Energy-saving control methods based on the spatial morphology of building complexes

Abstract

This invention discloses an energy-saving control method based on the spatial morphology of building complexes, belonging to the field of energy-saving control technology. The method includes the following steps: First, based on regions in the spatial shading perception matrix where the direction of thermal pressure difference changes, an airflow trajectory analysis region is constructed. Continuous wind direction and speed data are collected, and the presence of airflow direction anomalies is determined by the change in flow direction consistency entropy. Second, the spatial shading perception matrix and the regions with abnormal airflow directions are spatially overlapped. Based on the heat conduction rate and the stability of the airflow output path within the overlapped region, an exhaust vent ventilation and heat dissipation efficiency evaluation index is generated. Third, based on the exhaust vent ventilation and heat dissipation efficiency evaluation index, an exhaust response condition matrix including spatial shading parameters, airflow disturbance level, and heat retention level is constructed, generating a ventilation control judgment sequence. This invention solves the problem of exhaust failure under shading interference, achieves accurate judgment of ventilation efficiency and optimization of energy-saving control, and ensures the ventilation stability and energy utilization efficiency of building complexes.

Description

Technical Field

[0001] This invention relates to the field of energy-saving control technology, and more specifically to an energy-saving control method based on the spatial morphology of building complexes. Background Technology

[0002] Energy-saving control based on the spatial morphology of building complexes refers to a method that analyzes and models the spatial morphological characteristics of individual buildings within a complex, such as spatial layout relationships, orientation relationships, height differences, shading relationships, airflow unobstructedness, and sunlight conditions. This analysis, combined with environmental and energy usage data, leads to the development of energy-saving control strategies adapted to the overall operating environment of the building complex. This type of control technology emphasizes the impact of the building complex as a whole on energy consumption distribution, rather than controlling individual buildings in isolation. Existing energy-saving control technologies based on the spatial morphology of building complexes typically involve the following steps: First, spatial structural data of the building complex is acquired using BIM (Building Information Modeling), GIS (Geographic Information System), laser scanning, or remote sensing images, and a three-dimensional spatial model is established. Second, real-time environmental data collected by sensors (such as light intensity, temperature, wind speed, and occupancy density) is analyzed to assess the thermal environment, ventilation paths, and lighting conditions in different areas of the building complex. Next, energy consumption prediction models and optimization algorithms are used to evaluate the impact of different spatial morphologies on energy use (such as air conditioning systems, lighting systems, and shading systems). Finally, based on the analysis results, control strategies adapted to specific spatial conditions are generated, and intelligent scheduling and energy consumption optimization control of equipment are achieved through building automation systems (BAS) or energy management systems (EMS). Overall, this method achieves closed-loop management from "spatial cognition" to "strategy formulation" to "energy consumption execution," offering advantages such as improving the energy efficiency of building complexes, reducing overall carbon emissions, and enhancing the level of intelligent operation and maintenance.

[0003] The existing technology has the following shortcomings:

[0004] In the energy-saving control of building complex spatial form, natural ventilation is often achieved through high-rise exhaust vents based on the principle of thermal pressure difference to reduce air conditioning load and improve ventilation efficiency. However, when there are adjacent blocks of greater height near the top of the building, the original thermal pressure difference distribution is altered due to the spatial shading structure, causing the direction of hot airflow to reverse. This can lead to an abnormal phenomenon of hot air flowing back from the outside to the inside under certain meteorological conditions. At this time, although the exhaust vents are opened, they no longer have an effective heat removal function, and instead exacerbate the heat accumulation in the top-floor area. Existing energy-saving control technologies based on building complex spatial form cannot determine whether the high-rise exhaust vents still have ventilation and heat removal efficiency based on the abnormal airflow direction under the influence of thermal pressure difference changes. Their control logic still identifies thermal pressure response behavior as effective ventilation, leading to the continuous and erroneous opening of exhaust vents, resulting in increased cooling load, energy waste, and further disruption of the building's internal air pressure balance, producing a series of serious consequences such as top-floor temperature rise, heat recirculation in the middle and lower floors, odor diffusion, and smoke backflow.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an energy-saving control method based on the spatial morphology of building complexes to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an energy-saving control method based on the spatial morphology of building complexes, specifically comprising the following steps:

[0008] S1. Obtain the three-dimensional spatial block structure information and real-time meteorological parameters of the building complex, establish a spatial shading perception matrix, extract the spatial closure angle relationship between building blocks, and determine whether there are shading interference conditions that cause changes in the direction of thermal pressure difference.

[0009] S2. Based on the region in the spatial shading perception matrix where the thermal pressure difference direction changes, construct an airflow trajectory analysis region, collect continuous wind direction and wind speed data, and determine whether there is an abnormal airflow direction by the change in the flow direction consistency entropy value.

[0010] S3. Spatial overlap processing is performed between the spatial shading perception matrix and the abnormal airflow direction area. Based on the heat conduction rate and the stability of the airflow output path within the overlap area, an exhaust vent ventilation and heat dissipation efficiency evaluation index is generated.

[0011] S4. Based on the ventilation and heat dissipation efficiency evaluation index of the exhaust vent, construct an exhaust response condition matrix that includes space shading parameters, airflow disturbance level and heat retention level, and generate a ventilation control judgment sequence.

[0012] S5. Based on the control instructions in the ventilation control judgment sequence, perform dynamic adjustment of the exhaust vent opening status, and trigger corresponding ventilation behaviors or alternative energy-saving strategies according to different states, while recording the ventilation control data to the space disturbance dataset.

[0013] Preferably, S1 is as follows:

[0014] Acquire three-dimensional spatial block structure information and real-time meteorological parameters of the building complex. The three-dimensional spatial block structure information includes the height data, boundary outline data and facade orientation data of the building blocks. The real-time meteorological parameters include solar azimuth angle, solar altitude angle and wind direction data.

[0015] Based on the three-dimensional spatial block structure information of the building complex, a block ray projection model is constructed. A ray tracing algorithm is used to simulate the shadow superposition area of ​​each block under the current solar azimuth angle and form the spatial occlusion relationship between the blocks.

[0016] Based on the spatial occlusion relationship between building blocks, the spatial closure angle relationship between building blocks is extracted. The sum of the visible angles of each block in multiple directions is calculated using the included angle integral method, and a spatial closure angle distribution matrix is ​​generated.

[0017] The analysis is performed by coupling the spatial closure angle distribution matrix with the wind direction change trend in real-time meteorological parameters to determine whether spatial shading causes a change in the direction of thermal pressure difference. The determination method is as follows: when the rate of change of the spatial closure angle is greater than the preset change threshold, and the angle between the wind direction and the maximum shading of the closure area is less than the set interference angle threshold, it is marked as having shading interference conditions.

[0018] Preferably, S2 specifically includes the following steps:

[0019] S201. Based on the region in the spatial shading perception matrix where the thermal pressure difference direction changes, taking the center of the thermal pressure difference gradient change in this region as the starting point, and combining the relative height difference and orientation relationship between building blocks, an airflow trajectory analysis region containing multiple horizontal and vertical sections is constructed.

[0020] S202. Arrange wind direction and wind speed acquisition units along the prevailing wind direction in the airflow trajectory analysis area, collect wind direction and wind speed data in multiple consecutive time periods, map the data into an angle sequence on the unit circle according to the wind direction change trend, and establish a wind direction time series distribution model.

[0021] S203. Calculate the flow direction consistency entropy value in each time period based on the wind direction time series distribution model. When the rate of change of the flow direction consistency entropy value in a continuous period is greater than the set disturbance judgment threshold, and the dominant wind direction reverses at an angle exceeding the preset offset angle threshold in the region, it is determined that there is an abnormal airflow direction in the airflow trajectory analysis area.

[0022] Preferably, in S203, the flow direction consistency entropy value for each time period is calculated based on the wind direction temporal distribution model, specifically as follows:

[0023] The wind direction data in each time period of the wind direction time series distribution model is divided into a fixed number of wind direction intervals according to unit angles;

[0024] The frequency of wind direction data within each wind direction interval is counted, and the data is normalized to form a probability distribution function.

[0025] The flow direction consistency entropy value is calculated based on the probability distribution function and the Shannon entropy calculation formula to characterize the degree of dispersion of wind direction distribution within this time period.

[0026] Preferably, S3 specifically includes the following steps:

[0027] Spatial overlap processing is performed on the location regions with changes in thermal pressure difference direction and abnormal airflow direction regions in the spatial shading sensing matrix. The two regions are decomposed into voxels using a three-dimensional Euclidean mesh. Spatial registration is performed with the geometric center of each voxel in a unified coordinate system as the reference. The intersection region of the voxel coordinates is extracted to construct a set of thermal pressure disturbance coupled spatial units.

