A building space utilization efficiency evaluation method and system

Abstract

This invention discloses a method and system for evaluating the utilization efficiency of building space, relating to the field of building engineering technology. The key technical points include the following steps: collecting physical space data and usage demand data of the building space to be evaluated; constructing a digital twin model of the building space; extracting spatial representation information related to utilization efficiency from the digital twin model and marking it as the original information for efficiency evaluation; extracting the efficiency feature type, feature distribution density, and spatial correlation dimension of building space utilization from the original information for efficiency evaluation; determining whether simulation and deduction processing of the original information for efficiency evaluation is necessary based on the efficiency feature type and feature distribution density; and marking the corresponding original information for efficiency evaluation as information to be evaluated and processed. The effect is to achieve a deep integration of physical space and usage demand, realistically reflecting the actual operating status of the building space, avoiding a disconnect between the evaluation results and actual usage scenarios, and improving the comprehensiveness and authenticity of the evaluation.

Description

Technical Field

[0001] This invention relates to the field of building engineering technology, and more specifically, to a method and system for evaluating the efficiency of building space utilization. Background Technology

[0002] As urban buildings develop towards high density and multi-functionality, the demand for refined assessment of space utilization efficiency in scenarios such as smart communities and smart parks is becoming increasingly urgent. Although the current assessment of building space utilization efficiency has gradually shifted from manual experience-based judgment to data-driven methods, relying on IoT sensing devices to collect physical space and operational data, providing basic support for efficiency analysis, there are still many technical limitations that make it difficult to adapt to the full-dimensional assessment needs of intelligent buildings.

[0003] Existing assessment methods largely rely on single static parameter analysis, focusing only on physical data such as building area and structural layout. They fail to achieve deep integration of physical space data with usage demand data, neglecting the impact of dynamic usage behaviors such as personnel density, usage frequency, and circulation paths. This leads to a disconnect between assessment results and actual operational status, failing to accurately reflect the actual efficiency of space utilization. Furthermore, data sources are independent, lacking a unified digital model. Data on physical space, dynamic adaptation, and resource consumption are processed in a fragmented manner, making it difficult to form a holistic understanding of the building space. Consequently, the comprehensiveness and systematic nature of the assessment are insufficient. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for evaluating the efficiency of building space utilization.

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

[0006] A method and system for evaluating the efficiency of building space utilization, the method comprising the following steps:

[0007] Collect physical space data and usage demand data of the building space to be evaluated, construct a digital twin model of the building space, extract spatial representation information related to utilization efficiency from the digital twin model of the building space and mark it as the original information for efficiency evaluation;

[0008] Extract the efficiency feature type, feature distribution density and spatial correlation dimension of building space utilization from the original efficiency assessment information. Determine whether the original efficiency assessment information needs to be simulated and processed based on the efficiency feature type and feature distribution density. Mark the corresponding original efficiency assessment information as information to be assessed and processed.

[0009] Collect dynamic adaptation parameters of building space utilization and resource consumption efficiency parameters from the information to be evaluated and processed;

[0010] Based on the number of spatial association dimensions and efficiency feature types, determine whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, and generate an evaluation impact result set based on the judgment result of the mutual constraint effect.

[0011] The assessment results set determines the level of building space utilization efficiency and the corresponding optimization and renovation plan.

[0012] Preferably, the determination of whether the original efficiency evaluation information needs to be simulated and extrapolated based on the efficiency feature type and feature distribution density, and the marking of the corresponding original efficiency evaluation information as information to be evaluated and processed, specifically includes the following steps:

[0013] If the feature distribution density of the same efficiency feature type is fully presented on the same set of original efficiency evaluation information, then the set of original efficiency evaluation information is marked as information to be evaluated and processed.

[0014] If the feature distribution density of the same efficiency feature type appears in at least two sets of original efficiency evaluation information, then the corresponding original efficiency evaluation information will be partitioned and extracted to form a partitioned evaluation information unit containing the efficiency feature type.

[0015] Based on the spatial attributes of efficiency feature types, efficiency feature points at the boundaries of each partition evaluation information unit are identified. Based on the spatial correlation of efficiency feature points, the feature boundaries of each partition evaluation information unit are optimized and then simulated and fused to form the information to be evaluated and processed.

[0016] Preferably, based on the spatial correlation of efficiency feature points, the feature boundaries of each partition evaluation information unit are optimized and then simulated and fused to form the information to be evaluated and processed. This specifically includes the following steps:

[0017] Determine whether there is spatial overlap or correlation between efficiency feature points at the boundaries of at least two partition evaluation information units;

[0018] If the efficiency feature points at the boundaries of at least two partition evaluation information units have spatial overlap, the corresponding partition evaluation information units are marked as matching evaluation information units. Based on the overlapping efficiency feature points, the corresponding matching evaluation information units are simulated, deduced, and fused to form the information to be evaluated. The overlapping feature point information of one matching evaluation information unit is retained, and the overlapping feature point information of the other matching evaluation information units is deleted.

[0019] If there is no spatial overlap or correlation between the efficiency feature points at the boundary of each partition evaluation information unit, then the efficiency feature points at the boundary of each partition evaluation information unit will be spatially topologically matched. If the efficiency feature points form a continuous efficiency feature link through topological matching, then the corresponding partition evaluation information unit will be simulated, deduced, and fused to form the information to be evaluated.

[0020] Preferably, based on the number of spatial association dimensions and efficiency feature types, it is determined whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, specifically including the following steps:

[0021] If the building space in the information to be evaluated and processed has only one type of efficiency characteristic, then it is determined that there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated and processed.

[0022] If the building space in the information to be evaluated and processed has efficiency features of at least two types, then the spatial correlation dimension is used to determine whether the efficiency features of the building space in the information to be evaluated and processed have spatial overlap or dimensional intersection.

[0023] If the efficiency characteristics of the building space in the information to be evaluated do not overlap spatially and do not cross dimensions, then it is determined that there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated.

[0024] If the efficiency characteristics of the building space in the information to be evaluated overlap spatially or intersect in dimension, it is determined that there is a mutual constraint effect between at least two efficiency characteristics in the building space in the information to be evaluated.

[0025] Preferably, the evaluation impact result set is generated based on the judgment result of the interaction constraint effect, specifically including the following steps:

[0026] If there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated, then the first impact evaluation result of efficiency characteristics on building space utilization efficiency is determined based on the type of efficiency characteristics, dynamic adaptation parameters and resource consumption efficiency parameters.

[0027] If there are at least two interacting constraints between efficiency features in the building space of the information to be evaluated, then the second impact assessment result of the efficiency features with interacting constraints on the building space utilization efficiency is determined based on the efficiency feature type, dynamic adaptation parameters and resource consumption efficiency parameters.

[0028] The results of the first impact assessment and the second impact assessment are combined to form an impact assessment result set.

[0029] Preferably, the assessment result of the first impact of efficiency characteristics on building space utilization efficiency is determined based on the efficiency characteristic type, dynamic adaptation parameters, and resource consumption efficiency parameters, specifically including the following steps:

[0030] By comparing the dynamic adaptation parameters and resource consumption efficiency parameters of each efficiency characteristic type in the building space with the characteristic parameter efficiency evaluation table, the first efficiency adaptation degree and the first efficiency loss rate of each efficiency characteristic type to the building space utilization efficiency are obtained.

[0031] The first efficiency fit and first efficiency loss rate of each building space are classified and statistically analyzed according to the functional zoning of the space to obtain the comprehensive efficiency value of each functional zoning.

[0032] The first impact assessment result is obtained by weighting the comprehensive efficiency value of each functional zone based on the area proportion weight of each functional zone.

