High-efficiency heat dissipation method and system for plasma generator

Abstract

The application discloses a high-efficiency heat dissipation method and system of a plasma generator, and relates to the technical field of plasma heat dissipation.The method comprises the following steps: multiple metal blocks are arranged in a ring-shaped chamber; radial heat dissipation grooves on the outer side of the metal blocks are uniformly distributed in the circumferential direction and are in contact connection with liquid cooling pipes; heat dissipation monitoring and probability analysis are performed based on the ring-shaped chamber and the distribution of the metal blocks to identify the heat dissipation demand distribution; the position of the liquid cooling pipes is matched and control parameters are searched according to the heat dissipation demand distribution to obtain target control parameters of the liquid cooling pipes; collaborative optimization is performed according to the discrete distribution relationship of the liquid cooling pipes to generate liquid cooling heat dissipation control instructions for parameter control of the liquid cooling pipes.The application solves the technical problems that the existing technology has uneven heat dissipation of the ring-shaped chamber of the plasma generator, low heat dissipation efficiency and poor stability, and it is difficult to accurately match the heat dissipation demands of different areas, and achieves the technical effects of realizing accurate heat dissipation control of the ring-shaped chamber and improving the heat dissipation efficiency and stability.

Description

Technical Field

[0001] This invention relates to the field of plasma heat dissipation technology, and more specifically to a high-efficiency heat dissipation method and system for plasma generators. Background Technology

[0002] During the operation of a plasma generator, a large amount of heat is generated within its annular chamber due to ion reactions and component heating. This heat is unevenly distributed within the chamber; some areas, being close to the heat source, form heat dissipation cores, while others, due to structural obstruction or distance from heat dissipation components, become heat-deprived zones. If heat cannot be dissipated efficiently and promptly, the temperature within the chamber can easily become too high, affecting the operational stability and lifespan of the plasma generator. Existing heat dissipation technologies mostly employ single liquid cooling or metal heat conduction methods, lacking precise analysis of the annular chamber's structural distribution and heat flow paths. This makes it difficult to identify the different heat dissipation needs of different areas, often resulting in unreasonable allocation of heat dissipation resources: either insufficient heat dissipation in areas with high heat dissipation needs, leading to heat accumulation, or excessive heat dissipation in areas with low heat dissipation needs, resulting in resource waste. Furthermore, existing monitoring networks lack specificity and fail to comprehensively capture the status of key heat dissipation nodes, further affecting the accuracy of heat dissipation control and failing to meet the plasma generator's requirements for efficient and stable heat dissipation.

[0003] The existing technology of plasma generators suffers from uneven heat dissipation in the annular chamber, low heat dissipation efficiency and poor stability, making it difficult to accurately match the heat dissipation requirements of different areas. Summary of the Invention

[0004] This application provides a high-efficiency heat dissipation method and system for plasma generators, which addresses the technical problems of uneven heat dissipation in the annular chamber of plasma generators, low heat dissipation efficiency and poor stability, and difficulty in accurately matching the heat dissipation needs of different areas in the prior art.

[0005] In view of the above problems, this application provides a method and system for efficient heat dissipation of plasma generators.

[0006] A first aspect of this application provides a method for efficient heat dissipation of a plasma generator, the method comprising: Multiple metal blocks are placed within an annular chamber. Radial heat dissipation grooves on the outer side of the metal blocks are evenly distributed circumferentially and contact the liquid-cooled pipes. Heat dissipation monitoring and probability analysis are performed based on the annular chamber and the distribution of the metal blocks to identify the heat dissipation demand distribution. The liquid-cooled pipes are then matched for position and searched for control parameters according to the heat dissipation demand distribution to obtain the target control parameters of the liquid-cooled pipes. Based on the target control parameters of the liquid-cooled pipes, collaborative optimization is performed according to the discrete distribution relationship of the liquid-cooled pipes to determine the collaborative heat dissipation parameters and generate liquid cooling heat dissipation control commands for controlling the parameters of the liquid-cooled pipes.

[0007] A second aspect of this application provides a high-efficiency heat dissipation system for a plasma generator, the system comprising: The heat dissipation demand identification module is used to set multiple metal blocks in an annular chamber. The radial heat dissipation grooves on the outer side of the metal blocks are evenly distributed circumferentially and contact the liquid cooling pipes. Based on the annular chamber and the distribution of the metal blocks, heat dissipation monitoring and probability analysis are performed to identify the distribution of heat dissipation demand. The control parameter acquisition module is used to perform position matching and control parameter search of the liquid cooling pipes according to the heat dissipation demand distribution to obtain the target control parameters of the liquid cooling pipes. The control command generation module is used to perform collaborative optimization according to the discrete distribution relationship of the liquid cooling pipes based on the target control parameters of the liquid cooling pipes to determine the collaborative heat dissipation parameters and generate liquid cooling heat dissipation control commands for controlling the parameters of the liquid cooling pipes.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Multiple metal blocks are placed within an annular chamber. Radial heat dissipation grooves on the outer sides of these metal blocks are evenly distributed circumferentially and connect with the liquid-cooled pipes. Heat dissipation monitoring and probabilistic analysis are performed based on the annular chamber and the distribution of the metal blocks to identify the distribution of heat dissipation demand. Position matching and control parameter searching are performed on the liquid-cooled pipes to obtain their target control parameters. Cooperative optimization is then performed according to the discrete distribution relationship of the liquid-cooled pipes to determine cooperative heat dissipation parameters and generate liquid-cooled heat dissipation control commands for parameter control of the liquid-cooled pipes. This achieves precise heat dissipation control of the annular chamber, improving heat dissipation efficiency and stability. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic diagram of a high-efficiency heat dissipation method for a plasma generator provided in an embodiment of this application; Figure 2 A schematic diagram of the high-efficiency heat dissipation system of the plasma generator provided in the embodiments of this application.

[0011] Figure labeling: 10 heat dissipation requirement identification module, 20 control parameter acquisition module, 30 control command generation module. Detailed Implementation

[0012] This application provides a highly efficient heat dissipation method and system for plasma generators, which addresses the technical problems in the prior art of uneven heat dissipation in the annular chamber of plasma generators, low heat dissipation efficiency and poor stability, and difficulty in accurately matching the heat dissipation needs of different areas.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] Example 1, as Figure 1 As shown, this application provides a high-efficiency heat dissipation method for a plasma generator, the method comprising: Step S100: Multiple metal blocks are set in the annular cavity. The radial heat dissipation grooves on the outside of the metal blocks are evenly distributed along the circumference and connected to the liquid cooling pipes. Heat dissipation monitoring and probability analysis are performed based on the annular cavity and the distribution of metal blocks to identify the distribution of heat dissipation demand.

