Heat dissipation control method and control device for industrial personal computer and industrial personal computer

Abstract

The invention relates to the technical field of heat dissipation control, in particular to an industrial personal computer heat dissipation control method and device and an industrial personal computer. The method comprises the following steps: acquiring structural data of an industrial personal computer; performing thermal imaging scanning on the industrial personal computer based on the industrial personal computer structure data to obtain industrial personal computer thermal imaging scanning data; confirming hot spot area data of the industrial personal computer based on thermal imaging scanning data of the industrial personal computer; performing biological bionic heat dissipation simulation on the hot spot area data of the industrial personal computer to obtain micro heat dissipation data of the industrial personal computer; heat dissipation notch confirmation is carried out on the hot spot area data of the industrial personal computer based on the micro heat dissipation data of the industrial personal computer to obtain heat dissipation notch data of the industrial personal computer; and performing similar simulation structure area screening on the industrial personal computer structure data through the industrial personal computer heat dissipation notch data to obtain an optimal heat dissipation area of the industrial personal computer. According to the invention, through accurate hot spot positioning, bionic heat dissipation optimization, dynamic temperature monitoring and response and energy efficiency evaluation and adjustment, the accuracy, adaptability and heat dissipation energy efficiency of heat dissipation control of the industrial personal computer are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat dissipation control, and particularly to a heat dissipation control method, a control device and an industrial personal computer for an industrial personal computer. Background Art

[0002] Initially, heat dissipation mainly relied on natural convection and simple air-cooling systems, which could meet the requirements under low power and low load conditions. However, with the increase in power density and the complexity of the working environment, the deficiencies of traditional heat dissipation methods have gradually emerged. Especially under high-load operation, problems such as low heat dissipation efficiency and inaccurate temperature control have attracted wide attention. The heat pipe technology utilizes the phase change principle and can efficiently conduct heat in a smaller space, thereby enhancing the heat dissipation capacity. During this period, the design of the heat dissipation system gradually shifted from passive heat dissipation to active heat dissipation, that is, by controlling devices such as fans and liquid pumps to adjust the heat dissipation efficiency. With the rapid development of fields such as computers, communications, and new energy, the modern heat dissipation control method for industrial personal computers has entered the stage of intelligence and systematization. By using temperature sensors, dynamically adjusting the wind speed and liquid cooling flow rate, etc., it has been realized to automatically adjust the heat dissipation system according to the load change, optimize the heat dissipation performance, and improve the stability and reliability of the device. However, the existing heat dissipation methods currently are usually based on experience or preset heat dissipation modes, lacking a customized heat dissipation solution for specific devices, and at the same time lacking a dynamic response to the real-time operating state of the industrial personal computer, and it is easy to have insufficient or excessive heat dissipation when the load changes, thereby resulting in relatively low intelligence and accuracy in the heat dissipation control of the industrial personal computer. Summary of the Invention

[0003] Based on this, it is necessary to provide a heat dissipation control method, a control device and an industrial personal computer for an industrial personal computer to solve at least one of the above technical problems.

[0004] To achieve the above object, a heat dissipation control method for an industrial personal computer, the method includes the following steps: Step S1: Obtain the structural data of the industrial personal computer; perform a thermal imaging scan on the industrial personal computer based on the structural data of the industrial personal computer to obtain the thermal imaging scan data of the industrial personal computer; confirm the hot spot area data of the industrial personal computer based on the thermal imaging scan data of the industrial personal computer; Step S2: Perform a biomimetic heat dissipation simulation on the hot spot area data of the industrial personal computer to obtain the microscopic heat dissipation data of the industrial personal computer; confirm the heat dissipation cutout on the hot spot area data of the industrial personal computer based on the microscopic heat dissipation data of the industrial personal computer to obtain the heat dissipation cutout data of the industrial personal computer; screen the class-simulation structure area of the structural data of the industrial personal computer through the heat dissipation cutout data of the industrial personal computer to obtain the optimal heat dissipation area of the industrial personal computer; Step S3: Obtain the operating parameters of the industrial control computer; determine whether the temperature of the industrial control computer is abnormal according to the operating parameters of the industrial control computer. If so, perform a vortex flow analysis on the optimal heat dissipation area of the industrial control computer to generate microfluidic effect intensity data; perform acoustic wave excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate industrial control computer heat dissipation control data; Step S4: Evaluate the heat dissipation energy efficiency of the industrial control computer heat dissipation control data, and adjust the acoustic wave heat dissipation frequency based on the heat dissipation energy efficiency evaluation result to execute the industrial control computer heat dissipation control operation.

[0005] Through the thermal imaging scanning technology, the present invention can clearly obtain the temperature distribution of the industrial control computer, quickly and accurately locate the hot spot area. This step effectively avoids the situations of overheat dissipation or insufficient heat dissipation, making the subsequent heat dissipation design more targeted and reducing the damage or efficiency decline caused by overheating from the source. By accurately positioning the hot spot area, a response can be made before the temperature rises, thereby reducing the probability of the industrial control computer malfunctioning and improving the safety and operation stability of the device. Combining the principles of bionic biology for heat dissipation simulation, simulating the efficient heat dissipation methods in nature (such as the heat dissipation structures of leaves and marine organisms), the heat dissipation design of the industrial control computer can be optimized by referring to its structure and principle. This can not only improve the heat dissipation efficiency but also reduce energy consumption. Optimizing the heat dissipation structure of the industrial control computer, improving the heat dissipation efficiency, and reducing energy consumption provide support for environmentally friendly design and energy-saving requirements, with strong economic and ecological benefits. Conducting microfluidic effect analysis through vortex flow analysis in the optimal heat dissipation area of the industrial control computer to deeply explore the microscopic behavior of the fluid during heat conduction. Through the forced flow mode of the fluid (such as the vortex effect), the heat transfer efficiency is enhanced, enabling local hot spots to be quickly cooled. The introduction of the microfluidic effect can precisely control the flow mode of the cooling fluid, improving the cooling efficiency. Especially in a small space, it can greatly enhance the heat dissipation capacity and avoid system crashes or efficiency reduction caused by excessive temperature. Using acoustic wave excitation technology to adjust the heat dissipation frequency, vibration waves can be generated in the heat dissipation area of the industrial control computer through acoustic wave excitation to enhance the diffusion and conduction of heat. This technology helps to accelerate heat transfer without increasing additional energy consumption, especially prominent in high heat load areas. Acoustic wave excitation can not only improve the heat dissipation efficiency but also avoid energy waste caused by traditional heat dissipation methods, and has low energy consumption, providing an environmentally friendly and efficient heat dissipation solution. Therefore, through precise hot spot positioning, bionic heat dissipation optimization, dynamic temperature monitoring and response, and energy efficiency evaluation and adjustment, the present invention improves the accuracy, adaptability, and heat dissipation energy efficiency of the industrial control computer heat dissipation control.

[0006] Preferably, Step S1 includes the following steps: Step S11: Obtain the structural data of the industrial control computer; Step S12: Perform data preprocessing on the industrial control computer structure data to generate standard industrial control computer structure data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S13: Use a thermal imaging camera to perform thermal imaging scanning on the standard industrial control computer structure data to obtain industrial control computer structure thermal imaging scanning data; Step S14: Screen the temperature extreme value regions from the industrial control computer structure thermal imaging scanning data to obtain industrial control computer heat source region data; Step S15: Use fluid mechanics to perform regional heat aggregation analysis on the industrial control computer heat source region data to generate industrial control computer hot spot region data.

[0007] The present invention provides a basis for the entire heat dissipation analysis by accurately obtaining the structure data of the industrial control computer. The core of this step is to obtain information such as the geometry, materials, and components of the industrial control computer to ensure the accuracy and pertinence of subsequent analysis. The accurate structure data provides the necessary background information for subsequent thermal imaging scanning and heat dissipation analysis, ensuring the reliability of subsequent analysis. Data preprocessing ensures the quality of the input data through means such as data cleaning, denoising, missing value filling, and data standardization, which is a key step to ensure the accuracy and stability of the heat dissipation analysis model. The preprocessed standard data reduces the interference of abnormal data on the analysis results, ensuring the efficiency and accuracy of subsequent thermal imaging scanning and fluid mechanics analysis, and reducing the uncertainty brought by noise and missing data. By using a thermal imaging camera to perform thermal imaging scanning on the standard industrial control computer structure data, the thermal distribution of the industrial control computer can be obtained in real time, ensuring accurate grasp of the hot spot regions and providing data support for heat dissipation design. Thermal imaging scanning can visually display the temperature distribution of the industrial control computer during operation, timely detect high-temperature regions, and help quickly locate potential overheating problems to avoid equipment damage. By screening the temperature extreme value regions, the regions with high heat aggregation in the industrial control computer can be identified. This process helps to focus on the regions that need to be prioritized for heat dissipation treatment and avoid interference from irrelevant regions. By accurately screening the temperature extreme value regions, the focus can be concentrated on solving the heat source problem, reducing unnecessary waste of heat dissipation resources, and improving the heat dissipation efficiency. Using fluid mechanics to perform heat aggregation analysis on the hot spot regions can identify the specific regions where heat is concentrated and provide strong data support for subsequent heat dissipation design. This process applies the principles of fluid mechanics to the heat distribution to help optimize the heat dissipation plan. Regional heat aggregation analysis can help determine the distribution of heat sources, optimize the heat dissipation design plan, effectively handle the hot spot regions, improve the heat dissipation efficiency, and reduce the overheating risk.

[0008] Preferably, the biological bionic heat dissipation simulation of the industrial control computer hot spot region data in step S2 includes: Divide the data in the hot spot area of the industrial control computer into regional grids to obtain microscopic heat dissipation units; calculate the local unit temperature of the microscopic heat dissipation units to obtain grid local temperature data; Set bionic heat dissipation parameters; use the grid local temperature data to perform heat conduction calculations on the data in the hot spot area of the industrial control computer to generate grid heat distribution data; perform heat bionic convection analysis on the grid heat distribution data through the bionic heat dissipation parameters to generate hot spot area air heat transfer conduction data; Analyze the thermal radiation characteristics of the data in the hot spot area of the industrial control computer based on the hot spot area air heat transfer conduction data, and simulate the internal and external heat flow exchange of the data in the hot spot area of the industrial control computer through the thermal radiation characteristics, thereby generating microscopic heat dissipation data of the industrial control computer.