[0033] Preferably, based on the efficiency characteristic type, dynamic adaptation parameters, and resource consumption efficiency parameters, the assessment result of the second impact of efficiency characteristics with interactive constraints on building space utilization efficiency is determined, specifically including the following steps:

[0034] Determine the type of interaction constraint between architectural space efficiency characteristics in the information to be evaluated and processed, and determine whether the interaction constraint between efficiency characteristics is spatial overlapping type or dimensional cross-type interaction constraint.

[0035] If the building space in the information to be evaluated has efficiency characteristics of spatial overlap and interaction constraints, the direct constraint impact on the building space utilization efficiency is judged based on the parameter information of the overlap area, and the first constraint evaluation result is obtained.

[0036] If the building space in the information to be evaluated has efficiency characteristics of cross-dimensional interaction constraints, the indirect constraint impact on the building space utilization efficiency is judged based on the parameter information of the cross-dimensional interaction, and the second constraint evaluation result is obtained.

[0037] The results of the first constraint assessment and / or the second constraint assessment are integrated and revised to form the second impact assessment result.

[0038] Preferably, the first constraint assessment result is obtained by analyzing the direct restrictive impact on the building space utilization efficiency based on the parameter information of the overlapping area, specifically including the following steps:

[0039] Determine whether the efficiency characteristics of spatial overlapping and interaction constraints result in inefficiencies in the utilization of building space.

[0040] If an efficiency bottleneck is found, the type and degree information of the bottleneck are obtained. The type and degree information of the bottleneck are compared with the efficiency impact table of the bottleneck parameter to obtain the second efficiency loss rate and efficiency constraint dimension of the building space. The second efficiency loss rate and efficiency constraint dimension are marked as the first constraint assessment result.

[0041] If no efficiency bottlenecks are observed, it is determined that the efficiency characteristics of spatial overlapping interaction constraints do not directly restrict or reduce the efficiency of building space utilization, thus forming the first constraint assessment result.

[0042] Preferably, the indirect constraints on building space utilization efficiency are analyzed based on cross-dimensional parameter information to obtain the second constraint assessment result, which specifically includes the following steps:

[0043] By comparing the efficiency feature types, dynamic adaptation parameters, and resource consumption efficiency parameters of the cross-dimensional interaction constraints with the feature parameter efficiency evaluation table, the second efficiency adaptation degree and the third efficiency loss rate of each efficiency feature type to the building space are obtained.

[0044] Based on the dimensional correlation coefficients of each efficiency feature type, the second efficiency fit and the third efficiency loss rate are coupled to obtain the coupled comprehensive efficiency fit and comprehensive efficiency loss rate.

[0045] The overall efficiency fit and overall efficiency loss rate are marked as the second constraint assessment results.

[0046] A building space utilization efficiency evaluation system, comprising:

[0047] Data acquisition module: Collects physical space data and usage demand data of the building space to be evaluated, constructs a digital twin model of the building space, extracts spatial representation information related to utilization efficiency from the digital twin model of the building space and marks it as the original information for efficiency evaluation;

[0048] Information extraction module: Extracts the efficiency feature type, feature distribution density and spatial correlation dimension of building space utilization from the original efficiency assessment information. Based on the efficiency feature type and feature distribution density, it determines whether the original efficiency assessment information needs to be simulated and processed, and marks the corresponding original efficiency assessment information as information to be evaluated and processed.

[0049] Parameter acquisition module: Collects dynamic adaptation parameters of building space utilization and resource consumption efficiency parameters from the information to be evaluated and processed;

[0050] Efficiency Feature Judgment Module: Based on the spatial correlation dimension and the number of efficiency feature types, determine whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, and generate an evaluation impact result set based on the judgment result of the mutual constraint effect.

[0051] The rating and optimization module determines the rating of building space utilization efficiency and proposes optimization and renovation plans based on the assessment impact result set.

[0052] Compared with existing technologies, this invention has the following advantages: By collecting physical space data and usage demand data to construct a digital twin model of building space, static physical parameters and dynamic usage behavior data are uniformly mapped, achieving a deep integration of physical space and usage demand. This can truly reflect the actual operating status of building space, avoid the disconnect between evaluation results and actual usage scenarios, and improve the comprehensiveness and authenticity of the evaluation. By extracting and analyzing efficiency feature types, feature distribution density, and spatial correlation dimensions, a feature evaluation system covering space utilization, functional adaptability, and circulation convenience is established. This system can accurately identify the correlation between local space efficiency bottlenecks and the overall space, providing a reliable feature foundation for subsequent efficiency evaluation and optimization. By determining whether simulation processing is needed based on efficiency feature types and distribution density, feature data across spaces is extracted and optimized by partitioning. Through simulation and fusion, coherent information to be evaluated is formed, making the evaluation process more targeted. This accurately reflects the transmission and interaction patterns of efficiency features across spatial units, improving the accuracy of the evaluation results. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the steps of a method for evaluating the efficiency of building space utilization provided in an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram illustrating the steps in forming an evaluation impact result set in a method for evaluating the efficiency of building space utilization, as provided in an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of a building space utilization efficiency evaluation system provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0059] Reference Figures 1-3As shown.

[0060] This embodiment further illustrates the building space utilization efficiency evaluation method and system proposed in this invention.

[0061] A method and system for evaluating the efficiency of building space utilization, the method comprising the following steps:

[0062] Collect physical space data and usage demand data of the building space to be evaluated, construct a digital twin model of the building space, extract spatial representation information related to utilization efficiency from the digital twin model of the building space and mark it as the original information for efficiency evaluation.

[0063] First, we comprehensively collect two types of core data on the building space to be evaluated: 1. Physical space data, including static geometric and structural parameters such as building area, floor height, bay width and depth, location of traffic core, structural layout, and pipeline arrangement. For example, the total building area of ​​the office building is 20,000 square meters, the standard floor height is 3.6 meters, and the core tube is located at the center of the building plan; 2. Usage demand data, including dynamic behavior and demand parameters such as personnel density, functional zoning, frequency of use of each functional area, personnel circulation paths, and water and electricity resource allocation requirements. For example, the personnel density of the office area of ​​this office building is 0.12 people / square meter, the average daily usage frequency of the meeting room is 8 times, and the main circulation path is the straight-line distance from the elevator lobby to each office workstation.

[0064] After data collection, the system constructs a digital twin model of the building space based on BIM, IoT sensing, and 3D visualization technologies. This digital twin model can recreate the building's physical form, spatial topology, and functional layout in a 1:1 ratio, while simultaneously mapping real-time changes in usage demand data, creating a two-way linkage between the physical and digital spaces. For example, when personnel flow changes in the office area, the digital twin model will synchronously update personnel location and movement data, maintaining consistency with the physical space.

[0065] Subsequently, the system extracts spatial representation information related to utilization efficiency from the digital twin model of the building space through spatial analysis algorithms and feature extraction rules. This spatial representation information includes space availability, functional area connectivity, circulation path length, and resource coverage, and is uniformly labeled as raw information for efficiency assessment, providing fundamental data support for subsequent efficiency feature analysis and evaluation decisions. For example, by calculating the ratio of the actual usable area of ​​the office area to the building area, the space availability rate is found to be 78%; by analyzing the shortest path length between the meeting room and each workstation, the circulation convenience index is found to be 12 meters per workstation.

[0066] At the data quantification level, physical space data can be acquired through laser scanning and BIM model import, with an accuracy controllable within ±5 mm. Usage requirement data can be collected through IoT devices such as access control systems, video analytics, and energy consumption monitoring, with update frequencies set to minutes or hours depending on the assessment needs. The construction of the building space digital twin model follows the principle of spatial topological consistency, ensuring that the geometric coordinates and functional attributes of any spatial unit perfectly match the physical space. The extraction efficiency of its spatial representation information can reach a processing time of no more than 10 minutes per 10,000 square meters of building data, guaranteeing efficient assessment.

