Internal intelligent layout optimization and thermal management system of electric control cabinet

Abstract

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automatic control, and in particular to an intelligent layout optimization and thermal management system inside an electric control cabinet. Background Art

[0002] The layout and heat management of the electric control cabinet are two crucial aspects in its design. Reasonable layout can maximize space utilization, ensure orderly arrangement of electrical components, reduce electromagnetic interference, and optimize cable routing, thereby improving overall operating efficiency and maintenance convenience. At the same time, heat management is directly related to the stable operation and life of the equipment inside the electric control cabinet. Since electrical components will generate a lot of heat during operation, if it cannot be effectively dissipated in time, it will cause the temperature of the equipment to rise too high, affecting performance and even causing failures. Therefore, effective heat dissipation design, such as adopting a reasonable ventilation structure and installing high-efficiency heat dissipation components, is crucial to maintaining a suitable working temperature inside the electric control cabinet.

[0003] The defects of traditional electric control cabinets in layout design and thermal management. In terms of layout design, the space planning of traditional electric control cabinets is particularly conservative, failing to make full use of every inch of precious space. The arrangement of components often lacks compactness, which not only leads to a huge waste of space resources, but also makes the overall structure appear bloated and bulky. In addition, the layout of auxiliary facilities such as wire ducts and terminal blocks is often unreasonable, and the wiring is messy, like a maze. This not only affects the aesthetics of the electric control cabinet, but more importantly, the intricate wiring increases the risk of safety hazards such as cable wear and short circuits, and also brings great inconvenience to subsequent maintenance and repair work. What is more serious is that the unreasonable layout of strong and weak current components, such as strong current components surrounding weak current components, aggravates the problem of electromagnetic interference, which may interfere with the normal operation of the equipment and even damage the equipment. In terms of thermal management, the heat dissipation design of traditional electric control cabinets also has many shortcomings. Since the cabinet structure may not be conducive to air circulation, and the configuration of heat dissipation components such as heat sinks and fans is unreasonable or the number is insufficient, the heat generated by the electric control cabinet during operation cannot be discharged in a timely and effective manner. This not only causes the internal temperature to continue to rise, posing a serious threat to the performance and life of the equipment, but also increases the risk of equipment failure and downtime. What is more worrying is that in order to cope with this problem, traditional electric control cabinets often rely on high-energy air conditioning and refrigeration equipment. Although this method can alleviate the heat dissipation problem to a certain extent, it brings new problems such as low energy efficiency and high operating costs. A large amount of energy is wasted on cooling equipment and maintaining the temperature of the electric control cabinet, which not only increases the economic burden of the enterprise, but also creates an unnecessary burden on the environment. Summary of the invention

[0004] In order to solve the current technical problems, the main purpose of the present invention is to provide an intelligent layout optimization and thermal management system inside the electric control cabinet. By comprehensively using artificial intelligence, three-dimensional modeling, multi-dimensional heat dissipation technology and Internet of Things technology, the optimal layout and efficient thermal management of the internal equipment of the electric control cabinet are achieved, which significantly improves the operating efficiency, safety and maintainability of the electric control cabinet. The technical solution adopted by the present invention is: an intelligent layout optimization and thermal management system inside an electric control cabinet, comprising: Intelligent layout optimization module, which uses particle swarm optimization algorithm to receive device parameters input by the user or automatically extracted from the device database, and optimizes the objectives, including: maximizing space utilization, minimizing heat dissipation obstacles, optimizing maintenance paths, and ensuring safe spacing between devices; A three-dimensional modeling and simulation module, which uses three-dimensional modeling software to build a virtual model of the internal environment of the electric control cabinet, simulate the equipment installation location, wiring path and ventilation channel, predict the heat dissipation effect, space utilization and maintenance convenience under different layout schemes, and provide feedback for the intelligent layout optimization module; An efficient thermal management module, which monitors the internal environment of the electric control cabinet in real time through a sensor network and adjusts the heat dissipation strategy; Intelligent monitoring and early warning module, which uses wireless communication technology to achieve real-time data transmission between the electric control cabinet and the remote monitoring center, builds a cloud management platform to centrally display the operating status, and sets early warning thresholds. When the thresholds are exceeded, the early warning mechanism is automatically triggered and the emergency cooling plan is activated. The intelligent layout optimization module includes the following modules: A parameter input and extraction module, which receives device parameters input by a user through an interface, or automatically extracts device parameters of a device from a device database, wherein the device parameters include the physical dimensions of the device, such as length, width, height, weight, power consumption, and heat load; A particle swarm initialization module, wherein the particle swarm initialization module initializes the particle swarm according to the input device parameters and layout space constraints, wherein each particle represents a layout scheme, the position vector of each particle represents the specific position of the device in the layout space, and each particle has a velocity vector for moving in the search space to explore new layout schemes; A fitness evaluation module, which comprehensively evaluates each layout scheme by calculating the ratio of used space to total space, and evaluates the heat dissipation effect based on the heat dissipation requirements of the equipment and the ventilation conditions in the layout; Particle update and iteration module, which is used to update the speed and position of particles according to the fitness evaluation results, perform iterative optimization, combine the current speed, the difference between the best position and the current position, and the difference between the global best position and the current position, add the updated speed vector to the current position vector, and obtain a new position. Through continuous iteration, the particle swarm gradually converges to the optimal layout solution; A constraint processing module, which processes the constraint conditions in the layout process and guides the algorithm to find a better solution through the penalty term or reward term in the fitness function; The optimization result output module outputs the final optimized layout plan and its related indicators, wherein the layout plan includes a preview image or a layout image of the optimal layout plan, and the related indicators include space utilization, heat dissipation effect evaluation, and maintenance path planning indicators.

[0005] The intelligent layout optimization module is also provided with a user interaction and feedback module, which provides an interactive interface between the user and the intelligent layout optimization module, so that the user can input device parameters through the interface of the user interaction and feedback module or choose to extract parameters from the device database. During the operation of the algorithm, the user interaction and feedback module displays a preview image or related indicators of the current optimal layout solution in real time. After the optimization is completed, the detailed layout report can be viewed through the user interaction and feedback module, and adjustments can be made or re-optimization can be started as needed.

