Heat dissipation system and device

Abstract

The invention discloses a heat dissipation system and device, and relates to the technical field of thermal management, and the system comprises a training unit which is used for obtaining a heat dissipation assembly structure and an equivalent thermal resistance network, a supervising and training thermal conductance decision module, a fuzzy judgment unit which is used for connecting a state machine and being embedded into a first fuzzy judgment device, obtaining a real-time heat map and generating a thermal management instruction, the heat dissipation decision making unit is used for triggering a second heat dissipation decision making device according to the heat management instruction, determining a heat dissipation strategy based on a heat value and heat distribution heat dissipation regulation and control decision, and responding to a heat dissipation assembly to conduct heat dissipation regulation and control management, and is used for solving the problems that in the prior art, heat dissipation regulation and control accuracy is insufficient, heat dissipation efficiency is limited, and heat dissipation efficiency is poor. The heat dissipation efficiency and the regulation and control precision can be effectively improved, and the heat dissipation performance is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management, and particularly to a heat dissipation system and device. Background Art

[0002] With the continuous improvement of the integration level of electronic devices, the power consumption density increases accordingly, resulting in the accumulation of heat inside the device, which affects the system stability and service life. The existing heat dissipation technologies mainly include natural convection heat dissipation, air cooling heat dissipation, liquid cooling heat dissipation, heat pipe heat dissipation, etc.

[0003] At the same time, most of the existing heat dissipation systems rely on preset thresholds or simple temperature feedback control in formulating heat dissipation strategies, and fail to make full use of device operation data for intelligent optimization. For example, some systems only start the fan or adjust the heat dissipation module when the temperature exceeds the set threshold, and fail to accurately sense the heat distribution, resulting in the existence of local hot spot problems. In addition, the traditional thermal management system lacks a dynamic adjustment mechanism and cannot perform intelligent heat dissipation control according to the real-time working conditions of the device, resulting in increased energy consumption and decreased heat dissipation efficiency.

[0004] Therefore, the existing heat dissipation technologies still have technical problems such as insufficient accuracy of heat dissipation control, limited heat dissipation efficiency, and poor overall heat dissipation performance. Summary of the Invention

[0005] This application provides a heat dissipation system and device for solving the technical problems of insufficient accuracy of heat dissipation control, limited heat dissipation efficiency, and poor overall heat dissipation performance existing in the prior art.

[0006] In view of the above problems, this application provides a heat dissipation system and device.

[0007] In a first aspect, this application provides a heat dissipation system, which includes: a training unit for obtaining the heat dissipation component structure and the equivalent thermal resistance network, and supervising and training a thermal conductance decision module, where the thermal conductance decision module includes a first fuzzy discriminator and a second heat dissipation decision maker; a fuzzy decision unit for connecting a state machine and embedding the first fuzzy discriminator, obtaining a real-time heat map and generating a thermal management instruction, where the state machine is connected to the device sensing side; a heat dissipation decision unit for triggering the second heat dissipation decision maker according to the thermal management instruction, making a heat dissipation control decision based on the heat value and heat distribution, determining a heat dissipation strategy, and responding to the heat dissipation component for heat dissipation control management, where the second heat dissipation decision maker is embedded in the thermal management center.

[0008] Second aspect, the present application provides a heat dissipation device, the device includes: a heat conduction decision module, a heat dissipation component, a sensing array and a state machine; wherein, the sensing array is deployed on the sensing side of the device to sense the temperature state of the device; the heat dissipation component is used for heat dissipation execution; the heat conduction decision module includes a first fuzzy discriminator and a second heat dissipation decision maker; the state machine is embedded with a first fuzzy discriminator, one end of the state machine is connected to the sensing array, and one end is connected to the second heat dissipation decision maker embedded in the heat management center; the other end of the second heat dissipation decision maker is connected to the heat dissipation component.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages: A heat dissipation system provided by an embodiment of the present application includes: a training unit for obtaining the structure of the heat dissipation component and the equivalent thermal resistance network, and supervising and training the heat conduction decision module, wherein the heat conduction decision module includes a first fuzzy discriminator and a second heat dissipation decision maker; a fuzzy decision unit for connecting to the state machine and embedding the first fuzzy discriminator, obtaining a real-time thermal map and generating a heat management instruction, wherein the state machine is connected to the sensing side of the device; a heat dissipation decision unit for triggering the second heat dissipation decision maker according to the heat management instruction, determining a heat dissipation strategy based on the heat dissipation regulation decision of the heat value and the heat distribution, and responding to the heat dissipation component for heat dissipation regulation management, wherein the second heat dissipation decision maker is embedded in the heat management center. It is used to solve the technical problems of insufficient heat dissipation regulation accuracy and limited heat dissipation efficiency in the prior art, resulting in poor overall heat dissipation performance, and can effectively improve the heat dissipation efficiency and regulation accuracy, and enhance the heat dissipation performance. Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of a heat dissipation system provided by the present application; Figure 2 It is a schematic structural diagram of a heat dissipation system provided by the present application.

[0011] Description of the reference numerals: training unit 11, fuzzy decision unit 12, heat dissipation decision unit 13. Detailed Embodiments

[0012] The present application provides a heat dissipation system and device to solve the technical problems of insufficient heat dissipation regulation accuracy and limited heat dissipation efficiency in the prior art, resulting in poor overall heat dissipation performance.

