42 results about "Thermal aware" patented technology

The invention discloses a server BMC collaborative optimization method and system based on heat perception and energy consumption prediction, relates to the technical field of server hardware management and data center energy saving, and discloses the server BMC collaborative optimization method and system based on heat perception and energy consumption prediction. Comprising the steps that a heat-energy coupling model is established through multi-dimensional data collection and fusion analysis, accurate perception is achieved through three-dimensional heat distribution calculation and LSTM prediction, heat dissipation and power supply parameters are dynamically adjusted in combination with a multi-target optimization algorithm, a closed-loop feedback mechanism is formed, and the energy efficiency ratio of a server can be increased. And dynamic collaborative optimization of heat dissipation control and energy consumption management can be realized, the heat dissipation efficiency of the server is improved, and the service life of hardware is prolonged.

The invention discloses a condenser dynamic heat exchange regulation and control method and system based on multi-dimensional perception and AI, and relates to the technical field of intelligent heat exchange regulation and control. The condenser dynamic heat exchange regulation and control method and system based on multi-dimensional perception and AI comprises the steps that S1, condenser heat exchange perception data are collected and preprocessed; s2, performing two rounds of data screening on the sampling points, realizing emergency regulation and control instruction issuing and abnormal point data processing, and constructing an effective condenser operation data set; s3, the heat exchange adaptation degree of the condenser is evaluated, the load state category is recognized, and a regulation and control instruction is output; and S4, the regulation and control instruction is executed, execution feedback data is collected, the heat exchange execution performance of the condenser is evaluated, whether the current regulation and control instruction needs to be adjusted or not is judged based on the evaluation result, and the execution feedback data and the evaluation result are returned for threshold value updating and regulation and control strategy optimization. The problems that due to fixed heat exchange control of the condenser, phase change is limited under load change, and energy consumption is too high are solved.

The invention discloses a multi-core processor dynamic regulation and control method and system based on load and temperature sensing, and relates to the technical field of temperature control scheduling management, and the method comprises the steps: collecting and preprocessing load and temperature data in real time, and building a thermal sensing model to predict the temperature change; constructing a self-adaptive heuristic scheduling algorithm, and adjusting task distribution according to real-time data of the core load and the temperature; according to the load and temperature states, a dynamic voltage frequency adjustment strategy is adopted, and the frequency and the voltage are automatically adjusted when the load and the temperature are too high; and when the load and the temperature exceed set threshold values, a scheduling algorithm is optimized by adopting a self-learning mechanism, and a scheduling strategy and a temperature control scheme are automatically adjusted. According to the method, a more flexible and accurate dynamic regulation and control scheme is provided, the stability and high efficiency of the processor in a high-load and high-temperature environment can be ensured, performance reduction and hardware damage are avoided, and the obvious effects of saving energy and improving system stability are achieved.

The invention discloses an electromagnetic heating system, and a method and a device for detecting the thermistor of the electromagnetic heating system. The method comprises the following steps of: recording the temperature value obtained by the thermistor as a first temperature value when the electromagnetic heating system is powered on; recording the temperature value obtained by the thermistoras a second temperature value when the electromagnetic heating system is in a standby state; obtaining the maximum temperature value and the minimum temperature value of the temperature value obtainedby the thermistor in a continuously preset second preset time every other first preset time during the heating process of the electromagnetic heating system; and judging whether the thermistor is ina failure state or not according to the first temperature value and / or the second temperature value, the maximum temperature value, and the minimum temperature value. The method for detecting the thermistor of the electromagnetic heating system judges whether the thermistor fails or not according to the static temperature values at the time of power-on and standby and the dynamic temperature valuein the heating process, so that the accuracy and the reliability of the judgment are greatly improved, and the safety of the system is improved.

Systems and methods for thermal-aware network selection in an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive telemetry data from an Embedded Controller (EC) coupled to the processor; and in response to a determination that a policy is met based, at least in part, upon the telemetry data, modify a network communication of a pre-boot application.