[0028] Within the set of thermo-pressure perturbation coupled spatial units, the temperature gradient between adjacent units is calculated based on temperature sensing data, and the heat conduction rate of each unit is calculated using the Fourier heat conduction formula. Furthermore, an airflow output path map between units is constructed based on the wind speed direction sequence, and the path continuity coefficient and velocity stability factor are extracted to construct a joint thermo-fluid stability parameter set.

[0029] The heat conduction rate and airflow output path stability factor are normalized and assigned dynamic weight factors to highlight the local sensitivity of the shielding area. The local efficiency value of each spatial unit is generated based on the weighted scoring model. The local efficiency values ​​of all thermal pressure disturbance coupled spatial units are summarized to form the ventilation and heat exhaust efficiency evaluation index of the exhaust outlet.

[0030] Preferably, S4 is as follows:

[0031] S401. The ventilation and heat dissipation efficiency evaluation index of the exhaust vent is normalized and segmented, and the corresponding spatial shading parameters, airflow disturbance level and heat retention level are extracted based on the spatial unit location correlation, and assigned to the three-dimensional label vector to form the initial response factor set.

[0032] S402. Based on the combination relationship of the three types of level indicators in the initial response factor set, establish a multi-dimensional matrix mapping structure and construct an exhaust response condition matrix containing spatial shading parameters, airflow disturbance level and thermal retention level. Each element in the matrix corresponds to a unique level combination configuration.

[0033] S403. Based on the matching rules between each level combination in the exhaust response condition matrix and the exhaust control behavior, a ventilation control judgment sequence is generated according to the preset control threshold mapping logic. The ventilation behavior control instructions output by the ventilation control judgment sequence are used for subsequent exhaust control execution.

[0034] Preferably, S401 specifically refers to:

[0035] The ventilation and heat dissipation efficiency evaluation index of the exhaust vents is subjected to minimum-maximum normalization within the set upper and lower limit range, and divided into multiple discrete level segments according to the equal interval method, which are then mapped to each spatial unit in the building complex.

[0036] The spatial shielding parameters are calculated based on the relative height, closure angle, and azimuth angle between the spatial unit and the shielding source block; the airflow disturbance level is generated by combining the wind direction reversal probability and the rate of change of the flow direction consistency entropy value; and the heat retention level is calculated by combining the heat conduction rate and the heat accumulation duration.

[0037] The spatial occlusion parameters, airflow disturbance level, and thermal retention level are assigned as the respective dimensional components of the three-dimensional label vector, and then combined and stored according to the spatial unit coordinate index to form an initial response factor set.

[0038] Preferably, S402 is as follows:

[0039] The spatial shading parameter level set, airflow disturbance level set, and thermal retention level set are each mapped to an independent one-dimensional integer index axis, and an axial index system is defined to construct the three-dimensional matrix mapping structure.

[0040] The spatial obstruction parameter level, airflow disturbance level and thermal retention level of each group of response factors in the initial response factor set are input into the three-dimensional matrix structure as three-dimensional coordinates, and a unique identifier value is assigned to the corresponding coordinate unit for response configuration indexing.

[0041] Traverse all three-dimensional coordinate combinations to construct an exhaust response condition matrix, and explicitly map each cell element in the exhaust response condition matrix to a unique combination of space shading parameter level, airflow disturbance level, and thermal retention level configuration for use in the generation and retrieval of subsequent ventilation control judgment sequences.

[0042] Preferably, S5 is as follows:

[0043] The exhaust control execution unit is driven by the control commands in the ventilation control decision sequence. The opening angle and opening duration of the exhaust vents are dynamically adjusted by the opening degree adjustment mechanism. An exhaust vent opening state mapping model covering the building space area is established to represent the ventilation capacity boundary of each exhaust vent in real time.

[0044] Based on the linkage analysis of the state field of the exhaust vent opening state mapping model and the ventilation control judgment sequence, a ventilation behavior instruction matching the current state is triggered. When the current state is marked as a high-risk level of thermal pressure disturbance in the judgment sequence, an alternative energy-saving strategy is switched to be executed, including switching to a central exhaust system, starting a heat exchange recovery mechanism, or delaying the exhaust response strategy.

[0045] Key data such as the execution time of ventilation behavior or alternative energy-saving strategies, changes in exhaust vent opening, response type, and ventilation control decision sequence number are written into the spatial disturbance dataset in a unified structure format. The disturbance type index is marked with the spatial coordinates of the exhaust vent as the primary key, which is used for subsequent source tracing, modeling, and optimization learning of ventilation control behavior.

[0046] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0047] 1. This invention, by introducing a spatial shading perception matrix and an airflow trajectory analysis mechanism, can accurately identify changes in thermal pressure difference direction and airflow reversal phenomena under shading interference conditions, effectively avoiding the problem of misjudging ventilation status in traditional exhaust control systems. Through the joint discrimination method of wind direction temporal distribution model and flow direction consistency entropy, the system can monitor and determine in real time whether there is a risk of heat exhaust failure at the exhaust vents of high-rise buildings, ensuring that ventilation control behavior is based on the actual state of thermal pressure and airflow, improving the accuracy and dynamism of the thermal response mechanism, preventing phenomena such as increased cooling load, local heat accumulation, and airflow backflow caused by hot air backflow, and ensuring the thermal stability and air quality safety of the indoor environment.

[0048] 2. This invention further constructs an exhaust response condition matrix that integrates spatial shading parameters, airflow disturbance levels, and thermal retention levels, forming a ventilation control judgment logic based on multi-parameter collaboration, significantly improving the accuracy and adaptability of exhaust regulation. The system utilizes the ventilation and heat dissipation efficiency evaluation index of the exhaust vent as a key indicator. Based on the dynamic perception of local ventilation effectiveness, it links the implementation mechanism of alternative energy-saving strategies with the updating and storage of spatial disturbance datasets, achieving closed-loop management and intelligent optimization of ventilation behavior. Compared with existing methods that rely solely on single parameters such as thermal pressure or wind speed for control, this method possesses the ability to predict abnormal airflow states and the adaptive matching capability of exhaust strategies, and can be widely applied to ventilation energy efficiency management and energy-saving control scenarios in high-density urban building clusters. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0050] Figure 1 This is a flowchart illustrating the energy-saving control method based on the spatial morphology of building complexes according to the present invention. Detailed Implementation

[0051] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0052] This invention provides, for example Figure 1 The energy-saving control method based on the spatial morphology of building complexes, as shown, specifically includes the following steps:

[0053] S1. Obtain the three-dimensional spatial block structure information and real-time meteorological parameters of the building complex, establish a spatial shading perception matrix, extract the spatial closure angle relationship between building blocks, and determine whether there are shading interference conditions that cause changes in the direction of thermal pressure difference.

[0054] In this embodiment, S1 specifically refers to:

[0055] Acquire three-dimensional spatial block structure information and real-time meteorological parameters of the building complex. The three-dimensional spatial block structure information includes the height data, boundary outline data and facade orientation data of the building blocks. The real-time meteorological parameters include solar azimuth angle, solar altitude angle and wind direction data.

[0056] The process of acquiring three-dimensional spatial block structural information and real-time meteorological parameters of the building complex involves the dual acquisition and processing of building spatial morphology and dynamic environmental conditions. Building block height data can be obtained through LiDAR scanning by unmanned aerial vehicles (UAVs) or vertical elevation information extracted from Building Information Modeling (BIM) platforms. Boundary contour data can be extracted based on contour lines from remote sensing imagery, reality modeling, or existing two-dimensional vector layers in urban GIS platforms. Combined with measurements of building facade orientation, the orientation angle of each facade relative to geographic north can be calculated using a three-dimensional point cloud model for normal vector fitting. Real-time meteorological parameters are collected through environmental sensors deployed on the top of the building complex and at key ventilation path nodes. Solar azimuth and solar altitude angles are dynamically obtained through time, geographic coordinates, and astronomical calculation models (such as solar trajectory calculation formulas). Wind direction data is acquired and calibrated using multi-point anemometers. All the above spatial structural information and meteorological parameters are uniformly converted into a standard spatial data format and used as input data for subsequent construction of a spatial shading perception matrix. This data collection method not only ensures the accuracy of the spatial morphology data of the building complex, but also enables the dynamic coupling of environmental conditions and spatial structure, providing reliable data support for subsequent energy-saving control.

[0057] Based on the three-dimensional spatial block structure information of the building complex, a block ray projection model is constructed. A ray tracing algorithm is used to simulate the shadow superposition area of ​​each block under the current solar azimuth angle and form the spatial occlusion relationship between the blocks.

