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

Abstract

The application discloses a high-low voltage power distribution cabinet heat dissipation control method and system based on Internet of Things. The method comprises the following steps: collecting the temperature of multiple components in the high-low voltage power distribution cabinet in real time through an Internet of Things sensor array to obtain a temperature data set; generating a thermal distribution map of 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 map; generating a risk distribution map according to the heat flow distribution matrix; designing the geometric structure of the heat flow guide channel of the high-low voltage power distribution cabinet according to the risk distribution map to obtain channel layout parameters; determining the heat dissipation scheme of the high-low voltage power distribution cabinet according to the channel layout parameters, and controlling the heat dissipation of the high-low voltage power distribution cabinet based on the heat dissipation scheme.

Description

Technical Field

[0001] This application relates to the field of heat dissipation in high and low voltage switchgear, and more specifically, to a heat dissipation control method and system for high and low voltage switchgear based on the Internet of Things. Background Technology

[0002] As the core of power transmission and distribution, the stable operation of the power distribution system is crucial to social production and daily life. However, if the heat generated by the current load during the operation of high and low voltage switchgear 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, which, while alleviating the problem of heat accumulation to some extent, have many shortcomings.

[0003] First, traditional heat dissipation methods lack intelligent management tools, making it difficult to flexibly adjust to dynamic temperature changes. For example, under high-load scenarios, localized overheating is particularly prominent, and existing fixed heat dissipation devices cannot respond to these changes in a timely manner, resulting in low heat dissipation efficiency. Furthermore, due to the non-uniform heat distribution inside the distribution cabinet, traditional uniform heat dissipation methods are insufficient to effectively address the problem of heat accumulation in localized high-temperature areas.

[0004] Secondly, existing technologies lack sufficient analytical capabilities regarding the laws governing heat generation and transfer. Although existing industrial control software has been applied in some fields, it remains weak in the area of ​​heat dissipation control for power distribution cabinets. Current industrial control software is mostly used for simple data acquisition and monitoring, lacking the ability to deeply analyze complex heat distribution patterns and failing to identify potential overheating risks. Simultaneously, existing industrial control software also has limitations in airflow organization optimization, unable to design efficient heat flow guidance channels based on real-time data. Furthermore, traditional methods do not adequately utilize temperature difference characteristics, failing to fully guide heat from high-temperature areas to low-temperature areas, further reducing heat dissipation efficiency.

[0005] It is evident that existing technologies for heat dissipation control suffer from high energy consumption, low efficiency, and insufficient intelligence, particularly when dealing with complex heat distributions. With the widespread adoption of IoT technology, the application potential of industrial control software urgently needs to be explored to solve many of the current challenges in heat dissipation control.

[0006] In summary, given the inefficiency of controlling heat dissipation in high and low voltage distribution cabinets using the relevant technologies, it is necessary to improve these technologies to overcome the aforementioned deficiencies. Summary of the Invention

[0007] This application provides a method and system for controlling the heat dissipation of high and low voltage distribution cabinets based on the Internet of Things, so as to at least solve the problem of inefficiently controlling the heat dissipation of high and low voltage distribution cabinets.

[0008] According to one aspect of the embodiments of this application, a heat dissipation control method for high and low voltage distribution cabinets based on the Internet of Things (IoT) is provided, comprising: real-time temperature acquisition of multiple component locations in the high and low voltage distribution cabinet using an IoT sensor array to obtain a temperature dataset, wherein each temperature data in the temperature dataset includes a temperature value, a timestamp corresponding to the temperature value, and three-dimensional position coordinates; generating a thermal distribution map inside the high and low voltage distribution cabinet based on the temperature dataset, and generating a heat flow distribution matrix based on the thermal distribution map, wherein the heat flow distribution matrix has heat flow intensities of multiple grid points in the thermal distribution map; generating a risk distribution map based on the heat flow distribution matrix, wherein the risk distribution map describes the distribution of multiple risk grid points, and the absolute value of the three-dimensional temperature gradient vector of the risk grid points 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 based on the risk distribution map to obtain channel layout parameters; determining a heat dissipation scheme for the high and low voltage distribution cabinet based on 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, generating a thermal distribution map of the interior of the high- and low-voltage distribution cabinet based on the temperature dataset 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 dataset to obtain a normalized temperature dataset; clustering the normalized temperature dataset using a K-means clustering algorithm, 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 regions; and generating a thermal distribution map of the interior of the high- and low-voltage distribution cabinet based on a color mapping-based image generation method, according to 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: calculating the temperature gradient of each grid point in the thermal distribution map using the 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 dataset, wherein the target dataset includes the temperature value, temperature gradient, and heat flux intensity of each grid point; if the temperature gradient of a grid point is greater than a preset gradient threshold, tracing a heat transfer path along the gradient descent direction based on the target dataset to obtain a heat transfer path, wherein the heat transfer path has multiple grid points, including grid points with temperature gradients greater than the 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, generating a risk distribution map based on the heat flux distribution matrix includes: smoothing the heat flux distribution matrix using Gaussian filtering to obtain a smoothed heat flux distribution matrix; calculating the temperature gradient of each grid point in three dimensions using the finite difference method based on the smoothed heat flux distribution matrix to obtain a three-dimensional gradient vector for each grid point; determining a set of risk points based on the three-dimensional gradient vector of each grid point, wherein the grid points in the set of risk points are the risk grid points; and performing three-dimensional visualization processing using a stereomicroscope algorithm based on the set of risk points to obtain the 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 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 dataset; discretizing the high and low voltage distribution cabinet using the finite volume method according to the risk point coordinate dataset to generate three-dimensional finite volume grid cells; determining the spatial range of the three-dimensional finite volume grid cells to obtain a discretized grid dataset; calculating the airflow trajectory based on the SST k-ε turbulence model and momentum equation, according to the discretized grid dataset and the boundary conditions of the high and low voltage distribution cabinet, to obtain an airflow trajectory dataset, wherein the boundary conditions include the wall temperature of the high and low voltage distribution cabinet and the airflow inlet velocity; and calculating the cross-sectional shape and length of the heat flow guiding channel using an iterative optimization algorithm according to the airflow trajectory dataset 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 based on the channel layout parameters, the method further includes: generating a channel geometric model based on the channel layout parameters; meshing the channel geometric model using a finite element analysis tool, and calculating numerical expressions of the initial airflow velocity field and initial temperature field based on 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; performing coupled calculations of 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; and analyzing the airflow velocity distribution characteristics and pressure distribution characteristics based on the numerical expression of the target airflow velocity field to optimize the channel layout parameters.

