A building thermal performance intelligent energy-saving analysis method based on BIM

Abstract

The application relates to the technical field of architectural design, in particular to a building thermal performance intelligent energy-saving analysis method based on BIM (Building Information Modeling), which comprises the following steps: obtaining BIM (Building Information Modeling) data, extracting building geometric information, material attribute information and spatial topological relation information; performing three-dimensional space gridding division on the building information model to form spatial grid units; obtaining thermal information entropy values based on the thermal state information and the material attribute information of the spatial grid units; performing spatial interpolation processing on the thermal information entropy values of the spatial grid units by using the building geometric information and the spatial topological relation information, constructing a three-dimensional thermal information entropy field, calculating the spatial gradient distribution of the three-dimensional thermal information entropy field, identifying entropy value gradient abnormal areas as thermal performance key control points, and extracting building component thermal parameters; establishing a self-organizing balance model, adjusting the building component thermal parameters, making the three-dimensional thermal information entropy field tend to a preset ideal distribution, and generating a building thermal performance energy-saving optimization scheme.

Description

A BIM-based intelligent energy-saving analysis method for building thermal performance Technical Field

[0001] This invention relates to the field of architectural design technology, specifically to a BIM-based intelligent energy-saving analysis method for building thermal performance. Background Technology

[0002] Building thermal performance analysis is a core technical step in achieving energy-efficient building design, directly impacting energy consumption levels and indoor environmental quality. Traditional methods for building thermal performance analysis primarily rely on empirical formulas and simplified models, which suffer from insufficient calculation accuracy, incomplete parameter consideration, and an inability to accurately reflect the complex thermal behavior of buildings. With the development and application of BIM technology, Building Information Modeling (BIM) provides rich geometric information and attribute data for thermal performance analysis; however, existing BIM thermal analysis methods generally suffer from limitations such as limited analytical dimensions, low levels of intelligence, and a lack of comprehensive optimization capabilities.

[0003] Current building thermal performance analysis systems often require manual setting of numerous parameters, resulting in cumbersome analysis processes and limited output formats. This makes it difficult to provide designers with intuitive and effective energy-saving optimization suggestions, significantly reducing calculation accuracy and failing to meet the refined requirements of modern building energy-saving design. Therefore, this invention provides a BIM-based intelligent building thermal performance analysis method to improve analysis accuracy and efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a BIM-based intelligent energy-saving analysis method for building thermal performance. This method involves acquiring Building Information Modeling (BIM) data, extracting building geometric information, material property information, and spatial topological relationship information; dividing the BIM into three-dimensional spatial grids to form spatial grid units; obtaining thermal information entropy values ​​based on the thermal state and material property information of the spatial grid units; using building geometric information and spatial topological relationship information to perform spatial interpolation processing on the thermal information entropy values ​​of the spatial grid units to construct a three-dimensional thermal information entropy field, calculating its spatial gradient distribution, identifying abnormal entropy gradient regions as key control points for thermal performance, and extracting thermal parameters of building components; establishing a self-organizing equilibrium model, and adjusting the thermal parameters of building components to make the three-dimensional thermal information entropy field tend towards a preset ideal distribution, thereby generating an energy-saving optimization scheme for building thermal performance.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A BIM-based intelligent energy-saving analysis method for building thermal performance includes:

[0007] Acquire Building Information Model (BIM) data and extract building geometric information, material property information, and spatial topological relationship information;

[0008] The building information model is divided into three-dimensional spatial grids according to a preset precision to form spatial grid units;

[0009] Based on the thermal state information of the spatial grid cells and the material property information, the corresponding thermal information entropy value is obtained.

[0010] By using building geometric information and spatial topological relationship information, spatial interpolation processing is performed on the thermal information entropy value of spatial grid unit to construct a three-dimensional thermal information entropy field.

[0011] Calculate the spatial gradient distribution of the three-dimensional thermal information entropy field, identify regions with abnormal entropy gradients as key control points for thermal performance, and extract the corresponding thermal parameters of building components.

[0012] A self-organizing equilibrium model is established with the goal of optimizing the distribution of thermal information entropy values. By iteratively adjusting the thermal parameters of building components, the three-dimensional thermal information entropy field tends to the preset ideal distribution.

[0013] Based on the optimized three-dimensional thermal information entropy field distribution, an energy-saving optimization scheme for building thermal performance is generated.

[0014] Preferably, the specific process of the three-dimensional spatial meshing includes:

[0015] The geometric complexity coefficient of the building components is calculated based on the building geometry information. The geometric complexity coefficient is obtained by analyzing the surface curvature change, edge angle change and volume density distribution of the components.

[0016] The basic size of the mesh cell is determined based on the geometric complexity coefficient, with regions having higher geometric complexity coefficients corresponding to smaller mesh sizes;

[0017] The size of the basic grid is reduced at the junctions of building components, thermal bridges, and corners of the external envelope.

[0018] Each spatial grid cell is assigned a position identifier and geometric attribute identifier in a three-dimensional coordinate system to form a grid cell dataset.

