Method, device, medium and program product for dynamic visualization of temperature field in supercomputing center

Abstract

Embodiments of the present disclosure disclose a method, device, medium and program product for dynamic visualization of temperature field of an intelligent computing center. The method comprises: voxelizing a three-dimensional physical space model of the intelligent computing center to obtain spatial geometric parameters of each voxel in the three-dimensional physical space model; obtaining an ambient temperature value of the intelligent computing center, a collection position coordinate of the ambient temperature value, a device temperature value of each target device inside the intelligent computing center, and a collection position coordinate of the device temperature value; obtaining a comprehensive temperature value of each voxel based on the above parameters; setting a particle inside each voxel based on the comprehensive temperature value of each voxel; and performing layered rendering on the three-dimensional physical space model and the set particles to generate an intelligent computing center visualization model fused with a temperature cloud layer. The method can realize three-dimensional dynamic visualization of the temperature field of the intelligent computing center through three-dimensional voxelization and layered rendering of particles, and construct a continuous temperature cloud layer to truly present the spatial distribution characteristics of the temperature field.

Description

Technical Field

[0001] This disclosure relates to the field of density environment thermal management technology, and in particular to a method, device, medium and program product for dynamic visualization of temperature field in a computing center. Background Technology

[0002] As the scale of intelligent computing centers continues to expand and the computing power density continues to increase, their internal temperature fields have evolved from traditional two-dimensional planar forms to three-dimensional transient high-gradient fields with spatiotemporal dynamic characteristics. However, current mainstream temperature field visualization and operation and maintenance technologies are still limited to traditional methods such as two-dimensional heat map drawing or sparse point cloud modeling. The former directly projects the data collected by sensors deployed in the rack onto a two-dimensional planar color scale map, while the latter achieves temperature characterization in a three-dimensional engine through the schematic overlay of thousands of colored particles. The overall technical solutions all rely on a combination of manual threshold alarms and manual inspection to complete hotspot location.

[0003] In traditional methods, two-dimensional heat maps cannot accurately represent the longitudinal convection patterns of hot and cold channels because they lose Z-axis dimension information; sparse point clouds are limited by the number and distribution density of particles, making it difficult to construct a continuous "heat cloud" to reflect the true distribution characteristics of the temperature field. Summary of the Invention

[0004] In view of this, the present disclosure provides a method, device, medium and program product for dynamic visualization of temperature field in a computing center, which can realize three-dimensional dynamic visualization of temperature field in a computing center through three-dimensional voxel meshing and particle layer rendering, and construct a continuous temperature cloud to realistically present the spatial distribution characteristics of temperature field.

[0005] In a first aspect, embodiments of this disclosure provide a method for dynamically visualizing the temperature field of a smart computing center, employing the following technical solution: A three-dimensional physical space model of the intelligent computing center is constructed, and the three-dimensional physical space model is voxelized to obtain the spatial geometric parameters of each voxel in the three-dimensional physical space model. Acquire the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value was collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value was collected. Based on the ambient temperature value, the coordinates of the location where the ambient temperature value was collected, the device temperature value, the coordinates of the location where the device temperature value was collected, and the spatial geometric parameters of each voxel, the comprehensive temperature value of each voxel is obtained. Particles are set inside each voxel based on the overall temperature value of each voxel; The three-dimensional physical space model and the set particles are rendered in layers to generate a visualization model of the intelligent computing center that incorporates temperature clouds.

[0006] Optionally, obtaining the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value was collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value was collected includes: Environmental temperature sensors are deployed throughout the intelligent computing center to collect environmental temperature values ​​from multiple locations. The deployment coordinates of the ambient temperature sensor are converted to the unified world coordinate system in the three-dimensional physical space model to obtain the coordinates of the acquisition location of the corresponding ambient temperature value. Deploy a device temperature sensor on each target device to collect device temperature values; Based on the three-dimensional position coordinates of the target device in the three-dimensional physical space model, the relative coordinates of the device temperature sensor within the target device are converted to the unified world coordinate system in the three-dimensional physical space model to obtain the acquisition position coordinates of the corresponding device temperature value.

[0007] Optionally, obtaining the comprehensive temperature value of each voxel based on the ambient temperature value, the coordinates of the location where the ambient temperature value was acquired, the device temperature value, the coordinates of the location where the device temperature value was acquired, and the spatial geometric parameters of each voxel includes: The coordinates of the ambient temperature value acquisition location, the coordinates of the device temperature value acquisition location, and the spatial geometric parameters of each voxel are matched. If the spatial geometric parameters of the voxel only match the coordinates of the location where the ambient temperature value was acquired, then the comprehensive temperature value of the voxel is the ambient temperature value. If the spatial geometric parameters of the voxel match only the coordinates of the acquisition location of the device temperature value, then the comprehensive temperature value of the voxel is the device temperature value. If the spatial geometric parameters of the voxel are successfully matched with the coordinates of the acquisition location of the ambient temperature value and the coordinates of the acquisition location of the device temperature value, then the comprehensive temperature value of the voxel is the maximum value or weighted average value of the ambient temperature value and the device temperature value. If the spatial geometric parameters of the voxel fail to match the coordinates of the acquisition location of the ambient temperature value and the coordinates of the acquisition location of the device temperature value, the comprehensive temperature value of the voxel is obtained through a spatial interpolation algorithm.

[0008] Optionally, the step of setting particles within each voxel based on the comprehensive temperature value of each voxel includes: Based on preset temperature range and preset density range, obtain the mapping relationship between comprehensive temperature value and particle density; Based on the mapping relationship between the comprehensive temperature value and the particle density and the comprehensive temperature value of each voxel, the particle density value of each voxel is obtained. Based on the particle density value of each voxel, a corresponding number of particles are set inside each voxel.

[0009] Optionally, the method for dynamically visualizing the temperature field of the intelligent computing center further includes: The comprehensive temperature value of each voxel is matched with a preset color gradient specification list to determine the temperature value sub-range and the corresponding base color of each voxel; wherein, the preset color gradient specification list stores the mapping relationship between the temperature value sub-range and the base color. Based on the temperature sub-range and the corresponding base color of each voxel, the RGB color values ​​of the particles inside each voxel are obtained through an interpolation algorithm.

[0010] Optionally, the method for dynamically visualizing the temperature field of the intelligent computing center further includes: Based on preset temperature and transparency ranges, the mapping relationship between comprehensive temperature value and particle transparency is obtained; Based on the mapping relationship between the overall temperature value and particle transparency, and the overall temperature value of each voxel, the transparency value of the particles inside each voxel is obtained.