The present application discloses a heat dissipation system, a node server, a heat dissipation method, a device and a medium, and relates to the field of server technology. The heat dissipation array is composed of mobile heat dissipation components connected by magnetic attraction, and is positioned on the surface of the device to be cooled. The thermal sensing device is connected to the controller, and the controller is connected to the wireless power transmitting array. The controller determines the heat dissipation path based on the heat dissipation performance parameters and the power distribution strategy of the heat dissipation device, and performs power distribution processing on the wireless power transmitting array. The heat dissipation array knows the heat dissipation path constructed by the mobile heat dissipation component required by the device to be cooled by the amount of electric energy, so as to dissipate the heat of the components to be cooled. In order to solve the technical problems of reduced heat dissipation efficiency and low space utilization caused by the fixed position of the heat dissipation module, limited hardware layout and loss of cable power supply, the flexible movement of the magnetic connection of the mobile heat dissipation component and the wireless receiving method are achieved, which saves space utilization while improving the heat dissipation efficiency.

The application discloses a method for unmanned aerial vehicle assisted thermal sensing MEC network task scheduling and resource allocation. In the case of considering CPU thermal constraints, the cruising time of the unmanned aerial vehicle is minimized by jointly optimizing user admission policy, task scheduling, unmanned aerial vehicle hovering trajectory, flight time, and computing and communication resource allocation. The optimization problem is further decomposed into three sub-problems and iteratively processed in order. Simulation results show that the method has good performance improvement effect compared with other algorithms.

The invention provides a thermal-sensing hybrid task workflow collaborative scheduling method and system in a software defined satellite environment. The method comprises the following steps: acquiring a user request containing a plurality of sub-tasks, and establishing a dependency relationship among the sub-tasks based on an execution time sequence of each sub-task; allocating available time windows for the hardware sub-tasks in the user request, and organizing the software sub-tasks depending on the same hardware sub-task into an ordered batch topology; and obtaining performance parameters of the system when the hardware sub-tasks execute user requests in different time windows and / or the software sub-tasks are in different batch topologies, and obtaining an optimal sub-task scheduling strategy based on the performance parameters. According to the method, overheating and overload risks are actively avoided in the planning stage; and meanwhile, a task arrangement structure is collaboratively optimized, so that the traditional contradiction between high throughput and thermal safety is broken, and the utilization efficiency of satellite resources under complex constraints and the overall efficiency of the system are remarkably improved.

The invention provides a reversible thermochromic insulating material for thermal sensing of electrical equipment and a preparation method thereof, the insulating material comprises the following raw materials by weight: 19.2 to 25.6 parts of a core material emulsion, 6.2 to 8.8 parts of thermosensitive microcapsule powder, 173.8 to 186.2 parts of a base material, 92 to 108 parts of an auxiliary additive, and 4.5 to 6.2 parts of an emulsifier; the core material emulsion comprises the following components in parts by weight of the total components of the insulating material: 0.3-0.4 part of a composite color former, 18-24 parts of modified octadecanol and 0.9-1.2 parts of bisphenol A, and the modified octadecanol contains silicon-oxygen bonds. The core material emulsion is a core basis for realizing a thermochromic function and provides color change capability responding to temperature for the material, and the thermosensitive microcapsule powder plays roles in protecting the core material and maintaining thermochromic stability, ensures that the core material normally plays a role in a complex environment, assists the base material to enhance insulativity and mechanical support, and improves the thermochromic performance of the material. All the components are matched to guarantee stable exertion of the thermochromic function in nuclear power equipment.