[0013] Embodiment 1: As Figure 1 shown, the present application provides a heat dissipation system, the system includes: A training unit 11 for obtaining the structure of the heat dissipation component and the equivalent thermal resistance network, and supervising and training the heat conduction decision module, wherein the heat conduction decision module includes a first fuzzy discriminator and a second heat dissipation decision maker.

[0014] In the embodiment of the present application, the training unit 11 is used to construct a thermal conductivity decision module. First, based on the heat dissipation requirements of the electronic device, a structural model of the heat dissipation component is established. Exemplarily, the structural model includes a radiator, a heat pipe, a fan, and related heat-conducting materials, etc., and its geometric parameters, material properties, and heat conduction paths are characterized to construct a complete structural framework of the heat dissipation component.

[0015] Among them, the equivalent thermal resistance network refers to using the thermal resistance theory to equivalently simplify the heat conduction, heat convection, and heat radiation effects in the heat dissipation system, so that it can be quantitatively modeled with thermal resistance units and heat flow paths, in order to analyze the overall heat flow distribution characteristics and key thermal resistance points of the heat dissipation system. For example, the thermal resistance network can be composed of chip thermal resistance, interface thermal resistance, radiator thermal resistance, and air convection thermal resistance, etc. Each thermal resistance unit forms an equivalent thermal resistance network through series or parallel connection, thus providing a theoretical basis for subsequent heat management optimization.

[0016] Further, the thermal conductivity decision module is supervised and trained to optimize the heat dissipation control strategy. Among them, the thermal conductivity decision module is mainly used to analyze the thermal state of the device and make reasonable heat dissipation adjustments based on the heat flow distribution and temperature change trend. This module includes a first fuzzy decision maker and a second heat dissipation decision maker.

[0017] Among them, the first fuzzy decision maker is an intelligent decision-making unit based on fuzzy logic. Its function is to perform fuzzy processing on the device temperature data, that is, to judge the current heat management requirements according to factors such as temperature gradient, heat distribution uniformity, and change rate, to avoid hysteresis or misjudgment problems caused by triggering strategies with a single temperature threshold. For example, when the local temperature of the device is higher than the set value but the overall heat uniformity is good, the first fuzzy decision maker can determine that there is no need for excessive cooling, but adopt a local fine-tuning strategy to ensure heat dissipation stability.

[0018] The second heat dissipation decision maker is a heat dissipation control unit based on an optimization algorithm or a decision tree model. Its core function is to comprehensively analyze the real-time heat map and historical heat management data and select the optimal heat dissipation strategy. For example, in a high heat load scenario, the second heat dissipation decision maker can dynamically adjust the fan speed, the working mode of the heat-conducting material, or the liquid cooling flow rate according to the temperature gradient of different heat source areas to ensure that the heat dissipation system operates in an optimal state.

[0019] In a feasible embodiment, according to the above functional requirements, the module architecture of the thermal conductivity decision module is constructed. The sample-driven training method is adopted, with direct sensing as the input, the decision logic is determined based on the above functions, and then it is trained through samples until convergence to obtain the constructed thermal conductivity decision module.

[0020] Through the gradual optimization of the above process, the heat dissipation system can adaptively adjust the heat dissipation strategy based on the real-time heat state and dynamic change trend, so as to improve the heat dissipation efficiency, reduce energy consumption, and extend the service life of the device.

[0021] Further, the thermal conduction decision module includes a first fuzzy discriminator, and the training unit 11 is further configured to perform the following steps: Determine the heat dissipation management requirements of the device end, where the heat dissipation management requirements include a heat value management dimension and a heat distribution management dimension; interact with the historical operation and maintenance data of the device, and mine the heat management threshold based on the heat dissipation management requirements; construct the first fuzzy discriminator according to the heat dissipation management requirements and the heat management threshold.

[0022] In the embodiment of the present application, to determine the heat dissipation management requirements of the device end, it is first necessary to analyze the heat characteristics generated during the operation of the device, and summarize the heat dissipation requirements under different working states to ensure that the heat dissipation strategy can effectively adapt to the dynamic heat load change of the device.

[0023] Among them, the heat dissipation management requirements can be defined from two key dimensions, namely the heat value management dimension and the heat distribution management dimension. Among them, the heat value management dimension refers to the quantitative evaluation of the temperature of the whole or a local area of the device, mainly focusing on the absolute value of the temperature and its change trend to ensure that the working temperature of the device does not exceed the safe range. For example, when a high-power chip is running, if the temperature exceeds a certain set value, active heat dissipation measures need to be taken to prevent performance degradation or hardware damage caused by overheating. The heat distribution management dimension refers to paying attention to the temperature uniformity of different areas on the surface and inside of the device to prevent local overheating and optimize the overall heat flow distribution. For example, in the server heat dissipation system, even if the temperature of a certain area does not exceed the limit value, if the temperature difference between this area and the surrounding areas is too large, it may cause uneven heat dissipation, thereby affecting the system stability. Therefore, the heat dissipation management requirements not only need to control the overall temperature level, but also need to optimize the spatial distribution of heat to make the heat gradient within a reasonable range.

