286 results about "Load scheduling" patented technology

The invention provides a task load scheduling optimization method and system for distributed network nodes, and the method comprises the steps: determining a task transmission path between the distributed network nodes according to historical task congestion data and a real-time link load prediction result; adjusting a modulation coding strategy in a task transmission process according to the channel quality parameter on the task transmission path to obtain an adjusted modulation coding strategy; according to an energy consumption demand corresponding to the adjusted modulation coding strategy, combining load distribution of each communication link and each node on the task transmission path to generate a voltage frequency adjustment instruction corresponding to the distributed network node; performing collaborative optimization on the task transmission path, the channel quality parameter and the voltage frequency regulation instruction to generate a task load scheduling strategy; according to the invention, through collaborative optimization of dynamic path selection, adaptive modulation coding adjustment and precise energy consumption regulation, the transmission reliability, energy efficiency balance and system stability of the distributed network in a high dynamic environment are improved.

The invention discloses a power system load dynamic optimization method based on reinforcement learning. The method comprises the following steps: S1, collecting power system data to construct a state space; s2, constructing a hierarchical reinforcement learning model based on the state space, and dividing a high-level decision and a low-level execution task; s3, training a high-level decision model, and outputting a scheduling task target category instruction in a high-level state; s4, training a low-layer execution model, and outputting a control action in combination with a current node state and a high-layer instruction; s5, introducing an evolutionary mechanism to generate a strategy population and optimizing a low-layer execution model; s6, fusing an evolutionary mechanism and a strategy gradient to synchronously optimize individuals with excellent performance; s7, deploying the trained model to a power dispatching system; s8, performing model parameter fine tuning based on scheduling feedback; and S9, continuously applying the fine-tuned model to load scheduling control. According to the invention, power load accurate scheduling and strategy efficient adaptive optimization are realized, and system responsiveness and operation stability are improved.

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

The invention discloses a power grid data acquisition and analysis system based on big data, particularly relates to the technical field of power network monitoring, and is used for solving the problem of key feature loss caused by cross-hierarchy data semantic association missing of an existing hierarchical aggregation mechanism. Performing standardized filling and time label layered alignment processing on the original data through the equipment acquisition module; the feature extraction module screens an abnormal waveform fragment and a steady-state parameter offset based on the equipment type and the real-time fluctuation amplitude; the semantic association module generates a space-time association weight in combination with the regional physical topological relation and the reactive circulation path sensitivity; the feature clustering module reconstructs a region-level feature aggregation packet through kernel density estimation and spatial weighted fusion; the cross-layer analysis module dynamically corrects the aggregation weight based on the current flow direction and the transient energy distribution; and the decision generation module generates an equipment positioning instruction and load scheduling strategy combination through alarm template matching, and finally realizes accurate positioning of fault equipment and generation of a scheduling strategy.

The invention belongs to the technical field of intelligent power grids, and discloses an intelligent power grid load balance control method and system. The method comprises the steps of dividing a power grid into a plurality of regions based on a predetermined rule; based on the regional power supply quantity and the regional power consumption quantity, identifying an overload region with insufficient power supply quantity; formulating and executing a load balancing strategy for the overload area by adopting a natural heuristic optimization algorithm, wherein the load balancing strategy is a load scheduling strategy taking scheduling time minimization as a target; calculating the priority coefficient of the electric equipment in the overload area, and supplying power to each electric equipment according to the priority coefficient; effective balance and optimal allocation of intelligent power grid loads are achieved, emergency power supply is guaranteed to the maximum extent in natural disaster time, and therefore the power grid operation level and livelihood guarantee capacity are improved, and the power supply reliability and the power utilization efficiency of the power grid are improved.