[0011] Optionally, the method for dynamically visualizing the temperature field of the intelligent computing center further includes: The preset temperature value range is divided into multiple temperature value sub-ranges, and the preset transparency range is divided into an equal number of transparency sub-ranges. Sort all temperature value sub-intervals from low to high, and sort all transparency sub-intervals from low to high; A one-to-one mapping relationship between temperature value sub-intervals and transparency sub-intervals is constructed based on the sorting order; Based on the one-to-one mapping relationship, the temperature value sub-range to which the comprehensive temperature value of each voxel belongs is matched, and the transparency value of the particles inside the corresponding voxel is obtained.

[0012] Optionally, the method for dynamically visualizing the temperature field of the intelligent computing center further includes: Determine whether the distance between the voxel and all target devices is greater than a preset distance threshold; If the distance between the voxel and all target devices is greater than a preset distance threshold, then the transparency value of the particles inside the voxel remains unchanged. If the distance between the voxel and any target device is not greater than a preset distance threshold, then it is determined whether the voxel is located inside any target device or intersects with the target device; If the voxel is located inside or intersects with any target device, the transparency value of the particles inside the voxel is adjusted to a preset value. If the voxel is completely outside of all target devices, then determine whether the voxel is located in the specified direction of any target device and whether the distance between the voxel and the target device is within a preset distance range; If a voxel is located in any specified direction of a target device and the distance between it and the target device is within a preset distance range, then the transparency value of the particles inside the voxel is scaled according to a first preset ratio. If the voxel is not located in all the specified directions of the target device, or if the voxel is located in any of the specified directions of the target device but the distance between it and the target device is not within the preset distance range, then the transparency value of the particles inside the voxel remains unchanged.

[0013] Optionally, the method for dynamically visualizing the temperature field of the intelligent computing center further includes: A ray is emitted from the camera's perspective, and the opacity of all particles before reaching the surface of the target device is accumulated along the ray direction. Determine whether the accumulated opacity is greater than the preset opacity threshold; If the accumulated opacity is greater than the preset opacity threshold, the opacity value of all particles in the ray direction before reaching the surface of the target device is scaled according to the second preset ratio. If the accumulated opacity is not greater than the preset opacity threshold, the opacity value of all particles remains unchanged in the ray direction before reaching the surface of the target device.

[0014] Optionally, the method for dynamically visualizing the temperature field of the intelligent computing center further includes: Obtain the identification and risk level of risky equipment; Based on the identification of the risky device, the corresponding spatial region is located in the visualization model of the intelligent computing center; Based on the risk level, the brightness of particles within the spatial region is adjusted.

[0015] Secondly, this disclosure also provides a dynamic visualization system for the temperature field of an intelligent computing center, employing the following technical solution: The model building module is used to construct a three-dimensional physical space model of the intelligent computing center, perform voxel meshing on the three-dimensional physical space model, and obtain the spatial geometric parameters of each voxel in the three-dimensional physical space model. The coordinate acquisition module is used to acquire the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value was collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value was collected. The temperature acquisition module is used to acquire the comprehensive temperature value of each voxel based on the ambient temperature value, the coordinates of the acquisition location of the ambient temperature value, the device temperature value, the coordinates of the acquisition location of the device temperature value, and the spatial geometric parameters of each voxel. The particle setting module is used to set particles within each voxel based on the overall temperature value of each voxel. The layered rendering module is used to perform layered rendering of the three-dimensional physical space model and the set particles to generate a visualization model of the intelligent computing center that incorporates temperature clouds.

[0016] Thirdly, this disclosure also provides a computer device, which adopts the following technical solution: The computer device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform any of the above-described intelligent computing center temperature field dynamic visualization methods.

[0017] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing computer instructions for causing a computer to execute any of the above-described intelligent computing center temperature field dynamic visualization methods.

[0018] Fifthly, embodiments of this disclosure also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0019] The dynamic visualization method for temperature fields in intelligent computing centers provided in this disclosure constructs a three-dimensional physical space model of the intelligent computing center and completes voxel meshing. This achieves precise binding between ambient temperature, equipment temperature, and the physical space coordinates of the intelligent computing center. This allows discrete temperature acquisition data to be spatially mapped based on the spatial geometric parameters of voxels, laying a three-dimensional spatial foundation for accurate temperature field calculation. Simultaneously, by integrating multi-dimensional temperature acquisition data to solve for the comprehensive temperature value of each voxel, it effectively integrates all temperature information of the environment and equipment within the intelligent computing center, achieving comprehensive coverage and precise quantification of temperature field data. Based on the comprehensive temperature value of each voxel, particles are set within them, and a visualization model is generated by layered rendering and fusion of temperature clouds. This transforms abstract temperature field data into a concrete particle-based and cloud-like visual presentation, compensating for the lack of spatial dimension expression in traditional temperature visualization methods. It also enables a dynamic and continuous visualization of the distribution and changing characteristics of the temperature field in the intelligent computing center, significantly improving the spatial hierarchy and intuitiveness of temperature field visualization. This allows staff to clearly perceive the spatial distribution patterns of temperature within the intelligent computing center, providing precise visual reference for temperature control and heat dissipation optimization.

[0020] The above description is merely an overview of the technical solution disclosed herein. In order to better understand the technical means of this disclosure and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating the dynamic visualization method for the temperature field of a smart computing center provided in this embodiment of the disclosure; Figure 2 A flowchart illustrating the method for obtaining temperature values ​​and acquisition location coordinates provided in this embodiment of the disclosure; Figure 3 A schematic flowchart illustrating the method for obtaining voxel-based integrated temperature values ​​provided in this embodiment of the disclosure; Figure 4 A flowchart illustrating a method for setting particles for a voxel according to an embodiment of this disclosure; Figure 5 A schematic flowchart of the particle color acquisition method provided in the embodiments of this disclosure; Figure 6 A schematic flowchart illustrating the particle transparency acquisition method provided in this embodiment of the disclosure; Figure 7 Another schematic flowchart of the particle transparency acquisition method provided in this disclosure embodiment; Figure 8 A schematic flowchart illustrating the particle transparency adjustment method provided in this embodiment of the disclosure; Figure 9 A schematic flowchart of the particle dynamic brightness adjustment method provided in the embodiments of this disclosure; Figure 10 A schematic diagram of the intelligent computing center visualization model provided in the embodiments of this disclosure; Figure 11 A schematic diagram of the dynamic visualization system for temperature field in a smart computing center provided in this embodiment of the present disclosure; Figure 12 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. Detailed Implementation

[0023] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0024] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0025] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0026] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0027] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0028] Reference Figure 1 This disclosure provides a method for dynamic visualization of the temperature field in an intelligent computing center, comprising the following steps: S1: Construct a three-dimensional physical space model of the intelligent computing center, perform voxel meshing on the three-dimensional physical space model, and obtain the spatial geometric parameters of each voxel in the three-dimensional physical space model; S2: Obtain the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value was collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value was collected; S3: Based on the ambient temperature value, the coordinates of the location where the ambient temperature value was collected, the equipment temperature value, the coordinates of the location where the equipment temperature value was collected, and the spatial geometric parameters of each voxel, obtain the comprehensive temperature value of each voxel; S4: Based on the overall temperature value of each voxel, set particles inside each voxel; S5: Performs layered rendering of the 3D physical space model and set particles to generate a visualization model of the intelligent computing center that incorporates temperature clouds.

