1754 results about "Power demand" patented technology

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention relates to the technical field of cable laying management, and discloses a power industry cross-domain computing power dynamic scheduling system based on super intelligent fusion. According to the system, computing power demand data of each business domain in the power industry is collected in real time, computing resource demand characteristics in different business scenes are identified, and a cross-domain computing power demand characteristic graph is generated; meanwhile, the real-time load state and the available resource quantity of each computing node are continuously tracked, and a distributed computing power resource state matrix is constructed; analyzing and calculating a matching relationship between resource demands and available resources, and generating a multi-objective optimized computing power scheduling strategy scheme; according to the scheme, power business calculation tasks are distributed to optimal calculation nodes according to priorities and resource requirements, and a cross-domain execution process is triggered; and finally, the task execution state and the resource use condition are monitored in real time, an evaluation report is generated and fed back to a strategy generation module, closed-loop optimization is formed, and efficient collaboration and dynamic scheduling of cross-domain computing power resources in the power industry are achieved.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention relates to a motor power self-adaptive control method and system based on multi-dimensional sensor data, and the method comprises the steps: collecting multi-dimensional signals, such as ground resistance, motor temperature and battery voltage, in real time, carrying out the self-adaptive filtering and noise reduction, analyzing the correlation of all parameters through a multivariable decoupling algorithm, and determining a power demand weight; when it is detected that the ground resistance is a dominant factor, the system generates a load feature vector through weighted fusion, a database is matched to determine an optimal power target value, and PWM output is dynamically adjusted through a fuzzy control algorithm; and a closed-loop feedback mechanism continuously optimizes control parameters to ensure that the system quickly responds to sudden change of ground resistance. According to the method, intelligent adaptation of the motor power is achieved, the problems that adjustment lags behind and multi-parameter coupling processing is insufficient in the traditional technology are effectively solved, the stability of the sweeping robot under the complex ground working condition is remarkably improved, and the service life of the motor and the service life of a battery are remarkably prolonged.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention provides a coordination control method and device of a photovoltaic energy storage inverter for automobile charging based on artificial intelligence, and belongs to the technical field of automobile photovoltaic energy storage coordination control, and the method comprises the steps: monitoring the output power of a photovoltaic array, the state of charge (SOC) of an energy storage system, the state of a power grid and the power demand of a load in real time; a reinforcement learning algorithm is introduced, weather forecast and historical load data are combined, photovoltaic output and load demands in a future time period are predicted, and a power distribution instruction is generated; deciding a current operation mode and generating a mode switching instruction; seamless switching between grid connection and grid disconnection is controlled, and after switching is completed, the charging and discharging proportion of the lithium battery and the super capacitor is coordinated; integrating the energy storage real-time state and the power distribution instruction, and optimizing and adjusting the control parameters of the inverter in real time; continuously monitoring a running state and a switching process, and starting a standby mode when a fault is detected; and the data is uploaded to a monitoring platform. The system operation is efficiently optimized, the service life is prolonged, and stability is guaranteed.

The invention discloses a reactive compensation self-adaptive control method based on a power distribution network, which relates to the technical field of power systems, and comprises the following steps: collecting operation parameters of the power distribution network in real time and preprocessing the operation parameters, and identifying a load state and calculating a reactive demand by processing data; the state and parameter configuration of reactive compensation equipment are collected, an adjustment target table is obtained by calculating reactive deviation, and the equipment is dispatched and distributed; adjusting and controlling reactive power through a DDPG algorithm in combination with an OU noise mechanism and a curiosity mechanism based on the reactive demand, the reactive deviation and the equipment matching data, and issuing the adjusted reactive power to compensation equipment to execute reactive compensation; according to the method, a reinforcement learning framework fusing sensing, decision making, learning and control is constructed, multi-source state modeling, reward mechanism driving, refined Q value evaluation, strategy continuous optimization and physical constraint fusion are combined, and the reactive compensation adaptive control target in a complex power grid environment is achieved.

