High-low voltage power distribution cabinet heat dissipation control method and system based on Internet of Things

Abstract

The invention discloses a high-low voltage power distribution cabinet heat dissipation control method and system based on the Internet of Things, and the method comprises the steps: carrying out the real-time temperature collection of a plurality of part positions in a high-low voltage power distribution cabinet through an Internet of Things sensor array, and obtaining a temperature data set; generating a thermal distribution diagram in the high-low voltage power distribution cabinet according to the temperature data set, and generating a heat flow distribution matrix according to the thermal distribution diagram; generating a risk distribution map according to the heat flow distribution matrix; designing a geometric structure of a heat flow guide channel of the high-low voltage power distribution cabinet according to the risk distribution diagram to obtain channel layout parameters; and determining a heat dissipation scheme of the high-low voltage power distribution cabinet according to the channel layout parameters, and performing heat dissipation control on the high-low voltage power distribution cabinet based on the heat dissipation scheme.

Description

Technical Field

[0001] The present application relates to the field of heat dissipation of high and low voltage distribution cabinets, and specifically to a heat dissipation control method and system for high and low voltage distribution cabinets based on the Internet of Things. Background Art

[0002] As the core of power transmission and distribution, the stable operation of the power distribution system is crucial to social production and life. However, if the heat generated by the current load during operation of high and low voltage distribution cabinets cannot be effectively dissipated, it will lead to equipment aging, failure, and even safety accidents. Currently, traditional heat dissipation control methods mostly rely on mechanical fans or fixed heat dissipation devices. Although these methods can alleviate the problem of heat accumulation to a certain extent, they have many drawbacks.

[0003] First, traditional heat dissipation methods lack intelligent management capabilities, making it difficult to flexibly adjust to dynamic temperature changes. For example, under high-load scenarios, localized overheating is particularly problematic, and existing fixed heat dissipation devices are unable to respond promptly to these changes, resulting in inefficient heat dissipation. Furthermore, due to the non-uniform heat distribution within the power distribution cabinet, traditional uniform heat dissipation methods are unable to effectively address the problem of heat accumulation in localized high-temperature areas.

[0004] Secondly, existing technologies lack the ability to analyze the patterns of heat generation and transfer. Although existing industrial control software has been applied in certain fields, it is still weak in controlling heat dissipation in power distribution cabinets. Current industrial control software is mostly used for simple data collection and monitoring, lacking the ability to deeply analyze complex heat distribution patterns, making it difficult to identify potential overheating risk points. At the same time, existing industrial control software also has limitations in optimizing airflow organization and is unable to design efficient heat flow guidance channels based on real-time data. In addition, traditional methods do not fully utilize temperature difference characteristics, failing to fully guide heat flow from high-temperature areas to low-temperature areas, further reducing heat dissipation efficiency.

[0005] As can be seen, existing technologies for heat dissipation control suffer from high energy consumption, low efficiency, and insufficient intelligence, especially when dealing with complex heat distributions. With the widespread adoption of IoT technology, the potential for industrial control software applications is urgently needed to address the many challenges currently faced in heat dissipation control.

[0006] In summary, with respect to the problem that the related technology cannot efficiently control the heat dissipation of high and low voltage distribution cabinets, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the Invention

[0007] The embodiments of the present application provide a method and system for controlling heat dissipation of high and low voltage distribution cabinets based on the Internet of Things, so as to at least solve the problem of being unable to efficiently control the heat dissipation of high and low voltage distribution cabinets.

[0008] According to one aspect of an embodiment of the present application, a method for heat dissipation control of a high and low voltage distribution cabinet based on the Internet of Things is provided, comprising: performing real-time temperature acquisition of multiple component positions in the high and low voltage distribution cabinet through an Internet of Things sensor array to obtain a temperature data set, wherein each temperature data in the temperature data set includes a temperature value, a timestamp corresponding to the temperature value, and a three-dimensional position coordinate; generating a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set, and generating a heat flux distribution matrix according to the thermal distribution map, wherein the heat flux distribution matrix has heat flux intensities of multiple grid points in the thermal distribution map; generating a risk distribution map according to the heat flux distribution matrix, wherein the risk distribution map is used to describe the distribution of multiple risk grid points, and the absolute value of the three-dimensional temperature gradient vector of the risk grid point is greater than a first preset threshold; designing the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet according to the risk distribution map to obtain channel layout parameters; determining the heat dissipation scheme of the high and low voltage distribution cabinet according to the channel layout parameters, and performing heat dissipation control on the high and low voltage distribution cabinet based on the heat dissipation scheme.

[0009] In an exemplary embodiment, a thermal distribution map inside the high and low voltage distribution cabinet is generated based on the temperature data set, including: removing abnormal temperature data in the temperature data set according to preset data cleaning rules to obtain a cleaned temperature data set; normalizing the temperature value of each temperature data in the cleaned temperature data set to obtain a normalized temperature data set; clustering the normalized temperature data set based on the K-means clustering algorithm using the distance between the three-dimensional position coordinates in the temperature data as a distance metric to obtain K clusters, wherein K is an integer greater than or equal to 2, and different clusters correspond to different temperature areas; based on a color mapping image generation method, a thermal distribution map inside the high and low voltage distribution cabinet is generated based on the three-dimensional position coordinates and temperature values ​​corresponding to the K clusters.

[0010] In an exemplary embodiment, generating a heat flux distribution matrix based on the thermal distribution map includes: using a finite difference method to calculate the temperature gradient of each grid point in the thermal distribution map, and calculating the heat flux intensity of each grid point based on the temperature gradient of each grid point to obtain a target data set, wherein the target data set includes the temperature value, temperature gradient and heat flux intensity of each grid point; in the case where there is a grid point with a temperature gradient greater than a preset gradient threshold, tracing a heat transfer path along a gradient descent direction according to the target data set to obtain a heat transfer path, wherein the heat transfer path has multiple grid points, the multiple grid points include grid points with a temperature gradient greater than a preset gradient threshold, and the temperature gradients of the multiple grid points decrease; generating a heat flux distribution matrix based on the heat transfer path, wherein the heat flux distribution matrix includes the heat flux intensities of the multiple grid points.

[0011] In an exemplary embodiment, a risk distribution map is generated based on the heat flux distribution matrix, including: using Gaussian filtering to smooth the heat flux distribution matrix to obtain a smoothed heat flux distribution matrix; using a finite difference method to calculate the temperature gradient of each grid point in the three-dimensional direction based on the smoothed heat flux distribution matrix to obtain a three-dimensional gradient vector of each grid point; determining a risk point set based on the three-dimensional gradient vector of each grid point, wherein the grid points in the risk point set are the risk grid points; and using a stereo microscope algorithm to perform three-dimensional visualization processing based on the risk point set to obtain a risk distribution map.

[0012] In an exemplary embodiment, the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet is designed according to the risk distribution map to obtain the channel layout parameters, including: determining the three-dimensional coordinates of the multiple risk grid points according to the risk distribution map to obtain a risk point coordinate data set; discretizing the high and low voltage distribution cabinets according to the risk point coordinate data set using the finite volume method to generate three-dimensional finite volume grid units; determining the spatial range of the three-dimensional finite volume grid units to obtain a discretized grid data set; based on the SST k-ε turbulence model and momentum equation, calculating the airflow motion trajectory according to the discretized grid data set and the boundary conditions of the high and low voltage distribution cabinets to obtain an airflow trajectory data set, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinets; according to the airflow trajectory data set, using an iterative optimization algorithm to calculate the cross-sectional shape and length of the heat flow guiding channel to obtain the channel layout parameters.

[0013] In an exemplary embodiment, before determining the heat dissipation scheme of the high and low voltage distribution cabinet according to the channel layout parameters, the method also includes: generating a channel geometric model according to the channel layout parameters; meshing the channel geometric model through a finite element analysis tool, and calculating the numerical expression of the initial airflow velocity field and the initial temperature field according to the boundary conditions of the high and low voltage distribution cabinet, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinet; using the Navier-Stokes equation and the energy equation to couple the initial airflow velocity field and the initial temperature field to obtain a numerical expression of the target airflow velocity field; analyzing the airflow velocity distribution characteristics and the pressure distribution characteristics according to the numerical expression of the target airflow velocity field to optimize the channel layout parameters.

