247 results about "Load leveling" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention relates to the technical field of smart energy management, in particular to a public energy consumption equipment operation and maintenance management system suitable for a smart park. The system comprises an equipment operation state sensing module, an energy consumption characteristic dynamic analysis module, an operation parameter optimization module, an equipment state balance regulation and control module, an equipment operation safety constraint verification module and an equipment cooperative operation execution module. According to the invention, through combination of time sequence analysis and load fluctuation trend extraction, dynamic monitoring and trend arrangement are realized, the accuracy and comprehensiveness of operation data are improved, the operation diagnosis capability is improved, the parameter adjustment flexibility is enhanced, multi-device cooperative regulation and control and load balancing are realized, energy consumption fluctuation is reduced, and the system efficiency is improved. And in combination with safety verification and offset risk analysis, potential risk areas are adjusted in real time, the operation reliability is enhanced, a global regulation and control scheme is dynamically generated, energy consumption waste is reduced, the management efficiency of public energy consumption equipment is comprehensively improved, and the efficient energy-saving target of the smart park is achieved.

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention discloses an efficient control and adjustment method for power grid energy storage batteries, and relates to the technical field of electric power energy storage, and the method comprises the steps: obtaining a plurality of energy storage batteries in a preset power grid energy storage module; performing battery control consistent classification to generate a plurality of groups of battery control units; carrying out the optimization of the charging and discharging speed and the charging and discharging depth for multiple groups of battery control units, and generating a plurality of bidirectional optimal control parameter sets; when the control center sends a charging instruction, the charging control parameter with the minimum battery attenuation index is extracted to perform charging control on the multiple groups of battery control units; and when the control center sends a discharge instruction, carrying out load balance distribution according to the plurality of bidirectional optimal control parameter sets, and carrying out discharge control on the plurality of groups of battery control units according to a balance distribution result. According to the invention, the technical problem of non-uniform load distribution during charging and discharging of the battery in the prior art is solved, and the technical effects of balanced load distribution for charging and discharging of the battery and improvement of the overall performance of the energy storage system are achieved.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention discloses a charging pile intelligent distribution system based on dynamic load balancing and a charging pile, and relates to the technical field of charging piles, a distribution module is used for receiving an operation feature group of each charging pile at a first response moment, generating a feature weight set, and simulating importance judgment logic in the feature weight set according to a feature priority, dynamic load distribution of the charging piles is realized; the process module is used for receiving the data of each source in real time in the dynamic load distribution process, performing multi-source analysis, checking the fault burstiness of the charging pile, analyzing the communication interruption, predicting the charging demand performance and the power supply performance based on the data of each source at a second response moment, and constructing a comprehensive risk index in combination with an evidence theory; and the updating module is used for generating a judgment signal according to the comprehensive risk index value, and the judgment signal comprises an updating instruction and an iteration result.

The invention discloses an intelligent load balancing energy-saving control system and method for a fast charging station, and belongs to the technical field of power distribution of the fast charging station, and the method comprises the steps: carrying out the load imbalance trend prediction based on the operation data of a battery changing cabinet and the data of a vehicle battery which is being charged, and when the potential imbalance trend of the fast charging station is monitored, carrying out the control of the intelligent load balancing energy-saving control system; constructing a power distribution model, adjusting the power distribution of the power conversion cabinet, setting a trigger condition of a dynamic adjustment power distribution scheme, and dynamically adjusting the power distribution of the power conversion cabinet; according to the received power distribution instruction, adjusting the output power of the battery changing cabinet, formulating a user guiding strategy according to the current power distribution scheme, and dynamically adjusting the charging and discharging process of the energy storage battery based on the power grid load data of the rapid charging station and the charge state of the energy storage battery; the invention aims to solve the problems of difficulty in prediction of unbalanced load, difficulty in dynamic adjustment of power distribution, lack of user guiding strategies and the like in the quick charging station and realize intelligent load balancing and energy-saving operation of the quick charging station.