The invention relates to a charging pile system, a power scheduling method and device thereof and computer equipment. The method comprises the steps that under the condition that a first charging gun is connected with a load, a switch of an access between the first charging gun and a target power module is closed, a target charging gun corresponding to the target power module is different from the first charging gun, and the target charging gun is in an idle state; and under the condition that the charging demand power of the first charging gun is greater than the output power of the first power module, controlling at least one target power module to provide charging power for the first charging gun. The target power module can be quickly used for providing power for the first charging gun, so that the response speed to the power requirement is improved, and the charging efficiency is improved.

The invention relates to the technical field of power system automatic management, in particular to a virtual power plant optimal scheduling method and system.The virtual power plant optimal scheduling method comprises the steps that real-time multi-mode state data of all nodes are collected through an intelligent electric meter and a sensor, and a time sequence input tensor is constructed; a prediction tensor is generated based on the power system resource state and historical operation data, and abnormity is identified through difference operation with the real-time tensor; constructing an abnormal propagation map and identifying key linkage nodes; generating a candidate disposal path and an edge mapping strategy according to the key node data; testing a response index of each path in the virtual environment, collecting residual data, and selecting an optimal path; scheduling a processing sequence based on node priorities; and dynamically updating model parameters and a scoring function structure according to execution feedback. According to the method, the problem that tiny anomalies are difficult to perceive under the condition of complex power demands is solved, and model correction and strategy updating can be carried out according to disturbance response residual errors and simulation deviations after actual execution.

The invention discloses an intelligent charging priority distribution system and method based on multi-device identification, and particularly relates to the technical field of charging priority distribution. Equipment identity and charging demand information are obtained through an intelligent identification technology, a priority score is calculated in combination with a preset weight parameter, the real-time performance of priority distribution is ensured by adopting a dynamic threshold adjustment strategy, power demand fluctuation in a short time in the future is predicted based on a load prediction algorithm of machine learning, power distribution is optimized in advance, and the power distribution efficiency is improved. The method comprises the steps of reducing instantaneous load impact, monitoring equipment power in real time in the charging process, comparing the equipment power with the maximum distributable power, and dynamically adjusting the charging power or switching the charging sequence through a self-adaptive power adjustment mechanism if abnormality is detected, so as to guarantee the stability of a power grid and the safety of the equipment, and the method can effectively improve the utilization efficiency of charging resources and reduce the energy consumption. Overload of a power grid is prevented, multi-device charging scheduling is optimized, and safety, reliability and intelligence of the charging process are ensured.

The invention belongs to the technical field of charging pile power distribution, and discloses a charging pile power distribution method and system based on a depth-first algorithm, and the method comprises the steps: building a host power topology adjacency matrix as an initial adjacency matrix; obtaining a host fault state, and optimizing the initial adjacency matrix to obtain a real-time adjacency matrix; when a host fault state or charging gun power demand change is detected, each charging gun is taken as a starting point, the real-time adjacency matrix calculates each charging path and path efficiency through a depth-first algorithm, the charging paths with the path efficiency lower than a preset value are eliminated, weighting priorities of the remaining paths are calculated, and the charging paths are calculated according to the weighting priorities; selecting the charging path with the highest weighting priority as a target path; and performing cross verification on the target path and the parallel contactor path, if verification succeeds, outputting the target path, otherwise, outputting the path successfully verified last time, so that power distribution can be dynamically and flexibly adjusted according to the charging power demand changing in real time.