[0058] Constructing a ray projection model of building blocks is a crucial process for quantitatively modeling spatial occlusion relationships based on the 3D spatial block structure information of a building complex. This model is based on the geometric boundaries, facade orientation, and height information of each building block. By setting the solar azimuth and solar altitude angles at specific times and geographical locations, the direction vector of incident rays is determined. Ray tracing is a spatial lighting simulation algorithm originating from computer graphics. Its basic principle is to emit a large number of rays from the direction of the light source and calculate the intersection points of these rays with the object surface in 3D space, thereby determining whether the lighting is occluded, reflected, or penetrated. In this method, the ray tracing algorithm is used to simulate the projection process of sunlight onto the surface of building blocks, thereby identifying which blocks will cast shadows onto other blocks at the current solar azimuth angle. This simulation process can employ GPU-accelerated rendering computation to improve the efficiency of spatial occlusion analysis.

[0059] The construction of the block ray projection model first requires discretizing the 3D model of the building complex into a geometric structure composed of polygonal patches, and then introducing sunlight into the 3D coordinate system in the form of a vector array. Each ray is tested for intersection with the block surface; if a ray cannot propagate to the target point after entering a block, the target point is considered to be occluded. By traversing the ray occlusion relationships between multiple blocks, a layer of shadow overlay areas between buildings can be generated. In this layer, based on the distribution of occluded areas of different blocks in their respective time periods and directions, the spatial occlusion relationships between blocks under the current solar azimuth angle are statistically analyzed. The final spatial occlusion relationships include parameters such as the occluding building, the occluded building, the occlusion angle, and the occlusion time period, providing a quantitative basis for subsequent extraction of spatial closure angle relationships.

[0060] Based on the spatial occlusion relationship between building blocks, the spatial closure angle relationship between building blocks is extracted. The sum of the visible angles of each block in multiple directions is calculated using the included angle integral method, and a spatial closure angle distribution matrix is ​​generated.

[0061] When extracting the spatial closure angle relationship between building blocks based on the spatial occlusion relationship between blocks, it is necessary to establish a 3D model of the building complex with the building blocks as the observation center, and divide it into several equally divided directions (e.g., each 10° is a section). In each direction, it is determined whether the line of sight is blocked by other blocks, and the angle formed between the blocked building and the observed building is measured. The angle integral method is a calculation method used to quantify the intensity of multi-directional occlusion. Its core idea is to integrate the angle values ​​of each direction on a unit circle or sphere to calculate the sum of angles in the visible directions. Taking a low-rise block surrounded by three high-rise buildings as an example, if it is divided into 36 sectors in a 360° direction, and 20 of them are blocked, and the average angle of each blocked direction is 40°, then the sum of the visible angles is: (360° × 36 - 20 × 40°) ÷ 36 = 133.3°, indicating that the spatial closure degree of this block is relatively high. After normalizing the sum of the visible angles of all building blocks, a spatial closure angle distribution matrix is ​​formed. Each element in the matrix represents the degree of openness of the corresponding block in a given orientation, which helps to determine whether the building is in a "closed recessed area" or an "open edge area", providing a spatial structural basis for subsequent thermal pressure ventilation assessment.

[0062] The analysis is performed by coupling the spatial closure angle distribution matrix with the wind direction change trend in real-time meteorological parameters to determine whether spatial shading causes a change in the direction of thermal pressure difference. The determination method is as follows: when the rate of change of the spatial closure angle is greater than the preset change threshold, and the angle between the wind direction and the maximum shading of the closure area is less than the set interference angle threshold, it is marked as having shading interference conditions.

[0063] The coupling analysis of the spatial closure angle distribution matrix and the wind direction change trend in real-time meteorological parameters aims to identify areas within a building complex where the direction of thermal pressure difference changes abnormally due to spatial shading. This analysis first calculates the rate of change of the closure angle of each building block in the dominant ventilation direction based on the continuously generated spatial closure angle distribution matrix over historical periods. This rate of change represents the intensity of change in the closure degree of the building block per unit time, used to identify the dynamic impact of structural shading. Simultaneously, the wind direction change trend in real-time meteorological parameters is used to construct a wind direction trajectory curve through high-frequency wind direction acquisition points, extracting the offset range of the wind direction axis within the analysis period. When coupling these two types of data, it is determined whether the wind direction axis tends to align with the maximum shading angle of the target building block. If the rate of change of the closure angle exceeds a preset threshold (e.g., 15%) and the angle between the wind direction offset and the maximum shading angle is less than an interference angle threshold (e.g., 25°), it is inferred that the original thermal pressure driving channel may be blocked by the shading structure in this area, causing a reversal or failure of the ventilation direction. This judgment method integrates the evolution of spatial structure with the actual deviation of airflow, and can accurately screen out areas with shading and interference characteristics, providing a spatial basis for subsequent exhaust efficiency judgment.

[0064] S2. Based on the region in the spatial shading perception matrix where the thermal pressure difference direction changes, construct an airflow trajectory analysis region, collect continuous wind direction and wind speed data, and determine whether there is an abnormal airflow direction by the change in the flow direction consistency entropy value.

[0065] In this embodiment, S2 specifically includes the following steps:

[0066] S201. Based on the region in the spatial shading perception matrix where the thermal pressure difference direction changes, taking the center of the thermal pressure difference gradient change in this region as the starting point, and combining the relative height difference and orientation relationship between building blocks, an airflow trajectory analysis region containing multiple horizontal and vertical sections is constructed.

[0067] Constructing an airflow trajectory analysis region comprising multiple horizontal and vertical sections requires first spatially locating areas marked as having changes in thermal pressure gradient direction within the spatial shading perception matrix, and extracting the location with the most significant change in thermal pressure gradient as the initial calculation center point. This center point can be determined by calculating the maximum values ​​of each direction derivative in the thermal pressure gradient field, reflecting the core area of ​​disturbance to the thermal pressure conduction path caused by changes in spatial morphology. Based on this, and combining the relative height differences between building blocks and the orientation of the facades, ventilation channels most likely to traverse and deflect airflow are identified. When constructing the analysis region, a set of parallel horizontal sections are first unfolded along the prevailing wind direction axis in a three-dimensional coordinate system. The vertical spacing of each section is adaptively set based on the height of the gap between buildings, the wind speed attenuation coefficient, and the severity of shading. Subsequently, several vertical sections are inserted at the center point along a direction perpendicular to the prevailing wind direction to capture wind deflection or turbulent circulation behavior. Each profile and section forms a regular grid analysis volume. Multidimensional meteorological data such as wind speed and direction are mapped through grid point interpolation, providing spatial decoupling modeling capabilities for subsequent flow field trend analysis and anomaly detection. The entire construction process can be achieved by integrating computational fluid dynamics (CFD) modeling tools with a GIS platform, enabling the computational and visualization of high-resolution spatial airflow trajectories.

[0068] S202. Arrange wind direction and wind speed acquisition units along the prevailing wind direction in the airflow trajectory analysis area, collect wind direction and wind speed data in multiple consecutive time periods, map the data into an angle sequence on the unit circle according to the wind direction change trend, and establish a wind direction time series distribution model.

[0069] Wind direction and speed data acquisition units are deployed along the prevailing wind direction within the airflow trajectory analysis area. High-precision ultrasonic anemometers and multi-point wind direction sensors are uniformly placed along the centerline and boundary lines of the analysis area to capture microclimate disturbance characteristics at different heights and locations. The prevailing wind direction is determined by regression analysis based on the average wind direction over the past 72 hours, combined with building block shading characteristics to correct for deviations, ensuring that the sampling direction is consistent with the main airflow path. After acquisition, wind direction and speed data are segmented and summarized according to a uniform time resolution. The wind direction data within each time period is converted into angle values ​​relative to true north and mapped to vector points on a unit circle in a two-dimensional polar coordinate system. This set of vector points forms a wind direction angle sequence over time, creating a continuous angle trajectory in the time dimension. To establish a wind direction temporal distribution model, a weighted sliding window technique is used to locally smooth the angle sequence, extracting the prevailing wind direction change trend. Simultaneously, wind speed data is superimposed as a weight term to improve sensitivity to wind field disturbance fluctuations. The final generated wind direction time-series distribution model uses time as the horizontal axis and angle as the vertical axis, with an additional wind speed-weighted heatmap, serving as the basic data structure for subsequent calculation of flow direction consistency entropy. This model can be modeled and visualized using MATLAB or Python's SciPy and Matplotlib libraries, possessing high resolution and dynamic scalability.

