12210 results about "Thermal source" patented technology

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

The invention relates to an AI real-time intelligent guidance and self-adaptive obstacle avoidance method for multi-source sensing fusion of an unmanned aerial vehicle, and the method comprises the steps: collecting flight data through a visual sensor and an infrared sensor, obtaining a timestamp and space coordinate information, calculating a sampling frequency difference, and generating a fusion data set of time-space alignment through a self-adaptive interpolation algorithm; image and heat source features in the fusion data set are extracted, and a multi-dimensional feature set is generated, so that whether an obstacle exists or not is judged, and related information is extracted; and analyzing environment data, calculating an environment complexity index, generating a risk level in combination with obstacle information, predicting a dynamic weight of the sensor, and adjusting a working mode of the sensor. And then based on the obstacle condition, calculating a comprehensive risk value and predicting a motion trend through a path risk model, and finally generating an optimal obstacle avoidance path and a flight instruction, thereby realizing high-efficiency processing of sensor data, accurate risk assessment and real-time obstacle avoidance planning, and ensuring stable and safe flight of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of computer hardware monitoring, in particular to a case internal temperature intelligent monitoring system which comprises a temperature acquisition synchronization module, an environment difference analysis module, a cold control response judgment module, a heat source positioning recognition module and an energy consumption load evaluation module. According to the invention, by performing uniform timestamp sorting and collection on the temperature data at different positions, the time consistency of multi-node temperature information is improved, high-precision dynamic tracking of temperature change is realized, the timeliness and integrity of a time sequence are ensured, and the accuracy of the temperature change is improved. The temperature difference between the nodes and the environment and the time distribution characteristics of the nodes support accurate identification and positioning of thermal anomalies, fan response opportunities are judged according to the peak value and fluctuation rate of the temperature difference, fan numbers and control paths are calibrated, the pertinence of response is enhanced, and the core position of a heat source is positioned through temperature difference space comparison of adjacent nodes. The fan rotating speed and current load change analysis establishes dynamic association between a heat source and energy consumption, and thermal anomaly and energy efficiency trend evaluation is realized.

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention discloses an end-side cloud collaborative intelligent fire-fighting fire monitoring system based on image fusion, and relates to the technical field of intelligent fire-fighting, the system is composed of a plurality of functional modules, and the system comprises a multi-modal image fusion module which generates a dynamic scanning priority map based on prior data, distinguishes a natural heat source from an abnormal fire by using a dual-light fusion algorithm, and sends an image fusion result to a cloud server; a scanning area is divided according to the thermal risk grade, and the thermal imaging resolution is dynamically adjusted; the distributed edge computing module is used for carrying out space-time synchronization on cross-modal data through a multi-modal feature alignment network, and carrying out dynamic allocation on a CUDA core and CPU resources through adaptive computing scheduling; an improved artificial bee colony algorithm is adopted, the bandwidth of the multi-sensor data flow is dynamically allocated through a time-sharing multiplexing protocol, and three-dimensional path planning is carried out; and the end-side cloud collaborative decision module constructs a federated learning driven model sharing network, and each edge node trains a lightweight YOLOv5s pruning model based on local data.