[0024] Further, interact with the historical operation and maintenance data of the device to mine the heat management threshold based on the heat dissipation management requirements. The historical operation and maintenance data of the device refers to the data such as temperature changes, power consumption status, environmental factors, and the working conditions of heat dissipation components recorded during the long-term operation of the device. These data can provide long-term trend analysis and optimization basis for heat dissipation management.

[0025] Among them, the thermal management threshold refers to the critical value used to judge the heat dissipation state of the device, including the heat value threshold and the heat distribution threshold. Among them, the heat value threshold is used to limit the temperature range of the device under normal working conditions. For example, the highest safe temperature of the processor is set to 85°C. When the actual temperature approaches this threshold, the system needs to trigger the heat dissipation strategy; while the heat distribution threshold is used to measure the temperature difference in different areas of the device. For example, when the temperature of a local area exceeds the temperature of the adjacent area by more than 5°C, it is determined as a hot spot area, and local heat dissipation optimization measures need to be taken. Through in-depth analysis of historical operation and maintenance data, typical thermal management thresholds in different operating states can be summarized, providing accurate basic data support for the intelligent decision-making of the heat dissipation system.

[0026] Furthermore, based on the above heat dissipation management requirements and thermal management thresholds, the first fuzzy decision-making unit is constructed to realize the intelligent judgment of the heat dissipation state of the device. The first fuzzy decision-making unit refers to the use of a fuzzy logic reasoning mechanism to perform fuzzy analysis on the temperature state of the device to improve the flexibility and adaptability of heat dissipation decision-making. Among them, the core of fuzzy decision-making is to extend the temperature data from the traditional fixed threshold judgment to a fuzzy set, enabling the system to make intelligent judgments based on the temperature change trend, distribution characteristics, and environmental influencing factors.

[0027] For example, when the temperature of a certain area approaches but does not completely exceed the heat value threshold, the traditional heat dissipation system may not take measures, while the first fuzzy decision-making unit can combine historical data and the current temperature change rate to predict the future temperature rise trend and trigger appropriate heat dissipation measures in advance, thus avoiding performance loss or hardware damage caused by sudden temperature changes. In addition, the fuzzy decision-making unit can also combine the heat distribution management dimension to monitor the temperature uniformity of different areas of the device in real time and adjust the heat dissipation strategy according to the temperature gradient, such as dynamically adjusting the fan speed, optimizing the heat conduction path of the heat sink, or enabling the heat pipe conduction mechanism, etc., to ensure that the device operates in the best thermal management state.

[0028] In summary, by constructing the first fuzzy decision-making unit, the heat dissipation system can realize intelligent and dynamic thermal management optimization based on multi-dimensional thermal management requirements and historical operation and maintenance data, improve the heat dissipation efficiency, and enhance the long-term stability and reliability of the device.

[0029] The fuzzy decision-making unit 12 is used to connect to the state machine and embed the first fuzzy decision-making unit to obtain the real-time heat map and generate the thermal management instruction, where the state machine is connected to the device sensing side.

[0030] In the embodiments of the present application, the connection state machine is embedded in the first fuzzy decision-making unit. First, based on the thermal management requirements of the device, a state machine is constructed. The state machine is used to manage the operating state of the heat dissipation system and dynamically adjust the heat dissipation strategy according to the real-time temperature change of the device. In the embodiments of the present application, the state machine refers to a control model based on discrete state transitions, and its function is to determine the next operation according to the input environmental information and system state, so that the system can perform dynamic adjustment according to the preset logic.

[0031] In the heat dissipation system, the state machine can be divided into different thermal management states, such as normal heat dissipation state, warning state, high-temperature trigger state, and heat dissipation optimization state, to ensure the accurate execution of the heat dissipation strategy. For example, when the device is operating at low load, the state machine remains in the normal heat dissipation state and only performs basic heat dissipation; when the temperature gradually rises and approaches the set thermal management threshold, the state machine switches to the warning state and calls the first fuzzy decision-making unit for further analysis.

[0032] Furthermore, the state machine is embedded in the first fuzzy decision-making unit to make it the core unit for heat dissipation state decision-making. The first fuzzy decision-making unit is used to intelligently determine the thermal state of the device to avoid the lag or misjudgment of the heat dissipation strategy caused by the triggering of a single threshold. The traditional heat dissipation control system usually triggers based on a fixed threshold, that is, when the temperature exceeds the set value, heat dissipation measures are immediately taken, ignoring the trend and spatial distribution characteristics of the temperature change, which may lead to overly aggressive or inflexible heat dissipation strategies.

[0033] The first fuzzy decision-making unit, on the other hand, adopts a fuzzy logic reasoning method. Through the fuzzy processing of temperature data, combined with temperature gradient, heat distribution uniformity, and historical operation and maintenance data, it dynamically judges the heat dissipation requirements.

[0034] Among them, the real-time thermal map refers to the temperature distribution image generated by splicing the temperature data collected by the device sensors in combination with the relative spatial position, which is used to intuitively represent the thermal state of the device surface. To obtain the real-time thermal map, first, temperature sensing information needs to be obtained from the device sensing side. The device sensing side refers to the temperature sensor array deployed at various key positions of the device, and its function is to monitor the temperature change of each area of the device in real time. For example, in the heat dissipation system of a data center server, multiple temperature sensors are arranged at positions such as the CPU, power module, and heat dissipation channels to comprehensively sense the heat distribution of the device. The temperature data collected by these sensors is preprocessed and spatially mapped according to the internal structure of the device for subsequent thermal management decisions.

