6123 results about "High energy" patented technology

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention discloses an energy-efficient task scheduling method for an edge computing system, which comprises the following steps of: acquiring a task state, a computing node resource state, a link state and an energy consumption state according to a unified time slot under a computing power network control domain, and constructing a system state vector; performing priority evaluation on the to-be-scheduled task based on the residual delay budget, the candidate node energy efficiency coefficient and the queue position to obtain a target task set; inputting a system state vector and a target task set into an energy efficiency perception deep reinforcement learning scheduling model, outputting a task-node allocation decision under the constraint of computing node resources and task time delay, and introducing a system-level energy consumption ratio, self-adaptive energy consumption penalty and exploration bias facing high-energy-efficiency nodes into rewards; and scheduling tasks according to the allocation decision, recording state transition and instant rewards, updating a double-commentator and actor network, and performing iterative execution in continuous time slots. According to the method, task success rate, time delay, load balancing and energy-saving performance are considered, and system energy consumption is reduced.

A system and method for enabling real-time dispatch of flexibility resources to unlock grid capacity through AI-based orchestration. The invention addresses the challenge of connecting high energy demand users, such as data centers, to constrained electricity grids without requiring infrastructure upgrades. The system establishes a marketplace where flexible asset holders set temporal compensation prices and boundary conditions, enabling true market-based participation. An AI orchestration engine analyzes real-time grid conditions and modifies flexible asset behavior to create inverse consumption profiles that counterbalance new demand loads. The platform integrates hardware and software solutions for remote control and APIs for autonomous systems like electric vehicles. Aggregators and off-takers can establish long-term contracts for flexible capacity at agreed prices. The AI system ensures flexible assets meet user-defined boundary conditions while simultaneously masking high energy demand, making new loads invisible to the grid and enabling immediate connection of data centers essential for industrial deployment.

The invention belongs to the technical field of cleaning and dirt prevention and removal, and discloses a control system and a control method of an ultrasonic cleaning device, stable energy coupling and dead-corner-free cleaning are realized through double-closed-loop driving and dynamic modulation in an ultrasonic cleaning link, and the uniformity of tank liquid cavitation intensity is remarkably improved. Meanwhile, control strategies are customized in the aspects of a rinsing link, a water cutting and drying link, liquid level control, manipulator control and the like, so that the defects of high energy consumption, incomplete drying, low rinsing efficiency, manipulator conflict and the like in the prior art are overcome while the surface and the interior of the workpiece are thoroughly cleaned; and the cleaning effects of high cleanliness, complete drying and no physical or chemical damage are ensured.

The invention discloses an intelligent optimization decision-making method and system for an oilfield water injection system, and belongs to the technical field of oilfield development engineering. The method comprises the following steps: constructing a pipe network digital twinborn model and carrying out actual measurement verification; executing hydraulic-thermal simulation based on the model to generate a pressure-flow map; optimizing water distribution in combination with an injection allocation scheme and matching a pump set combination; generating a control instruction and executing a pressurization scheme; monitoring the state of the pipe network in real time and correcting model parameters. Through the digital twin technology and the differential evolution algorithm, global optimization decision of water distribution and pump set combination of the water injection system is achieved, and the problems that a traditional system is high in energy consumption and low in efficiency are solved.

The invention relates to the technical field of battery positive electrode materials, and provides a lithium iron phosphate positive electrode material and a preparation method thereof. The lithium iron phosphate positive electrode material has the characteristic of high energy density, the proportion of iron phosphide impurities in the total mass of the lithium iron phosphate positive electrode material is 0.001-0.008 PPM, the compaction density under the pressure of 3T is 2.68-2.8 g / cm < 3 >, the powder resistivity is 8-15 omega.cm, the 0.1 C discharge capacity is 161-163 mAh / g, the 0.1 C charge-discharge efficiency is 98.5-100%, and the 1C discharge capacity is 145-148 mAh / g. The preparation process is simple and safe, low in cost and high in stability. The content of iron phosphide impurities in a final lithium iron phosphate product can be regulated and controlled by regulating and controlling the phosphorus-iron ratio of the iron phosphate precursor and the ratio of the carbon source to the iron phosphate precursor in two-time burdening and controlling the sintering temperature in cooperation with secondary sintering, and the high-energy-density lithium iron phosphate positive electrode material is obtained.