The invention provides a power plant heat tracing system intelligent optimization method based on environment temperature fluctuation, and relates to the technical field of temperature control of a power plant heat tracing system, and the method comprises the following steps: S1, data collection and preprocessing: deploying a sensor array, collecting an operation data signal # imgabs0 # of a power plant heat tracing pipeline, carrying out the preprocessing of the operation data signal # imgabs1 #, and storing the preprocessed operation data signal # imgabs0 #; outputting a standardized feature vector; s2, model establishment and parameter optimization: constructing a physical-data dual-drive heat transfer model, and inputting standardized feature vector optimization initial parameters; compared with the prior art, the method has the following beneficial effects: firstly, control parameters can be automatically adjusted according to factors such as thermal load change, external environment temperature fluctuation and equipment aging; and secondly, the temperature trend in the future short time can be predicted by constructing a heat transfer dynamic model, the control strategy is adjusted in advance, the temperature is prevented from being out of control, heat dissipation can be increased in advance or heat source input can be adjusted in advance, and safe and efficient operation of the system is effectively guaranteed.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention provides a data center energy efficiency optimization method and system. A data acquisition node is arranged at the inlet end of a cooling unit to obtain refrigerant transportation data, the data are input into a path optimization model constructed based on a physical topological structure of the cooling unit, and a dynamic adjustment coefficient set containing heat exchange efficiency, phase change delay and pressure compensation parameters is generated. Meanwhile, the temperature difference generating capacity is converted into a driving parameter set based on the heat source distribution map by obtaining the surface waste heat of the cooling unit. And in combination with the dynamic adjustment coefficient set and the driving parameter set, the temperature gradient compensation amount, the phase change trigger threshold value and the pressure balance interval are determined, joint optimization is carried out, and a refrigerant flow rate control scheme adapting to machine room environment temperature fluctuation is generated. And energy efficiency optimization of the data center machine room environment is realized based on the refrigerant circulation parameters. According to the technical scheme provided by the invention, the energy efficiency optimization effect of the data center is remarkably improved by optimizing the flow speed control of the refrigerant.

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention relates to the field of grain drying, and discloses a grain drying energy-saving control system based on intelligent optimization, which comprises the following steps: after warehousing grains are detected, judging whether the water content of the grains exceeds a preset safety threshold value or not by collecting the initial water content of the grains and environment temperature and humidity information; according to the grain moisture content, the grain size distribution and the current environment condition, a multi-section type moisture change trend model fitting the drying process is constructed, and the decrease rate of the grain moisture content and the drying curve form in the future stage are predicted; analyzing the deviation value between the water content change slope and the target drying rate in the prediction curve; judging whether an adjustment potential exists or not by modeling a response relationship among the current hot air temperature, wind speed, heat source power and water content change; and according to the heat energy use analysis result, control instructions of hot air temperature, air speed and air supply modes are automatically generated. The self-adaptive energy-saving grain dryer has the advantage that the self-adaptive capacity and the energy-saving effect in the grain drying process are improved.

The invention discloses a temperature monitoring method for a photovoltaic grid-connected cabinet, and particularly relates to the technical field of photovoltaic grid-connected cabinets. The method comprises the following steps: arranging multiple types of temperature sensors in a grid-connected cabinet, collecting real-time temperature data, constructing a heat distribution model, comparing a current heat map with a historical steady-state heat field template, and identifying heat field offset and an atypical temperature rise trend; when the system detects that early warning is not triggered by an existing measuring point in a heat abnormal area, redundancy alarm logic is automatically started, temperature abnormal early warning is generated, and the speculated abnormal heat source position is recorded; according to the method, the sensing capability of the thermal anomaly in the blind area of the sensor is effectively improved, and the safety and the intelligent level of the system are enhanced.

The invention discloses a multi-heat-source coupling heat pump control system for recycling waste heat of industrial circulating cooling water, which comprises the following steps: monitoring key parameters of various heat sources in real time through an environment sensing module, screening and preprocessing through a data processing unit, screening out a heat source set suitable for the current condition, and sending the heat source set to a control module; the recovery potential and the use efficiency of the heat source are evaluated by utilizing an adaptive learning model, the use priority and the distribution proportion of the heat source are intelligently determined in combination with real-time load change, and the working time and the switching time of the heat source are dynamically adjusted by adopting a heat source time window distribution mechanism. The operation parameters and the heat source coupling mode of the heat pump are optimized through the intelligent complementary adjusting module, the heat recovery path is dynamically adjusted through the autonomous temperature control unit, the scheduling strategy and the operation mode of the heat source are optimized through the self-adaptive feedback mechanism and the intelligent learning module, and the adaptability and the energy-saving effect of the system are improved. According to the system, efficient cooperative utilization of multiple heat sources is achieved, and the heat recovery efficiency and the system stability are improved.