[0014] In an exemplary embodiment, the heat dissipation scheme of the high and low voltage distribution cabinet is determined according to the channel layout parameters, including: constructing a three-dimensional model of the high and low voltage distribution cabinet through digital twin technology, and obtaining the three-dimensional position of the heat dissipation device of the high and low voltage distribution cabinet; adjusting the three-dimensional position of the heat dissipation device according to the channel layout parameters, and determining the spatial layout of the high and low voltage distribution cabinet according to the adjusted three-dimensional position of the heat dissipation device; based on digital twin technology, performing convection simulation according to the spatial layout to obtain a multidimensional data set, wherein the multidimensional data set includes numerical expressions of the air flow velocity field and temperature field of the high and low voltage distribution cabinet; determining the heat dissipation scheme of the high and low voltage distribution cabinet according to the channel layout parameters and the multidimensional data set.

[0015] In an exemplary embodiment, the heat dissipation scheme of the high and low voltage distribution cabinet is determined based on the channel layout parameters and the multidimensional data set, including: determining the temperature change curve of the target area in the high and low voltage distribution cabinet based on the multidimensional data set, and determining the temperature change rate based on the temperature change curve, wherein the temperature of the target area is greater than a preset temperature; when the temperature change rate is greater than or equal to the preset change rate, determining the heat dissipation scheme of the high and low voltage distribution cabinet based on the numerical expression of the channel layout parameters and the airflow velocity field.

[0016] In an exemplary embodiment, the method further includes: when the temperature change rate is less than the preset change rate, using a geometric optimization tool to adjust the width and angle of the heat flow guiding channel according to the numerical expression of the air flow velocity field to obtain optimized channel layout parameters; based on the use of digital twin technology, performing convection simulation according to the optimized channel layout parameters to obtain a target multidimensional data set, wherein the target multidimensional data set includes the numerical expression of the updated air flow velocity field and temperature field of the high and low voltage distribution cabinet; when the temperature change rate determined according to the target multidimensional data set is greater than or equal to the preset change rate, determining the heat dissipation solution of the high and low voltage distribution cabinet based on the optimized channel layout parameters and the numerical expression of the updated air flow velocity field.

[0017] According to another aspect of the embodiment of the present application, a high and low voltage distribution cabinet heat dissipation control system based on the Internet of Things is also provided, including: an acquisition module for performing real-time temperature acquisition of multiple component positions in the high and low voltage distribution cabinet through an Internet of Things sensor array to obtain a temperature data set, wherein each temperature data in the temperature data set includes a temperature value, a timestamp corresponding to the temperature value, and a three-dimensional position coordinate; a first generation module for generating a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set, and generating a heat flow distribution matrix according to the thermal distribution map, wherein the heat flow distribution matrix has the The heat flux intensity of multiple grid points in the thermal distribution map; a second generation module, used to generate a risk distribution map according to the heat flux distribution matrix, wherein the risk distribution map is used to describe the distribution of multiple risk grid points, and the absolute value of the three-dimensional temperature gradient vector of the risk grid point is greater than a first preset threshold; a design module, used to design the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet according to the risk distribution map, and obtain the channel layout parameters; a control module, used to determine the heat dissipation scheme of the high and low voltage distribution cabinet according to the channel layout parameters, and perform heat dissipation control on the high and low voltage distribution cabinet based on the heat dissipation scheme.

[0018] According to another aspect of the embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored on the memory and runnable on the processor, characterized in that when the processor executes the computer program, the steps of the above-mentioned method for heat dissipation control of high and low voltage distribution cabinets based on the Internet of Things are implemented.

[0019] According to another aspect of the embodiment of the present application, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned Internet of Things-based high and low voltage distribution cabinet heat dissipation control method when running.

[0020] According to another aspect of the embodiments of the present application, a computer program product is further provided, including a computer program. When the computer program is executed by a processor, the above-mentioned method for controlling heat dissipation of high and low voltage distribution cabinets based on the Internet of Things is implemented.

[0021] This application utilizes an IoT sensor array to collect real-time temperatures at multiple component locations within a power distribution cabinet, generating a temperature dataset containing temperature values, timestamps, and three-dimensional location coordinates. This ensures a comprehensive understanding of the internal temperature distribution within the cabinet, providing accurate data support for subsequent analysis. A thermal distribution map is generated based on the temperature dataset, and a heat flux distribution matrix is ​​further generated to quantify the heat flux intensity at each grid point. This process reveals the patterns of heat transfer and the distribution characteristics of high-temperature areas, providing a basis for identifying heat dissipation bottlenecks. By analyzing the heat flux distribution matrix, a risk distribution map is generated, clearly identifying risk grid points where the absolute value of the three-dimensional temperature gradient vector exceeds a first preset threshold. This step accurately locates localized overheating areas and improves the efficiency of problem detection. Based on the risk distribution map, the geometric structure of the heat flow guidance channel is designed to optimize the airflow path and ensure efficient heat flow from high-temperature areas to low-temperature areas. Finally, a heat dissipation solution is determined and controlled based on the channel layout parameters, achieving efficient heat dissipation management for high- and low-voltage power distribution cabinets. This achieves closed-loop optimization from data collection to heat dissipation control, significantly improving heat dissipation efficiency. This solves the problem of inefficient heat dissipation control in high- and low-voltage power distribution cabinets in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] In order to more clearly illustrate the embodiments of the present application or the technology in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 This is a flow chart of a method for controlling heat dissipation of high and low voltage distribution cabinets based on the Internet of Things according to an embodiment of the present application;

[0025] Figure 2 This is a structural block diagram of a high and low voltage distribution cabinet heat dissipation control system based on the Internet of Things according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand this application, the following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technology in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] In this embodiment, a method for controlling heat dissipation of high and low voltage distribution cabinets based on the Internet of Things is provided, including but not limited to applications in industrial control software. Figure 1 FIG. 1 is a flow chart of a method for controlling heat dissipation of a high and low voltage distribution cabinet based on the Internet of Things according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps S102 to S110:

[0029] Step S102: Real-time temperature acquisition is performed on multiple component positions in the high and low voltage distribution cabinet using an IoT sensor array to obtain a temperature data set, wherein each temperature data in the temperature data set includes a temperature value, a timestamp corresponding to the temperature value, and a three-dimensional position coordinate;

[0030] In an exemplary embodiment, an IoT sensor array can be used to deploy high-precision thermistor sensors at key component locations inside a power distribution cabinet, collect temperature data once per second, and generate a temperature dataset containing timestamps and three-dimensional coordinates.

[0031] For example, in a typical medium-voltage distribution cabinet, sensors are deployed at key components such as busbars, circuit breakers, and cable connectors. They collect temperature data once per second, generating data in the format of (timestamp, x, y, z, temperature value), such as (2025-05-14-08:00:00, 1.2, 0.5, 0.3, 45.2). This high-frequency data acquisition ensures real-time and accurate data, providing a reliable foundation for subsequent anomaly detection. Thermistor sensors are chosen for their high sensitivity and low cost, effectively capturing subtle temperature changes and suiting them for complex industrial environments.

[0032] Optionally, after obtaining the temperature dataset, the temperature value corresponding to the three-dimensional coordinates of each key component can be obtained. If the temperature value exceeds a preset threshold, the data point is marked as an outlier (optionally, the temperature threshold is set to 60.0 degrees Celsius. If the temperature value of a component exceeds this threshold, it is marked as an outlier). A temperature dataset containing outlier marks is obtained. Using the temperature dataset containing outlier marks, a clustering algorithm is used to group the outliers, determine the spatial distribution of the outliers in the three-dimensional coordinates, and generate a spatial distribution dataset of the outliers. Based on the spatial distribution dataset of the outliers, a timestamp sequence of the outliers is obtained, and the continuity of the outliers in the time dimension is determined to generate a multidimensional temperature anomaly dataset containing time continuity information.

[0033] For example, the busbar position (1.2, 0.5, 0.3) recorded a temperature of 62.3 degrees Celsius at the timestamp 2025-05-14-08:00:05, which exceeded the threshold and was marked as an abnormality. This threshold setting is based on the operating specifications of the distribution cabinet and can be dynamically adjusted according to the equipment type. The abnormal marking dataset retains the timestamp and coordinate information to facilitate subsequent analysis of the spatial and temporal characteristics of the abnormality. In the abnormal point grouping, the K-means clustering algorithm is used to spatially group the abnormal points. For example, assuming that 10 abnormal points are detected in the distribution cabinet, the clustering algorithm divides them into 3 groups based on the three-dimensional coordinates, corresponding to the busbar area, circuit breaker area and cable joint area respectively. The spatial distribution dataset shows that the abnormal points in the busbar area are concentrated near (1.2, 0.5, 0.3), indicating that there may be overload or poor contact problems in this area. This spatial analysis helps to quickly locate the fault area and improve maintenance efficiency.