[0014] In an exemplary embodiment, determining the heat dissipation scheme of the high- and low-voltage distribution cabinet based on the channel layout parameters includes: constructing a three-dimensional model of the high- and low-voltage distribution cabinet using 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 based on the adjusted three-dimensional position of the heat dissipation device; performing convection simulation based on the spatial layout using digital twin technology 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 cabinet; and determining the heat dissipation scheme of the high- and low-voltage distribution cabinet based on the channel layout parameters and the multidimensional dataset.

[0015] In an exemplary embodiment, determining the heat dissipation scheme of the high and low voltage distribution cabinet based on the channel layout parameters and the multidimensional dataset includes: determining the temperature change curve of a target area in the high and low voltage distribution cabinet based on the multidimensional dataset, 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; and, if 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 channel layout parameters and the numerical expression of 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 airflow velocity field to obtain optimized channel layout parameters; performing convection simulation based on the optimized channel layout parameters using digital twin technology to obtain a target multidimensional dataset, wherein the target multidimensional dataset includes the numerical expression of the updated airflow velocity field and temperature field of the high and low voltage distribution cabinet; when the temperature change rate determined according to the target multidimensional dataset is greater than or equal to the preset change rate, determining the heat dissipation scheme of the high and low voltage distribution cabinet according to the optimized channel layout parameters and the updated numerical expression of the airflow velocity field.

[0017] According to another aspect of the embodiments of this application, a heat dissipation control system for high and low voltage distribution cabinets based on the Internet of Things is also provided, comprising: a data acquisition module, configured to acquire real-time temperatures of multiple component locations in the high and low voltage distribution cabinet through an Internet of Things sensor array to obtain a temperature dataset, wherein each temperature data in the temperature dataset includes a temperature value, a timestamp corresponding to the temperature value, and three-dimensional location coordinates; and a first generation module, configured to generate a thermal distribution map inside the high and low voltage distribution cabinet based on the temperature dataset, and generate a heat flow distribution matrix based on the thermal distribution map, wherein the heat flow distribution matrix contains the... The heat flow intensity of multiple grid points in the thermal distribution map; a second generation module, used to generate a risk distribution map based on the heat flow distribution matrix, wherein the risk distribution map describes the distribution of multiple risk grid points, and 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, used to design the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet based on the risk distribution map, and obtain channel layout parameters; a control module, used to determine the heat dissipation scheme of the high and low voltage distribution cabinet based on the channel layout parameters, and perform heat dissipation control of the high and low voltage distribution cabinet based on the heat dissipation scheme.

[0018] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the above-described Internet of Things-based high and low voltage distribution cabinet heat dissipation control method.

[0019] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described heat dissipation control method for high and low voltage distribution cabinets based on the Internet of Things when it is run.

[0020] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program, which, when executed by a processor, provides the above-described IoT-based high and low voltage power distribution cabinet heat dissipation control method.

[0021] This application utilizes an IoT sensor array to collect real-time temperature data 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 of the cabinet, providing accurate data support for subsequent analysis. Based on the temperature dataset, a thermal distribution map is generated, and further, a heat flow distribution matrix is ​​produced to quantify the heat flow intensity at each grid point. This process reveals the heat transfer patterns and distribution characteristics of high-temperature regions, providing a basis for identifying heat dissipation bottlenecks. By analyzing the heat flow distribution matrix, a risk distribution map is generated, clearly marking 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, improving the efficiency of problem detection. Based on the risk distribution map, the geometry of the heat flow guiding channel is designed, optimizing the airflow path to ensure efficient heat flow from high-temperature areas to low-temperature areas. Finally, a heat dissipation scheme is determined and controlled based on the channel layout parameters, achieving efficient management of heat dissipation in high and low voltage power distribution cabinets. This achieves closed-loop optimization from data acquisition to heat dissipation control, significantly improving heat dissipation efficiency. It solves the problem of inefficiently controlling heat dissipation in high and low voltage power distribution cabinets in related technologies. Attached Figure Description

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

[0023] To more clearly illustrate the technology in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a heat dissipation control method for high and low voltage distribution cabinets based on the Internet of Things, according to an embodiment of this application.

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

[0026] To enable those skilled in the art to better understand this application, the technology of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] This embodiment provides a heat dissipation control method for high and low voltage distribution cabinets based on the Internet of Things, which includes, but is not limited to, applications in industrial control software. Figure 1 This is a flowchart illustrating a heat dissipation control method for high and low voltage distribution cabinets based on the Internet of Things, according to an embodiment of this application. Figure 1 As shown, the process includes the following steps S102-S110:

[0029] Step S102: Real-time temperature data is collected from the locations of multiple components in the high and low voltage distribution cabinet using an IoT sensor array to obtain a temperature dataset. Each temperature data in the temperature dataset includes a temperature value, a timestamp corresponding to the temperature value, and three-dimensional location coordinates.

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

[0031] For example, in a typical medium-voltage switchgear, sensors are deployed at critical component locations such as busbars, circuit breakers, and cable joints, acquiring temperature data once per second. The generated data format is (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 acquisition ensures the real-time nature and accuracy of the data, providing a reliable foundation for subsequent anomaly detection. Thermistor sensors are chosen based on their high sensitivity and low cost, effectively capturing minute temperature changes and making them suitable 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 acquired. If the temperature value exceeds a preset threshold, the data point is marked as an anomaly (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 anomaly), resulting in a temperature dataset containing anomaly markers. Using the temperature dataset containing anomaly markers, a clustering algorithm is employed to group the anomalies, determining their spatial distribution in three-dimensional coordinates, and generating a spatial distribution dataset of anomalies. Based on the spatial distribution dataset of anomalies, the timestamp sequence of the anomalies is obtained, and the continuity of the anomalies in the time dimension is determined, generating a multidimensional temperature anomaly dataset containing time continuity information.

[0033] For example, the busbar location (1.2, 0.5, 0.3) recorded a temperature of 62.3 degrees Celsius at timestamp 2025-05-14-08:00:05, exceeding the threshold and being marked as abnormal. This threshold setting is based on the distribution cabinet's operating specifications and can be dynamically adjusted according to the equipment type. The abnormality marking dataset retains timestamp and coordinate information, facilitating subsequent analysis of the spatial and temporal characteristics of the abnormalities. In the abnormal point grouping, the K-means clustering algorithm is used to spatially group the abnormal points. For example, assuming 10 abnormal points are detected in the distribution cabinet, the clustering algorithm divides them into 3 groups based on 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 around (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 timestamp sequence of each anomaly is extracted from the spatial distribution dataset of anomalies. For example, the timestamps of anomalies in the bus region are (08:00:05, 08:00:06, 08:00:07), indicating that the anomaly lasts for 3 seconds, suggesting a potential risk of sustained overheating. The generated multidimensional temperature anomaly dataset integrates temporal continuity and spatial distribution information, such as (bus, 1.2, 0.5, 0.3, 62.3, continuous for 3 seconds). This multidimensional data provides a basis for fault prediction, and continuity analysis can provide early warning of potential thermal runaway risks.