[0029] The dynamic visualization method for temperature field in intelligent computing centers disclosed herein achieves precise binding of ambient temperature, equipment temperature, and physical space coordinates of the intelligent computing center by constructing a three-dimensional physical space model of the intelligent computing center and completing voxel meshing. This allows discrete temperature acquisition data to be spatialized based on the spatial geometric parameters of voxels, laying a three-dimensional spatial foundation for accurate temperature field calculation. At the same time, by integrating multi-dimensional temperature acquisition data to solve for the comprehensive temperature value of each voxel, it effectively integrates the full temperature information of the environment and equipment within the intelligent computing center, achieving comprehensive coverage and accurate quantification of temperature field data.

[0030] Based on the voxel-based comprehensive temperature value, particles are set inside, and a visual model is generated by merging temperature clouds through layered rendering. This transforms abstract temperature field data into a concrete particle-like and cloud-like visual presentation, which not only makes up for the lack of spatial dimension expression in traditional temperature visualization methods, but also enables a dynamic and continuous visualization of the distribution and changing characteristics of the temperature field in the intelligent computing center. This greatly improves the spatial hierarchy and intuitiveness of temperature field visualization, allowing staff to clearly perceive the spatial distribution pattern of temperature within the intelligent computing center, and providing accurate visual reference for temperature control and heat dissipation optimization in the intelligent computing center.

[0031] In S1, a 3D physical space model of the intelligent computing center (such as a computer room model) is constructed based on Building Information Modeling (BIM) or professional 3D modeling tools. This model includes physical space and infrastructure objects such as walls, floors, ceilings, server racks, air conditioning units, and fresh air systems. Each server rack and device is assigned a unique device identifier (such as F2M2-B06), and its bounding box parameters in a unified world coordinate system are calibrated, specifically including the 3D coordinates (x, y, x) of the bounding box center point. d y d , z d The bounding box refers to the smallest cubic space in a unified world coordinate system that can completely enclose the cabinet / equipment. The boundaries of this cube correspond one-to-one with the actual length, width, and height of the equipment. The three-dimensional coordinates of the center point of the bounding box are essentially the three-dimensional position of the geometric center of the cabinet / equipment in the unified world coordinate system.

[0032] Within the global bounding box of the intelligent computing center, the 3D physical space model is partitioned into voxel meshes based on a preset voxel resolution. For example, the entire 3D physical space model is divided into 0.1m × 0.1m × 0.1m cubic spatial units. Each cubic spatial unit is a voxel. Using the origin of the unified coordinate system of the computer room as a reference, the global space of the computer room is uniformly partitioned along the X, Y, and Z axes according to the preset voxel resolution, generating a voxel index set V(i, j, k), where the index parameters i, j, and k correspond to the voxel's sequence number on the X, Y, and Z axes, respectively. Based on the voxel resolution and index parameters, the spatial geometric parameters of each voxel are calculated. These spatial geometric parameters include at least one of the three-dimensional coordinates of the voxel's center point and its spatial geometric range, calculated using formula x. a =i×r x / 2、y a =j×r y / 2、z a =k×r z / 2 Determine the three-dimensional coordinates (x) of the center point of the voxel. a y a , z a ), r x r y r z These represent the voxel resolutions along the X, Y, and Z axes, respectively. Simultaneously, by combining the resolution parameters, the spatial geometric extent of each voxel is derived, i.e., the boundary of the voxel along the X-axis is [x...]. a -r x / 2, x a +r x / 2] Similarly, the boundaries of the Y-axis and Z-axis directions are calculated to mark the precise spatial position of each voxel in the three-dimensional physical space model. The voxel index set is used to establish the association mapping between voxels and the unified world coordinate system, providing index retrieval capability for subsequent accurate positioning of corresponding voxels based on coordinate information.

[0033] In S2, refer to Figure 2 The flowchart illustrating the method for obtaining temperature values ​​and data acquisition location coordinates shows that "obtaining the ambient temperature value of the intelligent computing center, the data acquisition location coordinates of the ambient temperature value, and the device temperature value and data acquisition location coordinates of each target device inside the intelligent computing center" includes the following steps: S21: Deploy ambient temperature sensors throughout the intelligent computing center to collect ambient temperature values ​​from multiple locations; S22: Convert the deployment coordinates of the ambient temperature sensor to the unified world coordinate system in the three-dimensional physical space model to obtain the coordinates of the acquisition location of the corresponding ambient temperature value; S23: Deploy a device temperature sensor on each target device to collect device temperature values; S24: Based on the three-dimensional position coordinates of the target device in the three-dimensional physical space model, the relative coordinates of the device temperature sensor in the target device are converted to the unified world coordinate system in the three-dimensional physical space model to obtain the acquisition position coordinates of the corresponding device temperature value.

[0034] In S21-S24, the deployment locations inside the intelligent computing center are determined in advance. For example, an ambient temperature sensor is deployed at a preset distance. Based on the actual monitoring needs, the target devices that the intelligent computing center needs to detect temperature are determined, and device temperature sensors are deployed on the target devices (such as server air vents and sensors inside the rack).

[0035] Within the unified world coordinate system of the three-dimensional physical space model, the three-dimensional coordinates (x, y, z) of the physical deployment of the environmental temperature sensor are directly extracted. s y s , z s The coordinates of the ambient temperature value acquisition location are used as the coordinates. For the device temperature sensor, the three-dimensional position coordinates of the target device in a unified world coordinate system are first obtained. Combined with the relative three-dimensional coordinates of the sensor inside the target device, the unified world coordinates of the device temperature sensor are obtained through coordinate superposition and transformation, and used as the coordinates of the device temperature value acquisition location.

[0036] In S3, refer to Figure 3 The flowchart illustrating the method for obtaining the comprehensive temperature value of a voxel includes the following steps: "Based on the ambient temperature value, the coordinates of the ambient temperature value acquisition location, the device temperature value, the coordinates of the device temperature value acquisition location, and the spatial geometric parameters of each voxel." S31: Match the location coordinates of the ambient temperature value acquisition point and the location coordinates of the device temperature value acquisition point with the spatial geometric parameters of each voxel; if the spatial geometric parameters of the voxel only match the location coordinates of the ambient temperature value acquisition point, then proceed to S32; if the spatial geometric parameters of the voxel only match the location coordinates of the device temperature value acquisition point, then proceed to S33; if the spatial geometric parameters of the voxel match both the location coordinates of the ambient temperature value acquisition point and the location coordinates of the device temperature value acquisition point, then proceed to S34; if the spatial geometric parameters of the voxel fail to match both the location coordinates of the ambient temperature value acquisition point and the location coordinates of the device temperature value acquisition point, then proceed to S35. S32: The overall temperature value of the voxel is the ambient temperature value; S33: The overall temperature value of the voxel is the equipment temperature value; S34: The overall temperature value of a voxel is the maximum or weighted average of the ambient temperature value and the equipment temperature value; S35: Obtain the comprehensive temperature value of voxels through spatial interpolation algorithm.