[0019] Preferably, the specific process for obtaining the thermal information entropy value includes:

[0020] The thermal state information includes temperature state, humidity state, and heat flow state;

[0021] The temperature state of each spatial grid cell is divided into multiple temperature levels according to a preset temperature range, the humidity state is divided into multiple humidity levels according to a preset humidity range, and the heat flux state is divided into multiple heat flux levels according to the magnitude and direction of the heat flux density.

[0022] Based on the material property information, the theoretical probability of each level in the spatial grid cell is calculated; the actual probability is calculated by statistically analyzing the actual occurrence frequency of the level; based on the degree of deviation between the theoretical probability and the actual probability, the entropy value of the thermal information of the spatial grid cell is obtained by using the entropy calculation method in information theory.

[0023] Preferably, the specific process of constructing the three-dimensional thermal information entropy field includes:

[0024] Based on the spatial topology information, a connectivity matrix is ​​constructed between adjacent grid cells, and the connectivity matrix records the adjacency relationship and connectivity path length between grid cells;

[0025] Using the component boundary information in the building geometry information, determine the geometric constraints for entropy propagation, and set entropy propagation hindrance coefficients at the component boundaries;

[0026] Entropy interpolation is performed between adjacent grid cells, taking into account spatial distance attenuation and material interface resistance effects; through iterative interpolation calculation, a continuously distributed three-dimensional thermal information entropy field is formed throughout the entire building space.

[0027] Preferably, the specific process for identifying key control points and extracting thermal parameters of building components includes:

[0028] The three-dimensional thermal information entropy field is subjected to three-dimensional gradient calculation to obtain the gradient components of the entropy field in the X, Y, and Z directions; the gradient magnitude at each spatial location is calculated to identify spatial regions where the gradient magnitude exceeds the dynamic threshold.

[0029] Extract the spatial coordinates of key control points within the spatial region, and assign influence weights to each key control point according to the gradient magnitude.

[0030] Based on the spatial coordinates of key control points, corresponding building components are matched from BIM data, and the heat transfer coefficient, thermal resistance, heat capacity, surface heat transfer coefficient, and material thickness of the components are extracted as a thermal parameter dataset.

[0031] Preferably, the self-organizing equilibrium model includes a perception layer, an analysis layer, and a decision-making layer:

[0032] The perception layer receives the thermal information entropy value, gradient component, and corresponding thermal parameter dataset of key control points, and establishes a multi-dimensional data vector.

[0033] The analysis layer processes the multidimensional data vector, calculates the sensitivity coefficients of changes in thermal parameters of building components and changes in local entropy, establishes a parameter-entropy response model, and transmits the sensitivity coefficient data to the decision layer.

[0034] The decision-making layer determines the priority sequence of parameter adjustments based on the sensitivity coefficient, formulates a parameter adjustment strategy, and generates parameter adjustment instructions to be fed back to the perception layer.

[0035] Preferably, the specific process of causing the three-dimensional thermal information entropy field to tend towards a preset ideal distribution includes:

[0036] Based on building functional zoning and thermal performance requirements, target entropy value ranges for each functional area are set, and an ideal entropy value distribution is constructed; the degree of deviation between the current thermal information entropy value and the ideal entropy value distribution is calculated, and the overall deviation value is quantified.

[0037] Based on the influence weight of the key control points, the thermal parameters of the corresponding building components are adjusted in descending order of weight.

[0038] After each parameter adjustment, the three-dimensional thermal information entropy field is recalculated to evaluate the change in the overall deviation value.

[0039] When the change in the overall deviation value is less than the preset convergence threshold for three consecutive iterations, the entropy field distribution is determined to have reached an ideal state.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. This invention establishes a continuously distributed three-dimensional thermal information entropy field by combining the thermal information entropy value with the spatial topology and material property data of the BIM model. The three-dimensional thermal information entropy field not only distinguishes the spatial heterogeneity of different components and regions under the states of heat conduction, heat capacity and heat flow, but also facilitates the rapid identification of areas with weak thermal performance and abnormal energy consumption in complex building structures.

[0042] 2. This invention performs spatial gradient analysis on the thermal information entropy field, calculates the rate of change of the entropy field in three dimensions, and identifies spatial points of gradient abrupt changes as key control points. Based on the BIM model, the corresponding building components are then located. By identifying the source of building thermal imbalance, such as thermal bridges, weak heat transfer layers, and structural interfaces, thermal parameters are further extracted. The key point extraction mechanism proposed in this invention improves the accuracy of anomaly location.

[0043] 3. The self-organizing equilibrium model proposed in this invention consists of a perception layer, an analysis layer, and a decision layer. The perception layer receives real-time entropy values ​​and gradient changes at key control points. The analysis layer constructs a model of the relationship between component thermal parameters and entropy response. The decision layer prioritizes adjusting high-impact parameters based on sensitivity coefficients and adjusts the optimization strategy through iterative feedback, gradually bringing the three-dimensional thermal information entropy field closer to the ideal distribution set for the functional zones. The self-organizing equilibrium model achieves closed-loop optimization control of building thermal energy status, improving the intelligence level of energy-saving design. Attached Figure Description

[0044] Figure 1 is a flowchart of a BIM-based intelligent energy-saving analysis method for building thermal performance provided by the present invention.