The invention provides a smart park operation and maintenance management method and system based on CIM and a medium, and belongs to the technical field of smart park management. The method comprises the following steps: constructing a CIM digital twinborn model based on a park building group, a geographic position, Internet of Things equipment and business data, and synchronously extracting equipment state and energy consumption information; analyzing the state data by using the equipment fault prediction model and generating a repair work order to realize predictive maintenance; performing gridding processing on the energy consumption data, generating a thermodynamic diagram and identifying an abnormal energy consumption area; and positioning a high-energy-consumption grid by combining an energy consumption analysis model, and generating a comprehensive operation and maintenance scheme of equipment regulation and control, energy scheduling and system optimization through matching of an energy management strategy library. According to the invention, the digital twinning technology and the CIM platform are fused, so that full-life-cycle management and dynamic energy consumption optimization of the equipment are realized, the operation and maintenance efficiency is remarkably improved, the failure rate and energy waste are reduced, and both economy and environmental protection are achieved.

The invention discloses a high-energy-efficiency logarithm floating point multiplier based on truncation error compensation. The system comprises a compensation parameter modeling unit and a floating point multiplier module. The compensation parameter modeling unit obtains an optimal compensation value k through linear fitting and parameter optimization, and realizes regional self-adaptive compensation in combination with block modeling so as to reduce truncation errors; the floating point multiplier module comprises a special value detection module, a symbol exclusive or module, an exponential addition module and a mantissa truncation compensation module. In a logarithm domain, multiplication is replaced by addition, and compensation logic is introduced to correct errors. The multiplier supports various floating point precisions and configurable truncation bit widths, provides three representative structures to give consideration to both the precision and the energy efficiency, and is suitable for high-energy-efficiency calculation scenes such as neural networks and image processing.

The invention discloses a wide-temperature-range sodium-lithium hybrid energy storage system and a control method thereof. According to the system, the working temperature range of the energy storage system is effectively expanded through sodium-lithium hybrid energy storage; the sodium ion battery unit bears a low-temperature working condition, and the lithium ion battery unit bears a normal-temperature basic load, so that the low-temperature and power characteristics of a sodium battery and the high-energy density advantage of a lithium battery are exerted, and complementation of energy density and temperature adaptability is realized; and the independent bidirectional DC / DC converter is adopted for power coupling, so that the problem of voltage mismatching possibly caused by direct parallel connection is effectively avoided, and the reliability and the control precision of the system are improved. The system has intelligent environment perception and adaptive control capabilities, can adaptively adjust a working mode and a power distribution strategy according to environment conditions, and is high in intelligent degree. The modular design is adopted, system expansion and maintenance are facilitated, and the operation and maintenance cost is reduced.

The invention discloses a low-power-consumption warning ground pile awakening method and system based on human activity recognition, and belongs to the technical field of image analysis and intelligent security and protection. According to the method, in a micro-power-consumption mode, the environment is monitored through an image sensor, and when environment changes meet awakening conditions, lightweight human body detection is conducted through an edge AI chip. And if the human activity probability exceeds the confidence coefficient, starting a main camera and a high-computing-power AI chip to carry out deep behavior analysis, fusing a behavior analysis result with a geographic position and a timestamp, generating a risk decision result, and triggering a dynamic response. According to the invention, a hierarchical wake-up and multi-source fusion technology is adopted, on-demand work is realized, power consumption is greatly reduced, early warning accuracy is improved through deep behavior analysis, and the problems of high energy consumption and inaccurate early warning of traditional equipment are effectively solved.

The invention discloses a mobile phone energy consumption optimization method, and relates to the technical field of information, and the method comprises the steps: obtaining a current game frame of a mobile phone game application, and extracting a key rendering parameter of the current game frame; according to the current game frame and the historical game frame, calculating a static scene element space consistency score and a dynamic rendering object state change rate; according to the static scene element space consistency score and the dynamic rendering object state change rate, generating a computing resource allocation scheme; according to the computing resource allocation scheme, GPU rendering pipelines and CPU computing resources are configured, corresponding game computing execution paths are selected, and the computing execution paths comprise a complete rendering pipeline path, an incremental rendering path or an inter-frame interpolation path; aiming at high energy consumption of 3D scene rendering in a mobile game, a three-level rendering strategy selection mechanism based on scene change degree and the like are established, and the energy consumption of a mobile phone is reduced while the game quality is kept.