[0037] In S31-S35, based on the voxel meshing rules, the voxel index (i) of the ambient temperature value acquisition location coordinates is calculated. s j s k s The collected ambient temperature value T env Directly bound to the corresponding voxel; the coordinates of the device temperature acquisition location are mapped to the corresponding voxel index (i) according to the same rules. d j d k d ), complete the equipment temperature value T dev Binding to voxels. For scenarios where a voxel is bound to only a single temperature value, the bound temperature value is the voxel's comprehensive temperature value. For scenarios where the same voxel is bound to multiple temperature values, a preset data fusion strategy is used to generate the comprehensive temperature value of the voxel. Depending on the actual operation and maintenance needs, methods such as weighted average or maximum value selection can be selected to fuse and calculate the ambient temperature value within the voxel and the temperature values ​​of multiple devices to obtain the voxel's unique comprehensive temperature value T[i][j][k].

[0038] If the spatial geometric parameters of a voxel fail to match the coordinates of the acquisition locations for both the ambient and device temperature values, it indicates that no sensor has been deployed in the physical space corresponding to that voxel, and no temperature data has been directly sampled. Based on its adjacent voxels with already bound valid temperature data, a spatial interpolation algorithm is used to fit and calculate the comprehensive temperature value of that voxel. For example, a distance-weighted average method can be used, assigning weights based on the spatial distance between adjacent voxels and the target voxel and calculating the mean to complete the temperature value.

[0039] The comprehensive temperature values ​​of all voxels are integrated according to their index sequence to form a three-dimensional temperature volume data array T[x][y][z] that corresponds one-to-one with the spatial position of the three-dimensional physical space model. This ultimately achieves precise binding of ambient temperature, equipment temperature and computer room physical space at the voxel level, providing accurate temperature data support for subsequent particle settings and visualization rendering.

[0040] In S4, refer to Figure 4 The flowchart illustrating the method for setting particles within voxels, "setting particles within each voxel based on the overall temperature value of each voxel," includes the following steps: S41: Based on the preset temperature range and preset density range, obtain the mapping relationship between the comprehensive temperature value and the particle density; S42: Based on the mapping relationship between the overall temperature value and the particle density and the overall temperature value of each voxel, obtain the particle density value of each voxel; S43: Based on the particle density value of each voxel, set the corresponding number of particles inside each voxel.

[0041] In S41-S43, a temperature range [T] is preset.min T max ] and the corresponding preset density range [ρ min , ρ max The overall temperature value is within the set temperature range. Based on these two ranges, a linear positive correlation mapping relationship between the overall temperature value and particle density can be established. That is, the higher the voxel temperature value, the greater the matching particle density value. Based on this mapping relationship, the overall temperature value of each voxel is substituted into the mapping formula to calculate the particle density value corresponding to each voxel. Then, according to the particle density value, a corresponding number of particles are generated and set in each voxel to achieve the distribution effect of more particles in high-temperature voxels and fewer particles in low-temperature voxels. Moreover, because the particle density is higher in the high-temperature region, it can form a sense of thickness and aggregation effect of "thermal cloud sea".

[0042] Optionally, refer to Figure 5 The flowchart illustrating the particle color acquisition method shows the following steps for acquiring the particle color within each voxel: S44: Match the comprehensive temperature value of each voxel with the preset color gradient specification list to determine the temperature value sub-range and the corresponding base color of each voxel; the preset color gradient specification list stores the mapping relationship between the temperature value sub-range and the base color. S45: Based on the temperature sub-range to which each voxel belongs and the corresponding base color, the RGB color value of the particles inside each voxel is obtained through an interpolation algorithm.

[0043] In S44 and S45, a color gradient specification list is pre-built. Based on the temperature monitoring needs of the intelligent computing center, the preset temperature value range is divided into continuous temperature value sub-ranges. For example, [0°C, 20°C] represents the cold aisle region; (20°C, 40°C) represents the normal operation zone; (40°C, 60°C) represents the mildly heated zone; and (60°C, 100°C) represents the high-temperature warning zone. Each temperature value sub-range uses a unified color gradient specification; for example, [0°C, 20°C] uses blue; (20°C, 40°C) uses green; (40–60°C) uses yellow; and (60°C, 100°C) uses orange-red.

[0044] RGB color values ​​are a digital coding system that uses different intensities of red, green, and blue light to represent colors. Reference colors are assigned to the two ends of each temperature sub-range. For example, in the cold channel range of 0~20℃, 0℃ corresponds to the deep blue light value RGB (0, 0, 102), and 20℃ corresponds to the light blue light value RGB (173, 216, 230). The color gradient specification list stores the mapping relationship between the temperature threshold values ​​of all ranges and their corresponding reference colors. Therefore, after matching the voxel's overall temperature value to the corresponding temperature sub-range, the reference temperature and reference color at both ends of the range are used as references. A linear interpolation algorithm is then used to calculate the RGB color value based on the relative proportion of the voxel's overall temperature value within its respective temperature sub-range. This value serves as the display color of the particles within the voxel, achieving a gradual change in color depth with temperature difference within the same temperature sub-range and a continuous and accurate correlation between temperature and color across the entire range. For example, if the proportion of voxels at 10℃ in the 0~20℃ range is 50%, take the reference RGB color values ​​of deep blue light at 0℃ and light blue light at 20℃ in this range, and perform linear interpolation on the RGB three color channel values ​​according to the proportion of 50% to calculate the accurate RGB color value corresponding to the voxel particle.

[0045] In one specific implementation scheme, refer to Figure 6 The flowchart illustrating the method for obtaining particle transparency is shown. The method for obtaining particle transparency within each voxel includes the following steps: S46: Based on preset temperature range and transparency range, obtain the mapping relationship between comprehensive temperature value and particle transparency; S47: Based on the mapping relationship between the overall temperature value and particle transparency, and the overall temperature value of each voxel, obtain the transparency value of the particles inside each voxel.