[0035] It should be noted that, through the above analysis, maintenance personnel can quickly identify anomalies in the busbar area, determine the severity of the fault by considering the time continuity, and prioritize maintenance. This method significantly improves the reliability and safety of the distribution cabinet operation, and reduces the risk of power outages or equipment damage caused by overheating.

[0036] Furthermore, by using industrial control software combined with temperature datasets collected by IoT sensor arrays, the internal temperature distribution of the power distribution cabinet can be monitored in real time, accurately locating hotspot areas. This helps the industrial control software quickly analyze heat accumulation trends, optimize heat dissipation strategies, improve equipment operational safety and reliability, while reducing energy consumption and maintenance costs.

[0037] Step S104: Generate a thermal distribution map inside the high and low voltage distribution cabinet based on the temperature dataset, and generate a heat flow distribution matrix based on the thermal distribution map, wherein the heat flow distribution matrix contains the heat flow intensity of multiple grid points in the thermal distribution map;

[0038] In an exemplary embodiment, the generation of the thermal distribution map inside the high and low voltage distribution cabinet based on the temperature dataset can be achieved through the following steps S11-S14:

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

[0040] In one possible implementation, the acquired temperature dataset typically contains timestamps, 3D coordinates, and temperature values, but may contain noisy or erroneous data. Data cleaning removes outliers through statistical outlier detection. For example, using a thresholding method based on the mean and standard deviation, assuming the mean temperature data for a key component is 50℃ and the standard deviation is 5℃, a threshold is set to the mean ± 2 times the standard deviation, i.e., 40℃ to 60℃. Data outside this range, such as 65℃, is marked as an outlier and removed. This method ensures that the cleaned dataset is more accurate and reflects the true temperature distribution.

[0041] Step S12: Normalize the temperature value of each temperature data in the cleaned temperature dataset to obtain a normalized temperature dataset.

[0042] Optionally, for the cleaned temperature dataset, a data standardization 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), where 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, thus obtaining the normalized temperature dataset.

[0043] For example, for a cleaned temperature dataset, data standardization involves linearly normalizing the temperature values ​​to make data of different dimensions comparable. Suppose a high- and low-voltage distribution cabinet has a temperature range of 30℃ to 70℃, and a point temperature is 50℃. Normalized using the formula (50-30) / (70-30) = 0.5, the normalized value is between 0 and 1, facilitating subsequent analysis.

[0044] It should be noted that linear normalization preserves the relative relationship of temperatures, making it suitable for algorithms such as clustering.

[0045] Step S13: Based on the K-means clustering algorithm, the normalized temperature dataset is clustered using 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 regions.

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

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

[0048] Step S14: Based on the color mapping image generation method, generate the 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.

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

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

[0051] It should be noted that using industrial control software to generate the aforementioned thermal distribution map offers the following advantages: 1) The industrial control software automatically removes abnormal temperature data according to preset rules, ensuring that the cleaned temperature dataset more accurately reflects the true temperature distribution and reduces noise interference; 2) Linear normalization of temperature values ​​makes data of different dimensions comparable, facilitating subsequent cluster analysis and algorithm processing, improving computational efficiency and result reliability; 3) Clustering of normalized data based on three-dimensional position coordinate distance metrics clearly delineates high-temperature, medium-temperature, and low-temperature regions within high- and low-voltage distribution cabinets, revealing heat distribution patterns; 4) The industrial control software, combined with color mapping, generates an intuitive thermal distribution map, helping 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 generation of the heat flow distribution matrix based on the thermal distribution map can be achieved through the following steps S21-S23:

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

[0054] Alternatively, temperature data points can be obtained from a thermal distribution map, and the temperature gradient at each grid point can be calculated using the finite difference method, where the finite difference formula is: T represents the temperature value, i represents the grid point index, and Δx represents the grid spacing. T_{i+1} and T_{i-1} represent the temperature values ​​of 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 measuring the temperature difference between adjacent grid points, reflecting the rate of heat change. This method can effectively capture regions with drastic temperature changes, such as areas near the heating element, where the gradient value is high.

[0056] Alternatively, according to Fourier's law, the heat flux intensity 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 represents the material's ability to conduct heat; It is a temperature gradient (unit: ℃ / m or K / m), which represents the rate of temperature change per unit distance.

[0057] Step S22: If the temperature gradient of a grid point is greater than a preset gradient threshold, the heat transfer path is traced along the gradient descent direction according to the target dataset to obtain the heat transfer path. The heat transfer path has multiple grid points, including grid points with temperature gradients greater than the preset gradient threshold, and the temperature gradients of the multiple grid points decrease.

[0058] Optionally, the heat transfer direction can be extracted from the target dataset. By setting a gradient threshold, such as 10°C / meter, high gradient regions can be selected. The heat transfer path can then be determined using a vector tracking algorithm along the gradient descent direction. The vector tracking algorithm simulates the flow of heat from high-temperature areas to low-temperature areas using the gradient vector direction. For example, in a high-temperature region near a transformer, the gradient points towards the heat dissipation vents, and the tracking path shows that heat flows towards well-ventilated areas. This path analysis helps identify potential points of heat accumulation.

[0059] Step S23: Generate a heat flow distribution matrix based on the heat transfer path, wherein the heat flow distribution matrix includes the heat flow intensity of the plurality of grid points.

[0060] Optionally, a two-dimensional heat flux distribution matrix can be generated using a matrix generation method based on the heat transfer path. Matrix elements represent the heat flux intensity at each grid point, i.e., M(i, j) represents the heat flux intensity at each grid point, and the value reflects the strength of the heat flow. For example, in a certain area of ​​the distribution cabinet, grid points near relays have higher heat flux intensity, resulting in larger matrix element values, such as 0.8, indicating concentrated heat; while values ​​near vents are lower, such as 0.2. This matrix intuitively reflects the heat distribution pattern, facilitating the location of high-temperature risk areas.