The invention provides an indoor environment monitoring method and system integrating temperature and humidity, and is applied to the field of sensing data processing. According to the method, the reading difference of the temperature sensor is collected, whether local deviation exists or not is judged, the arrangement type of the sensor is combined, a low temperature value generated by the wall-leaning sensor due to the cold bridge effect is dynamically compensated, misjudgment of an environment system is avoided, and the human body temperature is introduced as a sensing reference by constructing a temperature difference deviation function. According to the method, comprehensive temperature indexes closer to real experience can be generated, when abnormal fluctuation is detected, the space structure and heat source information are further combined, boundary conditions are constructed, dynamic heat flow and temperature and humidity distribution diagrams are simulated, temperature sensing deviation is effectively and reversely corrected, the precision, stability and energy-saving performance of an environment adjusting system are remarkably improved, and the comfort degree of a human body is improved.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention relates to the technical field of heat load prediction, and discloses a heat supply system load prediction method and system, and the method comprises the steps: collecting multi-source sensing data in real time, and collecting outdoor meteorological parameters and building structure information; based on building distribution, a pipe network structure and user load characteristics of a heat supply area, a multi-stage heat supply load prediction model is constructed. And constructing a thermal topological graph model of the heat supply area based on the graph neural network. And periodically collecting parameters of the building-level edge prediction model, the heat exchange station-level aggregation prediction model and the thermal topological graph model, performing global aggregation optimization, and updating and optimizing each edge node model. And obtaining an edge prediction result according to the optimized model, and jointly controlling the heat source output power, the main pump rotating speed and the area valve opening according to the edge prediction result and the heat source level scheduling prediction model. According to the method, the depiction capability of the system on the dynamic load change and the space heat conduction path is improved, and the generalization capability of model updating and the real-time responsiveness of edge deployment are ensured.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention belongs to the technical field of intelligent manufacturing and laser arc hybrid welding, and discloses a double-robot laser arc hybrid welding real-time regulation and control method based on digital twinning. According to the method, double robots are used, a laser-electric arc composite heat source digital twinborn model is constructed, a Gaussian distribution laser heat source and a double-ellipsoid electric arc heat source are integrated, and the dynamic behaviors (molten pool flowing and keyhole stability) of a molten pool and the generation probability of defects (air holes, cracks and the like) are predicted. And a visual sensor and an acoustic emission sensor on the two sides of the welding robot are used for capturing molten pool oscillation frequency and internal defect signals. And in combination with a reinforcement learning algorithm, welding path offset self-adaptive compensation and laser arc energy ratio adjustment are achieved, and closed-loop real-time regulation and control are formed by controlling the position of a welding gun of a welding robot and technological parameters. Based on a digital twinborn model, robot welding guns on the two sides can conduct synchronous welding on the two sides and can be independently adjusted while coupling is guaranteed according to the characteristics of specific materials, and high-quality welding is achieved.

The invention discloses a cold and heat supply unit and process capable of deeply utilizing a heat source in the technical field of absorption refrigeration. The cold and heat supply unit comprises a refrigerating unit, a heating unit, a heat source flash tank and a high-pressure absorber. The refrigerating unit utilizes an external heat source to produce cold energy, the heating unit utilizes the heat source used by the refrigerating unit to produce cold energy so as to cool circulating water of the refrigerating unit, and the heat source flash tank utilizes the heat source discharged by the high-pressure absorber to heat; the absorption refrigerating unit is optimally designed, so that the absorption refrigerating unit can deeply utilize a heat source and supply cold and high-level heat energy at the same time, the problem that the heat source utilization rate of the unit is not high is solved, and the requirement for high-temperature heat energy in the production process is met; and low-grade waste heat is converted into cold energy and high-level heat energy, transition and upgrading of energy quality can be achieved, the unit has the cold and heat combined supply function, and the application scene is greatly expanded.

The present application belongs to the technical field of material processing, and relates to a mold-free mirror image filament laying method and a mold-free mirror image filament laying system. The method lays a filament ribbon in a mirror image symmetry, without a mold, thereby improving production flexibility and efficiency. The method acquires the temperature and pressure of the filament ribbon in real time, calculates the output power of a heater and the modification value of a filament laying end motion mechanism path by using temperature and pressure differences, and optimizes controller parameters through reinforcement learning, so as to realize stable control of the temperature and pressure. The system comprises a hardware part and a control system. The hardware part includes pressure and heat source components. The control system adjusts through real-time feedback to ensure the quality of filament laying. The present application solves the problem that the temperature and pressure are difficult to accurately control in the process of mold-free mirror image filament laying, and improves the performance of composite products.