[0046] In S46 and S47, a temperature range [T] is preset. min T max The corresponding transparency range [α] min α max For example, [α] min α max The temperature range is [0.05, 0.6]. Higher temperatures result in lower transparency (less transparent), causing the cooler (blue) particles to be more transparent and appear more "ethereal," while the hotter (orange-red) particles are less transparent and appear more "solid," creating a sense of depth. The mapping relationship between overall temperature and particle transparency can be expressed by the following formula: α base = f(T) = α max - (T - T min ) / (T max - T min ) (α max - α min ); Where, α base f(T) represents the particle transparency value of a voxel; f(T) is a function expression that only indicates that the particle transparency value of a voxel is a single-valued function of the overall temperature value T, and has no additional calculation meaning.

[0047] In another specific implementation scheme, refer to Figure 7 The diagram illustrates another method for obtaining particle transparency, which includes the following steps: S48: Divide the preset temperature value range into multiple temperature value sub-ranges, and divide the preset transparency range into an equal number of transparency sub-ranges; S49: Sort all temperature value sub-intervals from low to high temperature, and sort all transparency sub-intervals from low to high transparency. S410: Construct a one-to-one mapping relationship between temperature value sub-intervals and transparency sub-intervals based on the sorting order; S411: Based on a one-to-one mapping relationship, match the temperature value sub-interval to which the comprehensive temperature value of each voxel belongs, and obtain the transparency value of the particles inside the corresponding voxel.

[0048] In steps S48-S411, the preset temperature value range and preset transparency range are divided into an equal number of sub-ranges to ensure that the mapping dimensions of temperature and transparency match. The temperature sub-ranges are sorted from low to high temperature, and the transparency sub-ranges are sorted from low transparency to high transparency, ensuring that the mapping logic of temperature and transparency aligns with visualization requirements. Following a one-to-one correspondence rule, a specific mapping relationship is constructed between the temperature value sub-ranges and the transparency sub-ranges. Specifically, the first low-temperature sub-range corresponds to the first low-transparency sub-range, the last high-temperature sub-range corresponds to the last high-transparency sub-range, and the intermediate sub-ranges are matched sequentially according to their sorting order. For example, the temperature sub-range [0°C, 20°C] maps to the transparency sub-range [0.05, 0.15], where particles set in this range form a cold channel layer that is extremely transparent and has a slight cloud-like feel; the temperature sub-range [20°C, 40°C] maps to the transparency sub-range [0.15, 0.25], where particles set in this range have a slight cloud-like feel, but do not significantly affect the device's observation; the temperature sub-range [40°C, 60°C] maps to the transparency sub-range [0.25, 0.4], where particles set in this range form a distinct cloud layer, highlighting the area of ​​interest; the temperature sub-range [60°C, 100°C] maps to the transparency sub-range [0.4, 0.6], where particles set in this range form the most concentrated and conspicuous cloud layer.

[0049] By substituting the comprehensive temperature value of each voxel into the mapping relationship, the temperature value sub-interval to which it belongs is first precisely matched. Then, the corresponding transparency sub-interval is retrieved. Based on the mapping relationship between the temperature value sub-interval and the corresponding transparency sub-interval, the transparency value of the voxel particle is calculated, achieving a precise mapping from temperature value to particle transparency value, allowing voxel particles at different temperatures to exhibit a matching transparency effect. The mapping relationship between the temperature value sub-interval to which the comprehensive temperature value belongs and the corresponding transparency sub-interval can also be expressed by a formula, as follows: α base = α kmax - (T - T kmin ) / (T kmax - T kmin ) (α kmax - α kmin ).

[0050] Among them, T kmin This represents the lower limit of the temperature sub-interval to which the overall temperature value belongs; T kmax This represents the upper limit of the temperature sub-interval to which the overall temperature value belongs; α kmin α represents the lower limit of the transparency sub-interval that has a mapping relationship with the temperature sub-interval to which the comprehensive temperature value belongs; kmax This represents the upper limit of the transparency sub-interval that has a mapping relationship with the temperature value sub-interval to which the overall temperature value belongs. Based on the basic transparency mapping, this method further divides the transparency intervals according to temperature levels, achieving a clearer "hot and cold layering" visual effect.

[0051] Reference Figure 8 The flowchart illustrating the particle transparency adjustment method shows the following steps for adjusting the transparency of particles within each voxel: S412: Determine whether the distance between the voxel and all target devices is greater than a preset distance threshold; if the distance between the voxel and all target devices is greater than the preset distance threshold, then proceed to S413; if the distance between the voxel and any target device is not greater than the preset distance threshold, then proceed to S414. S413: Maintain the transparency value of particles inside the voxel unchanged; S414: Determine whether the voxel is located inside any target device or intersects with any target device; if the voxel is located inside any target device or intersects with any target device, then execute S415; if the voxel is completely outside all target devices, then execute S416. S415: Adjust the transparency value of particles inside the voxel to the preset value; S416: Determine whether the voxel is located in any specified direction of the target device and the distance between it and the target device is within a preset distance range; if the voxel is located in any specified direction of the target device and the distance between it and the target device is within a preset distance range, then execute S417; if the voxel is not located in any specified direction of the target device, or if the voxel is located in any specified direction of the target device but the distance between it and the target device is not within a preset distance range, then execute S413; S417: Scale the transparency value of particles inside the voxel according to the first preset ratio; S418: A ray is emitted from the camera's perspective, and the opacity of all particles before reaching the surface of the target device is accumulated along the ray direction. S419: Determine whether the accumulated opacity is greater than the preset opacity threshold; if the accumulated opacity is greater than the preset opacity threshold, then execute S420; if the accumulated opacity is not greater than the preset opacity threshold, then execute S421. S420: Scale the transparency values ​​of all particles in the ray direction before they reach the surface of the target device according to a second preset ratio; S421: Maintain the transparency value of all particles in the ray direction until they reach the surface of the target device.

[0052] In S412-S421, focusing on the core objectives of ensuring that temperature clouds do not obscure the device model and taking into account the visual continuity of distant clouds, particle transparency is processed in layers based on the spatial positional relationship between voxels and target devices. The whole process is achieved through four stages: "global distance initial screening → device space determination → specified direction screening → depth buffer threshold constraint".

[0053] In the first stage, the shortest spatial distance from the center of each voxel to the bounding box of all target device models is pre-calculated, and a global far-end distance threshold (e.g., 2m) is set. When traversing all voxels, it is first determined whether the distance between the voxel and all target devices is greater than this distance threshold. If so, the transparency of the voxel particles is not adjusted. This method of not performing special weakening processing on voxels far from the target device model can ensure that the temperature cloud layer at the far end of the target device maintains complete visual continuity without consuming GPU computing power to perform subsequent complex judgments.