[0070] S203. Calculate the flow direction consistency entropy value in each time period based on the wind direction time series distribution model. When the rate of change of the flow direction consistency entropy value in a continuous period is greater than the set disturbance judgment threshold, and the dominant wind direction reverses at an angle exceeding the preset offset angle threshold in the region, it is determined that there is an abnormal airflow direction in the airflow trajectory analysis area.

[0071] Calculating the flow direction consistency entropy value within each time period based on a wind direction temporal distribution model, and determining whether the rate of entropy change within a continuous time period exceeds a set disturbance judgment threshold, aims to quantify the stability of airflow direction and capture sudden disturbance phenomena. The flow direction consistency entropy value essentially measures the concentration of wind direction distribution within a certain time period; a lower entropy value indicates a more consistent wind direction, while a higher entropy value indicates a more dispersed or turbulent wind direction. When the rate of entropy change within a continuous time period increases significantly, it usually means that the airflow in the area is subjected to shielding reflection, turbulent recirculation, or other physical disturbances, causing rapid fluctuations in wind direction. If, at this time, the dominant wind direction reverses at an angle within the analysis area, and this reversal angle exceeds a preset offset angle threshold, it further proves that the airflow behavior has deviated from the original ventilation design path. The disturbance judgment threshold is obtained through statistical modeling of entropy change samples under typical historical scenarios. Empirical percentiles are typically used to set the boundaries of the stable interval to ensure high-sensitivity detection. The preset offset angle threshold is determined based on the maximum passage angle between the building's geometry, the prevailing wind direction, and the available ventilation path. This ensures that an anomaly marker is triggered whenever the wind direction deviates from the normal path, effectively identifying backflow risks and ventilation failure hazards caused by obstruction. Using both thresholds together enables accurate identification of airflow anomalies, improving the energy-saving control system's response to disturbances in complex building spaces.

[0072] In this embodiment, in S203, the flow direction consistency entropy value for each time period is calculated based on the wind direction time series distribution model, specifically as follows:

[0073] The wind direction data in each time period of the wind direction time series distribution model is divided into a fixed number of wind direction intervals according to unit angles;

[0074] Dividing wind direction data within each time period in a wind direction time series distribution model into a fixed number of wind direction intervals by unit angle is to discretize the wind direction distribution and facilitate subsequent calculation of the statistical distribution characteristics of wind direction. This can be achieved by dividing a 360-degree circle into several equidistant angle segments. Common settings include 12 30-degree intervals, 24 15-degree intervals, or even more precise 36 10-degree intervals. The specific number of intervals can be adaptively set based on the complexity of the terrain surrounding the building complex and the frequency of wind direction fluctuations. Within each time period, the collected wind direction data is mapped to its corresponding wind direction interval according to its angle, and the counts are accumulated. For example, if a wind direction data point is 73 degrees and the interval is in 10-degree increments, it is classified into the 70-80 degree interval. This method transforms the original continuous wind direction data into a segmented counting structure, which not only helps reduce the interference of high-frequency noise on entropy value judgment but also improves the accuracy and stability of flow direction consistency measurement. This process can be implemented using discretization functions in programming languages, such as the `digitize` function in Python's NumPy library or the `discretize` method in Matlab. Combined with batch data processing operations, efficient real-time computation can be achieved. This operation, as the foundation for entropy calculation, determines the precision and classification rationality of the subsequent probability distribution function construction.

[0075] The frequency of wind direction data within each wind direction interval is counted, and the data is normalized to form a probability distribution function.

[0076] The key process in constructing the probability distribution function is to count the frequency of wind direction data within each wind direction interval and then normalize it. The aim is to transform the discretized wind direction data into a statistically meaningful probability model. Specifically, the process involves: first, classifying and counting all wind direction data according to defined angle intervals, recording the number of occurrences within each interval to form an initial frequency distribution vector. Then, dividing each element of this frequency vector by the total number of wind direction data samples within that time period converts the frequency into a corresponding relative frequency, thus obtaining the probability value for each wind direction interval. Mathematically, this process can be expressed as: if the number of samples within an interval is ni, and the total number of samples is N, then the probability of that interval is pi = ni / N. For example, in a 36 wind direction intervals divided into 10-degree units, if the 9th interval (80~90 degrees) shows wind direction data 45 times, and the total number of samples in that time period is 360, then the probability of that interval is 45 / 360 = 0.125. By combining all interval probability values ​​to form a complete probability distribution function, a probabilistic model of wind direction distribution within that time period is constructed. This probability distribution function serves as the input basis for subsequent calculations of flow direction consistency entropy, accurately reflecting the dispersion and concentration of current wind direction changes. It is commonly used in scenarios involving wind field disturbance identification, ventilation path determination, and abnormal airflow behavior detection. This calculation process can be implemented using the `value_counts(normalize=True)` function in Pandas in Python or frequency vector normalization operations in NumPy.

[0077] The flow direction consistency entropy value is calculated based on the probability distribution function and the Shannon entropy calculation formula to characterize the degree of dispersion of wind direction distribution within this time period.

[0078] This study calculates the wind direction consistency entropy value using the Shannon entropy formula based on the probability distribution function, aiming to quantitatively characterize the dispersion of wind direction data within a specific time period. Shannon entropy, originating from information theory, is a core tool proposed by Claude Shannon for measuring information uncertainty. Its formula is: H = -∑(pi × log2(pi)), where pi represents the probability value of the i-th wind direction interval in the wind direction probability distribution function. The closer each pi is to 1, the more concentrated the wind direction data is in a certain direction, and the lower the overall entropy value; conversely, if all pi are close to a uniform distribution, it indicates a higher degree of wind direction dispersion, and a larger entropy value. For example, if the wind direction is concentrated in the 90° direction within a certain time period, with a probability distribution of {0, 0, 1, 0, ..., 0}, the entropy value is 0; while if the distribution is {0.1, 0.1, ..., 0.1}, the entropy value is log2(n), where n is the number of wind direction intervals. By calculating the entropy value of the wind direction probability distribution function within each time period, the stability and consistency of the current wind direction can be quantified, facilitating the subsequent identification of sudden disturbances or anomalies such as obstruction and backflow. This process can be implemented using the `scipy.stats.entropy` function in Python or by writing custom code. The input is an array of wind direction probability distributions, and the output is the wind direction consistency entropy value corresponding to that time period. This entropy value serves as a key decision parameter in dynamic ventilation control and can be used to perceive the airflow field state within the building space in real time.

[0079] S3. Spatial overlap processing is performed between the spatial shading perception matrix and the abnormal airflow direction area. Based on the heat conduction rate and the stability of the airflow output path within the overlap area, an exhaust vent ventilation and heat dissipation efficiency evaluation index is generated.

[0080] In this embodiment, S3 specifically includes the following steps:

[0081] Spatial overlap processing is performed on the location regions with changes in thermal pressure difference direction and abnormal airflow direction regions in the spatial shading sensing matrix. The two regions are decomposed into voxels using a three-dimensional Euclidean mesh. Spatial registration is performed with the geometric center of each voxel in a unified coordinate system as the reference. The intersection region of the voxel coordinates is extracted to construct a set of thermal pressure disturbance coupled spatial units.

[0082] A 3D Euclidean mesh is a voxelized structure that regularly divides a continuous spatial region into uniformly sized, indexable cubic cells. Each voxel can be precisely located using the 3D coordinates of its geometric center, facilitating spatial registration and overlap calculations. In spatial overlap processing, the 3D Euclidean mesh can provide a consistent structure for spatial data from two independent sources, allowing them to be compared at a uniform spatial scale. Voxel decomposition maps a continuous volume to a set of smaller cubes, each recording spatial attribute information such as shading intensity, thermal pressure gradient, and airflow disturbance level. By comparing the spatial indices of two mesh voxels, overlapping regions in physical space can be efficiently identified, enabling joint modeling of multi-source spatial data.

[0083] To address the spatial overlap between regions exhibiting changes in thermal pressure differential direction and regions with abnormal airflow direction within the spatial shading sensing matrix, two types of regions are first modeled in 3D and a unified coordinate system is established. For example, the boundary surfaces of the corresponding blocks in the spatial shading sensing matrix can be extracted from the building complex BIM model and discretized into a 3D Euclidean mesh composed of 1m³ voxels. Similarly, the abnormal airflow trajectory regions generated based on wind direction disturbances are converted into voxel representations. Under the unified coordinate system, the center point of each voxel is used as the spatial registration reference point, and the coordinate matching relationship of all voxels in the x, y, and z dimensions is calculated. When the geometric center positions of voxels in two regions are completely consistent, the voxel is considered a spatially overlapping region. The set of all voxels satisfying this condition is defined as the set of thermal pressure disturbance coupled spatial units, and the voxels within the set are numbered, serving as the input basis for subsequent thermal-fluid stability analysis. For example, in the northwest corner of the top floor of a building, if the shading analysis results show that this is the center of thermal pressure reversal, and the wind direction data also indicates that there is abnormal backflow, the overlapping area after voxel registration in this area is identified as a key interference area.