The invention relates to a combinable modular automobile emergency power supply, and relates to the technical field of new energy automobiles and intelligent vehicle-mounted power supplies, and the combinable modular automobile emergency power supply comprises a standardized power supply module, a modular connection mechanism and an intelligent management system. Each group of standardized power supply module is an independent unit, is internally provided with a lithium ion battery, is uniform in size and is provided with a standard interface, two sides of the standard interface are provided with magnetic attraction type contacts, one side of each magnetic attraction type contact is provided with a buckle type physical lock catch, a main control chip is integrated, and the electric quantity, temperature and output state of the module are monitored in real time; output voltage and dynamic load balance are dynamically adjusted, so that the standardized power supply modules can be combined into a power supply pack, multiple protection mechanisms are provided, output is dynamically adjusted through a PID algorithm, safety is ensured in combination with a triple protection mechanism, and high-temperature stable operation is achieved in cooperation with a temperature control fan; and reliable connection is ensured through double verification of the magnetic contacts and the Hall sensors.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

Examples described herein relate to circuitry to access a request to perform operations written to a single queue. In some examples, the circuitry is to allocate the operations from the single queue to multiple queues associated with multiple accelerators based on load data of the multiple accelerators. In some examples, at least one of the multiple accelerators is associated with at least two queues.

The invention relates to the technical field of solid waste incineration automatic control, and discloses a solid waste incineration equipment automatic control method and system.The method comprises the steps that real-time data of solid waste components in a hearth are collected, and an initial multi-dimensional parameter set is obtained through preprocessing; calculating a balance point of the heat value and the water content through fuzzy reasoning; when the balance point exceeds a threshold value, parameter interaction influence is predicted, and a multi-dimensional control vector is generated; performing data fusion and historical data matching in combination with real-time incineration process indexes to obtain an enhanced load distribution adjustment factor; carrying out weight comparison based on the adjustment factor and the balance point, judging a load balance influence index, and carrying out matching correction if the index exceeds a compatible threshold value; updating a load distribution scheme according to a correction result, and generating a final operation strategy; and a control instruction is sent through a feedback cycle mechanism and is continuously optimized, so that closed-loop control is realized. The device can adaptively treat dynamic changes of solid waste components, and the incineration efficiency and stability are improved.

The invention discloses a power supply circuit and electrical equipment. The power supply circuit comprises a three-phase power supply and three rectification and inversion modules, wherein each rectification and inversion module comprises a first input end, a second input end and an output end; the first input ends of the three rectification inversion modules are respectively connected to three phase lines in a three-phase power supply in a one-to-one correspondence manner; second input ends of the three rectification inversion modules are connected to a neutral line in the three-phase power supply; the output ends of the three rectification inversion modules are respectively connected to three loads in a one-to-one correspondence manner; and the rectifying and inverting module is used for sequentially rectifying and inverting the input voltage and then supplying power to corresponding loads. According to the invention, the three-phase power supply is divided into three independent single-phase power supply paths for three loads, single-phase rectification is carried out on each phase independently, the voltage level is reduced, a low-voltage-level device can be selected, and the cost is reduced; the three-phase load is balanced, the harmonic dispersion is realized, and the harmonic pollution is reduced; fault area isolation is realized, and maintenance difficulty and maintenance cost are reduced.

The invention discloses a dynamic load balancing method and device for an intelligent charging pile management system, and the method comprises the steps: obtaining the operation parameters of a target area transformer corresponding to the intelligent charging pile management system in a current control period, so as to obtain the real-time total load of the target area transformer in the current control period; dynamically calculating the real-time available capacity of the transformer based on the rated power and the real-time total load of the transformer in the target station area; calculating the predicted available capacity of the transformer in the next control period through a load prediction model based on the historical power data and the external environment variables; and according to the real-time available capacity and the predicted available capacity, dynamically adjusting the operation power of the plurality of accessed charging piles, and expanding the number of the accessible charging piles in a load trough period. By fusing the real-time power grid state and the multivariable predicted future load demand, the dynamic fine adjustment of the power of the charging pile and the prospective expansion of the off-peak access amount are realized, the safety of the power grid is ensured, and the capacity utilization efficiency of the transformer is remarkably improved.