[0009] The present invention divides the hot spot area of the industrial control computer into regional grids, subdivides the hot spot area into multiple microscopic heat dissipation units, and can perform heat dissipation analysis for each small area, thereby achieving more refined heat dissipation optimization. The refined grid division ensures that the heat dissipation situation of each area can be analyzed separately, avoiding the deficiency of ignoring small-scale heat dissipation problems in traditional large-scale methods, and thus improving the overall performance of the heat dissipation system. By calculating the local unit temperature, specific temperature data is obtained for each microscopic heat dissipation unit. This step can accurately simulate the temperature change of each small area and further improve the accuracy of heat dissipation simulation. The accurate calculation of the local temperature can effectively identify the temperature differences in each small area of the industrial control computer, ensure that the heat dissipation system can be dynamically adjusted according to specific requirements, and avoid overheating or uneven temperature problems. Set bionic heat dissipation parameters, draw on the design principles of highly efficient heat dissipation structures in nature (such as insect wings, leaves, etc.), and optimize the heat dissipation mechanism. This step injects innovative ideas into the heat conduction process and improves the heat dissipation efficiency. The bionic heat dissipation parameters can draw on the highly efficient heat dissipation methods in nature, maximize the heat dissipation capacity of the industrial control computer in a specific environment, reduce energy waste, and improve the heat dissipation efficiency. Use the local temperature data of the grid to perform heat conduction calculations and generate grid heat distribution data. This step can accurately simulate the heat conduction situation in different areas and help optimize the heat dissipation design. The heat conduction calculation can accurately depict the heat distribution in the hot spot area, reveal the heat flow transfer relationship between the high-temperature area and the low-temperature area, and thus provide a basis for subsequent heat flow optimization to ensure the efficiency of the heat dissipation path. Through heat bionic convection analysis, simulate the heat conduction of air in the hot spot area and simulate the natural convection process of heat. This process can enhance the heat dissipation of the industrial control computer and avoid heat accumulation. The bionic convection analysis can simulate the natural convection process of heat, make the heat dissipation process in the hot spot area of the industrial control computer closer to the highly efficient heat dissipation method in nature, reduce the heat dissipation bottleneck in the artificial design, and optimize the overall heat dissipation effect. Analyze the thermal radiation characteristics of the hot spot area of the industrial control computer and perform simulation calculations of internal and external heat exchange through heat flow exchange. This process can accurately simulate the thermal radiation effect and further optimize the heat dissipation process. The analysis of thermal radiation characteristics can accurately simulate the propagation of heat through radiation, optimize the internal and external heat exchange process of heat flow, improve the heat dissipation effect, ensure the stable operation of the industrial control computer in a high-temperature environment, and prevent overheating problems caused by heat accumulation.

[0010] Preferably, the confirmation of the heat dissipation incision for the hot spot area data of the industrial control computer based on the microscopic heat dissipation data of the industrial control computer in step S2 includes: Identifying the heat dissipation bottleneck area based on the microscopic heat dissipation data of the industrial control computer to obtain the heat dissipation bottleneck area data; confirming the heat dissipation incision position for the heat dissipation bottleneck area data to obtain the heat dissipation incision position data; Extract the geometric shape of the industrial control computer based on the heat dissipation cut position data to obtain the heat dissipation cut shape data of the industrial control computer; use the heat dissipation cut shape data of the industrial control computer to calculate the opening degree and channel size of the heat dissipation cut for the heat dissipation cut position data to obtain the heat dissipation cut opening degree data and the heat dissipation channel size data; Perform a stability analysis of the heat dissipation cut on the heat dissipation cut position data through the heat dissipation cut opening degree data and the heat dissipation channel size data to generate the heat dissipation cut data of the industrial control computer.

[0011] The present invention can help designers avoid overheating of hot spots by accurately identifying the heat dissipation bottleneck area, improve the pertinence of the heat dissipation design, and ensure that heat is released in a timely and effective manner. By accurately determining the position of the heat dissipation cut, the blindness of the heat dissipation design is avoided, so that the heat dissipation cut can be made at the place where the heat is most concentrated, avoiding unnecessary energy loss, and at the same time improving the heat dissipation efficiency. Extracting the geometric shape ensures that the position of the heat dissipation cut is adapted to the physical structure and operating environment of the industrial control computer, thereby effectively improving the stability and functionality of the heat dissipation cut and avoiding the unsatisfactory heat dissipation effect caused by mismatched designs. By scientifically calculating the opening degree and channel size, it is ensured that the design of the heat dissipation cut not only meets the structural strength requirements but also can provide sufficient heat flow channels, avoiding insufficient heat dissipation caused by too small channels or structural instability caused by too large channels. Through stability analysis, it is ensured that the designed heat dissipation cut can withstand temperature changes and mechanical stresses during long-term operation, avoiding a decrease in heat dissipation effect or mechanical failure caused by unreasonable cut design, thereby improving the reliability and durability of the industrial control computer.

[0012] Preferably, the screening of the class simulation structure area for the industrial control computer structure data by the industrial control computer heat dissipation cut data in step S2 includes: Extract the external structure parameters of the heat dissipation cut for the industrial control computer heat dissipation cut data to obtain the external structure parameters of the heat dissipation cut, where the external structure parameters of the heat dissipation cut include shape, size, and cut edge characteristics; perform external structure calibration on the industrial control computer heat dissipation cut data based on the external structure parameters of the heat dissipation cut to obtain the external calibration data of the industrial control computer heat dissipation cut; Extract the heat dissipation cut structure material properties for the industrial control computer heat dissipation cut data to obtain the heat dissipation cut structure material properties; perform internal structure calibration on the industrial control computer heat dissipation cut data through the heat dissipation cut structure material properties to obtain the internal calibration data of the industrial control computer heat dissipation cut; Perform a structural fitting similarity calculation on the industrial control computer structure data according to the external calibration data of the industrial control computer heat dissipation cut and the internal calibration data of the industrial control computer heat dissipation cut, and perform screening of the class simulation structure area on the industrial control computer structure data based on the results of the structural fitting similarity calculation to obtain the optimal heat dissipation area of the industrial control computer, where the formula for the structural fitting similarity calculation is as follows:

[0013] In the formula, is the structural fitting similarity index, is the external structure adaptability index, is the internal structure adaptability index, is the geometric shape similarity index, is the air flow adaptability index, is the external structure adaptability weight coefficient, is the internal structure adaptability weight coefficient, is the geometric shape similarity weight coefficient, is the air flow adaptability weight coefficient.

[0014] Through the precise extraction and calibration of the external structure parameters of the present invention, it is ensured that the heat dissipation cutout can be perfectly matched with the external structure of the industrial control computer, maximizing the heat dissipation efficiency and avoiding unnecessary energy loss. Through the precise calibration of the internal structure, the heat dissipation flow can be reasonably distributed according to the thermal conductivity characteristics of different materials, avoiding local overheating and improving the overall heat dissipation performance and reliability of the industrial control computer. Through the precise calculation of the structural fitting similarity, it is ensured that the heat dissipation cutout design is perfectly matched with the structure of the industrial control computer, thereby improving the heat dissipation effect and system stability. The structural fitting similarity calculation comprehensively considers multiple factors such as external shape, internal structure, geometric shape, and air flow, comprehensively evaluating the adaptability of the heat dissipation cutout to the industrial control computer and avoiding insufficient heat dissipation efficiency caused by a single factor. Based on the calculation results of the structural fitting similarity, the optimal heat dissipation area can be intelligently selected to ensure the optimization of the heat dissipation path and further improve the performance of the heat dissipation system. The finally selected optimal heat dissipation area not only meets the requirements of heat conduction and air convection, but also can reduce the risk of heat accumulation, thereby ensuring that the heat dissipation system of the industrial control computer works efficiently and stably during operation. By dynamically adjusting each coefficient, the heat dissipation design can be optimized under different working conditions, improving the flexibility and adaptability of the system and realizing efficient heat dissipation control.

[0015] Preferably, step S3 includes the following steps: Step S31: Obtain the operating parameters of the industrial control computer; Step S32: Extract the temperature characteristics of the operating parameters of the industrial control computer to obtain the operating temperature characteristic data of the industrial control computer; Step S33: Perform abnormal detection of the operation of the industrial control computer on the operating temperature characteristic data of the industrial control computer according to the preset standard temperature threshold. When the operating temperature characteristic data of the industrial control computer is greater than or equal to the preset standard temperature threshold, abnormal industrial control computer operation data is generated; Step S34: Perform vortex flow analysis on the optimal heat dissipation area of the industrial control computer based on the abnormal industrial control computer operation data to generate microfluidic effect intensity data; perform acoustic wave excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate industrial control computer heat dissipation control data.

[0016] The present invention provides real-time data support for the operation of industrial control computers, ensuring the accuracy and relevance of subsequent processes and avoiding the impact of assumptions or inaccurate data on system optimization. By accurately extracting temperature characteristics, the thermal load of industrial control computers can be monitored in a timely manner, and abnormal situations such as too high or too low temperature can be detected, facilitating real-time adjustment of temperature control strategies. Through the setting of temperature thresholds, early warnings can be issued before the temperature reaches a dangerous level, avoiding equipment failures and damages. Temperature anomaly detection helps to determine whether problems such as insufficient heat dissipation occur in the system, ensuring that industrial control computers operate in an optimal state. By detecting anomalies early, equipment damages or performance degradation caused by overheating and other problems can be reduced, extending the service life of industrial control computers. Vortex flow analysis helps to accurately simulate the flow of fluids in the heat dissipation area, identify flow bottlenecks or non-smooth areas, and provide a theoretical basis for heat dissipation optimization. Through the analysis of flow effects, the heat transfer path can be adjusted, thereby enhancing the heat dissipation effect and avoiding overheated areas. Acoustic wave excitation can accelerate the conduction of heat through vibration, improve the heat dissipation efficiency, and prevent heat accumulation. Through the acoustic wave control mechanism, the heat dissipation system can be adjusted more precisely to maximize the heat dissipation effect and avoid equipment overheating caused by insufficient heat dissipation. Efficient heat dissipation control reduces energy consumption, ensures that the equipment achieves heat dissipation with lower energy consumption, and achieves the purpose of energy conservation. Through the generated heat dissipation control data, the heat dissipation system of industrial control computers can be adjusted precisely to adapt to different operating conditions, ensuring that the heat dissipation effect is always in the best state. The heat dissipation control data of industrial control computers can achieve real-time feedback, make rapid adjustments based on actual temperature changes, and ensure the stable operation of the equipment. Through effective heat dissipation control, failures caused by overheating are reduced, and the overall reliability of the equipment is improved.

[0017] Preferably, the vortex flow analysis of the optimal heat dissipation area of the industrial control computer based on abnormal industrial control computer operation data includes: Starting the acoustic wave duct system based on abnormal industrial control computer operation data, and using the acoustic wave duct system to perform acoustic wave excitation on the optimal heat dissipation area of the industrial control computer to generate industrial control computer acoustic wave excitation data; Extracting the acoustic wave frequency of the industrial control computer acoustic wave excitation data and calculating the acoustic wave excitation range of the acoustic wave frequency; performing an analysis of the influence of the acoustic-thermal effect on the optimal heat dissipation area of the industrial control computer through the acoustic wave excitation range to generate acoustic wave heat transfer data; Dividing the acoustic wave heat transfer data into a data set to generate a model training set and a model test set; training the model training set through a convolutional neural network algorithm to generate a vortex flow prediction pre-model; using the model test set to perform model test iteration on the vortex flow prediction model to generate a vortex flow prediction model; Importing the acoustic wave heat transfer data into the vortex flow prediction model to predict the microfluidic effect intensity and generate microfluidic effect intensity data.