[0006] The three-dimensional modeling and simulation module includes the following modules: A three-dimensional modeling module, which uses three-dimensional modeling software to build a virtual three-dimensional model according to the actual size, structural characteristics and equipment parameters of the electric control cabinet, simulate the shell, partitions, and mounting plate structural components of the electric control cabinet, place and adjust the equipment model in the simulated electric control cabinet to ensure the accuracy and rationality of the equipment installation position, build a virtual model of the wiring path to reflect the actual electrical connection situation, and design and simulate the ventilation channel to optimize the heat dissipation effect; A simulation analysis module, which uses simulation technology to predict the heat dissipation effect, space utilization and maintenance convenience under different layout schemes based on the constructed three-dimensional model, evaluates the impact of different layout schemes on heat dissipation performance, calculates and compares the space utilization under different layout schemes, identifies potential space waste areas, puts forward optimization suggestions, simulates the operation process of maintenance personnel, and evaluates the accessibility of equipment under different layout schemes, the smoothness of maintenance paths and the difficulty of maintenance work; Feedback and optimization module, the feedback and optimization module feeds back the results of simulation analysis to the optimization algorithm, provides data support and optimization direction for the algorithm, adjusts the equipment layout and optimizes the ventilation design according to the simulation results of heat dissipation effect to improve the heat dissipation performance, replans the equipment location according to the results of space utilization analysis to improve space utilization, and optimizes the equipment layout and channel design according to the maintenance convenience evaluation results to reduce the difficulty and cost of maintenance.

[0007] The three-dimensional modeling and simulation module also includes an interaction and visualization module, which is used to enable users to intuitively view the three-dimensional model and simulation results, render the three-dimensional model in real time, and provide interactive operations. The efficient thermal management module includes the following modules: A sensor network module, wherein the sensor network module monitors the environmental parameters inside the electric control cabinet in real time through sensor components, and provides real-time and accurate environmental data support for the adjustment of the heat dissipation strategy; wherein the sensor components include a temperature sensor, a humidity sensor, and a flow rate sensor; Semiconductor thermoelectric refrigeration module, which realizes refrigeration by utilizing the thermoelectric effect of semiconductor materials. Semiconductor thermoelectric refrigeration sheets are installed near the key heat source inside the electric control cabinet to directly cool the heat source; A heat pipe heat dissipation module, one end of which is in close contact with the heat source and the other end is connected to the heat dissipation fins, and the heat on the heat dissipation fins is dissipated into the air through forced convection of the fan; An intelligent fan speed regulation module, wherein the intelligent fan speed regulation module automatically regulates the speed of the fan according to real-time environmental data provided by the sensor network.

[0008] The efficient thermal management module also includes an advanced control algorithm or a machine learning model module, which performs real-time analysis on the data collected by the sensor network module, predicts future heat dissipation requirements, and automatically adjusts the working status of the semiconductor thermoelectric cooling module, the heat pipe heat dissipation module, and the intelligent fan speed control module.

[0009] The intelligent monitoring and early warning module includes the following modules: A data transmission module, which collects various environmental parameters and operating status data inside the electric control cabinet in real time, and transmits the collected data to a remote monitoring center or a cloud management platform in real time through wireless communication technology; A cloud management platform module, which centrally displays the operating status, environmental parameters, and historical data information of the electric control cabinet, provides a user-friendly operation interface, and supports multi-terminal access; An early warning threshold setting module allows users to set early warning thresholds according to actual needs, including upper and lower limits of key parameters such as temperature, humidity, and current. It supports user-defined thresholds to adapt to different operating environments and needs. When the collected data exceeds the preset threshold, the early warning mechanism is automatically triggered; The early warning mechanism and emergency response module are used to automatically trigger the early warning mechanism when the data exceeds the early warning threshold, notify relevant personnel through network information, and record the early warning information and emergency response process in detail.

[0010] The cloud management platform module has a built-in data analysis tool to process, analyze and display the collected data and generate operation trend charts and fault prediction reports.

[0011] It also includes a historical data query and report generation module and a data analysis and optimization suggestion module. The historical data query and report generation module is used for historical data query, and to perform statistics and analysis on historical data, generate operation trend reports and fault prediction reports, predict potential faults and optimize maintenance strategies. The data analysis and optimization suggestion module is used to combine historical data and real-time data to analyze the operating status of the electric control cabinet, and provide optimization suggestions to users based on the analysis results. Compared with the prior art, the technical solution of this application has the following beneficial technical effects: In terms of the intelligent layout optimization module, by introducing the particle swarm optimization algorithm, it is possible to comprehensively analyze the multi-dimensional parameters of the equipment inside the electric control cabinet, including size, weight, heat dissipation requirements, etc., thereby achieving the optimization of the layout. This optimization not only maximizes the space utilization and reduces the waste of space resources, but also effectively solves the problems of loose component arrangement, disorganized wiring and increased electromagnetic interference in traditional electric control cabinets by minimizing heat dissipation obstacles, optimizing maintenance paths and ensuring safe spacing between equipment.

[0012] In addition, the intelligent layout optimization module can automatically extract device parameters from the device database or receive user input, further improving the flexibility and accuracy of layout design.

[0013] Secondly, the application of 3D modeling and simulation modules enables the system to accurately simulate the internal environment of the electric control cabinet by building a virtual model in the early stage of design, including the equipment installation location, wiring path and ventilation channel. Through simulation technology, the system can predict the heat dissipation effect, space utilization and maintenance convenience under different layout schemes, and provide valuable feedback for the optimization algorithm. This method not only avoids the cost and time waste of "trial and error" in traditional design, but also significantly improves the design efficiency and accuracy, ensuring the excellent performance of the electric control cabinet in actual applications.