[0015] Specifically, the metal block deployment involves first constructing the distribution of the working structure and heat flow paths within the annular cavity using monitoring data or experimental samples. A heat dissipation efficiency thermal map is then generated based on this data. The thermal map is used to locate the core heat dissipation areas where heat is concentrated. Multiple metal blocks are then deployed within the annular cavity according to the distribution of these core areas. Simultaneously, radial heat dissipation grooves machined on the outer side of the metal blocks are ensured to be evenly distributed along the circumference of the annular cavity and to form a tight contact connection with the liquid cooling pipes, creating an efficient channel for heat transfer to the liquid cooling pipes. Next, preparatory work for heat dissipation monitoring is carried out: the heat generation paths are determined based on the distribution of the working structure within the annular cavity. The heat dissipation paths and probabilities are fitted using the metal block distribution. Through heat offset and node response support analysis of the heat generation paths and heat dissipation paths / probabilities, key nodes in the heat response are identified, and a monitoring network is deployed based on these key nodes. Subsequently, heat dissipation dead zones are identified based on the heat dissipation efficiency thermal map and the metal block distribution. Verification counterexamples are constructed using the heat dissipation dead zones and the core heat dissipation areas. If the feedback results from the monitoring network can influence the heat dissipation regulation of the verification counterexamples, the verification is passed; otherwise, the monitoring network is reset until the requirements are met. Finally, the heat energy distribution of each heat dissipation node is collected through the verified monitoring network to obtain heat dissipation monitoring data. Combined with the distribution of metal blocks, the heat dissipation of the annular chamber is probabilistically analyzed according to the current liquid cooling pipeline control parameters. The natural heat dissipation probability curve is fitted, and the heat dissipation compensation demand of each area is analyzed and the demand distribution is located based on the natural heat dissipation probability. Finally, the heat dissipation demand distribution in the annular chamber is accurately identified.

[0016] Step S200: Based on the heat dissipation demand distribution, perform position matching and control parameter search on the liquid cooling pipes to obtain the target control parameters of the liquid cooling pipes.

[0017] Specifically, based on the identified distribution of heat dissipation needs within the annular chamber, the heat dissipation needs of different areas within the annular chamber are clustered and segmented according to the intensity, scope, and heat aggregation characteristics of the heat dissipation needs. Areas with similar heat dissipation needs and adjacent spatial locations are divided into multiple independent heat dissipation zones. Each heat dissipation zone has a clearly defined corresponding heat dissipation target, such as the amount of heat to be removed per unit time, the rate of heat aggregation, and the rate of heat accumulation within the zone. Next, with efficient heat dissipation in each heat dissipation zone as the core optimization objective, and combined with the previously established contact connection structure between the metal block and the liquid cooling pipes, the liquid cooling pipes are positioned to ensure that each heat dissipation zone has a corresponding liquid cooling pipe section covering it. Furthermore, the pipe sections form an efficient heat conduction path with the high-heat-generating areas within the zone through radial heat dissipation grooves on the outside of the metal block, avoiding heat dissipation blind spots caused by pipe position deviations. Subsequently, the liquid cooling pipe parameters, including at least flow rate, temperature, pump power, and coolant properties, are used as variables. Parameter search algorithms, such as gradient descent and genetic algorithms, are employed to perform multivariate combination optimization. The goal is to maximize the heat dissipation target of each heat dissipation area. Different combinations of liquid cooling pipe parameters are continuously adjusted, and the deviation between the actual heat dissipation efficiency and the target value for each combination is calculated in real time. This process continues until the liquid cooling pipe parameter combination that satisfies the heat dissipation requirements of all heat dissipation areas and achieves the optimal overall heat dissipation efficiency is found. This parameter combination is then determined as the target control parameter for the liquid cooling pipe, providing a foundational parameter basis for subsequent heat dissipation collaborative optimization.

[0018] Step S300: Based on the target control parameters of the liquid cooling pipeline, perform collaborative optimization according to the discrete distribution relationship of the liquid cooling pipeline, determine the collaborative heat dissipation parameters, and generate liquid cooling heat dissipation control commands for controlling the parameters of the liquid cooling pipeline.

[0019] Specifically, by combining the target control parameters of the divided heat dissipation zones and their corresponding liquid cooling pipes, the heat dissipation is first aligned and sorted according to the heat dissipation requirements of each zone. A heat dissipation requirement gradient is then constructed based on the intensity of demand, from high to low, clarifying the priority of liquid cooling heat dissipation in different zones. Next, based on this heat dissipation requirement gradient, the control parameters of the liquid cooling pipes are configured hierarchically, starting with high-demand zones: the target control parameters of the liquid cooling pipes corresponding to high-demand zones, such as high flow rate and low temperature coolant parameters, are used as the basic configuration. During the configuration process, the impact of the heat dissipation effect of this zone on the heat of the surrounding low-demand zones is monitored in real time. The heat dissipation requirement gradient of the low-demand zones is dynamically adjusted based on the actual operating parameters of the liquid cooling pipes in the high-demand zones. If the heat dissipation efficiency of the high-demand zone exceeds expectations, the heat dissipation requirement intensity of adjacent low-demand zones can be appropriately reduced; conversely, it can be increased. This avoids localized overheating or resource waste caused by conflicts in liquid cooling parameters between different zones, gradually completing the adaptation and adjustment of the control parameters of the liquid cooling pipes in all heat dissipation zones. Subsequently, the adjusted control parameters of the liquid-cooled pipes at each level are spliced ​​and integrated according to the priority order of heat dissipation areas to form coordinated heat dissipation parameters covering all liquid-cooled pipes in the annular chamber. These parameters satisfy the independent heat dissipation needs of each area while achieving an optimal balance in overall heat dissipation efficiency. Finally, the coordinated heat dissipation parameters are converted into a standardized instruction format that conforms to the logic of the liquid-cooled pipe control system, such as digital coded instructions containing parameters like flow rate, temperature, and pump power. This generates liquid-cooled heat dissipation control instructions, which are sent to the liquid-cooled pipe control unit for precise regulation of the operating parameters of each section of the liquid-cooled pipe, achieving efficient and balanced heat dissipation in the annular chamber of the plasma generator.

[0020] In one possible implementation, step S100 further includes: Step S110: Using monitoring data or experimental samples, construct the working structure distribution and heat flow path within the annular cavity of the plasma generator.

[0021] Step S120: Based on the working structure distribution and heat flow path, construct a heat dissipation efficiency thermal map.

[0022] Step S130: Identify the heat dissipation core area based on the heat dissipation efficiency thermal map, and arrange the metal blocks according to the distribution location of the heat dissipation core area.