[0018] Through acoustic wave excitation, the present invention can affect fluid flow through vibration, increase the heat exchange efficiency, and thus optimize heat dissipation. By generating acoustic wave excitation data, the changes in the heat dissipation area can be truly reflected, providing basic data for subsequent modeling and analysis. It effectively promotes heat distribution, prevents local overheating, and improves the overall system heat dissipation capacity. Accurately calculating the range of acoustic wave excitation helps to directionally adjust the intensity and range of acoustic wave excitation, thereby achieving better heat dissipation effects in the optimal area. By analyzing the frequency and its range of acoustic wave excitation, the effect of acoustic waves can be optimized, and the heat dissipation efficiency can be improved. The calculation of acoustic wave frequency and excitation range provides detailed parameter inputs for fluid flow analysis, improving the accuracy of analysis. By analyzing the influence of the acoustic wave excitation range on the acoustic-thermal effect in the optimal heat dissipation area of the industrial control computer and generating acoustic wave heat transfer data, this analysis can reveal how acoustic waves affect the heat distribution in the heat dissipation area, providing a theoretical basis for subsequent optimization. By changing the fluid flow pattern, acoustic waves can promote heat transfer and distribution, reducing the phenomenon of heat accumulation. The acoustic wave heat transfer data provides key data support for the subsequent training of the convolutional neural network model, ensuring the accuracy of model training. Using a convolutional neural network for training can effectively extract features from the data and improve the prediction accuracy of vortex flow. Through the division and iterative testing of the data set, the model can show high accuracy and robustness in different situations. Using the CNN algorithm can accelerate the training process and obtain a more accurate vortex flow prediction model, improving the efficiency of system optimization. By predicting the microfluidic effect intensity through the vortex flow prediction model, the influence of fluid flow on the heat dissipation area can be understood, helping to optimize the heat dissipation effect of the industrial control computer. The prediction ability of the model can help identify heat dissipation bottlenecks and make improvements in advance, thus reducing the risk of equipment overheating caused by uneven heat dissipation. By predicting the microfluidic effect intensity, the industrial control computer can dynamically adjust the heat dissipation system according to the actual operating state, improving the stability and long-term operating efficiency of the equipment.

[0019] Preferably, step S4 includes the following steps: Step S41: Conduct a heat dissipation energy efficiency assessment on the industrial control computer heat dissipation control data to generate industrial control computer heat dissipation energy efficiency assessment data; Step S42: Adjust the acoustic wave heat dissipation frequency of the industrial control computer heat dissipation control data based on the heat dissipation energy efficiency assessment result to obtain industrial control computer heat dissipation control adjustment data; Step S43: Control the heat dissipation parameters of the acoustic wave duct system according to the industrial control computer heat dissipation control adjustment data to perform the industrial control computer heat dissipation control operation.

[0020] Through the heat dissipation energy efficiency evaluation data, the present invention can comprehensively analyze the effectiveness of the heat dissipation system of the industrial control computer and identify the deficiencies of the heat dissipation system. This evaluation can help optimize the various components of the heat dissipation system during the design phase and actual operation, improving the heat dissipation efficiency of the system operation. Through the heat dissipation energy efficiency evaluation, early warnings can be given in a timely manner when problems occur in the heat dissipation system, thereby reducing equipment failures caused by overheating. By adjusting the acoustic heat dissipation frequency, the frequency can be flexibly adjusted according to the feedback of the heat dissipation energy efficiency evaluation to achieve the best heat distribution and flow effect. The adjusted acoustic frequency can improve the heat conduction and flow in the heat dissipation area, further enhancing the heat dissipation efficiency and avoiding the formation of hot spots. Through frequency adjustment, the heat dissipation effect can be flexibly adjusted under different load conditions, avoiding uneven heat dissipation caused by load fluctuations and ensuring the stable operation of the industrial control computer. Through the control of the heat dissipation parameters of the acoustic waveguide system, it can be ensured that the heat dissipation system can maintain the best heat dissipation state in each operation cycle, reducing energy consumption waste. By precisely controlling the heat dissipation parameters, the phenomena of overheating or insufficient heat dissipation are avoided, thereby effectively reducing energy consumption. Through the intelligent adjustment of the heat dissipation parameters, the system can automatically optimize the heat dissipation performance in different environments, reducing manual intervention and improving the intelligence level of the overall system.

[0021] In this specification, a heat dissipation control device for an industrial control computer is provided, which is used to execute the above-mentioned heat dissipation control method for an industrial control computer. The heat dissipation control device for an industrial control computer includes: A thermal scanning module, configured to obtain the structural data of the industrial control computer; perform a thermal imaging scan on the industrial control computer based on the structural data of the industrial control computer to obtain the thermal imaging scan data of the industrial control computer; confirm the hot spot area data of the industrial control computer based on the thermal imaging scan data of the industrial control computer; A heat dissipation bionic analysis module, configured to perform a bionic heat dissipation simulation on the hot spot area data of the industrial control computer to obtain the microscopic heat dissipation data of the industrial control computer; confirm the heat dissipation cut data of the industrial control computer based on the microscopic heat dissipation data of the industrial control computer; screen the class simulation structure area of the structural data of the industrial control computer through the heat dissipation cut data of the industrial control computer to obtain the optimal heat dissipation area of the industrial control computer; An acoustic wave excitation heat dissipation module, configured to obtain the operating parameters of the industrial control computer; judge whether the temperature of the industrial control computer is abnormal according to the operating parameters of the industrial control computer. If so, perform a vortex flow analysis on the optimal heat dissipation area of the industrial control computer to generate microfluidic effect intensity data; perform acoustic wave excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate heat dissipation control data of the industrial control computer; A frequency adjustment module, configured to perform a heat dissipation energy efficiency evaluation on the heat dissipation control data of the industrial control computer, and adjust the acoustic wave heat dissipation frequency of the heat dissipation control data of the industrial control computer based on the heat dissipation energy efficiency evaluation result to execute the heat dissipation control operation of the industrial control computer.

[0022] The present invention also provides an industrial control computer, which includes a computer main body and controls a sound wave duct system connected to the industrial control computer to execute the above-mentioned industrial control computer heat dissipation control method.

[0023] The beneficial effects of the present invention are as follows: Through thermal scanning, the temperature distribution of the industrial control computer during operation can be monitored in real time, the hot spot area can be accurately determined, and rapid positioning of heat dissipation problems can be ensured. After confirming the hot spot area, the heat dissipation design can be further optimized to reduce the overheated area, thereby improving the overall heat dissipation efficiency. Through real-time thermal imaging scan data, potential heat dissipation problems can be discovered in advance, avoiding equipment failures due to overheating and increasing system reliability. Through biomimetic simulation, learning from the heat dissipation mechanisms in nature, the heat dissipation ability of the industrial control computer can be effectively improved, and the design can be ensured to be more efficient and environmentally friendly. The optimization of the heat dissipation cut helps to improve the heat dissipation efficiency, avoid heat accumulation, and ensure smooth air flow to avoid local overheating. Through the screening of the simulation-like structure area, the heat dissipation area can be accurately identified and optimized, maximizing the heat dissipation ability and improving the stability and efficiency of the industrial control computer. By real-time monitoring the temperature characteristics of the industrial control computer, abnormal temperature conditions can be detected in time, and active heat dissipation adjustment can be performed through acoustic wave excitation. Using the microfluidic effect intensity data generated by vortex flow analysis effectively enhances the heat transfer and distribution, improving the heat dissipation performance. Acoustic wave excitation can generate tiny airflows and vibrations during the heat dissipation process, increasing the heat transfer and diffusion, thereby improving the heat dissipation effect. Adjusting the acoustic wave frequency according to the heat dissipation energy efficiency evaluation results enables the heat dissipation effect to reach the best state and improves the energy efficiency ratio of the system. By adjusting the frequency, energy waste can be reduced, and unnecessary energy consumption can be reduced while ensuring the heat dissipation effect. Dynamically adjusting the frequency according to the real-time evaluation results, the system can adapt to different working environments and load conditions, improving the overall efficiency and stability. Therefore, the present invention improves the accuracy, adaptability, and heat dissipation energy efficiency of the industrial control computer heat dissipation control through precise hot spot positioning, biomimetic heat dissipation optimization, dynamic temperature monitoring and response, and energy efficiency evaluation and adjustment. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the step flow of an industrial control computer heat dissipation control method; Figure 2 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in Figure 3 It is Figure 1 a schematic diagram of the detailed implementation step flow of step S4 in The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0025] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although terms such as "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0028] To achieve the above object, please refer to Figures 1 to 3 , an industrial control computer heat dissipation control method, the method comprising the following steps: Step S1: Obtain the structural data of the industrial control computer; perform thermal imaging scanning on the industrial control computer based on the structural data of the industrial control computer to obtain the thermal imaging scanning data of the industrial control computer; confirm the hot spot area data of the industrial control computer based on the thermal imaging scanning data of the industrial control computer; Step S2: Perform biomimetic heat dissipation simulation on the hot spot area data of the industrial control computer to obtain the microscopic heat dissipation data of the industrial control computer; confirm the heat dissipation cut data of the industrial control computer based on the microscopic heat dissipation data of the industrial control computer; screen the structural data of the industrial control computer through the heat dissipation cut data of the industrial control computer to obtain the optimal heat dissipation area of the industrial control computer; Step S3: Obtain the operating parameters of the industrial control computer; determine whether the temperature of the industrial control computer is abnormal according to the operating parameters of the industrial control computer. If so, perform vortex flow analysis on the optimal heat dissipation area of the industrial control computer to generate microfluidic effect intensity data; perform acoustic excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate the heat dissipation control data of the industrial control computer; Step S4: Perform a heat dissipation energy efficiency assessment on the industrial control computer heat dissipation control data, and adjust the acoustic heat dissipation frequency based on the heat dissipation energy efficiency assessment result to execute the industrial control computer heat dissipation control operation.

[0029] Through the thermal imaging scanning technology, the present invention can clearly obtain the temperature distribution of the industrial control computer, quickly and accurately locate the hot spot area. This step effectively avoids the situations of overheating or insufficient heat dissipation, making the subsequent heat dissipation design more targeted and reducing the damage or efficiency decline caused by overheating from the source. By accurately locating the hot spot area, it is possible to react before the temperature rises, thereby reducing the probability of the industrial control computer malfunctioning and improving the safety and operation stability of the device. Combining the principle of bionic thermosiphon for heat dissipation simulation, simulating the highly efficient heat dissipation methods in nature (such as the heat dissipation structures of leaves and marine organisms), the heat dissipation design of the industrial control computer can be optimized by referring to their structures and principles. This can not only improve the heat dissipation efficiency but also reduce energy consumption. Optimizing the heat dissipation structure of the industrial control computer, improving the heat dissipation efficiency, and reducing energy consumption provide support for environmentally friendly design and energy-saving requirements, with strong economic and ecological benefits. Conducting microfluidic effect analysis through vortex flow analysis in the optimal heat dissipation area of the industrial control computer to deeply explore the microscopic behavior of the fluid during heat conduction. Through the forced flow mode of the fluid (such as the vortex effect), the heat transfer efficiency is enhanced, enabling local hot spots to be quickly cooled. The introduction of the microfluidic effect can precisely control the flow mode of the cooling fluid, improving the cooling efficiency. Especially in a small space, it can greatly enhance the heat dissipation capacity and avoid system crashes or efficiency reduction caused by excessive temperature. Using acoustic excitation technology to adjust the heat dissipation frequency, vibration waves can be generated in the heat dissipation area of the industrial control computer through acoustic excitation, enhancing the diffusion and conduction of heat. This technology helps to accelerate heat transfer without increasing additional energy consumption, especially prominent in high heat load areas. Acoustic excitation can not only improve the heat dissipation efficiency but also avoid energy waste caused by traditional heat dissipation methods, with low energy consumption, providing an environmentally friendly and efficient heat dissipation solution. Therefore, through precise hot spot location, bionic heat dissipation optimization, dynamic temperature monitoring and response, as well as energy efficiency assessment and adjustment, the present invention improves the accuracy, adaptability, and heat dissipation energy efficiency of the industrial control computer heat dissipation control.