The invention discloses an intelligent control method and system for a motor driver, and the method comprises the steps: building an acoustoelectric incidence matrix through collecting harmonic current and sound pressure fluctuation data in the operation process of a motor, and forming a fault feature library; identifying an available fault mode to determine an energy collection point, and performing phase deviation processing on the collection point to generate an auxiliary driving signal; performing spectrum analysis on the sound pressure fluctuation data to obtain an acoustic characteristic spectrum, extracting an efficiency indication tone through characteristic decomposition, mapping the efficiency indication tone into a torque optimization parameter, and establishing a noise reduction control table; performing spectrum analysis on the enhanced control domain to identify a high-energy frequency band, executing a frequency shift operation to generate a safety frequency band signal, and forming a multi-frequency band management strategy in combination with a noise reduction control table; a management strategy is decomposed into parallel control flows for sequential staggered arrangement, and an energy-saving control matrix is generated; and generating a PWM control signal based on the matrix, carrying out edge reconstruction, and obtaining a softened edge waveform through an anti-resonance injection technology to complete intelligent control of the motor driver.

The invention belongs to the technical field of crossing of intelligent manufacturing and thermal control systems, particularly relates to an intelligent cooling control system for a bionic micro-channel mold, and aims to solve the problems of low mold cooling efficiency, non-uniform temperature distribution, high energy consumption and the like. The system comprises a bionic microchannel mold body, a distributed temperature sensing network, a multi-area independent flow regulation and control module, a cooling medium dynamic supply unit and a central intelligent decision controller. Through cooperation of a bionic fractal micro-channel structure, partition flow regulation and control and an intelligent algorithm, fine regulation and control of a temperature field are achieved, the cooling efficiency is improved, energy consumption is reduced, and the self-adaptive optimization and fault redundancy capacity is achieved.

The invention provides an anti-ferroelectric lanthanum ferrite, bismuth ferrite and strontium titanate superlattice film with high energy storage density as well as a preparation method and application thereof. The anti-ferroelectric lanthanum ferrite, bismuth ferrite and strontium titanate superlattice film with high energy storage density comprises a substrate layer, a bottom electrode layer and a plurality of stacked relaxation ferroelectric layers, wherein the bottom electrode layer and the multiple stacked relaxation ferroelectric layers are sequentially arranged on the substrate layer; each relaxor ferroelectric layer sequentially comprises a first functional layer, a second functional layer and a third functional layer from bottom to top; the first functional layer comprises LaFeO3, the second functional layer comprises BiFeO3, and the third functional layer comprises SrTiO3. The anti-ferroelectric lanthanum ferrite, bismuth ferrite and strontium titanate superlattice thin film with the high energy storage density has small electric leakage performance and high polarization value, and more excellent energy storage performance is achieved.

The invention belongs to the technical field of wireless household electrical appliance intelligence, and discloses a wireless household electrical appliance intelligent identification and analysis method and system based on multi-protocol fusion, which can automatically extract new equipment protocol features through centralized middleware scheduling and adaptive learning mechanism of a protocol adaptation engine unit in combination with dynamic model training of an AI identification center unit. Generating a temporary adaptive interface and updating the protocol template; the dynamic protocol library supports OTA updating, a novel protocol and unknown equipment can be adapted without manual configuration, and the defects that the number of traditional system equipment is increased, and protocol type expansion is blocked are overcome; the intelligent optimization module dynamically adjusts the bandwidth priority and the data reporting frequency based on reinforcement learning and behavior prediction, guarantees smooth communication of key equipment in a high-concurrency scene, reduces standby power consumption of idle equipment, solves the problems of high-concurrency delay and high energy consumption of a traditional system, and improves the system reliability. And double improvement of identification reliability and system energy efficiency is realized.

The invention relates to the technical field of intelligent building environment control and energy management, and provides a building ventilation regulation and control method and equipment based on multi-target space-time analysis in order to solve the problems of high energy consumption, uncontrollable infection risk and response lag in the prior art. Original data and the spatial-temporal features are combined into a multi-modal fusion data set, an air quality change rule is learned based on an LSTM model, a future air quality state matrix is output, a multi-target dynamic optimization function is constructed, an optimal regulation and control strategy of a fan, fresh air and purification equipment is generated, and the optimal regulation and control strategy of the fan, the fresh air and the purification equipment is obtained. The response speed is improved while energy consumption is reduced and infection risks are controlled, and the system is particularly suitable for public places such as hospitals, schools and nursing homes with high requirements for indoor gas environments.