[0034] For temporal continuity analysis, the spatial distribution dataset of anomalies is used to extract the timestamp sequence for each anomaly. For example, the timestamps for the anomaly points in the busbar area are (08:00:05, 08:00:06, 08:00:07), indicating that the anomaly persisted for three seconds, suggesting a potential risk of sustained overheating. The resulting multidimensional temperature anomaly dataset integrates both temporal continuity and spatial distribution information, for example, (busbar, 1.2, 0.5, 0.3, 62.3, for three consecutive seconds). This multidimensional data provides a basis for fault prediction, and continuity analysis can provide early warning of potential thermal runaway risks.

[0035] It's important to note that this analysis allows maintenance personnel to quickly identify anomalies in the busbar area, determine the severity of the fault based on temporal continuity, and prioritize repairs. This approach significantly improves the reliability and safety of distribution panel operations, reducing the risk of power outages or equipment damage caused by overheating.

[0036] Furthermore, industrial control software, combined with temperature data sets collected by IoT sensor arrays, can monitor the temperature distribution inside the power distribution cabinet in real time and accurately locate hotspots. This helps the industrial control software quickly analyze heat accumulation trends and optimize cooling strategies, improving equipment operational safety and reliability while reducing energy consumption and maintenance costs.

[0037] Step S104: generating a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set, and generating a heat flow distribution matrix according to the thermal distribution map, wherein the heat flow distribution matrix includes heat flow intensities of multiple grid points in the thermal distribution map;

[0038] In an exemplary embodiment, the above-mentioned generation of the thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set can be achieved by the following steps S11-S14:

[0039] Step S11: removing abnormal temperature data from the temperature dataset according to a preset data cleaning rule to obtain a cleaned temperature dataset;

[0040] In one possible implementation, the acquired temperature dataset typically contains timestamps, three-dimensional coordinates, and temperature values, but may contain noise or erroneous data. Data cleaning removes abnormal points through statistical outlier detection. For example, using a threshold method based on mean and standard deviation, assuming the temperature data for a key component has a mean of 50°C and a standard deviation of 5°C, the threshold is set to the mean ± 2 times the standard deviation, or 40°C to 60°C. Data outside this range, such as 65°C, is marked as an outlier and removed. This method ensures that the cleaned dataset is more accurate and reflects the actual temperature distribution.

[0041] Step S12: normalizing the temperature value of each temperature data in the cleaned temperature data set to obtain a normalized temperature data set;

[0042] Optionally, for the cleaned temperature data set, a data normalization method can be used to process the temperature values ​​through linear normalization, wherein the linear normalization formula is x'=(x-x_min) / (x_max-x_min), x is the original temperature value, x_min is the minimum temperature value, x_max is the maximum temperature value, and x' is the normalized temperature value, to obtain the normalized temperature data set.

[0043] For example, for a cleaned temperature dataset, data standardization uses linear normalization to process temperature values, making data of different dimensions comparable. For example, suppose the temperature range of a high- and low-voltage distribution cabinet is 30°C to 70°C, and the temperature at a certain point is 50°C. The normalization formula is (50-30) / (70-30) = 0.5. The normalized value is between 0 and 1, making it easier to analyze later.

[0044] It should be noted that linear normalization retains the relative relationship of temperature and is suitable for processing by algorithms such as clustering.

[0045] Step S13: clustering the normalized temperature data set based on the K-means clustering algorithm using the distance between the three-dimensional position coordinates in the temperature data as a distance metric to obtain K clusters, where K is an integer greater than or equal to 2, and different clusters correspond to different temperature regions;

[0046] Optionally, spatial distribution features are extracted from the normalized temperature data set, and the k-means clustering algorithm is used with Euclidean distance as the distance metric, where the Euclidean distance formula is d = √(∑(x_i-y_i)^2), x_i is the coordinate of the temperature point, y_i is the coordinate of the cluster center, and d is the distance. The intra-cluster variance is minimized through iterative updating to obtain K clusters.

[0047] For example, assume there are 1000 data points in a power distribution cabinet, and set k = 3. The algorithm iteratively calculates the distance from each point to the cluster center, dividing the data into high-temperature, medium-temperature, and low-temperature areas. For example, the high-temperature cluster center might be located near the transformer at coordinates (10, 5, 3), with a normalized temperature close to 1; the low-temperature cluster center is located near the ventilation opening, with a normalized value close to 0. This grouping clearly reveals the heat distribution pattern and facilitates the identification of overheated areas.

[0048] Step S14: Based on a color mapping image generation method, a thermal distribution map inside the high and low voltage distribution cabinet is generated according to the three-dimensional position coordinates and temperature values ​​corresponding to the K clusters.

[0049] Optionally, an image generation method may be used to draw a non-uniform heat distribution map through color mapping to obtain a visualized image of the heat distribution inside the power distribution cabinet.

[0050] For example, after one hour of operation, a distribution cabinet's cluster near the relay showed persistently high temperatures, with a normalized value stabilizing at 0.9 and the image appearing red, prompting a need to check the heat dissipation or load conditions in that area. This multi-dimensional analysis improves the accuracy of temperature monitoring and optimizes equipment maintenance strategies.

[0051] It should be noted that implementing the thermal distribution map generation process described above through industrial control software offers the following benefits: 1) The industrial control software automatically removes abnormal temperature data according to preset rules, ensuring that the cleaned temperature dataset more accurately reflects the actual temperature distribution and reduces noise interference; 2) Temperature values ​​are linearly normalized to make data of different dimensions comparable, facilitating subsequent clustering analysis and algorithm processing, improving computational efficiency and result reliability; 3) Normalized data is clustered based on three-dimensional position coordinate distance metrics, clearly demarcating high-temperature, medium-temperature, and low-temperature areas within high- and low-voltage distribution cabinets and revealing heat distribution patterns; 4) The industrial control software combines color mapping to generate intuitive thermal distribution maps, helping operations and maintenance personnel quickly locate high-temperature risk areas and providing a scientific basis for heat dissipation optimization. The overall process is highly automated, significantly improving data analysis efficiency and decision-making accuracy.

[0052] In an exemplary embodiment, the above-mentioned generation of the heat flux distribution matrix according to the thermal distribution map can be achieved by the following steps S21-S23:

[0053] Step S21: using a finite difference method to calculate the temperature gradient of each grid point in the thermal distribution map, and calculating the heat flux intensity of each grid point based on the temperature gradient of each grid point, to obtain a target data set, wherein the target data set includes the temperature value, temperature gradient, and heat flux intensity of each grid point;

[0054] Alternatively, the temperature data points can be obtained from the thermal distribution map, and the temperature gradient of each grid point can be calculated using the finite difference method, where the finite difference formula is: T is the temperature value, i is the grid point index, and Δx is the grid spacing. T_{i+1} and T_{i-1} are the temperatures of the two adjacent grid points in opposite directions of the i-th grid point.

[0055] It should be noted that the finite difference method estimates the gradient by using the temperature difference between adjacent grid points, reflecting the rate of heat change. This method is effective in capturing areas with drastic temperature changes, such as those near heating elements, where the gradient is higher.

[0056] Alternatively, according to Fourier's law, the heat flux is proportional to the temperature gradient: Where q is the heat flux intensity, and k is the thermal conductivity of the material (unit: W / (m·K)), which indicates the ability of the material to conduct heat. It is the temperature gradient (unit: ℃ / m or K / m), which represents the rate of temperature change per unit distance.

[0057] Step S22: When there is a grid point whose temperature gradient is greater than a preset gradient threshold, tracing a heat transfer path along a gradient descent direction according to the target data set to obtain a heat transfer path, wherein the heat transfer path has a plurality of grid points, the plurality of grid points including a grid point whose temperature gradient is greater than the preset gradient threshold, and the temperature gradients of the plurality of grid points decrease in descending order;

[0058] Alternatively, the heat transfer direction can be extracted from the target dataset. By setting a gradient threshold, such as 10°C / meter, high-gradient areas can be identified. A vector tracing algorithm can then be used to determine the heat transfer path along the gradient descent direction. The vector tracing algorithm simulates the flow of heat from high-temperature to low-temperature areas by following the gradient vector direction. For example, in a high-temperature area near a transformer, where the gradient points toward a heat dissipation vent, the tracing path shows that heat flows toward well-ventilated areas. This path analysis helps identify potential risk points for heat accumulation.

[0059] Step S23: generating a heat flux distribution matrix according to the heat transfer path, wherein the heat flux distribution matrix includes heat flux intensities of the plurality of grid points.

[0060] Alternatively, a two-dimensional heat flow distribution matrix can be generated using a matrix generation method based on the heat transfer path. Matrix elements represent the heat flow intensity at a grid point, i.e., M(i, j) represents the heat flow intensity at a grid point, and the magnitude of the value reflects the strength of the heat flow. For example, if the heat flow intensity at a grid point near a relay in a certain area of ​​the distribution cabinet is high, the matrix element value will be large, such as 0.8, indicating heat concentration; while the value near the ventilation opening will be low, such as 0.2. This matrix intuitively reflects the heat distribution pattern and facilitates the identification of high-temperature risk areas.