[0030] In the embodiment of the present invention, referring to Figure 1 As shown, it is a schematic flow chart of the steps of a method for controlling the heat dissipation of an industrial control computer according to the present invention. In this example, the method for controlling the heat dissipation of an industrial control computer includes the following steps: Step S1: Obtain the structural data of the industrial control computer; perform a thermal imaging scan on the industrial control computer based on the structural data of the industrial control computer to obtain the thermal imaging scan data of the industrial control computer; confirm the hot spot area data of the industrial control computer based on the thermal imaging scan data of the industrial control computer. In the embodiments of the present invention, a three-dimensional scan of the industrial control computer is performed using a laser scanner (such as Leica RTC360 or Faro Focus) to obtain accurate structural data of the industrial control computer. The laser scanner quickly scans the surface with millions of laser pulses per second, generating high-precision three-dimensional point cloud data. The scanning resolution of the laser scanner is set to 1 mm to 2 mm, and the scanning range is 10 m to 50 m to ensure that detailed geometric structure data of each component of the body is obtained. By importing the scanning results into CAD modeling software (such as AutoCAD or SolidWorks), a complete three-dimensional model is generated, and the model includes the dimensions, connection methods, and material information of each component of the industrial control computer. After obtaining the detailed three-dimensional structure data, a thermal imager (such as FLIRT540 or FLIR E8) is used to perform a thermal imaging scan of the industrial control computer to capture the thermal distribution data during its operation. The working principle of the thermal imager is to detect the infrared energy radiated from the surface of an object through an infrared sensor and convert it into temperature information. To ensure accurate capture of the temperature distribution of the industrial control computer, the temperature range of the thermal imager is set to -20°C to +200°C to adapt to different working environments of the industrial control computer. To improve the fineness of the image, the resolution of the thermal imager should be set to 320x240 pixels or higher to ensure that small temperature differences can be distinguished. The frequency of the thermal imaging scan is set to 5 Hz to ensure real-time capture of dynamic thermal change information. After performing the thermal imaging scan, the device will generate thermal map data, and the image shows the temperature distribution of each component of the industrial control computer. The temperature value of each pixel point in the data corresponds to the temperature of the actual component, and usually these temperature values are presented after being captured by sensors and processed by a computer. According to the obtained thermal imaging scan data, the hot spot area is determined. A temperature threshold, such as 70°C, is set as the definition standard for hot spots. Any area where the surface temperature of a component exceeds this threshold will be marked as a hot spot. For example, if the temperature of a component is 80°C and the temperature threshold is set to 70°C, then the area of this component is identified as a hot spot. To ensure accurate identification of the hot spot area, an edge detection algorithm (such as Canny edge detection) can be used to identify the boundary of the high-temperature area in the thermal map. Then, thermal map clustering analysis (such as K-means clustering algorithm) is used to group the hot spot areas to further extract the most potentially risky hot spot areas. Through these technical means, the obtained hot spot area data will provide a basis for subsequent performance evaluation and maintenance decision-making.

[0031] Step S2: Perform a bio-inspired heat dissipation simulation on the hot spot area data of the industrial control computer to obtain the microscopic heat dissipation data of the industrial control computer; confirm the heat dissipation cutout based on the microscopic heat dissipation data of the industrial control computer to obtain the heat dissipation cutout data of the industrial control computer; screen the class simulation structure area of the industrial control computer structure data through the heat dissipation cutout data of the industrial control computer to obtain the optimal heat dissipation area of the industrial control computer; In the embodiments of the present invention, based on the hotspot area data obtained by thermal imaging scanning of the industrial control computer, biological bionic heat dissipation simulation is carried out. Biological bionic heat dissipation simulation is a process of imitating how organisms in nature effectively dissipate heat, such as the surface structure of fish scales, the vein distribution of leaves, etc. Using bionics design methods, a microscopic heat dissipation model based on the hotspot area is established through computer-aided design software (such as ANSYS Fluent or COMSOL Multiphysics). When conducting bionic simulation, first, appropriate heat conduction materials and structures need to be selected. For the hotspot area of the industrial control computer, a bionic heat conductor based on nanomaterials is used to optimize heat dissipation. The simulation parameters can be set as the following values: Select materials with high thermal conductivity, such as copper (about 385 W / m·K), or high thermal conductivity ceramic materials (>200 W / m·K). Based on air cooling or liquid cooling methods, thermal convection simulation is carried out, and the air flow velocity range is set between 0.5 m / s and 5 m / s to ensure sufficient heat dissipation effect. Designs such as wavy shapes similar to fish scales and metal honeycomb structures are adopted to simulate efficient heat dissipation performance. Through this biological bionic simulation method, the microscopic heat dissipation data of the industrial control computer are obtained, mainly including information such as the temperature field change, heat flux density distribution, and heat dissipation efficiency of each hotspot area. According to the microscopic heat dissipation data of the industrial control computer, cutout designs are made for the parts with higher temperatures in the hotspot areas, usually selecting areas with temperatures exceeding 70°C. Based on the heat dissipation requirements, the influence of cutouts of different sizes on the heat dissipation performance is simulated. The size of the cutout can be adjusted between 1 cm² and 10 cm² to ensure that it does not affect the structural strength of the industrial control computer. The shape of the cutout, such as rectangular, circular, or mesh structure, is analyzed through bionic simulation and selected according to the heat dissipation requirements and component structure of each area. Finally, combined with the heat dissipation cutout data obtained from simulation calculations, these data include the optimal position, size, shape, and other heat dissipation characteristics of the cutout, and are used for subsequent structural optimization. The coupling of structural mechanics and heat conduction is carried out, and thermal-structural coupling analysis is carried out through software (such as ABAQUS, ANSYS) to simulate the influence of the heat dissipation cutout on the structure of the industrial control computer. Through this simulation, areas that can ensure both structural strength and maximize heat dissipation performance are found. Based on the simulation results, areas that can maximize heat transfer conduction and do not affect the normal operation of the industrial control computer are selected. These areas should have a large surface area and be far from important mechanical components to avoid adverse effects of the cutout on structural stability. Structural strength analysis (such as Von Mises stress analysis) and heat conduction analysis (such as temperature gradient analysis) are adopted to ensure that when cutouts or heat dissipation devices are added in the selected heat dissipation areas, the structural integrity of the industrial control computer will not be reduced.

[0032] Step S3: Obtain the operating parameters of the industrial control computer; determine whether the temperature of the industrial control computer is abnormal according to the operating parameters of the industrial control computer. If so, perform a vortex flow analysis on the optimal heat dissipation area of the industrial control computer to generate microfluidic effect intensity data; perform acoustic excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate industrial control computer heat dissipation control data; In the embodiments of the present invention, the operating parameters of the industrial control computer are collected through sensors and monitoring devices, including but not limited to: obtaining the real-time temperature of key components of the industrial control computer by installing temperature sensors (such as thermocouples, RTD sensors), obtaining the load data of the industrial control computer under different working conditions, usually measured by load sensors or torque sensors, obtaining the air flow or liquid flow rate of the heat dissipation system of the industrial control computer through devices such as flow meters and anemometers, collecting the power consumption data of the industrial control computer through power meters or current / voltage sensors. These data are usually transmitted to the monitoring platform in real time through a data acquisition system (such as LabVIEW, SCADA system) for processing and analysis to ensure that the working parameters of each industrial control computer component are accurately monitored. Based on the obtained operating parameters, especially the temperature data, it is determined whether the temperature of the industrial control computer exceeds the normal working range. First, a temperature threshold is set, usually this value is determined according to the design parameters of the industrial control computer. For example, the upper limit of the normal working temperature of the industrial control computer is set to 85 °C. If the temperature of a certain component exceeds this threshold, the temperature is considered abnormal. When the temperature abnormality is confirmed, it is necessary to further analyze the optimal heat dissipation area of the industrial control computer, especially the heat flow and fluid dynamics performance of this area. At this time, vortex flow analysis is used to study how to improve the heat dissipation effect. Vortex flow is a flow method that enhances heat exchange through vortex structures. A flow model of the optimal heat dissipation area of the industrial control computer including heat dissipation cuts is established in CFD software. Factors such as heat conduction, convection, and radiation are considered in the simulation. The temperature and fluid velocity of the environment around the industrial control computer are set. For example, the wind speed can be set between 2 m / s and 5 m / s. Gas (air) or liquid (coolant) is used as the cooling medium, and appropriate fluid physical properties such as density and viscosity are input. By simulating different types of vortex structures (such as Taylor vortices, vortex flows, etc.), the improvement of the heat dissipation performance by vortex flow is observed. Key parameters such as vortex intensity, temperature gradient, and heat exchange rate in the flow field are analyzed. A sound wave propagation model is established using finite element analysis software (such as COMSOL Multiphysics) to simulate the propagation of sound waves in the heat dissipation area. The sound wave frequency range is usually set from 20 kHz to 100 kHz to achieve effective microfluidic perturbation. Appropriate excitation frequencies and sound wave intensities are selected, generally the sound wave intensity is selected to be 100 - 500 Pa, and the frequency is between 30 kHz and 50 kHz. The propagation direction of the sound wave and the position of the sound source need to be adjusted according to the position of the optimal heat dissipation area. By simulating the influence of sound wave excitation on the fluid flow and temperature field in the heat dissipation area of the industrial control computer, its heat dissipation effect is evaluated. According to the simulation results, the frequency and intensity of the sound wave excitation are adjusted to achieve the best heat dissipation effect.

[0033] Step S4: Perform heat dissipation energy efficiency evaluation on the heat dissipation control data of the industrial control computer, and adjust the sound wave heat dissipation frequency based on the heat dissipation energy efficiency evaluation result to execute the heat dissipation control operation of the industrial control computer.

[0034] In the embodiments of the present invention, the heat dissipation efficiency is calculated by comparing the input heat and the output heat of the optimal heat dissipation area of the industrial control computer. The formula is as follows: Wherein, is the heat dissipation efficiency, is the heat output from the heat dissipation area of the industrial control computer, is the heat input to the industrial control computer. The input heat can be estimated from the power consumption data of the industrial control computer, and the output heat is calculated from parameters such as the temperature change, specific heat capacity, and flow rate of the fluid. The energy efficiency ratio (COP) of the heat dissipation system is calculated. The higher the COP, the higher the efficiency of the heat dissipation system. The COP can be calculated by the following formula: Wherein, The input power consumed by the system. Generally, the COP of the cooling system is commonly between 2 and 6. Using temperature field analysis, evaluate the temperature uniformity of the cooling area. A too large temperature gradient means uneven heat dissipation and requires adjustment of the heat dissipation strategy. Monitor the energy used during the entire heat dissipation process, evaluate the energy consumption of the acoustic excitation system, liquid cooling system or air cooling system, and its contribution to overall heat dissipation. According to the temperature distribution, heat exchange efficiency and energy efficiency ratio in the heat dissipation energy efficiency evaluation, select the frequency range that needs to be adjusted. For different hot spots, adjust the frequency to obtain the best microfluidic perturbation effect. Generally, the frequency can be adjusted between 20 kHz and 100 kHz. At lower frequencies (such as 20 kHz to 40 kHz), the role of acoustic excitation is mainly to improve the microscopic flow of the fluid; at higher frequencies (such as 50 kHz to 100 kHz), it helps to further enhance the heat exchange efficiency of the fluid. Based on the evaluation results, select an initial acoustic excitation frequency. For example, select 30 kHz as the initial frequency. During the operation of the industrial control computer, monitor the temperature change and heat dissipation efficiency of the cooling area in real time. The temperature distribution of the cooling area can be monitored by sensors and the data can be fed back to the control system in real time. If the temperature distribution is uneven or the heat exchange efficiency is low, the frequency can be gradually increased (for example, from 30 kHz to 40 kHz) until the desired heat dissipation effect is achieved. At the same time, the frequency can also be appropriately reduced according to the energy efficiency evaluation results to reduce energy consumption. If the evaluation results show poor temperature uniformity or insufficient heat exchange efficiency, increase the frequency to enhance the microfluidic perturbation effect; if the evaluation results show a low energy efficiency ratio or high energy consumption, the frequency can be appropriately reduced to balance the heat dissipation efficiency and energy consumption. Start the acoustic excitation device through the control system, set the adjusted frequency and acoustic intensity, and make it act on the optimal heat dissipation area of the industrial control computer. Continuously monitor the temperature change of the industrial control computer to ensure that the acoustic excitation can continuously optimize the heat dissipation effect. By monitoring the real-time data, adjust the operating conditions to ensure that the cooling system is always in the best working state. Based on the real-time temperature feedback, use an automatic control system (such as the PID control algorithm) to adjust the frequency in real time to cope with the change of the operating state of the industrial control computer and ensure the continuity and stability of the heat dissipation control operation.

