Cold and hot channel simulation analysis method and system based on BIM and CFD

Abstract

The embodiment of the invention discloses a cold and hot channel simulation analysis method and system based on BIM and CFD, and the method comprises the steps: obtaining a building structure data set of a target building area, generating a BIM building model based on the building structure data set, and the BIM building model comprises the equipment distribution information and spatial topological relation of the target building area; performing channel division processing on the BIM building model to generate an initial layout scheme of a cold and hot channel, and performing fluid dynamic analysis based on the initial layout scheme to simulate an air flow state to obtain fluid distribution data of the cold and hot channel; performing coupling analysis on the BIM building model and the fluid distribution data to generate an air leakage state prediction result and a temperature distribution prediction result of the cold and hot channel; and generating a layout optimization strategy based on the air leakage state prediction result and the temperature distribution prediction result, and adjusting channel layout information in the initial layout scheme according to the layout optimization strategy to obtain a target layout scheme.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular, to a method and system for simulating and analyzing hot and cold channels based on BIM and CFD. Background Art

[0002] With the development of the construction industry, BIM (Building Information Modeling) and CFD (Computational Fluid Dynamics) technologies have been widely used in building design and analysis. The BIM technology can integrate various aspects of building information to construct a three-dimensional digital model, visually presenting the building structure, equipment distribution, etc.; the CFD technology focuses on the numerical simulation and analysis of fluid flow phenomena. The emergence of these two technologies provides a more scientific and accurate method for building design, enabling designers to evaluate building performance from different perspectives.

[0003] However, in the prior art, although the BIM and CFD technologies play their respective roles, there is a lack of effective combination and coordination between them. When dealing with problems such as the layout of hot and cold channels inside a building, usually only one of the technologies is used for analysis separately. When using the BIM technology alone, it is difficult to deeply analyze fluid-related problems such as air flow; while relying solely on the CFD technology, there is a lack of comprehensive building structure and equipment information support. This separated analysis method leads to an incomplete understanding of the complex conditions inside the building, making it difficult to formulate a scientific and reasonable optimization strategy for the layout of hot and cold channels. Summary of the Invention

[0004] The embodiments of the present invention provide a method and system for simulating and analyzing hot and cold channels based on BIM and CFD, which are used to effectively improve the rationality of the hot and cold channel layout, optimize the air flow and temperature distribution inside the building, and improve the overall functionality and comfort of the building.

[0005] In a first aspect, the embodiments of the present invention provide a method for simulating and analyzing hot and cold channels based on BIM and CFD, which is applied to a hot and cold channel simulation analysis system. The method includes: obtaining a set of building structure data of a target building area, generating a BIM building model based on the set of building structure data, where the BIM building model includes equipment distribution information and spatial topological relationships of the target building area; performing channel division processing on the BIM building model to generate an initial layout plan for hot and cold channels, performing fluid dynamics analysis based on the initial layout plan to simulate the air flow state, and obtaining fluid distribution data of the hot and cold channels; performing coupling analysis on the BIM building model and the fluid distribution data to generate a prediction result of the air leakage state and a prediction result of the temperature distribution of the hot and cold channels; generating a layout optimization strategy based on the prediction result of the air leakage state and the prediction result of the temperature distribution, and adjusting the channel layout information in the initial layout plan according to the layout optimization strategy to obtain a target layout plan.

[0006] In a second aspect, an embodiment of the present invention provides a hot and cold channel simulation analysis system, including: A processor; A storage device storing a computer program thereon, When the computer program is executed by the processor, the processor implements any one of the hot and cold channel simulation analysis methods based on BIM and CFD.

[0007] An embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the hot and cold channel simulation analysis method based on BIM and CFD are implemented.

[0008] Thus, the embodiments of the present invention have the following beneficial effects: First, by obtaining a set of building structure data to generate a BIM building model including equipment distribution information and spatial topological relationships, and performing channel division on the BIM building model and combining fluid dynamics analysis, the air flow state can be simulated and fluid distribution data can be obtained; Second, by coupling and analyzing the BIM building model and the fluid distribution data, the air leakage state and temperature distribution can be predicted, and through cross-domain data fusion analysis, it provides strong support for accurately grasping the hot and cold channel conditions; Then, based on the prediction results, a layout optimization strategy is generated and the channel layout is adjusted to obtain a target layout plan, which effectively improves the rationality of the hot and cold channel layout, optimizes the air flow and temperature distribution inside the building, improves the overall functionality and comfort of the building, and realizes a more efficient and scientific building layout planning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a flowchart of a hot and cold channel simulation analysis method based on BIM and CFD provided by an embodiment of the present invention.

[0010] Figure 2 It is a schematic diagram of the basic structure of a hot and cold channel simulation analysis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] To make the above objects, features, and advantages of the present invention more obvious and understandable, the embodiments of the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0012] Referring to Figure 1 as shown, this figure is a flowchart of a hot and cold channel simulation analysis method based on BIM and CFD provided by an embodiment of the present invention, and this method can be applied to a hot and cold channel simulation analysis system. As Figure 1 shown, this method includes steps 110 - step 140.

[0013] Step 110: Obtain the building structure data set of the target building area, and generate a BIM building model based on the building structure data set. The BIM building model includes the equipment distribution information and spatial topological relationship of the target building area.

[0014] In the embodiment of the present invention, take the computer room area of a large data center as an example of the target building area. First, through existing building data collection tools, obtain the building structure data set of this computer room area. The above data covers the structure information such as the walls, floors, and beams of the computer room, as well as the position information of various equipment such as server cabinets and air conditioning units. Then, use BIM modeling software to start generating a BIM building model based on the above building structure data set. During the modeling process, the data can be analyzed and processed through the BIM modeling software, accurately integrating the position information of the equipment into the model, and at the same time constructing the spatial topological relationship between the equipment, such as determining which equipment is connected by a channel and the direction of the channel. The generated BIM building model completely includes the equipment distribution information and spatial topological relationship of the computer room area.

[0015] As an alternative embodiment, generating the BIM building model based on the building structure data set in step 110 further includes: Step 111: Perform geometric analysis and processing on the building structure data set, and extract the structural contour features and equipment position coordinates of the target building area.

[0016] In the application scenario of the computer room of this data center, the collected building structure data set can be analyzed one by one through geometric analysis and processing. For the structural parts such as the walls and floors of the computer room, according to the geometric description in the data, the structural contour features can be extracted, such as the length, height, angle, etc. of the walls, so as to determine the overall geometric shape of the computer room. For the extraction of equipment position coordinates, the equipment-related information can be screened from the data to accurately determine the specific coordinate positions of each server cabinet, air conditioning unit, etc. in the computer room space. For example, the coordinates of server cabinet A in the computer room coordinate system are (x1, y1, z1), and the coordinates of air conditioning unit B are (x2, y2, z2), etc. The above coordinate information accurately locates the position of the equipment in the computer room.

[0017] Step 112: Generate a spatial grid model of the target building area based on the structural contour features, and mark the equipment identifiers corresponding to the equipment position coordinates in the spatial grid model.

[0018] When generating a spatial grid model, according to the structural contour features extracted previously, the space of the computer room is divided into small grid cells by using a grid generation algorithm. Taking the data center computer room as an example, the size of the above grid cells can be set according to the actual situation and the requirements of analysis accuracy. For example, it can be set as a cubic grid cell with a side length of ds meters. Then, in this spatial grid model, according to the device position coordinates extracted previously, the identifier corresponding to each device is marked at the corresponding grid cell position. For example, the identifier corresponding to server cabinet A is "S1", so "S1" is marked on the grid cell where it is located. The identifier corresponding to air conditioner unit B is "K1", and "K1" is also marked in its corresponding grid cell. Thus, the positions of various devices can be identified in the spatial grid model.

[0019] Step 113: Determine the heat dissipation information set of the target building area according to the device attribute data associated with the device identifier. The heat dissipation information set includes device power data, heat dissipation efficiency data, and heat radiation range data.