[0061] It's important to note that the heat flux distribution matrix can be visualized using color mapping, with red indicating high heat flux and blue indicating low heat flux. For example, areas near heat-generating components appear red, indicating that thermal design needs attention; areas with ventilation holes appear blue, indicating good heat dissipation. This visualization allows operators to quickly assess equipment status and optimize maintenance strategies.

[0062] It should be noted that generating a heat flux distribution matrix through industrial control software has the following benefits: 1) The finite difference method is used to calculate the temperature gradient at each grid point, and the target data set (including temperature value, temperature gradient, and heat flux intensity) is combined to quantify the heat transfer pattern and improve analysis accuracy; 2) When the temperature gradient at a grid point exceeds a preset threshold, the industrial control software automatically traces the heat transfer path along the descending gradient, screens out multiple key grid points with decreasing temperature gradients, and clarifies the direction of heat flow; 3) A heat flux distribution matrix is ​​generated based on the heat transfer path, intuitively reflecting the heat flux intensity distribution at each grid point, providing a scientific basis for heat dissipation optimization. The overall process is highly automated, significantly improving the efficiency and accuracy of heat transfer analysis and facilitating precise heat dissipation control.

[0063] Step S106: generating a risk distribution map according to the heat flux distribution matrix, wherein the risk distribution map is used to describe the distribution of a plurality of risk grid points, and the absolute value of the three-dimensional temperature gradient vector of the risk grid point is greater than a first preset threshold;

[0064] In an exemplary embodiment, the above step S106 can be implemented by the following steps S31-S34:

[0065] Step S31: using Gaussian filtering to smooth the heat flux distribution matrix to obtain a smoothed heat flux distribution matrix;

[0066] Optionally, before step S31, spatial interpolation can be used to estimate the heat flow value of the unknown point. Specifically, trilinear interpolation can be used. For example, an unknown point is adjacent to 8 known points, and the heat flow values ​​are 200, 210, 205, 198, 202, 208, 195, and 203 W / m 2 The heat flux at this point is estimated to be approximately 201.5 W / m2 by weighted average. This method ensures the continuity of the heat flux distribution and facilitates subsequent analysis.

[0067] In one possible implementation, Gaussian filtering is used to smooth the heat flux distribution matrix and eliminate measurement noise. Assuming a Gaussian kernel standard deviation of 1.5 grid cells (for example, corresponding to a physical scale of 0.015 meters), filtering reduces heat flux fluctuations. For example, if the original heat flux value sequence at a point is [200, 210, 195], it becomes [202, 205, 200] after smoothing, making the data more continuous and reflecting the true trend.

[0068] Step S32: Calculate the temperature gradient of each grid point in the three-dimensional direction using the finite difference method according to the smoothed heat flux distribution matrix to obtain the three-dimensional gradient vector of each grid point;

[0069] Step S33: determining a risk point set according to the three-dimensional gradient vector of each grid point, wherein the grid points in the risk point set are the risk grid points;

[0070] Assume that the heat flux values ​​of a grid point at adjacent points along the x, y, and z directions are:

[0071] The two heat flow values ​​of two adjacent grid points in opposite directions are q1 and q2, and the temperature gradient in the x direction can be calculated as: Where q is the heat flux intensity, in W / m 2 , k is the thermal conductivity of the material (unit: W / (m·K)), which indicates the ability of the material to conduct heat; is the temperature gradient (unit: ℃ / m or K / m), Δx is the grid spacing in meters; it corresponds to the temperature gradient in the y and z directions.

[0072] Then construct a three-dimensional gradient vector:

[0073] If the magnitude (or any component) of the three-dimensional gradient vector of a grid point exceeds a threshold, the point is marked as a risk point.

[0074] Step S34: Based on the risk point set, a stereo microscope algorithm is used to perform three-dimensional visualization processing to obtain a risk distribution map.

[0075] In one exemplary embodiment, suppose a region experiences excessive gradients at ten grid points due to insufficient cooling, forming a cluster of risk points. A stereomicroscope algorithm is used for 3D visualization, highlighting these risk points in red within the grid, visually demonstrating overheating areas. For example, a dense red grid near the exhaust port prompts designers to optimize cooling channels. This visualization allows engineers to quickly identify problem areas and improve design efficiency.

[0076] It should be noted that implementing the above process through industrial control software offers the following benefits: 1) The software performs Gaussian filtering on the heat flux distribution matrix, eliminating measurement noise, improving data continuity and authenticity, and providing a more reliable basis for subsequent analysis. 2) Based on the smoothed heat flux distribution matrix, the finite difference method is used to calculate the three-dimensional temperature gradient vector for each grid point, accurately quantifying areas of intense heat fluctuation and improving the accuracy of risk identification. 3) Risk grid points with temperature gradients exceeding the threshold are screened based on the three-dimensional gradient vectors, automatically generating a risk point set and quickly locating localized overheating areas. 4) A stereo microscope algorithm is used to perform three-dimensional visualization of the risk point set, generating a risk distribution map. This helps maintenance personnel intuitively understand the distribution of risk areas and optimize maintenance strategies. The entire process is highly automated, significantly improving analysis efficiency and decision-making accuracy.

[0077] Step S108: designing the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet according to the risk distribution map to obtain channel layout parameters;

[0078] In an exemplary embodiment, the above step S108 can be implemented by the following steps S41-S45:

[0079] Step S41: determining the three-dimensional coordinates of the plurality of risk grid points according to the risk distribution map to obtain a risk point coordinate data set;

[0080] Optionally, a stereo microscope algorithm is used to scan the risk distribution map to determine the three-dimensional coordinates of the multiple risk grid points. For example, assuming the dimensions of a power distribution cabinet are 1m×0.8m×0.5m, the algorithm identifies five risk points with coordinates of (0.2, 0.3, 0.1), (0.3, 0.4, 0.2), etc., in meters.

[0081] Step S42: discretizing the high and low voltage distribution cabinets using a finite volume method according to the risk point coordinate data set to generate a three-dimensional finite volume grid unit;

[0082] Optionally, based on the risk point coordinate dataset, an appropriate grid cell size (e.g., 0.01 m × 0.01 m × 0.01 m) is selected to ensure a balance between accuracy and computational efficiency. The internal space of the distribution cabinet is discretized using the finite volume method, generating approximately 4 million grid cells.

[0083] For example: the center coordinates of a grid cell are (0.25, 0.35, 0.15), covering a surrounding area of ​​0.01m 3 The meshing is denser near the risk point to capture the thermal flux variations. The discretized mesh dataset provides the basis for the airflow simulation.

[0084] Step S43: determining the spatial range of the three-dimensional finite volume grid unit to obtain a discretized grid data set;

[0085] Optionally, each grid cell records its spatial range. For example, if the center coordinates of a cell are (0.25, 0.35, 0.15), the range covers 0.01m around it. 3 By clarifying the spatial range of the grid cells, accurate geometric information is provided for subsequent airflow trajectory calculations.

[0086] Step S44: Based on the SST k-ε turbulence model and the momentum equation, the airflow trajectory is calculated according to the discretized grid data set and the boundary conditions of the high and low voltage distribution cabinets to obtain an airflow trajectory data set, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinets;

[0087] Optionally, boundary conditions include: a distribution cabinet wall temperature of 50°C, an air inlet velocity of 2 m / s, and the inlet located at the cabinet bottom. The SST k-ε turbulence model, combined with the momentum equation, is used to calculate airflow trajectories. This model simulates turbulent energy and dissipation rate to predict the airflow path within the cabinet. Assuming air enters from the bottom inlet and forms vortices upon encountering a high-temperature risk point, trajectory data shows that the airflow velocity drops to 0.5 m / s near (0.2, 0.3, 0.1), indicating insufficient heat dissipation. The airflow trajectory dataset intuitively reflects the heat transfer path, providing a basis for optimizing channel design.

[0088] It should be noted that the SST k-ε turbulence model is suitable for simulating near-wall flow and separated flow.

[0089] Step S45: Calculate the cross-sectional shape and length of the heat flow guiding channel using an iterative optimization algorithm based on the airflow trajectory data set to obtain the channel layout parameters.