[0035] Preferably, step S1 includes the following steps: Step S11: Obtain the structural data of the industrial control computer; Step S12: Perform data preprocessing on the structural data of the industrial control computer to generate standard structural data of the industrial control computer, where the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: Perform a thermal imaging scan of the industrial control computer structure on the standard structural data of the industrial control computer through a thermal imaging camera to obtain thermal imaging scan data of the industrial control computer structure; Step S14: Screen the temperature extreme value regions from the thermal imaging scan data of the industrial control computer structure to obtain the data of the heat source regions of the industrial control computer; Step S15: Use fluid mechanics to analyze the regional heat aggregation of the heat source region data of the industrial control computer to generate the data of the hot spot regions of the industrial control computer.

[0036] In the embodiments of the present invention, the structural data of the industrial control computer is obtained through precise three-dimensional scanning technology. A laser scanner (such as Leica RTC360 or Faro Focus) is used to measure the shape, dimensions, positions, and connection methods of the industrial control computer and its components by laser. The laser scanner generates high-precision three-dimensional point cloud data, which contains the complete geometric structure information of the industrial control computer. At this time, the scanning parameters are set as follows: the scanning accuracy is 1-2 mm to ensure that every detail of the industrial control computer can be accurately captured, and the scanning range is set to 10-50 m to cover the whole and key components of the industrial control computer. Noise data and invalid data points generated during the scanning process are removed. An outlier detection algorithm, such as the distance-based DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, is used to identify and remove abnormal data points. Gaussian filtering or median filtering is used to denoise the point cloud data to reduce the influence of environmental factors on the scanning data. Filtering parameter selection: the Gaussian filter kernel size is set to 3×3 or 5×5 and adjusted according to the noise level of the actual data, and the median filtering parameter is selected as 3×3 to ensure the smoothness of the point cloud data. Through interpolation methods, such as the k-nearest neighbor (KNN) interpolation method or polynomial fitting interpolation method, the missing data points during the scanning process are filled. By performing zero-mean normalization on the point cloud data, the data distribution in all directions is unified, facilitating subsequent processing. A suitable temperature range is selected, usually set to -20°C to +150°C to adapt to the temperature fluctuations during the normal operation of the industrial control computer. The specific temperature range can be adjusted according to the working environment of the industrial control computer. A resolution of 320×240 pixels is selected to ensure that subtle temperature differences can be accurately captured. The scanning frequency is set to 5 Hz to ensure that the temperature distribution changes of the industrial control computer can be captured in a short time. The thermal imaging camera will collect the surface temperatures of the components of the industrial control computer in real time and generate a temperature distribution heat map. Each pixel represents the temperature data of a certain point on the surface of the industrial control computer, and these data will be used to further analyze the hot spot areas. By analyzing the thermal imaging scanning data, the temperature extreme value areas (i.e., hot spot areas) are screened out. This process is carried out through the following steps: a temperature threshold (such as 70°C) is set, and all areas exceeding this temperature will be identified as heat source areas. For example, if the temperature of a certain component is 80°C and the threshold is 70°C, then this component is a heat source area. Based on the hot spot areas screened by the threshold, the boundaries of the heat source areas are further identified through image processing algorithms (such as edge detection algorithms) to ensure the accuracy of area calibration. The area of the heat source area is evaluated to ensure that the selected area can effectively represent the high-temperature components of the industrial control computer. Usually, the area of the heat source area should be greater than 1 cm². A fluid flow and heat conduction model of the heat source area is established using CFD (Computational Fluid Dynamics) software (such as ANSYS Fluent, COMSOL Multiphysics).This model will combine the geometric data of the heat source area and the thermal imaging data of the industrial control computer to simulate the heat propagation and accumulation. Simulate the temperature field distribution of the heat source area in CFD software, considering the flow conditions of different fluids (such as air or liquid coolant). Set the flow velocity range from 0.5 m / s to 5 m / s and select appropriate fluid physical parameters such as specific heat capacity and thermal conductivity. Evaluate the heat accumulation in each heat source area based on the fluid simulation results. Through temperature field analysis, obtain the heat flux density of each hot spot area and identify the areas with relatively serious heat accumulation.

[0037] Preferably, the biomimetic heat dissipation simulation of the data of the hot spot area of the industrial control computer in step S2 includes: Perform regional grid division on the data of the hot spot area of the industrial control computer to obtain microscopic heat dissipation units; calculate the local unit temperature of the microscopic heat dissipation units to obtain grid local temperature data; Set biomimetic heat dissipation parameters; use the grid local temperature data to perform heat conduction calculation on the data of the hot spot area of the industrial control computer to generate grid heat distribution data; perform biomimetic heat convection analysis on the grid heat distribution data through the biomimetic heat dissipation parameters to generate air heat conduction data of the hot spot area; Perform thermal radiation characteristic analysis on the data of the hot spot area of the industrial control computer based on the air heat conduction data of the hot spot area, and perform simulation of internal and external heat flux exchange on the data of the hot spot area of the industrial control computer through the thermal radiation characteristics, so as to generate microscopic heat dissipation data of the industrial control computer.

[0038] In the embodiment of the present invention, by using the grid generation technology in finite element analysis (FEA) or computational fluid dynamics (CFD), the data of the hot spot area is divided into multiple small units, and the size of these units is generally set from 1 cm² to 5 cm², which is adjusted according to the size and detail requirements of the hot spot area. The accuracy of the grid division is usually from 1 mm to 2 mm to ensure that the temperature and fluid changes in a small range can be accurately captured. Tetrahedral grids or hexahedral grids are used, depending on the geometric shape of the area, to ensure that the grids can accurately cover each hot spot component of the industrial control computer. Calculate each grid unit using the heat conduction equation. The heat conduction equation is set as follows: Where, is the temperature, is the thermal diffusivity, is the Laplacian operator of temperature. According to the material properties of each grid cell, physical parameters such as thermal conductivity and specific heat capacity are input. For metal components, the thermal conductivity is usually above 200 W / m·K; for plastics or composite materials, the thermal conductivity is relatively low, about 0.2 W / m·K to 1 W / m·K. The temperature values in the thermal imaging data are input as the initial conditions to calculate the local temperature of each microscopic heat dissipation unit. Through the above heat conduction calculation, the grid local temperature data of each microscopic heat dissipation unit is obtained, which will be further used for heat flow calculation and heat dissipation analysis. After the heat conduction calculation is completed, bionic heat dissipation parameters are set to optimize the heat dissipation design using the principles of bionics. The bionic heat dissipation parameters mainly include: imitating the heat management mechanisms in nature (such as fish scales, leaf vein structures, etc.) to design the surface morphology of the microscopic heat dissipation unit. Through structural parametric design, the shape, size, arrangement method, etc. of the heat dissipation unit are set. Generally, wavy, grid-like or honeycomb-like structures are selected to improve the heat exchange efficiency. Bionic materials with excellent thermal conductivity are selected, such as metals or composite materials with high thermal conductivity, to simulate the heat management system in organisms. The bionic structure is used to optimize the heat convection effect, and the air or liquid flow rate is set. Usually, the air flow rate is selected between 0.5 m / s and 2 m / s to ensure that the fluid can effectively carry away heat. Using the above heat conduction equation, combined with the bionic heat dissipation structure parameters, the heat transfer process is simulated. The boundary condition is set as the temperature of the external environment of the industrial control computer, and the heat transfer coefficient of the radiator is considered. According to the bionic structure design, the auxiliary effect of air flow on heat conduction is simulated. The Navier-Stokes equation is used to simulate fluid dynamics, the fluid viscosity and density are set, and the heat exchange between the surface of the heat dissipation unit and the fluid is calculated. Finally, through heat conduction calculation, grid heat distribution data is generated. After obtaining the grid heat distribution data, bionic heat convection analysis is carried out using the bionic heat dissipation parameters to simulate the influence of air or liquid flow on heat dispersion. Bionic heat transfer conduction analysis is carried out using the heat convection equation: where is the heat flux,[[]] is the convective heat transfer coefficient,[[]] is the surface area of the heat dissipation unit,[[]] is the surface temperature of the heat dissipation unit,[[]] is the fluid temperature far from the heat dissipation unit. With the help of the bionic structure, the convective heat transfer effect of the fluid is improved to ensure that heat can be effectively transferred to the fluid. Through the bionic optimization of fluid flow, the air flow rate is increased from 0.5 m / s to 2 m / s to enhance the heat convection ability. CFD software (such as ANSYS Fluent) is used to simulate the air flow in the hot spot area to obtain the heat transfer conduction data of the air. By setting appropriate fluid models and temperature boundary conditions, heat convection analysis is carried out, and the generated heat transfer conduction data of the air in the hot spot area will be used for subsequent heat radiation characteristic analysis. Calculate the heat radiation characteristics of the surface of the industrial control computer according to the Stefan-Boltzmann law: Among them, is the radiant heat flux, is the Stefan-Boltzmann constant, is the emissivity, is the heat dissipation surface area, and are the temperatures of the industrial control computer surface and the environment respectively. Through CFD or other simulation tools, the heat exchange between the industrial control computer surface and the surrounding air is simulated, and the heat transfer through radiation, convection, and heat conduction is analyzed. By simulating the heat conduction and heat radiation characteristics, the microscopic heat dissipation data of the industrial control computer is finally obtained, which includes the heat flux distribution, temperature gradient, and effective heat dissipation path in the hot spot area.

[0039] Preferably, the confirmation of the heat dissipation cut for the hot spot area data of the industrial control computer in step S2 includes: Identifying the heat dissipation bottleneck area based on the microscopic heat dissipation data of the industrial control computer to obtain the heat dissipation bottleneck area data; confirming the heat dissipation cut position for the heat dissipation bottleneck area data to obtain the heat dissipation cut position data; Extracting the geometric shape of the industrial control computer according to the heat dissipation cut position data to obtain the heat dissipation cut shape data of the industrial control computer; calculating the opening degree and channel size of the heat dissipation cut for the heat dissipation cut position data using the heat dissipation cut shape data of the industrial control computer to obtain the heat dissipation cut opening degree data and the heat dissipation channel size data; Analyzing the stability of the heat dissipation cut for the heat dissipation cut position data through the heat dissipation cut opening degree data and the heat dissipation channel size data to generate the heat dissipation cut data of the industrial control computer.