[0020] In the embodiment of the present invention, each device has its corresponding device attribute data stored in a preset database. When the device identifier is obtained, through database query operations, the attribute data of the corresponding device is associated according to the identifier. Taking the server cabinet as an example, the device power data of server cabinet S1 can be obtained by querying as P1 watts, the heat dissipation efficiency data is E1 (indicating the ratio of the heat dissipated by the device per unit time to the power consumed by the device), and the heat radiation range data can be determined by technical means such as thermal imaging analysis as a spherical area with a radius of R1 meters centered on the cabinet. Similarly, for air conditioner unit K1, its corresponding device power data P2, heat dissipation efficiency data E2, and heat radiation range data (such as the shape and range centered on the air conditioner outlet) can also be obtained. The above data together constitute the heat dissipation information set of the target building area.

[0021] Step 114: Perform data fusion on the spatial grid model and the heat dissipation information set to generate a BIM building model including the device distribution information and the spatial topological relationship. The spatial topological relationship is used to describe the relative positions between devices and the channel connectivity.

[0022] During the data fusion process, the device identifiers in the spatial grid model can be matched with those in the heat dissipation information set based on the data fusion algorithm. Taking the server cabinet S1 as an example, the position marked "S1" in the spatial grid model is determined through the data fusion algorithm, and then it is associated and fused with the heat dissipation information of the server cabinet S1 in the heat dissipation information set (such as power P1, heat dissipation efficiency E1, heat radiation range radius R1, etc.). After performing the above operations on all devices in the entire computer room, the data fusion between the spatial grid model and the heat dissipation information set is completed. The generated BIM building model not only contains the distribution information of the devices (the positions of the devices in the spatial grid model), but also describes the relative positions and channel connectivity between the devices through the previously constructed spatial topological relationship. For example, the server cabinet S1 is connected to the adjacent server cabinet S2 through a certain channel, and information such as the width and length of the channel is also included in the model, providing comprehensive data support for subsequent hot and cold aisle analysis.

[0023] Step 120: Perform channel division processing on the BIM building model to generate an initial layout plan for the hot and cold aisles, and perform computational fluid dynamics analysis based on the initial layout plan to simulate the air flow state, obtaining the fluid distribution data of the hot and cold aisles.

[0024] In the data center computer room of the embodiment of the present invention, when performing channel division processing on the BIM building model, first, channel planning is carried out according to the functional layout of the computer room and the heat dissipation requirements of the devices. For example, considering the heat dissipation direction of the server cabinets and the air supply direction of the air conditioning units, the space in the computer room is divided into different areas to determine which areas are cold aisles and which areas are hot aisles. Through the channel division algorithm, an initial layout plan for the hot and cold aisles is generated, and this plan clarifies information such as the positions, directions, and widths of the cold and hot aisles. For example, the cold aisle is arranged along one side of the computer room with a width of D1 meters, and the hot aisle is on the other side with a width of D2 meters. Then, based on this initial layout plan, computational fluid dynamics analysis software is used to simulate the air flow state. The software will set corresponding boundary conditions and parameters according to the channel information in the initial layout plan and the device distribution in the BIM building model, and perform simulation calculations to finally obtain the fluid distribution data of the hot and cold aisles. The above data reflects the air flow situation in the hot and cold aisles.

[0025] Among them, performing computational fluid dynamics analysis based on the initial layout plan in step 120 to simulate the air flow state and obtaining the fluid distribution data of the hot and cold aisles includes: Step 121: Determine the fluid inlet boundary conditions and fluid outlet boundary conditions according to the channel division information in the initial layout plan, and the boundary conditions include the inlet flow rate, outlet pressure, and initial temperature value.

[0026] In the embodiments of the present invention, boundary conditions are determined according to the channel division information in the initially generated hot and cold channel layout scheme. For the fluid inlet boundary condition, the air outlet of the air conditioning unit is the fluid inlet. By analyzing the performance parameters of the air conditioning unit and the design requirements of the channel, the inlet flow velocity is determined to be V1 m / s. The determination of this flow velocity should comprehensively consider the air supply capacity of the air conditioning unit and the bearing capacity of the channel. The outlet pressure is determined to be P0 Pascals according to factors such as the ventilation system design of the computer room and the atmospheric pressure. The initial temperature value is set according to the set temperature of the computer room environment, for example, set to T0 degrees Celsius. For the fluid outlet boundary condition, according to the discharge direction of the hot channel and the design of the ventilation system, parameters such as the outlet pressure and temperature are also determined. For example, the outlet pressure is Pa1 Pascals and the initial temperature value is T1 degrees Celsius. The above boundary conditions provide accurate initial parameters for the subsequent fluid dynamics simulation.

[0027] Step 122: Set the obstacle constraint characteristics based on the equipment distribution information in the BIM building model. The obstacle constraint characteristics include equipment geometric shape characteristics and surface roughness characteristics.

[0028] In the data center computer room, the BIM building model contains the distribution information of various equipment such as server cabinets and air conditioning units. According to the above information, the obstacle constraint characteristics are set. For the server cabinet, its geometric shape characteristics can be described as a cuboid with a length of L1 m, a width of W1 m, and a height of H1 m. The surface roughness characteristics can be determined as the roughness coefficient r1 through experimental measurement or reference to relevant standards. For the air conditioning unit, its geometric shape may be more complex, such as an irregular box shape. The accurate geometric shape characteristics are obtained through technologies such as three-dimensional modeling, and the surface roughness characteristics are also determined as the roughness coefficient r2 accordingly. The above obstacle constraint characteristics are used in the fluid dynamics simulation to simulate the obstacles and influences on the air when flowing through the equipment, making the simulation results closer to the actual situation.

[0029] Step 123: Combine the obstacle constraint characteristics and the target fluid dynamics control model to determine the air flow velocity distribution and pressure gradient distribution in the hot and cold channels.

[0030] In an embodiment of the present invention, a preset target fluid dynamics control model is utilized to incorporate the previously set obstacle constraint features into the model for calculation. This model will simulate and calculate the air flow in the cold and hot channels according to the basic principles of fluid mechanics, such as the continuity equation, momentum equation, etc., in combination with the geometric shape and surface roughness characteristics of the obstacles, as well as the previously determined boundary conditions. Through methods such as iterative calculation and numerical solution, the air velocity distribution and pressure gradient distribution at different positions in the cold and hot channels are finally determined. For example, at a certain position in the cold channel, the calculated air velocity is Vx m / s, and at another position in the hot channel, the pressure gradient is Gx Pa / m. The above distribution information provides detailed data for understanding the flow characteristics of air in the channels.

[0031] Step 124: Generate the fluid distribution data according to the air velocity distribution and the pressure gradient distribution, where the fluid distribution data includes a set of streamline trajectories, the positions of vortex regions, and the turbulence intensity index.

[0032] Optionally, according to the previously calculated air velocity distribution and pressure gradient distribution, relevant data generation algorithms are used to generate the fluid distribution data. For the set of streamline trajectories, the algorithm will draw the trajectories of air flow in the space of the cold and hot channels according to the direction and magnitude of the air velocity, forming a set of streamline trajectories. For example, in the cold channel, by analyzing the air velocities at different positions, a series of streamlines representing the air flow direction are drawn, and the above streamlines constitute the set of streamline trajectories. For the determination of the positions of vortex regions, the algorithm will identify the regions where vortices are formed in the air flow according to the changes in velocity and pressure, and determine their specific positions in the cold and hot channels. For the turbulence intensity index, the algorithm will calculate the turbulence intensity index at different positions according to the fluctuation of the velocity and relevant turbulence models, and the above index reflects the degree of disorder of the air flow. Finally, the above set of streamline trajectories, the positions of vortex regions, and the turbulence intensity index are integrated together to form the fluid distribution data of the cold and hot channels.

[0033] Step 130: Perform a coupled analysis on the BIM building model and the fluid distribution data to generate the prediction results of the air leakage state and the temperature distribution of the cold and hot channels.