[0035] After obtaining the real-time thermal map, the state machine generates corresponding thermal management instructions based on the determination result of the first fuzzy decision-making unit to guide the heat dissipation system to execute specific heat dissipation strategies. Among them, the thermal management instruction refers to the trigger instruction for whether heat dissipation regulation needs to be executed.

[0036] In summary, by connecting the state machine and embedding the first fuzzy decision maker, the heat dissipation system can make intelligent thermal management decisions based on the real-time thermal map, ensuring that the heat dissipation strategy can effectively respond to changes in the device's thermal load and avoid situations of excessive energy consumption or overheat dissipation, thereby improving the thermal management efficiency of the system and optimizing the overall heat dissipation performance of the device.

[0037] Further, to obtain the real-time thermal map and generate a thermal management instruction, the fuzzy decision unit 12 is further configured to perform the following steps: Obtain the temperature sensing information on the sensing side of the device, and determine the real-time thermal map based on relative spatio-temporal position stitching; traverse the real-time thermal map, and perform an over-limit determination based on the thermal management threshold; if the thermal management threshold is satisfied, generate a first determination information, where the first determination information is a decision termination information.

[0038] In the embodiment of the present application, to obtain the temperature sensing information on the sensing side of the device, first, extract the temperature data from the sensing side of the device. The sensing side of the device refers to an array of temperature sensors deployed inside or on the surface of the device, and this array is used to monitor the temperature status of each area of the device in real time and provide accurate temperature data support. For example, in a high-performance computing device, sensors can be arranged at key positions such as the processor, storage module, power module, heat dissipation channel, and chassis shell to ensure the comprehensiveness and accuracy of the temperature data. The temperature sensors collect the temperature data at multiple time points through regular sampling and transmit the data to the heat dissipation management system for further thermal state analysis.

[0039] Further, after the temperature data acquisition is completed, determine the real-time thermal map based on relative spatio-temporal position stitching. Relative spatio-temporal position stitching refers to mapping the temperature data at different time points in space according to the physical layout of each sensor and constructing a continuous temperature distribution map. The core of this step lies in time synchronization and space alignment to ensure that the generated thermal map accurately reflects the thermal state of the device. For example, in a server heat dissipation management system, the temperature data of different components may be collected by different sensors at different times. Therefore, it is necessary to perform time synchronization on the data based on the timestamp information of the sensors to ensure the temporal consistency of the data. At the same time, by analyzing the device structure and the sensor arrangement positions, perform spatial mapping on the temperature data to generate a complete real-time thermal map. This thermal map can intuitively reflect the temperature distribution on the surface of the device, identify high-temperature areas and possible hot spot problems, providing an important reference basis for heat dissipation management.

[0040] After determining the real-time heat map, further traverse the heat map and perform out-of-limit determination based on the thermal management threshold. That is, analyze the temperature value at each position in the heat map point by point and compare it with the preset thermal management threshold to determine whether there is an abnormal temperature situation. The thermal management threshold refers to the critical value used to judge the thermal state of the device, including the heat value threshold and the heat distribution threshold.

[0041] Among them, during the out-of-limit determination process, if the thermal management threshold is met, the first determination information is generated, where the first determination information is a decision termination information. Among them, meeting the thermal management threshold means that the current temperature state of the device is within the normal range, that is, the temperatures of all sensing points do not exceed the heat value threshold, and the heat distribution uniformity meets the set requirements. In this case, there is no need to further perform heat dissipation adjustment, so the first determination information is generated to instruct the thermal management system to terminate the current heat dissipation decision-making process.

[0042] In summary, by obtaining the temperature sensing information on the sensing side of the device and constructing a real-time heat map based on the relative spatio-temporal position splicing, the system can accurately identify the thermal distribution state of the device; by traversing the real-time heat map and performing out-of-limit determination based on the thermal management threshold, the system can dynamically monitor the temperature change and determine whether to trigger heat dissipation measures; when the temperature state meets the set thermal management threshold, the first determination information is generated and the current heat dissipation decision-making process is terminated, thereby realizing precise and efficient thermal management optimization, ensuring stable heat dissipation of the device while avoiding unnecessary energy consumption and improving the operation efficiency of the overall heat dissipation system.

[0043] Furthermore, the fuzzy determination unit 12 is further configured to perform the following steps: If the thermal management threshold is not met, generate the second determination information, where the second determination information is a thermal management instruction; identify the second determination information, perform position mapping on the real-time heat map and the thermal management threshold, and locate the out-of-limit position; perform heat value difference calculation on the out-of-limit position to reconstruct and obtain the out-of-limit heat map; associate the out-of-limit heat map with the thermal management instruction.

[0044] Among them, the thermal management threshold includes a heat value threshold and a heat distribution threshold, and the temperature uniformity is used as the heat distribution index.

[0045] In the embodiment of the present application, if the thermal management threshold is not met, the second determination information is generated, that is, the current temperature state of the device has exceeded the preset safe range, or the heat distribution uniformity of the local area of the device has a too large deviation, and there is a local hot spot problem, and further heat dissipation adjustment needs to be performed. At this time, the system generates the second determination information, that is, the thermal management instruction, to instruct the heat dissipation system to perform corresponding heat dissipation measures.