[0023] Specifically, basic data is obtained in two ways: First, during the actual operation of the annular chamber of the plasma generator, high-precision spatial positioning sensors, such as laser rangefinders, are used to collect the three-dimensional coordinates of core operating components such as ion generating electrodes, insulating supports, and gas guiding structures within the chamber. This data is combined with real-time heating power and temperature data of each component collected by temperature and power sensors to form a monitoring dataset. Second, a physical experimental model of the annular chamber is constructed to simulate different ion beam output powers and different working gas pressures. A high-speed infrared thermal imager is used to record the location and dynamics of heat generation and transfer within the chamber. At the same time, a 3D scanner is used to obtain the structural dimensions and spatial arrangement of the components inside the experimental model, forming an experimental sample. Subsequently, based on the monitoring dataset or experimental samples, a three-dimensional structural model of the annular chamber is built using finite element structural analysis software such as ANSYS. The spatial position, connection method, and functional zoning of each working component are restored according to the actual coordinates and dimensions, thus completing the construction of the working structure distribution. At the same time, heat data, such as heating power and temperature gradient, are input into the heat flow field simulation module. The computational fluid dynamics algorithm is used to simulate the speed, direction, and energy loss of heat transfer from the electrode heating area to the insulating components and chamber walls. Key nodes that are prone to heat accumulation during the heat transfer process, such as component gaps and structural corners, are marked, and finally, a visualized heat flow path map is generated.

[0024] Using the constructed annular chamber operating structure distribution as the spatial basis, the three-dimensional coordinates, dimensions, and functional zoning of each component in the operating structure, such as ion generating electrodes, insulating supports, and gas guiding structures, serve as the spatial framework for the heat map, ensuring that the heat map accurately corresponds to the actual structural location of the chamber. Next, key data from the heat flow path are integrated, including the heat generation intensity along each path, such as electrode heating power, heat transfer rate, and thermal conductivity efficiency at different component interfaces, such as the thermal conductivity difference between metal and non-metal components and information on heat accumulation nodes. This data is mapped to the operating structure distribution framework according to the corresponding spatial coordinates. Subsequently, heat map visualization technology is employed, using color gradients as the core expression dimension. For example, dark red represents areas with high heat accumulation density, fast transfer speed, and low heat dissipation efficiency, while dark blue represents areas with low heat accumulation density, slow transfer speed, and high heat dissipation efficiency. Simultaneously, the specific heat dissipation efficiency values ​​for each area are marked on the heat map, such as the amount of heat dissipated per unit time and the heat accumulation risk level. Finally, through multiple data calibrations, combined with the heat dissipation characteristics of components in the working structure, such as the thermal conductivity of component materials and the actual heat transfer path, the color gradient and numerical labels of local areas in the heat map are corrected to ensure that the heat map can truly and intuitively reflect the differences in heat dissipation efficiency in different areas of the annular cavity, and finally form a complete heat dissipation efficiency heat map.

[0025] Regional feature analysis was performed on the generated heat dissipation efficiency heat map. Based on the color gradient of the heat map, the heat dissipation efficiency value, and the heat accumulation risk level, areas with high heat accumulation density, fast heat transfer speed, and low heat dissipation efficiency were screened out. If heat dissipation in these areas is not strengthened in time, heat accumulation is likely to occur, which will affect the normal operation of the plasma generator or the ion generation efficiency. These areas are the core heat dissipation areas. Subsequently, the specific coordinate range, area, and heat accumulation intensity of each core heat dissipation area in the three-dimensional space of the annular chamber were extracted. Combined with the distribution of the constructed working structure, the operation paths of key functional components such as ion generation electrodes and gas guiding structures in the chamber were avoided, and the layout area and density of metal blocks were planned: For the core heat dissipation areas with high heat accumulation intensity and large area, a high-density layout of metal blocks was adopted, such as 3 to 5 metal blocks per square meter, to ensure that the metal blocks can fully contact the high-heat parts in the area; for the core heat dissipation areas with medium heat accumulation intensity and small area, the layout density of metal blocks was appropriately reduced, such as 1 to 2 metal blocks per square meter, to ensure the heat dissipation effect while avoiding waste of space resources. Finally, during the deployment process, it was ensured that there was no physical interference between the metal blocks and the walls of the annular chamber and the surrounding working parts, and space was reserved on the outside of the metal blocks for subsequent processing of radial heat dissipation grooves, laying the foundation for subsequent contact and connection with liquid cooling pipes, and finally completing the precise deployment of metal blocks based on the distribution of the heat dissipation core area.

[0026] In one possible implementation, step S100 further includes: Step S140: Collect the heat energy distribution of the heat dissipation nodes through the monitoring network to obtain heat dissipation monitoring data.

[0027] Step S150: Based on the heat dissipation monitoring data and the distribution of the metal blocks, analyze the heat dissipation probability of the annular cavity according to the current liquid cooling pipe control parameters, and fit the natural heat dissipation probability.

[0028] Step S160: Analyze the heat dissipation compensation demand and locate the demand distribution based on the natural heat dissipation probability to obtain the heat dissipation demand distribution.

[0029] Specifically, the monitoring network is a dedicated network that has been deployed and validated in the early stages. Previously, heat generation paths were determined based on the distribution of the annular chamber's working structure. Heat dissipation paths and probabilities were fitted by combining the distribution of metal blocks. Key nodes in the heat response were identified through heat impact and node response support analysis, and deployment was completed based on these key nodes. Simultaneously, heat dissipation dead zones and core heat dissipation areas identified using heat dissipation efficiency thermal maps were used to construct validation counterexamples, ensuring that the monitoring network's feedback results can influence heat dissipation regulation of these counterexamples, achieving full coverage and data validity. During the data acquisition phase, monitoring elements deployed at each key heat response node in the monitoring network, such as high-precision temperature sensors and heat flux density sensors, were activated. The focus was on collecting thermal energy data from key heat dissipation nodes within the annular chamber, including the surface of the metal blocks, the contact area between the metal blocks and liquid-cooled pipes, the edges of heat dissipation dead zones, and the periphery of the core heat dissipation areas. This included real-time temperature values, temperature change rates, heat transfer directions, and heat flux density for each node. During the data acquisition process, data is continuously acquired at preset time intervals, such as once every 10 seconds, to avoid missing changes in heat distribution due to excessively long data acquisition intervals. At the same time, the acquired raw data is initially screened to remove invalid data caused by sensor malfunctions, ultimately forming heat dissipation monitoring data that accurately reflects the real-time heat distribution status of each heat dissipation node in the annular chamber.