The invention relates to a low-voltage power distribution network inter-station power mutual aid method and system. The method comprises the following steps: monitoring the load state of each transformer area of the low-voltage power distribution network and the low-voltage side current of the transformer area; when an overload transformer area is detected, searching the adjacent transformer area meeting a load capacity margin condition and a low-voltage side current margin condition as a target light-load transformer area, and if the adjacent transformer areas are both overloaded, starting an orderly power utilization three-level progressive transfer scheme; and determining a target unloading load according to a transfer strategy, controlling the section switch to execute switching operation, and switching the target unloading load from the overload platform area to the target light-load platform area. According to the invention, load balance between stations can be effectively optimized, the power supply safety and reliability are improved, and the method has the characteristics of low investment cost, high automation degree and strong adjustment capability.

The invention discloses an automobile charging pile charging allocation method based on dynamic load balancing, and the method comprises the following steps: S1, collecting the real-time operation data of a charging pile, and storing the real-time operation data in a charging pile load database; s2, constructing a charging scheduling optimization model, and setting an optimization target and a constraint condition; s3, a charging power distribution scheme is initialized, population individuals are generated by adopting a doliolaria group algorithm, and a fitness value is calculated; s4, running the dogvessel squirt group algorithm for optimization, adjusting the power by the navigator according to the optimal solution, and updating the power distribution scheme by the follower; s5, population diversity is calculated, and if the proportion of low-diversity individuals is lower than a threshold value, differential evolution algorithm optimization is triggered; s6, the fitness values are compared, and an optimal charging power distribution scheme is selected; and S7, scheduling the charging power in real time and monitoring the state by applying the optimal charging power distribution scheme. According to the invention, charging power balanced distribution is realized through an intelligent optimization algorithm, user waiting time and power grid load fluctuation are reduced, and charging station operation efficiency is improved.

The invention belongs to the technical field of power data transmission, provides a power data release path planning method and system based on load balancing, and provides a multi-path transmission strategy optimization method based on LSTM prediction and MPC, and the method comprises the steps: firstly constructing a multi-path data transmission state prediction model through LSTM; acquiring a data transmission state prediction value of each path at the next moment; then, inputting the predicted value at the next moment into the LSTM model so as to obtain the data transmission predicted quantity of a period of time in the future; and finally, on the basis of taking the set minimum release time as a target function, adopting rolling optimization and feedback correction of MPC to obtain each path transmission control quantity meeting the target function. The efficiency that the electricity consumption information acquisition system publishes the total acquisition data to the marketing business application system is improved, and a load balancing strategy is realized.

The invention relates to the technical field of flywheel energy storage, in particular to a flywheel energy storage controller for a data center and a control method, and the controller comprises a flywheel rotating speed dynamic regulation and control module, an energy storage and release optimization module, a load balancing evaluation module, a real-time monitoring and feedback module and an overall efficiency evaluation module. According to the invention, energy storage and release can be optimized in real time, the energy utilization rate of the system can be improved, energy storage and release conditions can be accurately judged, control of a driving motor can be optimized, and the driving motor can be timely adjusted and maximized in energy output and storage according to load changes by accurately monitoring the rotating speed of the flywheel and dynamically regulating and controlling the rotating speed of the flywheel; energy conversion efficiency is remarkably improved, energy loss is reduced, stable operation of the system is ensured, a load distribution path is optimized through load balancing, load balance is ensured, control parameters are adjusted through real-time feedback, load matching efficiency is improved, energy consumption is reduced, and the overall efficiency of the flywheel energy storage system is remarkably improved.

The invention relates to the technical field of loader control, in particular to an extended-range loader multi-power-source cooperative work and load balancing control method which comprises the following steps: S1, collecting working parameters of each power source in the operation process of a loader; s2, calculating a current total load demand based on the working parameters collected in the S1; s3, analyzing and outputting the output capacity of each power source, including the maximum power output of the internal combustion engine, the power range of the motor and the working pressure range of the hydraulic system; s4, calculating the optimal output power of each power source according to the total load demand in S2 and the output capacity of each power source; and S5, dynamically adjusting the output power of each power source according to the optimal output power determined in the step S4. According to the invention, by dynamically optimizing the power distribution and the load balance of each power source, the energy efficiency and the working efficiency of the extended-range loader are improved, the energy waste is reduced, and meanwhile, the stability and the reliability of the system are ensured.