[0045] Figure 2 is a flowchart of the thermal information entropy value acquisition process provided by the present invention;

[0046] Figure 3 is a flowchart of the identification of key control points provided by the present invention. Detailed Implementation

[0047] 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.

[0048] Example 1:

[0049] Please refer to Figure 1. This invention provides a BIM-based intelligent energy-saving analysis method for building thermal performance, the technical solution of which is as follows:

[0050] Acquire Building Information Modeling (BIM) data, extract building geometry, material properties, and spatial topology, and perform noise reduction, standardization, and normalization processing.

[0051] The building geometry information specifically includes: spatial coordinate information of building components, geometric dimension parameters of building components, geometric shape characteristics of building components, spatial orientation information of building components, and boundary definition information of building components;

[0052] The material property information specifically includes: the material's basic thermal properties, the material's heat transfer characteristic parameters, the material's physical structure parameters, the material's environmental adaptability parameters, and the material's thermal bridge characteristic parameters.

[0053] The spatial topology information specifically includes: topology at the grid cell level, topology at the component level, topology at the spatial level, topology at the system level, and topology at the boundary level.

[0054] The building information model is divided into three-dimensional spatial grids according to a preset precision to form spatial grid units;

[0055] The specific process of the three-dimensional spatial meshing includes:

[0056] The geometric complexity coefficient of the building components is calculated based on the building geometry information. The geometric complexity coefficient is obtained by analyzing the surface curvature change, edge angle change and volume density distribution of the components.

[0057] The surface of the building component is sampled in three dimensions. The principal and secondary curvatures at each sampling point are calculated, and the curvature variation between adjacent sampling points is statistically analyzed. The curvature variation values ​​of the entire component surface are weighted and averaged to obtain the surface curvature variation coefficient. All edge lines of the building component are identified, including the intersection edges between components, the contact edges between components and air, and the geometric feature edges inside the component. The degree of angle variation between adjacent edge segments is calculated, and the angle variation values ​​of all edges of the component are statistically summarized to obtain the edge angle variation coefficient. The building component is divided into multiple sub-regions according to the principle of equal volume. The material volume occupancy and space utilization rate of each sub-region are calculated. The degree of density difference and distribution uniformity between sub-regions are analyzed. The overall density distribution characteristics of the component are comprehensively considered to obtain the volume density distribution coefficient. The surface curvature variation coefficient, edge angle variation coefficient, and volume density distribution coefficient are weighted and averaged according to a weight ratio of 4:3:3 to obtain the comprehensive geometric complexity coefficient of the component.

[0058] The basic size of the mesh cell is adaptively determined based on the geometric complexity coefficient, with regions having higher geometric complexity coefficients corresponding to smaller mesh sizes;

[0059] When the geometric complexity coefficient is in the range of 0.1-0.3, the corresponding basic size of the mesh cell is set to 1.5-2.0 meters;

[0060] When the geometric complexity coefficient is in the range of 0.4-0.6, the corresponding basic size of the mesh cell is set to 0.8-1.2 meters;

[0061] When the geometric complexity coefficient is in the range of 0.7-0.9, the corresponding basic size of the mesh cell is set to 0.3-0.6 meters;

[0062] The size of the basic grid is reduced at the junctions of building components, thermal bridges, and corners of the external envelope.

[0063] In the area where building components meet, the size of the basic mesh corresponding to the geometric complexity coefficient is reduced to half of the original size to ensure the accuracy of thermal analysis at the interface.

[0064] At thermal bridge locations and corners of the outer envelope, the size of the base mesh corresponding to the geometric complexity coefficient is reduced to one-third to one-quarter of its original size to capture the characteristics of abrupt changes in local thermal performance.

[0065] In areas where equipment passes through and pipelines penetrate, the base mesh size corresponding to the geometric complexity coefficient is reduced to one-quarter to one-fifth of its original size to ensure meshing accuracy under complex geometric boundary conditions.

[0066] Each spatial grid cell is assigned a position identifier and geometric attribute identifier in a three-dimensional coordinate system to form a grid cell dataset.

[0067] In this embodiment, by extracting the geometric information, material properties, and spatial topological relationships of the building information model, and adaptively dividing the three-dimensional spatial mesh using a geometric complexity coefficient, the meshing accuracy and efficiency are improved. Smaller mesh sizes are used in high-complexity regions, enhancing the accuracy and applicability of the building model in thermal, structural, and energy consumption analyses.

[0068] Based on the thermal state information of the spatial grid cells and the material property information, the corresponding thermal information entropy value is obtained. The thermal state information includes temperature state, humidity state and heat flow state.