The invention relates to the technical field of data processing, and particularly provides a low-altitude aircraft power supply facility cooperative control method, system and device and a medium, and the method comprises the steps: predicting the renewable energy power generation power of a power supply facility and the charging load power of a low-altitude aircraft based on obtained multi-source information, and generating a prediction scene set representing uncertainty; based on the prediction scene set, through a multi-time scale optimization strategy, solving a scheduling model with optimal operation cost as a target, and successively obtaining a pre-scheduling plan of a day-ahead stage and a real-time control instruction of an intra-day stage; the multi-time scale optimization strategy comprises random optimization based on a scene and feedback optimization based on a rolling time domain; according to the real-time control instruction, power distribution is conducted on the hybrid energy storage system, and a power type energy storage unit preferentially responds to the transient power requirement for charging of the low-altitude aircraft. The comprehensive operation cost is effectively reduced, and particularly, the service life of equipment is prolonged through quantitative energy storage degradation.

The invention discloses a flexible dynamic distribution method and system for a power module based on a charging pile, and relates to the field of power distribution, and the method comprises the steps: collecting dynamic demand parameters of a charging terminal in real time, and calculating a target power demand in combination with a battery safety constraint; obtaining the total number of available power modules and rated power, and generating an effective module set; calculating the minimum module demand number according to the total demand and the module average power; if the modules are sufficient, an efficiency priority strategy is adopted, and distribution is optimized through a greedy algorithm; if not, high-power terminals are preferentially distributed, and then residual power is distributed in a balanced mode; through real-time data, distribution is dynamically adjusted when a threshold value is exceeded; starting dormancy for the module with the load rate lower than 30%, migrating the terminal to other modules, and awakening when the load rises to 40%, so as to improve the efficiency and the resource utilization rate. The method has the advantages that through real-time monitoring and dynamic distribution of the power modules, the charging efficiency and safety are optimized, and the resource utilization rate and the system reliability of the charging pile are remarkably improved.

The invention provides an intelligent optimization control method for the energy consumption of a packaging production line, and the method comprises the steps: collecting the processing data of the production of products of two specifications of vacuum can sealing and normal pressure can sealing, extracting the load change of a conveying belt during the switching of different specifications, and obtaining the fluctuation range of the load of the conveying belt; predicting the operation power scheduling of the equipment according to the instantaneous peak value and high-frequency fluctuation characteristics of the power demand, and outputting energy consumption peak value prediction data of the equipment during specification switching; determining and outputting an operation time sequence plan according to the energy consumption peak prediction data of the equipment during specification switching; monitoring the dynamic change of the conveyor belt load in real time according to the operation time sequence plan, obtaining the deviation value of the current load and the predicted load, and judging whether the deviation value affects the energy consumption distribution mode of the whole production line or not; and through the new energy consumption distribution result, generating and executing an equipment cooperative operation control instruction in combination with the operation time sequence plan.

The invention provides a hydropower station fusion computing power intelligent server system, and relates to the technical field of carbon emission. The method comprises the following steps: an acquisition correction module acquires hydropower station generation power data and server cluster instantaneous energy consumption data in real time, establishes a time sequence corresponding relation between a power generation side and a power utilization side, and obtains clean energy supply quantity data; the prediction scheduling module generates a non-tampering carbon footprint tracking chain number, and predicts a clean energy supply fluctuation trend sequence by adopting a long short-term memory network algorithm; training a random forest model to predict a computing power demand trend sequence; optimizing task scheduling according to a carbon emission minimization target, and generating dynamic balance configuration data of a computing power task and clean energy supply; and the monitoring evaluation module outputs computing power task level carbon emission and a clean energy use ratio in real time through an application programming interface according to the dynamic balance configuration data, and generates quantitative evaluation data of a computing power service carbon neutralization target by adopting a data visualization technology monitoring technology.

The invention is applicable to the field of power demand analysis, and provides a user demand-oriented power transaction matching recommendation method and system. The method comprises the following steps of: acquiring power consumption data of a user, and generating a resonance group and a feature packet with similar power consumption modes and risk tolerance; generating a power utilization comprehensive bidding plan in combination with the resonance group characteristics and the real-time market data; performing transaction matching based on the bidding plan, preferentially selecting the power generation resource with the closest electrical distance, introducing a green energy premium coefficient, and outputting a transaction result; and performing investment portfolio optimization analysis according to a transaction result, and generating a customized power package containing traditional energy, new energy and stored energy in combination with user risk preference. Through multi-dimensional data integration and intelligent algorithm application, accurate matching of power generation resources and user demands is realized, power transaction efficiency and user satisfaction are improved, and new energy consumption and sustainable development of the power market are promoted.

