal elevation difference, is the distance between grid nodes, where the distance between grid nodes is obtained through experimental calculation;
[0091] By adaptively adjusting the grid cell scale (P*P or q*q), it dynamically responds to different terrain undulations and improves the local sensitivity and overall accuracy of slope calculation. Compared with the traditional fixed grid method, this solution can automatically refine the grid in areas with severe elevation differences, thereby improving slope resolution. It can also automatically expand the grid in areas with gentle terrain, reducing computational redundancy, thereby achieving high-precision and high-efficiency slope extraction. In addition, the comprehensive gradient modeling method, which combines horizontal and vertical elevation differences with node spacing, avoids directional errors, enhances the adaptability of slope calculation to complex landforms, and provides more scientific basic data support for subsequent building site selection and geological risk analysis.
[0092] For suitable units, the specific methods of generating intelligent pile foundation data include:
[0093] For suitable units, obtain the bearing capacity data, slope value and water level depth value of each grid node in the suitable unit;
[0094] Preset pile foundation rule library to match pile type, pile length and pile diameter according to water level depth value;
[0095] For the foundation pile length and foundation pile diameter, the bearing capacity data and slope value are used to optimize the foundation pile length and foundation pile diameter to obtain the optimized foundation pile length and optimized foundation pile diameter;
[0096] The single pile bearing capacity data is calculated based on the optimized foundation pile diameter, and the number of piles is calculated using the total building load, single pile bearing capacity data, and safety factor;
[0097] Pile foundation data includes optimized foundation pile length, optimized foundation pile diameter and number of piles;
[0098] The preset pile foundation rule library is set according to the technical specifications for building pile foundations. When the water level depth is greater than 3m, cast-in-place piles are used with a foundation pile length of 18m and a foundation pile diameter of 1m. They have good water resistance and are suitable for deep water areas. When the water level depth is less than or equal to 3m and the bearing capacity is greater than or equal to 200kPa, prestressed pipe piles are used with a foundation pile length of 10m and a foundation pile diameter of 0.5m. They are highly economical and suitable for shallow water areas. When the water level depth is less than or equal to 3m and the bearing capacity is less than 200kPa, precast piles are used with a precast column foundation pile length of 5m and a foundation pile diameter of 0.4m.
[0099] When the slope value is in the high-risk area, the pile length and diameter are increased by 20%; when the slope value is in the potential risk area, the pile length and diameter are increased by 15%; when the slope value is in the safe area, the foundation pile length and diameter are used; when the bearing capacity data is less than 200kPa, the pile length and diameter are increased to 15%; when the bearing capacity data is greater than or equal to 200kPa, the benchmark pile length and diameter are used;
[0100] If the water level is 3m deep and the bearing capacity is 185kPa, precast piles are used. The bearing capacity and slope values are used to modify the foundation pile length to the foundation pile diameter.
[0101] For example, in coordinate system A, the bearing capacity data is 185 kPa, the slope value is 15%, and the water level depth is 3 m. Using the preset pile foundation rule library, cast-in-place piles are used. If the slope value is 15% and P1 is set to 10%, a slope value of 15% indicates a high-risk area. The foundation pile length is 5 m, and the foundation pile diameter is 0.4 m. Using the slope value and bearing capacity data to correct the foundation pile length and diameter, the formula for correcting the foundation pile length is: 5 m * (1 + 15% + 15%) = 6.5 m. The formula for correcting the foundation pile diameter is: 0.4 * (1 + 15% + 15%) = 0.52 m.
[0102] The number of piles is calculated based on the total building load, single pile bearing capacity data and safety factor; the single pile bearing capacity data is calculated by correcting the foundation pile diameter to calculate the pile cross-sectional area, and then calculated based on the pile cross-sectional area and bearing capacity data;
[0103] The purpose of calculating intelligent pile foundation data is to optimize the design of pile foundation according to the specific conditions of the building site (such as geological characteristics, hydrological conditions, slope, etc.) to ensure the stability, safety and economy of the foundation;
[0104] By accurately acquiring data such as the building site's bearing capacity, slope, and water depth, combined with a pre-set pile foundation rule library and optimization algorithm, the most appropriate pile foundation design can be tailored for each suitable unit. Dynamically optimizing pile length, diameter, and number of piles not only improves design accuracy and safety, but also avoids over-design and resource waste, achieving cost control. Furthermore, calculating the number of piles based on the total building load and optimizing pile foundation parameters makes the pile foundation solution more adaptable, enabling efficient, safe, and economical foundation design under varying geological conditions, significantly improving design efficiency and construction reliability.