[0069] The specific process for obtaining the thermal information entropy value is shown in Figure 2, including:

[0070] The temperature state of each spatial grid cell is divided into multiple temperature levels according to a preset temperature range, the humidity state is divided into multiple humidity levels according to a preset humidity range, and the heat flux state is divided into multiple heat flux levels according to the magnitude and direction of the heat flux density.

[0071] Combining the thermal conductivity, specific heat capacity, density, and moisture conductivity information in the material properties, the theoretical occurrence weight of each state level in the grid cell is calculated;

[0072] By statistically analyzing the actual frequency of occurrence of each level of state, the actual probability distribution of occurrence is calculated.

[0073] Based on the degree of deviation between theoretical weights and actual occurrence probabilities, the entropy value of the thermal information of the spatial grid cell is obtained by using the entropy calculation method in information theory.

[0074] The entropy calculation method in information theory is specifically as follows:

[0075] The ratio of the actual occurrence probability to the theoretical weight is used as the corrected probability. For the i-th state level, the logarithmic ratio of its corrected probability to the theoretical probability is calculated.

[0076] The relative entropy value of the spatial grid cell is obtained by weighting and summing the logarithmic ratios of each state level according to the corrected probability.

[0077] The relative entropy value is converted into a standard entropy value in the range of zero to one through standardization, which is then used as the final thermal information entropy value of the grid cell.

[0078] In this embodiment, the complexity of the building's thermal state is quantified by calculating the thermal information entropy value based on the thermal state information and material property information of spatial grid cells. Temperature, humidity, and heat flux are graded, and material properties such as thermal conductivity are combined to calculate the deviation between theoretical weights and actual probabilities. The relative entropy value of the grid cell is obtained using the information theory entropy calculation method and standardized to a standard entropy value in the range of 0 to 1. This improves the reliability of thermal analysis, provides a scientific basis for building energy consumption assessment, thermal bridge optimization, and environmental control, and enhances the intelligence level of building thermal performance analysis.

[0079] By using building geometric information and spatial topological relationship information, spatial interpolation processing is performed on the thermal information entropy value of spatial grid unit to construct a three-dimensional thermal information entropy field.

[0080] The specific process of constructing the three-dimensional thermal information entropy field includes:

[0081] Based on the spatial topology information, a connectivity matrix is ​​constructed between adjacent grid cells, and the connectivity matrix records the adjacency relationship and connectivity path length between grid cells;

[0082] Using the component boundary information in the building geometry information, determine the geometric constraints for entropy propagation, and set entropy propagation hindrance coefficients at the component boundaries;

[0083] A multivariate interpolation algorithm based on distance weight and topological relationship is adopted to perform entropy interpolation calculation between adjacent grid cells. The interpolation algorithm takes into account spatial distance attenuation and material interface resistance effect.

[0084] Through iterative interpolation calculations, a continuously distributed three-dimensional thermal information entropy field data is formed throughout the entire building space.

[0085] The multivariate interpolation algorithm specifically includes:

[0086] For the spatial location to be interpolated, search for known entropy value grid cells within a preset range around it and establish a neighborhood grid cell set;

[0087] The distance weight is calculated based on the spatial distance, the connectivity weight is calculated based on the topological connectivity path, and the product of the distance weight and the connectivity weight is used as the comprehensive weight.

[0088] Considering the hindering effect of material interfaces, the weights for crossing different material interfaces are attenuated and corrected.

[0089] The entropy values ​​of the neighboring grid cells are weighted and averaged using a comprehensive weighting method to obtain the entropy value of the location to be interpolated.

[0090] In this embodiment, by utilizing building geometry and spatial topology information, spatial interpolation is performed on the thermal entropy values ​​of spatial grid cells to construct a continuous three-dimensional thermal entropy field, thereby improving the accuracy of building thermal analysis. A multivariate interpolation algorithm combining connectivity matrix and component boundary information with distance weights and topological relationships is employed, taking into account spatial distance attenuation and material interface resistance effects to effectively optimize the accuracy of entropy propagation. Iterative interpolation calculations generate continuous entropy field data, providing high-precision support for building thermal performance evaluation, energy consumption optimization, and thermal bridge analysis.

[0091] The spatial gradient distribution of the three-dimensional thermal information entropy field is calculated, and the regions with abnormal entropy gradients are identified as key control points for thermal performance. The thermal parameters of the building components corresponding to the key control points are extracted. See Figure 3 for details.

[0092] The specific process for identifying key control points and extracting thermal parameters of building components includes:

[0093] The three-dimensional gradient of the entropy field of the three-dimensional thermal information is calculated to obtain the gradient components of the entropy field in the X, Y and Z directions;

[0094] Calculate the gradient magnitude at each spatial location, identify spatial regions where the gradient magnitude exceeds a dynamic threshold, which is determined based on the statistical characteristics of the overall entropy field distribution;

[0095] Within the spatial region, a clustering algorithm is used to extract the spatial coordinates of key control points, and influence weights are assigned to each key control point according to the magnitude of the gradient.