The invention discloses a rescue material transportation following robot based on oil-electric hybrid power and a control method, and aims to solve the problems of endurance, carrying and energy complementation of a pure electric rescue material following robot under the conditions of long-time heavy load, complex terrain and inconvenience in charging. The method comprises the following steps: acquiring multi-source state, motion and environment data, and predicting a power demand; dynamically selecting a pure electric charging mode, a range extending charging mode, a parallel charging mode or a parking charging mode; and generating parallel power and a motion instruction to drive the robot to follow operation. The system comprises a hybrid power unit, a following positioning module, an environment sensing module, a main controller, an energy management unit, a motion control unit and a driving execution unit. The problems of endurance and energy complementation of the pure electric robot are effectively solved, the operation continuity, the environment adaptability, the carrying efficiency and the man-machine cooperation safety are remarkably improved, and the system energy efficiency is optimized.

The invention provides a management scheduling system and method for computing power resources, relates to the field of management scheduling of computing power resources, and solves the technical problems that a large number of resources are idle or wasted and efficient resource allocation and scheduling are difficult to realize due to the fact that a resource allocation mode in the prior art cannot be dynamically adjusted according to actual requirements. The method comprises the following steps: a cloud server extracts a current idle computing power node and a corresponding computing power resource, and analyzes the computing power resource of the idle computing power node; the client obtains the computing power demand task, and calculates a difference index between the computing power demand task and the idle computing power node; based on the difference index, obtaining a matching computing power node of the computing power demand task; and when the plurality of computing power demand tasks correspond to the same matched computing power node, the priorities of the plurality of computing power demand tasks are analyzed, and the matched computing power node sequentially executes the tasks based on the priorities of the computing power demand tasks. The method and the device are used in a computing power resource management scheduling process.

The invention discloses a super-capacitor module voltage balancing method and device, a terminal and a medium, and the method comprises the steps: collecting the terminal voltage value of each single capacitor in a super-capacitor module according to a to-be-balanced super-capacitor module; then, according to the terminal voltage value, constructing a time-varying fractional order capacitor model to accurately describe the non-ideal characteristics of the super capacitor, and obtaining an adaptive parameter set comprising a fractional order parameter, an equivalent series resistance parameter, a capacitor capacity parameter and an aging factor parameter in combination with historical equilibrium data; and then a differential game decision-making device is adopted to carry out multi-target collaborative optimization on capacitance balance, power grid reactive power demand and energy consumption limitation, and a current instruction considering capacitance balance, power grid support and energy consumption constraint is dynamically generated by solving Nash equilibrium points of three optimization targets, so that precise balance of the voltage of the super-capacitor module is realized, and the accuracy of the voltage balance of the super-capacitor module is improved. The technical problem of insufficient model precision in a traditional strategy is solved, and the reliability of the system under complex working conditions is improved.

The invention discloses a wind power generation and gravity energy storage combined power supply system and method, and the system comprises a wind power generation unit, a gravity energy storage unit and an intelligent control unit. The gravity energy storage unit is dynamically controlled to store or release energy according to the difference value between the wind power generation real-time power and the threshold value, so that the combined output power is stable. The gravity energy storage unit adopts a symmetrical double-slope supporting tower and double-energy-storage structural design, and bidirectional conversion between electric energy and gravitational potential energy is achieved through a clutch mechanism. And the intelligent control unit dynamically adjusts the floating threshold by combining the predicted wind speed and the historical power fluctuation rate, so that the adaptability of the system to different wind fields is improved. The intermittent problem of wind power generation is solved through cooperative control of power generation and energy storage, the fluctuation amplitude of output power is reduced, peak clipping and valley filling can be achieved through a two-way energy conversion mechanism, energy release and power supply are achieved in the power demand peak period, energy storage is achieved in the valley period, the overall energy efficiency of a power grid is improved, and waste of energy in the power generation peak period is avoided.