[0067] Extract the efficiency feature type, feature distribution density and spatial correlation dimension of building space utilization from the original efficiency assessment information. Determine whether the original efficiency assessment information needs to be simulated and processed based on the efficiency feature type and feature distribution density. Mark the corresponding original efficiency assessment information as information to be assessed and processed.

[0068] Collect dynamic adaptation parameters of building space utilization and resource consumption efficiency parameters from the information to be evaluated and processed;

[0069] Based on the number of spatial association dimensions and efficiency feature types, determine whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, and generate an evaluation impact result set based on the judgment result of the mutual constraint effect.

[0070] The assessment results set determines the level of building space utilization efficiency and the corresponding optimization and renovation plan.

[0071] First, the system standardizes the multi-dimensional data in the assessment impact set, mapping spatial correlation dimension, efficiency feature distribution density, dynamic adaptation parameters, resource consumption efficiency parameters, and interaction constraint effects into a quantifiable scoring system. For example, space utilization rate, circulation convenience, and resource consumption rate are assigned weights of 30%, 25%, and 20%, respectively, with the remaining 25% weight used to measure the degree of influence of interaction constraint effects. The comprehensive efficiency score is obtained by weighted summation, and the calculation formula is that the comprehensive efficiency score is equal to the sum of the products of each indicator score and its corresponding weight.

[0072] After calculating the overall score, the system determines the efficiency level based on a preset threshold range. Efficiency levels are typically divided into four tiers: an overall score of 90 or higher is considered excellent, 70-89 is good, 50-69 is average, and below 50 is in need of improvement. For example, if an office building's overall efficiency score is 62, corresponding to an average level, it indicates that the building space has issues such as redundant circulation paths and uneven resource allocation in some functional areas, as well as local efficiency characteristics that interact and restrict each other.

[0073] Subsequently, the system combines the efficiency level assessment results with the bottleneck indicators identified in the evaluation results to generate targeted optimization and renovation plans. For buildings rated "excellent," the plans primarily focus on maintaining the status quo, making minor adjustments, and implementing preventative maintenance. For buildings rated "good," the plans focus on optimizing local efficiency shortcomings, such as adjusting meeting room layouts to shorten circulation distances. For buildings rated "average," the plans propose moderate renovation measures such as functional zoning restructuring and traffic core optimization. For buildings rated "needing optimization," the plans include in-depth renovation suggestions such as partial structural adjustments and resource system restructuring. For example, the aforementioned office building with a comprehensive score of 62 points, after analysis, was found to have a core bottleneck: the interaction between high-density office areas and low circulation convenience. The system-generated optimization plan involves adding auxiliary staircases and passageways in the middle of the building, while converting some unused spaces into temporary office areas to disperse personnel density and shorten the average circulation distance. Simulation results verified that this plan can improve the comprehensive efficiency score to 78 points, achieving a "good" rating.

[0074] During the scheme generation process, the system will simultaneously output the priority and cost-benefit analysis of the renovation implementation. It will assist decision-making by calculating the input-output ratio of the renovation. The calculation formula is that the input-output ratio is equal to the expected improvement in comprehensive efficiency after the renovation divided by the total cost required for the renovation. The higher the input-output ratio, the higher the priority of the renovation measures. This ensures that the optimized renovation scheme achieves a balance between technical feasibility and economic rationality, and ultimately provides a feasible decision-making basis for the efficient use of building space.

[0075] Based on the type and density of efficiency features, determine whether simulation processing of the original efficiency evaluation information is necessary. Mark the corresponding original efficiency evaluation information as information to be evaluated and processed. The specific steps include:

[0076] If the feature distribution density of the same efficiency feature type is fully presented on the same set of original efficiency evaluation information, then the set of original efficiency evaluation information is marked as information to be evaluated and processed.

[0077] If the feature distribution density of the same efficiency feature type appears in at least two sets of original efficiency evaluation information, then the corresponding original efficiency evaluation information will be partitioned and extracted to form a partitioned evaluation information unit containing the efficiency feature type.

[0078] Based on the spatial attributes of efficiency feature types, efficiency feature points at the boundaries of each partition evaluation information unit are identified. Based on the spatial correlation of efficiency feature points, the feature boundaries of each partition evaluation information unit are optimized and then simulated and fused to form the information to be evaluated and processed.

[0079] First, the system systematically analyzes the extracted raw efficiency assessment information, categorizing and calculating the corresponding feature distribution density for each type of efficiency characteristic to determine the data presentation scope and processing method. Efficiency characteristic types cover space utilization, functional adaptability, circulation convenience, and resource allocation balance. Feature distribution density reflects the concentration of similar efficiency characteristics within the building space, and its calculation formula is: Feature distribution density = Frequency of occurrence of a certain efficiency characteristic in the target space / Area of ​​the target space. This formula yields standardized density values, facilitating cross-space comparative analysis.

[0080] After calculating the feature distribution density, the system enters the first type of processing logic. When the feature distribution density of the same efficiency feature type is fully presented in the same set of raw efficiency assessment information, the raw efficiency assessment information of that set is directly marked as information to be evaluated and processed. For example, for the efficiency feature type of space utilization rate of a standard floor in an office building, if its feature distribution density data fully covers the entire office area of ​​the standard floor, is not dispersed to other floors or functional areas, and the density value is continuously distributed within a reasonable range of 0.75 to 0.85, then the raw efficiency assessment information containing the complete space utilization rate distribution can be directly marked as information to be evaluated and processed without additional processing, and used for subsequent dynamic adaptation parameter collection and interaction constraint effect analysis.

[0081] If the feature distribution density of the same efficiency characteristic type appears in at least two sets of raw efficiency assessment information, the system will initiate a partitioned extraction process. For example, for the efficiency characteristic type of convenient circulation in a shopping mall, the feature distribution density appears in two sets of raw efficiency assessment information in the first-floor retail area and the second-floor dining area. In this case, the system will partition and extract these two sets of raw information, forming a first-floor partitioned assessment information unit and a second-floor partitioned assessment information unit containing the convenient circulation characteristic. Each partitioned unit accurately carries the efficiency characteristic data within its corresponding spatial range, ensuring that the spatial ownership of the data is clearly identifiable.

[0082] The system then identifies efficiency feature points at the boundaries of each zone's assessment information unit based on the spatial attributes of efficiency feature types. Taking a shopping mall as an example, the spatial attribute of circulation convenience is reflected in the continuity and connectivity of pedestrian flow. The system locates efficiency feature points at the boundaries of escalators, stairwells, and atrium corridors between the first and second-floor zone assessment information units. These efficiency feature points are key nodes connecting different zones and are the core locations for efficiency feature transmission and interaction. Based on the spatial correlation of efficiency feature points, the feature boundaries of each zone's assessment information unit are optimized. By analyzing the correlation parameters of path distance, traffic flow, and pedestrian density between feature points, abrupt changes in zone boundaries are smoothed, making the distribution of efficiency features in different zones more consistent with actual spatial usage logic. The optimized boundaries can more accurately reflect the transmission patterns of efficiency features across zone spaces.

[0083] The system performs simulation and fusion of the optimized evaluation information units from each partition to form unified information to be evaluated. During the simulation, the system simulates the interactive effects of efficiency characteristics of different partitions, such as simulating the flow transmission of the accessibility characteristics of the first and second floors at boundary feature points, to verify the overall efficiency performance after the fusion of different partition features. Through multiple rounds of iterative simulation, the system eliminates the evaluation bias caused by the fragmentation of partition data, and finally marks the fused complete feature data as the information to be evaluated, providing reliable and consistent data support for subsequent dynamic adaptation parameter acquisition, interaction constraint effect judgment, and efficiency level evaluation.