The invention relates to the technical field of electric vehicle thermal management, discloses an electric vehicle motor and driver integrated intelligent cooling system and a control method, and aims at solving the problems that an existing motor driver thermal management system is low in cooling efficiency, high in energy consumption, complex and insufficient in space utilization rate. The method comprises the following steps: acquiring operation, thermal state and environment data; constructing a collaborative thermal model and predicting a thermal load; generating an optimal cooling strategy based on the predicted value and the target; the cooling liquid pump, the three-way valve and the cooling fan are adjusted, and cooling resources are dynamically distributed to achieve collaborative cooling. The system comprises a data acquisition module, a collaborative thermal model construction prediction module, an intelligent decision control module and an integrated cooling circulation execution module. By the adoption of the technical scheme, the cooling performance can be effectively improved, system energy consumption and complexity are reduced, space layout is optimized, the service life of parts is prolonged, and the reliability, the power performance and the endurance of the whole vehicle are improved.

The invention relates to the technical field of bubble generation, and provides a micro-nano bubble stable generation method and a micro-nano bubble stable generation device.The method comprises the steps that a liquid-phase working medium flows through a Venturi tube, so that negative pressure is formed in the throat portion of the Venturi tube, a gas-phase working medium is sucked in, preliminary mixing is conducted, and a gas-liquid mixture is obtained; the gas-liquid mixture is tangentially guided into a rotary-cut type excitation cavity to drive a free rotating impeller in the rotary-cut type excitation cavity to rotate, and fine shearing is conducted on bubbles; the gas-liquid mixture obtained after fine shearing is guided into a diffusion and flow stabilization cavity for speed reduction and flow stabilization; the gas-liquid mixture subjected to flow stabilization flows through a nanocrystallization foam stabilizing unit, a plurality of parallel micro-channels are arranged in the nanocrystallization foam stabilizing unit, and the surface of the inner wall of each micro-channel is covered with a layer of nanofiber net-shaped structure; and carrying out final nanocrystallization and interface stabilization treatment on the gas-liquid mixture through a nanofiber net structure, and outputting stable micro-nano bubble liquid. The problems of poor stability, short service life and high energy consumption of the traditional technology are solved.

The invention belongs to the technical field of hard and brittle crystal material micropore machining, and particularly relates to an ultra-precision micropore multi-energy field coupling machining method and system.The method comprises the steps that the laser pulse frequency and power are set according to the workpiece material type and the micropore depth-diameter ratio, and a laser focus is positioned at the inner hole bottom of a workpiece; a modified structure extending in the axial direction of a hole is formed by controlling matching of a light spot and a hole diameter and the movement track of the light spot, in the machining process, a plasma nozzle and laser coaxially move, a modified area is removed in real time, a heat affected zone and the hole shape taper are restrained, meanwhile, vibration is applied to a workpiece through an axial ultrasonic vibration device, and machining precision is improved. And chip discharge is promoted, and stress and crack generation are reduced. The problems of serious thermal damage, poor hole shape precision, low machining efficiency and high energy consumption in the micropore machining process in the prior art are solved, and high-quality and high-precision machining of the high-depth-diameter-ratio micropore of the hard and brittle transparent crystal material is achieved.

The invention discloses multi-pump collaborative variable-frequency energy-saving water supply equipment and a control method, belongs to the technical field of intelligent control of water supply equipment, and aims to solve the problems of high energy consumption and poor adaptability of traditional water supply equipment. The method comprises the following steps: collecting water consumption data, extracting features through a sliding window, screening parameters through principal component analysis to construct a feature parameter set, dividing feature clustering groups in combination with time attribute clustering, and associating water consumption time periods and demand intensity grades to construct a typical water consumption mode; according to the water pump performance curve and the attenuation index, constructing an updated dynamic capability matrix; matching a target water consumption mode, screening water pumps to construct an execution pump set, decomposing a flow demand and distributing a load to generate a regulation and control instruction; and executing the instruction and counting the deviation accumulated duration, and judging whether to update the instruction. Dynamic adaptation of the water pump set and the water demand is achieved, and the water supply stability and the energy saving performance are improved.

The invention discloses a power transmission line insulator dual defense system and method, and belongs to the technical field of power transmission line maintenance, in the system, a power supply subsystem generates, stores and conveys high-pressure gas, and a primary defense subsystem and a secondary defense subsystem are respectively communicated with the power supply subsystem through high-pressure conveying hoses to obtain a high-pressure gas source; the first defense subsystem is arranged around an insulator of the power transmission line and forms an annular air curtain to prevent rain and snow from being attached to the surface of the insulator; the secondary defense subsystem detects and removes rain and snow breaking through the annular air curtain; the photovoltaic power supply subsystem is used for providing power for the whole defense system; all the subsystems are electrically connected with the intelligent control subsystem, and the intelligent control subsystem is used for coordinating starting, operation and parameter adjustment of all the subsystems. The problems that an existing anti-icing technology is high in energy consumption and slow in response, an icing micromechanism is difficult to capture, and protection is insufficient due to the fact that extreme working conditions are difficult to reproduce are solved, and efficient blocking and removing of rain and snow are achieved.