[0084] Within the set of thermo-pressure perturbation coupled spatial units, the temperature gradient between adjacent units is calculated based on temperature sensing data, and the heat conduction rate of each unit is calculated using the Fourier heat conduction formula. Furthermore, an airflow output path map between units is constructed based on the wind speed direction sequence, and the path continuity coefficient and velocity stability factor are extracted to construct a joint thermo-fluid stability parameter set.

[0085] In a thermo-pressure-coupled spatial unit set, the thermal conductivity rate is a key parameter for measuring the heat transfer capacity within a region. Calculating the thermal conductivity rate between adjacent voxels requires first acquiring temperature sensing data; the temperature gradient is obtained by dividing the temperature difference between two voxels by their center-to-center distance. The Fourier heat transfer formula describes steady-state heat transfer: q = -k × A × (dT / dx), where q represents the thermal conductivity rate, k is the thermal conductivity of the material, A is the area of ​​the thermal interface, and dT / dx is the temperature gradient. This formula is based on the principle of natural heat diffusion from high-temperature to low-temperature regions and is applicable to calculations of heat transfer between building blocks. The airflow output path map is constructed using a sequence of wind speed directions, its core being the tracking of the continuous path of the dominant wind direction between voxels. The path continuity coefficient indicates whether the wind direction remains consistent between adjacent voxels, while the velocity stability factor assesses the amplitude of wind speed fluctuations. The joint thermo-fluid stability parameter set is a set of indicators that integrates thermal conductivity performance and airflow continuity characteristics, used to evaluate the effectiveness of the exhaust path.

[0086] In the analysis of thermal pressure disturbance in the building complex, real-time temperature data within the thermal pressure coupling area is first collected by temperature sensors placed inside each voxel. The temperature gradient is obtained by dividing the temperature difference between two adjacent voxels by their geometric center distance, and then the heat transfer rate is calculated using the Fourier heat conduction formula. For example, if the temperature difference between the centers of two voxels is 4°C and the distance between them is 1 meter, and the thermal conductivity is taken as 1.7 W / (m·K) for concrete, then the heat flux density is q = -1.7 × A × 4. Simultaneously, wind speed and direction in each voxel are obtained using wind speed sensors, and a voxel connection diagram is constructed. Each edge connects two voxels with a dominant wind direction transmission relationship, and the wind speed change trend is recorded. When the wind direction is continuous and the wind speed fluctuation is small, a higher path continuity coefficient and velocity stability factor are assigned. Taking the voxel of the building's top-floor corridor as an example, if its wind direction is stably pointing towards the exhaust vent and heat can be continuously conducted to that point, its heat transfer parameter score is high, indicating that the ventilation and heat exhaust path is efficient.

[0087] The heat conduction rate and airflow output path stability factor are normalized and assigned dynamic weight factors to highlight the local sensitivity of the shielding area. The local efficiency value of each spatial unit is generated based on the weighted scoring model. The local efficiency values ​​of all thermal pressure disturbance coupled spatial units are summarized to form the ventilation and heat exhaust efficiency evaluation index of the exhaust outlet.

[0088] Heat transfer rate and airflow path stability factor measure the heat transfer efficiency and ventilation path reliability of a space unit, respectively. To achieve standardized evaluation, these two indicators need to be normalized, commonly using linear normalization, which transforms the original values ​​to the [0,1] interval using a min-max normalization method. Dynamic weighting factors are used to assign higher weights to specific areas during the evaluation process. For example, areas with strong shading effects should have a stronger influence on the airflow stability factor, while areas with drastic wind direction changes should have a higher emphasis on heat transfer rate. Dynamic weighting factors can be calculated based on spatial location, meteorological disturbance intensity, or historical disturbance frequency. The weighted scoring model can use a linear weighted sum model, in the form: Local effectiveness value = α × Normalized heat transfer rate + β × Normalized airflow stability factor, where α and β are dynamically adjusted weighting parameters, adaptively determined through contextual thermal disturbance sensitivity. This model can integrate the combined effects of the two parameters on ventilation effectiveness, ensuring evaluation accuracy in complex shading environments.

[0089] In the thermo-pressure coupled spatial units in areas with significant shading interference, the heat conduction rate and airflow path stability factor of each unit are first normalized. For example, if a unit has a heat conduction rate of 0.85 (relatively high among all units) and a wind speed stability factor of 0.60, the normalized values ​​are 0.90 and 0.65, respectively. Based on the historical frequency of thermal disturbances and the intensity of shading in its region, weights α=0.4 and β=0.6 are assigned to reflect the region's greater sensitivity to airflow continuity. The final local efficiency value is calculated as 0.4×0.90 + 0.6×0.65 = 0.75. This process is repeated for all thermo-pressure coupled spatial units, and their local efficiency values ​​are weighted averaged or summed to form the ventilation and heat dissipation efficiency evaluation index for the region's exhaust vents. If the final evaluation index is significantly lower than the normal threshold, it can be determined that the exhaust channel has failed due to shading and airflow reversal, and the ventilation strategy should be adjusted. This method enables intelligent evaluation of complex shading interference areas, avoiding the misjudgment of ventilation failure as successful ventilation.

[0090] S4. Based on the ventilation and heat dissipation efficiency evaluation index of the exhaust vent, construct an exhaust response condition matrix that includes space shading parameters, airflow disturbance level and heat retention level, and generate a ventilation control judgment sequence.

[0091] In this embodiment, S4 specifically refers to:

[0092] S401. The ventilation and heat dissipation efficiency evaluation index of the exhaust vent is normalized and segmented, and the corresponding spatial shading parameters, airflow disturbance level and heat retention level are extracted based on the spatial unit location correlation, and assigned to the three-dimensional label vector to form the initial response factor set.

[0093] S402. Based on the combination relationship of the three types of level indicators in the initial response factor set, establish a multi-dimensional matrix mapping structure and construct an exhaust response condition matrix containing spatial shading parameters, airflow disturbance level and thermal retention level. Each element in the matrix corresponds to a unique level combination configuration.

[0094] S403. Based on the matching rules between each level combination in the exhaust response condition matrix and the exhaust control behavior, a ventilation control judgment sequence is generated according to the preset control threshold mapping logic. The ventilation behavior control instructions output by the ventilation control judgment sequence are used for subsequent exhaust control execution.

[0095] The generation of ventilation control decision sequences is based on the exhaust response condition matrix. A mapping mechanism is formed by establishing matching rules between each level combination and the corresponding exhaust control behavior. The matching rules are defined through expert system modeling, historical control data training, or scenario feedback based on simulated environments to ensure that the most suitable ventilation response strategy is selected under different combinations of thermal and wind disturbances. For example, a combination of high spatial shading parameter level, severe airflow disturbance level, and persistently high heat retention level will be mapped to "closing the exhaust vents and enabling alternative heat removal channels," while in a scenario with slight shading impact but moderate heat retention level, it may trigger "opening some exhaust vents to enhance local heat removal." This mapping relationship is encoded in the form of a rule table or conditional logic tree, and the system can quickly match and output control behaviors in the exhaust response condition matrix based on real-time monitoring data labels.

[0096] The preset control threshold mapping logic is used to refine the dynamic response relationship between level combinations and exhaust behavior. This logic not only limits which behavior should be triggered under which combination of conditions, but also allows for setting gradual strategies and fault-tolerant mechanisms for trigger thresholds. For example, when the airflow disturbance level critically fluctuates between medium and high, it can be configured to trigger a strategy of completely closing the exhaust vent only after the heat retention level has been continuously higher than a certain duration, in order to avoid frequent control switching caused by short-cycle fluctuations. This logic is implemented in the control system using state transition functions, condition matching trees, or fuzzy logic mapping, which ensures both response stability and improves the system's adaptability to complex dynamic environments. The final generated ventilation control decision sequence is a time-sequential set of control instructions, supporting linked execution, priority scheduling, and feedback closed-loop correction, used to subsequently drive the exhaust device to complete zonal response, energy-saving switching, or alternative ventilation strategy execution.

[0097] In this embodiment, S401 specifically refers to:

[0098] The ventilation and heat dissipation efficiency evaluation index of the exhaust vents is subjected to minimum-maximum normalization within the set upper and lower limit range, and divided into multiple discrete level segments according to the equal interval method, which are then mapped to each spatial unit in the building complex.