[0105] Set up static interfaces, dynamic interfaces, and environment interfaces, encapsulate the data in the dynamic interfaces and environment interfaces into BIM data using the dynamic feature-driven method, and use the feature check code comparison method to check whether the encapsulated BIM data is abnormal. The specific methods for discarding abnormal data include:
[0106] The multi-source data access stage includes static interfaces, dynamic interfaces, and environmental data integration interfaces;
[0107] Import IFC files, CAD drawings and equipment parameter tables into the static interface;
[0108] Import the equipment's operating status data and smart pile foundation data into the dynamic interface;
[0109] Import the building's internal and external environmental data into the environmental interface;
[0110] Based on the equipment's operating status data, intelligent pile foundation data, and building internal and external environment data, the dynamic feature-driven method is used to encapsulate them into BIM data;
[0111] The characteristic check code comparison method is used to check the benchmark quantum check code of the encapsulated BIM data, and abnormal BIM data is discarded;
[0112] Among them, the building's internal and external environmental data includes external environmental data and internal environmental data; external environmental data includes meteorological data, air quality data, and lighting data; meteorological data includes temperature data, humidity data, wind speed data, precipitation data, air pressure data, and wind direction data;
[0113] Air quality data package including PM2.5 data, PM10 data, carbon dioxide concentration data, and VOC concentration data;
[0114] Light data includes sunlight intensity data, ultraviolet intensity data, and skyline data;
[0115] Internal environmental data includes internal temperature data, internal humidity data, internal air quality data, internal light intensity data, noise data, and basic personnel flow data.
[0116] Specific ways to use the dynamic feature-driven method to encapsulate BIM data include:
[0117] Each data point in each group of equipment operating status data, smart pile foundation data, and building internal and external environment data is defined as a characteristic data point; the local fluctuation rate within Fd timestamps before the characteristic data point is calculated; and the stability data of the characteristic data point is calculated based on the local fluctuation rate;
[0118] The calculation process of local volatility is as follows: calculate the average value within the first Fd timestamps, and take the weighted average of the difference between each feature data point and the average value as the local volatility; for local volatility, use the formula: ,in, is the local volatility, is stability data;
[0119] Local volatility refers to the degree of fluctuation of the current feature data point relative to its historical feature data within a specific time window, which is used to measure the volatility of the data;
[0120] Add a small noise to each feature data point to obtain a feature data noise point; calculate the disturbance rate of the data point based on the ratio of the difference between the feature data noise point and the feature data point and the small noise; calculate the feature data disturbance point of the previous timestamp and the next timestamp of the feature data point, and obtain the disturbance rate of the feature data point of the previous timestamp and the next timestamp; calculate the sensitivity data of the feature data point of the current timestamp based on the average disturbance rate of the feature data points of the previous timestamp, the next timestamp and the current timestamp;
[0121] For the stability data and sensitivity data of the characteristic data points, for example, the importance data value of the characteristic data points is calculated using a weighted average method; the importance data value of each characteristic data point is arranged in descending order to obtain a descending sequence;
[0122] Define the first 50% of the descending sequence as key parameters, the 50% to 80% of the descending sequence as non-key parameters, and the last 20% of the descending sequence as unstable parameters;
[0123] Encapsulate key parameters and non-key parameters into BIM data;
[0124] A disturbance rate and sensitivity modeling mechanism is introduced to achieve data importance grading and precise screening, building a BIM data system with dynamic adaptability. Specifically, by first defining equipment operating status, intelligent pile foundation data, and building internal and external environment data as feature data points, a highly refined basic data granularity is established, which is conducive to the precise identification of subsequent feature behaviors. Secondly, through local volatility analysis within the first Fd timestamps, not only the stability of feature points in the time dimension is quantified, but also data anomalies and dynamic trends are effectively identified. After adding small noise and introducing disturbance rate calculation, the system's response sensitivity to tiny data changes is further enhanced, allowing the disturbance response characteristics of the data to be modeled and explicitly expressed.