The invention discloses a virtual power plant building key load priority distribution method and system, and relates to the technical field of load dispatching management. The virtual power plant building key load priority distribution method comprises the following steps: S1, acquiring load switching scheduling data, and preprocessing the load switching scheduling data; s2, identifying an executable switching window between the main power supply and the standby power supply, quantifying a switching impact index of a load, and screening a candidate load set; s3, evaluating the switching priority of the load and generating a sorting list, and sequentially selecting according to the upper limit of the residual impact capacity of the bus to form a current period execution plan; and S4, executing a scheduling instruction, recording operation parameters before and after switching in real time, constructing a disturbance evaluation input set, evaluating a load switching disturbance degree, judging a switching state based on an evaluation result, and triggering a feedback mechanism to adjust a subsequent sorting weight and an impact tolerance. The problem of bus impact caused by concurrent access of multiple loads in the main and standby power supply switching process is solved.

The invention provides a network load balancing algorithm based on a credential platform and a credential terminal, and relates to the technical field of credential, and the algorithm comprises the following steps: collecting real-time traffic data from a plurality of ports; carrying out preprocessing of normalization, abnormal value elimination and protocol field standardization on the collected data; performing multi-dimensional protocol fingerprint feature extraction on the flow of each port based on a protocol analysis algorithm; modeling historical and real-time traffic of each port by using a time sequence neural network, and outputting a traffic prediction value and abnormal traffic probability assessment in a future short time period; dynamically generating a multi-port distribution weight factor based on the protocol characteristics and the flow change trend of each port; inputting the prediction result and the real-time load state into a reinforcement learning scheduling agent; upon detection of an abnormal traffic trend, feed-forward load distribution adjustments are performed immediately. According to the method, the data processing efficiency can be improved, the load scheduling intelligence is realized, the resource allocation priority of the port of the credential terminal is ensured, and the service continuity is guaranteed.

The invention discloses a multi-model dynamic image analysis system, and relates to the technical field of artificial intelligence. The system adopts a heterogeneous computing architecture and comprises a preprocessing model, a multi-modal analysis model, an anisotropic correction model, a large language model, a task knowledge base embedding model and a KV cache management system. An incongruous task reconstruction mechanism based on hardware isomerism is constructed, a large language reasoning model is nested for analysis, and efficient reuse of computing resources is achieved by using a cross-model KV cache optimization technology. The system is provided with multiple graphics cards and a double-channel NUMA, and a multi-mode large language model carries out unsupervised or weak supervised training on a target detection model in the operation of the system. According to the system, through a dynamic load scheduling strategy, a mechanism for reasoning by using a processor and a memory enhanced display card in a full-load mode is innovatively designed, and three-section real-time streaming enhanced output is achieved. According to the system, through a dynamic load scheduling strategy, a mechanism for reasoning by using a processor and a memory enhanced graphics card in a full-load mode is innovatively designed.

The invention relates to the technical field of distributed computing, and discloses a heavy truck charging load scheduling-oriented distributed energy intelligent matching method, which comprises the following steps of: establishing a charging service computing power mapping table at a scheduling node; maintaining a shadow counter in a local memory, extracting a pre-estimated computing power consumption value according to an event type and accumulating the pre-estimated computing power consumption value to the shadow counter, and executing linear numerical deduction on the shadow counter according to a reference logic subtraction rate so as to simulate a scheduling data throughput evolution process; adjusting a linear deduction rate parameter according to the deviation between the state feedback data and the numerical value of the shadow counter; and distributing the charging matching task to a charging station edge computing node of which the shadow counter value does not exceed a preset logic saturation threshold value. According to the invention, through an open-loop estimation and closed-loop calibration mechanism of a local logic state, an instantaneous congestion risk caused by physical feedback lag is eliminated; and logic state consistency and self-adaptive distribution of the whole network computing power resources are realized.