[0034] In an embodiment of the present invention, when coupling and analyzing a BIM building model with fluid distribution data, a corresponding coupling analysis algorithm is used. This algorithm correlates and integrates the equipment distribution, heat dissipation information, etc. in the BIM building model with the air flow information in the fluid distribution data. By analyzing the heat dissipation situation of the equipment and the influence of air flow on heat dissipation, prediction results of the air leakage state and temperature distribution of the cold and hot channels are generated. For example, by analyzing whether the air flow in the cold and hot channels will cause air to leak from the cold channel to the hot channel, or vice versa, the air leakage state can be predicted. At the same time, according to factors such as the heat dissipation power of the equipment, the heat carried away by the air flow, and the heat conduction in the channel, the temperature distribution of different channel regions is predicted.

[0035] In a preferred embodiment, step 130 includes: Step 131: Extract the heat source characteristics from the equipment distribution information in the BIM building model to obtain the heat source intensity characteristics corresponding to each equipment.

[0036] In a data center computer room, extract the heat source characteristics from the equipment distribution information in the BIM building model. Taking a server cabinet as an example, by analyzing the equipment power data and heat dissipation efficiency data of the server cabinet, the heat source intensity characteristics are calculated. For example, if the equipment power of server cabinet S1 is P1 watts and the heat dissipation efficiency is E1, then its heat source intensity characteristics can be calculated by multiplying the power by the heat dissipation efficiency, that is, the heat source intensity is P1×E1 watts. For other equipment, such as air conditioning units, similar methods are used to calculate the corresponding heat source intensity characteristics according to their equipment attribute data. The above heat source intensity characteristics reflect the amount of heat dissipated by each equipment per unit time.

[0037] Step 132: Determine the spatial overlapping region between the air flow path and the heat source intensity characteristics according to the streamline trajectory set in the fluid distribution data, and the spatial overlapping region represents the coverage range of the air flowing through the heat source.

[0038] In an embodiment of the present invention, the spatial overlapping region is determined according to the streamline trajectory set in the fluid distribution data. For example, the streamline trajectory set shows that the air flows along a certain path from the air outlet of the air conditioning unit. When this streamline trajectory intersects with the heat source region of server cabinet S1, the spatial overlapping region between the air flow path and the heat source intensity characteristics is determined. By analyzing the specific direction of the streamline trajectory and the range of the heat source, the shape and size of the spatial overlapping region are accurately determined. The spatial overlapping region represents the coverage range of the air flowing through heat sources such as server cabinet S1, which is of great significance for understanding the influence range of air on equipment heat dissipation.

[0039] Step 133: Conduct a thermodynamic equilibrium analysis within the spatial overlap region to obtain the heat exchange state characteristics of the cold and hot channels. The heat exchange state characteristics are used to quantify the contribution degree of air flow to the heat dissipation of the device.

[0040] This step conducts a thermodynamic equilibrium analysis within the determined spatial overlap region. Using thermodynamic principles and related heat exchange models, factors such as the air flow rate, temperature, and heat source intensity of the device are considered. For example, air flows through the spatial overlap region at a certain flow rate Vx, the air temperature is Tx, and the heat source intensity of the server cabinet S1 is Q1. Through the heat exchange formula and the law of conservation of energy, the heat exchange amount between the air and the device within this spatial overlap region is calculated. Based on the heat exchange amount and the relevant parameters of the air and the device, the heat exchange state characteristics of the cold and hot channels are obtained. This characteristic can be represented by a quantified index, such as the heat exchange efficiency η, which reflects the contribution degree of air flow to the heat dissipation of the device.

[0041] Step 134: Generate a leakage state prediction model based on the heat exchange state characteristics and the turbulence intensity index, and calculate the leakage state prediction result of the cold and hot channels through the leakage state prediction model.

[0042] In an embodiment of the present invention, a leakage state prediction model is generated based on the heat exchange state characteristics and the turbulence intensity index. First, a large amount of historical engineering data is collected. The above data includes different heat exchange efficiencies, turbulence intensities, and corresponding leakage rate values. Then, a multiple linear regression analysis is performed on the above data. Using the regression analysis algorithm, the first regression coefficient k1 between the heat exchange state characteristics and the leakage rate, and the second regression coefficient k2 between the turbulence intensity index and the leakage rate are determined. Next, the heat exchange state characteristics are multiplied by the first regression coefficient to obtain the first prediction component, that is, the first prediction component = heat exchange state characteristics × k1. The turbulence intensity index is multiplied by the second regression coefficient to obtain the second prediction component, that is, the second prediction component = turbulence intensity index × k2. Finally, the first prediction component and the second prediction component are superimposed to generate the leakage state prediction result of the cold and hot channels, that is, the leakage state prediction result = the first prediction component + the second prediction component.

[0043] Exemplarily, the generating a leakage state prediction model based on the heat exchange state characteristics and the turbulence intensity index includes: Step 1340: Obtain the air leakage state quantization information in the historical engineering data. The air leakage state quantization information includes the air leakage rate values corresponding to different heat exchange efficiencies and turbulence intensities. Perform multiple linear regression analysis on the air leakage state quantization information to determine the first regression coefficient between the heat exchange state characteristics and the air leakage rate, and the second regression coefficient between the turbulence intensity index and the air leakage rate. Perform a multiplication operation on the heat exchange state characteristics and the first regression coefficient to obtain the first prediction component, and perform a multiplication operation on the turbulence intensity index and the second regression coefficient to obtain the second prediction component. Perform a superposition operation on the first prediction component and the second prediction component to generate the air leakage state prediction result of the cold and hot channels.

[0044] In actual operation, extract the air leakage state quantization information from the historical engineering database. For example, find a set of data where when the heat exchange efficiency is E1, the turbulence intensity is TF1, and the corresponding air leakage rate is L1; when the heat exchange efficiency is E2, the turbulence intensity is TF2, and the corresponding air leakage rate is L2, etc. Input the above data into the multiple linear regression analysis software. The software determines the first regression coefficient k1 between the heat exchange state characteristics and the air leakage rate, and the second regression coefficient k2 between the turbulence intensity index and the air leakage rate through fitting and calculation of the data. Among them, the heat exchange state characteristics of the current cold and hot channels are E, and the turbulence intensity index is TF. Calculate the first prediction component as E×k1, the second prediction component as TF×k2, and the superposition of the two gives the air leakage state prediction result as E×k1 + TF×k2.

[0045] Step 135: Generate the temperature distribution prediction result according to the heat conduction relationship between the air flow path and the heat source intensity characteristics. The temperature distribution prediction result reflects the temperature gradient changes in different channel regions.

[0046] In the embodiment of the present invention, generate the temperature distribution prediction result according to the heat conduction relationship between the air flow path and the heat source intensity characteristics. Consider the influence of factors such as the air flow velocity, heat source intensity, and channel material on heat conduction. For example, in the cold channel, air flows from the air conditioner unit to the server cabinet at a certain speed, and the heat dissipated by the server cabinet is transferred to the surrounding space through air flow and heat conduction. Use the heat conduction equation and related heat transfer models, combined with the air flow path and heat source intensity characteristics, to calculate the temperature changes at different positions. Through the calculation of different regions of the entire cold and hot channels, generate the temperature distribution prediction result, which can be represented by a temperature distribution matrix. Each element in the matrix corresponds to the temperature value of a different channel region, thus clearly reflecting the temperature gradient changes in different channel regions.

[0047] Step 140: Generate a layout optimization strategy based on the predicted air leakage state and the predicted temperature distribution result, and adjust the channel layout information in the initial layout plan according to the layout optimization strategy to obtain a target layout plan.

[0048] In the data center computer room, a layout optimization strategy is generated based on the predicted air leakage state and the predicted temperature distribution result. If the predicted air leakage state shows that the air leakage rate in one area is relatively high and the predicted temperature distribution shows that the temperature in some areas is uneven, then a corresponding optimization strategy needs to be formulated. For example, for the air leakage problem, the tightness of the channel can be considered for adjustment; for the temperature unevenness problem, the width of the channel or the placement position of the equipment can be adjusted. According to the above analysis results, a layout optimization strategy including parameter adjustment instructions is generated using an optimization algorithm, and then the channel layout information in the initial layout plan, such as the channel width and the channel inclination angle, is adjusted according to this strategy, and finally a target layout plan is obtained to improve the performance of the cold and hot channels.