[0125] In addition, by constructing a two-way evaluation system for stability data and sensitivity data, and combining it with the weighted average method (or other fusion strategies) to calculate the importance value of each feature point, not only does it make feature sorting more scientific and objective, but it also provides a quantitative basis for subsequent parameter screening and BIM packaging. Ultimately, the importance data values are divided into three categories in descending order: key parameters, non-key parameters, and unstable parameters. Only key parameters and non-key parameters are selected and packaged into the BIM data, effectively eliminating invalid data with large noise disturbances and poor stability, ensuring the quality, efficiency, and reliability of the packaged BIM data.
[0126] This mechanism realizes the closed-loop link of "dynamic screening - disturbance analysis - importance determination - efficient packaging" from data collection to data packaging, which greatly improves the dynamic expression ability, real-time adaptation ability and intelligent judgment ability of BIM data. It is a high-precision and robust packaging strategy suitable for BIM data construction in complex building scenarios; the operating status data of each group of equipment, intelligent pile foundation data and internal and external environment data of the building will be packaged into different BIM data.
[0127] The specific methods of using the feature check code comparison method to check the baseline quantum check code of the encapsulated BIM data and discarding abnormal BIM data include:
[0128] The key parameters in the BIM data are set as Qw, and the non-key parameters are set as Qe; the reference quantum check code is calculated according to the key parameter weight, the key parameter feature data point, the non-key parameter weight and the non-key feature data point;
[0129] For the encapsulated BIM data, the reference quantum check code of the encapsulated BIM data is calculated, the reference quantum check code of the encapsulated BIM data is matched with the reference quantum check code of the BIM data, and the matching is inconsistent, indicating that the encapsulated BIM data is abnormal, and the encapsulated BIM data is discarded;
[0130] The reference quantum check code of the encapsulated BIM data and the reference quantum check code of the BIM data are equal, indicating that the matching is consistent;
[0131] The reference quantum check code of the encapsulated BIM data and the reference quantum check code of the BIM data are not equal, indicating that the matching is inconsistent;
[0132] By distinguishing key parameters and non-key parameters and giving different weights (Qw, Qe), a data content driven check code generation logic is constructed. Compared with the traditional redundant check or hash check method, this mechanism can better reflect the logical association and feature importance of the data structure, thereby improving the discriminant sensitivity and semantic expression ability of the check code;
[0133] Secondly, the characteristic value and weight of the key / non-key parameter are used to generate the reference quantum check code, and the reference quantum check code of the encapsulated BIM data is calculated again, and the comparison between the two is used to judge whether the data has semantic level deviation, loss or pollution, so as to quickly capture and judge the small abnormal changes in the encapsulation process;
[0134] In addition, this mechanism has good scalability and robustness: when the data source, device or external environment fluctuates, as long as the check code matches, it means that the data is still within the acceptable range and does not need human intervention; if the matching fails, the abnormal data is immediately excluded, avoiding the entry of false information into the subsequent design or decision-making process, effectively improving the overall data reliability and engineering safety boundary of the system;
[0135] In summary, the advantages of this method are the fusion of feature importance + check structure + multi-dimensional comparison strategy, which establishes a data check framework with logical rigor, recognition sensitivity and self-adaptive ability, especially suitable for automatic encapsulation and quality control of multi-source heterogeneous BIM data, and is an important guarantee mechanism for intelligent building data processing.