[0030] The acquired heat dissipation monitoring data, including real-time temperature, temperature change rate, and heat flux density of each heat dissipation node, is spatially correlated with the actual distribution of metal blocks within the annular cavity. This clarifies the thermal energy distribution characteristics of the area covered by each metal block and the surrounding heat dissipation nodes, such as the heat accumulation intensity of nodes on the surface of the metal block and the heat transfer efficiency of the nodes in contact with the liquid cooling pipes. This provides a spatial positioning basis for subsequent heat dissipation probability analysis. Next, the control parameters of the current liquid cooling pipes are retrieved, including at least the coolant flow rate, temperature, pump power, and coolant properties. Using these parameters as constraints, combined with the thermal conductivity of the metal blocks and the heat dissipation characteristics of the annular cavity walls, a heat flow field simulation algorithm, such as the finite element thermal analysis algorithm, is used to perform a probabilistic analysis of the overall heat dissipation process of the annular cavity. This simulates the rate at which the heat absorbed by the metal blocks and conducted to the liquid cooling pipes is carried away by the coolant under the current liquid cooling parameters. By comparing the real-time heat generated and the heat carried away by each heat dissipation node, it is determined whether each area can achieve heat dissipation under the current liquid cooling control parameters, i.e., the amount of heat generated ≤ the amount of heat dissipated. Finally, based on the above analysis results, the heat dissipation balance state of different regions of the annular chamber is probabilistically quantified. Regions that can dissipate heat are marked as having a high heat dissipation probability, while regions that cannot dissipate heat are marked as having a low heat dissipation probability. A natural heat dissipation probability curve covering the entire annular chamber is generated using a data fitting tool, which intuitively presents the heat dissipation capacity of each region under the current liquid cooling control parameters: regions with probability values ​​higher than the preset balance threshold indicate that the current parameters can meet the heat dissipation requirements and no additional control is needed; regions with probability values ​​lower than the threshold indicate that the heat cannot be completely dissipated and heat dissipation compensation adjustments need to be made in subsequent steps.

[0031] Based on the fitted natural heat dissipation probability curve of the annular chamber, a heat dissipation probability balance threshold is first set. This threshold is determined based on the normal operating temperature range and heat tolerance limit of the plasma generator. Areas with a natural heat dissipation probability below this threshold are designated as target areas requiring heat dissipation compensation. These areas cannot fully absorb the generated heat under the current liquid cooling pipeline control parameters, posing a risk of heat accumulation. Next, a heat dissipation compensation requirement analysis is conducted for each compensation target area: combining the acquired heat dissipation monitoring data, the difference between the heat generated per unit time in each area and the heat dissipated by the current liquid cooling system is calculated. This difference represents the additional heat dissipation compensation required for each area. Simultaneously, based on the heat accumulation rate, the compensation priority is determined from the temperature change rate; areas with a faster heat accumulation rate have a higher compensation priority. Subsequently, combining the distribution of the annular chamber's operating structure and the location of the metal blocks, the compensation amount and priority of each compensation target area are correlated with the corresponding three-dimensional spatial coordinates to accurately locate the specific range of each area within the annular chamber, such as the electrode perimeter area covered by a metal block, or specific corner areas of the chamber wall, avoiding conflicts between the compensation area and the operating path of the core components of the ion generator. Finally, the location information, compensation amount, and compensation priority of all compensation target areas are integrated into a visualized distribution map, clearly marking key information within the annular cavity, ultimately forming a complete distribution of heat dissipation requirements.

[0032] In one possible implementation, step S140 further includes: Step S141: Determine the heat generation path based on the working structure distribution of the annular chamber.

[0033] Step S142: Fit the heat dissipation path and heat dissipation probability based on the distribution of the metal blocks.

[0034] Step S143: Based on the heat generation path, the heat dissipation path, and the heat dissipation probability, perform heat offset and node response support analysis to identify key nodes in the heat response.

[0035] Step S144: Deploy a monitoring network based on the key nodes of the heat response for monitoring heat dissipation status data.

[0036] Specifically, based on the existing operational structure distribution within the annular chamber, the spatial arrangement, material properties, and working principles of key components for ion generation within the chamber are clearly defined, including ionization electrodes, accelerating electrodes, supporting structures such as insulating supports, and functional channels such as gas input channels and ion output channels. These core ion generation components are the primary heat sources; for example, heat is generated due to resistance when the electrodes are energized, and additional heat may be generated during component operation due to particle collisions and energy conversion. Subsequently, combining the heating mechanisms of each component with the connections within the operational structure, the trajectory of heat transfer from its source, such as the heating area on the electrode surface, to the surrounding area is traced. For instance, heat generated by the electrodes is first conducted to the adjacent insulating support through direct contact, and then transferred from the support to the annular chamber wall. Simultaneously, some heat diffuses into the surrounding space in the form of thermal radiation or flows through the gas channels with the airflow. During this process, the component interfaces, structural gaps, channel paths, and areas where heat is easily trapped, such as component connection corners and narrow channel sections, are marked, ultimately forming a heat generation path covering the entire annular chamber and clearly reflecting the origin and direction of heat diffusion.

[0037] Based on the existing metal block distribution within the annular chamber, the spatial coordinates, dimensions, and relative positions of each metal block with surrounding components such as the ion-generating electrodes and the annular chamber walls are first determined. Particular attention is paid to the spatial intersections between the metal blocks and the previously determined heat generation paths. Key areas marked include where the metal blocks directly contact or are close to heat sources, such as electrode heating zones and critical heat transfer sections. This clarifies the range within which the metal blocks, as heat conduction media, can capture heat. Subsequently, considering the thermal conductivity of the metal blocks (e.g., the high thermal conductivity of copper blocks), the structural parameters of the radial heat dissipation grooves on the outer side of the metal blocks (e.g., groove depth and spacing), their impact on the contact area with the liquid cooling pipes, and the pre-defined contact connection method between the metal blocks and the liquid cooling pipes, the complete process of heat entering the metal block from the generation path, being conducted along the metal block body to the radial heat dissipation grooves, and then being transferred to the liquid cooling pipes through the heat dissipation grooves is simulated. Based on this, the corresponding heat dissipation path for each metal block is fitted, clearly marking the direction of heat transfer between the metal blocks and the liquid cooling pipes, and key conduction nodes such as the connection between the metal block and the heat dissipation groove, and the contact point between the heat dissipation groove and the liquid cooling pipe. Finally, based on the fitted heat dissipation paths, a heat conduction efficiency calculation model is introduced. Parameters such as the thermal conductivity of the metal block, the tightness of contact between the heat dissipation tank and the liquid cooling pipe, and the distance between the metal block and the heat source are used as variables to calculate the probability that heat is successfully transferred to the liquid cooling pipe along each heat dissipation path and carried away by the coolant. For example, metal blocks that are close to the heat source and in close contact with the liquid cooling pipe have a higher heat transfer success rate and a higher heat dissipation probability value; while metal blocks that are far from the heat source or have a larger contact gap have a lower heat dissipation probability value. Finally, the heat dissipation paths and heat dissipation probabilities of all metal block coverage areas are fitted.