[0049] In an alternative embodiment, the generating a layout optimization strategy based on the predicted air leakage state and the predicted temperature distribution result includes: Step 141: Identify the air leakage hazard areas in the initial layout plan according to the predicted air leakage state result, and extract the channel boundary geometric vectors of the air leakage hazard areas.

[0050] In the embodiment of the present invention, an analysis is performed according to the predicted air leakage state result. If the predicted air leakage state result shows that the air leakage rate in one area exceeds a preset standard, then this area is an air leakage hazard area. For example, at the junction of the cold and hot channels, the predicted air leakage state result shows a relatively high air leakage rate, and this area is determined as an air leakage hazard area. Then, using a geometric analysis algorithm, the channel boundary geometric vectors of this air leakage hazard area are extracted. For the air leakage hazard area at the junction of the cold and hot channels, the channel boundary may be an irregular shape, but it can be approximated as a polygon by mathematical methods for processing. For example, the channel boundary is divided into multiple line segments, and each line segment can be represented by a vector. For example, if the channel boundary is composed of three points PA, PB, and PC connected in sequence, then the line segment PAPB can be represented as the vector PAPB (its coordinates are the coordinates of point PB minus the coordinates of point PA), and the line segment PBPC can be represented as the vector PBPC (its coordinates are the coordinates of point PC minus the coordinates of point PB), and the above vectors together constitute the channel boundary geometric vectors of the air leakage hazard area. The above vectors contain information such as the direction and length of the channel boundary, providing an important data basis for subsequent layout optimization.

[0051] Step 142: Determine the temperature abnormal areas in the cold and hot channels based on the predicted temperature distribution result, and calculate the heat radiation superposition range of adjacent devices according to the spatial coordinates of the temperature abnormal areas.

[0052] In the data center computer room, analysis is carried out based on the predicted temperature distribution results. The predicted temperature distribution results are presented in matrix form. By comparing and analyzing the temperature values represented by each element in the matrix, if the temperature in one area deviates significantly from the overall average temperature and exceeds the preset normal range, then this area is determined as a temperature anomaly area. For example, in one corner of the hot aisle, the temperature is significantly higher than other areas, and this corner is the temperature anomaly area. Then, according to the spatial coordinates of this temperature anomaly area, the devices located in this area and its surrounding areas are found. For example, there are server cabinets S3 and S4 in this area. Based on the previously determined data on the heat radiation range of the devices (such as the heat radiation range of server cabinet S3 is a spherical area with a radius of R3 centered on it, and the heat radiation range of S4 is a spherical area with a radius of R4 centered on it) and their spatial coordinates, the overlapping range of the heat radiation of adjacent devices is calculated using spatial geometric algorithms. For the overlap of two spherical heat radiation ranges, first calculate the distance d between the centers of the two spheres (i.e., the device positions), and then judge the intersection situation of the two spheres according to geometric relationships. If d is less than R3 + R4, the two spheres intersect, and the shape and range of the intersection part are determined through geometric calculations (such as using geometric models such as spherical caps and spherical segments). This intersection part is the overlapping range of the heat radiation of adjacent devices, and the determination of this range helps to understand the degree of heat interference between devices and provides a basis for optimizing the layout.

[0053] Step 143: Obtain a multi-objective optimization function including a minimum air leakage rate index, a maximum temperature uniformity index, and a minimum device heat interference index.

[0054] In the scenario of optimizing the layout of a data center computer room, to comprehensively consider multiple performance metrics for optimizing the hot and cold aisle layout, a multi-objective optimization function needs to be constructed. Minimizing the air leakage rate metric aims to reduce the air leakage between the hot and cold aisles and improve energy utilization efficiency. The air leakage rate can be defined as a function related to the prediction result of the air leakage state. For example, let the air leakage rate be L, which can be the ratio of the total air leakage flow at the junction of the hot and cold aisles and other possible air leakage areas to the total supply air volume. By optimizing the layout, L is made as small as possible. Maximizing the temperature uniformity metric requires that the temperature difference in each area within the hot and cold aisles be as small as possible to ensure that the equipment operates in a stable temperature environment. The temperature uniformity can be measured by the standard deviation of temperature σ. The smaller σ is, the more uniform the temperature is. By adjusting the layout parameters, σ is minimized. Minimizing the equipment heat interference metric is to reduce the mutual influence of thermal radiation between adjacent equipment and ensure the heat dissipation efficiency of the equipment. The equipment heat interference can be defined as the total heat within the superposition range of the thermal radiation of adjacent equipment, denoted as H. By optimizing the equipment spacing and layout, H is minimized. Combining these three metrics, a multi-objective optimization function F is constructed. For example, F = q1×L + q2×σ + q3×H, where q1, q2, and q3 are weight coefficients, representing the importance of each metric in the overall optimization, and are set according to actual requirements. For example, q1 = 0.3, q2 = 0.3, q3 = 0.4. By adjusting the above weights, the optimization priorities between different metrics can be balanced, thereby achieving a comprehensive optimization of the hot and cold aisle layout.

[0055] Step 144: Adjust the channel boundary geometric vector and the thermal radiation superposition range through an iterative optimization algorithm to make the multi-objective optimization function reach a preset convergence condition, and generate the layout optimization strategy.

[0056] During the optimization of the data center computer room layout, an iterative optimization algorithm is used to adjust the channel boundary geometric vector and the thermal radiation superposition range. First, relevant parameters are initialized. For example, the channel boundary geometric vector is set to an initial value (the above initial value can be determined according to the channel boundary information in the initial layout plan), and the initial value of the thermal radiation superposition range is also set according to the previous calculation results. Then, the value of the multi-objective optimization function F is calculated at the current parameter values (i.e., the current channel boundary geometric vector and the thermal radiation superposition range). Next, the gradient vector of the multi-objective optimization function F at the current parameter is calculated using an iterative optimization algorithm (such as the gradient descent algorithm). The gradient vector indicates the direction in which the function value increases fastest, so the opposite direction is the direction in which the function value decreases fastest, that is, the direction of parameter adjustment. Along the opposite direction of the gradient vector, the channel boundary geometric vector and the thermal radiation superposition range are gradually updated according to a certain step size. After each update, the value of the multi-objective optimization function F is recalculated, and it is checked whether the preset convergence condition is reached. The preset convergence condition can be that the change amount of the multi-objective optimization function F is less than one of the set thresholds. For example, the set threshold is ε. If the change amount of F is less than ε, it is considered that the algorithm converges. At this time, the layout adjustment plan corresponding to the obtained parameter values (i.e., the updated channel boundary geometric vector and the thermal radiation superposition range) is the generated layout optimization strategy; if the convergence condition is not reached, the next round of iterative calculation is continued, and the parameters are continuously adjusted until the convergence condition is met.

[0057] Specifically, step 144 may include: Step 1440: Initialize the channel boundary geometric vector as the first initial feature parameter set, and the thermal radiation superposition range as the second initial feature parameter set; calculate the gradient vector of the multi-objective optimization function at the first initial feature parameter set and the second initial feature parameter set, and the gradient vector indicates the parameter adjustment direction; gradually update the first initial feature parameter set and the second initial feature parameter set along the gradient vector direction until the change amount of the multi-objective optimization function is less than the set threshold; generate a layout optimization strategy including parameter adjustment instructions according to the updated first initial feature parameter set and the second initial feature parameter set.