[0054] In the second stage, for near-field voxels that are less than or equal to a preset distance threshold from any target device, the voxel position is further determined using the device bounding box geometry. If the voxel center falls inside the target device bounding box, or if the voxel mesh intersects with the surface of the target device model, the transparency value of the voxel particle is directly set to a preset value (such as 0 or close to 0, i.e., completely transparent or nearly completely transparent), completely preventing the rendering of temperature clouds inside the device and retaining only the visualization effect of the target device model itself, thus avoiding the cloud layer from covering the details of the target device itself; if the voxel is completely outside of all devices, the next stage of determination is performed.

[0055] In the third stage, for near-field voxels outside the device, key visible directions of the target device (such as the front of the cabinet, the direction of the heat dissipation vents, etc.) are pre-marked, and a near-distance range (e.g., 0.1–0.2m) is set in that direction. If a voxel is located in any of the specified directions of the target device and the distance is within this range (i.e., the foreground cloud layer of the target device), the original transparency is multiplied by a first preset ratio (e.g., 0.3–0.5) to forcibly reduce the particle opacity, thereby reducing the risk of cloud layer occlusion in front of the target device from the source; if the voxel does not meet the conditions of specified direction and near distance, the transparency value of the particles inside the voxel is temporarily kept unchanged.

[0056] In the fourth stage, regardless of whether the voxel opacity value has been adjusted in the first three nodes, a final depth buffer threshold constraint is required to determine whether it needs to be adjusted again or remains unchanged. Fine-grained control is performed in the GPU fragment shader using the depth buffer. A ray is emitted from the camera's viewpoint towards the target device, and the opacity (Σα) of all particles before reaching the corresponding target device surface is accumulated along the ray direction. An opacity threshold α is then set. threshold (e.g., 0.6). If the accumulated value exceeds this threshold, it will be adjusted according to the second preset ratio (i.e., α). threshold / Σα) Scales the transparency value of all particles in the ray direction before they reach the surface of the target device, so that Σα≈threshold; if the accumulated value is less than or equal to the threshold, the current transparency of the particles remains unchanged, which ensures the visualization effect of the clouds near the target device, and avoids the target device outline being occluded due to excessive superposition of particle layers through threshold constraints.

[0057] After obtaining T[x][y][z], the above scheme generates one or more temperature particles for each voxel and assigns them color and density attributes, enabling a three-dimensional understanding of temperature data. It transforms abstract temperature values ​​into a visualized point cloud, making the temperature distribution within the intelligent computing center readily apparent and effectively solving the problem of spatial perception deficiency in traditional two-dimensional thermal maps. Simultaneously, the blue and orange-red particle clouds visually represent the layered state of hot and cold channels, clearly reflecting the airflow convection patterns of air conditioning supply and server heat dissipation, and accurately revealing the causes of the heat island effect. The thermal cloud formed by temperature particles can achieve dynamic flow effects, providing an immersive visual impact, allowing visitors to intuitively perceive the "heat breathing" within the intelligent computing center, and also supporting maintenance personnel to drill down and view precise temperature data at the equipment level. The particle layer is transparently superimposed on the 3D physical model. To achieve a high-quality layered visual effect and ensure that the equipment in the data center is not obscured, the above solution implements graded configuration and depth constraint control of particle transparency through particle transparency configuration and layered display strategy. Specifically, this includes voxel-level transparency mapping, layered partition transparency strategy, and occlusion control with the target device model. This design ensures that the device itself is not obscured and successfully solves the industry pain point of conflict between performance and visual effect in existing temperature visualization solutions. In terms of occlusion control of the target device model, the combined strategy of "basic transparency mapping + temperature layer interval + weakening near the device + depth cumulative constraint" achieves basic control of "maintaining continuity of distant cloud layers, no cloud layers inside the device, and weakening of foreground cloud layers first". This allows the cloud layers to form natural layers and thickness in the depth direction, and the cloud layers near the target device automatically become "thinner" so as not to completely block the appearance of the cabinet and equipment. From any viewing angle, the outline of the target device model is always clearly visible, ensuring that the operation and maintenance personnel can identify the details of the target device.

[0058] In S5, the ThingJS VolumeLayeredMesh volume rendering engine is used to perform layered rendering of the 3D physical space model and set particles. First, the ThingJSVolumeLayeredMesh instance is initialized and a volume rendering object is created. The voxel temperature data T[x][y][z] is converted into a 3D texture or volume data buffer and then uploaded to the GPU to provide a data foundation for subsequent rendering. Then, in the vertex / fragment shader of the GPU, the voxel data is sampled according to the camera view. Combined with the preset temperature and color gradient mapping rules, each voxel particle is assigned a precise RGB color value. At the same time, according to the spatial position relationship between the voxel and the device (such as inside the device, in a specified direction, in a far region, etc.), the corresponding transparency value is configured for the particle to generate voxel fragments with color and transparency attributes, realizing layered transparent point cloud rendering. On this basis, a multi-layer particle fusion algorithm is used to make the thermal cloud layer present a sense of flow and spatial thickness. At the same time, the rendering space is clipped using the data center bounding box, and volume rendering calculations are performed only in the target area to reduce unnecessary GPU overhead. Finally, a visualization model of the intelligent computing center with fused temperature cloud layer is generated.

[0059] This implementation scheme uses particle color and transparency as core rendering attributes, permeating the entire process from GPU shader sampling to particle generation. This ensures both the visual expressiveness of the temperature cloud layer and maintains rendering performance through rendering range constraints, ultimately achieving a natural integration of the temperature cloud layer with the 3D physical space model. Technically, it employs a VolumeLayeredMesh layered rendering scheme combined with bounding box constraints to achieve real-time, lag-free updates of tens of thousands of voxels.

[0060] In traditional methods, temperature acquisition data and device physical coordinates are decoupled. Abnormal temperature values ​​require manual secondary mapping to correspond to specific cabinet locations. Hotspot location relies entirely on minute-level manual inspections, which has drawbacks such as slow response and easy omission of hidden hotspots. Even if we try to build a voxel model with tens of thousands of voxels to improve accuracy, we will face the problem of a sharp drop in system frame rate during real-time updates. Moreover, when transparent voxels are overlaid, they can easily obscure the underlying device model, resulting in a serious disconnect between the temperature field visualization effect and the actual operation and maintenance scenario.

[0061] To address the above problems, this solution provides a method for dynamic particle brightness adjustment, referring to... Figure 9 The flowchart illustrating the particle dynamic brightness adjustment method demonstrates a method for dynamically adjusting particle brightness based on changes in the risk level of the intelligent computing center. The method includes the following steps: S6: Obtain the identification and risk level of the risky equipment; S7: Based on the identification of risky devices, locate the corresponding spatial area in the visualization model of the intelligent computing center; S8: Adjust the brightness of particles within the space area based on the risk level.