[0014] In terms of thermal management, the high-efficiency thermal management module integrates semiconductor thermoelectric refrigeration, heat pipe cooling and intelligent fan speed regulation technology. These advanced technologies work together to achieve efficient heat dissipation inside the electrical control cabinet. Real-time monitoring of the sensor network and automatic analysis and adjustment of advanced control algorithms or machine learning models enable the heat dissipation strategy to be dynamically optimized according to actual conditions, which not only ensures the stable operation of the equipment but also reduces energy consumption. Compared with traditional electrical control cabinets that rely on high-energy air-conditioning and refrigeration equipment, this system has significantly improved energy efficiency, reduced operating costs, and reduced environmental burden.

[0015] Finally, the intelligent monitoring and early warning module realizes real-time data transmission between the electric control cabinet and the remote monitoring center through wireless communication technology, providing users with powerful remote monitoring and management capabilities. The cloud management platform centrally displays the operating status of the electric control cabinet and sets the early warning threshold. Once an abnormal situation is found, the system will automatically trigger the early warning mechanism and start the emergency heat dissipation plan to ensure the stable operation of the electric control cabinet under abnormal conditions. In addition, the application of historical data query function and big data analysis technology enables users to generate operation trend reports and fault prediction reports, providing strong data support for further optimization and maintenance. These functions jointly improve the intelligence level of the electric control cabinet and reduce the difficulty of maintenance and safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation or the prior art description are briefly introduced below. Obviously, the drawings in the following description are some implementations of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work: Figure 1 This is the overall structural diagram of the intelligent layout optimization and thermal management system inside the electric control cabinet of the present invention; Figure 2 It is a structural block diagram of the intelligent layout optimization module of the internal intelligent layout optimization and thermal management system of the electric control cabinet of the present invention; Figure 3 It is a structural block diagram of the three-dimensional modeling and simulation module of the internal intelligent layout optimization and thermal management system of the electric control cabinet of the present invention; Figure 4 It is a structural block diagram of the efficient thermal management module of the intelligent layout optimization and thermal management system inside the electric control cabinet of the present invention; Figure 5 This is a structural block diagram of the intelligent monitoring and early warning module of the intelligent layout optimization and thermal management system inside the electric control cabinet of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Combination Figure 1-5 As shown, the intelligent layout optimization and thermal management system inside the electric control cabinet of the present invention includes: Intelligent layout optimization module: The intelligent layout optimization module adopts particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameters, size, weight, heat dissipation requirements, etc. of the equipment inside the electric control cabinet to achieve the optimization of the layout. It receives the equipment parameters input by the user or automatically extracted from the equipment database. The equipment parameters include but are not limited to the physical size, power consumption, heat load, etc. of the equipment, and optimizes the goals, including: maximizing space utilization, minimizing heat dissipation obstacles, optimizing maintenance paths, and ensuring safe spacing between equipment; Combination Figure 2 , the intelligent layout optimization module specifically includes the following modules: Parameter input and extraction module: The parameter input and extraction module is used to receive device parameters input by the user through the interface, or automatically extract relevant parameters of the device from the device database, including but not limited to the physical dimensions of the device, such as length, width, height, weight, power consumption, heat load, etc., in order to provide basic data support for subsequent layout optimization; Particle swarm initialization module, the particle swarm initialization module initializes the particle swarm according to the input device parameters and layout space constraints, where each particle represents a possible layout scheme, and its position vector represents the specific position of the device in the layout space. Each particle also has a velocity vector for moving in the search space to explore new layout schemes, and sets the control parameters of the algorithm, such as particle swarm size, maximum number of iterations, learning factor, and inertia weight; Fitness evaluation module, which is used to design fitness functions and conduct a comprehensive evaluation of each layout scheme by calculating the ratio of used space to total space. Based on the heat dissipation requirements of the equipment and the ventilation conditions in the layout, the heat dissipation effect is evaluated, the accessibility between equipment and the length of the mobile path of maintenance personnel are evaluated, and whether the distance between all equipment meets the safety standards is checked. Its purpose is to provide optimization direction for the particle swarm optimization algorithm and guide the algorithm to find the optimal layout scheme; Particle update and iteration module: The particle update and iteration module updates the speed and position of the particles according to the fitness evaluation results, performs iterative optimization, considers the current speed, the difference between the personal best position and the current position, and the difference between the global best position and the current position, adds the updated speed vector to the current position vector, and obtains the new position. Through continuous iteration, the particle swarm gradually converges to the optimal layout solution; Constraint processing module,The constraint processing module is used to process various constraints in the layout process, such as the minimum safe distance between devices, which must be strictly met, otherwise the layout plan is considered invalid, heat dissipation effect, maintenance path, etc., and guides the algorithm to find a better solution through the penalty term or reward term in the fitness function. Its purpose is to ensure that the layout plan meets the actual requirements and improve the feasibility and practicality of the layout; The optimization result output module is used to output the final optimized layout plan and its related indicators, the preview image or layout image of the optimal layout plan, detailed indicators such as space utilization, heat dissipation effect evaluation, maintenance path planning, and a detailed report of the layout plan for user reference and decision-making.

[0019] A user interaction and feedback module is also provided in the intelligent layout optimization module, wherein the user interaction and feedback module provides an interactive interface between the user and the intelligent layout optimization module. The user can input device parameters through the interface or choose to extract parameters from the device database. During the operation of the algorithm, a preview image or related indicators of the current optimal layout solution can be displayed in real time. After the optimization is completed, the user can view a detailed layout report and make adjustments or re-optimization as needed.