[0061] It should be noted that the heat flux distribution matrix can be presented using color mapping, with red indicating high heat flux intensity and blue indicating low intensity. For example, areas near heat-generating components are displayed in red, indicating a need to pay attention to the heat dissipation design; vent areas are displayed in blue, indicating good heat dissipation. This visualization method allows maintenance personnel to quickly assess the equipment status and optimize maintenance strategies.

[0062] It should be noted that generating the heat flux distribution matrix through industrial control software offers the following advantages: 1) It calculates the temperature gradient at each grid point using the finite difference method and quantifies the heat transfer pattern by combining it with the target dataset (containing temperature values, temperature gradients, and heat flux intensity), thus improving analysis accuracy; 2) When the temperature gradient at a grid point exceeds a preset threshold, the industrial control software automatically tracks the heat transfer path along the gradient descent direction, selecting multiple key grid points with decreasing temperature gradients to clarify the heat flow direction; 3) The heat flux distribution matrix generated based on the heat transfer path intuitively reflects 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: Generate a risk distribution map based on 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 points is greater than a first preset threshold.

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

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

[0066] Optionally, prior to step S31 above, spatial interpolation can be used to estimate the heat flux value at the unknown point. Specifically, trilinear interpolation can be used. For example, the heat flux values ​​of eight known points near an unknown point are 200, 210, 205, 198, 202, 208, 195, and 203 W / m², respectively. 2 The heat flux at this point was estimated to be approximately 201.5 W / m² using a weighted average. This method ensures the continuity of the heat flux distribution, facilitating subsequent analysis.

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

[0068] Step S32: Based on the smoothed heat flow distribution matrix, calculate the temperature gradient of each grid point in three dimensions using the finite difference method to obtain the three-dimensional gradient vector of each grid point;

[0069] Step S33: Determine the 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;

[0070] Suppose that the heat flux values ​​of a certain grid point along the x, y, and z directions are respectively those of its adjacent points:

[0071] The heat flux values ​​of two adjacent grid points in opposite directions along the x-axis are q1 and q2, respectively. Therefore, the temperature gradient along the x-axis can be calculated as follows: Where q is the heat flux intensity, with units of W / m 2 k is the thermal conductivity of the material (unit: W / (m·K)), which represents the material's ability to conduct heat; The temperature gradient is represented by Δx (unit: ℃ / m or K / m), where Δx is the grid spacing in meters; it corresponds to the temperature gradient in the y and z directions.

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

[0073] If the magnitude (or any component) of the 3D gradient vector of a certain grid point exceeds the threshold, then the point is marked as a risk point.

[0074] Step S34: Based on the set of risk points, perform three-dimensional visualization processing using a stereomicroscope algorithm to obtain a risk distribution map.

[0075] In one exemplary embodiment, suppose a region experiences insufficient cooling, resulting in 10 grid points exceeding gradient limits, forming a set of risk points. A stereomicroscope algorithm is used for 3D visualization, highlighting these risk points in red within the grid to visually represent the overheated area. For example, the grid near the exhaust end appears densely red, prompting designers to optimize the cooling channels. This visualization helps engineers quickly locate problem areas, improving design efficiency.

[0076] It should be noted that implementing the above process through industrial control software offers the following advantages: 1) The industrial control software performs Gaussian filtering on the heat flux distribution matrix, eliminating measurement noise, improving data continuity and accuracy, 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 of each grid point, accurately quantifying areas of drastic heat changes and improving the accuracy of risk identification; 3) Risk grid points exceeding the temperature gradient threshold are selected based on the three-dimensional gradient vector, automatically generating a risk point set and quickly locating local overheating areas; 4) The risk point set is visualized in three dimensions using a stereomicroscope algorithm, generating a risk distribution map to help 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: 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;

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

[0079] Step S41: Determine the three-dimensional coordinates of the multiple risk grid points based on the risk distribution map to obtain a risk point coordinate dataset;

[0080] Optionally, a stereomicroscope algorithm can be used to scan the risk distribution map and determine the three-dimensional coordinates of the multiple risk grid points. Example: Assuming the distribution cabinet has dimensions of 1m × 0.8m × 0.5m, the algorithm identifies 5 risk points with coordinates of (0.2, 0.3, 0.1), (0.3, 0.4, 0.2), etc., in meters.

[0081] Step S42: Based on the risk point coordinate dataset, the high and low voltage distribution cabinets are discretized using the finite volume method to generate three-dimensional finite volume mesh elements;

[0082] Optionally, based on the risk point coordinate dataset, an appropriate grid cell size (e.g., 0.01m × 0.01m × 0.01m) 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 certain grid cell are (0.25, 0.35, 0.15), covering an area of ​​0.01m around it. 3 The grid is denser near the risk points to capture changes in heat flow. The discretized grid dataset provides the foundation for airflow simulation.

[0084] Step S43: Determine the spatial range of the three-dimensional finite volume mesh element to obtain a discretized mesh dataset;

[0085] Optionally, each grid cell records its spatial extent. For example, if the center coordinates of a cell are (0.25, 0.35, 0.15), its extent covers a radius of 0.01m. 3 The region is defined. By clearly defining the spatial extent 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 momentum equation, calculate the airflow trajectory according to the discretized grid dataset and the boundary conditions of the high and low voltage distribution cabinet to obtain the airflow trajectory dataset. The boundary conditions include the wall temperature of the high and low voltage distribution cabinet and the airflow inlet velocity.

[0087] Optionally, the boundary conditions include: a cabinet wall temperature of 50℃, an airflow inlet velocity of 2 m / s, and the inlet located at the bottom of the cabinet. The SST k-ε turbulence model, combined with the momentum equation, is used to calculate the airflow trajectory. The model predicts the airflow path within the cabinet by simulating turbulent energy and dissipation rate. Assuming the airflow 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 visually reflects the heat transfer path, providing a basis for optimizing the 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: Based on the airflow trajectory dataset, use an iterative optimization algorithm to calculate the cross-sectional shape and length of the heat flow guiding channel to obtain the channel layout parameters.

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

[0091] It should be noted that using industrial control software to design heat flow guiding channels has the following advantages: 1) It extracts the three-dimensional coordinates of risk grid points based on the risk distribution map, generating a risk point coordinate dataset to ensure that the channel design is tailored to actual high-temperature areas; 2) It discretizes the distribution cabinet using the finite volume method to generate three-dimensional mesh cells, and combines the SST k-ε turbulence model and momentum equation to simulate the airflow trajectory, accurately predicting the heat transfer path; 3) It uses iterative optimization algorithms to adjust the cross-sectional shape and length of the heat flow guiding channel, enhancing the cooling effect of the airflow on the risk points and improving heat dissipation efficiency; 4) The industrial control software runs through the entire process, from data acquisition to trajectory calculation to channel design, achieving automated processing, while providing intuitive visualization results of airflow trajectory and channel layout, significantly improving design accuracy and efficiency.