[0096] Based on the spatial coordinates of key control points, corresponding building components are matched from the BIM data, and the heat transfer coefficient, thermal resistance, heat capacity, surface heat transfer coefficient, and material thickness of the components are extracted as a thermal parameter dataset.

[0097] In this embodiment, by calculating the spatial gradient distribution of the three-dimensional thermal information entropy field, key control points are identified, and relevant building component thermal parameters are extracted, thus improving the accuracy of building thermal performance analysis. Three-dimensional gradient calculation and dynamic threshold identification are employed, combined with clustering algorithms to extract key control points and their influence weights, ensuring efficient identification of key areas. Matching components from BIM data and extracting thermal parameter datasets improves the efficiency and intelligence of thermal analysis.

[0098] A self-organizing equilibrium model is established with the goal of optimizing the distribution of thermal information entropy values. By iteratively adjusting the thermal parameters of building components, the three-dimensional thermal information entropy field tends to the preset ideal distribution.

[0099] The self-organizing equilibrium model includes a perception layer, an analysis layer, and a decision-making layer:

[0100] The perception layer receives entropy data, gradient data, and corresponding thermal parameter data of building components from key control points, and establishes a multi-dimensional data vector.

[0101] The analysis layer uses correlation analysis to process multidimensional data vectors, calculates the sensitivity coefficients of changes in thermal parameters of building components and changes in local entropy, establishes a parameter-entropy response relationship model, and transmits the sensitivity coefficient data to the decision layer.

[0102] The decision-making layer determines the priority sequence of parameter adjustments based on the sensitivity coefficient data, formulates a parameter adjustment strategy using the gradient descent optimization algorithm, generates parameter adjustment instructions and feeds them back to the perception layer to achieve closed-loop optimization control.

[0103] In this embodiment, by establishing a self-organizing equilibrium model with the goal of optimizing thermal information entropy, the energy waste caused by uneven thermal performance is effectively reduced, thermal bridges and heat transfer characteristics are optimized, and building energy efficiency and comfort are improved.

[0104] The parameter-entropy response relationship model includes a data preprocessing sublayer, a feature extraction sublayer, a relationship modeling sublayer, and a response prediction sublayer:

[0105] The data preprocessing sublayer receives the multidimensional data vector transmitted from the perception layer, normalizes the thermal parameter data using the Z-score normalization method, identifies and removes outliers in the data using the 3σ criterion and box plot method, and eliminates data noise and random fluctuations using the moving average and Kalman filtering methods to obtain a standardized thermal parameter dataset, a corresponding entropy value dataset, and a data quality assessment report, and then transmits the cleaned data to the feature extraction sublayer.

[0106] The feature extraction sublayer uses principal component analysis to reduce the dimensionality of standardized thermal parameter data and extract the main components. It uses Pearson correlation coefficient and Spearman rank correlation coefficient to calculate the linear and nonlinear correlation between each thermal parameter and the entropy value. It uses mutual information and maximum information coefficient methods to quantify the complex dependence between parameters and entropy value. It uses analysis of variance to evaluate the contribution of parameters to the change of entropy value, and obtains parameter importance weight vector, parameter feature matrix, correlation coefficient matrix and feature contribution ranking table. The feature analysis results are then passed to the relationship modeling sublayer.

[0107] The relationship modeling sublayer uses multiple linear regression to establish a linear response model between thermal parameters and entropy values, supports vector regression and random forest to establish a nonlinear response model between thermal parameters and entropy values, and multilayer perceptron neural network to establish a deep mapping relationship model between thermal parameters and entropy values. It uses ensemble learning and model fusion techniques to combine multiple response models into a comprehensive response relationship model. The optimal model parameters are determined through cross-validation and model evaluation, resulting in the parameter-entropy response function, sensitivity coefficient matrix, model accuracy evaluation report, and parameter influence ranking. The modeling results are then passed to the response prediction sublayer.

[0108] The response prediction sublayer, based on the thermal parameter adjustment scheme input by the decision layer, uses a comprehensive response relationship model to predict the trend of the impact of single parameter adjustment and multi-parameter coordinated adjustment on the entropy value. It calculates the quantitative impact of parameter adjustment on the local entropy distribution and the global entropy field, uses sensitivity analysis to evaluate the effectiveness and feasibility of the parameter adjustment scheme, and uses uncertainty analysis to evaluate the confidence interval of the prediction results. It obtains the entropy change prediction results, parameter adjustment effect evaluation, scheme feasibility report, and prediction confidence analysis, and feeds the prediction results and evaluation suggestions back to the decision layer for optimizing parameter adjustment strategies and iterative control.

[0109] Based on the optimized three-dimensional thermal information entropy field distribution, an energy-saving optimization scheme for building thermal performance is generated.

[0110] The specific process of making the three-dimensional thermal information entropy field tend towards a preset ideal distribution includes:

[0111] Based on building functional zoning and thermal performance requirements, target entropy value ranges for each functional area are set, and an ideal entropy value distribution template is constructed.