[0058] In the actual optimization operation of the data center computer room, the channel boundary geometric vector is set as the first initial characteristic parameter set Set1. For example, Set1 contains the initial coordinate values of multiple channel boundary vectors, and the above values are determined according to the initial layout scheme. The heat radiation superposition range is set as the second initial characteristic parameter set Set2. Set2 may contain parameters such as the initial shape and size of the heat radiation superposition range of each device. Then, use specialized optimization calculation software or an algorithm implemented by programming to calculate the gradient vector G of the multi-objective optimization function F at Set1 and Set2. This gradient vector G is a multi-dimensional vector, and each dimension corresponds to the change direction of a parameter. For example, for one of the vector coordinate parameters x in the channel boundary geometric vector, the corresponding dimension value in the gradient vector G represents the influence degree and direction of the change of x on the multi-objective optimization function F. Next, update Set1 and Set2 in the opposite direction of the gradient vector G according to a certain step size α (such as α = 0.1). For example, for one of the vector coordinate parameters x in Set1, the updated x value is x - α × Gx (Gx is the dimension value corresponding to x in the gradient vector G). Similar update operations are performed on all parameters in Set1 and Set2 to obtain new parameter sets Set1' and Set2'. Calculate the values of the multi-objective optimization function F at Set1' and Set2', and compare them with the F value calculated in the previous round, and calculate the change amount ΔF. If ΔF is less than the set threshold (such as the set threshold is 0.01), it is considered that the algorithm converges. At this time, a layout optimization strategy including parameter adjustment instructions is generated according to Set1' and Set2'. For example, according to the channel boundary geometric vector information in Set1', instructions for adjusting the channel width and tilt angle are generated, and according to the heat radiation superposition range information in Set2', instructions for adjusting the device spacing, etc. are generated. The above instructions constitute the layout optimization strategy.

[0059] In a preferred embodiment, adjusting the channel layout information in the initial layout scheme according to the layout optimization strategy to obtain a target layout scheme includes: Step 145: Modify the channel width and channel tilt angle of the initial layout scheme according to the parameter adjustment instructions in the layout optimization strategy to obtain a modified channel layout scheme.

[0060] In an embodiment of the present invention, operations are performed according to the parameter adjustment instructions in the layout optimization strategy. For example, the instructions in the layout optimization strategy require increasing the width of the cold aisle from the original 2 meters to 2.5 meters, decreasing the width of the hot aisle from 1.5 meters to 1.2 meters, and adjusting the tilt angle of one of the aisles from 30 degrees to 45 degrees. According to the above instructions, the initial layout plan is modified. During the modification process, a professional layout design software is used, which can adjust the geometric shape of the aisle according to the input parameters. For the modification of the aisle width, the software will expand or shrink the boundaries of the corresponding aisle according to the instructions. For the adjustment of the aisle tilt angle, the software will use the selected point as the rotation center and rotate the aisle according to the angle value of the instruction, so as to obtain the modified aisle layout plan, which meets the requirements of the layout optimization strategy in terms of aisle width and tilt angle, laying a foundation for further optimizing the performance of the cold and hot aisles.

[0061] Step 146: Re-determine the equipment spacing characteristics in the modified aisle layout plan to obtain an aisle layout division plan, where the equipment spacing characteristics are used to constrain that there is no overlapping area in the heat radiation range between adjacent equipment.

[0062] After obtaining the modified aisle layout plan, the equipment spacing characteristics are re-determined. Since the aisle layout has changed, the relative position relationship between the equipment has also changed accordingly. Therefore, it is necessary to re-consider the equipment spacing to avoid overlapping of the heat radiation ranges between adjacent equipment. Taking server cabinets as an example, by analyzing the heat radiation range of each server cabinet (such as a spherical area with a radius of R centered on the cabinet), combined with the modified aisle layout plan, a spatial geometry algorithm is used to determine the appropriate equipment spacing. In the new aisle layout, the minimum spacing between two adjacent server cabinets should ensure that their heat radiation ranges do not overlap. By calculating the boundary distance between the heat radiation ranges of the two cabinets and considering factors such as space utilization and air flow in the aisle, the equipment spacing is determined. For all the equipment in the entire computer room, the equipment spacing characteristics are re-determined according to the above method, and finally an aisle layout division plan is obtained. This plan not only considers the layout change of the aisle, but also effectively avoids the heat interference between adjacent equipment through reasonable equipment spacing constraints, improving the heat dissipation efficiency of the computer room.

[0063] Step 147: Perform a hydrodynamic verification analysis on the aisle layout division plan to obtain a verification result. If the verification result meets the preset air leakage tolerance threshold and temperature uniformity threshold, generate the target layout plan; otherwise, re-execute the steps of the coupled analysis until the verification conditions are met.

[0064] In an embodiment of the present invention, a hydrodynamic verification analysis is performed on the channel layout division scheme. First, the channel layout division scheme is converted into boundary condition input information required for hydrodynamic analysis. For example, the new dimensions of the channel (such as the modified channel width, length, etc.) and the new position information of the equipment are accurately input into the hydrodynamic analysis software. Then, the mesh division parameters for the hydrodynamic simulation are set based on the geometric complexity of the channel. If the channel shape is relatively complex, a finer mesh division may be required. For example, the side length of the mesh element is set to 0.2 meters; if the channel shape is relatively simple, the mesh element size can be appropriately increased, such as set to 0.5 meters. Based on the above mesh division parameters, a hydrodynamic simulation is performed in the hydrodynamic analysis software. During the simulation process, the software calculates the air flow in the channel according to the set boundary conditions and mesh division, and outputs the verification data of the air leakage state and the verification data of the temperature distribution. The verification data of the air leakage state is compared and analyzed with the preset air leakage tolerance threshold. For example, the preset air leakage tolerance threshold is 5%. If the verification data of the air leakage state shows that the actual air leakage rate is less than 5%, the air leakage situation meets the requirements. At the same time, the verification data of the temperature distribution is analyzed for consistency with the preset temperature uniformity threshold. For example, the preset temperature uniformity threshold requires that the temperature standard deviation does not exceed 3 degrees Celsius. If the temperature standard deviation calculated from the verification data of the temperature distribution is less than 3 degrees Celsius, the temperature uniformity meets the requirements. If the verification data of the air leakage state is less than the air leakage tolerance threshold and the verification data of the temperature distribution meets the temperature uniformity threshold, it is confirmed that the verification result meets the preset conditions, and a target layout scheme is generated; otherwise, it indicates that there are still problems with the current channel layout division scheme, and the steps of the coupling analysis need to be re-executed, that is, the BIM building model and the fluid distribution data are coupled and analyzed again, and the layout scheme is adjusted until the verification conditions are met.

[0065] In an exemplary embodiment, the hydrodynamic verification analysis of the channel layout division scheme to obtain a verification result includes: Step 1470: Convert the target layout scheme into boundary condition input information required for hydrodynamic analysis, where the boundary condition input information includes the updated channel dimensions and equipment position information; set the mesh division parameters for the hydrodynamic simulation based on the geometric complexity of the channel; perform a hydrodynamic simulation based on the mesh division parameters and the mesh division parameters, and output the verification data of the air leakage state and the verification data of the temperature distribution; compare and analyze the verification data of the air leakage state with the air leakage tolerance threshold, and analyze the consistency of the verification data of the temperature distribution with the temperature uniformity threshold; if the verification data of the air leakage state is less than the air leakage tolerance threshold and the verification data of the temperature distribution meets the temperature uniformity threshold, confirm that the verification result meets the preset air leakage tolerance threshold and temperature uniformity threshold.