The invention relates to the technical field of energy storage load scheduling, in particular to an energy storage load scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining historical electricity price, load power and renewable energy power generation power data of each time period before the current time as input variables, and carrying out the normalization processing to obtain input data; inputting the input data into an LSTM-Transformer prediction model, and predicting to obtain the power grid electricity price, the load demand and the renewable energy power generation amount in a future time period; establishing a multi-objective optimization model including electric charge cost, operating profit and energy storage life; substituting the prediction data into the multi-objective optimization model, and solving a Pareto frontier based on a multi-objective optimization algorithm to obtain a non-inferior solution set satisfying the multi-objective optimization model; and taking each solution in the non-inferior solution set as input, and screening the solution with the maximum Sharpley value as an optimal energy storage charging and discharging adjustment strategy. By accurately controlling the working state of the energy storage system, the interactive operation mode of the energy storage system and the power grid is optimized.

The invention discloses a space-time price-based computing power and electric power space-time collaborative flexible scheduling method and device, and relates to the technical field of electric power systems, and the method comprises the steps: obtaining the architecture and operation characteristics of a data center, pre-establishing an energy consumption model and a load scheduling model of the data center, and pre-establishing a computing power-electric power collaborative regulation double-layer optimization model, the calculation power-electric power coordinated regulation double-layer optimization model is based on an energy consumption model and a load scheduling model of the data center, takes the initiative of the data center as an independent operator and the influence of the load of the data center on the electricity price of the system into consideration, and is constructed by taking the data center as a decision-making main body; a lower-layer power grid direct current optimal power flow problem is converted into a KKT condition to serve as a constraint to be substituted into an upper-layer problem, a strong dual theorem and a Big-M method are utilized to linearize and solve the double-layer optimization model of computing power-power coordinated regulation, and a computing power-power space-time coordinated flexible scheduling result is obtained.

The invention discloses a glass factory load low-carbon regulation and control method considering capacity compensation and carbon quota, and aims to realize refined carbon emission control and flexible scheduling in the production process of a glass factory. By fusing multi-dimensional input of historical load power, load change rate, environment temperature, humidity, wind speed, electricity price, time and the like and utilizing an STFormer architecture to model a complex time dependency relationship, a kilowatt-hour electricity carbon emission factor in a future time period is predicted, and prior information is provided for low-carbon scheduling; with minimization of carbon emission and electric charge cost and maximization of capacity income as targets, a future load scheduling strategy is optimized under constraints of carbon quota, an electric power regulation range and equipment operation; and globally searching an optimal load control scheme through a quantum particle swarm algorithm, and generating a multi-scene-oriented dynamic regulation and control strategy under the constraints of a multi-target fitness function and a penalty mechanism. The method not only improves the precision and robustness of carbon factor prediction, but also realizes flexible scheduling of the glass factory load driven by carbon economy.

The present invention discloses a Hadoop cluster task scheduling method based on a positive and negative feedback load scheduling algorithm, which is applied to a Hadoop resource allocation center. The cluster task scheduling method comprises: an idle computing node applies to a management node for a task execution request; the management node receives the task request and queries whether there is a historical record of running the task request; the historical records are compared and analyzed, and if the task configuration meets the resource requirements, the task is calculated in this computing node; otherwise, the computing node is re-matched with a new task request; and computing tasks can be dynamically allocated in cluster task scheduling according to the resource usage of different servers and the differences in server performance, thereby achieving efficient completion of computing tasks and improving CPU and resource utilization efficiency.

The invention provides a large language model application workload scheduling method, system and device, and relates to the technical field of model load scheduling, and the method comprises the steps: modeling a composite large language model application into a directed acyclic graph comprising a conventional stage, an LLM stage and a dynamic stage; modeling execution correlation among stages in the directed acyclic graph through a Bayesian network, dynamically predicting duration distribution of uncompleted stages, and calibrating a duration estimated value of an LLM stage in combination with a real-time batch processing size of an LLM executor; the uncertainty reduction amount of each ready stage is quantitatively scheduled based on information entropy; an epsilon-greedy strategy is adopted, and a JCT priority queue and an uncertainty reduction priority queue are combined to allocate scheduling resources; and assigning the task to a corresponding executor for execution, and repeating the above process until all operations are completed. The technical problem that the scheduling technology in the prior art is difficult to effectively deal with the execution time uncertainty and the structure uncertainty of the composite LLM application is solved.