[0136] Calculate the effectiveness index of the BIM model component attributes and evaluate the performance level of the BIM model based on the effectiveness index. The performance levels include high adaptability level and medium adaptability level. The specific methods include:
[0137] Input CAD drawings and packaged BIM data into BIM software to build a BIM model;
[0138] Use the FilteredElementCollector function to traverse the components of the BIM model, and use the Element.LookupParameter(string) function to obtain the properties of the components of the BIM model. If the property is null or an empty string, it is considered missing. Calculate the property completeness rate of the BIM model;
[0139] Compare the attributes in the BIM model components with the standard attributes to check whether the attributes are normal and calculate the attribute accuracy of the BIM model components;
[0140] Use the FilteredElementCollector function to count the total types of component attributes in the BIM model, and compare them with the total types of component attributes in the standard BIM model to calculate the BIM model attribute coverage;
[0141] The performance index of component attributes in the BIM model is calculated based on the attribute completeness, attribute accuracy, and attribute coverage of the BIM model. If the attribute performance index is greater than the benchmark value, it means that the BIM model has a high adaptability level; if the attribute performance index is less than or equal to the benchmark value, it means that the BIM model has a medium adaptability level. For example, based on the attribute completeness, attribute accuracy, and attribute coverage of the BIM model, the performance index of component attributes in the BIM model can be calculated using the weighted average method; the benchmark value is obtained by consulting industry standards;
[0142] First, by leveraging built-in functions in BIM software, such as FilteredElementCollector and Element.LookupParameter, we automatically traverse model components and extract attribute data, greatly improving the automation and comprehensiveness of data collection. Second, we comprehensively measure the information quality of BIM components from three dimensions: attribute completeness, accuracy, and coverage, effectively avoiding omissions of details or attribute deviations that are easily overlooked in traditional manual inspections.
[0143] At the same time, by comparing and verifying with the standard attribute library, the consistency of model output with industry standards is ensured, improving the interoperability and reuse value of the model;
[0144] Finally, the three indicators are weighted and integrated into an "efficiency index", and based on this, they are divided into "high adaptability level" and "medium adaptability level", which not only provides clear guidance for subsequent design optimization and construction application, but also establishes an extensible and reusable BIM model quality evaluation standard framework; this method is particularly suitable for the intelligent management and screening of model data integrity and accuracy in large-scale and complex structural projects, and has the significant advantages of high efficiency, objectivity and strong standardization.
[0145] For the medium adaptability level, the specific methods of supplementing component attributes and re-evaluating the performance level of the BIM model include:
[0146] For high adaptability levels, it can be used directly for subsequent construction and operation and maintenance;
[0147] For the medium adaptability level, standard attributes are used to supplement the attributes that are null or empty strings. For the supplemented attributes, the attribute completeness rate, attribute accuracy rate and attribute coverage rate are calculated innovatively to calculate the attribute performance index. The performance level of the BIM model is evaluated based on the attribute performance index. If the performance level is high adaptability, it is used directly. If the performance level is medium adaptability, it is re-evaluated until the evaluation result is high adaptability.
[0148] This embodiment integrates multiple data sources, such as elevation grids, structured soil parameters, and water levels, and combines them with BIM to construct layers, enabling comprehensive digital representation and analysis of the construction site. Using data such as drone lidar point clouds and borehole exploration reports, it accurately calculates slope values, bearing capacity data, and water level distribution, and scientifically demarcates suitable and unsuitable units. This significantly improves the design's adaptability and accuracy to the natural environment, reducing construction risks and subsequent maintenance costs.
[0149] For suitable units, the pile type, length, and diameter are dynamically matched based on bearing capacity data, slope values, and water level depth values. By optimizing foundation pile parameters, the conservative and wasteful nature of traditional empirical design is avoided. The bearing capacity of individual piles and the number of piles are calculated based on the optimized foundation pile diameter, achieving intelligent and efficient design, improving foundation stability and safety, while reducing material and construction costs.
[0150] Integrate multi-source data through static interfaces, dynamic interfaces, and environmental interfaces, and encapsulate them into BIM data using a dynamic feature-driven approach. Combined with feature checksum comparison methods, abnormal data is automatically detected and discarded to ensure the accuracy and integrity of BIM data and enhance the reliability of subsequent design, analysis, and management work.
[0151] A model is constructed based on the encapsulated BIM data, and the performance index is calculated using indicators such as attribute completeness, accuracy, and coverage to achieve a scientific assessment of the BIM model's performance level. For models with a medium adaptability level, missing attributes are automatically supplemented, and repeated assessments are performed until a high adaptability level is reached, ensuring the high quality and applicability of the final model to support subsequent intelligent design and construction applications.
[0152] Extract key data from comprehensive multi-source maps, effectively integrate complex terrain, geological and environmental information, automatically generate comprehensive building suitability values, realize intelligent and refined evaluation of construction sites, and greatly improve the scientific rationality of design solutions.