[0090] In one possible implementation, an iterative optimization algorithm is used to design heat-guiding channels. Based on a dataset of airflow trajectories, the algorithm adjusts the channel's cross-sectional shape and length, aiming to enhance the cooling effect of the airflow on risk points. The initial channel design had a rectangular cross-section, 0.05m wide, 0.03m high, and 0.4m long. After optimization, the cross-section was adjusted to an elliptical shape with a short axis of 0.04m, a long axis of 0.06m, and a length extended to 0.5m. The optimized channel guides airflow more concentratedly through risk points, improving heat dissipation efficiency. The design was verified through 3D modeling to ensure that the channel geometry fits the interior space of the power distribution cabinet.

[0091] It should be noted that the use of industrial control software to implement heat flow guidance channel design has the following benefits: 1) The three-dimensional coordinates of the risk grid points are extracted based on the risk distribution map, and a risk point coordinate data set is generated to ensure that the channel design is targeted at the actual high-temperature area; 2) The finite volume method is used to discretize the distribution cabinet to generate three-dimensional grid units, and the SST k-ε turbulence model and momentum equation are combined to simulate the airflow trajectory and accurately predict the heat transfer path; 3) The cross-sectional shape and length of the heat flow guidance channel are adjusted using an iterative optimization algorithm to enhance the cooling effect of the airflow on the risk points and improve the heat dissipation efficiency; 4) Industrial control software runs through the entire process, from data acquisition to trajectory calculation to channel design, to achieve automated processing, while providing intuitive airflow trajectory and channel layout visualization results, significantly improving design accuracy and efficiency.

[0092] In an exemplary embodiment, before step S110, the method further includes the following steps S51-S54:

[0093] Step S51: generating a channel geometric model according to the channel layout parameters;

[0094] Alternatively, in the scenario of optimizing the airflow and temperature field within a power distribution cabinet, the geometric model is generated based on the channel layout design. Assuming the dimensions of the power distribution cabinet are 1.2m × 0.9m × 0.6m, the optimized channel layout results in a set of elliptical airflow channels with a short axis of 0.05m, a long axis of 0.08m, and a length of 0.6m, arranged from the bottom to the top of the cabinet. The geometric model is generated using 3D modeling software, describing the relative position of the channels to the inner walls of the cabinet to ensure that the airflow path covers high-temperature areas. After the model is generated, it is imported into a finite element analysis tool for meshing.

[0095] Step S52: Meshing the channel geometric model using a finite element analysis tool, and calculating numerical expressions of the initial airflow velocity field and the initial temperature field based on the boundary conditions of the high and low voltage distribution cabinets, wherein the boundary conditions include the wall temperature and the airflow inlet velocity of the high and low voltage distribution cabinets;

[0096] Optionally, the grid is divided into unstructured tetrahedral grids, with the grid size near the channel inlet and high-temperature area set to 0.008m to capture the complex flow field changes, and the grid size away from the channel area is relaxed to 0.015m, generating a total of approximately 3.5 million grid cells. The boundary conditions are set as a cabinet wall temperature of 45°C, a bottom airflow inlet velocity of 1.5m / s, and a top outlet at constant pressure. The numerical expression of the initial airflow velocity field and the initial temperature field is calculated using finite element tools, for example, a velocity of 1.5m / s near the inlet and a temperature of 30°C.

[0097] Optionally, a sensitivity analysis can be performed on the grid division to compare the differences in airflow trajectories under grid sizes of 0.01m, 0.005m, and 0.002m, and finally select the 0.005m grid to ensure a balance between accuracy and computational efficiency.

[0098] Step S53: using the Navier-Stokes equation and the energy equation to perform coupled calculation on the initial airflow velocity field and the initial temperature field to obtain a numerical expression of the target airflow velocity field;

[0099] Optionally, the Navier-Stokes equations and the energy equation are used to couple the calculation of the airflow velocity field and temperature field. The Navier-Stokes equations describe the velocity and pressure changes of the airflow, and the energy equations deal with heat transfer. The iterative solution uses a pressure-based coupling algorithm. The initial airflow velocity field shows that the airflow forms laminar flow in the channel and turns into weak turbulence near the high-temperature area. After the residual of the airflow velocity field is calculated, if it is greater than the preset threshold of 0.001, the turbulent viscosity parameter is adjusted and the iterative calculation is performed again. For example, after a certain iteration, the velocity distribution in the middle of the channel is updated to 1.2m / s, and the pressure distribution shows that the top outlet pressure is reduced to 0.98 times the atmospheric pressure. After multiple iterations, the residual is reduced to 0.0008, which meets the convergence criteria, and the numerical expression of the target airflow velocity field is obtained.

[0100] Step S54: analyzing the airflow velocity distribution characteristics and pressure distribution characteristics according to the numerical expression of the target airflow velocity field to optimize the channel layout parameters.

[0101] Alternatively, the airflow velocity and pressure distribution characteristics were analyzed based on the numerical representation of the target airflow velocity field. The velocity distribution showed that the airflow velocity was high at the channel inlet, reaching 1.5 m / s. This velocity decreased to 1.0 m / s in the middle due to channel geometry constraints, and then rebounded to 1.3 m / s at the top outlet. The pressure distribution indicated a low-pressure zone in the middle of the channel, promoting air circulation. Based on this, the optimization solution was determined to be a 15° inclination of the channel outlet to enhance the efficiency of top airflow discharge.

[0102] It should be noted that by adjusting the channel geometry, the airflow can be evenly distributed across high-temperature areas, improving heat dissipation. For example, adding a 0.02m wide diverter plate in the middle of the channel can further direct the airflow to specific high-temperature points, optimizing the stability of the convection cycle.

[0103] It should be noted that implementing the above steps through industrial control software has the following benefits: 1) Automatically generate a geometric model based on the channel layout parameters to ensure that the design is consistent with the actual scenario, providing an accurate basis for subsequent analysis; 2) Use finite element analysis tools to mesh the model, and combine boundary conditions (such as wall temperature and inlet velocity) to calculate the initial airflow velocity field and temperature field to improve simulation accuracy; 3) Use the Navier-Stokes equation and energy equation to couple the initial field to obtain a numerical expression of the target airflow velocity field, and deeply analyze the airflow velocity and pressure distribution characteristics; 4) The industrial control software automatically adjusts the channel layout parameters according to the analysis results, optimizes the heat dissipation performance, significantly improves the design efficiency and heat dissipation effect, and reduces the risk of high temperature.

[0104] Step S110: determining a heat dissipation solution for the high and low voltage power distribution cabinet according to the channel layout parameters, and performing heat dissipation control on the high and low voltage power distribution cabinet based on the heat dissipation solution.

[0105] Optionally, a heat dissipation solution is determined based on channel layout parameters. This includes optimizing airflow organization and adjusting heat dissipation device location and parameters (such as fan speed and channel cross-sectional shape) to effectively cool high-temperature areas. Industrial control software monitors temperature changes in real time and dynamically adjusts the operating status of heat dissipation devices to ensure uniform temperature distribution within the distribution cabinet, reducing the risk of overheating and improving equipment reliability and safety.

[0106] In an exemplary embodiment, the above step S110 can be implemented by the following steps S61-S64:

[0107] Step S61: constructing a three-dimensional model of the high and low voltage distribution cabinet by using digital twin technology, and obtaining the three-dimensional position of the heat dissipation device of the high and low voltage distribution cabinet;

[0108] Alternatively, digital twin technology can be used to build a 3D model of the distribution cabinet's interior. High-precision scanning equipment can be used to capture the geometric data of the cabinet's internal components, generating an accurate 3D model. For example, if the cabinet contains a transformer, circuit breaker, and cooling fan, a laser scanner can capture the dimensions and position of each component, generating point cloud data that can then be converted into a 3D model.

[0109] Optionally, the digital twin model is updated every 10 seconds, or a real-time update is triggered when the temperature change rate exceeds 1°C / s.

[0110] It's important to note that the core of digital twin technology lies in the real-time mapping of physical entities to virtual models, ensuring that the model aligns with the actual spatial layout of the distribution cabinet. For example, the transformer's coordinates are set at (0.5, 0.3, 0.2) meters, and the cooling fan is located at (0.8, 0.1, 0.4) meters, with model accuracy controlled to the millimeter level. This method effectively reproduces the real scene and facilitates subsequent simulation analysis.

[0111] Step S62: adjusting the three-dimensional position of the heat sink according to the channel layout parameters, and determining the spatial layout of the high and low voltage distribution cabinets according to the adjusted three-dimensional position of the heat sink;

[0112] Optionally, channel layout parameters define the geometry of the heat flow channel (e.g., cross-sectional shape, length, etc.). These parameters determine the airflow path. Based on these channel layout parameters, the heat sink can be repositioned to move it closer to high-temperature areas or optimize airflow coverage. For example, assuming the initial fan position is (0.8, 0.1, 0.4) meters, the genetic algorithm iterative optimization recommends moving the fan to (0.7, 0.2, 0.3) meters.