The invention provides an intelligent harmonic dynamic compensation method and system for a data center. According to the method, server heat source surface temperature field distribution is monitored in real time through a distributed optical fiber temperature measurement array, and a dynamic impedance spectrum is generated; when the temperature gradient exceeds the flash evaporation critical value of the fluorinated cooling liquid, an active filter is activated to carry out dynamic impedance matching compensation on high-frequency harmonic current in the liquid cooling tank cabinet, and a pulse width modulation control signal of a circulating pump is generated; the compensation power of the filter and the turbulence intensity of the cooling liquid are synchronously adjusted by analyzing the coupling relation between the flowing vortex intensity of the cooling liquid and the dielectric polarization loss; when phase mismatch of a temperature field is caused by heat dissipation of multiple heat sources, a multi-band compensation strategy is reconstructed by utilizing frequency domain characteristics of a pulse width modulation signal and a compensation power historical value, and a cooling liquid branch flow distribution ratio and a filter frequency response curve are cooperatively adjusted. According to the technical scheme, the thermal stability and the electrical safety of the data center cooling machine room are improved.

The invention discloses a waste heat recovery scheduling method and system for a two-phase cold plate of a data center, and relates to the technical field of refrigeration and heat management. The waste heat recovery scheduling method and system for the two-phase cold plate of the data center comprises the following steps that S1, key thermal parameters and tail end state variables are collected through multiple types of thermal monitoring devices, and thermal state monitoring data are obtained and preprocessed; s2, on the basis of the thermal state monitoring data and in combination with an SLA configuration file, performing priority division on the heat utilization terminals, and constructing a scheduling deviation judgment mechanism to identify thermal state changes; s3, under driving of the regulation and control instruction, accessing multi-source waste heat and distributing the multi-source waste heat to each terminal scene, and evaluating a matching condition of the waste heat and a terminal demand; and S4, based on the change of the heat distribution state and the thermal state monitoring data, identifying an overload risk in combination with a multi-parameter linkage mechanism. The problems that waste heat recovery is insufficient and the energy utilization rate is limited under the light-load and multi-heat-source cooperative working condition are solved.

The invention relates to the technical field of hydrogen energy, and discloses an auxiliary-machine-free solid oxide electrolytic hydrogen production system and an auxiliary coal electricity deep adjustment method, the hydrogen production system comprises an SOE heat box system for electrolytic hydrogen production, a hydrogen circulation subsystem and a hydrogen storage subsystem; the ejector is driven by external heat source steam, part of hydrogen at an outlet of the SOE heat box system is ejected to a low-pressure suction inlet of the ejector through the hydrogen circulation subsystem, and formed reducing mixed gas of steam and hydrogen enters a cathode inlet of the SOE heat box system. The system is driven by an external heat source and operates in an internal stack heating mode, so that the power consumption of the electrolytic hydrogen production system is reduced; by adopting the modes of external material supply, cathode ejector material self-circulation and multi-stream heat exchange, a complex cold and hot auxiliary machine of a traditional solid oxide electrolytic hydrogen production system is omitted, the structure is simple, and the system compactness is greatly improved; electric power-steam-air-condensed water flow coupling is established with a coal-fired power plant, and efficient, flexible and deep peak shaving is adjusted in combination with steam extraction multi-stage adjustment.

The invention relates to the technical field of battery safety management, in particular to a battery thermal runaway early warning method and system based on multi-dimensional feature fusion. The method comprises the following steps: collecting thermal characteristic data, gas characteristic data and electrochemical characteristic data of a battery; calculating a temperature gradient vector and an isothermal consistency index by using the thermal characteristic data, and capturing abnormal changes of a battery hot spot region by using a local dynamic sampling increasing method; calculating an internal and external field temperature difference phase difference by utilizing the thermal characteristic data and the gas characteristic data, and judging a heat source attribute by combining a heat contribution ratio model; and calculating a cross-modal early response phase difference, constructing a phase difference feature matrix, combining the temperature gradient vector, the heat source attribute and the phase difference feature matrix to form a fusion feature matrix, and inputting the fusion feature matrix into a dynamic weight fusion discrimination model to calculate a thermal runaway risk probability. According to the invention, based on a multi-dimensional feature fusion method, the thermal runaway risk probability is calculated in real time, and the early-stage accurate early warning of the thermal runaway of the battery is realized.