[0153] Example 2
[0154] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A BIM-based building intelligent design optimization method is provided, comprising:
[0155] Step SS1: coordinate system 1 is performed using the elevation grid surface, structured parameter table, water level contour map, and BIM construction layer to obtain a comprehensive multi-source map; the comprehensive multi-source map is divided into L grid cells, and the slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method; the comprehensive building suitability value of the grid cell is calculated; and the grid cell is divided into suitable cells and unsuitable cells;
[0156] Step SS2: Generate intelligent pile foundation data for suitable units; obtain bearing capacity data, slope value, and water level depth value of each grid node in the suitable unit; match the water level depth value with a preset pile foundation rule library to determine the pile type, foundation pile length, and foundation pile diameter; optimize the foundation pile length and foundation pile diameter using the bearing capacity data and slope value; calculate the single pile bearing capacity data based on the optimized foundation pile diameter, and calculate the number of piles using the single pile bearing capacity data;
[0157] Step SS3: Set up static interfaces, dynamic interfaces, and environment interfaces, encapsulate the data in the dynamic interfaces and environment interfaces into BIM data using a dynamic feature-driven method, and use a feature check code comparison method to check whether the encapsulated BIM data is abnormal. If abnormal, discard it.
[0158] Step SS4: Generate a BIM model for the encapsulated BIM data, obtain the model's attribute completeness, attribute accuracy, and attribute coverage, and calculate the performance index of the BIM model component attributes. Based on the BIM model's performance index, evaluate the BIM model's performance level, which includes a high adaptability level and a medium adaptability level. For the medium adaptability level, supplement the component attributes and re-evaluate the BIM model's performance level.
[0159] Example 3
[0160] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the BIM-based building intelligent design optimization system and method provided above is implemented.
[0161] Since the electronic device introduced in this embodiment is an electronic device used to implement a BIM-based building intelligent design optimization system and method in the embodiment of this application, based on the BIM-based building intelligent design optimization system and method introduced in the embodiment of this application, those skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the BIM-based building intelligent design optimization system and method in the embodiment of this application, they all fall within the scope of protection to be provided by this application.
[0162] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.
[0163] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A BIM-based building intelligent design optimization system, characterized by: include: Land adaptability assessment module: The elevation grid surface, structured parameter table, water level contour map, and BIM construction layer are used to establish a coordinate system to obtain a comprehensive multi-source map. The comprehensive multi-source map is divided into L grid cells, and the slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method. The comprehensive building suitability value of the grid cell is also calculated, and the grid cells are divided into suitable cells and unsuitable cells. Intelligent pile foundation optimization module: Generates intelligent pile foundation data for suitable units. This module obtains the bearing capacity data, slope value, and water level depth value for each grid node in the suitable unit. The water level depth value is matched with a preset pile foundation rule library to determine the pile type, pile length, and pile diameter. The bearing capacity data and slope value are used to optimize the pile length and diameter. The bearing capacity data of a single pile is calculated based on the optimized pile diameter, and the number of piles is calculated using the single pile bearing capacity data. BIM data integration module: Set up static interfaces, dynamic interfaces, and environment interfaces, encapsulate the data in the dynamic interfaces and environment interfaces into BIM data using the dynamic feature-driven method, and use the feature check code comparison method to check whether the encapsulated BIM data is abnormal. If abnormal, discard it; BIM model performance optimization module: Generates a BIM model for the encapsulated BIM data, obtains the model's attribute completeness, attribute accuracy, and attribute coverage, and calculates the performance index of the BIM model component attributes. Based on the BIM model's performance index, the module evaluates the BIM model's performance level, which includes high and medium adaptability levels. For the medium adaptability level, the module supplements the component attributes and re-evaluates the BIM model's performance level.