[0020] Through the parameter input and extraction module, the system can flexibly obtain device parameters. Whether it is manually input by the user or automatically extracted from the database, it greatly improves the convenience and accuracy of data acquisition, laying a solid foundation for subsequent layout optimization. Secondly, the introduction of the particle swarm initialization module and the particle update and iteration module makes the layout optimization process efficient and intelligent. By simulating group behavior, it continuously explores and iterates in the search space until the optimal layout solution is found. The fitness evaluation module and the constraint processing module ensure that the optimization process not only pursues the maximization of space utilization, but also takes into account the requirements of heat dissipation effect, maintenance convenience and safe spacing between equipment, effectively avoiding the problems of heat dissipation obstacles, messy wiring and electromagnetic interference that may occur in traditional designs. Finally, the combination of the optimization result output module and the user interaction and feedback module enables users to intuitively see the optimization results and adjust or re-optimize according to actual needs, greatly improving the user experience and design flexibility.

[0021] Take the electric control cabinet on a large industrial automation production line as an example. The electric control cabinet integrates a large number of electrical components and control devices. The traditional layout design can no longer meet the requirements of efficient operation and easy maintenance. The specific implementation is as follows: Parameter input and extraction: Users input parameters such as the physical dimensions (such as length, width, height), weight, power consumption and heat load of the main equipment through the system interface. At the same time, the system automatically extracts the parameters of some auxiliary equipment from the equipment database to ensure the integrity and accuracy of the data.

[0022] Particle swarm initialization: Based on the input device parameters and the layout space constraints inside the electric control cabinet, the system initializes a particle swarm containing multiple particles, each of which represents a possible layout solution. The particle swarm size is set to 50, the maximum number of iterations is 1000, and the control parameters such as the learning factor and inertia weight are adjusted to adapt to the search process.

[0023] Fitness evaluation and particle update: The system designs a fitness function that takes into account factors such as space utilization, heat dissipation, maintenance paths, and safe spacing between devices. In each iteration, the system calculates the fitness value of each particle and updates the particle's speed and position based on the fitness evaluation results. Through continuous iterations, the particle swarm gradually converges to the optimal layout solution.

[0024] Constraint handling: During the optimization process, the system strictly handles the minimum safe spacing constraint between devices to ensure that any layout solution will not violate this safety requirement. At the same time, through the penalty or reward terms in the fitness function, the algorithm is guided to consider factors such as heat dissipation and maintenance paths when finding the optimal solution.

[0025] Optimization result output and user interaction: After multiple iterations, the system outputs the final optimized layout plan and its related indicators, including detailed indicators such as space utilization, heat dissipation effect evaluation, and maintenance path planning. The user views the preview image and detailed report of the layout plan through the system interface, fine-tunes the location of some equipment, and re-optimizes it.

[0026] The three-dimensional modeling and simulation module uses three-dimensional modeling software to build a virtual model of the internal environment of the electric control cabinet, accurately simulates the equipment installation position, wiring path and ventilation channel, and predicts the heat dissipation effect, space utilization and maintenance convenience under different layout schemes through simulation technology, providing feedback for the optimization algorithm.

[0027] Combination Figure 3 , the 3D modeling and simulation module specifically includes the following modules: 3D modeling module: The 3D modeling module uses 3D modeling software such as AutoCAD, SolidWorks, 3ds Max, etc. to build a high-precision virtual 3D model based on the actual size, structural characteristics and equipment parameters of the electric control cabinet, accurately simulate the shell, partitions, mounting plates and other structural components of the electric control cabinet, place and adjust the equipment model to ensure the accuracy and rationality of the equipment installation position, build a virtual model of the wiring path, including cables, wires, terminal blocks, etc., to reflect the actual electrical connection situation, and design and simulate the ventilation channel, including air inlets, air outlets, cooling fans, etc., to optimize the heat dissipation effect; Simulation analysis module: Based on the constructed three-dimensional model, the simulation analysis module uses simulation technology to predict the heat dissipation effect, space utilization and maintenance convenience under different layout schemes. Through thermal simulation software such as ANSYS Fluent and Icepak, it simulates the temperature distribution, heat flow path and heat dissipation effect inside the electric control cabinet, evaluates the impact of different layout schemes on heat dissipation performance, calculates and compares the space utilization under different layout schemes, identifies potential space waste areas, puts forward optimization suggestions, simulates the operation process of maintenance personnel, and evaluates the accessibility of equipment under different layout schemes, the smoothness of maintenance paths and the difficulty of maintenance work; Feedback and optimization module: The feedback and optimization module feeds back the results of simulation analysis to the optimization algorithm, providing data support and optimization direction for the algorithm. According to the simulation results of heat dissipation effect, the equipment layout is adjusted and the ventilation design is optimized to improve the heat dissipation performance. According to the results of space utilization analysis, the equipment location is replanned to improve space utilization. According to the results of maintenance convenience evaluation, the equipment layout and channel design are optimized to reduce the difficulty and cost of maintenance. The interaction and visualization module provides a user-friendly interactive interface and visualization tools, allowing users to intuitively view the 3D model and simulation results, render the 3D model in real time, and show the realism of the internal environment of the electric control cabinet. It provides interactive operations such as rotation, zooming, and translation, which facilitates users to observe the model from different angles and display the results of simulation analysis, such as temperature distribution diagrams, space utilization charts, maintenance path animations, etc., to help users understand the simulation results and make decisions.

[0028] Through the high-precision 3D modeling module, all details of the electric control cabinet and its internal equipment can be accurately simulated, including the shell, partitions, mounting plates, equipment locations, wiring paths, and ventilation channels. This refined modeling provides a solid foundation for subsequent simulation analysis and ensures the accuracy and reliability of the simulation results. Secondly, the simulation analysis module uses advanced simulation technology to comprehensively predict the heat dissipation effect, space utilization, and maintenance convenience under different layout schemes, providing designers with scientific data support and optimization directions. This not only helps to improve the heat dissipation performance of the electric control cabinet and reduce equipment failures caused by overheating, but also optimizes the space layout, improves space utilization, and reduces the difficulty and cost of subsequent maintenance. In addition, the feedback and optimization module realizes the closed-loop process of design-simulation-optimization by feeding back the simulation results to the optimization algorithm in real time, making the design process more efficient and intelligent. Finally, the interaction and visualization module provides a user-friendly interactive interface and visualization tools, allowing designers and users to intuitively view and understand the 3D model and simulation results, improving the transparency and user participation of the design process.