[0113] The spatial layout includes the three-dimensional positions of all components within the PDC (such as transformers, circuit breakers, and cooling fans) and their interrelationships. Based on the adjusted three-dimensional positions of the cooling devices, the spatial distribution of the components within the PDC is updated. Ensure that the new layout does not affect the functionality and installation of other equipment.

[0114] Step S63: Based on the digital twin technology, perform convection simulation according to the spatial layout to obtain a multidimensional dataset, wherein the multidimensional dataset includes numerical expressions of the airflow velocity field and temperature field of the high and low voltage distribution cabinets;

[0115] Alternatively, computational fluid dynamics (CFD) software (such as OpenFOAM or ANSYS) can be used for convection simulation. Discretize the interior of the distribution cabinet into a finite volume grid of 1 million cells. Set boundary conditions, such as a fan inlet velocity of 2 m / s and a no-slip boundary on the outer wall.

[0116] The simulation involves the following: 1) calculating the trajectory and velocity distribution of the airflow within the distribution cabinet; 2) generating temperature distribution data using the heat transfer equation Q = h·A·ΔT. Q is the heat transfer rate in watts (W), or joules per second (J / s); h is the convective heat transfer coefficient (or surface heat transfer coefficient), measured in watts per square meter Kelvin (W / (m²·K)). A is the heat transfer area in square meters, which refers to the surface area involved in heat transfer. For example, the total surface area of ​​a transformer in contact with the air. The larger the area, the higher the heat transfer rate. ΔT is the temperature difference in Kelvin (K) or degrees Celsius, which can be the difference between the high-temperature and low-temperature fluid temperatures.

[0117] Step S64: determining a heat dissipation solution for the high and low voltage distribution cabinet according to the channel layout parameters and the multidimensional data set.

[0118] In an exemplary embodiment, the above step S64 can be implemented by the following steps S71-S75:

[0119] Step S71: determining a temperature change curve of a target area in the high and low voltage distribution cabinet according to the multidimensional data set, and determining a temperature change rate according to the temperature change curve, wherein the temperature of the target area is greater than a preset temperature;

[0120] Optionally, in the heat dissipation optimization scenario of the power distribution cabinet, when extracting the temperature change curve data of the high-temperature risk area from the multidimensional dataset, it can be achieved through a data visualization tool. Assuming that the temperature of a certain area in the power distribution cabinet drops from 85°C to 65°C within 30 minutes of initial operation, a curve can be drawn using time-temperature data points. Use a data screening tool, such as the Python-based Pandas library, to set a temperature threshold of 80°C to filter out a data subset in the high-temperature area. Then, the temperature drop rate is calculated using the numerical differentiation method. For example, the time interval is 5 minutes, and the temperature drops from 85°C to 65°C, with a drop rate of about 4°C / minute. If the target drop rate needs to reach 5°C / minute, the current rate is insufficient and needs to be optimized.

[0121] Step S72: When the temperature change rate is greater than or equal to a preset change rate, a heat dissipation solution for the high and low voltage distribution cabinet is determined according to the channel layout parameters and the numerical expression of the airflow velocity field.

[0122] Step S73: When the temperature change rate is less than the preset change rate, a geometric optimization tool is used to adjust the width and angle of the heat flow guiding channel according to the numerical expression of the airflow velocity field to obtain optimized channel layout parameters;

[0123] Optionally, for temperature change rates below the target value, the channel geometry can be adjusted based on the airflow velocity field data. The airflow velocity field shows that the inlet velocity of a certain channel is only 2m / s, which is far lower than the ideal 5m / s. Using geometric optimization tools, such as CAD software combined with optimization algorithms, the channel width is adjusted from 50mm to 70mm, and the angle is optimized from 30° to 45° to reduce airflow resistance. The optimized channel geometry parameters generate a new layout data set. It should be noted that increasing the channel width can increase the airflow flow, while adjusting the angle can guide the airflow to cover the high-temperature area more evenly.

[0124] Step S74: Based on the digital twin technology, a convection simulation is performed according to the optimized channel layout parameters to obtain a target multidimensional dataset, wherein the target multidimensional dataset includes numerical expressions of the updated airflow velocity field and temperature field of the high and low voltage distribution cabinets;

[0125] Step S75: When the temperature change rate determined according to the target multidimensional data set is greater than or equal to the preset change rate, the heat dissipation solution of the high and low voltage distribution cabinet is determined according to the numerical expression of the optimized channel layout parameters and the updated air flow velocity field.

[0126] Optionally, based on the optimized channel layout parameters, computational fluid dynamics software, such as ANSYS Fluent, is used to reconstruct the virtual convection model. The simulation shows that after optimization, the channel inlet velocity rises to 4.8m / s, and the peak temperature of the high-temperature area in the temperature field drops from 85°C to 60°C in 5 minutes. The updated heat dissipation efficiency data set reflects that the temperature drop rate has increased to 5°C / minute, reaching the target value. At this point, the optimized channel layout parameters (such as width 70mm, angle 45°) are combined with the airflow velocity field data (inlet velocity 4.8m / s) through the data integration tool to generate the final heat dissipation solution parameters. Optionally, the data integration tool can use Excel or dedicated database software to associate geometric parameters with simulation data to ensure that the solution can be directly applied to the distribution cabinet design.

[0127] It should be noted that the above-mentioned step S72 and steps S73-S75 are performed in different situations and there is no specific order of execution.

[0128] It should be noted that implementing the cooling solution determination process described above using industrial control software offers the following significant benefits: 1) Digital twin technology is used to construct a 3D model of the power distribution cabinet and determine the 3D position of the heat sink. The heat sink position is adjusted based on channel layout parameters, generating a precise spatial layout and providing a reliable foundation for subsequent optimization. 2) Based on a multidimensional dataset (air velocity and temperature fields), the temperature variation curve and rate of change in the target area are analyzed to quickly locate high-temperature risk points. The industrial control software automatically extracts key data, improving analysis efficiency. 3) When the temperature rate of change falls below a preset value, geometric optimization tools are used to adjust the width and angle of the heat flow channel to generate optimized channel layout parameters. Convection simulation is then performed again using digital twin technology to generate updated air velocity and temperature field data, ensuring that the optimization results are verifiable. This closed-loop optimization process significantly improves the adaptability and effectiveness of the cooling solution. 4) Based on the optimized channel layout parameters and the updated numerical representation of the air velocity field, the industrial control software automatically determines whether the cooling requirements are met. If not, it continues the iterative optimization process until the preset rate of change is achieved. 5) Virtual simulation replaces traditional physical testing, reducing the number of experiments and time costs. At the same time, potential heat dissipation problems can be discovered in advance and the design can be optimized to avoid equipment failure or damage due to high temperature; 6) The entire process is driven by industrial control software, which supports real-time monitoring and dynamic adjustment to adapt to the heat dissipation requirements under different working conditions and ensure the safety and reliability of the operation of high and low voltage distribution cabinets.

[0129] In summary, industrial control software runs through the entire process from modeling, analysis to optimization, enabling efficient formulation and precise implementation of cooling solutions, significantly improving the cooling performance and operation and maintenance efficiency of distribution cabinets.

[0130] In steps S102-S110, an IoT sensor array is used to collect real-time temperatures at multiple component locations within the distribution cabinet, generating a temperature dataset containing temperature values, timestamps, and three-dimensional location coordinates. This ensures a comprehensive understanding of the temperature distribution within the distribution cabinet, providing accurate data support for subsequent analysis. A thermal distribution map is generated based on the temperature dataset, and a heat flux distribution matrix is ​​further generated to quantify the heat flux intensity at each grid point. This process reveals the patterns of heat transfer and the distribution characteristics of high-temperature areas, providing a basis for identifying heat dissipation bottlenecks. By analyzing the heat flux distribution matrix, a risk distribution map is generated, clearly identifying risk grid points where the absolute value of the three-dimensional temperature gradient vector exceeds a first preset threshold. This step accurately locates localized overheating areas and improves the efficiency of problem detection. Based on the risk distribution map, the geometric structure of the heat flow guidance channel is designed to optimize the airflow path and ensure efficient heat flow from high-temperature areas to low-temperature areas. Finally, a heat dissipation solution is determined and implemented based on the channel layout parameters, achieving efficient heat dissipation management for high- and low-voltage distribution cabinets. This achieves a closed-loop optimization from data collection to heat dissipation control, significantly improving heat dissipation efficiency. The invention solves the problem in related art that high and low voltage distribution cabinets cannot be efficiently controlled for heat dissipation.