[0046] Further, perform position mapping on the real-time heat map and the thermal management threshold to locate the overlimit positions. That is, perform spatial comparative analysis on the temperature data in the real-time heat map and the thermal management threshold to determine which areas have temperatures exceeding the allowable range. For example, assume that the real-time temperature in the processor core area reaches 90 °C, while the preset heat value threshold is 85 °C, then this area can be determined as an overlimit position. During the position mapping process, the system will compare all sensor data and combine the structural information of the device to accurately calibrate the specific area where overheating occurs, so as to implement targeted heat dissipation optimization measures.

[0047] Further analyze the temperature data at this position, that is, calculate the difference in heat value for the overlimit position and reconstruct to obtain the overlimit heat map. Here, calculating the difference in heat value means calculating the temperature difference between the actual temperature value at the overlimit position and its corresponding thermal management threshold to evaluate the degree of temperature overlimit. Subsequently, based on the calculation result of the difference in heat value, reconstruct the overlimit heat map, that is, reconstruct the heat map with the overlimit position and the overlimit value. This heat map represents the temperature distribution that needs to be adjusted, so that the heat dissipation system can formulate the optimal heat dissipation strategy accordingly.

[0048] After obtaining the overlimit heat map, further associate the overlimit heat map with the thermal management instruction to ensure the accuracy and pertinence of the heat dissipation adjustment strategy. The thermal management instruction needs to be combined with the overlimit heat map during execution to implement the execution instruction for local precise control.

[0049] In summary, by generating the second determination information and parsing the thermal management instruction, accurately identify the overlimit temperature area of the device; through the spatial mapping of the real-time heat map and the thermal management threshold, accurately locate the overlimit positions; by calculating the difference in heat value and reconstructing the overlimit heat map, visually present the situation of temperature overlimit; finally, by associating the overlimit heat map with the thermal management instruction, ensure the accurate execution of the heat dissipation measures. The entire process realizes efficient heat dissipation optimization through intelligent dynamic thermal management, improves the adaptive control ability of the heat dissipation system, and ensures the safe and stable operation of the device under high-performance working conditions.

[0050] The heat dissipation decision-making unit 13 is used to trigger the second heat dissipation decision maker according to the thermal management instruction, determine the heat dissipation strategy based on the heat dissipation regulation decision of the heat value and the heat distribution, and respond to the heat dissipation component to perform heat dissipation regulation management, where the second heat dissipation decision maker is embedded in the thermal management center.

[0051] In the embodiment of the present application, after receiving the thermal management instruction, the thermal management center immediately activates the second heat dissipation decision maker, so that it performs in-depth heat dissipation strategy analysis according to the current thermal state and historical data.

[0052] The second heat dissipation decision maker refers to an intelligent heat dissipation control unit embedded in the thermal management center. Its core function is to determine the heat dissipation strategy based on the heat dissipation regulation decision of the heat value and heat distribution. Among them, the heat value refers to the absolute temperature value of the device at present, which is used to evaluate whether the device exceeds the normal working range; the heat distribution refers to the temperature gradient in different areas of the device, which is used to judge the internal heat uniformity of the device to ensure that there is no local overheating phenomenon. The heat dissipation regulation decision refers to comprehensively analyzing the current heat value, heat distribution state and historical operation and maintenance data of the device to select the optimal heat dissipation strategy to ensure the stable temperature of the device and improve the heat dissipation efficiency.

[0053] In the process of determining the heat dissipation strategy, the second heat dissipation decision maker adopts an adaptive heat dissipation optimization algorithm, combines the historical data of thermal management and the real-time temperature feedback, and dynamically adjusts the heat dissipation measures.

[0054] Exemplarily, in a high-temperature load scenario, the second heat dissipation decision maker can select one of the following heat dissipation strategies: Fan speed regulation strategy: When the overall temperature of the device is close to the heat value threshold but the heat distribution is relatively uniform, the system can appropriately increase the fan speed to enhance the heat dissipation effect while avoiding the formation of local hot spots. Local heat conduction optimization strategy: If the temperature in a certain area is higher than other areas, but the overall temperature is still within the normal range, the system can adjust the heat pipe heat conduction direction or enable the phase change heat dissipation technology to optimize the local heat dissipation effect. Dynamic power consumption regulation strategy: If the device temperature continues to rise and the heat dissipation components have reached their maximum working capacity, the system can reduce the processor frequency or adjust the workload to reduce the heat generation and prevent the device from entering the overheat protection state.

[0055] After determining the heat dissipation strategy, it further responds to the heat dissipation components for heat dissipation regulation management. Among them, the heat dissipation components refer to the hardware units that perform heat dissipation tasks, including fans, heat pipes, heat sinks, liquid cooling systems, and phase change heat dissipation materials, etc. For example, in a data center server, the heat dissipation components include cabinet fans, cooling plates, and liquid cooling pipes, while in a mobile device, the heat dissipation components may include graphene heat dissipation films or liquid cooling pipes. The system sends control signals to the corresponding heat dissipation components according to the decision results of the second heat dissipation decision maker to adjust their working states.