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

[0093] Step S51: Generate a channel geometric model based on the channel layout parameters;

[0094] Optionally, in the scenario of optimizing the airflow and temperature field inside the distribution cabinet, the geometric model is generated based on the channel layout design. Assuming the distribution cabinet dimensions are 1.2m × 0.9m × 0.6m, the optimized channel layout forms a set of elliptical guide channels with a minor axis of 0.05m, a major 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 positions of the channels and the inner wall of the cabinet to ensure that the airflow path covers the high-temperature area. After the model is generated, it is imported into a finite element analysis tool for mesh generation.

[0095] Step S52: Mesh the channel geometry model using a finite element analysis tool, and calculate the numerical expressions of the initial airflow velocity field and initial temperature field based on the boundary conditions of the high and low voltage distribution cabinet. The boundary conditions include the wall temperature of the high and low voltage distribution cabinet and the airflow inlet velocity.

[0096] Optionally, the mesh is divided into unstructured tetrahedral meshes. The mesh size near the channel inlet and high-temperature region is set to 0.008m to capture complex flow field changes, while the mesh size in regions far from the channel is widened to 0.015m, generating a total of approximately 3.5 million mesh elements. The boundary conditions are set as follows: cabinet wall temperature 45℃, bottom airflow inlet velocity 1.5m / s, and top outlet at atmospheric pressure. The initial airflow velocity field and initial temperature field are numerically expressed using finite element analysis tools, for example, a velocity of 1.5m / s and a temperature of 30℃ near the inlet.

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

[0098] Step S53: 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;

[0099] Optionally, the Navier-Stokes equations and energy equations are used to couple the calculation of the airflow velocity and temperature fields. The Navier-Stokes equations describe the velocity and pressure changes of the airflow, while the energy equations handle heat transfer. An iterative solution employs a pressure-based coupled algorithm. The initial airflow velocity field shows laminar flow in the channel, transitioning to weak turbulence near the high-temperature region. After calculating the residual of the airflow velocity field, if it exceeds a preset threshold of 0.001, the turbulent viscosity parameter is adjusted, and the iterative calculation is repeated. For example, after one iteration, the velocity distribution in the middle of the channel is updated to 1.2 m / s, and the pressure distribution shows that the pressure at the top outlet has decreased to 0.98 times atmospheric pressure. After multiple iterations, the residual decreases to 0.0008, satisfying the convergence criterion, and the numerical expression of the target airflow velocity field is obtained.

[0100] Step S54: Analyze the airflow velocity distribution characteristics and pressure distribution characteristics based on the numerical expression of the target airflow velocity field in order to optimize the channel layout parameters.

[0101] Optionally, the velocity and pressure distribution characteristics of the airflow are analyzed based on the numerical expression of the target airflow velocity field. The velocity distribution shows that the airflow velocity is high at the inlet of the channel, reaching 1.5 m / s, decreasing to 1.0 m / s in the middle due to channel geometric constraints, and rising back to 1.3 m / s at the top outlet. The pressure distribution indicates the existence of a low-pressure zone in the middle of the channel, which promotes airflow circulation. Based on this, the optimized scheme is determined to be a 15° tilt at the channel outlet to enhance the efficiency of the top airflow discharge.

[0102] It should be noted that by adjusting the channel geometry, the airflow can be evenly distributed over the high-temperature area, thereby improving heat dissipation. For example, adding a 0.02m wide diverter in the middle of the channel can further guide the airflow to specific high-temperature points and optimize the stability of the convection circulation.

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

[0104] Step S110: 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.

[0105] Optionally, a heat dissipation scheme can be determined based on the channel layout parameters, specifically including optimizing airflow organization and adjusting the position and parameters of the heat dissipation devices (such as fan speed and channel cross-sectional shape) to achieve effective cooling of high-temperature areas. Industrial control software is used to monitor temperature changes in real time and dynamically adjust the operating status of the heat dissipation equipment to ensure uniform temperature distribution inside the distribution cabinet, reduce the risk of overheating, and improve the reliability and safety of equipment operation.

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

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

[0108] Optionally, when constructing a 3D model of the internal components of the distribution cabinet, digital twin technology can be used. High-precision scanning equipment can be used to acquire the geometric data of the internal components of the distribution cabinet, generating an accurate 3D model. Assuming the distribution cabinet contains a transformer, circuit breaker, and cooling fan, the size and position of each component can be collected using a laser scanner to form point cloud data, which 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 real-time mapping of physical entities to virtual models, ensuring that the model's spatial layout matches that of the actual distribution cabinet. For example, the transformer's location 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 the model's accuracy controlled at the millimeter level. This method efficiently recreates the real-world scenario, facilitating subsequent simulation and analysis.

[0111] Step S62: 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 according to the adjusted three-dimensional position of the heat dissipation device;

[0112] Optionally, the channel layout parameters define the geometry of the heat flow guiding channels (such as cross-sectional shape and length), which determines the airflow path. Based on these parameters, the location of the heat dissipation device can be replanned to bring 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 suggests moving the fan to (0.7, 0.2, 0.3) meters.

[0113] Spatial layout includes the three-dimensional positions and interrelationships of all components inside the distribution cabinet (such as transformers, circuit breakers, cooling fans, etc.). Based on the adjusted three-dimensional positions of the cooling devices, the spatial distribution of components inside the distribution cabinet is updated. It is necessary to ensure that the new layout does not affect the function and installation of other equipment.

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

[0115] Optionally, computational fluid dynamics (CFD) software (such as OpenFOAM or ANSYS) can be used for convection simulation. The internal space of the distribution cabinet is discretized into finite volume grid elements, assuming a grid size of 1 million, and boundary conditions are set, such as a fan inlet velocity of 2 m / s and a no-slip boundary on the outer wall.

[0116] The simulation includes: 1) Calculating the airflow trajectory and velocity distribution inside the distribution cabinet; 2) Generating temperature distribution data using the heat transfer equation Q = h·A·ΔT, where Q is the heat transfer rate in watts (W), i.e., joules per second (J / s); h is the convective heat transfer coefficient (or surface heat transfer coefficient) in watts per square meter (Kelvin / (m²·K)); A is the heat transfer area in square meters, referring to the surface area involved in heat transfer, such as the total area of ​​the transformer surface in contact with 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 temperature difference between the high-temperature fluid and the low-temperature fluid.