[0084] Based on the spatial correlation of efficiency feature points, the feature boundaries of each partition evaluation information unit are optimized and then simulated and fused to form the information to be evaluated and processed. The specific steps include:

[0085] Determine whether there is spatial overlap or correlation between efficiency feature points at the boundaries of at least two partition evaluation information units;

[0086] If the efficiency feature points at the boundaries of at least two partition evaluation information units have spatial overlap, the corresponding partition evaluation information units are marked as matching evaluation information units. Based on the overlapping efficiency feature points, the corresponding matching evaluation information units are simulated, deduced, and fused to form the information to be evaluated. The overlapping feature point information of one matching evaluation information unit is retained, and the overlapping feature point information of the other matching evaluation information units is deleted.

[0087] If there is no spatial overlap or correlation between the efficiency feature points at the boundary of each partition evaluation information unit, then the efficiency feature points at the boundary of each partition evaluation information unit will be spatially topologically matched. If the efficiency feature points form a continuous efficiency feature link through topological matching, then the corresponding partition evaluation information unit will be simulated, deduced, and fused to form the information to be evaluated.

[0088] First, the system performs spatial overlap correlation judgment on the efficiency feature points at the boundaries of each zone's evaluation information unit. It compares the spatial coordinates and attribute information of the boundary feature points of different units one by one to determine whether there is any overlapping or functional overlap correlation. Efficiency feature points refer to key spatial nodes that carry the transmission of efficiency features, such as the intersection of escalator entrances, stairwells, and passageways in a building space. Spatial overlap correlation means that the boundary feature points of at least two zone evaluation information units completely overlap in spatial location or have completely identical functional attributes, and can be used as shared feature nodes across units.

[0089] When efficiency feature points at the boundaries of at least two zone evaluation information units have spatial overlap, the system marks the corresponding zone evaluation information units as matching evaluation information units. For example, in a shopping mall, the escalator entrance feature points at the boundaries of the first-floor retail area and the second-floor food and beverage area completely overlap spatially and both carry the efficiency feature of convenient circulation. In this case, these two zone units are marked as matching evaluation information units. Subsequently, the system performs simulation and fusion processing on the matching evaluation information units based on the overlapping efficiency feature points. During the fusion process, the system retains the overlapping feature point information of one matching evaluation information unit and deletes the overlapping feature point information of the other matching evaluation information units to avoid duplicate data interfering with subsequent evaluations. For example, the system retains the escalator entrance feature point information of the first-floor retail zone unit and deletes the same escalator entrance feature point information of the second-floor food and beverage zone unit. Then, the system simulates the transmission effect of the overlapping feature point on the efficiency features of the two zones through simulation, ultimately forming a unified information to be evaluated.

[0090] If the efficiency feature points at the boundaries of each partition assessment information unit do not have spatial overlap, the system initiates a spatial topology matching process to analyze the topological relationships of the efficiency feature points at each partition boundary. The core of topology matching is determining whether there are connectable spatial paths between feature points, such as continuous paths formed by corridors, stairs, and escalators. The matching logic can be expressed as: Topology matching degree = Path connectivity score between feature points × Functional attribute similarity score of feature points. When the topology matching degree reaches a preset threshold, it is determined that the efficiency feature points can form a continuous efficiency feature link. For example, in the partition assessment information unit of the office area and meeting area in an office building, the corridor endpoint feature point and the stairwell feature point at their boundaries do not have spatial overlap, but they can achieve functional connectivity through a continuous path formed by the corridor and stairs. In this case, these two feature points form a continuous efficiency feature link through topology matching. Subsequently, the system performs simulation and fusion processing on the corresponding partition assessment information units, simulating the transmission and interaction of efficiency features in the continuous link, integrating the scattered partition data into coherent and unified information to be evaluated, providing complete data support for subsequent efficiency evaluation and optimization.

[0091] Based on the number of spatial association dimensions and efficiency feature types, determine whether there are interactive constraints on efficiency features in the architectural space of the information to be evaluated and processed. This includes the following steps:

[0092] If the building space in the information to be evaluated and processed has only one type of efficiency characteristic, then it is determined that there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated and processed.

[0093] If the building space in the information to be evaluated and processed has efficiency features of at least two types, then the spatial correlation dimension is used to determine whether the efficiency features of the building space in the information to be evaluated and processed have spatial overlap or dimensional intersection.

[0094] If the efficiency characteristics of the building space in the information to be evaluated do not overlap spatially and do not cross dimensions, then it is determined that there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated.

[0095] If the efficiency characteristics of the building space in the information to be evaluated overlap spatially or intersect in dimension, it is determined that there is a mutual constraint effect between at least two efficiency characteristics in the building space in the information to be evaluated.

[0096] First, the system will traverse the information to be evaluated and process, and count the number of efficiency feature types contained therein. The efficiency feature types cover space utilization, functional adaptability, circulation convenience, resource allocation balance, etc. Different types represent different evaluation dimensions of building space utilization efficiency.

[0097] When the building space in the information to be evaluated contains only one type of efficiency characteristic, the system directly determines that there is no interaction constraint effect. For example, if a part of the building space only involves the efficiency characteristic of space utilization and there is no interference or influence from other types of characteristics, then a single characteristic cannot form a mutually restrictive relationship, and the determination result is no interaction constraint effect.

[0098] If the building space in the information to be evaluated contains at least two types of efficiency characteristics, the system further combines spatial correlation dimensions to determine whether these efficiency characteristics have spatial overlap or dimensional intersection. Spatial correlation dimensions characterize the connectivity, dependence, and synergy between different spatial units. Spatial overlap refers to overlapping areas within the building space covered by different efficiency characteristics. Dimensional intersection refers to the mutual influence between the evaluation logics of different efficiency characteristics; for example, circulation convenience and resource allocation balance interact in the evaluation logic. This can be quantified using the formula: Dimensional Intersection = Percentage of Spatial Overlap Area × Product of Feature Influence Weights. Here, the percentage of spatial overlap area = Area of ​​Overlapping Area ÷ Total Area of ​​Evaluation Area, and the feature influence weights are pre-assigned based on the contribution of different efficiency characteristics to overall efficiency.

[0099] If the efficiency characteristics of the building space in the information to be evaluated do not overlap spatially and have no dimensional intersection, the system determines that there is no mutual constraint effect. For example, the office area of ​​a building only involves the space utilization characteristic, and the independent meeting room only involves the functional adaptability characteristic. The spatial scope covered by the two characteristics is completely separate, and the evaluation logic does not affect each other. In this case, the two efficiency characteristics act independently and there is no mutual constraint relationship.

[0100] If the efficiency characteristics of building space in the information to be evaluated overlap spatially or intersect dimensionally, the system determines that there is a mutually restrictive effect between at least two efficiency characteristics. For example, the core office area of ​​an office building has both space utilization and circulation convenience as efficiency characteristics. The spatial ranges covered by these two characteristics completely overlap, and an increase in space utilization will lead to an increase in circulation distance and a decrease in convenience, forming a typical dimensional cross-effect. In this case, the system determines that these two efficiency characteristics have a mutually restrictive effect, and this effect will be included in the subsequent evaluation impact result set to analyze efficiency bottlenecks and generate optimization solutions.

[0101] The assessment result set is generated based on the judgment of the interaction constraint effect, which specifically includes the following steps:

[0102] If there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated, then the first impact evaluation result of efficiency characteristics on building space utilization efficiency is determined based on the type of efficiency characteristics, dynamic adaptation parameters and resource consumption efficiency parameters.