The invention discloses an infrared thermal imaging diagnosis system and method based on a converter station intelligent gateway, and the system comprises an infrared thermal imaging collection unit which is used for collecting the image data of a core thermal load region through arranging a visual and infrared thermal sensing terminal in the core thermal load region of to-be-monitored equipment, sending the data to the edge computing intelligent gateway; the edge computing intelligent gateway is deployed in a core thermal load area, and the edge computing intelligent gateway is used for preprocessing the image data and performing preliminary judgment on the preprocessed image data to obtain a preliminary thermal analysis result of the image data; generating alarm information based on the preliminary thermal analysis result, and sending the alarm information to an operation and maintenance management platform; the operation and maintenance management platform is used for fusing the image data in the alarm information with the multi-source data to generate fused data; and the operation and maintenance management platform is used for analyzing the fusion data through a deep learning image recognition model and outputting a diagnosis result of the to-be-monitored equipment.

The invention discloses a silicon bridge interconnection core particle thermal sensing layout method based on reinforcement learning. The method comprises the following steps: S1, receiving a configuration file containing a core particle set, a size, an identifier and a silicon bridge connection relation network; s2, constructing a reinforcement learning environment, setting the size of a substrate and the size of a grid, and generating a fixed placement sequence of core particles based on a connection relationship; s3, determining legal candidate positions in sequence, sampling a preset area of the substrate in an empty layout, taking an intersection of candidate positions of adjacent core particles in a non-empty layout, and filtering illegal positions; s4, the intelligent agent selects an action containing a placement coordinate and a rotation state from the legal action set for execution; s5, calculating a dual-stage reward, guiding a legal layout, and optimizing thermal performance, area and bus length; s6, neural network parameters are updated based on a reinforcement learning algorithm; and S7, circulating the steps S3-S6 until the termination condition is met, and outputting the optimal layout meeting the core constraint. According to the method, the exploration space is effectively reduced, the solving efficiency is improved, and the cooperation of the silicon bridge interconnection reliability and multi-objective optimization is realized.

The invention discloses a GPU (Graphic Processing Unit) chip temperature sensing thread bundle management method, relates to the technical field of GPU chip application, and solves the problems of low efficiency of a thermal management strategy and the like caused by the fact that a thermal prediction method in the prior art cannot cover hot spots of a whole chip, is high in modeling complexity and poor in real-time performance. The thermal prediction model is used for predicting the hot spot temperature of the chip in the running process of the graphics processor; the TAWS scheduling module is constructed based on a two-stage Warp scheduling strategy and is used for comparing the hot spot temperature with a preset maximum temperature threshold value and selectively executing a normal scheduling mode or a temperature control scheduling mode; the temperature of the chip is regulated and controlled; and when the duration of the high-temperature state exceeds the preset maximum allowable duration, the TAWS scheduling module triggers a scheduling freezing mechanism to realize safety control of the chip temperature. According to the method, cross-architecture high-precision generalization, a low-overhead instruction-level thermal sensing scheduling mechanism, refined analysis of instruction-level thermal characteristics and the like are realized.

The invention discloses a thermal sensing core particle layout method for silicon bridge interconnection, and relates to the technical field of packaging. According to the method, a multi-core particle layout optimization problem oriented to silicon bridge interconnection is formalized into a normalized mathematical programming model. Based on an existing mature mathematical solution method, a legal layout scheme meeting the silicon bridge interconnection constraint is rapidly obtained. Meanwhile, solution constraints are eliminated, differentiated legal layout solution sets can be generated through the constraints, then systematic thermal characteristic evaluation is carried out on different layout schemes, and finally a silicon bridge interconnection-oriented multi-core-particle layout scheme considering interconnection constraints and thermal performance optimization is obtained.

The invention belongs to the technical field of thermoforming of plastic vacuum forming machines, and provides a thermoforming energy-saving system and method for a plastic vacuum forming machine based on double-station time sequence collaboration.The thermoforming energy-saving method comprises the steps that a heat needing sensing system is constructed through a heating station inlet sensor, heating partitions are divided, the heat needing state of a sheet is dynamically matched, and an outlet sensor conducts real-time feedback and correction; relying on the process parameter knowledge base and the energy consumption model, a heating strategy is optimized through a genetic algorithm; a fault judgment mechanism is introduced, a standby heating pipe is rapidly switched when a fault occurs, a double-station cooperation mechanism is synchronously established, and power pre-compensation of a rear station is achieved through temperature deviation transmission of a front station; a digital twin management module is constructed, faults and time sequence deviation are actively predicted through a long-short-term memory network in combination with historical data, a forming time sequence is accurately regulated and controlled, the double-station precision consistency and the energy utilization rate are improved, and production continuity is guaranteed.