The invention discloses a double-clutch variable-stiffness knee joint exoskeleton, and relates to the technical field of wearable lower limb exoskeletons.The double-clutch variable-stiffness knee joint exoskeleton comprises a gait sensing module arranged outside and a limb connecting arm module used for being connected with a human body, and an energy storage module is installed on one side of the limb connecting arm module; a sensor module is mounted on the other side of the limb connecting arm module; the limb connecting arm module comprises a limb connecting arm assembly I used for being connected with the thigh of the human body and a limb connecting arm assembly II used for being connected with the shank of the human body, the limb connecting arm assembly I and the limb connecting arm assembly II are each provided with a clutch unit, and the core speed increaser is installed between the limb connecting arm assembly I and the limb connecting arm assembly II. The energy storage module and the sensor module are arranged on the two sides of the core speed increaser, and the core speed increaser is connected with the energy storage module. The device has the advantages of compact structure, variable rigidity bandwidth and good motion adaptability, can realize high energy recovery efficiency in a multi-gait mode, and reduces the metabolic consumption of a human body.

The application discloses a high-energy-density lithium ion battery, which comprises a positive electrode sheet and a negative electrode sheet, the positive electrode sheet comprises a positive electrode active material, the positive electrode active material comprises a lithium iron phosphate material and a lithium-rich material, the mass ratio of the lithium-rich material in the positive electrode active material is 0.2%-8%, and the chemical formula of the lithium iron phosphate material is Li 1+x Fe 1‑ y M y (PO4) 1+z , 0<=x<0.1, 0<=y<0.01, 0<=z<0.04, M comprises at least one of Ti, V, Zr, Nb, Mg, Mo, W and Al, the lithium-rich material comprises at least one of LFO and LNO, the chemical formula of the LFO is Li 5+a Fe 1‑b Z b O 4+c , 0<=a<0.5, 0<=b<0.01, 0<=c<0.03, Z comprises at least one of Ti, V, Zr, Nb, Mg, Mo, W and Al, and the chemical formula of the LNO is Li 2+d Ni 1‑ e Y e O 2+f , -0.5<=d<=0.5, e>=0, -0.5<=f<=0.5, Y comprises at least one of Ti, V, Al, Mg, Mn and Fe, and the negative electrode active material comprises a silicon negative electrode material, the mass ratio of the silicon negative electrode material in the negative electrode active material is 1%-50%. The application has the advantages of improving the energy density, rate performance and cycle performance of the lithium battery.

Main characteristic of the invention is the exploitation of the very large flux of high energy neutrons generated in a nuclear fusion tokamak with beam-driven plasma for the purpose of producing radioisotopes. It specifically relates to the deployment of precursor materials into the core of a nuclear fusion tokamak with beam-driven plasma for the purpose of exposing these materials to the neutron flux generated in the plasma by the process of nuclear fusion of Deuterium (D) and Tritium (T) to generate radioactive isotopes, so-called radioisotopes. The precursor materials contained in metallic capsules are incorporated into the blankets that surround the plasma.

The invention discloses a boiler combustion energy efficiency improving method and system based on digital twinning and a medium, and relates to the technical field of boilers, and the method comprises the steps that design information and historical data of a target boiler combustion system are obtained, digital twinning modeling is carried out, and a digital twinning model is built; key parts are identified based on design information, sensors are deployed, a combustion system sensing assembly is constructed, and real-time combustion state data are collected; constructing a boiler combustion energy efficiency evaluation system, performing multi-dimensional evaluation on real-time data, and determining a current energy efficiency coefficient; and performing energy efficiency improvement control on the target boiler combustion system based on the energy efficiency coefficient through a digital twin model. The technical problems of low combustion efficiency and high energy consumption caused by difficulty in real-time monitoring and optimization of energy efficiency in the boiler combustion process are solved, and accurate evaluation and intelligent optimization control of the boiler combustion state are realized through digital twin modeling and real-time sensing. The combustion energy efficiency of the boiler is improved, the energy consumption is reduced, and the operation stability is ensured.