[0056] In summary, by triggering the second heat dissipation decision maker, the heat dissipation system can dynamically adjust the heat dissipation strategy according to the heat value and heat distribution state; through the adaptive heat dissipation optimization decision, it can ensure that the heat dissipation solution can not only meet the thermal management requirements of the device but also reduce unnecessary energy consumption; finally, through the thermal management center coordinating the operation of each heat dissipation component, it realizes precise and efficient heat dissipation management and improves the overall heat dissipation performance and stability of the device.

[0057] Further, to determine the heat dissipation strategy, the heat dissipation decision unit 13 is further used to perform the following steps: Receive the thermal management instruction and activate the second heat dissipation decision maker embedded in the thermal management center; import the over-limit heat map into the second heat dissipation decision maker, use the over-limit heat map as the heat dissipation adjustment target, make greedy decisions, and determine the heat dissipation strategy.

[0058] In the embodiment of the present application, receive the thermal management instruction and activate the second heat dissipation decision maker embedded in the thermal management center. After receiving the thermal management instruction, immediately activate the second heat dissipation decision maker embedded inside the thermal management center. Among them, the thermal management center is the core module responsible for global thermal management, and its main function is to monitor the temperature status of the device in real time and dynamically adjust the heat dissipation strategy to optimize the thermal management efficiency. The second heat dissipation decision maker is an intelligent heat dissipation control unit embedded in the thermal management center, and its role is to comprehensively analyze the heat dissipation requirements based on the thermal state data and generate the optimal heat dissipation strategy.

[0059] Furthermore, import the over-limit heat map into the second heat dissipation decision maker to ensure that the heat dissipation decision takes targeted measures for the over-limit heat map. Specifically, use this heat map as the heat dissipation adjustment target, make greedy decisions, and determine the optimal heat dissipation strategy. Among them, the heat dissipation adjustment target refers to the core target of heat dissipation optimization, including reducing the temperature of the over-limit area, improving the uniformity of the device's heat distribution, and reducing the overall power consumption. To achieve the optimal heat dissipation strategy, the second heat dissipation decision maker uses the greedy decision algorithm to gradually optimize the heat dissipation parameters to approach the global optimum on the premise of local optimum. For example, if the over-limit heat map marks the processor temperature over-limit area, the system can adopt a greedy strategy to first adjust the fan speed to reduce the overall temperature, and then optimize the heat conduction path to evenly spread the heat to the fuselage to prevent local hot spots from overheating.

[0060] In summary, by receiving the thermal management instruction and activating the second heat dissipation decision maker embedded in the thermal management center, the system can dynamically sense the thermal state of the device and make intelligent heat dissipation decisions; by importing the over-limit heat map, the heat dissipation strategy optimization is based on accurate thermal data support; finally, through the greedy decision method, quickly determine the optimal heat dissipation strategy and execute targeted heat dissipation adjustment measures to improve the overall response speed, energy-saving efficiency and device stability of the heat dissipation system.

[0061] Furthermore, after the heat dissipation component performs heat dissipation regulation management, the system is also used to execute the following steps: Along with the execution of the heat dissipation strategy, synchronously trigger the sensing array, perform heat dissipation response perception, and determine the response heat map; based on the response heat map, perform deviation determination based on the temperature adjustment trend and adjustment amount, and perform feedback management on the heat dissipation regulation.

[0062] In the embodiments of the present application, along with the execution of the heat dissipation strategy, the sensing array is synchronously triggered to perform heat dissipation response perception. Herein, the heat dissipation strategy refers to a heat dissipation optimization plan formulated by the second heat dissipation decision maker based on the overlimit heat map and the device heat management requirements. During the execution of the heat dissipation strategy, in order to monitor the heat dissipation effect in real time, the system synchronously triggers the sensing array deployed on the device to collect the latest data on the device temperature status.

[0063] Furthermore, the temperature change data after the execution of the heat dissipation strategy is collected by the sensor, and the heat dissipation effect is evaluated based on this data. Preferably, to ensure the timeliness of the data, the sensing array usually adopts a periodic sampling or event-triggered sampling mode. For example, data is collected once per second, or data is automatically collected when the device temperature change exceeds a preset threshold. Through heat dissipation response perception, the system can grasp the execution situation of the heat dissipation strategy in real time and provide data support for subsequent optimization and adjustment.

[0064] The response heat map is further determined, that is, the heat distribution map generated by the temperature data collected by the sensing array after the execution of the heat dissipation strategy. This heat map is used to intuitively reflect the heat dissipation effect. By analyzing the response heat map, the effectiveness of the current heat dissipation strategy can be evaluated, and data support can be provided for subsequent heat dissipation optimization. According to the response heat map, a deviation determination based on the temperature adjustment trend and the adjustment amount is performed. Herein, the temperature adjustment trend refers to the overall change trend of the device temperature after the execution of the heat dissipation strategy, and the deviation of the adjustment amount refers to the difference between the actual temperature change amplitude and the expected heat dissipation effect. For example, in a server heat dissipation system, if the goal of the fan speed regulation strategy is to reduce the rack temperature by 10 °C, but the response heat map shows that the actual temperature only drops by 5 °C, it indicates that the heat dissipation effect does not meet the expectation, there is an adjustment amount deviation, and the heat dissipation strategy needs to be further optimized.