[0112] Calculate the degree of deviation between the current entropy field distribution and the ideal distribution template, and use the root mean square error method to quantify the overall deviation value;

[0113] Based on the influence weight of the key control points, the thermal parameters of the corresponding building components are adjusted sequentially in descending order of weight, with each adjustment ranging from 1% to 5% of the initial parameter value.

[0114] After each parameter adjustment, the three-dimensional thermal information entropy field is recalculated to evaluate the change in the overall deviation value.

[0115] When the change in the overall deviation value is less than the preset convergence threshold for three consecutive iterations, the entropy field distribution is determined to have reached an ideal state.

[0116] The generation of the building thermal performance energy-saving optimization scheme specifically includes:

[0117] Analyze the regions with high entropy values ​​that still exist in the optimized entropy field distribution and identify the corresponding weak links in thermal performance.

[0118] Based on the entropy characteristics and spatial location characteristics of the weak links, suitable energy-saving measures are matched from a pre-set improvement measures database;

[0119] Conduct technical feasibility and economic rationality assessments of various energy-saving measures, and calculate implementation costs and expected energy-saving effects;

[0120] The measures are ranked according to the ratio of energy saving effect to implementation cost, and an energy-saving optimization plan with clear priority is generated.

[0121] In this embodiment, by optimizing the three-dimensional thermal information entropy field distribution, an energy-saving optimization scheme for building thermal performance is generated, thereby improving building energy efficiency. An ideal entropy value template is set based on functional zoning, and the deviation is quantified using root mean square error. Thermal parameters are iteratively adjusted by combining the weights of key control points until the entropy field approaches an ideal distribution. Simultaneously, by analyzing high-entropy regions, preset energy-saving measures are matched, and optimized schemes are generated by ranking them according to their energy-saving effects. This invention significantly reduces energy costs and improves building thermal performance by identifying weak points in thermal engineering.

[0122] This invention improves the efficiency of building thermal performance analysis by extracting BIM data and dividing it into a three-dimensional spatial mesh using geometric complexity coefficients. It constructs a thermal information entropy field. An information-theoretic entropy calculation method is employed to quantify the complexity of the thermal state, and a continuous three-dimensional entropy field is generated using topological relationships and multivariate interpolation algorithms. Key control points are identified through gradient analysis, and thermal parameters are extracted. These parameters are then iteratively adjusted using a self-organizing equilibrium model to bring the entropy field towards an ideal distribution, generating energy-saving optimization schemes. By identifying weak points in the thermal system and optimizing thermal bridges and heat transfer characteristics, building energy efficiency is improved.

[0123] Example 2:

[0124] This embodiment takes an energy-saving renovation project of a garden expo as an example, and the specific process is as follows:

[0125] Obtain Building Information Model (BIM) data and extract building geometry, material properties, and spatial topology information:

[0126] First, obtain the BIM model data of the garden expo, including information on building structure, electromechanical equipment, decoration and renovation, etc.

[0127] In this example, a reinforced concrete structure with an external insulation system is used, the interior walls are made of lightweight blocks, and the exterior windows are a double-glazed curtain wall system. Geometric information and corresponding material property data of the building components are extracted using BIM data.

[0128] The building information model is divided into three-dimensional spatial grids according to a preset method to form spatial grid units:

[0129] Based on the extracted building geometry information, the geometric complexity coefficient of each building component is calculated. The basic size of the grid unit is determined according to the geometric complexity coefficient. The grid size of 1.0m×1.0m×1.0m is generally used in the general area, the size of the grid is reduced to 0.25m×0.25m×0.25m in the thermal bridge area, and the size of the grid size at the corner of the outer envelope is reduced to 0.5m×0.5m×0.5m.

[0130] Each spatial grid cell is assigned a position identifier and geometric attribute identifier in a three-dimensional coordinate system, forming a dataset of the grid cells. Each grid cell records its spatial coordinates, the type of component it belongs to, its material attribute number, and a list of adjacent cells.

[0131] Based on the thermal state information and material property information of spatial grid cells, the corresponding thermal information entropy value is obtained:

[0132] Thermal status information is collected, including temperature, humidity, and heat flow. Temperature is divided into 2°C intervals, humidity into 10% intervals, and heat flow is divided into 6 levels based on the magnitude and direction of heat flux density (large outward loss, medium outward loss, small outward loss, small inward inflow, medium inward inflow, and large inward inflow).

[0133] By combining the thermal conductivity, specific heat capacity, density, and moisture conductivity information from the material properties, the theoretical occurrence weight of each state level in the grid cell is calculated. Actual thermal state data of each grid cell at different time periods are obtained using building energy consumption simulation software, and the actual occurrence frequency of each state level is statistically analyzed to calculate the actual occurrence probability distribution.

[0134] By using building geometry and spatial topology information, spatial interpolation is performed on the thermal entropy values ​​of spatial grid cells to construct a three-dimensional thermal entropy field.