[0099] The ventilation and heat dissipation efficiency evaluation index of the exhaust vents is normalized to a minimum-maximum range within a set upper and lower limit interval. The purpose is to standardize the original index values ​​to a unified numerical range for subsequent grading and comparison. This process typically constructs a normalization mapping range based on the historical maximum and minimum values ​​of the actual evaluation index, ensuring uniform comparability of evaluation values ​​between different spatial units. After normalization, the numerical range is graded using the equal-interval method. The equal-interval method is a discretization method that uniformly divides the normalized continuous numerical range into several sub-intervals. Its core idea is to divide the index space into several grade segments with a fixed interval step size, each segment corresponding to a specific discrete grade. For example, when the normalization result is limited to between 0 and 1, the equal-interval method can divide it into five grade segments, corresponding to five grade labels: extremely low, relatively low, medium, relatively high, and extremely high. Each spatial unit is automatically classified into the corresponding grade segment based on its normalized value. Through this division process, each spatial unit in the building complex can be assigned a clear discrete level, thereby realizing the structural expression of the ventilation and heat dissipation efficiency evaluation index of the exhaust vents on a spatial scale, laying the foundation for the subsequent construction of the ventilation control matrix.

[0100] The spatial shielding parameters are calculated based on the relative height, closure angle, and azimuth angle between the spatial unit and the shielding source block; the airflow disturbance level is generated by combining the wind direction reversal probability and the rate of change of the flow direction consistency entropy value; and the heat retention level is calculated by combining the heat conduction rate and the heat accumulation duration.

[0101] Spatial shading parameters are calculated by analyzing the three-dimensional spatial relationship between each spatial unit and its corresponding shading source block. Relative height measures the vertical shading intensity of the block above the unit, closure angle assesses the degree of visual obstruction between the two blocks in their planar projection, and azimuth angle reflects the spatial orientation influence of the shading source block relative to the main facade of the spatial unit. Quantifying these three dimensions allows for the construction of a multi-factor weighted model to express the overall shading level. Airflow disturbance level is determined by the probability of wind direction reversal and the rate of change of flow direction consistency entropy within the airflow trajectory analysis area. The probability of wind direction reversal represents the frequency of a 180-degree reversal in wind direction per unit time, while the rate of change of flow direction consistency entropy measures the disturbance trend of local airflow direction stability. By setting disturbance level classification criteria, disturbance levels are divided into high, medium, and low levels. Thermal retention level is evaluated based on a composite assessment of the thermal conduction rate and heat accumulation duration within each spatial unit. Spatial units with low thermal conduction rates and long heat accumulation durations are marked as high thermal retention areas. Taking a unit at the top of a high-rise building as an example, if it is blocked by a taller block above it, with a positive relative height, a closure angle close to 90 degrees, and an azimuth angle less than 30 degrees, it is judged to have significant shading. At the same time, if the wind direction of this unit reverses frequently and the flow entropy value fluctuates significantly in the past two hours, the disturbance level is judged to be high. If the temperature remains for a long time without a clear diffusion path, its heat retention level is also high, thus it is identified as a high-risk ventilation control point in the spatial response analysis.

[0102] The spatial occlusion parameters, airflow disturbance level, and thermal retention level are assigned as the respective dimensional components of the three-dimensional label vector, and then combined and stored according to the spatial unit coordinate index to form an initial response factor set.

[0103] After spatial shading parameters, airflow disturbance levels, and thermal retention levels are mapped to discrete numerical labels, they can be sequentially assigned as the three independent dimensional components of a three-dimensional label vector. For example, spatial shading parameters can be divided into five levels (0-4), airflow disturbance levels into three levels (0-2), and thermal retention levels into four levels (0-3), thus forming a three-dimensional vector structure with unique combinational meaning. For each spatial unit within the building complex, its three-dimensional coordinates are indexed in a unified spatial grid system as the primary key for combined storage, and the corresponding three-dimensional label vector is bound to it, thus forming a structured data set. The entire initial response factor set construction process is based on spatial mapping and label aggregation mechanisms, and sparse tensor or multidimensional array structures can be used to achieve data organization and fast retrieval. Each response factor in this set is indexed by spatial coordinates and its value is a three-dimensional vector, possessing spatial uniqueness and level combination discrimination capabilities. For example, a spatial unit at the top of a building, designated A-12-5, has a spatial shading parameter of 3, an airflow disturbance level of 2, and a heat retention level of 1. Its label vector is [3,2,1]. This vector, bound to the spatial coordinates, is written into the initial response factor set for subsequent construction of the exhaust response condition matrix and ventilation control decision sequence. This method ensures that the environmental state of each spatial point is uniformly modeled and accurately participates in the ventilation control logic.

[0104] In this embodiment, S402 specifically refers to:

[0105] The spatial shading parameter level set, airflow disturbance level set, and thermal retention level set are each mapped to an independent one-dimensional integer index axis, and an axial index system is defined to construct the three-dimensional matrix mapping structure.

[0106] The spatial oppression parameter level set, airflow disturbance level set, and thermal retention level set each contain multiple discretized level labels. For example, the spatial oppression parameter might consist of five levels, the airflow disturbance level three levels, and the thermal retention level four levels. To uniquely identify each level combination in the data structure, each level set needs to be mapped to a one-dimensional integer index axis, where each level corresponds to a unique integer index number. For example, spatial oppression parameter levels 0 to 4 are mapped to positions 0 to 4 on index axis X, airflow disturbance levels 0 to 2 to positions 0 to 2 on index axis Y, and thermal retention levels 0 to 3 to positions 0 to 3 on index axis Z. The three types of indicators occupy the three coordinate axes of the three-dimensional matrix, respectively. In this way, a three-dimensional matrix mapping structure can be established, where each three-dimensional coordinate point represents a combination configuration of spatial oppression parameter level, airflow disturbance level, and thermal retention level, and has a unique index address. This index system not only facilitates rapid matching of ventilation control rules but also provides a data structure foundation for rapid retrieval of response configurations and automatic mapping of level combinations. This mapping structure can be implemented based on a three-dimensional array, a sparse matrix, or a hash mapping. It features a clear structure, high operational efficiency, and strong scalability, making it suitable for complex control scenarios under dynamic changes in the building complex environment.

[0107] The spatial obstruction parameter level, airflow disturbance level and thermal retention level of each group of response factors in the initial response factor set are input into the three-dimensional matrix structure as three-dimensional coordinates, and a unique identifier value is assigned to the corresponding coordinate unit for response configuration indexing.

[0108] Each initial response factor consists of three dimensions, corresponding to the spatial shading parameter level, airflow disturbance level, and thermal retention level, respectively. The level value of each dimension has been mapped to an integer index value. These three index values ​​are treated as three-dimensional coordinate points and input into a predefined three-dimensional matrix structure, thereby accurately locating a unique position unit in the matrix. This position unit represents a specific combination of response factors, and its coordinates are unique, which can be used to accurately associate and retrieve the corresponding ventilation control rules. A response configuration identifier value is assigned to this coordinate unit to encode the control behavior label of the current level combination. This identifier value can be a preset ventilation command code, control mode index, or priority factor. This method forms an efficient data indexing mechanism through a static mapping between spatial level combinations and behavioral responses, enabling rapid level identification and control strategy linkage under complex environmental parameter fluctuations. This three-dimensional matrix structure can be implemented using a multidimensional array. In actual deployment, it can be combined with a key-value mapping table to improve access efficiency, and an index caching mechanism can improve the real-time performance and stability of the control response. The entire process relies on a multidimensional label structure and matrix mapping mechanism to enhance the ventilation control system's ability to respond precisely to building shading disturbances and their thermal effects.

[0109] Traverse all three-dimensional coordinate combinations to construct an exhaust response condition matrix, and explicitly map each cell element in the exhaust response condition matrix to a unique combination of space shading parameter level, airflow disturbance level, and thermal retention level configuration for use in the generation and retrieval of subsequent ventilation control judgment sequences.