A method includes identifying a structure of interest in an IC design; assigning a heat affected region to a region of the IC design, the region containing at least a portion of the structure of interest; identifying a plurality of structures, all or part of which are located in the heat affected zone, and which correspond to IC device elements that are self-heated during operation of the IC device based on the IC design; modeling a corresponding plurality of operating temperatures for the plurality of structures, and identifying a highest temperature of the plurality of operating temperatures; identifying, among the plurality of structures, a structure having the highest temperature as a first adjacent structure; specifying a rating factor according to a distance between the structure of interest and the first adjacent structure; and modeling an operating temperature of the structure of interest according to the maximum temperature and the rating factor.

This invention discloses a dynamic heat exchange control method and system for condensers based on multi-dimensional sensing and AI, belonging to the field of intelligent heat exchange control technology. The method and system include: S1, collecting condenser heat exchange sensing data and preprocessing the data; S2, performing two rounds of data filtering on sampling points to issue emergency control commands and process abnormal data, constructing an effective condenser operation dataset; S3, evaluating the condenser's heat exchange adaptability, identifying load state categories, and outputting control commands; S4, executing the control commands and collecting execution feedback data, evaluating the condenser's heat exchange performance, determining whether the current control command needs adjustment based on the evaluation results, and transmitting the execution feedback data and evaluation results back for threshold updates and control strategy optimization. This solves the problems of limited phase change and excessive energy consumption under load changes caused by fixed condenser heat exchange control.

The invention relates to a point cloud thermal prediction method for a large-scale unstructured 2.5 D integrated circuit. The network comprises three core modules: an adaptive multi-path coupling diffusion module (AMD), a wavelet-based fine-grained recovery module (WFR) and a thermal sensing material expert hybrid adapter (TA-MoME). The AMD module adaptively learns interaction of a thermal diffusion path through an attention mechanism based on serialized gating; the WFR module recovers fine-grained thermal gradient features by using a high-frequency wavelet domain enhancement technology; and the TA-MoME adapter dynamically routes the material specific expert to adapt to the heat distribution difference between the heterogeneous materials. Particularly, the framework also introduces a continuous learning mechanism with efficient parameters to ensure that when the model adapts to a new geometric scale, catastrophic forgetting can be prevented, and cross-scale adaptation and mobility can be realized. Experimental results show that in 80K-scale point cloud prediction, compared with an existing FSA-Heat method, the average precision index is remarkably improved (errors are reduced by about 78% or above), compared with commercial COMSOL, acceleration of 147 times is achieved, and the generalization ability for larger-scale nodes (0.4 M) and zero samples without geometrical shapes is shown.

Hot-aware interrupt routing in an interrupt controller in a processor-based system and related methods are disclosed. The processor-based system includes one or more interrupt controllers each configured to prioritize interrupts received from components in the processor-based system and direct each received interrupt to a CPU core in a designated processor of the interrupt controller to execute an interrupt service routine (ISR) to process the interrupt. To avoid or reduce the likelihood that the interrupt controller directs an interrupt to a CPU core and / or CPU core cluster that can exceed its thermal limit by accepting and handling the interrupt, the interrupt controller is configured to be aware of the temperature of the CPU cores and / or CPU core clusters in its designated processor. The interrupt controller is configured to selectively route a received interrupt based on the temperature of the CPU core and / or its CPU core cluster that is determined to be eligible to receive and handle the interrupt.