The invention discloses a cloud-side collaborative task allocation method and device based on task difficulty analysis, terminal equipment and a storage medium, and belongs to the technical field of load scheduling, and the method comprises the steps: obtaining a calculation amount index, a data amount index and a real-time requirement index of a to-be-allocated task, and carrying out the calculation to obtain task complexity; and according to the task complexity and the terminal computing power index of the terminal where the task to be allocated is located, whether the task should be independently processed by the terminal equipment or cooperatively processed by other terminals in the cloud server or the local area network is judged. According to the method, the task complexity is considered in the task allocation process, particularly the calculated amount index, the data volume index and the real-time requirement index of the task are concerned, and the cloud edge collaborative task allocation result obtained by implementing the method better meets the calculated amount condition, the data volume condition and the real-time requirement of the to-be-allocated task; the problem that the cloud edge collaborative task allocation result is low in practicability due to the fact that the task difficulty cannot be effectively perceived can be solved.

The invention provides a database query prediction and load scheduling method and device. The method comprises the following steps: obtaining a plurality of query tasks corresponding to a database system; respectively inputting the plurality of query tasks into a preset memory prediction model, and obtaining memory prediction results which are output by the memory prediction model and respectively correspond to the plurality of query tasks; the memory prediction result is a memory demand prediction value; performing load scheduling on the plurality of query tasks based on the memory prediction result and a dynamic scheduling strategy to obtain load scheduling information, and performing memory allocation processing on the plurality of query tasks based on the load scheduling information; wherein the dynamic scheduling strategy is a load scheduling strategy based on memory perception of the database system; the load scheduling information comprises a query execution sequence of a plurality of query tasks. According to the method provided by the invention, the memory prediction precision and the load scheduling efficiency of the query task can be effectively improved.

The invention discloses a server cluster task migration method, electronic equipment, a storage medium and a product, and relates to the technical field of server cluster management.The method comprises the steps that a task migration prediction result is obtained through prediction of a pre-trained load prediction model, and the task migration prediction result is obtained based on the task migration prediction result; and when the current running task meets the task migration triggering condition, determining a migration strategy of the current running task according to the current running task, and migrating the current running task to the target service equipment according to the migration strategy. The technical problems of non-uniform load scheduling, unreasonable migration opportunity, high migration cost, computing resource waste and excessive energy consumption in a server cluster in related technologies are solved, and efficient allocation and operation optimization of server cluster resources are realized through multi-dimensional operation data acquisition, intelligent load prediction, task migration strategies and the like.

The invention discloses a multi-port flexible interconnection control method, system, equipment and medium, and the method comprises the steps: obtaining first historical load information and real-time load information of a first flexible interconnection device, obtaining a plurality of clustering results based on the first historical load information, based on each clustering result and the real-time load information, judging whether the first flexible interconnection device breaks down or not; if a fault occurs, respectively acquiring second historical load information of each second flexible interconnection device, determining a plurality of standby devices based on each second historical load information, calculating a target distance between the first flexible interconnection device and each standby device, and determining the standby device with the minimum target distance as a target port flexible interconnection device; and connecting the load of the first flexible interconnection device and the distributed power supply to the target port flexible interconnection device, so that the target port flexible interconnection device performs load scheduling. Therefore, resource waste can be reduced while stable operation of the power grid is guaranteed.