[0065] After completing the analysis of the temperature adjustment trend and the adjustment amount deviation, further feedback management of the heat dissipation regulation is performed, that is, based on the execution effect of the heat dissipation strategy, the current heat dissipation strategy is adjusted and optimized to improve the adaptive ability of the heat dissipation system.

[0066] In summary, by synchronously triggering the sensing array and performing heat dissipation response perception, the system can monitor the execution effect of the heat dissipation strategy in real time; by obtaining the response heat map and analyzing the deviation of the temperature adjustment trend and the adjustment amount, the system can accurately evaluate the effectiveness of the current heat dissipation strategy; finally, through the feedback management mechanism, the heat dissipation system has the ability of adaptive optimization, ensuring that the device can maintain a stable temperature under the high-performance operation state, and further improving the overall heat dissipation efficiency and energy consumption management level.

[0067] A heat dissipation system provided by the present application has the following technical effects: 1. Adopt a hierarchical structure of a sensing array - heat conduction decision - making module - heat dissipation component - state machine to achieve precise perception, intelligent decision - making, and efficient heat dissipation execution for thermal management. It can dynamically adjust the heat dissipation strategy to adapt to different thermal load environments, improve the heat dissipation efficiency, and reduce energy consumption.

[0068] 2. Collect device temperature data through a multi - point distributed temperature sensor and splice them based on relative spatio - temporal positions to generate a real - time thermal map. Achieve high - precision temperature monitoring to ensure that the heat dissipation strategy is dynamically adjusted according to the real - time thermal state.

[0069] 3. Deploy the first fuzzy discriminator in the state machine for front - end perception and fuzzy discrimination to determine whether heat dissipation control needs to be executed and determine the heat dissipation requirements. Deploy the second heat dissipation decision - making device in the thermal management center. When there is a heat dissipation requirement, the first fuzzy discriminator generates an instruction to trigger the second heat dissipation decision - making device to execute heat dissipation decision - making processing, making the heat dissipation control process more orderly, improving the device's response speed to complex heat dissipation requirements, and avoiding overheating or excessive heat dissipation.

[0070] 4. After the heat dissipation strategy is executed, collect the response thermal map through the sensing array, calculate the temperature adjustment trend and adjustment amount deviation, analyze the heat dissipation optimization effect, and dynamically adjust the heat dissipation strategy. Achieve closed - loop control to ensure continuous improvement of the heat dissipation efficiency during long - term operation.

[0071] Embodiment 2: Based on the same inventive concept as a heat dissipation system in the foregoing embodiment, as Figure 2 shown, the present application provides a heat dissipation device, and the heat dissipation device includes a heat conduction decision - making module, a heat dissipation component, a sensing array, and a state machine; Among them, the sensing array is deployed on the sensing side of the device to sense the temperature state of the device; the heat dissipation component is used for heat dissipation execution; the heat conduction decision - making module includes a first fuzzy discriminator and a second heat dissipation decision - making device; the state machine has a first fuzzy discriminator embedded therein. One end of the state machine is connected to the sensing array, and one end is connected to the second heat dissipation decision - making device embedded in the thermal management center; the other end of the second heat dissipation decision - making device is connected to the heat dissipation component.

[0072] The heat dissipation device includes a heat conduction decision - making module, a heat dissipation component, a sensing array, and a state machine. Among them, each component module cooperates with each other to achieve intelligent heat dissipation management of the device. The heat conduction decision - making module is responsible for analyzing the thermal state of the device and formulating a heat dissipation strategy. The heat dissipation component is used for specifically executing heat dissipation measures. The sensing array is used for real - time sensing of the temperature state of the device, and the state machine is used for managing the logical execution process of each heat dissipation control unit to ensure that the heat dissipation system can dynamically adapt to the device operation environment and achieve high - performance heat dissipation regulation.

[0073] Among them, the sensing array is deployed on the sensing side of the device to sense the temperature state of the device. The sensing side of the device refers to the area inside or on the surface of the device for temperature monitoring, which usually includes key positions such as chip modules, storage modules, power modules, heat dissipation channels, and the body shell. The sensing array includes multiple distributed temperature sensors, which adopt periodic sampling or event-triggered sampling methods to collect temperature data of different areas of the device in real time and provide heat distribution information.

[0074] The heat dissipation component is used to perform heat dissipation execution, that is, to specifically implement corresponding heat dissipation measures according to the heat dissipation strategy generated by the heat conduction decision module. The execution logic of the heat dissipation component is controlled by the heat conduction decision module to ensure accurate heat dissipation adjustment and energy consumption optimization.

[0075] The heat conduction decision module includes a first fuzzy discriminator and a second heat dissipation decision maker. Among them, the first fuzzy discriminator is mainly responsible for preliminarily judging the heat management requirements of the device, while the second heat dissipation decision maker makes an optimization decision on the heat dissipation strategy according to the specific heat state.

[0076] The state machine is embedded with a first fuzzy discriminator for dynamically managing the heat dissipation control logic, enabling the system to adjust the heat dissipation mode according to the operating state of the device. The state machine refers to a control logic module based on discrete state transitions, which is used to manage the operating state of the heat dissipation system and dynamically adjust the heat dissipation strategy according to the heat management requirements of the device.