[0103] If there are at least two interacting constraints between efficiency features in the building space of the information to be evaluated, then the second impact assessment result of the efficiency features with interacting constraints on the building space utilization efficiency is determined based on the efficiency feature type, dynamic adaptation parameters and resource consumption efficiency parameters.

[0104] The results of the first impact assessment and the second impact assessment are combined to form an impact assessment result set.

[0105] First, the system will retrieve the judgment conclusions of the previous interaction constraint effect, divide the information to be evaluated into two scenarios: no interaction constraint effect and interaction constraint effect, and carry out targeted efficiency impact assessments for each.

[0106] When there is no interaction constraint effect between efficiency features in the building space within the information to be evaluated, the system enters the judgment process for the first impact assessment result. At this point, efficiency features act independently on the building space. The system combines efficiency feature types, dynamic adaptation parameters, and resource consumption efficiency parameters to quantify the degree of independent impact of each efficiency feature on space utilization efficiency. Efficiency feature types include space utilization rate and functional adaptability; dynamic adaptation parameters cover space usage switching frequency and personnel flow adaptability; and resource consumption efficiency parameters include energy consumption utilization rate and operation and maintenance cost efficiency. The impact assessment calculation formula in this scenario is: First Impact Assessment Result = Efficiency Feature Type Score × Dynamic Adaptation Parameter Weight + Resource Consumption Efficiency Parameter Score × Resource Weight. Each parameter score is standardized and converted to a value from 0 to 100, and the sum of the weights is 1 to ensure the consistency and comparability of the calculation results. For example, if a part of a building space only has the efficiency characteristic of functional adaptability and has no interaction constraint effect, its functional adaptability score is 85, the dynamic adaptability parameter weight is 0.4, the resource consumption efficiency parameter score is 90, and the resource weight is 0.6. The first impact assessment result is calculated as 85×0.4+90×0.6=88, which reflects that this characteristic has a high degree of positive impact on space utilization efficiency.

[0107] When at least two efficiency features in the building space under evaluation exhibit interactive constraints, the system initiates the second impact assessment result judgment process. In this case, efficiency features are mutually influential and mutually constraining. The system needs to comprehensively calculate the coupled impact of each feature on space utilization efficiency, taking into account the interactive effects. The impact assessment calculation formula in this scenario is: Second Impact Assessment Result = Efficiency Feature Type Coupling Score × Dynamic Adaptation Parameter Weight + Resource Consumption Efficiency Parameter Coupling Score × Resource Weight + Interaction Constraint Effect Correction Value. The efficiency feature type coupling score is calculated by analyzing the superposition or cancellation relationship between features, and the interaction constraint effect correction value is assigned according to the degree of constraint, with positive constraints resulting in positive values ​​and negative constraints in negative values. For example, in the core area of ​​an office building, there is a mutual constraint effect between space utilization and circulation convenience. The space utilization score is 90, the circulation convenience score is 70, the coupling score is 80, the dynamic adaptation parameter weight is 0.4, the resource consumption efficiency parameter coupling score is 82, the resource weight is 0.6, and the mutual constraint effect correction value is -3. The calculated second impact assessment result is 80×0.4+82×0.6-3=64.2, reflecting that the mutual constraint effect has led to a decrease in the overall efficiency impact.

[0108] The results of the first and second impact assessments are combined to form a complete set of impact assessment results. This set comprehensively covers efficiency impact data in both unconstrained and constrained scenarios, including independent impact assessments of single features as well as coupled impact assessments of multiple features interacting. It can fully reflect the actual state and potential bottlenecks of building space utilization efficiency, providing a comprehensive and accurate basis for subsequent efficiency level determination and optimization and renovation scheme generation.

[0109] Based on the efficiency characteristic type, dynamic adaptation parameters, and resource consumption efficiency parameters, the assessment result of the first impact of efficiency characteristics on building space utilization efficiency is determined, specifically including the following steps:

[0110] By comparing the dynamic adaptation parameters and resource consumption efficiency parameters of each efficiency characteristic type in the building space with the characteristic parameter efficiency evaluation table, the first efficiency adaptation degree and the first efficiency loss rate of each efficiency characteristic type to the building space utilization efficiency are obtained.

[0111] The first efficiency fit and first efficiency loss rate of each building space are classified and statistically analyzed according to the functional zoning of the space to obtain the comprehensive efficiency value of each functional zoning.

[0112] The first impact assessment result is obtained by weighting the comprehensive efficiency value of each functional zone based on the area proportion weight of each functional zone.

[0113] The dynamic adaptation parameters and resource consumption efficiency parameters for each efficiency characteristic type in the building space are matched one by one with a pre-set characteristic parameter efficiency evaluation table to obtain the first efficiency adaptation degree and the first efficiency loss rate for the corresponding efficiency characteristic type. Efficiency characteristic types cover space utilization, functional adaptability, and circulation convenience, while dynamic adaptation parameters include space usage switching frequency and personnel flow adaptability. Resource consumption efficiency parameters include energy consumption utilization rate and operation and maintenance cost efficiency. The characteristic parameter efficiency evaluation table is a pre-established mapping rule between parameters and efficiency indicators. For example, the personnel flow adaptability in the dynamic adaptation parameters is divided into multiple levels, each level corresponding to a specific first efficiency adaptation degree value, and the energy consumption utilization rate in the resource consumption efficiency parameters is mapped to a specific first efficiency loss rate value.

[0114] After completing the parameter comparison, the first efficiency fit and first efficiency loss rate of each building space are categorized and statistically analyzed according to the functional zones of the space to obtain the comprehensive efficiency value of each functional zone. The formula for calculating the comprehensive efficiency value is: Comprehensive Efficiency Value = First Efficiency Fit - First Efficiency Loss Rate. The difference between the fit and the loss rate directly reflects the actual performance of the efficiency characteristics within a single functional zone. For example, an office building includes three functional zones: office area, meeting area, and support area. The first efficiency fit of the office area is 86, and the first efficiency loss rate is 12, so the comprehensive efficiency value is 86 - 12 = 74; the first efficiency fit of the meeting area is 78, and the first efficiency loss rate is 8, so the comprehensive efficiency value is 78 - 8 = 70; the first efficiency fit of the support area is 82, and the first efficiency loss rate is 15, so the comprehensive efficiency value is 82 - 15 = 67.

[0115] Finally, the system performs a weighted calculation of the overall efficiency value of each functional zone based on its area proportion weight, yielding the final first impact assessment result. The formula for calculating the area proportion weight is: Area Proportion Weight = Area of ​​a Functional Zone ÷ Total Assessed Building Area. The formula for calculating the first impact assessment result is: E i w represents the overall efficiency value of the i-th functional partition. i Let this be the area percentage weight for the i-th functional zone. For example, if the total assessed area of ​​the office building is 10,000 square meters, the office area is 6,000 square meters, and the area percentage weight is 6,000 ÷ 10,000 = 0.6; the meeting area is 2,500 square meters, and the area percentage weight is 2,500 ÷ 10,000 = 0.25; and the logistics area is 1,500 square meters, and the area percentage weight is 1,500 ÷ 10,000 = 0.15. Substituting these values ​​into the calculation, we get the first impact assessment result = 74 × 0.6 + 70 × 0.25 + 67 × 0.15 = 71.95. This value directly reflects the overall utilization efficiency of the building space under the condition of no interaction constraints, providing a precise quantitative basis for subsequent assessment and optimization.

[0116] Based on the efficiency characteristic type, dynamic adaptation parameters, and resource consumption efficiency parameters, the assessment results of the second impact of efficiency characteristics with mutual constraints on building space utilization efficiency are determined, specifically including the following steps:

[0117] Determine the type of interaction constraint between architectural space efficiency characteristics in the information to be evaluated and processed, and determine whether the interaction constraint between efficiency characteristics is spatial overlapping type or dimensional cross-type interaction constraint.