[0029] Taking the electric control cabinet on a large industrial automation production line as an example, in order to improve its heat dissipation performance, optimize the space layout and reduce maintenance costs, a 3D modeling and simulation module was used for design and optimization. The specific implementation process is as follows: 3D modeling: First, using SolidWorks 3D modeling software, a high-precision virtual 3D model was constructed based on the actual size, structural characteristics and equipment parameters of the electric control cabinet. The model accurately simulated the housing, partitions, mounting plates and other structural components of the electric control cabinet, and placed and adjusted the equipment models one by one to ensure the accuracy and rationality of the equipment installation position. At the same time, a virtual model of the wiring path was also constructed, including cables, wires, terminal blocks, etc., to reflect the actual electrical connection situation. In addition, a ventilation channel, including air inlets, air outlets and cooling fans, was specially designed to optimize the heat dissipation effect.

[0030] Simulation analysis: Based on the constructed 3D model, the heat dissipation effect was simulated using ANSYS Fluent thermal simulation software. By simulating the temperature distribution, heat flow path and heat dissipation effect inside the electric control cabinet, the impact of different layout schemes on heat dissipation performance was evaluated. At the same time, the space utilization under different layout schemes was calculated and compared, potential space waste areas were identified, and optimization suggestions were put forward. In addition, the operation process of maintenance personnel was simulated to evaluate the accessibility of equipment, the smoothness of maintenance paths and the difficulty of maintenance work under different layout schemes.

[0031] Feedback and optimization: Based on the results of simulation analysis, feedback data is input into the optimization algorithm in a timely manner, providing a clear optimization direction for the algorithm. Based on the simulation results of heat dissipation effect, the equipment layout is adjusted and the ventilation design is optimized to improve heat dissipation performance. At the same time, based on the results of space utilization analysis, the equipment location is replanned to improve space utilization. In addition, based on the results of maintenance convenience evaluation, the equipment layout and channel design are optimized to reduce maintenance difficulty and cost.

[0032] Interaction and visualization: A user-friendly interactive interface and visualization tools are provided throughout the design and optimization process. Designers and users can intuitively view the 3D model and simulation results through interactive operations such as rotation, zooming, and translation. In particular, the results of the simulation analysis, such as temperature distribution diagrams, space utilization charts, and maintenance path animations, are clearly displayed in the interactive interface, helping users better understand the simulation results and make decisions. Ultimately, through this series of optimization measures, the heat dissipation performance of the electric control cabinet was successfully improved, the space layout was optimized, and the maintenance cost was reduced.

[0033] The efficient thermal management module combines semiconductor thermoelectric cooling, heat pipe cooling and intelligent fan speed regulation technology to achieve efficient heat dissipation inside the electric control cabinet. The sensor network monitors the internal environment of the electric control cabinet in real time, and uses advanced control algorithms or machine learning models to automatically analyze and adjust the heat dissipation strategy. Combination Figure 4 The efficient thermal management module specifically includes the following modules: Sensor network module: The sensor network module monitors key environmental parameters such as temperature, humidity, and air flow rate inside the electric control cabinet in real time through sensor components. The sensor components include temperature sensors, humidity sensors, and flow rate sensors. These sensors are distributed in key locations inside the electric control cabinet to ensure the comprehensiveness and accuracy of the data, and provide real-time and accurate environmental data support for subsequent cooling strategy adjustments; Semiconductor thermoelectric refrigeration module: The semiconductor thermoelectric refrigeration module uses the thermoelectric effect of semiconductor materials to achieve refrigeration. Semiconductor thermoelectric refrigeration sheets are installed near the key heat source inside the electric control cabinet to directly cool the heat source; Heat pipe cooling module: The heat pipe cooling module uses the evaporation and condensation process of the working fluid inside the heat pipe and the capillary action to achieve rapid heat transmission and diffusion. It specifically includes heat pipes, cooling fins, fans and other components. One end of the heat pipe is close to the heat source, and the other end is connected to the cooling fins. The heat is dissipated into the air through forced convection heat dissipation of the fan; Intelligent fan speed control module, the intelligent fan speed control module automatically adjusts the fan speed through intelligent algorithms according to the real-time environmental data provided by the sensor network to achieve the best heat dissipation effect. The intelligent control unit receives the sensor data, sends a control signal to the motor driver after algorithm calculation, and adjusts the fan speed.

[0034] The efficient thermal management module is also equipped with an advanced control algorithm or machine learning model module to perform real-time analysis on the data collected by the sensor network, predict future cooling needs, and automatically adjust the working states of modules such as semiconductor thermoelectric cooling, heat pipe cooling, and intelligent fan speed regulation. It uses advanced control strategies or machine learning models such as predictive control, fuzzy control, and neural control to improve the response speed and stability of the cooling system.

[0035] The sensor network module realizes comprehensive real-time monitoring of the internal environmental parameters of the electric control cabinet, providing accurate data support for thermal management decisions. The semiconductor thermoelectric refrigeration module directly performs efficient cooling on the heat source, effectively reducing the temperature of the heat source. The heat pipe cooling module uses physical principles to achieve rapid heat transmission and diffusion, further improving the heat dissipation efficiency. The intelligent fan speed control module automatically adjusts the fan speed according to real-time environmental data through intelligent algorithms, realizing the optimal configuration of heat dissipation resources. In addition, the introduction of advanced control algorithms or machine learning model modules not only improves the system's predictive ability, enabling the heat dissipation strategy to respond to environmental changes in advance, but also improves the response speed and stability of the heat dissipation system by optimizing the control strategy, thereby ensuring the long-term stable operation of the electric control cabinet.

[0036] Take the electric control cabinet on a large industrial automation production line as an example: First, sensor network modules are deployed at key locations inside the electric control cabinet, including temperature sensors, humidity sensors, and flow rate sensors. These sensors monitor and collect key environmental parameters such as temperature, humidity, and air flow rate inside the electric control cabinet in real time. These data are transmitted to the intelligent control unit via wired or wireless means, providing real-time and accurate environmental data support for subsequent cooling strategy adjustments.