[0066] During the actual verification process in the data center computer room, the channel dimension information in the target layout plan (such as the cold channel width becomes 2.5 meters, the hot channel width becomes 1.2 meters, etc.) and the equipment location information (such as the server cabinet S1 is moved to a new coordinate position, etc.) are accurately sorted into the format required by the computational fluid dynamics analysis software and used as boundary condition input information. Determine the mesh generation parameters according to the complexity of the channel. For example, if there are many corners and obstacles in the channel, the mesh is made finer, and the side length of the mesh element is set to 0.2 meters. Input the above mesh generation parameters into the computational fluid dynamics analysis software and start the simulation. After a series of calculations by the software, the air leakage state verification data (such as the actual air leakage rate is 3%) and the temperature distribution verification data (such as the calculated temperature standard deviation is 2 degrees Celsius) are output. Compare the air leakage state verification data with the preset air leakage tolerance threshold (set to 5%). Since 3% is less than 5%, it indicates that the air leakage situation meets the requirements. Conduct a consistency analysis of the temperature distribution verification data with the preset temperature uniformity threshold (requiring the temperature standard deviation not to exceed 3 degrees Celsius). Since 2 degrees Celsius is less than 3 degrees Celsius, it indicates that the temperature uniformity also meets the requirements. Since the air leakage state verification data is less than the air leakage tolerance threshold and the temperature distribution verification data meets the temperature uniformity threshold, it is confirmed that the verification result meets the preset air leakage tolerance threshold and temperature uniformity threshold. At this time, the target layout plan passes the verification and can be applied to the actual computer room construction or optimization.

[0067] As an independently implementable technical solution, after adjusting the channel layout information in the initial layout plan according to the layout optimization strategy to obtain a target layout plan, it further includes: deploying a temperature sensor array and an air velocity sensor array based on the target layout plan, and collecting the actual temperature distribution data and actual air velocity data of the cold and hot channels in real time; calculating the residuals between the actual temperature distribution data and the temperature distribution prediction result to generate a temperature prediction error distribution map, and extracting abnormal monitoring areas where the temperature deviation exceeds a preset threshold according to the error distribution map; performing dynamic path matching degree analysis on the actual air velocity data and the streamline trajectory set in the fluid distribution data to identify channel sections with a flow velocity matching degree lower than the preset matching degree; generating a feedback optimization instruction based on the abnormal monitoring area and the channel section, and adjusting the equipment spacing characteristics and channel tilt angle in the target layout plan to generate a first optimized layout plan.

[0068] In the data center computer room, the sensor array is deployed based on the target layout plan. Temperature sensor arrays and air velocity sensor arrays are installed at key positions in the cold and hot aisles. For example, a temperature sensor is installed every 2 meters in the cold aisle, a temperature sensor is installed every 3 meters in the hot aisle, and air velocity sensors are installed at different height positions in the aisle to comprehensively collect actual temperature distribution data and actual air flow velocity data. After collecting the above data, residual calculation is performed on the actual temperature distribution data and the previously generated temperature distribution prediction results. For example, for a certain position in the cold aisle, the actual temperature is T_actual, the temperature at this position in the temperature distribution prediction result is T_prediction, and the residual is T_actual - T_prediction. By performing the above calculations on each position in the entire cold and hot aisles, a temperature prediction error distribution map is generated. A preset threshold is set, such as 5 degrees Celsius. According to the error distribution map, the areas where the temperature deviation exceeds 5 degrees Celsius are extracted as abnormal monitoring areas. For the actual air flow velocity data, dynamic path matching degree analysis is performed with the streamline trajectory set in the fluid distribution data. For example, based on the streamline trajectory set, the theoretical air flow velocity and direction of a certain aisle section are determined, and the actual air flow velocity data is compared with it to calculate the flow velocity matching degree. If the preset matching degree is 80%, when the flow velocity matching degree of a certain aisle section is lower than 80%, this aisle section is identified as abnormal. Based on the above abnormal monitoring areas and aisle sections, a feedback optimization instruction is generated. For example, if it is found that the temperature of a certain abnormal monitoring area is too high due to the small distance between devices resulting in heat radiation superposition, an instruction to increase the device distance is generated; if the flow velocity matching degree of a certain aisle section is low because the aisle tilt angle is unreasonable, an instruction to adjust the aisle tilt angle is generated. According to the above instructions, the device distance characteristics and aisle tilt angle in the target layout plan are adjusted, thereby generating the first optimized layout plan to further optimize the performance of the cold and hot aisles.

[0069] As an independently implementable technical solution, after adjusting the channel layout information in the initial layout plan according to the layout optimization strategy to obtain the target layout plan, it further includes: calculating the heat radiation superposition influence factor of each device based on the device distribution information in the target layout plan, and the heat radiation superposition influence factor is determined by the product of the overlapping area of the heat radiation ranges of adjacent devices and the device heat dissipation power; constructing a thermal environment energy efficiency evaluation matrix according to the heat radiation superposition influence factor and the temperature distribution prediction result, and each element in the matrix represents the unit area heat dissipation efficiency and energy consumption ratio of the corresponding channel area; performing regional division on the thermal environment energy efficiency evaluation matrix based on a preset energy efficiency threshold, and screening out the low-efficiency channel areas where the energy efficiency value is lower than the energy efficiency threshold; adjusting the device installation direction or adding auxiliary heat dissipation devices according to the spatial coordinates of the low-efficiency channel areas to generate the second optimized layout plan.

[0070] In an embodiment of the present invention, a heat radiation superposition influence factor is calculated based on the device distribution information in the target layout plan. Taking a server cabinet as an example, first, the adjacent devices of each server cabinet are determined. Among them, the adjacent devices of server cabinet S1 are S2 and S3. According to the previously determined device heat radiation range data, the overlapping areas of the heat radiation ranges between S1 and S2, and between S1 and S3 are calculated. For example, through spatial geometry calculation, the overlapping area of the heat radiation ranges between S1 and S2 is A12, and the overlapping area of the heat radiation ranges between S1 and S3 is A13. Given that the heat dissipation power of S1 is P1, the heat radiation superposition influence factor of S1 is (A12 × P1) + (A13 × P1). The above calculations are performed for all devices to obtain the heat radiation superposition influence factors of each device. Then, a heat environment energy efficiency evaluation matrix is constructed based on the above heat radiation superposition influence factors and the temperature distribution prediction results. The rows and columns of the matrix correspond to different channel areas, and each element in the matrix represents the heat dissipation efficiency per unit area and the energy consumption ratio of the corresponding channel area. For example, for one of the channel areas, the heat dissipation efficiency per unit area can be calculated by dividing the total heat dissipated by the devices in the area by the area of the area, and the energy consumption is determined according to the energy required for air-conditioning and other devices to provide cooling for the area. The ratio of the two is the value of this element. Next, based on a preset energy efficiency threshold, the heat environment energy efficiency evaluation matrix is regionally divided. For example, the preset energy efficiency threshold is E0. Each element in the matrix is traversed, and the channel areas corresponding to the elements smaller than E0 are screened out. The above areas are the low-efficiency channel areas. For the screened low-efficiency channel areas, the devices involved are determined according to their spatial coordinates. If one of the low-efficiency channel areas is caused by unreasonable installation directions of the devices, resulting in mutual interference of heat radiation and affecting the heat dissipation efficiency and energy consumption ratio, the installation directions of the relevant devices are adjusted to make the heat radiation directions of the devices more conducive to air flow and heat dissipation; if the energy efficiency of this area is low due to insufficient heat dissipation capacity, auxiliary heat dissipation devices are added, such as installing small fans near the devices. Through the above operations, a second optimized layout plan is generated to further improve the heat environment energy efficiency of the cold and hot channels in the data center computer room.

[0071] As an independently implementable technical solution, after adjusting the channel layout information in the initial layout plan according to the layout optimization strategy to obtain the target layout plan, it further includes: obtaining the real-time device load data of the target building area, and generating a dynamic heat source intensity correction coefficient according to the load change cycle; fusing the dynamic heat source intensity correction coefficient with the heat dissipation information set in the BIM building model to update the device power data and the heat radiation range data; performing dynamic channel zoning on the target layout plan based on the updated heat dissipation information set, and adjusting the channel width priority and the cold and hot channel isolation distance according to the peak heat source intensity period; performing periodic computational fluid dynamics verification according to the dynamically divided channel zones to generate an elastic channel layout plan adapted to load fluctuations.