[0118] If the building space in the information to be evaluated has efficiency characteristics of spatial overlap and interaction constraints, the direct constraint impact on the building space utilization efficiency is judged based on the parameter information of the overlap area, and the first constraint evaluation result is obtained.

[0119] If the building space in the information to be evaluated has efficiency characteristics of cross-dimensional interaction constraints, the indirect constraint impact on the building space utilization efficiency is judged based on the parameter information of the cross-dimensional interaction, and the second constraint evaluation result is obtained.

[0120] The results of the first constraint assessment and / or the second constraint assessment are integrated and revised to form the second impact assessment result.

[0121] Determine the type of interaction constraints among the building space efficiency characteristics in the information to be evaluated, clarifying whether it is a spatial overlap type or a dimensional cross-type interaction constraint. Spatial overlap type interaction constraints refer to overlapping areas within the building space covered by different efficiency characteristics; for example, space utilization and circulation convenience characteristics simultaneously affect the same office area. Dimensional cross-type interaction constraints refer to mutual influence between the evaluation logics of different efficiency characteristics; for example, functional adaptability and resource allocation balance are mutually constraining in their evaluation logic, where optimization of one leads to limitations in the other.

[0122] If the building space in the information to be evaluated exhibits efficiency characteristics of spatial overlap and interaction constraints, the system will determine the direct constraint impact on the building space utilization efficiency based on the parameter information of the overlap area, thus obtaining the first constraint evaluation result. The parameter information of the overlap area includes the area of ​​the overlap area, the dynamic adaptation parameters of each efficiency feature within the overlap area, and resource consumption efficiency parameters, etc. The calculation formula is: First constraint evaluation result = Overlapping area area ratio × (Feature A adaptation degree - Feature B loss rate + Feature B adaptation degree - Feature A loss rate) ÷ 2, where the overlapping area area ratio = Overlapping area area ÷ Total evaluation area area. This formula quantifies the degree of direct constraint caused by spatial overlap. For example, the core office area of ​​an office building is an overlapping area of ​​space utilization and circulation convenience. The overlapping area accounts for 0.7, the space utilization adaptability is 90, the loss rate is 10, the circulation convenience adaptability is 75, and the loss rate is 8. Substituting these values ​​into the calculation, the first constraint assessment result is 0.7×(90-10+75-8)÷2=0.7×147÷2=51.45. This value reflects the direct constraint intensity brought about by the space overlap.

[0123] If the building space in the information to be evaluated exhibits efficiency characteristics with cross-dimensional interactions and constraints, the system will determine the indirect constraints on the building space utilization efficiency based on the parameter information of the cross-dimensional interactions, thus obtaining a second constraint evaluation result. The parameter information of the cross-dimensional interactions includes the influence weights of each efficiency characteristic, the correlation degree of dynamic adaptation parameters, and the coupling degree of resource consumption parameters. The calculation formula is: Second constraint evaluation result = Feature influence weight product × Dynamic adaptation parameter correlation degree × Resource consumption parameter coupling degree × 100, where the feature influence weight product is the product of the preset weights of each cross-dimensional interaction, and the correlation degree of dynamic adaptation parameters and the coupling degree of resource consumption parameters are both standardized values ​​between 0 and 1. For example, the functional adaptability and resource allocation balance of a shopping mall have cross-dimensional interactions. The functional adaptability weight is 0.6, the resource allocation balance weight is 0.4, the dynamic adaptation parameter correlation degree is 0.8, and the resource consumption parameter coupling degree is 0.7. Substituting these values, the second constraint evaluation result is calculated as 0.6 × 0.4 × 0.8 × 0.7 × 100 = 13.44. This value reflects the strength of the indirect constraints brought about by the cross-dimensional interactions.

[0124] The system integrates and corrects the results of the first constraint assessment and / or the second constraint assessment to form the second impact assessment result. If only one type of constraint exists, the assessment result of that constraint is used directly as the basis for correction, and adjustments are made in conjunction with the basic efficiency score under the unconstrained scenario. If two types of constraints exist simultaneously, the assessment results of the two constraints are first weighted and summed, and then integrated with the basic efficiency score. The integration and correction formula is: Second Impact Assessment Result = Basic Efficiency Score - (First Constraint Assessment Result × First Constraint Weight + Second Constraint Assessment Result × Second Constraint Weight), where the basic efficiency score is the comprehensive efficiency value under the unconstrained scenario, and the constraint weight is preset based on the degree of influence of the constraint type on the overall efficiency. For example, if the basic efficiency score of an office building is 72, the first constraint assessment result is 51.45, the first constraint weight is 0.7, and there is no second constraint type, the second impact assessment result is calculated as 72 - 51.45 × 0.7 = 35.985. This value directly reflects the actual level of building space utilization efficiency under the interaction constraint effect, providing an accurate basis for subsequent efficiency level determination and optimization.

[0125] Based on the analysis of parameter information of the overlapping area, the direct constraint on the efficiency of building space utilization is determined, resulting in the first constraint assessment result, which includes the following steps:

[0126] Determine whether the efficiency characteristics of spatial overlapping and interaction constraints result in inefficiencies in the utilization of building space.

[0127] If an efficiency bottleneck is found, the type and degree information of the bottleneck are obtained. The type and degree information of the bottleneck are compared with the efficiency impact table of the bottleneck parameter to obtain the second efficiency loss rate and efficiency constraint dimension of the building space. The second efficiency loss rate and efficiency constraint dimension are marked as the first constraint assessment result.

[0128] If no efficiency bottlenecks are observed, it is determined that the efficiency characteristics of spatial overlapping interaction constraints do not directly restrict or reduce the efficiency of building space utilization, thus forming the first constraint assessment result.

[0129] First, regarding the efficiency characteristics of spatial overlap and interaction constraints, it is determined whether they create efficiency bottlenecks in the utilization of building space. An efficiency bottleneck refers to a situation where, within the spatial overlap area, the performance level of at least one efficiency characteristic is significantly lower than a preset threshold, and this will reduce the overall space utilization efficiency. For example, when space utilization rate and circulation convenience are overlapped, if the circulation convenience score is far below the threshold, it becomes a bottleneck restricting overall efficiency.

[0130] If an efficiency bottleneck is identified, the system further acquires information on the type and severity of the bottleneck. The bottleneck type information clarifies which efficiency characteristic constitutes the bottleneck, such as insufficient accessibility or unbalanced resource allocation. The bottleneck severity information uses quantitative indicators to reflect the severity of the bottleneck; for example, bottleneck severity = threshold - current feature score, with a higher value indicating a more severe bottleneck. Subsequently, the system compares and matches the bottleneck type and severity information with a pre-defined bottleneck parameter efficiency impact table to obtain the second efficiency loss rate and efficiency constraint dimension of the building space. The second efficiency loss rate quantifies the direct loss value caused by the efficiency bottleneck, while the efficiency constraint dimension clarifies the spatial scope and evaluation direction covered by the constraint impact, such as the accessibility dimension or resource dimension covering overlapping areas. Finally, the second efficiency loss rate and efficiency constraint dimension are jointly marked as the first constraint assessment result. For example, the ease of movement within the overlapping area of ​​an office building becomes an efficiency bottleneck, with a bottleneck level of 15. After referring to the efficiency impact table of bottleneck parameters, the second efficiency loss rate is 22, and the efficiency constraint dimension is the movement dimension. At this time, the first constraint assessment result is the combination of the second efficiency loss rate of 22 and the efficiency constraint dimension of the movement dimension.