The invention discloses a power equipment anomaly detection method and device, equipment and a storage medium. According to the dynamic graph division method based on computational load awareness, partition layout of load balance is achieved, the problem of load imbalance between processing nodes is remarkably reduced, hotspot bottlenecks caused by high-frequency equipment concentration are avoided, the overall throughput and parallel processing capacity of a system are improved, and the performance requirement for real-time processing of high-frequency data is met. And a constructed partition-unit-device multi-level anomaly detection mechanism triggers the detection task of each level as required, so that the system resource overhead is effectively reduced, and the overall detection efficiency is improved. By analyzing the abnormal propagation path in the electrical topology and combining the spatial position information of the equipment, the abnormal region boundary expression is generated, so that the conversion from point-level identification to chain-level analysis and region-level visualization is realized, the operation and maintenance personnel can quickly position and respond, and the interpretability of the abnormal result and the practical value of scheduling are improved.

The invention discloses an analysis method and system for energy conservation and loss reduction of transformers, and the method comprises the steps: collecting the standby state data of a transformer group, recognizing a high-loss unit, and predicting the parameter drift trend of the high-loss unit; acquiring an electromagnetic impact strength signal in a working state; the parameter drift trend and the electromagnetic impact strength signal are fused, and the collaborative imbalance risk is evaluated; and according to a risk assessment result and a risk cause, dynamic voltage regulation and load balancing optimization are adaptively executed. The cross-working-condition correlation analysis and closed-loop control are carried out on the transformer group serving as the power supply core, so that the standby no-load loss can be effectively reduced, the equipment aging can be delayed, the operation cost can be reduced, the long-term stability and reliability of the power supply system can be ensured, and the method can be widely applied to the industrial fields of pulsed electric fields, precision manufacturing and the like.

The invention provides a heterogeneous computing power distribution method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of cloud computing resource energy efficiency evaluation. In the embodiment of the invention, the energy consumption condition of the server is quantified by using a power consumption function, the resource utilization efficiency is measured by using a load balance degree function, an energy + efficiency dual-objective function model is established to evaluate the energy efficiency of heterogeneous computing power, the two objective functions are optimized at the same time by using a dynamic multi-objective optimization algorithm, and meanwhile, the service index is taken as a constraint condition. According to the method, calculation tasks are distributed to the server with the highest energy efficiency, the dual purposes of high performance and low energy consumption are achieved, the purposes of improving the energy efficiency and reducing the cost are achieved, service level indexes are taken into consideration, and the response speed is improved while stable operation of a service system is guaranteed.

The invention provides an intelligent charging method and system for a charging and swapping cabinet, and the method comprises the steps: extracting the opening frequency of the charging and swapping cabinet and the swapping frequency of each battery pack from the historical swapping record of the charging and swapping cabinet, and determining the electricity load of the charging and swapping cabinet through the opening frequency and the charging performance in each battery state. Determining the charging priority of each battery pack according to the electricity load and the battery replacement frequency of each battery pack; extracting load characteristics of the charging and swapping cabinet from all the battery states based on a mapping relation among the battery packs in the charging and swapping cabinet, and determining a load balance degree of charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-path charging power; and according to the load balance degree and the charging priority of each battery pack, dynamically adjusting the charging power of each battery pack in the charging and swapping cabinet in the subsequent charging process. Based on the scheme, the power distribution efficiency in the charging process can be improved.

The invention discloses a multi-level flexible interconnection device configuration method considering maximum power supply capacity improvement, and belongs to the technical field of power distribution network optimization operation. According to the multi-level flexible interconnection device configuration method considering maximum power supply capacity improvement, the optimal configuration strategy of the flexible interconnection device is obtained through optimization model solving, and the maximum power supply capacity of the system is improved while the multi-level load balancing rate of the interconnection system is optimized. The method comprises the following steps: providing a load growth space analysis method based on the safe operation boundary of the power distribution network, constructing an SOP site selection optimization model based on multi-level load balancing, constructing an SOP constant volume optimization model based on maximum power supply capacity improvement, and realizing the maximum mining of the power supply potential of the power distribution network. According to the method, a multi-level flexible interconnection device configuration method considering maximum power supply capacity improvement is analyzed, the influence of the access position and capacity of the flexible interconnection device on the multi-level load balance degree and the load increase space of the power distribution network is explored, and reference is hoped to be provided for guiding optimal configuration of the flexible interconnection power distribution network.