The present application provides an entropy-regulated composite dielectric film, which comprises a polymer matrix; and fillers dispersed in the polymer matrix, wherein the fillers comprise Bi 4‑a M 1a Ti (3‑3b) M 2b O 12 , wherein M1 comprises at least one of lanthanum, neodymium, samarium and praseodymium, M2 comprises at least one of zirconium, hafnium and tin, 0 The entropy-regulated composite dielectric film has high temperature resistance, high breakdown strength, high polarization strength, high energy storage density and good stability.

The invention relates to the technical field of wireless communication, and discloses an unloading method in an ultra-dense millimeter wave MEC network. Aiming at the problems of insufficient terminal capability, high energy consumption of an ultra-dense base station, millimeter wave coverage limitation, communication security risk and the like under the condition of sharp increase of a calculation-intensive task, the method comprises the following steps: firstly, obtaining network basic information, constructing a network architecture containing communication, calculation unloading and a security model, and establishing a multi-constraint optimization problem; based on the optimization problem, initializing a multi-strategy black-wing plinuary optimization algorithm population through logic mapping and elite selection; updating individual positions through global and local search in algorithm iteration, and screening historical optimal individuals; and finally, configuring and unloading resources according to the configuration. According to the method, the NOMA technology, a multi-step unloading framework and a hybrid communication mode are combined, the optimization efficiency is improved through a multi-strategy black-wing optimization algorithm, the local energy consumption is reduced, the communication rate is improved, the safety is guaranteed, the optimal scheme meeting time delay and safety constraints is rapidly converged, and the user experience is improved.

The invention relates to the technical field of underwater robots, discloses an underwater autonomous flight control method and system for a submersible type unmanned aerial vehicle, and aims to solve the technical defects of rigid route planning, high energy consumption and poor task adaptability of an existing submersible type unmanned aerial vehicle in complex dynamic underwater environment flight control. The method comprises the steps of executing multi-modal underwater environment perception, constructing an environment state fusion model under a unified space-time reference, generating a predictive hydrodynamic field four-dimensional model, executing multi-target route optimization based on energy and risk cost, and implementing self-adaptive flight control based on feedforward compensation. By adopting the above technical scheme, the method can effectively reduce the energy consumption of the unmanned aerial vehicle, remarkably prolong the operation endurance time, improve the intelligence and task adaptability of route planning, and improve the flight stability and trajectory tracking precision. Therefore, the autonomy, the economy and the task success rate in an unknown, unstructured and strong dynamic underwater environment are enhanced.

The invention relates to the technical field of wind power generation, and discloses an anti-icing and deicing system and method for a wind turbine generator, electronic equipment and a storage medium, the system comprises a heating module which is laid on the surface of a blade and is divided into a plurality of heating subareas, and the heating subareas are used for independent control to provide local heating; the monitoring module is used for detecting and outputting the icing thickness and environmental parameters of the blade; and the control module is in communication connection with the monitoring module and the heating module, and is used for dynamically adjusting the power of the heating subareas based on the icing thickness and the environmental parameters, and realizing anti-icing control through an icing prediction model. Through the partition design of the heating module, the local icing area of the blade can be accurately heated, and energy waste caused by heating of the whole blade is avoided; the icing thickness and environmental parameters are monitored in real time, so that the control module can dynamically adjust the power, anti-icing operation is triggered in advance in combination with the icing prediction model, the heating energy consumption is remarkably reduced, and the problem of high energy consumption of an anti-icing and deicing system in the related technology is solved.

The invention discloses a fuel cell vehicle control method integrating fault diagnosis and energy management. According to the method, vehicle key operation parameters are collected in real time, a whole vehicle operation state vector is constructed, and fuel cell health diagnosis and preliminary power action generation are carried out at the same time based on the state vector. The diagnosis module obtains a health factor and a severity index through residual calculation, particle filtering and generalized likelihood ratio test, and the action generation module outputs an intelligent agent power distribution vector. And according to the severity index, the dynamic reconstruction power, the SOC and the power change rate constraint, optimal power distribution is obtained through optimization solution, and meanwhile, a high-severity state is intervened in combination with a hierarchical fault-tolerant strategy. The method also comprises a demand power prediction and online learning module which is used for improving the robustness and the adaptive capability of the optimization strategy. According to the invention, by tightly combining the health state of the fuel cell with energy management optimization, high-energy-efficiency, long-service-life and reliable dynamic control of the fuel cell system is realized.