[0040] In the embodiments of the present invention, by using the microscopic heat dissipation data of the industrial control computer, the heat flux density of each region is calculated. The calculation formula of the heat flux density is: q = -k∇T; where q is the heat flux density, k is the thermal conductivity, and ∇T is the temperature gradient. According to the heat flux density data, the regions with restricted heat conduction are identified. In the calculated heat flux density data, the regions with lower heat flux density or faster temperature rise are selected as the heat dissipation bottleneck regions. A temperature threshold (such as 70°C) and a heat flux threshold are set to identify the regions with heat dissipation bottlenecks. Through image processing algorithms, such as region growing algorithm or edge detection algorithm, the heat dissipation bottleneck regions are extracted from the heat flux density map and the temperature distribution map. This region generally shows uneven heat dissipation and heat accumulation. Finally, the heat dissipation bottleneck region data is generated, including the spatial coordinates, heat flux density, temperature values, etc. of each bottleneck region. According to the heat dissipation bottleneck region data, the geometric shape, temperature distribution, and heat flux density distribution of the bottleneck region are analyzed. Usually, the heat dissipation bottleneck appears in the regions with obvious heat accumulation, such as the regions with concentrated heat sources or the parts with poor fluid flow. Using fluid mechanics simulation and heat conduction analysis, the optimal position of the heat dissipation cut is determined. According to the heat flux density and temperature distribution, the part with the largest heat accumulation or the most blocked flow in the bottleneck region is selected as the position for opening the cut. Usually, the cut positions in these regions are located in the regions with higher temperature or lower heat flux density. Optimization algorithms such as genetic algorithm or simulated annealing algorithm are used to perform multiple iterative calculations to confirm the optimal cut position. The position of the cut needs to ensure the maximum heat dissipation efficiency and does not affect the structural strength of the industrial control computer. Finally, the heat dissipation cut position data is obtained, including the coordinates, size, and position relationship of the cut relative to other components of the industrial control computer. Using three-dimensional modeling tools (such as SolidWorks, AutoCAD) or CAD data processing software, the geometric shape corresponding to the heat dissipation cut position is extracted from the standard structure data of the industrial control computer. According to the position and size of the heat dissipation cut, the specific shape of the cut is designed. The cut shape can usually be selected as rectangular, circular, diamond-shaped, or a specially customized shape, and the specific shape is optimized according to the geometric structure and heat dissipation requirements of the bottleneck region. Parametric design of the cut shape is carried out to ensure that the geometric shape of the cut can be compatible with the overall structure of the industrial control computer and does not affect the structural stability. Finally, the heat dissipation cut shape data is obtained, which includes information such as the shape, size, depth, and relative position of the cut to the surrounding structure. According to the heat flux density and temperature distribution, the opening size of the cut is determined. Generally speaking, the opening size of the cut is closely related to the heat dissipation effect. Too small an opening cannot dissipate heat effectively, and too large an opening will affect the structural stability. The opening of the cut is adjusted through optimization algorithms (such as particle swarm optimization algorithm, simulated annealing) to ensure the best heat dissipation effect. According to the heat conduction path and the fluid dynamics model, the channel size of the heat dissipation cut is calculated. The length, width, and depth of the channel are calculated to ensure that the fluid can flow smoothly through the cut and take away the heat.The specific dimensions can be optimized through hydrodynamic simulations (such as CFD analysis) to ensure the flow efficiency of air or cooling liquid in the cutout channels. Adjust the channel dimensions according to the actual situation of the heat dissipation cutout. Generally, the channel width can be set between 2 mm and 10 mm, and the channel depth can be adjusted according to the structural and heat dissipation requirements. Finally, generate the heat dissipation cutout opening data and the heat dissipation channel dimension data, which will provide a basis for the processing of the actual cutout and the optimization of the heat dissipation performance. Use finite element analysis (FEA) or computer-aided engineering analysis (CAE) to perform a structural analysis of the heat dissipation cutout. Ensure that the opening and channel dimensions of the cutout do not affect the structural strength and stability of the industrial control computer. Especially for high-temperature areas, analyze the impact of the cutout opening on material fatigue. According to the opening and channel dimensions of the heat dissipation cutout, simulate the heat conduction through the cutout to ensure that the cutout can effectively remove heat without heat accumulation or overheating. Use thermodynamic simulations to analyze the thermal stress distribution of the cutout and avoid material damage caused by excessive temperature differences. Through CFD simulations, analyze the flow stability of the air flow or cooling liquid in the heat dissipation cutout channels to ensure that the flow velocity and flow pattern of the fluid can be continuously stable, avoiding local overheating or excessive flow resistance. Finally, obtain the industrial control computer heat dissipation cutout data, which includes information such as the position, opening, channel dimensions, structural stability, and heat flow and fluid stability of the heat dissipation cutout, providing a detailed basis for subsequent manufacturing and heat dissipation system optimization.

[0041] Preferably, the screening of the industrial control computer structure data for the class simulation structure area through the industrial control computer heat dissipation cutout data in step S2 includes: Extract the external structure parameters of the heat dissipation cutout from the industrial control computer heat dissipation cutout data to obtain the external structure parameters of the heat dissipation cutout, where the external structure parameters of the heat dissipation cutout include the shape, dimensions, and cutout edge characteristics; based on the external structure parameters of the heat dissipation cutout, calibrate the external structure of the industrial control computer heat dissipation cutout data to obtain the external calibration data of the industrial control computer heat dissipation cutout; Extract the structural material properties of the heat dissipation cutout from the industrial control computer heat dissipation cutout data to obtain the structural material properties of the heat dissipation cutout; through the structural material properties of the heat dissipation cutout, calibrate the internal structure of the industrial control computer heat dissipation cutout data to obtain the internal calibration data of the industrial control computer heat dissipation cutout; Perform a structural fitting similarity calculation on the industrial control computer structure data according to the external calibration data of the industrial control computer heat dissipation cutout and the internal calibration data of the industrial control computer heat dissipation cutout, and based on the results of the structural fitting similarity calculation, screen the industrial control computer structure data for the class simulation structure area to obtain the optimal heat dissipation area of the industrial control computer, where the formula for the structural fitting similarity calculation is as follows:

[0042] In the formula, is the structural fitting similarity index, is the external structure adaptability index, is the internal structure adaptability index, is the geometric shape similarity index, is the air flow adaptability index, is the external structure adaptability weight coefficient, is the internal structure adaptability weight coefficient, is the geometric shape similarity weight coefficient, is the air flow adaptability weight coefficient.

[0043] In the embodiments of the present invention, the geometric form of the heat dissipation cutout is extracted by using CAD modeling software (such as SolidWorks, AutoCAD), including the shape of the cutout (such as rectangle, circle, rhombus, etc.), the smoothness of the edge, the regularity or irregularity of the shape, etc. The dimensional data of the cutout is extracted through 3D modeling, mainly including the opening size, depth, and length of the channel of the cutout. Usually, the cutout opening is set to be between 2 mm and 10 mm, and the depth is adjusted according to the heat dissipation requirements, generally between 10 mm and 50 mm. The edge of the cutout is analyzed by scanning and image processing algorithms (such as edge detection algorithms) to extract features such as the smoothness, sharpness, and irregularity of the edge. The characteristics of the cutout edge will affect the fluid flow and heat exchange efficiency. Use finite element analysis (FEA) software to simulate the contact and interaction between the heat dissipation cutout and the surrounding structures. Analyze the adaptability between the edge of the cutout and the surrounding structures to ensure that the cutout position does not affect the overall structural stability of the industrial control computer. Based on the geometric structure of the industrial control computer and the external parameters of the heat dissipation cutout, external calibration is performed to calculate the adaptability between the cutout and the industrial control computer structure. The external calibration data includes factors such as the geometric fitness of the cutout, the edge smoothness, the strength, and stability of the cutout. Through the material database or experimental data, the material properties of the part where the heat dissipation cutout is located are extracted, including thermal conductivity, specific heat capacity, strength, elastic modulus, etc. Common heat dissipation materials such as aluminum and copper have relatively high thermal conductivities (about 200 - 400 W / m·K) and are suitable for high-efficiency heat dissipation applications. Through thermodynamic simulation, the influence of different materials on heat conduction is evaluated, and the most suitable material for heat dissipation requirements is selected. For example, in a high-temperature environment, copper or aluminum alloy is the best choice, while in a low-temperature environment, it is more suitable to use other materials with lower thermal conductivities. Use CFD (Computational Fluid Dynamics) simulation to perform heat conduction calculations on the internal structure of the heat dissipation cutout based on the properties such as the thermal conductivity and specific heat capacity of the material. During the simulation process, factors such as the temperature difference inside and outside the cutout, the fluid flow direction, and heat convection are considered. By analyzing the heat conduction path inside the cutout, an optimal configuration of the heat flow is carried out. Comparative analysis is performed on cutouts with different forms, sizes, and materials to obtain the best internal structure calibration results to ensure that heat can be effectively conducted through the cutout. Based on the external calibration data, internal calibration data, and geometric shape data, similarity calculations are performed on each area of the industrial control computer. The formula for the structural fitting similarity index is as follows: In the formula, is the structural fitting similarity index, is the external structure adaptability index, is the internal structure adaptability index, is the geometric shape similarity index, is the air flow adaptability index, is the external structure adaptability weight coefficient, is the internal structure adaptability weight coefficient, is the geometric shape similarity weight coefficient, is the air flow adaptability weight coefficient. The similarity index obtained through weighted calculation is used to screen out the area with higher structural fitting degree as the optimal heat dissipation area. Through multiple simulation calculations, the area with the best heat dissipation performance in the industrial control computer can be accurately selected. Finally, the optimal heat dissipation area of the industrial control computer is obtained, which can maximize the heat dissipation effect while ensuring the structural integrity and stability of the industrial control computer.

[0044] As an example of the present invention, referring to Figure 2 shown, in this example, the step S3 includes: Step S31: Obtain the operating parameters of the industrial control computer; Step S32: Extract the temperature characteristics from the operating parameters of the industrial control computer to obtain the operating temperature characteristic data of the industrial control computer; Step S33: Perform abnormal detection of the operation of the industrial control computer on the operating temperature characteristic data of the industrial control computer according to the preset standard temperature threshold. When the operating temperature characteristic data of the industrial control computer is greater than or equal to the preset standard temperature threshold, abnormal industrial control computer operation data is generated; Step S34: Perform vortex flow analysis on the optimal heat dissipation area of the industrial control computer based on the abnormal industrial control computer operation data to generate microfluidic effect intensity data; perform acoustic excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate industrial control computer heat dissipation control data.