[0037] Next, a semiconductor thermoelectric cooling module was installed near the key heat source inside the electric control cabinet, using the thermoelectric effect of semiconductor materials to directly cool the heat source. This measure effectively lowered the temperature of the heat source and reduced the diffusion of heat to the surrounding environment.

[0038] At the same time, a heat pipe cooling module is introduced, which realizes the rapid transmission and diffusion of heat through the evaporation and condensation process of the working fluid inside the heat pipe and the capillary action. One end of the heat pipe is close to the heat source, and the other end is connected to the heat sink fins, and the heat is dissipated into the air through the forced convection of the fan. This module further improves the heat dissipation efficiency of the electric control cabinet.

[0039] In addition, it is equipped with an intelligent fan speed control module, which automatically adjusts the fan speed through an intelligent algorithm based on the real-time environmental data provided by the sensor network. After receiving the sensor data, the intelligent control unit sends a control signal to the motor driver after algorithm calculation, thereby achieving precise adjustment of the fan speed. This measure not only optimizes the configuration of heat dissipation resources, but also reduces unnecessary energy consumption.

[0040] Finally, in order to further improve the intelligence level of the system, an advanced control algorithm or machine learning model module is introduced. This module analyzes the data collected by the sensor network in real time, predicts future cooling needs, and automatically adjusts the working status of modules such as semiconductor thermoelectric cooling, heat pipe cooling, and intelligent fan speed regulation. By adopting advanced control strategies or machine learning models such as predictive control, fuzzy control, and neural control, the response speed and stability of the cooling system are successfully improved, ensuring the long-term stable operation of the electric control cabinet in high temperature and high load operating environments.

[0041] Combination Figure 5 , intelligent monitoring and early warning module, using wireless communication technology to achieve real-time data transmission between the electric control cabinet and the remote monitoring center, building a cloud management platform to centrally display the operating status, set early warning thresholds, automatically trigger the early warning mechanism and start the emergency cooling plan to ensure the stable operation of the electric control cabinet under abnormal conditions, provide historical data query function, use big data analysis technology to generate operation trend reports and fault prediction reports, and provide users with optimization suggestions.

[0042] Specifically, the intelligent monitoring and early warning module includes the following modules: Data transmission module: The data transmission module collects various environmental parameters and operating status data inside the electric control cabinet in real time, and transmits the collected data to the remote monitoring center or cloud management platform in real time through wireless communication technologies such as Wi-Fi, Zigbee, LoRa, NB-IoT, etc., to provide real-time and accurate data support for subsequent monitoring, analysis and early warning; Cloud management platform module: The cloud management platform module builds a cloud management platform to centrally display the operating status, environmental parameters, historical data and other information of the electric control cabinet, provide a user-friendly operation interface, support multi-terminal access such as computers, mobile phones, tablets, etc., so that users can check the operating status of the electric control cabinet anytime and anywhere. It has built-in data analysis tools to process, analyze and display the collected data, and generate operating trend charts, fault prediction reports, etc. Warning threshold setting module: The warning threshold setting module allows users to set warning thresholds according to actual needs, including upper and lower limits of key parameters such as temperature, humidity, and current. It supports user-defined thresholds to adapt to different operating environments and needs. When the collected data exceeds the preset threshold, the warning mechanism is automatically triggered; Early warning mechanism and emergency response module: In the early warning mechanism and emergency response module, when the data exceeds the early warning threshold, the early warning mechanism is automatically triggered, and relevant personnel are notified through SMS, email, APP push, etc., and emergency cooling plans are initiated, such as adjusting the fan speed, starting semiconductor thermoelectric cooling equipment, etc., to deal with abnormal situations, and record the early warning information and emergency response process in detail to provide data support for subsequent troubleshooting and optimization.

[0043] The system also has a historical data query and report generation module and a data analysis and optimization suggestion module. The historical data query and report generation module provides a historical data query function. Users can query the historical operation data of the electric control cabinet according to time range, data type and other conditions, and use big data analysis technology to count and analyze historical data to generate operation trend reports, fault prediction reports, etc., to provide users with optimization suggestions to help users understand the operation rules of the electric control cabinet, predict potential faults, and optimize maintenance strategies. The data analysis and optimization suggestion module combines historical data and real-time data, uses big data analysis technology, and conducts in-depth analysis of the operating status of the electric control cabinet. According to the analysis results, it provides users with optimization suggestions, such as adjusting equipment layout, optimizing heat dissipation strategy, replacing aging components, etc.

[0044] The data transmission module ensures the real-time and accuracy of data, providing a solid foundation for monitoring and early warning; the cloud management platform module provides the convenience of centralized display and remote access, enhancing the user experience; the early warning threshold setting module allows users to flexibly set the early warning threshold according to actual needs, improving the pertinence and effectiveness of early warning; the early warning mechanism and emergency response module realizes the automation of early warning and the timeliness of emergency handling, reducing the impact of faults on the system. In addition, the use of the historical data query and report generation module and the data analysis and optimization suggestion module not only helps users better understand the operating rules and potential faults of the electric control cabinet, but also provides users with scientific optimization suggestions, further improving the reliability and maintenance efficiency of the system.

[0045] Taking the electric control cabinet on a large industrial automation production line as an example, in order to improve the real-time monitoring and the accuracy of early warning, the intelligent monitoring and early warning module was used for transformation. The specific implementation process is as follows: First, a variety of sensors are installed inside the electric control cabinet to collect real-time environmental parameters and operating status data such as temperature, humidity, and current. Through the data transmission module, these data are transmitted to the cloud management platform in real time. Wi-Fi is selected as the main communication method to ensure the stability and speed of data transmission.

[0046] In the cloud management platform module, a user-friendly operation interface is built to support multi-terminal access. Users can view the operating status, environmental parameters and historical data of the electric control cabinet anytime and anywhere through devices such as computers, mobile phones or tablets. The platform has built-in data analysis tools that can automatically generate operating trend charts, fault prediction reports, etc., to help users better understand the operation of the electric control cabinet.