[0110] The process of constructing the exhaust response condition matrix requires traversing all possible three-dimensional combinations of spatial shading parameter levels, airflow disturbance levels, and thermal retention levels. Each level combination is mapped to a unique cell position in the matrix structure, with each cell representing a specific environmental state combination configuration. The three-dimensional coordinate axis is formed by the Cartesian product of the spatial shading parameter level set, airflow disturbance level set, and thermal retention level set. The traversal process is completed in a nested loop manner to ensure that all possible combinations are enumerated and mapped. In the matrix, each cell element is precisely bound to a unique level combination through the index value corresponding to its three-dimensional coordinates. This binding relationship is implemented in a table-driven manner during the initialization phase, and each combination is associated with a specific ventilation control strategy or instruction through the response rule configuration file. This matrix structure is used as a core index library during operation. When actual monitoring values ​​generate new level label combinations, the system can quickly locate the corresponding matrix cell and then retrieve the preset control response. The entire matrix structure is implemented using sparse storage or a three-dimensional array structure. Combined with bitmap identification and caching mechanisms, it can significantly improve the efficiency of control logic matching and system response speed, while also having good scalability, making it easy to introduce more control factors or adjust the level granularity in the future.

[0111] S5. Based on the control instructions in the ventilation control judgment sequence, perform dynamic adjustment of the exhaust vent opening status, and trigger corresponding ventilation behaviors or alternative energy-saving strategies according to different states, while recording the ventilation control data to the space disturbance dataset.

[0112] In this embodiment, S5 specifically refers to:

[0113] The exhaust control execution unit is driven by the control commands in the ventilation control decision sequence. The opening angle and opening duration of the exhaust vents are dynamically adjusted by the opening degree adjustment mechanism. An exhaust vent opening state mapping model covering the building space area is established to represent the ventilation capacity boundary of each exhaust vent in real time.

[0114] The control commands in the ventilation control decision sequence establish a communication channel with the exhaust control execution unit through the logic parsing module, driving the exhaust control execution unit to electrically open and close the exhaust vents. During this process, the opening adjustment mechanism finely adjusts the opening angle and duration of the exhaust vents based on the exhaust intensity level and duration parameters included in the control commands. To uniformly manage the operating status of multiple exhaust vents, the exhaust equipment can be spatially labeled using a 3D building model. Combined with the current actual opening status and time information, a mapping model of the exhaust vent opening status covering the entire building space can be constructed. This model can dynamically track the operating status boundaries of each exhaust vent on the time axis, thus reflecting the distribution of ventilation capacity at different locations within the building over a specific time period. For example, in a high-rise building block, when the control command determines low heat dissipation efficiency, the corresponding exhaust vent in the mapping model will display "low opening + short duration," triggering the subsequent alternative strategy response module to intervene.

[0115] The exhaust control execution unit consists of electric actuators connected to the building automation control system. It receives precise numerical commands from the control sequence to control the opening and closing of exhaust vents. The opening adjustment mechanism is based on exhaust level parameters, typically divided into multiple discrete levels, such as 0%, 25%, 50%, 75%, and 100%, each corresponding to a set of estimated ventilation capacity values. The opening duration is synchronously managed by the system's timing module, ensuring that exhaust behaviors at different locations do not interfere with each other and achieve coordinated heat dissipation. The exhaust vent opening state mapping model is constructed based on Building Information Modeling (BIM) or a GIS spatial information system. The mapping data structure includes the exhaust vent's spatial location, opening percentage, opening and closing time, and corresponding control sequence number to support subsequent visualization analysis, predictive modeling, and energy strategy optimization. This structure enables dynamic representation of ventilation capacity boundaries across time and space dimensions, serving as a crucial foundation for control efficiency and strategy adaptation.

[0116] Based on the linkage analysis of the state field of the exhaust vent opening state mapping model and the ventilation control judgment sequence, a ventilation behavior instruction matching the current state is triggered. When the current state is marked as a high-risk level of thermal pressure disturbance in the judgment sequence, an alternative energy-saving strategy is switched to be executed, including switching to a central exhaust system, starting a heat exchange recovery mechanism, or delaying the exhaust response strategy.

[0117] A linkage and analysis mechanism is established between the exhaust vent opening status mapping model and the ventilation control judgment sequence to achieve dynamic response and strategy adaptation of the control logic. This mechanism matches the opening status and opening / closing time of the current exhaust vent in the mapping model, and links the corresponding status field values ​​in the judgment sequence, such as ventilation intensity level, airflow disturbance level, and heat retention level. When the analysis finds that the current state corresponds to a high thermal pressure disturbance risk level, the system will trigger the strategy diversion module, terminate the output of the regular ventilation behavior command, and instead execute the backup ventilation strategy or energy-saving mechanism. Specifically, conditional trigger statements can be set in the control system through programming logic, such as "when the state is marked as high risk level and ventilation is ineffective for three consecutive cycles, the heat recovery device will be activated." Taking a specific implementation as an example, in the south area of ​​a high-rise building, if strong shading occurs and is accompanied by airflow reversal during a continuous monitoring period, the exhaust vents in this area are marked as low ventilation efficiency in the mapping model and high risk in the judgment sequence. At this time, the system will automatically activate the central exhaust unit or the delayed opening strategy to alleviate the ventilation load.

[0118] The exhaust vent opening status mapping model is a multi-dimensional data structure that records the current spatial location, opening angle, opening duration, and opening / closing timestamp of each exhaust vent, used to reflect the exhaust behavior boundaries of various areas of the building in real time. The status fields in the ventilation control judgment sequence include the combination of three-dimensional label vectors corresponding to the exhaust response condition matrix and their matching strategy weights. During the linkage analysis process, the status fields are matched with the current data items in the mapping model to drive the execution module to output specific ventilation behaviors. When thermal pressure disturbances are determined to be of a high-risk level, the alternative energy-saving strategy module will select a strategy path based on energy-saving priorities, including activating the centrally located exhaust system to break through the ventilation blind spots caused by pressure blockages, activating the heat exchange device to recover energy from the high-temperature exhaust gas, or using a dynamic scheduling algorithm to temporarily suspend exhaust behavior to prevent further reduction in local heat dissipation efficiency. This design enhances the system's intelligent adaptability under complex aerodynamic disturbances.

[0119] Key data such as the execution time of ventilation behavior or alternative energy-saving strategies, changes in exhaust vent opening, response type, and ventilation control decision sequence number are written into the spatial disturbance dataset in a unified structure format. The disturbance type index is marked with the spatial coordinates of the exhaust vent as the primary key, which is used for subsequent source tracing, modeling, and optimization learning of ventilation control behavior.

[0120] Ventilation behavior or alternative energy-saving strategies involve several key variables during execution, including the execution time period, dynamic changes in exhaust vent opening, triggered response types, and ventilation control decision sequence numbers used for triggering. Recording these data items uniformly in a spatial disturbance dataset requires standardization using a structured format, such as a field-oriented data table structure. Each record uses the three-dimensional spatial coordinates of the exhaust vent as the primary key index, and additional fields indicate the specific disturbance type and response mechanism. This approach helps to quickly locate the historical behavioral trajectory of the ventilation response area in subsequent system evaluations, enabling causal tracing and optimization modeling of exhaust behavior. For example, when a persistent high thermal pressure disturbance occurs in the northern area of ​​a building, system records show that the exhaust vents in that area implemented a delayed exhaust and heat exchange mechanism during a certain period. The ventilation control decision sequence number differs from that of adjacent areas. The system can track the effect of this specific response pattern through the structured records of the spatial disturbance dataset, thus providing a reference sample for response strategies in similar future disturbance scenarios.

[0121] The spatial disturbance dataset employs a spatial coordinate-based indexing method to achieve spatial management of exhaust control behavior. Execution time records reflect the timeliness of response strategies, changes in exhaust vent opening reflect the physical behavior of the exhaust control unit, response types distinguish the switching logic between conventional ventilation and alternative energy-saving mechanisms, and ventilation control decision sequence numbers serve as the logical basis, linking the upstream decision mechanism with the downstream behavioral response. The application of a unified structural format ensures that the dataset can be directly read and processed by machine learning modules or optimization algorithms, providing a data foundation for the intelligent evolution of the ventilation control system. Through this mechanism, the system can not only identify control behavior patterns under different shading and airflow disturbance scenarios, but also train predictive models based on historical data accumulation to adjust future exhaust behavior strategies in advance to avoid thermal pressure interference risks.

[0122] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0123] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0124] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0128] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy-saving control method based on the spatial morphology of building complexes, characterized in that, Specifically, the following steps are included: S1. Obtain the three-dimensional spatial block structure information and real-time meteorological parameters of the building complex, establish a spatial shading perception matrix, extract the spatial closure angle relationship between building blocks, and determine whether there are shading interference conditions that cause changes in the direction of thermal pressure difference. S2. Based on the region in the spatial shading perception matrix where the thermal pressure difference direction changes, construct an airflow trajectory analysis region, collect continuous wind direction and wind speed data, and determine whether there is an abnormal airflow direction by the change in the flow direction consistency entropy value. S3. Spatial overlap processing is performed between the spatial shading perception matrix and the abnormal airflow direction area. Based on the heat conduction rate and the stability of the airflow output path within the overlap area, an exhaust vent ventilation and heat dissipation efficiency evaluation index is generated. S4. Based on the ventilation and heat dissipation efficiency evaluation index of the exhaust vent, construct an exhaust response condition matrix that includes space shading parameters, airflow disturbance level and heat retention level, and generate a ventilation control judgment sequence. S5. Based on the control instructions in the ventilation control judgment sequence, perform dynamic adjustment of the exhaust vent opening status, and trigger corresponding ventilation behaviors or alternative energy-saving strategies according to different states, while recording the ventilation control data to the space disturbance dataset.