The invention discloses a load balancing method and device for a heavy-load electric carrier, and the method comprises the steps: receiving various types of sensing data, the multiple types of sensing data comprise gravity distribution data used for representing the gravity distribution conditions of the cargos in the front, back, left and right directions of the heavy-load electric carrier, the acceleration of the heavy-load electric carrier and the total weight of the cargos; constructing a load distribution image of the heavy-load electric carrier according to the gravity distribution data and the total weight; inputting the load distribution image into a pre-trained gravity distribution uniformity identification model, and outputting a gravity distribution identification result corresponding to the load distribution image; and under the condition that the gravity distribution identification result indicates that the gravity distribution of the cargoes is not uniform, the gravity center of the cargoes is adjusted based on the gravity distribution identification result and the acceleration so as to carry out load balancing on the heavy-load electric carrier. By the adoption of the method, the efficiency of the mode of automatically adjusting load distribution is high, accurate load balancing can be achieved, and therefore the running stability of the heavy-load electric carrier is improved.

The invention, which relates to the technical field of the new energy vehicle power supply system, discloses a dynamic load balancing power supply system for a new energy vehicle charging pile cluster, comprising a data acquisition module, a load prediction and decision module, a dynamic power distribution execution module and a safety protection and communication module. The data acquisition module is used for monitoring three-phase electrical parameters, temperature and electric energy quality data of the charging pile cluster in real time; the load prediction and decision module is connected with the data acquisition module and is used for predicting a load trend based on historical and real-time data and generating a dynamic regulation and control strategy; the dynamic power distribution execution module is connected with the load prediction and decision module, accurate prediction and rapid dynamic regulation and control of the charging load can be achieved, the problems of three-phase imbalance, transformer loss, harmonic pollution and the like of a power distribution system caused by randomness and volatility of charging behaviors are effectively solved, and the intelligent graded safety protection capability is achieved.

The invention discloses a charging terminal, a charging control method, a storage medium and computer equipment. A switch assembly and a charging controller are arranged in the charging terminal, the switch assembly comprises a plurality of switch devices, a detection device and a load balancing controller are arranged in a charging station where the charging terminal is located, and the detection device is used for detecting current detection signals of three phase lines in the charging station; the load balancing controller is used for generating a scheduling instruction based on the current detection signal; the charging controller is used for generating a switch control instruction based on the scheduling instruction; and the switch assembly is used for controlling two switch devices in the switch assembly to be closed based on the switch control instruction, so that a load to be charged is connected with two phase lines in the three phase lines, and charging is carried out. According to the charging terminal, dynamic adjustment of the power load of the double-phase line charging terminal can be achieved, the power supply capacity of a station is utilized to the maximum extent, and the installation and maintenance cost of a charging system is reduced.

The invention relates to a task scheduling method and device, electronic equipment and a storage medium, and relates to the field of task scheduling. The method comprises the steps of obtaining a target task and configuration information of the target task; obtaining resource state information of each actuator in the actuator cluster in real time; according to the configuration information and the resource state information, a load balancing strategy is utilized to allocate a target actuator for the target task; in the process that the target task waits for the execution of the target executor, in response to the detection that the target executor meets a preset condition, determining whether an idle executor exists in the executor cluster or not; and in response to determining that the idle executors exist in the executor cluster, allocating the target task to the idle executors. The system performance can be improved, and the resource utilization efficiency can be improved.

The invention relates to a transformer area group optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: obtaining voltage, current and frequency of each edge node in a to-be-optimized transformer area group, photovoltaic output data of the transformer area group, energy storage charge and discharge state data and electric vehicle charging power data, and presetting various operation constraints set for the transformer area group; and inputting the voltage, the current, the frequency, the photovoltaic output data, the energy storage charge and discharge state data, the electric vehicle charging power data and the various operation constraints into a pre-constructed multi-target optimization model, obtaining optimization strategies meeting the various operation constraints, and performing optimization guidance on the operation of the transformer area group according to the optimization strategies. The transformer area group level multi-objective optimization task is calculated through the cloud high-performance computing cluster, so that an optimization strategy which is more adaptive to the current transformer area group is obtained, edge setting is controlled according to the optimization strategy to execute optimization output, and the load balance and operation robustness of the transformer area group are ensured.