[0045] In the embodiments of the present invention, temperature sensors (such as thermocouples, RTD sensors) and load sensors are installed at key parts of the industrial control computer. The temperature sensors can monitor the temperature changes of various components (such as motors, cooling systems, coolant channels, etc.) in real time. Through a data acquisition system (such as SCADA system, LabVIEW, etc.), the operating data of the industrial control computer are recorded in real time, and these data include but are not limited to temperature, load, power, wind speed, fluid flow rate, etc. Through wireless communication or wired connection, the data are transmitted from the sensors to the central control system or the cloud to ensure real-time monitoring of the operating status of the industrial control computer. Signal processing techniques are used to process the temperature data, including methods such as moving average, Fourier transform, etc., to extract features such as the trend of temperature change, fluctuation frequency, etc. The temperature signal is smoothed to remove noise, obtaining a stable temperature change trend, analyzing the frequency components of the temperature change, and identifying whether there are periodic fluctuations. The extracted temperature feature data can include: temperature mean, temperature fluctuation range, and temperature change rate. According to the designed temperature range and normal working environment of the industrial control computer, a standard temperature threshold is set. For example, the normal temperature upper limit is set to 80 °C, and if the temperature exceeds this threshold, it is considered that the temperature is abnormal. The threshold comparison method is used for anomaly detection. When the operating temperature feature data of the industrial control computer is greater than or equal to the standard temperature threshold, an abnormal state is triggered. By comparing the extracted temperature feature data (such as temperature mean, fluctuation range, etc.) with the threshold. If the temperature exceeds the threshold, it is marked as "abnormal". When an abnormal state is detected, abnormal industrial control computer operating data is generated, and this data includes information such as the time point of the anomaly occurrence, specific temperature values, temperature change trend, etc. Through CFD software (such as ANSYS Fluent or COMSOL Multiphysics), a hydrodynamic analysis is carried out on the optimal heat dissipation area of the industrial control computer to simulate the vortex flow phenomenon. The vortex flow can enhance the heat exchange ability of the fluid, thereby improving the heat dissipation efficiency. A three-dimensional flow field model of the heat dissipation area is established, and by setting the physical properties of the fluid (such as density, viscosity) and flow velocity (such as 0.5 m / s to 2 m / s), data such as heat flux density, temperature field distribution, etc. are calculated. The vortex flow generated in the high-temperature area is simulated, the intensity and distribution of the vortex are analyzed, its contribution to heat dissipation is evaluated, and the influence of the vortex flow on heat conduction is calculated to obtain the microfluidic effect intensity data, which show the heat transfer conduction effect and efficiency of the vortex flow in the heat dissipation area. Through acoustic excitation technology, the microscopic perturbation of the fluid is enhanced by the acoustic wave vibration, thereby improving the heat exchange efficiency. The frequency range of the acoustic wave is generally set to 20 kHz to 100 kHz, and the excitation intensity is 100 - 500 Pa to increase the degree of fluid mixing and improve heat convection. Using CFD software, based on the microfluidic effect intensity data, a simulation of acoustic excitation is carried out to simulate the influence of the acoustic wave on fluid flow. The amplitude, frequency, and direction of the acoustic wave will affect the stability of the air flow and temperature uniformity.Improve the fluid flow through acoustic wave excitation, thereby enhancing the heat dissipation effect, and calculate the changes in heat dissipation performance at different frequencies. According to the simulation results of acoustic wave excitation, generate industrial computer heat dissipation control data, including acoustic wave frequency, intensity, excitation effect, and the final data on the improvement of heat dissipation efficiency.

[0046] Preferably, the vortex flow analysis of the optimal heat dissipation area of the industrial computer based on abnormal industrial computer operation data includes: Start the acoustic wave duct system based on abnormal industrial computer operation data, and use the acoustic wave duct system to perform acoustic wave excitation on the optimal heat dissipation area of the industrial computer to generate industrial computer acoustic wave excitation data; Extract the acoustic wave frequency of the industrial computer acoustic wave excitation data, and calculate the acoustic wave excitation range of the acoustic wave frequency; perform an analysis of the influence of the acoustic-thermal effect on the optimal heat dissipation area of the industrial computer through the acoustic wave excitation range to generate acoustic wave heat transfer data; Divide the acoustic wave heat transfer data into a model training set and a model test set; perform model training on the model training set through the convolutional neural network algorithm to generate a pre-model for vortex flow prediction; use the model test set to perform model test iteration on the vortex flow prediction model to generate a vortex flow prediction model; Import the acoustic wave heat transfer data into the vortex flow prediction model to predict the microfluidic effect intensity and generate microfluidic effect intensity data.

[0047] In the embodiments of the present invention, by based on the operating data of the abnormal industrial control computer (such as temperature change, load data, etc.), the heat dissipation area to be excited is determined, and the acoustic waveguide system is started. This system generates high-frequency sound waves through piezoelectric materials or ultrasonic emission devices. The frequency is usually set to 20 kHz to 100 kHz to ensure effective microfluidic perturbation. The fluid flow is optimized by adjusting the excitation frequency and power. The acoustic waveguide needs to be connected to the optimal heat dissipation area of the industrial control computer, and the sound waves are transmitted to the heat dissipation area through the waveguide. The design of the waveguide needs to ensure that the acoustic signal can be evenly distributed throughout the heat dissipation area. The acoustic waveguide system enhances the microscopic perturbation of the air flow or coolant by generating high-frequency vibrations, thereby promoting the heat exchange efficiency. During the excitation process, temperature sensors and acoustic sensors are used to record the acoustic excitation data in real time, such as frequency, amplitude, etc. The monitoring system continuously records the data changes during the excitation process to generate the acoustic excitation data of the industrial control computer, which includes excitation frequency, amplitude, excitation time, and duration, etc. By analyzing the acoustic excitation data, the frequency components of the sound waves are extracted. The fast Fourier transform (FFT) method can be used to extract the frequency information in the signal to obtain the main frequency and harmonic information of the sound waves. According to the frequency data, the effective frequency range of the acoustic excitation is calculated, which is usually determined by identifying the frequency peak and its bandwidth. Combining the frequency data of the sound waves and the excitation power, the propagation range of the sound waves, that is, the effective influence range of the acoustic excitation in the heat dissipation area, is calculated. At this time, factors such as the geometric shape, material properties, and fluid state of the industrial control computer heat dissipation area need to be considered. The propagation range of the sound waves in the optimal heat dissipation area of the industrial control computer is calculated by the wave equation: ; where is the acoustic power, is the acoustic intensity, is the surface area of the acoustic wave propagation, is the angle between the propagation direction and the surface normal. Combining the acoustic wave excitation range, the acoustic-thermal effect analysis is carried out on the optimal heat dissipation area of the industrial computer. The vibration of the acoustic wave can not only enhance the fluid flow, but also cause temperature fluctuations in the fluid, thereby affecting the heat conduction and heat convection processes. The CFD simulation is used to analyze the heat transfer effect caused by the acoustic wave. Consider the local temperature change and the enhancement of heat flux caused by the acoustic wave. Finally, the acoustic-thermal effect analysis is carried out on the optimal heat dissipation area of the industrial computer through the acoustic wave excitation range, and the acoustic wave heat transfer data is generated, which includes information such as the heat transfer efficiency and heat flux distribution caused by the acoustic wave. The acoustic wave heat transfer data is divided into a training set and a test set according to a certain proportion, and the common division ratio is 70% for the training set and 30% for the test set. The training set is used to train the vortex flow prediction model, and the test set is used to evaluate the prediction effect of the model. Data preprocessing techniques such as normalization and standardization are used to ensure that the data in the training set can balance the weights between different features. The test set is used for model verification to ensure that the model has good generalization ability and can make accurate predictions on new data. Design a CNN model, which usually includes multiple convolutional layers, pooling layers, fully connected layers, etc. The convolutional layer is used to extract features from the acoustic wave heat transfer data, and the pooling layer is used for dimensionality reduction. Finally, the vortex flow prediction result is output through the fully connected layer. Use the backpropagation algorithm for training and adopt the Adam optimizer to optimize the weights. Through iterative training, continuously adjust the parameters of the model, reduce the error, and optimize the prediction effect. Monitor the loss function (such as mean square error) and accuracy of the model during the training process, and adjust the hyperparameters (such as learning rate, batch size, etc.) to improve the model performance. Use the test set to verify the model, calculate the prediction accuracy and error (such as MSE, RMSE), evaluate the performance of the model, and perform model iteration. Each iteration adjusts the model according to the feedback of the test set until the expected accuracy is achieved. Finally, a vortex flow prediction model is obtained, which can predict the intensity and influence of the vortex flow according to the input acoustic wave heat transfer data. Through the vortex flow prediction model, the acoustic wave heat transfer data is used as the input to calculate and predict the intensity of the microfluidic effect. The intensity of the microfluidic effect reflects the enhancement effect of the vortex flow caused by the acoustic wave on heat transfer. The output microfluidic effect intensity data includes information such as the intensity of the vortex in the fluid, the change of temperature gradient, and the velocity distribution of the flow.

[0048] As an example of the present invention, refer to Figure 3 shown. In this example, step S4 includes: Step S41: Perform heat dissipation energy efficiency evaluation on the industrial computer heat dissipation control data to generate industrial computer heat dissipation energy efficiency evaluation data; Step S42: Adjust the acoustic wave heat dissipation frequency of the industrial computer heat dissipation control data based on the heat dissipation energy efficiency evaluation result to obtain the industrial computer heat dissipation control adjustment data; Step S43: Control the heat dissipation parameters of the acoustic waveguide system according to the industrial control computer heat dissipation control adjustment data to perform the industrial control computer heat dissipation control operation.

[0049] In the embodiment of the present invention, based on the heat dissipation control data of the industrial control computer, a heat dissipation energy efficiency evaluation model is first constructed. The model includes multiple input parameters (such as: heat dissipation area temperature, heat dissipation channel flow rate, acoustic excitation frequency, air flow direction, ambient temperature, etc.) and output results (such as: heat exchange efficiency, heat dissipation power, energy efficiency ratio, etc.). The energy efficiency ratio can be calculated by the following formula: Energy efficiency ratio = heat dissipation power ÷ input power; Use the sensor network to collect real-time data, such as temperature sensors, flow rate sensors, pressure sensors, etc., to record the heat dissipation performance of the industrial control computer. The sensors should be installed in the key heat dissipation areas of the industrial control computer to monitor the dynamic state of the industrial control computer heat dissipation. Generate the industrial control computer heat dissipation energy efficiency evaluation data through the heat dissipation energy efficiency evaluation model. This data includes heat dissipation energy efficiency indicators (such as EER), heat dissipation effect, heat dissipation efficiency, etc. These evaluation data help to judge the energy efficiency level of the current industrial control computer heat dissipation system. According to the heat dissipation energy efficiency evaluation data, analyze the frequency adjustment space of the current heat dissipation system. Use the generated heat dissipation energy efficiency data to judge the optimal frequency range of the heat dissipation effect (for example, when the heat dissipation energy efficiency is lower than a certain threshold, the acoustic excitation frequency needs to be increased or decreased to optimize the heat dissipation). Based on the heat dissipation characteristics of the industrial control computer and the actual working environment, design an intelligent adjustment mechanism, specifically including: when the heat dissipation energy efficiency is insufficient, increase the acoustic wave frequency or adjust its modulation method to improve the microfluidic perturbation effect and improve the heat dissipation efficiency. When the system is overheated or over-dissipated, reduce the acoustic excitation frequency to avoid excessive perturbation of the fluid and optimize the heat exchange. The control basis for frequency adjustment can be real-time energy efficiency feedback. For example, when the heat dissipation efficiency reaches a certain set value, stop the frequency adjustment and maintain a stable state. According to the adjustment strategy, fine-tune the frequency by adjusting the frequency generator of the acoustic excitation device in the industrial control computer to adjust it to the value most suitable for the current working conditions. Monitor the heat dissipation effect after adjustment through real-time temperature and flow rate sensors to verify whether better heat dissipation effect is produced at the new frequency and ensure the optimization effect of the system. Record all the change data during the adjustment process, including the frequency values before and after adjustment, temperature change data, energy efficiency change data, etc., to generate the industrial control computer heat dissipation control adjustment data. These data will be used for subsequent optimization adjustment and system monitoring. Based on the industrial control computer heat dissipation control adjustment data, design a heat dissipation parameter control model for the acoustic waveguide system. This model can consider multiple factors, such as the transmission efficiency of the acoustic waveguide, the geometric shape of the heat dissipation area, the flow characteristics of the fluid, etc. Adjust the working parameters of the acoustic waveguide (such as pressure, acoustic wave frequency, excitation power, etc.) according to the current heat dissipation requirements to optimize the influence of acoustic excitation on the heat dissipation area. Through the acoustic waveguide control system, based on the industrial control computer heat dissipation control adjustment data, adjust the key parameters such as the heat dissipation flow rate, acoustic excitation frequency, and waveguide position of the waveguide.