2. The energy-saving control method based on the spatial morphology of building complexes according to claim 1, characterized in that, S1 specifically refers to: Acquire three-dimensional spatial block structure information and real-time meteorological parameters of the building complex. The three-dimensional spatial block structure information includes the height data, boundary outline data and facade orientation data of the building blocks. The real-time meteorological parameters include solar azimuth angle, solar altitude angle and wind direction data. Based on the three-dimensional spatial block structure information of the building complex, a block ray projection model is constructed. A ray tracing algorithm is used to simulate the shadow superposition area of ​​each block under the current solar azimuth angle and form the spatial occlusion relationship between the blocks. Based on the spatial occlusion relationship between building blocks, the spatial closure angle relationship between building blocks is extracted. The sum of the visible angles of each block in multiple directions is calculated using the included angle integral method, and a spatial closure angle distribution matrix is ​​generated. The analysis is performed by coupling the spatial closure angle distribution matrix with the wind direction change trend in real-time meteorological parameters to determine whether spatial shading causes a change in the direction of thermal pressure difference. The determination method is as follows: when the rate of change of the spatial closure angle is greater than the preset change threshold, and the angle between the wind direction and the maximum shading of the closure area is less than the set interference angle threshold, it is marked as having shading interference conditions.

3. The energy-saving control method based on the spatial morphology of building complexes according to claim 1, characterized in that, S2 specifically includes the following steps: S201. Based on the region in the spatial shading perception matrix where the thermal pressure difference direction changes, taking the center of the thermal pressure difference gradient change in this region as the starting point, and combining the relative height difference and orientation relationship between building blocks, an airflow trajectory analysis region containing multiple horizontal and vertical sections is constructed. S202. Arrange wind direction and wind speed acquisition units along the prevailing wind direction in the airflow trajectory analysis area, collect wind direction and wind speed data in multiple consecutive time periods, map the data into an angle sequence on the unit circle according to the wind direction change trend, and establish a wind direction time series distribution model. S203. Calculate the flow direction consistency entropy value in each time period based on the wind direction time series distribution model. When the rate of change of the flow direction consistency entropy value in a continuous period is greater than the set disturbance judgment threshold, and the dominant wind direction reverses at an angle exceeding the preset offset angle threshold in the region, it is determined that there is an abnormal airflow direction in the airflow trajectory analysis area.

4. The energy-saving control method based on the spatial morphology of building complexes according to claim 3, characterized in that, In S203, the flow direction consistency entropy value for each time period is calculated based on the wind direction temporal distribution model, specifically as follows: The wind direction data in each time period of the wind direction time series distribution model is divided into a fixed number of wind direction intervals according to unit angles; The frequency of wind direction data within each wind direction interval is counted, and the data is normalized to form a probability distribution function. The flow direction consistency entropy value is calculated based on the probability distribution function and the Shannon entropy calculation formula to characterize the degree of dispersion of wind direction distribution within this time period.

5. The energy-saving control method based on the spatial morphology of building complexes according to claim 1, characterized in that, S3 specifically includes the following steps: Spatial overlap processing is performed on the location regions with changes in thermal pressure difference direction and abnormal airflow direction regions in the spatial shading sensing matrix. The two regions are decomposed into voxels using a three-dimensional Euclidean mesh. Spatial registration is performed with the geometric center of each voxel in a unified coordinate system as the reference. The intersection region of the voxel coordinates is extracted to construct a set of thermal pressure disturbance coupled spatial units. Within the set of thermo-pressure perturbation coupled spatial units, the temperature gradient between adjacent units is calculated based on temperature sensing data, and the heat conduction rate of each unit is calculated using the Fourier heat conduction formula. Furthermore, an airflow output path map between units is constructed based on the wind speed direction sequence, and the path continuity coefficient and velocity stability factor are extracted to construct a joint thermo-fluid stability parameter set. The heat conduction rate and airflow output path stability factor are normalized and assigned dynamic weight factors to highlight the local sensitivity of the shielding area. The local efficiency value of each spatial unit is generated based on the weighted scoring model. The local efficiency values ​​of all thermal pressure disturbance coupled spatial units are summarized to form the ventilation and heat exhaust efficiency evaluation index of the exhaust outlet.

6. The energy-saving control method based on the spatial morphology of building complexes according to claim 1, characterized in that, S4 specifically refers to: S401. The ventilation and heat dissipation efficiency evaluation index of the exhaust vent is normalized and segmented, and the corresponding spatial shading parameters, airflow disturbance level and heat retention level are extracted based on the spatial unit location correlation, and assigned to the three-dimensional label vector to form the initial response factor set. S402. Based on the combination relationship of the three types of level indicators in the initial response factor set, establish a multi-dimensional matrix mapping structure and construct an exhaust response condition matrix containing spatial shading parameters, airflow disturbance level and thermal retention level. Each element in the matrix corresponds to a unique level combination configuration. S403. Based on the matching rules between each level combination in the exhaust response condition matrix and the exhaust control behavior, a ventilation control judgment sequence is generated according to the preset control threshold mapping logic. The ventilation behavior control instructions output by the ventilation control judgment sequence are used for subsequent exhaust control execution.

7. The energy-saving control method based on the spatial morphology of building complexes according to claim 6, characterized in that, S401 specifically refers to: The ventilation and heat dissipation efficiency evaluation index of the exhaust vents is subjected to minimum-maximum normalization within the set upper and lower limit range, and divided into multiple discrete level segments according to the equal interval method, which are then mapped to each spatial unit in the building complex. The spatial shielding parameters are calculated based on the relative height, closure angle, and azimuth angle between the spatial unit and the shielding source block; the airflow disturbance level is generated by combining the wind direction reversal probability and the rate of change of the flow direction consistency entropy value; and the heat retention level is calculated by combining the heat conduction rate and the heat accumulation duration. The spatial occlusion parameters, airflow disturbance level, and thermal retention level are assigned as the respective dimensional components of the three-dimensional label vector, and then combined and stored according to the spatial unit coordinate index to form an initial response factor set.

8. The energy-saving control method based on the spatial morphology of building complexes according to claim 6, characterized in that, S402 specifically refers to: The spatial shading parameter level set, airflow disturbance level set, and thermal retention level set are each mapped to an independent one-dimensional integer index axis, and an axial index system is defined to construct the three-dimensional matrix mapping structure. The spatial obstruction parameter level, airflow disturbance level and thermal retention level of each group of response factors in the initial response factor set are input into the three-dimensional matrix structure as three-dimensional coordinates, and a unique identifier value is assigned to the corresponding coordinate unit for response configuration indexing. Traverse all three-dimensional coordinate combinations to construct an exhaust response condition matrix, and explicitly map each cell element in the exhaust response condition matrix to a unique combination of space shading parameter level, airflow disturbance level, and thermal retention level configuration for use in the generation and retrieval of subsequent ventilation control judgment sequences.

9. The energy-saving control method based on the spatial morphology of building complexes according to claim 1, characterized in that, S5 specifically refers to: The exhaust control execution unit is driven by the control commands in the ventilation control decision sequence. The opening angle and opening duration of the exhaust vents are dynamically adjusted by the opening degree adjustment mechanism. An exhaust vent opening state mapping model covering the building space area is established to represent the ventilation capacity boundary of each exhaust vent in real time. Based on the linkage analysis of the state field of the exhaust vent opening state mapping model and the ventilation control judgment sequence, a ventilation behavior instruction matching the current state is triggered. When the current state is marked as a high-risk level of thermal pressure disturbance in the judgment sequence, an alternative energy-saving strategy is switched to be executed, including switching to a central exhaust system, starting a heat exchange recovery mechanism, or delaying the exhaust response strategy. Key data such as the execution time of ventilation behavior or alternative energy-saving strategies, changes in exhaust vent opening, response type, and ventilation control decision sequence number are written into the spatial disturbance dataset in a unified structure format. The disturbance type index is marked with the spatial coordinates of the exhaust vent as the primary key, which is used for subsequent source tracing, modeling, and optimization learning of ventilation control behavior.

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