2. The BIM-based building intelligent design optimization system according to claim 1, characterized in that: The specific method of calculating the comprehensive building suitability value of the grid unit includes: Use a drone equipped with a LiDAR to scan the construction site, obtain the original point cloud data of the land, and convert the original point cloud data of the land into an elevation grid surface with geographic coordinates; Obtaining N drilling exploration reports of the construction sites, extracting soil stratification parameters from the drilling exploration reports, the soil stratification parameters including bearing capacity data and permeability coefficient, and generating a structured parameter table based on the bearing capacity data and permeability coefficient; Obtain regional groundwater monitoring data, monitor water level depth, and use Kriging interpolation to generate water level contour maps; Import CAD drawings or SHP files from municipal archives and convert them into BIM construction layers using BIM software; The elevation grid surface, structured parameter table, water level contour map and BIM construction layer are aligned to form a coordinate system to obtain a comprehensive multi-source map; For the comprehensive multi-source map, the comprehensive multi-source map is divided into L o*o grid cells. The slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method. According to the slope value, the disaster risk area is generated. The disaster risk area includes the high-risk area for landslide, the potential risk area for landslide and the safe area. The comprehensive building suitability value of each grid cell is calculated based on the multi-directional data and slope values in the comprehensive multi-source map; and the comprehensive building suitability value is compared with the theoretical building suitability value. When the comprehensive building suitability value is greater than the theoretical building suitability value, the grid cell is defined as an unsuitable cell; when the comprehensive building suitability value is less than or equal to the theoretical building suitability value, the grid cell is defined as a suitable cell.
3. The BIM-based building intelligent design optimization system according to claim 2 is characterized in that: The specific method of calculating the slope value of the grid node in the grid unit using the adjacent elevation difference gradient method includes: Calculate the horizontal elevation difference and the vertical elevation difference between the elevation values of the grid node and the adjacent grid nodes in the corresponding grid unit; calculate the comprehensive elevation difference based on the horizontal elevation difference and the vertical elevation difference; When the comprehensive elevation difference is less than the standard elevation difference, the grid cell is expanded to P*P to obtain a changed grid cell; when the comprehensive elevation difference is greater than the standard elevation difference, the grid cell is reduced to q*q to obtain a changed grid cell; For the changed grid cell corresponding to the grid node, the comprehensive horizontal elevation difference and the comprehensive vertical elevation difference between the grid node and the adjacent grid nodes in the changed grid cell are calculated; the slope value of the grid node is calculated by the comprehensive horizontal elevation difference, the comprehensive vertical elevation difference and the distance between the grid nodes.
4. The BIM-based building intelligent design optimization system according to claim 3 is characterized in that: The specific method of generating intelligent pile foundation data for suitable units includes: For suitable units, obtain the bearing capacity data, slope value and water level depth value of each grid node in the suitable unit; Preset pile foundation rule library to match pile type, pile length and pile diameter according to water level depth value; For the foundation pile length and foundation pile diameter, the bearing capacity data and slope value are used to optimize the foundation pile length and foundation pile diameter to obtain the optimized foundation pile length and optimized foundation pile diameter; The single pile bearing capacity data is calculated based on the optimized foundation pile diameter, and the number of piles is calculated using the total building load, single pile bearing capacity data and safety factor.
5. The BIM-based building intelligent design optimization system according to claim 4, characterized in that: The specific method of setting up a static interface, a dynamic interface, and an environment interface, encapsulating the data in the dynamic interface and the environment interface into BIM data using a dynamic feature-driven method, and using a feature check code comparison method to check whether the encapsulated BIM data is abnormal, and discarding the abnormal BIM data includes: The multi-source data access stage includes static interfaces, dynamic interfaces, and environmental data integration interfaces; Import IFC files, CAD drawings and equipment parameter tables into the static interface; Import the equipment's operating status data and smart pile foundation data into the dynamic interface; Import the building's internal and external environmental data into the environmental interface; Based on the equipment's operating status data, intelligent pile foundation data, and building internal and external environment data, the dynamic feature-driven method is used to encapsulate them into BIM data; The characteristic check code comparison method is used to check the benchmark quantum check code of the encapsulated BIM data, and the abnormal BIM data is discarded.
6. The BIM-based building intelligent design optimization system according to claim 5, characterized in that: The specific method of encapsulating the BIM data using the dynamic feature-driven method includes: Calculate the local volatility within Fd timestamps before the feature data point; calculate the stability data of the feature data point based on the local volatility; Add small noise to each characteristic data point to obtain the characteristic data noise point; calculate the disturbance rate of the data point and the sensitivity data of the characteristic data point; Calculate the importance data value of the characteristic data point based on the stability data and sensitivity data of the characteristic data point; arrange the importance data value of each characteristic data point in descending order to obtain a descending sequence, and divide the characteristic data points in the descending sequence into key parameters, non-key parameters, and unstable parameters; Encapsulate key parameters and non-key parameters into BIM data.