[0038] The determined heat generation path and the fitted heat dissipation path are spatially superimposed to identify the intersection and non-intersection regions of the two paths. The intersection region is the core overlapping segment of heat generation and heat dissipation, while the non-intersection region may have gaps in heat dissipation coverage. For the intersection region, the heat offset effect is calculated based on heat dissipation probability data: using the heat output of the heat generation path per unit time as a benchmark, the heat absorption of the heat dissipation path in that region under the corresponding heat dissipation probability is compared to determine whether the heat dissipation can offset the generated heat, and subdivided regions of insufficient heat offset (heat absorption < heat output), balanced offset (heat absorption ≈ heat output), and excessive offset (heat absorption > heat output) are marked. Next, a node response support analysis was conducted on all potential monitoring nodes within the annular cavity, including intersections of heat generation and dissipation paths, nodes in areas with insufficient heat dissipation, key conduction nodes in heat dissipation paths, and nodes at corners and component connections within the cavity structure. On one hand, the sensitivity of each node to heat changes was assessed by simulating heat fluctuations, such as small changes in heat source power, monitoring the magnitude and response speed of changes in node temperature and heat flux density, and selecting nodes that respond sensitively to heat changes. On the other hand, the representativeness of the node data was determined, analyzing whether a node could reflect the heat generation and dissipation status within a certain surrounding area. For example, a node that could cover the heat dissipation situation of two adjacent heat dissipation paths was considered more representative. Finally, combining the heat dissipation analysis results with the node response support assessment, nodes with high sensitivity and strong representativeness in areas with insufficient heat dissipation were prioritized as core candidate nodes. Sensitive nodes reflecting heat dissipation gaps in non-intersection areas were also included, while nodes with sluggish responses or weak representativeness in areas with excessive heat dissipation were eliminated. Ultimately, key heat response nodes that are crucial for subsequent heat dissipation monitoring were identified.

[0039] Based on the identified key nodes in the thermal response, the three-dimensional spatial coordinates and their respective regions within the annular chamber are extracted, such as areas with insufficient heat dissipation, key segments of the heat dissipation path, and non-intersecting heat dissipation gaps, along with their corresponding thermal characteristics, such as sensitivity to heat changes and data representativeness range. This serves as the core basis for the deployment of the monitoring network, ensuring that the deployment scheme accurately matches the functional attributes of the key nodes. Subsequently, suitable monitoring elements are selected according to the monitoring requirements of each key node: high-precision platinum resistance temperature sensors are deployed for nodes requiring precise temperature change capture; heat flux density sensors are configured for nodes requiring monitoring of heat transfer rates. Simultaneously, to avoid physical interference or impact on heat conduction between the sensors and internal working components within the annular chamber, such as ion generating electrodes and gas flow channels, the sensor installation angle and fixing method are determined based on the spatial arrangement of components surrounding the node, such as using non-contact bracket fixing or embedded installation, ensuring that the sensors can directly collect heat dissipation status data of key nodes without interfering with the normal operation of the plasma generator. Finally, the monitoring network was built according to the principle of full coverage without blind spots and dense deployment in key areas: at key nodes in high-risk areas such as insufficient heat dissipation, the spacing between sensors was appropriately reduced to improve the frequency and accuracy of data acquisition; at key nodes with strong data representativeness, data transmission modules were simultaneously connected to realize the real-time uploading of monitoring data; at the same time, the entire monitoring network was initially debugged to verify whether each sensor could stably collect heat dissipation status data such as temperature and heat flux density, ensuring that the network as a whole has continuous and reliable heat dissipation status monitoring capabilities, laying the foundation for the acquisition of heat dissipation monitoring data in subsequent steps.

[0040] In one possible implementation, step S144 further includes: Step S1441: Identify heat dissipation dead zones based on the heat dissipation efficiency thermal map and the distribution of metal blocks.

[0041] Step S1442: Construct a verification counterexample using the aforementioned heat dissipation dead zone and heat dissipation core region.

[0042] Step S1443: Verify the monitoring coverage response of the monitoring network using the verification counterexamples.

[0043] Specifically, the heat dissipation efficiency thermal map is used to screen out areas with extremely low heat dissipation efficiency, where heat easily accumulates and is difficult to dissipate through natural heat dissipation or conduction by metal blocks. These areas are usually far from the metal blocks, blocked by the internal structure of the chamber, or located in heat transfer dead zones, resulting in heat not being effectively transferred to the liquid cooling pipes, which are heat dissipation dead zones. At the same time, combined with the distribution of the metal blocks, it is further confirmed whether the heat dissipation dead zone is outside the heat dissipation coverage of the metal blocks, eliminating local inefficient heat dissipation areas caused by insufficient density or positional deviation of the metal blocks, and accurately locating the true heat dissipation dead zone in the annular chamber.

[0044] The key differences between the heat dissipation dead zone and the heat dissipation core area were clearly defined. The heat dissipation dead zone is an area with extremely low heat dissipation efficiency and easy heat retention, identified in the early stage through heat dissipation efficiency thermal maps and metal block distribution. The heat dissipation core area is an area with high heat accumulation density, fast transfer speed, and requires focused heat dissipation. Based on the thermal characteristics and risk points of the two areas, the direction of constructing verification counterexamples was determined, namely, to verify the monitoring network's monitoring capabilities for two scenarios: "slow heat accumulation under low heat dissipation efficiency" and "rapid heat change under high heat dissipation demand." Subsequently, specific simulated thermal scenario parameters were set for the two types of areas: for the heat dissipation dead zone, the heat accumulation process under low-power operation of the ion source was simulated, setting the initial temperature, heat increment per unit time, and the time to accumulate to the critical risk temperature, clarifying the requirement for the monitoring network to accurately capture this slow temperature rise trend; for the heat dissipation core area, the scenario of a sudden heat rise under high-power operation of the ion source was simulated, setting the temperature fluctuation range and rate of change within a short period of time, clarifying the requirement for the monitoring network to quickly respond to this instantaneous heat change. Finally, the simulated scenarios, temperature parameters, and corresponding monitoring standards for the two types of areas were integrated into a complete verification counterexample.

[0045] Using the previously constructed verification counterexamples as evaluation criteria, the deployed monitoring network was activated to ensure all sensors within the network were functioning correctly and capable of collecting real-time thermal data from the heat dissipation dead zone and the core heat dissipation area within the annular cavity. Next, following the simulated thermal scenarios set in the verification counterexamples, the corresponding thermal change process was reproduced within the annular cavity: for the heat dissipation dead zone, the slow accumulation of heat under low-power conditions was simulated, continuously tracking the temperature data fed back by the monitoring sensors in this area; for the core heat dissipation area, a scenario of rapid heat rise under high-power conditions was simulated, recording the sensor response time and data accuracy to instantaneous thermal changes. Then, the actual feedback results of the monitoring network were compared with the preset standards of the verification counterexamples: if the temperature monitoring data of the heat dissipation dead zone accurately reflects the heat accumulation trend and the data error is controlled within a preset range, and the sensors in the core heat dissipation area can capture the rapid heat rise within a specified time, and the feedback data meets the expected requirements, then the monitoring network is deemed to have passed verification; if the monitoring data in any area shows excessive error, response delay, or failure to capture key thermal changes, then the monitoring network is deemed to have failed verification. Finally, for monitoring networks that fail verification, adjustments and optimizations are made based on the problems exposed in the verification results. For example, additional sensors are deployed in heat dissipation dead zones to improve data acquisition accuracy, or the installation positions of sensors in the core heat dissipation area are adjusted to shorten the response time. After the adjustments are completed, verification is carried out again based on the verification counterexamples until the feedback results of the monitoring network fully meet the requirements of the verification counterexamples, ensuring that it has effective coverage and rapid response capabilities for key heat dissipation areas.