[0072] In the data center computer room, real-time device load data of the target building area is obtained through a dedicated data acquisition system. This data acquisition system is connected to the monitoring ports of each device and can monitor the load conditions of devices such as server cabinets and air-conditioning units in real time. For example, the real-time load data of server cabinet S1 can be obtained through the monitoring module inside it, and the load data may be presented in the form of power values during device operation, task processing volumes, etc. According to the above real-time device load data, analyze its load change cycle. For example, after a period of monitoring, it is found that server cabinet S1 has a high load from 10 am to 4 pm every day, showing a certain periodic pattern. Using the corresponding algorithm, generate a dynamic heat source intensity correction factor according to the load change cycle. For example, this algorithm analyzes historical load data and current real-time load data, considering factors such as load peaks, averages, and change trends, and obtains that the dynamic heat source intensity correction factor of server cabinet S1 during the load peak period is k1.

[0073] Integrate the generated dynamic heat source intensity correction factor with the heat dissipation information set in the BIM building model. For server cabinet S1, its original device power data is P1, and the heat radiation range data is a spherical area with a radius of R1 centered on the cabinet. During integration, update the device power data to P1×k1, and also adjust the heat radiation range data according to the correction factor and relevant physical models. For example, the heat radiation range radius becomes R1×k1' (k1' is an adjustment factor determined according to the heat radiation principle and the correction factor). Perform the above integration operation on all devices, thereby updating the heat dissipation information set in the entire BIM building model.

[0074] Based on the updated heat dissipation information set, perform dynamic channel zoning on the target layout plan. According to the peak period of heat source intensity, analyze the heat dissipation requirements of different devices during the peak period. For example, during the load peak period of server cabinets, the heat source intensity of multiple cabinets increases significantly, and at this time, a larger channel space is required to ensure air flow and heat dissipation. Therefore, adjust the priority of the channel width. For areas with high heat dissipation requirements, increase the channel width first. At the same time, adjust the isolation distance between the cold and hot channels to ensure that cold and hot air do not mix excessively and affect the heat dissipation effect.

[0075] Perform periodic hydrodynamic verification according to the channel partitions after dynamic partitioning. Set the verification period, for example, perform verification every hour. At each verification, convert the channel partition information after dynamic partitioning into the boundary condition input information required for hydrodynamic analysis, including the updated channel dimensions, equipment positions, and new heat dissipation information, etc. Set the mesh generation parameters for the hydrodynamic simulation based on the channel geometric complexity, and perform the hydrodynamic simulation. After the simulation ends, output the verification data of the air leakage state and the verification data of the temperature distribution. Analyze the above verification data to check whether it meets the preset performance index requirements, such as the air leakage rate, temperature uniformity, etc. If the verification result meets the requirements, the current channel layout plan can continue to be used; if it does not meet the requirements, further adjust the channel layout according to the verification result, and re-perform the hydrodynamic verification until an elastic channel layout plan adapted to load fluctuations is generated. The elastic channel layout plan generated in this way can adjust the channel layout in real time according to the dynamic changes of the equipment load, ensuring that the data center computer room has good heat dissipation performance and air flow effect under different load conditions.

[0076] It should be noted that when implementing the embodiments of the present invention, those skilled in the art can rely on the existing mature technology system for algorithm adaptation and engineering implementation in each link.

[0077] First, in the BIM modeling stage, realize the parsing of building structure data through the built-in geometric engine of the Building Information Modeling (BIM) software, complete the semantic alignment and topological reconstruction of multi-source data using the IFC standard protocol, and extract the equipment space coordinates in combination with the point cloud data processing technology. The hydrodynamic analysis uses the Computational Fluid Dynamics (CFD) method. Based on the Reynolds-averaged Navier-Stokes equation framework, select the standard k-ε turbulence model to simulate the air flow state, discretize and solve the flow control equation by the finite volume method, and use the boundary condition setting module built in commercial CFD software (such as ANSYS Fluent) to define the inlet flow velocity and outlet pressure.

[0078] Secondly, the thermodynamic analysis uses the coupled calculation method of the energy conservation equation and the Fourier heat conduction law, and combines the equipment heat source intensity data to simulate the heat transfer process. When constructing the air leakage prediction model, apply the multiple linear regression algorithm, fit the variable relationship in the historical data by the least squares method, and use the standardized preprocessing to eliminate the dimension difference. The multi-objective optimization process uses the gradient descent algorithm or the genetic algorithm, determines the iteration direction through parameter sensitivity analysis, and balances the conflicts of multiple optimization objectives in combination with the Pareto front theory.

[0079] Then, the Kalman filter algorithm is introduced in sensor data processing to achieve noise suppression, and the dynamic time warping (DTW) technique is used to analyze the matching degree between the measured data and the predicted streamline. The Monte Carlo radiative heat transfer method is applied to calculate the superposition range of equipment thermal radiation, and the ray tracing technique is used to simulate the thermal radiation path. The dynamic load response uses a time series prediction algorithm (such as the LSTM neural network) to analyze the load cycle characteristics of the equipment, and combines real-time data stream processing technology to dynamically correct the heat source parameters.

[0080] In addition, all physical quantity calculations follow the specifications of the International System of Units, and the key parameter assignments refer to industry standard manuals (such as the ASHRAE Thermal Environment Design Guide).

[0081] It is worth mentioning that in the embodiments of the present invention, a BIM model including equipment distribution and spatial topological relationships is constructed through building information modeling technology. The air flow in the cold and hot channels is simulated by combining computational fluid dynamics methods. The Navier-Stokes equation is discretely solved by the finite volume method to obtain fluid distribution data. A quantitative relationship between the equipment heat source intensity and the air flow is established through thermodynamic coupling analysis. An innovative channel layout optimization method based on the calculation of the thermal radiation superposition range and multi-objective optimization functions (including air leakage rate, temperature uniformity, and thermal interference index) is proposed. The gradient descent algorithm is used to iteratively optimize the channel geometric parameters, and a closed-loop optimization system is constructed by combining dynamic sensor data feedback, forming a complete technical system covering data acquisition, model construction, simulation analysis, parameter optimization, and verification implementation. The technical means such as the spatial grid generation algorithm, k-ε turbulence model, and Monte Carlo thermal radiation calculation method involved all have clear physical meanings and engineering implementation paths. The technical solution can be repeatedly implemented by setting grid division standards, heat source intensity calculation formulas, and verification thresholds. It creatively solves technical problems such as air leakage control, temperature balance adjustment, and dynamic load adaptation in the cold and hot channel layout of the data center computer room, significantly improves the equipment heat dissipation efficiency and reduces energy consumption, and belongs to a substantial technical improvement of the building environment control method.

[0082] In summary, in the embodiments of the present invention, first, a BIM building model including equipment distribution information and spatial topological relationships is generated by obtaining the building structure data set, and the BIM building model is channel-divided and combined with fluid dynamics analysis to simulate the air flow state and obtain fluid distribution data; secondly, the BIM building model and the fluid distribution data are coupled and analyzed to predict the air leakage state and temperature distribution, providing strong support for accurately grasping the cold and hot channel conditions through cross-domain data fusion analysis; then, a layout optimization strategy is generated based on the prediction results and the channel layout is adjusted to obtain the target layout plan, effectively improving the rationality of the cold and hot channel layout, optimizing the air flow and temperature distribution inside the building, improving the overall functionality and comfort of the building, and realizing a more efficient and scientific building layout planning method.

[0083] See Figure 2 As shown, this figure is a schematic diagram of the basic structure of a hot and cold channel simulation analysis system 200 provided by an embodiment of the present invention. The hot and cold channel simulation analysis system 200 includes: A processor 201; A storage device 202, on which a computer program 2020 is stored; When the computer program 2020 is executed by the processor 201, the processor 201 is enabled to implement any one of the above-mentioned hot and cold channel simulation analysis methods based on BIM and CFD.

[0084] On the above basis, a readable storage medium is provided. A program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the above method are implemented.

[0085] It should be noted that the various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method part.