The invention discloses a power supply circuit, a power supply method, a server, a product and a medium, and relates to the technical field of server power supply, the total power demand of the server is dispersed through the parallel design of multiple power panels, the bus current of a single board connected to a first voltage is reduced to the safe bearing range of an MOS tube, the impact risk is reduced, and the service life of the server is prolonged. The electronic fuse is matched with the single-board power to set an overcurrent protection threshold value, and the short-circuit protection response time is shortened; each board is independently provided with a power supply brick to realize second voltage localization conversion and single board output current reduction, a multi-board output end combined power supply design is combined, a heat source is dispersed to eliminate heat accumulation, the through-current capability is multiplied through a parallel path, and the heat dissipation and electrical design feasibility of the circuit board is improved. The technical problems of MOS tube over-limit, overcurrent protection delay, circuit board heat accumulation and difficult through-flow caused by large current brought by a power supply in a high-power server are solved, and the technical effects of enhancing power supply reliability and optimizing energy efficiency are achieved.

The invention discloses a heat dissipation driver and electronic equipment, and relates to the technical field of electronic equipment heat dissipation, the heat dissipation driver comprises a frame, a magnetic circuit system, a vibration system and a flow adjusting assembly, the vibration system comprises a vibrating diaphragm and a coil, the coil is located in a magnetic field of the magnetic circuit system, and the outer edge of the vibrating diaphragm is connected with the frame; the vibrating diaphragm, the magnetic circuit system and the frame define a rear cavity, and the vibrating diaphragm is provided with a communication hole communicated with the rear cavity. When the vibrating diaphragm vibrates in the direction close to the magnetic circuit system, the flow adjusting assembly is in an open state, and the flow adjusting assembly is exposed out of the communicating hole so that airflow in the rear cavity can flow out of the rear cavity through the communicating hole. When the vibrating diaphragm vibrates in the direction away from the magnetic circuit system, the flow adjusting assembly is in a closed state, and the flow adjusting assembly blocks the communicating hole, so that airflow in a heat source area of the equipment flows into the rear cavity, and heat dissipation of the equipment is achieved.

The invention belongs to the technical field of power system control, and discloses an intelligent adjusting system and method for a transformer. The method comprises the following steps: inputting transformer operation data and weather data collected in real time into a pre-trained load prediction model to obtain predicted load data; inputting the acquired physical parameters and operation data and preset calibration parameters into a pre-constructed digital twinborn model to obtain a cooling capacity index; calculating the total heat source power of the copper loss and the iron loss according to the predicted load data and the electrical parameters of the transformer collected in real time, and dynamically quantifying the balance state of the total heat source power and the cooling capacity index through a matching degree evaluation function in combination with the cooling capacity index to obtain a matching degree evaluation index; and the problem of dynamic unbalance between the heat dissipation capability of the transformer and the load demand in an extreme environment is systematically solved.

The invention discloses a low-carbon flexible coal-fired unit power control system and method, and relates to the technical field of power systems, and the system comprises a cascade waste heat recovery module, a boiler deep regulation and control module, a thermal coordination module, an environmental protection and emission reduction module, an intelligent operation scheduling module and an energy efficiency monitoring module. The gradient waste heat recovery module carries out gradient recovery on flue gas heat energy according to temperature zone grades; the boiler depth regulation and control module is used for regulating combustion air supply and heat absorption paths to realize low-load stable combustion; the thermal coordination module dynamically distributes thermal loads between the multiple heat sources and the thermal units; the environmental protection and emission reduction module jointly controls desulfurization, denitration and wastewater resourceful treatment; the intelligent operation scheduling module generates a load regulation and control instruction and coordinates multi-module response; the energy efficiency monitoring module collects and analyzes energy consumption and emission indexes and assists strategy optimization. The energy efficiency utilization and emission control capability of the unit under the deep peak regulation condition is improved, and flexible, efficient and low-carbon operation is achieved.