The invention provides a non-contact thermal sensing system and method, and belongs to the technical field of robot sensing and flexible electronics. In order to solve the problems that physical contact is needed for temperature sensing of an existing robot, response is slow, and a heat source cannot be pre-positioned, the system comprises a flexible temperature sensor array composed of sensor units containing two-dimensional material film temperature-sensitive layers, and the flexible temperature sensor array has millisecond-level thermal response time; a non-thermal sensor; and a processor. The processor is configured to: acquire temperature data of the array and signals of the non-thermal sensors; judging whether a heat source approaches without physical contact based on the two signals; in response to the determination, a spatial location of the heat source is calculated based on a temperature distribution formed over the array. According to the invention, by fusing the multi-mode signals, rapid and non-contact pre-sensing and spatial positioning of the heat source are realized, and the operation safety of the robot is improved.

The invention discloses a global sensing type intelligent temperature control cable and a preparation method thereof, and the cable comprises a conductor, an inner insulating layer, an intelligent sensing response layer, an outer insulating layer, a fireproof layer, an electromagnetic shielding layer and a sheath layer, and the intelligent sensing response layer comprises a polyimide composite belt, a sensing micropipe and a liquid cooling micropipe. Perfluoropolyether cooling liquid and gallium-indium-tin liquid metal micro-droplets are filled in the sensing micro-tubes, high-thermal-conductivity nano-fluid is filled in the liquid-cooling micro-tubes, and the sensing micro-tubes and the liquid-cooling micro-tubes form a sensing channel and a temperature control channel through parallel design respectively; inlets and outlets of the sensing channel and the temperature control channel are arranged at the end part of the cable and are integrated with a double-cavity micro piezoelectric pump and a double-cavity micro radiator to form a sensing array and a temperature control array, and an embedded edge controller electrically connected with a sensing micro tube is integrated in the double-cavity micro piezoelectric pump. According to the invention, transmission, heat dissipation and sensing are integrated, and accurate positioning of various faults such as extrusion, cutting, overheating and the like is realized.

The invention discloses a heat sensing data center energy consumption and service life optimization method and system based on the health degree and a storage medium thereof, and belongs to the technical field of data center energy saving and equipment service life management. A sustainable updating server health degree scoring model is constructed, a temperature fluctuation memory window is introduced to describe a thermal fatigue effect, IT energy consumption, cooling energy consumption and life loss cost converted from health degree reduction are uniformly incorporated into a multi-time-period joint optimization model, and a two-stage time scale scheduling strategy is adopted to optimize the health degree of a server. On the premise that service requirements and temperature safety are guaranteed, the server operation environment and the load state are guided to evolve towards the direction of low energy consumption, low temperature impact and low failure rate, and collaborative optimization of the total energy consumption of the data center and the service life of the server is achieved. On the premise of meeting business requirements and temperature safety, comprehensive energy consumption is reduced, health degree attenuation is slowed down, the service life of the server is prolonged, and long-term operation stability is improved.

The application discloses a server BMC cooperative optimization method and system based on heat sensing and energy consumption prediction, relates to the technical field of server hardware management and data center energy saving, and discloses a server BMC cooperative optimization method and system based on heat sensing and energy consumption prediction, which comprises the following steps: a heat-energy coupling model is established through multidimensional data acquisition and fusion analysis; precise sensing is realized by adopting three-dimensional heat distribution calculation and LSTM prediction; heat dissipation and power supply parameters are dynamically adjusted in combination with a multi-objective optimization algorithm; a closed-loop feedback mechanism is formed; the energy efficiency ratio of the server can be improved; in addition, dynamic cooperative optimization of heat dissipation control and energy consumption management, improvement of server heat dissipation efficiency and prolongation of the service life of hardware can be realized.