[0077] One end of the state machine is connected to the sensing array, and the other end is connected to the second heat dissipation decision maker embedded in the heat management center. Among them, the connection end of the sensing array is used to receive real-time temperature data and transmit the temperature information to the first fuzzy discriminator for preliminary determination. The connection end of the second heat dissipation decision maker is used to activate the second heat dissipation decision maker to execute higher-level heat dissipation optimization measures when the heat dissipation strategy needs to be adjusted. The other end of the second heat dissipation decision maker is connected to the heat dissipation component, which is used to control the heat dissipation component to perform corresponding heat dissipation operations according to the heat dissipation decision result.

[0078] In summary, by integrating a heat conduction decision module, a heat dissipation component, a sensing array, and a state machine in the heat dissipation device, the system can sense the temperature state of the device in real time and dynamically adjust the heat dissipation strategy according to the heat dissipation requirements; through the collaborative work of the first fuzzy discriminator and the second heat dissipation decision maker, it is ensured that the heat dissipation control can not only meet the heat management requirements of the device but also optimize the heat dissipation energy consumption; finally, through the logical control of the state machine, the intelligent and adaptive adjustment of the heat dissipation system is realized to ensure that the device maintains a stable temperature under efficient operation and improve the accuracy and reliability of the overall heat dissipation management.

[0079] Through the foregoing detailed description of a heat dissipation system, those skilled in the art can clearly understand a heat dissipation system and device in this embodiment. For the device disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple. For related parts, reference can be made to the description of the system part.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A heat dissipation system, characterized in that, The system includes: A training unit, configured to obtain the structure of the heat dissipation component and the equivalent thermal resistance network, and supervise and train a thermal conductance decision module, where the thermal conductance decision module includes a first fuzzy discriminator and a second heat dissipation decision maker; A fuzzy determination unit, configured to connect to a state machine and embed the first fuzzy discriminator, obtain a real-time thermal map, and generate a thermal management instruction, where the state machine is connected to the device sensing side; A heat dissipation decision unit, configured to trigger the second heat dissipation decision maker according to the thermal management instruction, make a heat dissipation regulation decision based on the heat value and heat distribution, determine a heat dissipation strategy, and perform heat dissipation regulation management in response to the heat dissipation component, where the second heat dissipation decision maker is embedded in the thermal management center.

2. The heat dissipation system according to claim 1, wherein, The training unit includes: A requirement determination unit, configured to determine the heat dissipation management requirements of the device end, where the heat dissipation management requirements include a heat value management dimension and a heat distribution management dimension; A mining unit, configured to interact with the device historical operation and maintenance data and mine the thermal management threshold based on the heat dissipation management requirements; A construction unit, configured to construct the first fuzzy discriminator according to the heat dissipation management requirements and the thermal management threshold.

3. The heat dissipation system according to claim 2, wherein The fuzzy determination unit includes: A thermal map determination unit, configured to obtain the temperature sensing information of the device sensing side and determine the real-time thermal map based on the relative spatio-temporal position splicing; An overlimit determination unit, configured to traverse the real-time thermal map and perform overlimit determination based on the thermal management threshold; A first determination unit, configured to generate first determination information if the thermal management threshold is satisfied, where the first determination information is a decision termination information.

4. The heat dissipation system according to claim 3, wherein, The fuzzy determination unit includes: A second determination unit, configured to generate second determination information if the thermal management threshold is not satisfied, where the second determination information is a thermal management instruction; A positioning unit, configured to identify the second determination information, perform position mapping on the real-time thermal map and the thermal management threshold, and locate the overlimit position; A reconstruction unit, configured to perform heat value difference calculation on the overlimit position and reconstruct and obtain an overlimit thermal map; An association unit, configured to associate the overlimit thermal map with the thermal management instruction.

5. The heat dissipation system according to claim 2, characterized in that, The thermal management threshold includes a heat value threshold and a heat distribution threshold, and the temperature uniformity is used as the heat distribution index.

6. The heat dissipation system according to claim 4, wherein, The heat dissipation decision unit includes: An activation unit, configured to receive the thermal management instruction and activate the second heat dissipation decision maker embedded in the thermal management center; A strategy determination unit, configured to import the overlimit thermal map into the second heat dissipation decision maker, take the overlimit thermal map as the heat dissipation adjustment target, perform greedy decision, and determine the heat dissipation strategy.

7. The heat dissipation system according to claim 1, characterized in that, The system further includes: A response perception unit, configured to synchronously trigger a sensing array along with the execution of the heat dissipation strategy, perform heat dissipation response perception, and determine a response thermal map; A feedback management unit, configured to perform deviation determination based on the temperature adjustment trend and the adjustment amount according to the response thermal map, and perform feedback management on the heat dissipation regulation.

8. A heat dissipation device, characterized in that, For performing the steps of a heat dissipation system according to any one of claims 1-7, the heat dissipation device includes a thermal conduction decision module, a heat dissipation component, a sensing array, and a state machine; Among them, the sensing array is deployed on the sensing side of the device and is used to sense the temperature state of the device; the heat dissipation component is used to perform heat dissipation; the thermal conduction decision module includes a first fuzzy decision maker and a second heat dissipation decision maker; the state machine is embedded with the first fuzzy decision maker, one end of the state machine is connected to the sensing array, and one end is connected to the second heat dissipation decision maker embedded in the thermal management center; the other end of the second heat dissipation decision maker is connected to the heat dissipation component.

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