[0117] Step S64: Determine the heat dissipation scheme of the high and low voltage distribution cabinet based on the channel layout parameters and the multidimensional dataset.

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

[0119] Step S71: Determine the temperature change curve of the target area in the high and low voltage distribution cabinet based on the multidimensional dataset, 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.

[0120] Optionally, in the scenario of optimizing the heat dissipation of power distribution cabinets, the extraction of temperature change curve data for high-temperature risk areas from a multidimensional dataset can be achieved using data visualization tools. Assuming the temperature in a certain area of ​​the power distribution cabinet drops from 85℃ to 65℃ within the initial 30 minutes of operation, a curve can be plotted using time-temperature data points. Using a data filtering tool, such as the Python-based Pandas library, a temperature threshold of 80℃ can be set to filter out a subset of data for high-temperature areas. Next, the temperature decrease rate is calculated using numerical differentiation. For example, with a time interval of 5 minutes, the temperature drops from 85℃ to 65℃ at a rate of approximately 4℃ / minute. If the target decrease rate needs to reach 5℃ / minute, the current rate is insufficient and needs optimization.

[0121] Step S72: When the temperature change rate is greater than or equal to the preset change rate, determine the heat dissipation scheme of the high and low voltage distribution cabinet 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, use a geometric optimization tool to adjust the width and angle of the heat flow guiding channel according to the numerical expression of the airflow velocity field to obtain the optimized channel layout parameters;

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

[0124] Step S74: Based on the adoption of digital twin technology, perform convection simulation 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 cabinet;

[0125] Step S75: If the temperature change rate determined based on the target multidimensional dataset is greater than or equal to the preset change rate, determine the heat dissipation scheme of the high and low voltage distribution cabinet based on the optimized channel layout parameters and the numerical expression of the updated airflow velocity field.

[0126] Optionally, based on the optimized channel layout parameters, a virtual convection model is reconstructed using computational fluid dynamics software, such as ANSYS Fluent. Simulations show that the optimized channel inlet velocity increases to 4.8 m / s, and the peak temperature in the high-temperature region of the temperature field drops from 85°C to 60°C within 5 minutes. The updated heat dissipation efficiency dataset reflects a temperature decrease rate of 5°C / minute, reaching the target value. At this point, the optimized channel layout parameters (e.g., width 70 mm, angle 45°) are combined with the airflow velocity field data (inlet velocity 4.8 m / s) using a data integration tool to generate the final heat dissipation scheme parameters. Optionally, the data integration tool can use Excel or dedicated database software to correlate geometric parameters with simulation data, ensuring that the scheme can be directly applied to the design of the distribution cabinet.

[0127] It should be noted that steps S72 and S73-S75 are performed under different circumstances and there is no specific order in which they are performed.

[0128] It should be noted that implementing the above heat dissipation scheme determination process through industrial control software has the following significant advantages: 1) A three-dimensional model of the power distribution cabinet is constructed using digital twin technology, and the three-dimensional position of the heat dissipation device is obtained. The position of the heat dissipation device is adjusted in conjunction with the channel layout parameters to generate an accurate spatial layout, providing a reliable foundation for subsequent optimization; 2) The temperature change curve and rate of change of the target area are analyzed based on multidimensional datasets (airflow velocity field and temperature field) to quickly locate high-temperature risk points. The industrial control software can automatically extract key data, improving analysis efficiency; 3) When the temperature change rate is lower than the preset value, the width and angle of the heat flow guiding channel are adjusted using geometric optimization tools to generate optimized channel layout parameters. Convection simulation is performed again using digital twin technology to generate updated airflow velocity field and temperature field data, ensuring that the optimization results are verifiable. This closed-loop optimization process significantly improves the adaptability and effectiveness of the heat dissipation scheme; 4) The industrial control software automatically determines whether the heat dissipation requirements are met based on the optimized channel layout parameters and the updated airflow velocity field numerical expression. If not, iterative optimization continues until the preset rate of change standard is reached; 5) Virtual simulation replaces traditional physical experiments, reducing the number of experiments and time costs. At the same time, potential heat dissipation problems can be identified 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 heat dissipation requirements under different working conditions and ensure the safety and reliability of high and low voltage distribution cabinet operation.

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

[0130] Steps S102-S110 above utilize an IoT sensor array to collect real-time temperature data from multiple components within the 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 of the distribution cabinet, providing accurate data support for subsequent analysis. A thermal distribution map is generated based on the temperature dataset, and a heat flow distribution matrix is ​​further generated to quantify the heat flow intensity at each grid point. This process reveals the heat transfer patterns and distribution characteristics of high-temperature areas, providing a basis for identifying heat dissipation bottlenecks. By analyzing the heat flow distribution matrix, a risk distribution map is generated, clearly marking risk grid points where the absolute value of the three-dimensional temperature gradient vector exceeds a first preset threshold. This step accurately locates local overheating areas, improving the efficiency of problem detection. Based on the risk distribution map, the geometry of the heat flow guiding channel is designed, optimizing the airflow path to ensure efficient heat flow from high-temperature areas to low-temperature areas. Finally, a heat dissipation scheme is determined and controlled based on the channel layout parameters, achieving efficient management of heat dissipation in both high and low voltage distribution cabinets. This achieves closed-loop optimization from data acquisition to heat dissipation control, significantly improving heat dissipation efficiency. This solves the problem of inefficiently controlling heat dissipation in high and low voltage distribution cabinets in related technologies.

[0131] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they 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, in essence or the part of this application that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0132] This embodiment also provides a heat dissipation control system and device for high and low voltage distribution cabinets based on the Internet of Things (IoT). This IoT-based heat dissipation control system and device are used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, 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 switchgear heat dissipation control system based on the Internet of Things, according to an embodiment of this application. The system includes:

[0134] The acquisition module 202 is used to acquire the real-time temperature of multiple components in the high and low voltage distribution cabinet through an Internet of Things sensor array to obtain a temperature dataset. Each temperature data in the temperature dataset includes a temperature value, a timestamp corresponding to the temperature value, and three-dimensional location coordinates.

[0135] The first generation module 204 is used to generate a thermal distribution map inside the high and low voltage distribution cabinet based on the temperature dataset, and to generate a heat flow distribution matrix based on the thermal distribution map, wherein the heat flow distribution matrix has the heat flow intensity of multiple grid points in the thermal distribution map;

[0136] The second generation module 206 is used to generate a risk distribution map based on the heat flow 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 points is greater than a first preset threshold.