[0131] If no efficiency bottleneck is identified, the system directly determines that the efficiency characteristics of the spatially overlapping interaction do not directly restrict or reduce the building space utilization efficiency. In this case, the first constraint assessment result is marked as having no direct restrictive loss, indicating that the efficiency characteristics within the spatially overlapping area are balanced and have not formed a bottleneck that lowers the overall efficiency, thus requiring no additional quantification of loss values. This judgment logic can be quantified using the formula: Efficiency Bottleneck Judgment Value = Minimum Score of Each Efficiency Characteristic ÷ Preset Threshold. When the efficiency bottleneck judgment value is less than 1, it is determined that an efficiency bottleneck has occurred; when the efficiency bottleneck judgment value is greater than or equal to 1, it is determined that no efficiency bottleneck has occurred, ensuring the objectivity and accuracy of the judgment results.

[0132] Based on the analysis of cross-dimensional parameter information, the indirect constraints on building space utilization efficiency are determined, resulting in a second constraint assessment. This assessment includes the following steps:

[0133] By comparing the efficiency feature types, dynamic adaptation parameters, and resource consumption efficiency parameters of the cross-dimensional interaction constraints with the feature parameter efficiency evaluation table, the second efficiency adaptation degree and the third efficiency loss rate of each efficiency feature type to the building space are obtained.

[0134] Based on the dimensional correlation coefficients of each efficiency feature type, the second efficiency fit and the third efficiency loss rate are coupled to obtain the coupled comprehensive efficiency fit and comprehensive efficiency loss rate.

[0135] The overall efficiency fit and overall efficiency loss rate are marked as the second constraint assessment results.

[0136] First, the efficiency feature types, dynamic adaptation parameters, and resource consumption efficiency parameters that interact and constrain each other across dimensions are matched one by one with a pre-set feature parameter efficiency evaluation table to obtain the second efficiency adaptability and the third efficiency loss rate for each efficiency feature type to the building space. Efficiency feature types include functional adaptability and resource allocation balance, dynamic adaptation parameters cover space usage switching frequency and personnel flow adaptability, and resource consumption efficiency parameters include energy utilization rate and operation and maintenance cost efficiency. The feature parameter efficiency evaluation table is a pre-established mapping rule between parameters and efficiency indicators. For example, the personnel flow adaptability in the dynamic adaptation parameters is divided into multiple levels, each level corresponding to a specific second efficiency adaptability value, and the energy utilization rate in the resource consumption efficiency parameters is mapped to a specific third efficiency loss rate value.

[0137] After parameter comparison, the system performs coupling calculations on the second efficiency fit and the third efficiency loss rate based on the dimensional correlation coefficients of each efficiency feature type, obtaining the coupled comprehensive efficiency fit and comprehensive efficiency loss rate. The dimensional correlation coefficients characterize the degree of mutual influence between different efficiency feature types in dimensional cross-scenarios. These coefficients are pre-assigned based on the contribution of different features to the overall space utilization efficiency, and the sum of all dimensional correlation coefficients is 1 to ensure the consistency and comparability of the calculation results. The calculation formula for the coupling operation is: A com For overall efficiency and adaptability, A i For the second efficiency fit of the i-th efficiency feature type, k i Let L be the dimensional correlation coefficient corresponding to the i-th efficiency feature type; L be the overall efficiency loss rate. com The calculation formula is: L i Let k be the third efficiency loss rate of the i-th efficiency feature type. iLet be the dimensional correlation coefficient corresponding to the i-th efficiency feature type. Through weighted summation, the independent performance of multiple dimensional features is integrated into a coupled performance under cross-scenario conditions, accurately reflecting the indirect constraints brought about by dimensional intersection. For example, a shopping mall has two efficiency features with overlapping dimensions: functional adaptability and resource allocation balance. The second efficiency adaptability of functional adaptability is 82, the third efficiency loss rate is 10, and the dimensional correlation coefficient is 0.6. The second efficiency adaptability of resource allocation balance is 76, the third efficiency loss rate is 8, and the dimensional correlation coefficient is 0.4. The calculated comprehensive efficiency adaptability is 82 × 0.6 + 76 × 0.4 = 79.6, and the comprehensive efficiency loss rate is 10 × 0.6 + 8 × 0.4 = 9.2.

[0138] Finally, the system marks the overall efficiency fit degree and overall efficiency loss rate obtained from the coupled operation as the second constraint evaluation result. The second constraint evaluation result includes the overall fit level of space utilization efficiency in the cross-dimensional scenario, and also quantifies the degree of indirect loss caused by the interaction of multiple features. This provides accurate data support for subsequent integration and correction to form the second impact evaluation result, and provides core basis for identifying cross-dimensional efficiency bottlenecks and formulating targeted optimization and transformation schemes.

[0139] A building space utilization efficiency evaluation system, comprising:

[0140] Data acquisition module: Collects physical space data and usage demand data of the building space to be evaluated, constructs a digital twin model of the building space, extracts spatial representation information related to utilization efficiency from the digital twin model of the building space and marks it as the original information for efficiency evaluation;

[0141] Information extraction module: Extracts the efficiency feature type, feature distribution density and spatial correlation dimension of building space utilization from the original efficiency assessment information. Based on the efficiency feature type and feature distribution density, it determines whether the original efficiency assessment information needs to be simulated and processed, and marks the corresponding original efficiency assessment information as information to be evaluated and processed.

[0142] Parameter acquisition module: Collects dynamic adaptation parameters of building space utilization and resource consumption efficiency parameters from the information to be evaluated and processed;

[0143] Efficiency Feature Judgment Module: Based on the spatial correlation dimension and the number of efficiency feature types, determine whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, and generate an evaluation impact result set based on the judgment result of the mutual constraint effect.

[0144] The rating and optimization module determines the rating of building space utilization efficiency and proposes optimization and renovation plans based on the assessment impact result set.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the efficiency of building space utilization, characterized in that, The method includes the following steps: Collect physical space data and usage demand data of the building space to be evaluated, construct a digital twin model of the building space, extract spatial representation information related to utilization efficiency from the digital twin model of the building space and mark it as the original information for efficiency evaluation; Extract the efficiency feature types, feature distribution density, and spatial correlation dimensions of building space utilization from the original efficiency assessment information. Determine whether the original efficiency assessment information needs to be simulated and processed based on the efficiency feature types and feature distribution density, and mark the corresponding original efficiency assessment information as information to be assessed and processed. Collect dynamic adaptation parameters of building space utilization and resource consumption efficiency parameters from the information to be evaluated and processed; Based on the number of spatial association dimensions and efficiency feature types, determine whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, and generate an evaluation impact result set based on the judgment result of the mutual constraint effect. The assessment results set determines the level of building space utilization efficiency and the corresponding optimization and renovation plan.

2. The method for evaluating the efficiency of building space utilization according to claim 1, characterized in that, Based on the type and density of efficiency features, determine whether simulation processing of the original efficiency evaluation information is necessary. Mark the corresponding original efficiency evaluation information as information to be evaluated and processed. The specific steps include: If the feature distribution density of the same efficiency feature type is fully presented on the same set of original efficiency evaluation information, then the set of original efficiency evaluation information is marked as information to be evaluated and processed. If the feature distribution density of the same efficiency feature type appears in at least two sets of original efficiency evaluation information, then the corresponding original efficiency evaluation information will be partitioned and extracted to form a partitioned evaluation information unit containing the efficiency feature type. Based on the spatial attributes of efficiency feature types, efficiency feature points at the boundaries of each partition evaluation information unit are identified. Based on the spatial correlation of efficiency feature points, the feature boundaries of each partition evaluation information unit are optimized and then simulated and fused to form the information to be evaluated and processed.