The application discloses a kind of laser cutting machine rail searching path planning optimization method and system, it is related to laser cutting machine path optimization technical field, specifically includes the following steps: when the prediction result is that there is heat distribution overlap, all path groups with this condition are screened out, and it is calibrated as to be adjusted cutting path group;Real-time acquisition of the thermal sensitive characteristic information of each group of to-be-adjusted cutting path group, and after acquisition, analysis is carried out, the heat interference intensity generated by each group of to-be-adjusted cutting path group under current order is evaluated, and each group of to-be-adjusted cutting path group is divided into low heat interference path group, medium heat interference path group and high heat interference path group according to the evaluation result;According to the division result, corresponding cutting path order optimization measures are respectively executed.The application solves the problem that path heat overlap cannot be dynamically avoided, realizes intelligent optimization of cutting order based on heat sensing and accurate control of heat interference.

This invention provides a temperature derating method in thermally sensed electromigration (EM) analysis, which may include identifying a heat-sensitive structure, a first heat-generating structure, and a first heat-dissipating structure. The temperature derating method includes adjusting the evaluation temperature of the heat-sensitive structure by adjusting the operating temperature of the first heat-generating structure, and calculating the temperature increase of the heat-sensitive structure.

A processor-implemented method for thermal aware performance / power management for concurrent operation of large models includes monitoring a first temperature of one or more of the plurality of processors. Each of the one or more processors executes a machine learning model workload. The processor-implemented method also includes determining if the first temperature of the one or more of the plurality of processors is within one of a set of temperature zones. Power to the one or more of the plurality of processors is controlled based on the one of the set of temperature zones in which the first temperature of the one or more of the plurality of processors lies.

A machine-learning based, rapid, physics-aware thermal simulator, drawing inspiration from the Fourier's law and the Fourier-Biot equation, the first and second derivatives of the temperature map, is provided. The learning objective evolves from merely translating images to approximating natural phenomena such as the thermal gradient and thermal Laplacian. By adding an additional encoder during training and substituting the image-based loss with the thermal-aware loss, the proposed model achieves lower prediction error, higher data efficiency, and more physically accurate behavior.

The invention relates to the technical field of semiconductor thermal management, and discloses an edge computing task scheduling system of a diamond-based semiconductor device, which comprises an instruction preprocessing module, a thermal sensing control module and a scheduling execution module. The instruction preprocessing module reorganizes the instruction sequence into micro-slices and marks the micro-slices as a high-power burst type or a low-power gap-filling type. The thermal sensing control module calculates a predicted junction temperature based on the real-time voltage and current using a transient thermal impedance model. The scheduling execution module regulates and controls hardware according to a comparison result of the junction temperature and a safety threshold; when the junction temperature is safe, burst slicing is executed; when the junction temperature reaches the limit, the clock frequency and the voltage are kept unchanged, and a gap-filling slice is inserted and executed. According to the invention, the rapid thermal relaxation characteristic of the diamond substrate is utilized, active cooling of the device is realized through instruction flow gap filling on the premise of no frequency reduction and no voltage regulation, the switching overhead of the traditional frequency and voltage regulation technology is avoided, and the energy efficiency ratio and the thermal stability of the system are improved.

The application discloses a kind of AGC instruction dynamic distribution methods of fusing boiler heat storage state, it is related to thermal power generation control technical field, the present application includes the following steps: using pre-training parameters, the flue gas temperature of furnace outlet, water-cooled wall outlet working medium temperature, steam-water separator pressure and main steam and feedwater flow difference are mapped into nonlinear feature vector that characterizes heat storage space form feature and antagonistic latent heat state;Receive AGC instruction and actual power, extract heat storage perception from feature vector, calculate the equivalent load deviation of compensation heat storage, generate virtual guide rail;The time gradient of feature vector is calculated to determine the potential energy direction factor, generate instruction: when indicating violent confrontation, boiler proportional response deviation, steam turbine inhibition and feedforward;Indicate stable migration, boiler integral response, steam turbine fast tracking rail;Predict future main steam pressure, compare with high and low limit value and clamp gate and combustion rate instruction;Output clamped instruction, store parameters and vector into history window.