Claims

1. A simulation and analysis method for cold and hot channels based on BIM and CFD, characterized in that, Including: Obtain the building structure data set of the target building area, generate a BIM building model based on the building structure data set, and the BIM building model includes the equipment distribution information and spatial topological relationship of the target building area; Perform channel division processing on the BIM building model to generate an initial layout plan for the cold and hot channels, perform fluid dynamics analysis based on the initial layout plan to simulate the air flow state, and obtain the fluid distribution data of the cold and hot channels; Perform coupling analysis on the BIM building model and the fluid distribution data to generate the air leakage state prediction result and temperature distribution prediction result of the cold and hot channels; Generate a layout optimization strategy based on the air leakage state prediction result and the temperature distribution prediction result, and adjust the channel layout information in the initial layout plan according to the layout optimization strategy to obtain the target layout plan.

2. The method for simulating and analyzing cold and hot channels based on BIM and CFD according to claim 1, wherein The generating the BIM building model based on the building structure data set includes: Perform geometric analysis processing on the building structure data set, and extract the structural contour features and equipment position coordinates of the target building area; Generate a spatial grid model of the target building area based on the structural contour features, and mark the equipment identifiers corresponding to the equipment position coordinates in the spatial grid model; Determine the heat dissipation information set of the target building area according to the equipment attribute data associated with the equipment identifier, and the heat dissipation information set includes equipment power data, heat dissipation efficiency data, and heat radiation range data; Perform data fusion on the spatial grid model and the heat dissipation information set to generate a BIM building model including the equipment distribution information and the spatial topological relationship, and the spatial topological relationship is used to describe the relative positions between equipment and channel connectivity.

3. The method for simulating and analyzing hot and cold channels based on BIM and CFD according to claim 2, wherein The performing fluid dynamics analysis based on the initial layout plan to simulate the air flow state and obtain the fluid distribution data of the cold and hot channels includes: Determine the fluid inlet boundary condition and fluid outlet boundary condition according to the channel division information in the initial layout plan, and the boundary conditions include inlet flow velocity, outlet pressure, and initial temperature value; Set the obstacle constraint characteristics based on the equipment distribution information in the BIM building model, and the obstacle constraint characteristics include equipment geometric shape characteristics and surface roughness characteristics; Combine the obstacle constraint characteristics and the target fluid dynamics control model to determine the air flow velocity distribution and pressure gradient distribution in the cold and hot channels; Generate the fluid distribution data according to the air flow velocity distribution and the pressure gradient distribution, and the fluid distribution data includes a streamline trajectory set, a vortex region position, and a turbulence intensity index.

4. The method for simulating and analyzing cold and hot channels based on BIM and CFD according to claim 3, wherein The performing coupling analysis on the BIM building model and the fluid distribution data to generate the air leakage state prediction result and temperature distribution prediction result of the cold and hot channels includes: Extract the heat source characteristics of the equipment distribution information in the BIM building model to obtain the heat source intensity characteristics corresponding to each equipment; Determine a spatial overlap region between the air flow path and the heat source intensity characteristic according to the set of streamline trajectories in the fluid distribution data, where the spatial overlap region represents the coverage range of the air flowing through the heat source; Conduct a thermodynamic equilibrium analysis within the spatial overlap region to obtain the heat exchange state characteristic of the cold and hot channels, where the heat exchange state characteristic is used to quantify the contribution degree of air flow to the heat dissipation of the device; Generate a leakage state prediction model based on the heat exchange state characteristic and the turbulence intensity index, and calculate the leakage state prediction result of the cold and hot channels through the leakage state prediction model; Generate the temperature distribution prediction result according to the heat conduction relationship between the air flow path and the heat source intensity characteristic, where the temperature distribution prediction result reflects the temperature gradient change in different channel regions.

5. The method for simulating and analyzing hot and cold channels based on BIM and CFD according to claim 4, wherein The generating the leakage state prediction model based on the heat exchange state characteristic and the turbulence intensity index includes: Obtain the leakage state quantification information in the historical engineering data, where the leakage state quantification information includes the leakage rate values corresponding to different heat exchange efficiencies and turbulence intensities; Conduct a multiple linear regression analysis on the leakage state quantification information to determine the first regression coefficient between the heat exchange state characteristic and the leakage rate, and the second regression coefficient between the turbulence intensity index and the leakage rate; Perform a multiplication operation on the heat exchange state characteristic and the first regression coefficient to obtain a first prediction component, and perform a multiplication operation on the turbulence intensity index and the second regression coefficient to obtain a second prediction component; Perform a superposition operation on the first prediction component and the second prediction component to generate the leakage state prediction result of the cold and hot channels.

6. The method for simulating and analyzing cold and hot channels based on BIM and CFD according to claim 1, characterized in that The generating the layout optimization strategy based on the leakage state prediction result and the temperature distribution prediction result includes: Identify the leakage hazard areas in the initial layout scheme according to the leakage state prediction result, and extract the channel boundary geometric vectors of the leakage hazard areas; Determine the temperature abnormal areas in the cold and hot channels based on the temperature distribution prediction result, and calculate the heat radiation superposition range of adjacent devices according to the spatial coordinates of the temperature abnormal areas; Obtain a multi-objective optimization function including a minimum leakage rate index, a maximum temperature uniformity index, and a minimum device heat interference index; Adjust the channel boundary geometric vectors and the heat radiation superposition range through an iterative optimization algorithm to make the multi-objective optimization function reach a preset convergence condition, and generate the layout optimization strategy.

7. The method for simulating and analyzing hot and cold channels based on BIM and CFD according to claim 6, wherein The adjusting the channel boundary geometric vectors and the heat radiation superposition range through an iterative optimization algorithm to make the multi-objective optimization function reach a preset convergence condition and generate the layout optimization strategy includes: Initialize the channel boundary geometric vectors as a first set of initial characteristic parameters, and the heat radiation superposition range as a second set of initial characteristic parameters; Calculate the gradient vectors of the multi-objective optimization function at the first set of initial characteristic parameters and the second set of initial characteristic parameters, where the gradient vectors indicate the parameter adjustment directions; Gradually update the first set of initial characteristic parameters and the second set of initial characteristic parameters along the direction of the gradient vector until the change amount of the multi-objective optimization function is less than a set threshold; Generate a layout optimization strategy including parameter adjustment instructions according to the updated first set of initial characteristic parameters and the second set of initial characteristic parameters.

8. The method for simulating and analyzing hot and cold channels based on BIM and CFD according to claim 1, wherein The adjusting the channel layout information in the initial layout plan according to the layout optimization strategy to obtain a target layout plan includes: Modify the channel width and channel tilt angle of the initial layout plan according to the parameter adjustment instructions in the layout optimization strategy to obtain a modified channel layout plan; Redetermine the device spacing characteristics in the modified channel layout plan to obtain a channel layout division plan, where the device spacing characteristics are used to constrain that there is no overlapping area in the heat radiation range between adjacent devices; Perform a hydrodynamic verification analysis on the channel layout division plan to obtain a verification result. If the verification result meets the preset air leakage tolerance threshold and temperature uniformity threshold, generate the target layout plan; otherwise, re-execute the steps of the coupling analysis until the verification conditions are met.

9. The method for simulating and analyzing cold and hot channels based on BIM and CFD according to claim 8, characterized in that The performing a hydrodynamic verification analysis on the channel layout division plan to obtain a verification result includes: Convert the target layout plan into boundary condition input information required for hydrodynamic analysis, where the boundary condition input information includes the updated channel size and device position information; Set the mesh division parameters for hydrodynamic simulation based on the channel geometric complexity; Execute a hydrodynamic simulation based on the mesh division parameters and the mesh division parameters, and output air leakage state verification data and temperature distribution verification data; Perform a comparison analysis on the air leakage state verification data and the air leakage tolerance threshold, and perform a consistency analysis on the temperature distribution verification data and the temperature uniformity threshold; If the air leakage state verification data is less than the air leakage tolerance threshold and the temperature distribution verification data meets the temperature uniformity threshold, confirm that the verification result meets the preset air leakage tolerance threshold and temperature uniformity threshold.

10. A hot and cold channel simulation analysis system, characterized in that, Including: A processor; A storage device storing a computer program, which when executed by the processor enables the processor to implement the BIM and CFD-based hot and cold channel simulation analysis method according to any one of claims 1-9.

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