[0131] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technology of this application, the part of this application that contributes to the prior art in essence, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0132] In this embodiment, a high- and low-voltage power distribution cabinet heat dissipation control system and device based on the Internet of Things are also provided. The high- and low-voltage power distribution cabinet heat dissipation control system and device based on the Internet of Things are used to implement the above-mentioned embodiments and preferred implementation methods. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0133] Figure 2 This is a structural block diagram of a high and low voltage distribution cabinet heat dissipation control system based on the Internet of Things according to an embodiment of the present application, the system includes:

[0134] The acquisition module 202 is configured to acquire real-time temperature data from multiple component locations in the high and low voltage distribution cabinet using an IoT sensor array to obtain a temperature data set, wherein each temperature data in the temperature data set includes a temperature value, a timestamp corresponding to the temperature value, and a three-dimensional position coordinate;

[0135] A first generating module 204 is configured to generate a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set, and generate a heat flow distribution matrix according to the thermal distribution map, wherein the heat flow distribution matrix includes heat flow intensities of multiple grid points in the thermal distribution map;

[0136] A second generating module 206 is configured to generate a risk distribution map based on the heat flux distribution matrix, wherein the risk distribution map is used to describe the distribution of a plurality of risk grid points, where the absolute value of the three-dimensional temperature gradient vector of the risk grid point is greater than a first preset threshold;

[0137] A design module 208 is configured to design the geometric structure of the heat flow guiding channel of the high and low voltage power distribution cabinet according to the risk distribution map, and obtain channel layout parameters;

[0138] The control module 210 is configured to determine a heat dissipation scheme for the high and low voltage power distribution cabinets according to the channel layout parameters, and perform heat dissipation control on the high and low voltage power distribution cabinets based on the heat dissipation scheme.

[0139] The system utilizes an IoT sensor array to collect real-time temperatures at multiple component locations within a power distribution cabinet (PDC), generating a temperature dataset containing temperature values, timestamps, and three-dimensional location coordinates. This ensures a comprehensive understanding of the internal PDC temperature distribution, providing accurate data support for subsequent analysis. Based on the temperature dataset, a thermal distribution map is generated, which is then further generated into a heat flux distribution matrix to quantify the heat flux intensity at each grid point. This process reveals the patterns of heat transfer and the distribution characteristics of high-temperature areas, providing a basis for identifying heat dissipation bottlenecks. By analyzing the heat flux distribution matrix, a risk distribution map is generated, clearly identifying risk grid points where the absolute value of the three-dimensional temperature gradient vector exceeds a first preset threshold. This step accurately locates localized overheating areas and improves the efficiency of problem detection. Based on the risk distribution map, the geometric structure of the heat flow guidance channel is designed to optimize the airflow path and ensure efficient heat flow from high-temperature to low-temperature areas. Finally, a heat dissipation solution is determined and implemented based on the channel layout parameters, achieving efficient heat dissipation management for high- and low-voltage PDCs. This system achieves a closed-loop optimization from data collection to heat dissipation control, significantly improving heat dissipation efficiency. This solves the problem of inefficient heat dissipation control in PDCs, which has been a problem in related technologies.

[0140] In an exemplary embodiment, the first generation module 204 is also used to remove abnormal temperature data in the temperature data set according to preset data cleaning rules to obtain a cleaned temperature data set; normalize the temperature value of each temperature data in the cleaned temperature data set to obtain a normalized temperature data set; based on the K-means clustering algorithm, cluster the normalized temperature data set with the distance between the three-dimensional position coordinates in the temperature data as the distance metric to obtain K clusters, where K is an integer greater than or equal to 2, and different clusters correspond to different temperature areas; based on the color mapping image generation method, generate a thermal distribution map inside the high and low voltage distribution cabinet according to the three-dimensional position coordinates and temperature values ​​corresponding to the K clusters.

[0141] In an exemplary embodiment, the first generation module 204 is further used to calculate the temperature gradient of each grid point in the thermal distribution map using a finite difference method, and calculate the heat flux intensity of each grid point based on the temperature gradient of each grid point to obtain a target data set, wherein the target data set includes the temperature value, temperature gradient and heat flux intensity of each grid point; when there is a grid point with a temperature gradient greater than a preset gradient threshold, according to the target data set, the heat transfer path is traced along the gradient descent direction to obtain a heat transfer path, wherein the heat transfer path has multiple grid points, the multiple grid points include grid points with a temperature gradient greater than the preset gradient threshold, and the temperature gradients of the multiple grid points decrease; a heat flux distribution matrix is ​​generated according to the heat transfer path, wherein the heat flux distribution matrix includes the heat flux intensities of the multiple grid points.

[0142] In an exemplary embodiment, the second generation module 206 is further used to use Gaussian filtering to smooth the data of the heat flux distribution matrix to obtain a smoothed heat flux distribution matrix; based on the smoothed heat flux distribution matrix, the finite difference method is used to calculate the temperature gradient of each grid point in the three-dimensional direction to obtain the three-dimensional gradient vector of each grid point; based on the three-dimensional gradient vector of each grid point, a risk point set is determined, wherein the grid points in the risk point set are the risk grid points; based on the risk point set, a stereo microscope algorithm is used to perform three-dimensional visualization processing to obtain a risk distribution map.

[0143] In an exemplary embodiment, the design module 208 is also used to determine the three-dimensional coordinates of the multiple risk grid points based on the risk distribution map to obtain a risk point coordinate data set; based on the risk point coordinate data set, the high and low voltage distribution cabinets are discretized using the finite volume method to generate three-dimensional finite volume grid units; the spatial range of the three-dimensional finite volume grid units is determined to obtain a discretized grid data set; based on the SST k-ε turbulence model and momentum equation, the airflow motion trajectory is calculated according to the discretized grid data set and the boundary conditions of the high and low voltage distribution cabinets to obtain an airflow trajectory data set, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinets; based on the airflow trajectory data set, an iterative optimization algorithm is used to calculate the cross-sectional shape and length of the heat flow guide channel to obtain the channel layout parameters.

[0144] In an exemplary embodiment, the system also includes an optimization module for generating a channel geometric model according to the channel layout parameters before determining the heat dissipation scheme of the high and low voltage distribution cabinet according to the channel layout parameters; meshing the channel geometric model through a finite element analysis tool, and calculating the numerical expression of the initial airflow velocity field and the initial temperature field according to the boundary conditions of the high and low voltage distribution cabinet, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinet; coupling the initial airflow velocity field and the initial temperature field using the Navier-Stokes equation and the energy equation to obtain a numerical expression of the target airflow velocity field; analyzing the airflow velocity distribution characteristics and pressure distribution characteristics according to the numerical expression of the target airflow velocity field to optimize the channel layout parameters.

[0145] In an exemplary embodiment, the control module 210 is also used to construct a three-dimensional model of the high and low voltage distribution cabinet through digital twin technology, and obtain the three-dimensional position of the heat dissipation device of the high and low voltage distribution cabinet; adjust the three-dimensional position of the heat dissipation device according to the channel layout parameters, and determine the spatial layout of the high and low voltage distribution cabinet based on the adjusted three-dimensional position of the heat dissipation device; based on the digital twin technology, perform convection simulation according to the spatial layout to obtain a multidimensional data set, wherein the multidimensional data set includes numerical expressions of the air flow velocity field and temperature field of the high and low voltage distribution cabinet; determine the heat dissipation scheme of the high and low voltage distribution cabinet based on the channel layout parameters and the multidimensional data set.

[0146] In an exemplary embodiment, the control module 210 is also used to determine the temperature change curve of the target area in the high and low voltage distribution cabinet based on the multidimensional data set, and determine the temperature change rate based on the temperature change curve, wherein the temperature of the target area is greater than the preset temperature; when the temperature change rate is greater than or equal to the preset change rate, the heat dissipation solution of the high and low voltage distribution cabinet is determined based on the numerical expression of the channel layout parameters and the air flow velocity field.

[0147] In an exemplary embodiment, the control module 210 is also used to, when the temperature change rate is less than the preset change rate, use a geometric optimization tool to adjust the width and angle of the heat flow guiding channel according to the numerical expression of the air flow velocity field to obtain optimized channel layout parameters; based on the use of digital twin technology, perform convection simulation according to the optimized channel layout parameters to obtain a target multidimensional data set, wherein the target multidimensional data set includes the numerical expression of the updated air flow velocity field and temperature field of the high and low voltage distribution cabinet; when the temperature change rate determined according to the target multidimensional data set is greater than or equal to the preset change rate, determine the heat dissipation solution of the high and low voltage distribution cabinet based on the optimized channel layout parameters and the numerical expression of the updated air flow velocity field.

[0148] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any of the above method embodiments when run.

[0149] Optionally, in this embodiment, the above-mentioned storage medium may be configured to store a computer program for executing the following steps.