The invention discloses an intelligent load scheduling method and system for a virtual power plant user side. The method comprises the following steps: acquiring original load data of the virtual power plant user side; performing feature extraction according to the original load data to obtain a feature vector; performing load demand prediction according to the feature vector to obtain a predicted load demand; according to the predicted load demand, adopting a load scheduling model based on the game theory to obtain a load scheduling scheme; wherein the load scheduling model comprises a local game layer and a global game layer, and in each game process, the load scheduling model determines the load decision weight of the user side according to the fault-tolerant factor, and adjusts the load decision of the user side based on the fault-tolerant factor. According to the method, the flexibility and accuracy of virtual power plant user side load scheduling can be effectively improved.

The invention relates to the technical field of power system optimization scheduling, in particular to a multi-region decentralized scheduling method based on demand flexibility aggregation, which comprises the following steps: (1) grouping and clustering transferable loads and interruptible loads to generate a flexible load aggregation model; (2) constructing an optimal scheduling model of the multi-region power system based on the flexible load aggregation model; and (3) decomposing each load cluster according to the optimal scheduling model of the multi-region power system, and ensuring that the decomposed load scheduling plan conforms to the user preference and system constraint of each load. According to the multi-region decentralized scheduling method based on demand flexibility aggregation, the scale of the scheduling problem is reduced through load aggregation, it is ensured that the scheduling result conforms to user preference and system constraints through load decomposition, the calculation complexity can be effectively reduced, and the renewable energy consumption capacity and economical efficiency of the system are improved.

The invention discloses a transformer area flexible load robust optimization method and device, electronic equipment and a storage medium, relates to the field of low-voltage transformer area flexible load scheduling, and realizes gradient utilization of flexible loads and improves the distribution transformer capacity utilization rate through a multi-time scale two-stage robust optimization model and considering distributed photovoltaic uncertainty. The method comprises the steps that an air conditioner load model, an electric vehicle charge state model and an energy storage charge state model are adopted to construct a transformer area flexible load model; establishing a multi-time-scale two-stage robust optimization model based on a robust optimization algorithm and the transformer area flexible load model; solving the day-ahead stage of the robust optimization model by adopting a column constraint generation algorithm to obtain an air conditioning load, energy storage station and charging station scheduling plan of the day-ahead stage; and based on the scheduling plan of the air conditioner load, the energy storage station and the charging station in the day-ahead stage, solving the intra-day stage of the robust optimization model by adopting a solver to obtain a real-time charging and discharging power scheduling plan of the energy storage equipment and the electric vehicle.

The invention belongs to the technical field of comprehensive energy management, and provides a comprehensive energy management and control method and system, a storage medium and electronic equipment, and the method comprises the steps: generating a power generation prediction power sequence corresponding to a preset time length based on historical power generation data through employing a first prediction algorithm; based on the historical energy consumption data of the plurality of energy consumption devices, using a second prediction algorithm to generate a device prediction load sequence corresponding to a preset duration; constructing a load scheduling algorithm of the adjustable load equipment, and generating a load scheduling model according to the power generation prediction power sequence and the equipment prediction load sequence; constructing a scheduling optimization algorithm of energy supply and energy load, and generating a scheduling optimization model based on the power generation prediction power sequence, the equipment prediction load sequence and the load scheduling model; and based on the load scheduling model and the scheduling optimization model, managing and controlling the energy consumption condition of the energy consumption equipment in a future preset duration range. The flexibility and the accuracy of load scheduling are improved; the solving efficiency is improved, and the precision of an optimization result is ensured.

The invention relates to a fault network card isolation method and device applied to a GPU cluster, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: determining whether a fault network card exists on any GPU node in the GPU cluster or not according to the state information of each network card in the GPU node; when it is determined that fault network cards exist on the GPU node and the number of the fault network cards is smaller than or equal to a preset threshold value, an NCCL topological file of the GPU node is updated, and the NCCL topological file of the GPU node is used for indicating the topological relation between available GPU devices and available network cards included in the GPU node; and according to the NCCL topology file of the GPU node, isolating a fault network card in the GPU node when carrying out load scheduling on the GPU node. According to the embodiment of the invention, the fault isolation granularity can be effectively refined to a single network card in the GPU cluster.