[0047] In order to meet the early warning needs of different users, an early warning threshold setting module is set up. Users can customize the upper and lower limits of key parameters such as temperature, humidity, and current according to actual conditions. Once the collected data exceeds the preset threshold, the early warning mechanism will be automatically triggered and relevant personnel will be notified via SMS, email, or APP push.

[0048] In the early warning mechanism and emergency response module, a detailed emergency cooling plan has been formulated. When the data exceeds the early warning threshold, the system will automatically adjust the fan speed, start the semiconductor thermoelectric cooling equipment and other measures to deal with abnormal situations. At the same time, the system will record the early warning information and emergency response process in detail to provide data support for subsequent troubleshooting and optimization.

[0049] In addition, the historical data query and report generation module and the data analysis and optimization suggestion module are fully utilized. By querying historical data, users can understand the long-term operation rules of the electric control cabinet; through big data analysis technology, the system can generate operation trend reports, fault prediction reports, etc., and provide optimization suggestions for users. For example, the system may suggest that users adjust the equipment layout to improve heat dissipation, or replace aging parts to improve system stability. These suggestions provide users with a scientific decision-making basis, further improving the maintenance efficiency and reliability of the electric control cabinet.

[0050] The intelligent layout optimization and thermal management system inside the electric control cabinet is a highly integrated and intelligent system, which aims to improve the operating efficiency, reliability and maintenance convenience of the electric control cabinet by optimizing the layout and strengthening thermal management. The system is mainly composed of four core parts: intelligent layout optimization module, 3D modeling and simulation module, efficient thermal management module and intelligent monitoring and early warning module. The modules work together to optimize and manage the internal environment of the electric control cabinet. The intelligent layout optimization module is the starting point of the entire system. It uses advanced algorithms such as particle swarm optimization algorithm to comprehensively consider the multi-dimensional parameters of the internal equipment of the electric control cabinet, such as size, weight, heat dissipation requirements, etc., as well as the number of users or equipment. Based on the specific information provided by the database, optimization goals are set, such as maximizing space utilization, minimizing heat dissipation obstacles, optimizing maintenance paths, ensuring safe spacing between equipment, etc. Through continuous iteration and optimization of the algorithm, this module can generate a series of layout plans to provide a basis for subsequent 3D modeling and simulation. After obtaining the layout plan provided by the intelligent layout optimization module, the 3D modeling and simulation module uses 3D modeling software to build a virtual model of the internal environment of the electric control cabinet. The model accurately simulates key factors such as the installation location of the equipment, wiring paths and ventilation channels. Through simulation technology, this module can predict the heat dissipation effect, space utilization and maintenance convenience under different layout plans. After the layout optimization is completed, the efficient thermal management module is responsible for realizing efficient heat dissipation inside the electric control cabinet. It combines a variety of advanced technologies such as semiconductor thermoelectric refrigeration, heat pipe heat dissipation and intelligent fan speed regulation. Through the sensor network, the internal environmental parameters of the electric control cabinet, such as temperature, humidity, air flow rate, etc., are monitored in real time. By using advanced control algorithms or machine learning models, the module can automatically analyze and adjust the heat dissipation strategy to ensure that the electric control cabinet can maintain good heat dissipation performance under various working conditions, thereby extending the life of the equipment and improving the system stability. The intelligent monitoring and early warning module uses Wireless communication technology enables real-time data transmission between the electric control cabinet and the remote monitoring center. On the cloud management platform, users can centrally view the operating status, environmental parameters, historical data and other information of the electric control cabinet. By setting the warning threshold, the module can automatically trigger the warning mechanism and notify relevant personnel through SMS, email, APP push and other methods. Once an abnormal situation is detected, the system will immediately activate the emergency cooling plan to ensure the stable operation of the electric control cabinet. At the same time, the module also provides historical data query function, and uses big data analysis technology to generate operation trend reports and fault prediction reports, providing users with scientific optimization suggestions to further improve the overall performance of the system.

[0051] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0052] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent layout optimization and thermal management system inside an electric control cabinet, characterized in that: include: Intelligent layout optimization module, which uses particle swarm optimization algorithm to receive device parameters input by the user or automatically extracted from the device database, and optimizes the objectives, including: maximizing space utilization, minimizing heat dissipation obstacles, optimizing maintenance paths, and ensuring safe spacing between devices; A three-dimensional modeling and simulation module, which uses three-dimensional modeling software to build a virtual model of the internal environment of the electric control cabinet, simulate the equipment installation location, wiring path and ventilation channel, predict the heat dissipation effect, space utilization and maintenance convenience under different layout schemes, and provide feedback for the intelligent layout optimization module; An efficient thermal management module, which monitors the internal environment of the electric control cabinet in real time through a sensor network and adjusts the heat dissipation strategy; Intelligent monitoring and early warning module, which uses wireless communication technology to achieve real-time data transmission between the electric control cabinet and the remote monitoring center, builds a cloud management platform to centrally display the operating status, and sets early warning thresholds. When the thresholds are exceeded, the early warning mechanism is automatically triggered and the emergency cooling plan is activated.

2. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 1 is characterized in that: The intelligent layout optimization module includes the following modules: A parameter input and extraction module, which receives device parameters input by a user through an interface, or automatically extracts device parameters of a device from a device database, wherein the device parameters include the physical dimensions of the device, such as length, width, height, weight, power consumption, and heat load; A particle swarm initialization module, wherein the particle swarm initialization module initializes the particle swarm according to the input device parameters and layout space constraints, wherein each particle represents a layout scheme, the position vector of each particle represents the specific position of the device in the layout space, and each particle has a velocity vector for moving in the search space to explore new layout schemes; A fitness evaluation module, which comprehensively evaluates each layout scheme by calculating the ratio of used space to total space, and evaluates the heat dissipation effect based on the heat dissipation requirements of the equipment and the ventilation conditions in the layout; Particle update and iteration module, which is used to update the speed and position of particles according to the fitness evaluation results, perform iterative optimization, combine the current speed, the difference between the best position and the current position, and the difference between the global best position and the current position, add the updated speed vector to the current position vector, and obtain a new position. Through continuous iteration, the particle swarm gradually converges to the optimal layout solution; A constraint processing module, which processes the constraint conditions in the layout process and guides the algorithm to find a better solution through the penalty term or reward term in the fitness function; The optimization result output module outputs the final optimized layout plan and its related indicators, wherein the layout plan includes a preview image or a layout image of the optimal layout plan, and the related indicators include space utilization, heat dissipation effect evaluation, and maintenance path planning indicators.

3. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 1 or 2, characterized in that: The intelligent layout optimization module is also provided with a user interaction and feedback module, which provides an interactive interface between the user and the intelligent layout optimization module, so that the user can input device parameters through the interface of the user interaction and feedback module or choose to extract parameters from the device database. During the operation of the algorithm, the user interaction and feedback module displays a preview image or related indicators of the current optimal layout solution in real time. After the optimization is completed, the detailed layout report can be viewed through the user interaction and feedback module, and adjustments can be made or re-optimization can be started as needed.

4. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 1 is characterized in that: The three-dimensional modeling and simulation module includes the following modules: A three-dimensional modeling module, which uses three-dimensional modeling software to build a virtual three-dimensional model according to the actual size, structural characteristics and equipment parameters of the electric control cabinet, simulate the shell, partitions, and mounting plate structural components of the electric control cabinet, place and adjust the equipment model in the simulated electric control cabinet to ensure the accuracy and rationality of the equipment installation position, build a virtual model of the wiring path to reflect the actual electrical connection situation, and design and simulate the ventilation channel to optimize the heat dissipation effect; A simulation analysis module, which uses simulation technology to predict the heat dissipation effect, space utilization and maintenance convenience under different layout schemes based on the constructed three-dimensional model, evaluates the impact of different layout schemes on heat dissipation performance, calculates and compares the space utilization under different layout schemes, identifies potential space waste areas, puts forward optimization suggestions, simulates the operation process of maintenance personnel, and evaluates the accessibility of equipment under different layout schemes, the smoothness of maintenance paths and the difficulty of maintenance work; Feedback and optimization module, the feedback and optimization module feeds back the results of simulation analysis to the optimization algorithm, provides data support and optimization direction for the algorithm, adjusts the equipment layout and optimizes the ventilation design according to the simulation results of heat dissipation effect to improve the heat dissipation performance, replans the equipment location according to the results of space utilization analysis to improve space utilization, and optimizes the equipment layout and channel design according to the maintenance convenience evaluation results to reduce the difficulty and cost of maintenance.

5. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 4 is characterized in that: The three-dimensional modeling and simulation module also includes an interaction and visualization module, which is used to enable users to intuitively view the three-dimensional model and simulation results, render the three-dimensional model in real time, and provide interactive operations.

6. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 1 is characterized in that: The efficient thermal management module includes the following modules: A sensor network module, wherein the sensor network module monitors the environmental parameters inside the electric control cabinet in real time through sensor components, and provides real-time and accurate environmental data support for the adjustment of the heat dissipation strategy; wherein the sensor components include a temperature sensor, a humidity sensor, and a flow rate sensor; Semiconductor thermoelectric refrigeration module, which realizes refrigeration by utilizing the thermoelectric effect of semiconductor materials. Semiconductor thermoelectric refrigeration sheets are installed near the key heat source inside the electric control cabinet to directly cool the heat source; A heat pipe heat dissipation module, one end of which is in close contact with the heat source and the other end is connected to the heat dissipation fins, and the heat on the heat dissipation fins is dissipated into the air through forced convection of the fan; An intelligent fan speed regulation module, wherein the intelligent fan speed regulation module automatically regulates the speed of the fan according to real-time environmental data provided by the sensor network.

7. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 6 is characterized in that: The efficient thermal management module also includes an advanced control algorithm or a machine learning model module, which performs real-time analysis on the data collected by the sensor network module, predicts future heat dissipation requirements, and automatically adjusts the working status of the semiconductor thermoelectric cooling module, the heat pipe heat dissipation module, and the intelligent fan speed control module.

8. The intelligent layout optimization and thermal management system for the electric control cabinet according to claim 1 is characterized in that: The intelligent monitoring and early warning module includes the following modules: A data transmission module, which collects various environmental parameters and operating status data inside the electric control cabinet in real time, and transmits the collected data to a remote monitoring center or a cloud management platform in real time through wireless communication technology; A cloud management platform module, which centrally displays the operating status, environmental parameters, and historical data information of the electric control cabinet, provides a user-friendly operation interface, and supports multi-terminal access; An early warning threshold setting module allows users to set early warning thresholds according to actual needs, including upper and lower limits of key parameters such as temperature, humidity, and current. It supports user-defined thresholds to adapt to different operating environments and needs. When the collected data exceeds the preset threshold, the early warning mechanism is automatically triggered; The early warning mechanism and emergency response module are used to automatically trigger the early warning mechanism when the data exceeds the early warning threshold, notify relevant personnel through network information, and record the early warning information and emergency response process in detail.

9. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 8, characterized in that: The cloud management platform module has a built-in data analysis tool to process, analyze and display the collected data and generate operation trend charts and fault prediction reports.

10. The intelligent layout optimization and thermal management system inside the electric control cabinet according to claim 1, characterized in that: It also includes a historical data query and report generation module and a data analysis and optimization suggestion module. The historical data query and report generation module is used for historical data query, and to perform statistics and analysis on historical data, generate operation trend reports and fault prediction reports, predict potential faults and optimize maintenance strategies. The data analysis and optimization suggestion module is used to combine historical data and real-time data to analyze the operating status of the electric control cabinet, and provide optimization suggestions to users based on the analysis results.

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