[0150] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0151] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0152] An embodiment of the present application further provides a computer program product, including a computer program, and the computer program performs the steps of any of the above method embodiments when executed by a processor.

[0153] An embodiment of the present application further provides another computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0154] An embodiment of the present application further provides an electronic device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the steps of any of the above method embodiments through the computer program.

[0155] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0156] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0157] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A heat dissipation control method for high and low voltage distribution cabinets based on the Internet of Things, characterized in that: include: Real-time temperature acquisition is performed on multiple component locations in a high and low voltage distribution cabinet using an IoT sensor array to obtain a temperature data set, wherein each temperature data in the temperature data set includes a temperature value, a timestamp corresponding to the temperature value, and a three-dimensional position coordinate; Generating a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set, and generating a heat flow distribution matrix according to the thermal distribution map, wherein the heat flow distribution matrix includes heat flow intensities of multiple grid points in the thermal distribution map; generating a risk distribution map according to the heat flux distribution matrix, wherein the risk distribution map is used to describe the distribution of a plurality of risk grid points, wherein the absolute value of the three-dimensional temperature gradient vector of the risk grid point is greater than a first preset threshold; Designing the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet according to the risk distribution map to obtain channel layout parameters; A heat dissipation scheme for the high and low voltage power distribution cabinet is determined according to the channel layout parameters, and heat dissipation control is performed on the high and low voltage power distribution cabinet based on the heat dissipation scheme.

2. The method according to claim 1, characterized in that Generating a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set includes: removing abnormal temperature data from the temperature dataset according to a preset data cleaning rule to obtain a cleaned temperature dataset; Normalizing the temperature value of each temperature data in the cleaned temperature data set to obtain a normalized temperature data set; Based on the K-means clustering algorithm, the normalized temperature data set is clustered using the distance between the three-dimensional position coordinates in the temperature data as a distance metric to obtain K clusters, where K is an integer greater than or equal to 2, and different clusters correspond to different temperature regions; An image generation method based on color mapping generates a thermal distribution map inside the high and low voltage distribution cabinet according to the three-dimensional position coordinates and temperature values ​​corresponding to the K clusters.

3. The method according to claim 1, characterized in that Generating a heat flow distribution matrix according to the thermal distribution map includes: Calculating the temperature gradient of each grid point in the thermal distribution map using a finite difference method, and calculating the heat flux intensity of each grid point based on the temperature gradient of each grid point to obtain a target data set, wherein the target data set includes the temperature value, temperature gradient, and heat flux intensity of each grid point; In a case where there is a grid point whose temperature gradient is greater than a preset gradient threshold, tracing a heat transfer path along a gradient descent direction according to the target data set to obtain a heat transfer path, wherein the heat transfer path has a plurality of grid points, the plurality of grid points including a grid point whose temperature gradient is greater than the preset gradient threshold, and the temperature gradients of the plurality of grid points decrease; A heat flux distribution matrix is ​​generated according to the heat transfer path, wherein the heat flux distribution matrix includes heat flux intensities of the plurality of grid points.

4. The method according to claim 1, wherein Generating a risk distribution map according to the heat flow distribution matrix includes: Gaussian filtering is used to smooth the heat flux distribution matrix to obtain a smoothed heat flux distribution matrix; Calculating the temperature gradient of each grid point in a three-dimensional direction using a finite difference method according to the smoothed heat flux distribution matrix to obtain a three-dimensional gradient vector of each grid point; Determine a risk point set according to the three-dimensional gradient vector of each grid point, wherein the grid points in the risk point set are the risk grid points; According to the risk point set, a stereo microscope algorithm is used to perform three-dimensional visualization processing to obtain a risk distribution map.

5. The method according to claim 1, wherein The geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet is designed according to the risk distribution map to obtain channel layout parameters, including: determining the three-dimensional coordinates of the plurality of risk grid points according to the risk distribution map to obtain a risk point coordinate data set; According to the risk point coordinate data set, the high and low voltage distribution cabinets are discretized using the finite volume method to generate three-dimensional finite volume grid units; Determining the spatial range of the three-dimensional finite volume grid unit to obtain a discretized grid data set; Based on the SST k-ε turbulence model and the momentum equation, the airflow motion trajectory is calculated according to the discretized grid data set and the boundary conditions of the high and low voltage distribution cabinets to obtain an airflow trajectory data set, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinets; According to the airflow trajectory data set, an iterative optimization algorithm is used to calculate the cross-sectional shape and length of the heat flow guiding channel to obtain the channel layout parameters.

6. The method according to claim 1, characterized in that Before determining the heat dissipation solution of the high and low voltage distribution cabinet according to the channel layout parameters, the method further includes: generating a channel geometric model according to the channel layout parameters; Meshing the channel geometric model using a finite element analysis tool, and calculating the numerical expression of the initial airflow velocity field and the initial temperature field according to the boundary conditions of the high and low voltage distribution cabinets, wherein the boundary conditions include the wall temperature and the airflow inlet velocity of the high and low voltage distribution cabinets; The initial airflow velocity field and the initial temperature field are coupled and calculated using the Navier-Stokes equation and the energy equation to obtain a numerical expression of the target airflow velocity field; The airflow velocity distribution characteristics and pressure distribution characteristics are analyzed according to the numerical expression of the target airflow velocity field to optimize the channel layout parameters.

7. The method according to any one of claims 1 to 6, characterized in that Determining a heat dissipation solution for the high and low voltage distribution cabinet according to the channel layout parameters includes: Constructing a three-dimensional model of the high and low voltage distribution cabinet through digital twin technology, and obtaining the three-dimensional position of the heat dissipation device of the high and low voltage distribution cabinet; Adjusting the three-dimensional position of the heat sink according to the channel layout parameters, and determining the spatial layout of the high and low voltage distribution cabinets according to the adjusted three-dimensional position of the heat sink; Based on the digital twin technology, a convection simulation is performed according to the spatial layout to obtain a multidimensional dataset, wherein the multidimensional dataset includes numerical expressions of the airflow velocity field and the temperature field of the high and low voltage distribution cabinets; A heat dissipation solution for the high and low voltage distribution cabinet is determined based on the channel layout parameters and the multidimensional data set.

8. The method according to claim 7, characterized in that Determining a heat dissipation solution for the high and low voltage distribution cabinet according to the channel layout parameters and the multidimensional data set includes: Determining a temperature change curve of a target area in the high and low voltage distribution cabinet according to the multidimensional data set, and determining a temperature change rate according to the temperature change curve, wherein the temperature of the target area is greater than a preset temperature; When the temperature change rate is greater than or equal to a preset change rate, a heat dissipation solution for the high and low voltage distribution cabinet is determined according to the channel layout parameters and the numerical expression of the airflow velocity field.

9. The method according to claim 8, characterized in that The method further comprises: When the temperature change rate is less than the preset change rate, a geometric optimization tool is used to adjust the width and angle of the heat flow guiding channel according to the numerical expression of the airflow velocity field to obtain optimized channel layout parameters; Based on the digital twin technology, a convection simulation is performed according to the optimized channel layout parameters to obtain a target multidimensional dataset, wherein the target multidimensional dataset includes numerical expressions of the updated airflow velocity field and temperature field of the high and low voltage distribution cabinets; When the temperature change rate determined according to the target multidimensional data set is greater than or equal to the preset change rate, the heat dissipation scheme of the high and low voltage distribution cabinet is determined according to the numerical expression of the optimized channel layout parameters and the updated air flow velocity field.

10. A heat dissipation control system for high and low voltage distribution cabinets based on the Internet of Things, characterized in that: include: An acquisition module is used to collect real-time temperature data from multiple component positions in the high and low voltage distribution cabinet through an Internet of Things sensor array to obtain a temperature data set, wherein each temperature data in the temperature data set includes a temperature value, a timestamp corresponding to the temperature value, and a three-dimensional position coordinate; A first generating module is configured to generate a thermal distribution map inside the high and low voltage distribution cabinet according to the temperature data set, and generate a heat flow distribution matrix according to the thermal distribution map, wherein the heat flow distribution matrix includes heat flow intensities of multiple grid points in the thermal distribution map; a second generating module, configured to generate a risk distribution map according to the heat flux distribution matrix, wherein the risk distribution map is used to describe the distribution of a plurality of risk grid points, wherein the absolute value of the three-dimensional temperature gradient vector of the risk grid points is greater than a first preset threshold; A design module is used to design the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet according to the risk distribution map to obtain channel layout parameters; A control module is used to determine the heat dissipation scheme of the high and low voltage distribution cabinet according to the channel layout parameters, and to control the heat dissipation of the high and low voltage distribution cabinet based on the heat dissipation scheme.

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