[0062] In S6-S8, an alarm model is used to monitor the device status within the 3D physical space model, detecting devices with risks. Based on the unique identifier, risk level, and 3D location and bounding box information of the risky device output by the alarm model, the system automatically matches the physical space region associated with the identifier and accurately extracts the set of voxels intersecting with the device's spatial region. For particles within the voxel set, the brightness / emissivity channels are enhanced on top of the original transparency (e.g., by increasing the emissive color component), and the upper limit of particle opacity is appropriately increased (e.g., from 0.6 to 0.7). Combined with the orange-red thermal zone visual characteristics of the temperature cloud itself, a more striking dual visual highlighting effect is formed, achieving automated visual identification of risk areas. Simultaneously, the system will link the camera's viewpoint, automatically flying the viewpoint to the area where the risky device is located, completing one-click location of the abnormal cabinet. This design provides efficient technical support for intelligent operation and maintenance of the intelligent computing center, significantly reducing the response time for operation and maintenance personnel to locate risky devices from the traditional minutes to the seconds, and significantly improving the efficiency of emergency response in abnormal temperature scenarios.

[0063] Optionally, a variety of viewing modes and interaction methods can be configured for the temperature visualization scenario of the intelligent computing center to comprehensively meet the operational needs of different operation and maintenance scenarios. For example, in the global mode, the macroscopic distribution of the temperature cloud layer of the entire intelligent computing center can be displayed from the floor-view or oblique angle, making it easy for managers to intuitively observe the layout of hot and cold aisles and the overall thermal risk of the computer room; the local mode can accurately focus on the local perspective of a single row of cabinets or a single cabinet. Combined with the aforementioned particle transparency layer control strategy, it can clearly observe the temperature cloud layer distribution characteristics of the local area while ensuring the visibility of the equipment on the front and back sides of the cabinet, making it easy to view the detailed status of the equipment; at the same time, it supports convenient information interaction functions. When the user hovers the mouse over or clicks on any temperature voxel and its area, the interface will pop up detailed information such as the associated equipment number, real-time temperature value, and alarm status of the area in real time (such as "F2M2-B06, 55.0°C, mild warning"); it is also equipped with a historical playback function. Combined with the time axis control, users can freely select any time period to replay the dynamic evolution process of the temperature cloud layer, intuitively observe the entire process of the formation and dissipation of thermal risks, and provide data support for thermal risk tracing and analysis.

[0064] Optionally, multi-dimensional data caching and performance optimization strategies are used to ensure the stable operation of real-time updates of tens of thousands of voxel particles on the large screen. A version number mechanism is adopted, and the monitoring system assigns a new version number to the dataset each time new temperature data is collected. Compared with the previous version, only voxels with temperature changes are updated, and the corresponding voxel data is uploaded to the GPU and a local re-rendering is triggered, avoiding the computational cost of updating all data. Voxel block updates are implemented, dividing the voxel mesh into multiple sub-blocks in space. When the temperature change in a sub-block is lower than a threshold, the re-rendering of that sub-block is skipped, further reducing unnecessary calculations. The transparency blending logic is optimized. During the volume rendering process, pre-multiplication of alpha and the aforementioned depth accumulation threshold control method are combined to reduce the excessive mixing computation overhead caused by the superposition of multiple cloud layers. At the same time, relying on the VolumeLayeredMesh volume rendering engine and GPU shaders, large-scale parallel computing is achieved, which efficiently supports the real-time updates of tens of thousands of particle points, keeping the system frame rate stable at around 60fps, balancing rendering effect and running smoothness.

[0065] Reference Figure 10 The schematic diagram of the intelligent computing center visualization model on display is directly derived through the logical chain of voxel mapping, GPU volume rendering, and layered transparent fusion. Compared with the existing two-dimensional / point-and-surface rendering solutions, it can improve the efficiency of high temperature risk identification by more than 5 times and the rendering performance by 10 times in complex data center scenarios, and significantly enhance the sense of technology in the display and the accuracy of operation and maintenance decisions.

[0066] By using a predefined 3D voxel mesh, the ambient temperature and internal equipment temperature of the intelligent computing center are precisely bound to physical coordinates. Particles with color, transparency, and density attributes are generated within each voxel, achieving a true 3D volume rendering "temperature cloud". Through layered control of particle transparency and control of device model occlusion, the cloud has both spatial thickness and does not obscure the cabinet and equipment models. Combined with the alarm model, the brightness / transparency of the spatial particles corresponding to the risky equipment is enhanced, enabling automatic highlighting and one-click location of risky areas.

[0067] Reference Figure 11 This disclosure provides a dynamic visualization system for the temperature field of an intelligent computing center, comprising: The model building module 101 is used to build a three-dimensional physical space model of the intelligent computing center, perform voxel meshing on the three-dimensional physical space model, and obtain the spatial geometric parameters of each voxel in the three-dimensional physical space model. The coordinate acquisition module 102 is used to acquire the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value is collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value is collected. The temperature acquisition module 103 is used to acquire the comprehensive temperature value of each voxel based on the ambient temperature value, the coordinates of the acquisition location of the ambient temperature value, the device temperature value, the coordinates of the acquisition location of the device temperature value, and the spatial geometric parameters of each voxel. Particle setting module 104 is used to set particles within each voxel based on the comprehensive temperature value of each voxel; The layered rendering module 105 is used to perform layered rendering of the three-dimensional physical space model and the set particles to generate a visualization model of the intelligent computing center that incorporates temperature clouds.

[0068] The various variations and specific examples in the above-described method for dynamic visualization of the temperature field of a computing center are also applicable to the dynamic visualization system for the temperature field of a computing center provided in this disclosure. Through the foregoing detailed description of the method for dynamic visualization of the temperature field of a computing center, those skilled in the art can clearly understand the implementation method of the dynamic visualization system for the temperature field of a computing center. For the sake of brevity, it will not be described in detail here.

[0069] A computer device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0070] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory, causing the computer device to perform all or part of the steps of the intelligent computing center temperature field dynamic visualization method described in the foregoing embodiments of this disclosure.

[0071] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0072] like Figure 12 This is a schematic diagram of a computer device provided for an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the computer device in the embodiments of the present disclosure. Figure 12The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0073] like Figure 12 As shown, a computer device may include a processor (such as a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or programs loaded from storage devices into random access memory (RAM). The RAM also stores various programs and data required for the operation of the computer device. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0074] Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard drives; and communication devices. Communication devices allow the computer device to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. Although Figure 12 A computer apparatus with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or included alternatively.

[0075] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the intelligent computing center temperature field dynamic visualization method of embodiments of this disclosure are performed.

[0076] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0077] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the intelligent computing center temperature field dynamic visualization method described in the foregoing embodiments of the present disclosure are performed.