[0137] Design module 208 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, and obtain the channel layout parameters;

[0138] The control module 210 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.

[0139] The aforementioned system utilizes an IoT sensor array to collect real-time temperature data from multiple components within the 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 of the cabinet, providing accurate data support for subsequent analysis. Based on the temperature dataset, a thermal distribution map is generated, and further, a heat flow distribution matrix is ​​produced to quantify the heat flow intensity at each grid point. This process reveals the heat transfer patterns and distribution characteristics of high-temperature areas, providing a basis for identifying heat dissipation bottlenecks. By analyzing the heat flow distribution matrix, a risk distribution map is generated, clearly marking 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, improving the efficiency of problem detection. Based on the risk distribution map, the geometry of the heat flow guiding channel is designed, optimizing the airflow path to ensure efficient heat flow from high-temperature areas to low-temperature areas. Finally, a heat dissipation scheme is determined and controlled based on the channel layout parameters, achieving efficient management of heat dissipation in both high- and low-voltage power distribution cabinets. This achieves closed-loop optimization from data acquisition to heat dissipation control, significantly improving heat dissipation efficiency. It solves the problem of inefficiently controlling heat dissipation in high- and low-voltage power distribution cabinets in related technologies.

[0140] In an exemplary embodiment, the first generation module 204 is further configured to remove abnormal temperature data from the temperature dataset according to a preset data cleaning rule to obtain a cleaned temperature dataset; normalize the temperature value of each temperature data in the cleaned temperature dataset to obtain a normalized temperature dataset; cluster the normalized temperature dataset 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; and generate a thermal distribution map inside the high and low voltage distribution cabinet based on a color mapping-based image generation method, 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 configured to calculate the temperature gradient of each grid point in the thermal distribution map using the 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 dataset, wherein the target dataset includes the temperature value, temperature gradient, and heat flux intensity of each grid point; if the temperature gradient of a grid point is greater than a preset gradient threshold, the heat transfer path is traced along the gradient descent direction based on the target dataset to obtain a heat transfer path, wherein the heat transfer path has multiple grid points, the multiple grid points include grid points with temperature gradients greater than the preset gradient threshold, and the temperature gradients of the multiple grid points decrease; and a heat flux distribution matrix is ​​generated based on the heat transfer path, wherein the heat flux distribution matrix includes the heat flux intensity of the multiple grid points.

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

[0143] In an exemplary embodiment, the design module 208 is further configured to determine the three-dimensional coordinates of the plurality of risk grid points according to the risk distribution map, thereby obtaining a risk point coordinate dataset; discretize the high and low voltage distribution cabinet using the finite volume method according to the risk point coordinate dataset, thereby generating a three-dimensional finite volume grid cell; determine the spatial range of the three-dimensional finite volume grid cell, thereby obtaining a discretized grid dataset; calculate the airflow trajectory based on the SST k-ε turbulence model and momentum equation, according to the discretized grid dataset and the boundary conditions of the high and low voltage distribution cabinet, thereby obtaining an airflow trajectory dataset, wherein the boundary conditions include the wall temperature of the high and low voltage distribution cabinet and the airflow inlet velocity; and calculate the cross-sectional shape and length of the heat flow guiding channel using an iterative optimization algorithm according to the airflow trajectory dataset, thereby obtaining the channel layout parameters.

[0144] In an exemplary embodiment, the system further includes an optimization module, configured to generate a channel geometric model based on the channel layout parameters before determining the heat dissipation scheme of the high and low voltage distribution cabinet based on the channel layout parameters; mesh the channel geometric model using a finite element analysis tool, and calculate 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 cabinet, wherein the boundary conditions include the wall temperature and airflow inlet velocity of the high and low voltage distribution cabinet; perform coupled calculations of 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; and analyze the airflow velocity distribution characteristics and pressure distribution characteristics based on 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 further configured to construct a three-dimensional model of the high- and low-voltage distribution cabinet using 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 according to the adjusted three-dimensional position of the heat dissipation device; perform convection simulation based on the spatial layout using digital twin technology 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 cabinet; and determine the heat dissipation scheme of the high- and low-voltage distribution cabinet according to the channel layout parameters and the multidimensional dataset.

[0146] In an exemplary embodiment, the control module 210 is further configured to determine the temperature change curve of the target area in the high and low voltage distribution cabinet based on the multidimensional dataset, and determine the temperature change rate based on the temperature change curve, wherein the temperature of the target area is greater than a preset temperature; and when the temperature change rate is greater than or equal to the preset change rate, determine the heat dissipation scheme of the high and low voltage distribution cabinet based on the channel layout parameters and the numerical expression of the airflow velocity field.

[0147] In an exemplary embodiment, the control module 210 is further configured 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 airflow velocity field to obtain optimized channel layout parameters; based on digital twin technology, perform convection simulation according to the optimized channel layout parameters to obtain a target multidimensional dataset, wherein the target multidimensional dataset includes the numerical expression of the updated airflow velocity field and temperature field of the high and low voltage distribution cabinet; and when the temperature change rate determined according to the target multidimensional dataset is greater than or equal to the preset change rate, determine the heat dissipation scheme of the high and low voltage distribution cabinet according to the optimized channel layout parameters and the updated numerical expression of the airflow velocity field.

[0148] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when run.

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

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

[0151] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0152] Embodiments of this application also provide a computer program product, including a computer program, wherein the computer program, when executed by a processor, performs the steps in any of the above method embodiments.

[0153] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0154] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to perform the steps of any of the above method embodiments via the computer program.

[0155] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0156] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0157] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this 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: The temperature of multiple components in the high and low voltage distribution cabinet is collected in real time by an Internet of Things sensor array to obtain a temperature dataset. Each temperature data in the temperature dataset includes a temperature value, a timestamp corresponding to the temperature value, and three-dimensional location coordinates. A thermal distribution map of the interior of the high and low voltage distribution cabinet is generated based on the temperature dataset, and a heat flow distribution matrix is ​​generated based on the thermal distribution map, wherein the heat flow distribution matrix contains the heat flow intensity of multiple grid points in the thermal distribution map; A risk distribution map is generated based on 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 points is greater than a first preset threshold. Based on the risk distribution map, the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet is designed to obtain the channel layout parameters; The heat dissipation scheme of the high and low voltage distribution cabinet is determined according to the channel layout parameters, and the heat dissipation of the high and low voltage distribution cabinet is controlled based on the heat dissipation scheme. Before determining the heat dissipation scheme for the high and low voltage distribution cabinet based on the channel layout parameters, the method further includes: Generate a channel geometric model based on the channel layout parameters; The channel geometry model is meshed using a finite element analysis tool, and the initial airflow velocity field and initial temperature field are numerically expressed based on the boundary conditions of the high and low voltage distribution cabinet. The boundary conditions include the wall temperature of the high and low voltage distribution cabinet and the airflow inlet velocity. The initial airflow velocity field and the initial temperature field are coupled and calculated using the Navier-Stokes equations and the energy equations to obtain a numerical expression of the target airflow velocity field. The airflow velocity distribution characteristics and pressure distribution characteristics are analyzed based on the numerical expression of the target airflow velocity field in order to optimize the channel layout parameters.