[0135] A connectivity matrix between adjacent grid cells is constructed based on spatial topological relationship information, recording the adjacency relationships and path lengths between grid cells. Utilizing component boundary information from building geometry, an entropy propagation hindrance coefficient is set at the interface between the concrete wall and the insulation layer, and a hindrance coefficient is set at the interface between indoor and outdoor air.

[0136] Entropy is calculated using a multivariate interpolation algorithm based on distance weights and topological relationships. For the spatial location to be interpolated, entropy values ​​of surrounding grid cells within a 2-meter radius are obtained to establish a neighborhood grid cell set. Distance weights are calculated based on spatial distance, and connectivity weights are calculated based on topological connectivity paths. The product of these two weights is used as the overall weight.

[0137] Through iterative interpolation calculations, a continuously distributed three-dimensional thermal information entropy field data is generated. The entropy field shows that the entropy value in the corner area of ​​the exterior wall is generally between 0.7 and 0.9, indicating that the thermal state is unstable; the entropy value in the central area of ​​the interior is between 0.2 and 0.4, indicating that the thermal state is relatively stable.

[0138] Calculate the spatial gradient distribution of the three-dimensional thermal information entropy field, and identify regions with abnormal entropy gradients as key control points for thermal performance.

[0139] Three-dimensional gradient calculations were performed on the three-dimensional thermal entropy field to obtain the gradient components of the entropy field in the X, Y, and Z directions. The gradient magnitude at each spatial location was calculated, and spatial regions where the gradient magnitude exceeded the dynamic threshold were identified. Statistical analysis revealed that gradient anomaly regions were mainly concentrated at the junction of the exterior wall and floor slab, the vertical keel of the curtain wall, and the wall penetration points of equipment shafts.

[0140] The K-means clustering algorithm is used to cluster the abnormal regions into key control points. Each key control point represents a region with weak thermal performance. The influence weights are assigned to each key control point according to the magnitude of the gradient.

[0141] Based on the spatial coordinates of key control points, corresponding building components are matched from BIM data, and the heat transfer coefficient, thermal resistance, heat capacity, surface heat transfer coefficient, and material thickness of the components are extracted as a thermal parameter dataset.

[0142] Establish a self-organizing equilibrium model with the goal of optimizing the distribution of thermal information entropy values:

[0143] A self-organizing equilibrium model comprising a perception layer, an analysis layer, and a decision-making layer is established. The perception layer receives entropy data, gradient data, and corresponding thermal parameter data of building components from key control points, and establishes a multi-dimensional data vector. The data vector for each control point includes nine dimensions: entropy value, gradient in the X direction, gradient in the Y direction, gradient in the Z direction, heat transfer coefficient of the corresponding component, thermal resistance, heat capacity, surface heat transfer coefficient, and material thickness.

[0144] The analysis layer employs Pearson correlation analysis to process multidimensional data vectors and calculates the sensitivity coefficients of changes in building component thermal parameters and local entropy. A parameter-entropy response model is established, which can predict the degree of impact of parameter adjustments on entropy changes.

[0145] The decision-making team determines the priority sequence of parameter adjustments based on sensitivity coefficient data and formulates parameter adjustment strategies using a gradient descent optimization algorithm. Priority is given to adjusting the thickness of the external wall insulation layer, which has a higher heat transfer coefficient; secondly, the construction details of thermal bridge areas are adjusted; and finally, the surface heat transfer coefficient is optimized.

[0146] To make the three-dimensional thermal information entropy field tend towards the preset ideal distribution:

[0147] Based on functional zoning and thermal performance requirements, the target entropy range for each functional area is set.

[0148] The root mean square error method is used to calculate the deviation between the current entropy field distribution and the ideal distribution template. Based on the influence weights of key control points, the thermal parameters of the corresponding building components are adjusted sequentially in descending order of weight.

[0149] After each parameter adjustment, the three-dimensional thermal information entropy field is recalculated to evaluate the change in the overall deviation value. Through iterative adjustments, the overall deviation value is reduced, and the change amplitude in three consecutive iterations is less than the preset convergence threshold, confirming that the entropy field distribution has reached an ideal state.

[0150] Based on the optimized 3D thermal information entropy field distribution, an energy-saving optimization scheme for building thermal performance is generated:

[0151] The analysis revealed persistent high-entropy regions in the optimized entropy field distribution, identifying corresponding weak points in thermal performance. These weak points were primarily located around equipment manholes and along some window frames. Based on the entropy and spatial characteristics of these weak points, suitable energy-saving measures were matched from a pre-defined database of improvement measures. These measures included increasing insulation thickness, replacing windows with high-performance ones, implementing thermal bridging measures, and optimizing external shading systems.