[0046] In one possible implementation, step S1443 further includes: If the feedback results obtained based on the monitoring network affect the verification counterexample, then the verification is passed.

[0047] If the feedback results obtained based on the monitoring network cannot affect the heat dissipation regulation of the verification counterexample, then the verification will fail.

[0048] Specifically, the monitoring network is reset based on the verification results and the verification counterexamples to satisfy the verification counterexamples.

[0049] Specifically, the deployed monitoring network is activated to continuously collect thermal energy data from the heat dissipation dead zone and the core heat dissipation area within the annular cavity. Simultaneously, based on previously constructed verification counterexamples, the corresponding heat change scenarios are reproduced within the cavity, such as the slow accumulation of heat in the heat dissipation dead zone and the rapid surge in heat in the core heat dissipation area. Subsequently, the verification results from the monitoring network are assessed: if the temperature, heat flux density, and other data output by the monitoring network accurately reflect the heat change trends in the verification counterexamples, and if these data allow for further control of the heat dissipation status in the corresponding areas (e.g., adjusting liquid cooling parameters based on high-temperature feedback from the core heat dissipation area), meaning the feedback results have a real impact on the verification counterexamples, then the monitoring network is deemed to have passed verification. Conversely, if the data from the monitoring network fails to accurately capture the heat changes in the verification counterexamples, or if these data cannot be used to effectively control the heat dissipation in the corresponding areas, meaning the feedback results do not have a heat dissipation control effect on the verification counterexamples, then the monitoring network is deemed to have failed verification. Finally, for cases that fail verification, the monitoring network is reset based on the verification results and the specific requirements of the verification counterexamples: if there are monitoring blind spots in the heat dissipation dead zone, appropriate temperature sensors need to be deployed in that area; if the sensor response is delayed in the core heat dissipation area, the sensor installation position needs to be adjusted or replaced with a monitoring element with higher sensitivity; after the reset, the test is carried out again based on the original verification counterexamples until the feedback results of the monitoring network can meet the requirements of the verification counterexamples, ensuring that it has the ability to effectively monitor and control key heat dissipation areas.

[0050] In one possible implementation, step S200 further includes: Step S210: Based on the distribution of heat dissipation demand, perform heat dissipation demand clustering and segmentation to establish heat dissipation areas.

[0051] Step S220: Based on the heat dissipation demand target and heat accumulation rate of the heat dissipation area, establish an efficient heat dissipation optimization target. Use the liquid cooling pipe parameters as variables to perform target search until the liquid cooling pipe parameter combination that maximizes the target of each heat dissipation area is obtained, and the target control parameters of the liquid cooling pipe are obtained.

[0052] Specifically, based on the heat dissipation demand distribution obtained through prior analysis of natural heat dissipation probability, a clustering algorithm, such as K-means clustering, is used to segment the heat dissipation demand of different regions within the annular cavity. Regions with similar heat dissipation demand intensity, heat accumulation rate, and spatial location are grouped into the same category. Simultaneously, the correlation between the distribution of metal blocks and the heat flow path is considered to ensure consistency in heat dissipation characteristics within the same cluster, avoiding imbalances in subsequent parameter configurations due to overly fine or coarse region divisions. Through clustering, the entire annular cavity is divided into multiple independent and well-defined heat dissipation regions. Each region corresponds to a clear set of heat dissipation demand indicators, such as the heat to be dissipated per unit time and the maximum tolerable temperature.

[0053] For each defined heat dissipation zone, its core parameters are clearly defined. The heat dissipation target is the desired heat dissipation effect for that zone, such as stabilizing the zone temperature at 30-35℃ and dissipating no less than 500W of heat per unit time. The heat accumulation rate is the rate at which heat increases within the zone per unit time, such as a temperature increase of 1.2℃ per minute and an additional heat gain of 3000W per hour. Based on this, the core guiding principle for efficient heat dissipation optimization is determined: balancing the energy consumption and heat dissipation response speed of the liquid cooling system while meeting the heat dissipation target, for example, "achieving the heat dissipation target with the lowest pump power" or "achieving the heat dissipation target as quickly as possible with fixed energy consumption." Subsequently, the optimization variable range of the liquid cooling pipeline parameters is determined. These parameters include at least flow rate, temperature, pump power, and coolant properties. Parameter boundaries need to be set in conjunction with the actual space constraints of the heat dissipation zone and the hardware capabilities of the liquid cooling system, such as controlling the flow rate range to 5~20L / min, ensuring the coolant temperature is no less than 15℃, and limiting the pump power to no more than 1.5kW. Next, a target search algorithm, such as a genetic algorithm, is used to transform the optimization target of each heat dissipation area into a mathematical function. The liquid cooling pipe parameters are then substituted into the function for iterative calculation. In each iteration, the heat dissipation effect under different parameter combinations is simulated. For example, the temperature change and heat dissipation of the area are calculated through thermal flow field simulation. The difference between this effect and the heat dissipation target is compared, and the corresponding system energy consumption is calculated. The parameter combination that is closer to the optimization target in the current iteration is selected. The iterative search continues until a stable result is obtained, that is, a certain parameter combination can make the heat dissipation effect of the corresponding heat dissipation area fully meet the target and achieve optimal energy consumption or response speed. At this point, the parameter search for that area is stopped. After all heat dissipation areas have obtained the liquid cooling pipe parameter combination that maximizes the target, these parameter combinations are integrated and summarized to form the target control parameters for the liquid cooling pipes covering all heat dissipation areas of the annular chamber.

[0054] In one possible implementation, step S220 further includes: The parameters of the liquid cooling pipeline include at least: flow rate, temperature, pump power, and coolant properties.