2. The method according to claim 1, characterized in that, Generate a thermal distribution map of the interior of the high and low voltage distribution cabinet based on the temperature dataset, including: Abnormal temperature data in the temperature dataset are removed according to preset data cleaning rules to obtain a cleaned temperature dataset. The temperature value of each temperature data in the cleaned temperature dataset is normalized to obtain a normalized temperature dataset. Based on the K-means clustering algorithm, the normalized temperature dataset is clustered using the distance between the three-dimensional position coordinates in the temperature data as the distance metric, resulting in K clusters, where K is an integer greater than or equal to 2, and different clusters correspond to different temperature regions. A color mapping-based image generation method generates a thermal distribution map inside the high and low voltage distribution cabinet based on 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 based on the aforementioned thermal distribution map includes: The temperature gradient of each grid point in the thermal distribution map is calculated using the finite difference method, and the heat flux intensity of each grid point is calculated based on the temperature gradient of each grid point to obtain the target dataset, wherein the target dataset includes the temperature value, temperature gradient and heat flux intensity of each grid point; If the temperature gradient at a grid point is greater than a preset gradient threshold, the heat transfer path is traced along the gradient descent direction according to the target dataset to obtain the heat transfer path. The heat transfer path has multiple grid points, including grid points with temperature gradients greater than the preset gradient threshold, and the temperature gradients of the multiple grid points decrease. A heat flow distribution matrix is ​​generated based on the heat transfer path, wherein the heat flow distribution matrix includes the heat flow intensity of the plurality of grid points.

4. The method according to claim 1, characterized in that, Generate a risk distribution map based on the heat flow distribution matrix, including: Gaussian filtering is used to smooth the heat flux distribution matrix to obtain a smoothed heat flux distribution matrix. Based on the smoothed heat flow distribution matrix, the temperature gradient of each grid point in three dimensions is calculated using the finite difference method to obtain the three-dimensional gradient vector of each grid point. A set of risk points is determined based on the three-dimensional gradient vector of each grid point, wherein the grid points in the set of risk points are the risk grid points; Based on the set of risk points, a three-dimensional visualization process is performed using a stereomicroscope algorithm to obtain a risk distribution map.

5. The method according to claim 1, characterized in that, Based on the risk distribution map, the geometric structure of the heat flow guiding channel of the high and low voltage distribution cabinet is designed to obtain the channel layout parameters, including: Based on the risk distribution map, the three-dimensional coordinates of the multiple risk grid points are determined to obtain a risk point coordinate dataset. Based on the risk point coordinate dataset, the high and low voltage distribution cabinets are discretized using the finite volume method to generate three-dimensional finite volume mesh elements. Determine the spatial extent of the three-dimensional finite volume mesh element to obtain a discretized mesh dataset; Based on the SST k-ε turbulence model and momentum equation, the airflow trajectory is calculated according to the discretized grid dataset and the boundary conditions of the high and low voltage distribution cabinet, and the airflow trajectory dataset is obtained. The boundary conditions include the wall temperature of the high and low voltage distribution cabinet and the airflow inlet velocity. Based on the airflow trajectory dataset, the cross-sectional shape and length of the heat flow guiding channel are calculated using an iterative optimization algorithm to obtain the channel layout parameters.

6. The method according to any one of claims 1-5, characterized in that, The heat dissipation scheme for the high and low voltage distribution cabinet is determined based on the channel layout parameters, including: A three-dimensional model of the high and low voltage distribution cabinet is constructed using digital twin technology, and the three-dimensional position of the heat dissipation device of the high and low voltage distribution cabinet is obtained. The three-dimensional position of the heat dissipation device is adjusted according to the channel layout parameters, and the spatial layout of the high and low voltage distribution cabinet is determined according to the adjusted three-dimensional position of the heat dissipation device. Based on digital twin technology, convection simulation is performed according to the spatial layout to obtain a multidimensional dataset, wherein the multidimensional dataset includes numerical representations of the airflow velocity field and temperature field of the high and low voltage distribution cabinet. The heat dissipation scheme for the high and low voltage distribution cabinet is determined based on the channel layout parameters and the multidimensional dataset.

7. The method according to claim 6, characterized in that, The heat dissipation scheme for the high and low voltage distribution cabinet is determined based on the channel layout parameters and the multidimensional dataset, including: The temperature change curve of the target area in the high and low voltage distribution cabinet is determined based on the multidimensional dataset, and the temperature change rate is determined 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, the heat dissipation scheme of the high and low voltage distribution cabinet is determined according to the channel layout parameters and the numerical expression of the airflow velocity field.

8. The method according to claim 7, characterized in that, The method further includes: 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 the optimized channel layout parameters. Based on the adoption of digital twin technology, 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 cabinet. If the temperature change rate determined based on the target multidimensional dataset is greater than or equal to the preset change rate, the heat dissipation scheme of the high and low voltage distribution cabinet is determined based on the optimized channel layout parameters and the numerical expression of the updated airflow velocity field.

9. A heat dissipation control system for high and low voltage distribution cabinets based on the Internet of Things, characterized in that, A method for controlling the heat dissipation of high and low voltage distribution cabinets based on the Internet of Things as described in any one of claims 1 to 8 includes: The data acquisition module is used to collect real-time temperature data of multiple components in the high and low voltage distribution cabinet through an Internet of Things sensor array to obtain a temperature dataset. Each temperature data in the temperature dataset includes a temperature value, a timestamp corresponding to the temperature value, and three-dimensional location coordinates. The first generation module is used to generate a thermal distribution map inside the high and low voltage distribution cabinet based on the temperature dataset, and to generate a heat flow distribution matrix based on the thermal distribution map, wherein the heat flow distribution matrix has the heat flow intensity of multiple grid points in the thermal distribution map; The second generation module is used to generate a risk distribution map based on the heat flow 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 points is greater than a first preset threshold. The 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, and obtain the channel layout parameters; The 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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