[0050] Therefore, in all respects, the embodiments should be considered exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes that fall within the meaning and scope of the equivalent elements of the application documents are intended to be embraced by the present invention.

[0051] The above description is only a specific implementation manner of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A heat dissipation control method for an industrial control computer, characterized in that, Including the following steps: Step S1: Obtain the industrial control computer structure data; perform thermal imaging scanning on the industrial control computer based on the industrial control computer structure data to obtain the industrial control computer thermal imaging scanning data; confirm the industrial control computer hot spot area data based on the industrial control computer thermal imaging scanning data; Step S2: Conduct biomimetic heat dissipation simulation on the industrial control computer hot spot area data to obtain the industrial control computer microscopic heat dissipation data; confirm the heat dissipation cutout for the industrial control computer hot spot area data based on the industrial control computer microscopic heat dissipation data to obtain the industrial control computer heat dissipation cutout data; screen the industrial control computer structure data through the industrial control computer heat dissipation cutout data to obtain the optimal heat dissipation area of the industrial control computer; Step S3: Obtain the operating parameters of the industrial control computer; determine whether the temperature of the industrial control computer is abnormal according to the operating parameters of the industrial control computer. If so, perform vortex flow analysis on the optimal heat dissipation area of the industrial control computer to generate microfluidic effect intensity data; perform acoustic excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate the industrial control computer heat dissipation control data; Step S4: Evaluate the heat dissipation energy efficiency of the industrial control computer heat dissipation control data, and adjust the acoustic heat dissipation frequency of the industrial control computer heat dissipation control data based on the heat dissipation energy efficiency evaluation result to execute the industrial control computer heat dissipation control operation.

2. The industrial control computer heat dissipation control method according to claim 1, wherein, Step S1 includes the following steps: Step S11: Obtain the industrial control computer structure data; Step S12: Perform data preprocessing on the industrial control computer structure data to generate standard industrial control computer structure data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S13: Perform industrial control computer structure thermal imaging scanning on the standard industrial control computer structure data through a thermal imaging camera to obtain the industrial control computer structure thermal imaging scanning data; Step S14: Screen the temperature extreme value area of the industrial control computer structure thermal imaging scanning data to obtain the industrial control computer heat source area data; Step S15: Use fluid mechanics to perform regional heat aggregation analysis on the industrial control computer heat source area data to generate the industrial control computer hot spot area data.

3. The industrial control computer heat dissipation control method according to claim 1, characterized in that The biomimetic heat dissipation simulation of the industrial control computer hot spot area data in Step S2 includes: Perform regional grid division on the industrial control computer hot spot area data to obtain microscopic heat dissipation units; calculate the local unit temperature of the microscopic heat dissipation units to obtain grid local temperature data; Set biomimetic heat dissipation parameters; perform heat conduction calculation on the industrial control computer hot spot area data using the grid local temperature data to generate grid heat distribution data; perform heat biomimetic convection analysis on the grid heat distribution data through the biomimetic heat dissipation parameters to generate hot spot area air heat transfer conduction data; Perform thermal radiation characteristic analysis on the industrial control computer hot spot area data according to the hot spot area air heat transfer conduction data, and perform heat flow internal and external exchange simulation on the industrial control computer hot spot area data through the thermal radiation characteristics, so as to generate the industrial control computer microscopic heat dissipation data.

4. The industrial control computer heat dissipation control method according to claim 1, characterized in that The confirmation of the heat dissipation cutout for the industrial control computer hot spot area data in Step S2 includes: Identify the heat dissipation bottleneck area for the industrial control computer hot spot area data based on the industrial control computer microscopic heat dissipation data to obtain the heat dissipation bottleneck area data; confirm the heat dissipation cutout position for the heat dissipation bottleneck area data to obtain the heat dissipation cutout position data; Extract the geometric shape of the industrial control computer based on the heat dissipation cut position data to obtain the heat dissipation cut shape data of the industrial control computer; use the heat dissipation cut shape data of the industrial control computer to calculate the opening degree and channel size of the heat dissipation cut for the heat dissipation cut position data to obtain the heat dissipation cut opening degree data and the heat dissipation channel size data; Conduct a heat dissipation cut stability analysis on the heat dissipation cut position data through the heat dissipation cut opening degree data and the heat dissipation channel size data to generate the heat dissipation cut data of the industrial control computer.

5. The industrial control computer heat dissipation control method according to claim 1, wherein The screening of the class-simulation structure area for the industrial control computer structure data through the heat dissipation cut data of the industrial control computer in step S2 includes: Extract the external structure parameters of the heat dissipation cut for the heat dissipation cut data of the industrial control computer to obtain the external structure parameters of the heat dissipation cut, where the external structure parameters of the heat dissipation cut include shape, size, and cut edge characteristics; calibrate the external structure of the heat dissipation cut for the heat dissipation cut data of the industrial control computer based on the external structure parameters of the heat dissipation cut to obtain the external calibration data of the heat dissipation cut of the industrial control computer; Extract the material property of the heat dissipation cut structure for the heat dissipation cut data of the industrial control computer to obtain the material property of the heat dissipation cut structure; calibrate the internal structure of the heat dissipation cut for the heat dissipation cut data of the industrial control computer through the material property of the heat dissipation cut structure to obtain the internal calibration data of the heat dissipation cut of the industrial control computer; Calculate the structural fitting similarity for the industrial control computer structure data based on the external calibration data of the heat dissipation cut of the industrial control computer and the internal calibration data of the heat dissipation cut of the industrial control computer, and screen the class-simulation structure area for the industrial control computer structure data based on the result of the structural fitting similarity calculation to obtain the optimal heat dissipation area of the industrial control computer, where the formula for the structural fitting similarity calculation is as follows: Wherein, is the structural fitting similarity index, is the external structure adaptability index, is the internal structure adaptability index, is the geometric shape similarity index, is the air flow adaptability index, is the external structure adaptability weight coefficient, is the internal structure adaptability weight coefficient, is the geometric shape similarity weight coefficient, is the air flow adaptability weight coefficient.

6. The industrial control computer heat dissipation control method according to claim 1, wherein, Step S3 includes the following steps: Step S31: Obtain the operating parameters of the industrial control computer; Step S32: Extract the temperature characteristics of the operating parameters of the industrial control computer to obtain the operating temperature characteristic data of the industrial control computer; Step S33: Conduct an abnormal detection of the operation of the industrial control computer on the operating temperature characteristic data of the industrial control computer according to the preset standard temperature threshold. When the operating temperature characteristic data of the industrial control computer is greater than or equal to the preset standard temperature threshold, abnormal operating data of the industrial control computer is generated; Step S34: Conduct a vortex flow analysis on the optimal heat dissipation area of the industrial control computer based on the abnormal operating data of the industrial control computer to generate the microfluidic effect intensity data; control the heat dissipation by acoustic excitation on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate the heat dissipation control data of the industrial control computer.

7. The industrial control computer heat dissipation control method according to claim 6, wherein The vortex flow analysis on the optimal heat dissipation area of the industrial control computer based on the abnormal operating data of the industrial control computer includes: Start the acoustic waveguide system based on the abnormal operating data of the industrial control computer, and use the acoustic waveguide system to conduct acoustic excitation on the optimal heat dissipation area of the industrial control computer to generate the acoustic excitation data of the industrial control computer; Extract the acoustic frequency of the acoustic excitation data of the industrial control computer, and calculate the acoustic excitation range of the acoustic frequency; conduct an analysis of the influence of the acoustic-thermal effect on the optimal heat dissipation area of the industrial control computer through the acoustic excitation range to generate the acoustic heat transfer data; Partition the dataset of acoustic wave heat transfer data to generate a model training set and a model test set; train a model using the model training set through a convolutional neural network algorithm to generate a pre-model for vortex flow prediction; use the model test set to perform model test iteration on the vortex flow prediction model to generate a vortex flow prediction model. Import the acoustic wave heat transfer data into the vortex flow prediction model to predict the microfluidic effect intensity and generate microfluidic effect intensity data.

8. The industrial control computer heat dissipation control method according to claim 1, wherein Step S4 includes the following steps: Step S41: Evaluate the heat dissipation energy efficiency of the industrial control computer heat dissipation control data to generate industrial control computer heat dissipation energy efficiency evaluation data. Step S42: Adjust the acoustic wave heat dissipation frequency of the industrial control computer heat dissipation control data based on the heat dissipation energy efficiency evaluation result to obtain industrial control computer heat dissipation control adjustment data. Step S43: Control the heat dissipation parameters of the acoustic wave duct system according to the industrial control computer heat dissipation control adjustment data to perform the industrial control computer heat dissipation control operation.

9. An industrial control computer heat dissipation control device, characterized in that, An industrial control computer heat dissipation control device for performing the industrial control computer heat dissipation control method as described in claim 1, the industrial control computer heat dissipation control device includes: A thermal scanning module, configured to obtain industrial control computer structure data; perform thermal imaging scanning on the industrial control computer based on the industrial control computer structure data to obtain industrial control computer thermal imaging scanning data; confirm industrial control computer hot spot area data based on the industrial control computer thermal imaging scanning data. A heat dissipation bionic analysis module, configured to perform biological bionic heat dissipation simulation on the industrial control computer hot spot area data to obtain industrial control computer microscopic heat dissipation data; confirm heat dissipation cuts on the industrial control computer hot spot area data based on the industrial control computer microscopic heat dissipation data to obtain industrial control computer heat dissipation cut data; screen the industrial control computer structure data through the industrial control computer heat dissipation cut data to obtain the optimal heat dissipation area of the industrial control computer. An acoustic wave excitation heat dissipation module, configured to obtain industrial control computer operating parameters; determine whether the temperature of the industrial control computer is abnormal according to the industrial control computer operating parameters, and if so, perform vortex flow analysis on the optimal heat dissipation area of the industrial control computer to generate microfluidic effect intensity data; perform acoustic wave excitation heat dissipation control on the optimal heat dissipation area of the industrial control computer through the microfluidic effect intensity data to generate industrial control computer heat dissipation control data. A frequency adjustment module, configured to evaluate the heat dissipation energy efficiency of the industrial control computer heat dissipation control data and adjust the acoustic wave heat dissipation frequency of the industrial control computer heat dissipation control data based on the heat dissipation energy efficiency evaluation result to perform the industrial control computer heat dissipation control operation.

10. An industrial control computer, characterized in that, It includes a computer main body and controls an acoustic wave duct system connected to the industrial control computer to perform the industrial control computer heat dissipation control method as described in claims 1-8 above.

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