[0055] Specifically, liquid cooling pipeline parameters are the core variables for achieving precise heat dissipation control, and their selection and configuration must closely match the needs of different heat dissipation areas. These parameters include at least four key categories: flow rate, temperature, pump power, and coolant properties. Flow rate directly determines the total amount of coolant flowing through the pipeline per unit time; a higher flow rate generally indicates stronger heat dissipation capacity, suitable for areas with high heat dissipation requirements. Temperature refers to the initial temperature of the coolant upon entering the pipeline; a lower coolant temperature increases the temperature difference with the heat-generating area, accelerating heat absorption, and needs to be dynamically adjusted based on the rate of heat accumulation in the area. Pump power provides the power for coolant flow, affecting flow rate stability and coolant delivery distance, and must be matched to the flow rate requirements to avoid energy waste. Coolant properties encompass characteristics such as thermal conductivity, specific heat capacity, and viscosity. For example, coolants with high thermal conductivity have higher heat dissipation efficiency, while low-viscosity coolants reduce pump power loss; the appropriate coolant type must be selected based on the temperature range and heat dissipation efficiency requirements of the heat dissipation area. These four types of parameters are interrelated and work synergistically to form the foundation of liquid cooling pipeline control.

[0056] In one possible implementation, step S300 further includes: Step S310: Sort the heat dissipation areas according to their heat dissipation requirements to obtain the heat dissipation requirement gradient of the areas.

[0057] Step S320: Based on the regional heat dissipation demand gradient, perform layered configuration from high to low, wherein the regional heat dissipation demand gradient is dynamically adjusted according to the control parameters of the higher layer configuration to gradually complete the heat dissipation target of all heat dissipation areas, and sequentially splice the layered control parameters to obtain the collaborative heat dissipation parameters.

[0058] Specifically, taking each of the defined heat dissipation zones as the object, the heat dissipation target of each zone is extracted, such as the heat to be dissipated per unit time and the target temperature range. The heat is then aligned and sorted according to the intensity of demand from high to low. For example, high-demand zones with a heat dissipation of 1000W per unit time and a target temperature ≤30℃ are ranked first, while low-demand zones with a heat dissipation of 300W per unit time and a target temperature ≤38℃ are ranked last. By sorting, the urgency of each zone for cooling resources is clarified, and a regional heat dissipation demand gradient is finally formed, providing a priority basis for subsequent resource allocation.

[0059] Following the core strategy of prioritizing the highest demand, the system employs a tiered allocation approach based on the regional heat dissipation demand gradient, from high to low. First, for the highest-demand regions at the top of the gradient, sufficient cooling resources are allocated, such as maximizing the flow rate of liquid cooling pipes and reducing coolant temperature to prioritize meeting their heat dissipation targets. During this process, due to the locality and thermal inertia of the thermal system, the rapid cooling of high-demand regions has a passive heat dissipation effect on surrounding low-demand regions. For example, while the coolant temperature rises after absorbing heat in high-demand regions, it can still carry away some heat from adjacent low-demand regions as it flows through them. This allows some low-demand regions to meet their heat dissipation targets without separate allocation, reducing redundant scheduling. Meanwhile, the regional heat dissipation demand gradient is not fixed and needs to be dynamically adjusted according to the control parameters of the high-level configuration: if the heat in the surrounding low-demand areas has been reduced to the target range due to passive heat dissipation after the high-demand areas are configured, these areas are removed from the gradient; if some low-demand areas still do not meet their needs, such as due to their rapid heat accumulation and long continuous heat generation time, making passive heat dissipation unable to fully cover them, the gradient order is readjusted, and these unmet areas are designated as new "layers to be configured," continuing to be allocated cooling resources, such as appropriately increasing the pump power of the corresponding pipes to increase flow. This process is repeated to gradually achieve the heat dissipation targets of all heat dissipation areas. Finally, the liquid cooling pipe control parameters determined in each layer configuration stage are spliced ​​and integrated according to the configuration order to form a coordinated heat dissipation parameter that covers the entire region and adapts collaboratively.

[0060] Example 2, based on the same inventive concept as the high-efficiency heat dissipation method for the plasma generator in the foregoing examples, such as... Figure 2 As shown, this application provides a high-efficiency heat dissipation system for a plasma generator. The system and method embodiments in this application are based on the same inventive concept. The system includes: The heat dissipation demand identification module 10 is used to set multiple metal blocks in the annular cavity. The radial heat dissipation grooves on the outside of the metal blocks are evenly distributed along the circumference and contact the liquid cooling pipes. Based on the annular cavity and the distribution of the metal blocks, heat dissipation monitoring and probability analysis are performed to identify the distribution of heat dissipation demand.

[0061] The control parameter acquisition module 20 is used to perform position matching and control parameter search of the liquid cooling pipe according to the heat dissipation demand distribution, and obtain the target control parameters of the liquid cooling pipe.

[0062] The control command generation module 30 is used to perform collaborative optimization based on the target control parameters of the liquid cooling pipeline and the discrete distribution relationship of the liquid cooling pipeline, determine the collaborative heat dissipation parameters, and generate liquid cooling heat dissipation control commands for controlling the parameters of the liquid cooling pipeline.

[0063] Furthermore, the system is also used to implement the following functions: Using monitoring data or experimental samples, the distribution of the working structure and the heat flow path within the annular cavity of the plasma generator are constructed; based on the distribution of the working structure and the heat flow path, a heat dissipation efficiency thermal map is constructed; the heat dissipation core area is identified according to the heat dissipation efficiency thermal map, and the metal blocks are arranged according to the distribution location of the heat dissipation core area.

[0064] Furthermore, the system is also used to implement the following functions: Heat energy distribution of heat dissipation nodes is collected by monitoring network to obtain heat dissipation monitoring data; based on the heat dissipation monitoring data and the distribution of the metal block, the heat dissipation probability of the annular cavity is analyzed according to the current liquid cooling pipeline control parameters, and the natural heat dissipation probability is fitted; heat dissipation compensation demand analysis and demand distribution location are performed according to the natural heat dissipation probability to obtain the heat dissipation demand distribution.

[0065] Furthermore, the system is also used to implement the following functions: Based on the working structure distribution of the annular chamber, the heat generation path is determined; the heat dissipation path and heat dissipation probability are fitted based on the metal block distribution; heat offset and node response support analysis are performed based on the heat generation path, the heat dissipation path, and the heat dissipation probability to identify key nodes of heat response; a monitoring network is deployed based on the key nodes of heat response for monitoring heat dissipation status data.

[0066] Furthermore, the system is also used to implement the following functions: Based on the heat dissipation efficiency heat map and the distribution of metal blocks, heat dissipation dead zones are identified; using the heat dissipation dead zones and the core heat dissipation area, verification counterexamples are constructed; and the monitoring network is verified for monitoring coverage response using the verification counterexamples.

[0067] Furthermore, the system is also used to implement the following functions: If the feedback results obtained based on the monitoring network affect the verification counterexample, the verification is passed; if the feedback results obtained based on the monitoring network cannot affect the heat dissipation regulation of the verification counterexample, the verification is not passed; wherein, the monitoring network is reset based on the verification results and the verification counterexample to satisfy the verification counterexample.