[0078] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0079] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0080] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0081] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0082] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0083] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0084] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0085] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0086] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for dynamic visualization of temperature field in an intelligent computing center, characterized in that, include: A three-dimensional physical space model of the intelligent computing center is constructed, and the three-dimensional physical space model is voxelized to obtain the spatial geometric parameters of each voxel in the three-dimensional physical space model. Acquire the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value was collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value was collected. Based on the ambient temperature value, the coordinates of the location where the ambient temperature value was collected, the device temperature value, the coordinates of the location where the device temperature value was collected, and the spatial geometric parameters of each voxel, the comprehensive temperature value of each voxel is obtained. Particles are set inside each voxel based on the overall temperature value of each voxel; The three-dimensional physical space model and the set particles are rendered in layers to generate a visualization model of the intelligent computing center that incorporates temperature clouds.

2. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, The acquisition of the ambient temperature value of the intelligent computing center, the coordinates of the location where the ambient temperature value was collected, the device temperature value of each target device inside the intelligent computing center, and the coordinates of the location where the device temperature value was collected include: Environmental temperature sensors are deployed throughout the intelligent computing center to collect environmental temperature values ​​from multiple locations. The deployment coordinates of the ambient temperature sensor are converted to the unified world coordinate system in the three-dimensional physical space model to obtain the coordinates of the acquisition location of the corresponding ambient temperature value. Deploy a device temperature sensor on each target device to collect device temperature values; Based on the three-dimensional position coordinates of the target device in the three-dimensional physical space model, the relative coordinates of the device temperature sensor within the target device are converted to the unified world coordinate system in the three-dimensional physical space model to obtain the acquisition position coordinates of the corresponding device temperature value.

3. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, The process of obtaining the comprehensive temperature value of each voxel based on the ambient temperature value, the coordinates of the location where the ambient temperature value was acquired, the device temperature value, the coordinates of the location where the device temperature value was acquired, and the spatial geometric parameters of each voxel includes: The coordinates of the ambient temperature value acquisition location, the coordinates of the device temperature value acquisition location, and the spatial geometric parameters of each voxel are matched. If the spatial geometric parameters of the voxel only match the coordinates of the location where the ambient temperature value was acquired, then the comprehensive temperature value of the voxel is the ambient temperature value. If the spatial geometric parameters of the voxel match only the coordinates of the acquisition location of the device temperature value, then the comprehensive temperature value of the voxel is the device temperature value. If the spatial geometric parameters of the voxel are successfully matched with the coordinates of the acquisition location of the ambient temperature value and the coordinates of the acquisition location of the device temperature value, then the comprehensive temperature value of the voxel is the maximum value or weighted average value of the ambient temperature value and the device temperature value. If the spatial geometric parameters of the voxel fail to match the coordinates of the acquisition location of the ambient temperature value and the coordinates of the acquisition location of the device temperature value, the comprehensive temperature value of the voxel is obtained through a spatial interpolation algorithm.

4. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, The method of setting particles within each voxel based on the comprehensive temperature value of each voxel includes: Based on preset temperature range and preset density range, obtain the mapping relationship between comprehensive temperature value and particle density; Based on the mapping relationship between the comprehensive temperature value and the particle density and the comprehensive temperature value of each voxel, the particle density value of each voxel is obtained. Based on the particle density value of each voxel, a corresponding number of particles are set inside each voxel.

5. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, Also includes: The comprehensive temperature value of each voxel is matched with a preset color gradient specification list to determine the temperature value sub-range and the corresponding base color of each voxel; wherein, the preset color gradient specification list stores the mapping relationship between the temperature value sub-range and the base color. Based on the temperature sub-range and the corresponding base color of each voxel, the RGB color values ​​of the particles inside each voxel are obtained through an interpolation algorithm.

6. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, Also includes: Based on preset temperature and transparency ranges, the mapping relationship between comprehensive temperature value and particle transparency is obtained; Based on the mapping relationship between the overall temperature value and particle transparency, and the overall temperature value of each voxel, the transparency value of the particles inside each voxel is obtained.

7. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, Also includes: The preset temperature value range is divided into multiple temperature value sub-ranges, and the preset transparency range is divided into an equal number of transparency sub-ranges. Sort all temperature value sub-intervals from low to high, and sort all transparency sub-intervals from low to high; A one-to-one mapping relationship between temperature value sub-intervals and transparency sub-intervals is constructed based on the sorting order; Based on the one-to-one mapping relationship, the temperature value sub-range to which the comprehensive temperature value of each voxel belongs is matched, and the transparency value of the particles inside the corresponding voxel is obtained.

8. The method for dynamic visualization of temperature field in a smart computing center according to claim 6 or 7, characterized in that, Also includes: Determine whether the distance between the voxel and all target devices is greater than a preset distance threshold; If the distance between the voxel and all target devices is greater than a preset distance threshold, then the transparency value of the particles inside the voxel remains unchanged. If the distance between the voxel and any target device is not greater than a preset distance threshold, then it is determined whether the voxel is located inside any target device or intersects with the target device; If the voxel is located inside or intersects with any target device, the transparency value of the particles inside the voxel is adjusted to a preset value. If the voxel is completely outside of all target devices, then determine whether the voxel is located in the specified direction of any target device and whether the distance between the voxel and the target device is within a preset distance range; If a voxel is located in any specified direction of a target device and the distance between it and the target device is within a preset distance range, then the transparency value of the particles inside the voxel is scaled according to a first preset ratio. If the voxel is not located in all the specified directions of the target device, or if the voxel is located in any of the specified directions of the target device but the distance between it and the target device is not within the preset distance range, then the transparency value of the particles inside the voxel remains unchanged.

9. The method for dynamic visualization of temperature field in a smart computing center according to claim 8, characterized in that, Also includes: A ray is emitted from the camera's perspective, and the opacity of all particles before reaching the surface of the target device is accumulated along the ray direction. Determine whether the accumulated opacity is greater than the preset opacity threshold; If the accumulated opacity is greater than the preset opacity threshold, the opacity value of all particles in the ray direction before reaching the surface of the target device is scaled according to the second preset ratio. If the accumulated opacity is not greater than the preset opacity threshold, the opacity value of all particles remains unchanged in the ray direction before reaching the surface of the target device.

10. The method for dynamic visualization of temperature field in a smart computing center according to claim 1, characterized in that, Also includes: Obtain the identification and risk level of risky equipment; Based on the identification of the risky device, the corresponding spatial region is located in the visualization model of the intelligent computing center; Based on the risk level, the brightness of particles within the spatial region is adjusted.

11. A computer device, characterized in that, The computer device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the dynamic visualization method for temperature field of the intelligent computing center as described in any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the intelligent computing center temperature field dynamic visualization method as described in any one of claims 1-10.

13. A computer program product comprising computer instructions, characterized in that, When executed by a processor, the computer instructions implement the steps of the method for dynamic visualization of the temperature field of a smart computing center as described in any one of claims 1-10.

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