[0152] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A BIM-based intelligent energy-saving analysis method for building thermal performance, characterized in that, include: Acquire Building Information Model (BIM) data and extract building geometric information, material property information, and spatial topological relationship information; The building information model is divided into three-dimensional spatial grids according to a preset precision to form spatial grid units; Based on the thermal state information of the spatial grid cells and the material property information, the corresponding thermal information entropy value is obtained. A three-dimensional thermal information entropy field is constructed by spatially interpolating the thermal information entropy values ​​of spatial grid cells using building geometric information and spatial topological relationship information. The spatial gradient distribution of the three-dimensional thermal information entropy field is calculated, and regions with abnormal entropy gradients are identified as key control points for thermal performance. The corresponding thermal parameters of building components are then extracted. The specific process of identifying key control points and extracting thermal parameters of building components includes: calculating the three-dimensional gradient of the three-dimensional thermal information entropy field to obtain the gradient components in the X, Y, and Z directions; calculating the gradient magnitude at each spatial location; and identifying spatial regions where the gradient magnitude exceeds a dynamic threshold. Within the spatial region, the spatial coordinates of key control points are extracted, and influence weights are assigned to each key control point according to the gradient modulus. Based on the spatial coordinates of the key control points, corresponding building components are matched from the BIM data, and the heat transfer coefficient, thermal resistance, heat capacity, surface heat transfer coefficient, and material thickness of the components are extracted as a thermal parameter dataset. A self-organizing equilibrium model is established with the goal of optimizing the distribution of thermal information entropy. By iteratively adjusting the thermal parameters of the building components, the three-dimensional thermal information entropy field tends to the preset ideal distribution. Based on the optimized three-dimensional thermal information entropy field distribution, an energy-saving optimization scheme for building thermal performance is generated.

2. The intelligent energy-saving analysis method for building thermal performance based on BIM according to claim 1, characterized in that: The specific process of the three-dimensional spatial meshing includes: calculating the geometric complexity coefficient of building components based on the building geometric information, wherein the geometric complexity coefficient is obtained by analyzing the surface curvature change, edge angle change and volume density distribution of the components; determining the basic size of the mesh unit according to the geometric complexity coefficient; reducing the basic mesh size at the junction of building components, thermal bridges and corners of the external envelope structure; and assigning a position identifier and geometric attribute identifier in the three-dimensional coordinate system to each spatial mesh unit to form a mesh unit dataset.

3. The intelligent energy-saving analysis method for building thermal performance based on BIM according to claim 1, characterized in that: The specific process for obtaining the thermal information entropy value includes: the thermal state information includes temperature state, humidity state, and heat flow state; the temperature state of each spatial grid cell is divided into multiple temperature levels according to a preset temperature range, the humidity state is divided into multiple humidity levels according to a preset humidity range, and the heat flow state is divided into multiple heat flow levels according to the magnitude and direction of heat flow density; combined with the material property information, the theoretical probability of each level in the spatial grid cell is calculated; the actual probability is calculated by statistically analyzing the actual occurrence frequency of the level; based on the degree of deviation between the theoretical probability and the actual probability, the entropy calculation method in information theory is used to obtain the thermal information entropy value of the spatial grid cell.

4. The intelligent energy-saving analysis method for building thermal performance based on BIM according to claim 1, characterized in that: The specific process of constructing the three-dimensional thermal information entropy field includes: constructing a connectivity matrix between adjacent grid cells based on the spatial topological relationship information, wherein the connectivity matrix records the adjacency relationship and connectivity path length between grid cells; using the component boundary information in the building geometry information to determine the geometric constraints for entropy propagation, and setting an entropy propagation hindrance coefficient at the component boundary; performing entropy interpolation calculations between adjacent grid cells, considering spatial distance attenuation and material interface hindrance effects; and forming a continuously distributed three-dimensional thermal information entropy field throughout the entire building space through iterative interpolation calculations.

5. The intelligent energy-saving analysis method for building thermal performance based on BIM according to claim 1, characterized in that: The self-organizing equilibrium model comprises a perception layer, an analysis layer, and a decision layer: the perception layer receives the thermal information entropy values, gradient components, and corresponding thermal parameter datasets of key control points, and establishes a multidimensional data vector; the analysis layer processes the multidimensional data vector, calculates the sensitivity coefficient between changes in building component thermal parameters and changes in local entropy values, establishes a parameter-entropy response relationship model, and transmits the sensitivity coefficient data to the decision layer; the decision layer determines the priority sequence of parameter adjustments based on the sensitivity coefficients, formulates parameter adjustment strategies, and generates parameter adjustment instructions to be fed back to the perception layer.

6. The intelligent energy-saving analysis method for building thermal performance based on BIM according to claim 1, characterized in that: The specific process of bringing the three-dimensional thermal information entropy field toward a preset ideal distribution includes: setting the target entropy value range for each functional area based on building functional zoning and thermal performance requirements, and constructing an ideal entropy value distribution; calculating the degree of deviation between the current thermal information entropy value and the ideal entropy value distribution, and quantifying the overall deviation value; adjusting the thermal parameters of the corresponding building components in descending order of influence weight according to the influence weight of key control points; recalculating the three-dimensional thermal information entropy field after each parameter adjustment, and evaluating the change in the overall deviation value; and determining that the entropy field distribution has reached an ideal state when the change in the overall deviation value is less than a preset convergence threshold for three consecutive iterations.

Images