7. The BIM-based building intelligent design optimization system according to claim 6, characterized in that: The specific method of using the feature check code comparison method to check the benchmark quantum check code of the encapsulated BIM data and discarding abnormal BIM data includes: The weights of key parameters in BIM data are set as Qw, and non-key parameters are set as Qe; a reference quantum check code is calculated based on the key parameter weights, key parameter feature data points, non-key parameter weights, and non-key feature data points; For the encapsulated BIM data, the reference quantum check code of the encapsulated BIM data is calculated, and the encapsulated reference quantum check code is matched with the reference quantum check code of the BIM data. If the match is inconsistent, it means that the encapsulated BIM data is abnormal, and the encapsulated BIM data is discarded.
8. The BIM-based building intelligent design optimization system according to claim 7, characterized in that: The method of calculating the effectiveness index of the BIM model component attribute and evaluating the performance level of the BIM model according to the effectiveness index of the BIM model, wherein the performance level includes a high adaptability level and a medium adaptability level, includes: Input CAD drawings and packaged BIM data into BIM software to build a BIM model; Traverse the components of the BIM model and obtain the attributes of the components of the BIM model. If the attribute is null or an empty string, it is considered missing. Calculate the attribute completeness rate of the BIM model; Compare the attributes in the BIM model components with the standard attributes to check whether the attributes are normal and calculate the attribute accuracy of the BIM model components; Count the total types of component attributes in the BIM model and compare them with the total types of component attributes in the standard BIM model to calculate the BIM model attribute coverage; The performance index of component attributes in the BIM model is calculated based on the attribute completeness rate, attribute accuracy rate and attribute coverage rate of the BIM model. If the attribute performance index is greater than the benchmark value, it means that the BIM model has a high adaptability level; if the attribute performance index is less than or equal to the benchmark value, it means that the BIM model has a medium adaptability level.
9. The BIM-based building intelligent design optimization system according to claim 8, characterized in that: For the medium adaptability level, the specific methods of supplementing component attributes and re-evaluating the performance level of the BIM model include: For high adaptation levels, use it directly; For the medium adaptability level, standard attributes are used to supplement the attributes that are null or empty strings. For the supplemented attributes, the attribute completeness rate, attribute accuracy rate and attribute coverage rate are calculated innovatively to calculate the attribute performance index. The performance level of the BIM model is evaluated based on the attribute performance index. If the performance level is high adaptability, it is used directly. If the performance level is medium adaptability, it is re-evaluated until the evaluation result is high adaptability.
10. A BIM-based building intelligent design optimization method, which is based on the BIM-based building intelligent design optimization method according to any one of claims 1 to 9, characterized in that: include: Step SS1: coordinate system 1 is performed using the elevation grid surface, structured parameter table, water level contour map, and BIM construction layer to obtain a comprehensive multi-source map; the comprehensive multi-source map is divided into L grid cells, and the slope value of the grid node in the grid cell is calculated using the adjacent elevation difference gradient method; the comprehensive building suitability value of the grid cell is calculated; and the grid cell is divided into suitable cells and unsuitable cells; Step SS2: Generate intelligent pile foundation data for suitable units; obtain bearing capacity data, slope value, and water level depth value of each grid node in the suitable unit; match the water level depth value with a preset pile foundation rule library to determine the pile type, foundation pile length, and foundation pile diameter; optimize the foundation pile length and foundation pile diameter using the bearing capacity data and slope value; calculate the single pile bearing capacity data based on the optimized foundation pile diameter, and calculate the number of piles using the single pile bearing capacity data; Step SS3: Set up static interfaces, dynamic interfaces, and environment interfaces, encapsulate the data in the dynamic interfaces and environment interfaces into BIM data using a dynamic feature-driven method, and use a feature check code comparison method to check whether the encapsulated BIM data is abnormal. If abnormal, discard it. Step SS4: Generate a BIM model for the encapsulated BIM data, obtain the model's attribute completeness, attribute accuracy, and attribute coverage, and calculate the performance index of the BIM model component attributes. Based on the BIM model's performance index, evaluate the BIM model's performance level, which includes a high adaptability level and a medium adaptability level. For the medium adaptability level, supplement the component attributes and re-evaluate the BIM model's performance level.