The present disclosure relates to a standby power supply, including: a first sub power supply configured to provide alternating current electrical energy, where the first sub power supply is connected to an input terminal of an alternating current / direct current (AC / DC) converter; the AC / DC converter configured to convert the alternating current electrical energy into direct current electrical energy, where an output terminal of the AC / DC converter is connected to an output terminal of the standby power supply; and second sub power supplies configured to provide the direct current electrical energy, where the second sub power supplies are connected to the output terminal of the standby power supply. Through the present disclosure, a standby power supply solution having high reliability and capable of meeting power consuming requirements of a load with a great change in power demand such as a wind generator may be provided.

The invention relates to an electric engine, a power device and an aircraft, and belongs to the technical field of aircraft power equipment.The electric engine comprises a power motor and a liquid cooling flow path arranged in the power motor and comprises a stator support; the heat dissipation system comprises a fan, a radiator and a heat dissipation motor, an outlet of the radiator is connected with an inlet of the liquid cooling flow path, an inlet of the radiator is communicated with an outlet of the liquid cooling flow path, and the fan is used for cooling the radiator and / or the power motor; the motor rear cover is fixedly connected with the stator bracket; and the two direct-current converters are both mounted on the motor rear cover and are both electrically connected with the heat dissipation motor. The two direct-current converters convert high-voltage direct current accessed by the electric engine into low-voltage direct current capable of being used by the heat dissipation motor, and the electricity utilization requirement of the heat dissipation motor is met. Due to the redundancy design of the two direct-current converters, when one path of low-voltage power supply breaks down, the other path of low-voltage power supply can still supply power to the heat dissipation motor in a low-voltage mode, and enough fault handling time can be provided for the whole power supply system.

The invention relates to the technical field of unmanned aerial vehicle power systems, in particular to a low-altitude unmanned aerial vehicle hydrogen-electricity hybrid energy management system and control device.According to the low-altitude unmanned aerial vehicle hydrogen-electricity hybrid energy management system and control device, the residual hydrogen amount can be accurately quantified through combined collection and fusion calculation of hydrogen flow signals and air pressure signals; and the opening state of the proportional valve and the air inlet valve is adjusted in real time under data driving, so that the hydrogen input is matched with the actual power requirement, the voltage, the current and the temperature are synchronously monitored, and fluctuation feature extraction and life parameter calibration are performed on a power output sequence. The operation health degree and the life loss trend of the fuel cell can be dynamically obtained before power distribution, an accurate reference basis is provided for follow-up power allocation, the internal resistance change trend is measured and calculated through voltage and current sampling, and dynamic adjustment of the charge-discharge depth and charge-discharge process parameters is carried out in combination with the health degree. And the usability and the load capacity of the lithium battery are accurately represented.

The invention discloses a power system demand control method and device, equipment and a medium, and relates to the technical field of power control. The method comprises the following steps: collecting multi-dimensional sensing data and generating a power system state vector through denoising and filtering; inputting the power system state vector into a BP neural network, and outputting a PID initial control parameter; adjusting the PID initial control parameter based on the load change rate to obtain a PID control parameter; according to the target power demand and the actual power demand, demand deviation, a demand deviation grade coefficient and demand deviation acceleration are determined; according to the PID control parameters, the demand deviation, the demand deviation grade coefficient and the demand deviation acceleration, determining energy storage charging and discharging target power; adjusting the energy storage charging and discharging actual power through a sliding mode control algorithm; pID control parameters are adjusted according to the energy storage charging and discharging actual power so as to achieve the demand control target. By adopting the demand control method of the power system, the demand control precision can be improved.