[0068] Furthermore, the system is also used to implement the following functions: Based on the distribution of heat dissipation demand, heat dissipation demand is clustered and segmented to establish heat dissipation areas. Based on the heat dissipation demand target and heat aggregation rate of the heat dissipation area, an efficient heat dissipation optimization target is established. The target search is performed with liquid cooling pipe parameters as variables until the liquid cooling pipe parameter combination that maximizes the target of each heat dissipation area is obtained, thus obtaining the target control parameters of the liquid cooling pipe.

[0069] Furthermore, the system is also used to implement the following functions: The parameters of the liquid cooling pipeline include at least: flow rate, temperature, pump power, and coolant properties.

[0070] Furthermore, the system is also used to implement the following functions: According to the heat dissipation demand targets of the heat dissipation areas, the heat dissipation demand gradient of each area is obtained. Based on the heat dissipation demand gradient of each area, a hierarchical configuration is performed from high to low. The heat dissipation demand gradient of each area is dynamically adjusted according to the control parameters configured at higher levels to gradually complete the heat dissipation targets of all heat dissipation areas. The hierarchical control parameters are then sequentially concatenated to obtain the collaborative heat dissipation parameters.

[0071] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0072] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0073] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A highly efficient heat dissipation method for a plasma generator, characterized in that, include: Multiple metal blocks are set in an annular chamber. Radial heat dissipation grooves on the outer side of the metal blocks are evenly distributed along the circumference and contact the liquid cooling pipes. Heat dissipation monitoring and probability analysis are performed based on the annular chamber and the distribution of metal blocks to identify the distribution of heat dissipation demand. Based on the heat dissipation demand distribution, the liquid cooling pipes are matched for location and control parameters are searched to obtain the target control parameters of the liquid cooling pipes. Based on the target control parameters of the liquid cooling pipeline, collaborative optimization is performed according to the discrete distribution relationship of the liquid cooling pipeline to determine the collaborative heat dissipation parameters and generate liquid cooling heat dissipation control commands for controlling the parameters of the liquid cooling pipeline. The annular cavity contains multiple metal blocks, including: Using monitoring data or experimental samples, the working structure distribution and heat flow path within the annular cavity of the plasma generator are constructed. Based on the work structure distribution and heat flow path, a heat dissipation efficiency thermal map is constructed. Identify the core heat dissipation region based on the heat dissipation efficiency thermal map, and arrange the metal blocks according to the distribution location of the core heat dissipation region; Among them, heat dissipation monitoring and probability analysis based on the distribution of the annular chamber and metal blocks are used to identify the distribution of heat dissipation demand, including: Heat dissipation monitoring data is obtained by collecting heat energy distribution data from heat dissipation nodes through a monitoring network; Based on the heat dissipation monitoring data and the distribution of the metal blocks, the heat dissipation probability of the annular cavity is analyzed according to the current liquid cooling pipe control parameters, and the natural heat dissipation probability is fitted. Based on the natural heat dissipation probability, the heat dissipation compensation demand is analyzed and the demand distribution is located to obtain the heat dissipation demand distribution. This includes monitoring the heat distribution of heat dissipation nodes through a network, which previously included: Based on the working structure distribution of the annular chamber, the heat generation path is determined; The heat dissipation path and probability are fitted based on the distribution of the metal blocks; Based on the heat generation path, the heat dissipation path, and the heat dissipation probability, heat offset and node response support analysis are performed to identify key nodes in the heat response. A monitoring network is deployed based on the aforementioned key nodes of the heat response for monitoring heat dissipation status data; The system includes deploying a monitoring network based on the key nodes of the heat response, and further includes: Based on the heat dissipation efficiency thermal map and the distribution of metal blocks, heat dissipation dead zones are identified; Using the aforementioned heat dissipation dead zone and core heat dissipation area, construct a counterexample for verification; The monitoring network is subjected to monitoring coverage response verification using the aforementioned verification counterexamples. This verification includes: If the feedback results obtained based on the monitoring network affect the verification counterexample, then the verification is passed; If the feedback results obtained based on the monitoring network cannot affect the heat dissipation regulation of the verification counterexample, then the verification will not pass. Specifically, the monitoring network is reset based on the verification results and the verification counterexamples to satisfy the verification counterexamples. Specifically, based on the heat dissipation demand distribution, the liquid cooling pipes are matched for location and control parameters are searched to obtain the target control parameters for the liquid cooling pipes, including: Based on the distribution of heat dissipation demand, heat dissipation demand is clustered and segmented to establish heat dissipation areas; Based on the heat dissipation requirements and heat accumulation rate of the heat dissipation area, an efficient heat dissipation optimization target is established. The target search is performed using the liquid cooling pipe parameters as variables until the combination of liquid cooling pipe parameters that maximizes the target of each heat dissipation area is obtained, thus obtaining the target control parameters of the liquid cooling pipe.

2. The high-efficiency heat dissipation method for a plasma generator according to claim 1, characterized in that, The parameters of the liquid cooling pipeline include at least: flow rate, temperature, pump power, and coolant properties.

3. The high-efficiency heat dissipation method for a plasma generator according to claim 1, characterized in that, Based on the target control parameters of the liquid cooling pipeline, collaborative optimization is performed according to the discrete distribution relationship of the liquid cooling pipeline to determine the collaborative heat dissipation parameters, including: The heat dissipation requirements of the heat dissipation areas are aligned and sorted according to the heat dissipation requirements of the heat dissipation areas to obtain the heat dissipation requirement gradient of the areas. Based on the regional heat dissipation demand gradient, a layered configuration is performed from high to low. The regional heat dissipation demand gradient is dynamically adjusted according to the control parameters of the higher layer configuration to gradually achieve the heat dissipation target of all heat dissipation areas. The layered control parameters are sequentially spliced ​​together to obtain the coordinated heat dissipation parameters.

4. A high-efficiency heat dissipation system for a plasma generator, characterized in that, The system is used to implement the efficient heat dissipation method for the plasma generator according to any one of claims 1-3, and the system comprises: The heat dissipation demand identification module is used to set multiple metal blocks in an annular cavity. The radial heat dissipation grooves on the outside of the metal blocks are evenly distributed in the circumference and contact the liquid cooling pipes. Based on the annular cavity and the distribution of metal blocks, heat dissipation monitoring and probability analysis are performed to identify the distribution of heat dissipation demand. The control parameter acquisition module is used to perform position matching and control parameter search of the liquid cooling pipe according to the heat dissipation demand distribution, and obtain the target control parameters of the liquid cooling pipe. The control command generation module is used to perform collaborative optimization based on the target control parameters of the liquid cooling pipeline and the discrete distribution relationship of the liquid cooling pipeline to determine the collaborative heat dissipation parameters and generate liquid cooling heat dissipation control commands for controlling the parameters of the liquid cooling pipeline.

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