3. The method for evaluating the efficiency of building space utilization according to claim 2, characterized in that, Based on the spatial correlation of efficiency feature points, the feature boundaries of each partition evaluation information unit are optimized and then simulated and fused to form the information to be evaluated and processed. The specific steps include: Determine whether there is spatial overlap or correlation between efficiency feature points at the boundaries of at least two partition evaluation information units; If the efficiency feature points at the boundaries of at least two partition evaluation information units have spatial overlap, the corresponding partition evaluation information units are marked as matching evaluation information units. Based on the overlapping efficiency feature points, the corresponding matching evaluation information units are simulated, deduced, and fused to form the information to be evaluated. The overlapping feature point information of one matching evaluation information unit is retained, and the overlapping feature point information of the other matching evaluation information units is deleted. If there is no spatial overlap or correlation between the efficiency feature points at the boundary of each partition evaluation information unit, then the efficiency feature points at the boundary of each partition evaluation information unit will be spatially topologically matched. If the efficiency feature points form a continuous efficiency feature link through topological matching, then the corresponding partition evaluation information unit will be simulated, deduced, and fused to form the information to be evaluated.

4. The method for evaluating the efficiency of building space utilization according to claim 3, characterized in that, Based on the number of spatial association dimensions and efficiency feature types, determine whether there are interactive constraints on efficiency features in the architectural space of the information to be evaluated and processed. This includes the following steps: If the building space in the information to be evaluated and processed has only one type of efficiency characteristic, then it is determined that there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated and processed. If the building space in the information to be evaluated and processed has efficiency features of at least two types, then the spatial correlation dimension is used to determine whether the efficiency features of the building space in the information to be evaluated and processed have spatial overlap or dimensional intersection. If the efficiency characteristics of the building space in the information to be evaluated do not overlap spatially and do not cross dimensions, then it is determined that there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated. If the efficiency characteristics of the building space in the information to be evaluated overlap spatially or intersect in dimension, it is determined that there is a mutual constraint effect between at least two efficiency characteristics in the building space in the information to be evaluated.

5. The method for evaluating the efficiency of building space utilization according to claim 4, characterized in that, The assessment result set is generated based on the judgment of the interaction constraint effect, which specifically includes the following steps: If there is no interaction constraint effect of efficiency characteristics in the building space in the information to be evaluated, then the first impact evaluation result of efficiency characteristics on building space utilization efficiency is determined based on the type of efficiency characteristics, dynamic adaptation parameters and resource consumption efficiency parameters. If there are at least two interacting constraints between efficiency features in the building space of the information to be evaluated, then the second impact assessment result of the efficiency features with interacting constraints on the building space utilization efficiency is determined based on the efficiency feature type, dynamic adaptation parameters and resource consumption efficiency parameters. The results of the first impact assessment and the second impact assessment are combined to form an impact assessment result set.

6. The method for evaluating the efficiency of building space utilization according to claim 5, characterized in that, Based on the efficiency characteristic type, dynamic adaptation parameters, and resource consumption efficiency parameters, the assessment result of the first impact of efficiency characteristics on building space utilization efficiency is determined, specifically including the following steps: By comparing the dynamic adaptation parameters and resource consumption efficiency parameters of each efficiency characteristic type in the building space with the characteristic parameter efficiency evaluation table, the first efficiency adaptation degree and the first efficiency loss rate of each efficiency characteristic type to the building space utilization efficiency are obtained. The first efficiency fit and first efficiency loss rate of each building space are classified and statistically analyzed according to the functional zoning of the space to obtain the comprehensive efficiency value of each functional zoning. The first impact assessment result is obtained by weighting the comprehensive efficiency value of each functional zone based on the area proportion weight of each functional zone.

7. The method for evaluating the efficiency of building space utilization according to claim 5, characterized in that, Based on the efficiency characteristic type, dynamic adaptation parameters, and resource consumption efficiency parameters, the assessment results of the second impact of efficiency characteristics with mutual constraints on building space utilization efficiency are determined, specifically including the following steps: Determine the type of interaction constraint between architectural space efficiency characteristics in the information to be evaluated and processed, and determine whether the interaction constraint between efficiency characteristics is spatial overlapping type or dimensional cross-type interaction constraint. If the building space in the information to be evaluated has efficiency characteristics of spatial overlap and interaction constraints, the direct constraint impact on the building space utilization efficiency is judged based on the parameter information of the overlap area, and the first constraint evaluation result is obtained. If the building space in the information to be evaluated has efficiency characteristics of cross-dimensional interaction constraints, the indirect constraint impact on the building space utilization efficiency is judged based on the parameter information of the cross-dimensional interaction, and the second constraint evaluation result is obtained. The results of the first constraint assessment and / or the second constraint assessment are integrated and revised to form the second impact assessment result.

8. The method for evaluating the efficiency of building space utilization according to claim 7, characterized in that, Based on the analysis of parameter information of the overlapping area, the direct constraint on the efficiency of building space utilization is determined, resulting in the first constraint assessment result, which includes the following steps: Determine whether the efficiency characteristics of spatial overlapping and interaction constraints result in inefficiencies in the utilization of building space. If an efficiency bottleneck is found, the type and degree information of the bottleneck are obtained. The type and degree information of the bottleneck are compared with the efficiency impact table of the bottleneck parameter to obtain the second efficiency loss rate and efficiency constraint dimension of the building space. The second efficiency loss rate and efficiency constraint dimension are marked as the first constraint assessment result. If no efficiency bottlenecks are observed, it is determined that the efficiency characteristics of spatial overlapping interaction constraints do not directly restrict or reduce the efficiency of building space utilization, thus forming the first constraint assessment result.

9. The method for evaluating the efficiency of building space utilization according to claim 7, characterized in that, Based on the analysis of cross-dimensional parameter information, the indirect constraints on building space utilization efficiency are determined, resulting in a second constraint assessment. This assessment includes the following steps: By comparing the efficiency feature types, dynamic adaptation parameters, and resource consumption efficiency parameters of the cross-dimensional interaction constraints with the feature parameter efficiency evaluation table, the second efficiency adaptation degree and the third efficiency loss rate of each efficiency feature type to the building space are obtained. Based on the dimensional correlation coefficients of each efficiency feature type, the second efficiency fit and the third efficiency loss rate are coupled to obtain the coupled comprehensive efficiency fit and comprehensive efficiency loss rate. The overall efficiency fit and overall efficiency loss rate are marked as the second constraint assessment results.

10. A building space utilization efficiency evaluation system, applied to a building space utilization efficiency evaluation method as described in any one of claims 1-9, characterized in that, include: Data acquisition module: Collects physical space data and usage demand data of the building space to be evaluated, constructs a digital twin model of the building space, extracts spatial representation information related to utilization efficiency from the digital twin model of the building space and marks it as the original information for efficiency evaluation; Information extraction module: Extracts the efficiency feature type, feature distribution density and spatial correlation dimension of building space utilization from the original efficiency assessment information. Based on the efficiency feature type and feature distribution density, it determines whether the original efficiency assessment information needs to be simulated and processed, and marks the corresponding original efficiency assessment information as information to be evaluated and processed. Parameter acquisition module: Collects dynamic adaptation parameters of building space utilization and resource consumption efficiency parameters from the information to be evaluated and processed; Efficiency Feature Judgment Module: Based on the spatial correlation dimension and the number of efficiency feature types, determine whether there is a mutual constraint effect of efficiency features in the building space of the information to be evaluated and processed, and generate an evaluation impact result set based on the judgment result of the mutual constraint effect. The rating and optimization module determines the rating of building space utilization efficiency and proposes optimization and renovation plans based on the assessment impact result set.

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