The invention relates to the field of intelligent power grid and energy management, and discloses a park distribution box peak-valley scheduling monitoring method, system and device and a storage medium, and the method comprises the following steps: collecting and uploading the power data of a park distribution box in real time; on the basis of historical power data and meteorological information, predicting future power demands and identifying wave crests and wave troughs by using a deep learning model; generating a load scheduling strategy by adopting a reinforcement learning algorithm in combination with the prediction result and the real-time data; optimizing a scheduling strategy by using a particle swarm optimization algorithm, and outputting an optimal load distribution scheme; adjusting the load of the distribution box according to an optimization result, and balancing power resources; and when abnormity is detected, starting an emergency response mechanism to guarantee key load power supply. Through deep learning, reinforcement learning and a particle swarm optimization algorithm, accurate prediction and scheduling optimization of the park power load are realized, power resources are effectively balanced, the load response capability is improved, and key load power supply under abnormal conditions is guaranteed.

The invention relates to the field of computing power scheduling, and discloses an artificial intelligence-based computing power scheduling method, which comprises the following steps of S1, full-dimensional data acquisition: acquiring hardware resource data, task operation data, user demand data and heterogeneous computing power characteristic data through distributed sensing nodes; s2, data processing and feature construction: preprocessing the data collected in the S1, extracting multi-dimensional features through feature engineering, and constructing a feature matrix adapted to an AI model; s3, computing power demand intelligent prediction: learning the feature data preprocessed in the S2 by using a federal deep learning model, and outputting time-phased and type-divided computing power demand prediction results; and S4, scheduling strategy optimization generation. Hardware resources, task operation, user requirements and heterogeneous computing power characteristic data are comprehensively collected by relying on edge-core two-level distributed sensing nodes, the collection frequency can be dynamically adjusted between 50ms and 1mi n through a self-adaptive algorithm, and real-time performance and resource economy are both considered.

The invention relates to the technical field of ship energy control, and provides an energy efficiency optimization scheduling method for a marine SOFC heat recovery system, and the method comprises the steps: dynamically distributing an SOFC waste heat path through a heat energy distribution optimization algorithm according to the type of a current ship operation condition, and for a high-load condition, taking a propulsion power demand as a priority target, and carrying out the optimal scheduling of the SOFC waste heat path; the SOFC waste heat is preferentially guided into a ship power position to assist in propelling; for a medium load working condition, SOFC waste heat is distributed to auxiliary propulsion and life service energy according to a preset proportion, and the preset proportion is finely adjusted according to real-time requirements; and for a low-load working condition, the SOFC waste heat is preferentially introduced into life service energy, and the distribution sequence and proportion are dynamically adjusted according to environmental parameters. A machine learning classification model matched with a multi-threshold rule is introduced for working condition recognition, and recognition of different working conditions is achieved; by constructing a dynamic heat energy scheduling mechanism, dynamic optimal allocation of SOFC waste heat among different energy utilization paths is achieved, and the flexibility and adaptability of heat energy utilization are improved.

The invention discloses an intelligent scheduling control system for computing power and power load of a data center, and the system obtains key data of computing power and power in real time through a data collection module, thereby helping the system to master the operation state in time. The load evaluation module calculates a comprehensive load value based on the data, comprehensively considers calculation power and electric power dimensions, accurately reflects an actual load, clarifies calculation power demand and electric power supply balance, and avoids misjudgment of single-dimension evaluation; the comprehensive load value is compared with a preset threshold range through a scheduling strategy generation module, a strategy is generated in combination with service priority and computing power resource distribution, actual requirements are met, and the problems that in a traditional mode, consideration on the service priority is insufficient, and flexible scheduling cannot be achieved according to resource distribution are solved; computing power and power distribution are adjusted in real time through the scheduling control module according to a strategy, load changes and service requirements can be quickly responded, efficient operation of the data center is ensured, the resource utilization efficiency is improved, the operation cost is reduced, and the defect that traditional scheduling is difficult to adjust in real time is overcome.