The invention provides a load optimization scheduling method and system based on a transformer area energy storage device, and belongs to the technical field of load scheduling, and the method specifically comprises the steps: determining the residual energy storage capacity of a standby energy storage device and a standby association relationship between the standby energy storage device and different power distribution transformer areas, in combination with the residual energy storage capacity of the transformer area energy storage devices of the power distribution transformer area with the standby association relationship, when it is determined that the standby adjustment coefficients of the standby energy storage devices meet the requirements, the standby adjustment coefficients of the different standby energy storage devices with the standby adjustment coefficients meeting the requirements and the coincidence condition of the power transmission lines of the power distribution transformer area are determined; the working modes of different standby energy storage devices are determined, and the stability of the load of the power distribution area is improved.

The invention discloses a micro-grid energy scheduling optimization method based on artificial intelligence load prediction, and the method comprises the following steps: collecting original data of a micro-grid, and carrying out the preprocessing of the original data, and obtaining the preprocessing data; constructing a causal directed acyclic graph by adopting a causal discovery method based on the preprocessed data to obtain a prediction feature set; constructing an improved causal convolutional network based on the prediction feature set; inputting the preprocessed data and the prediction feature set into an improved causal convolutional network for training, and generating a load prediction result; constructing a micro-grid energy scheduling model; inputting the load prediction result into the micro-grid energy scheduling model for solving, and generating an energy storage unit power distribution strategy, a renewable energy source output distribution strategy and a controllable load scheduling strategy; and updating the preprocessed data and the prediction feature set, generating a new load prediction result, inputting the new load prediction result into the micro-grid energy scheduling model for solving, obtaining an updated scheduling scheme, and executing the updated scheduling scheme.

The invention provides a micro-grid management method and system based on source grid load storage, and relates to the technical field of micro-grids, and the method comprises the steps: S1, obtaining renewable energy power generation data in a micro-grid, the state of charge (SOC) of an energy storage device, a load demand, and power grid operation state information; and S2, performing real-time prediction on the load demand by using an intelligent algorithm and generating a load scheduling plan. According to the micro-grid management method and system based on source grid load storage, load prediction is carried out by introducing an intelligent algorithm, power scheduling between renewable energy sources and an energy storage device is optimized by a dynamic coordination mechanism, and resources are allocated by a multi-objective optimization algorithm, so that efficient management of the micro-grid is successfully realized. By acquiring various data in the micro-grid in real time and combining with a dynamic adjustment scheduling strategy, the method can effectively deal with the uncertainty of load fluctuation and renewable energy output, optimize the operation efficiency of the power grid, and ensure the stability and economy of the system.

The invention discloses an artificial intelligence body for intelligent switching and scheduling of multiple heat sources of a hospital, and relates to the technical field of hospital heating management. The artificial intelligence body comprises an influence parameter acquisition module, a state space construction module, an action space construction module, a reward function calculation module and a reinforcement learning execution module which cooperate with one another to comprehensively consider heat source static capability parameters and dynamic operation data, and a load prediction and reinforcement learning optimization mechanism is combined, so that the dynamic performance of the heat source is improved. On-demand starting and stopping, dynamic switching and load self-adaptive distribution of heat sources are achieved, the energy saving performance, responsiveness and operation safety of the whole heating system are improved, then the optimal switching and load dispatching of the multi-heat-source heating system can be achieved, the intelligent level and operation efficiency of the system are remarkably improved, and practical application and popularization are facilitated.

The invention provides a multi-element load joint prediction method and system based on sequence decomposition and feature screening, and the method comprises the steps: collecting multi-element load data and related influence factor data of an integrated energy system, and carrying out the preprocessing, and obtaining the processed multi-element load data and related influence factor data; performing sequence decomposition on the processed multivariate load data, and fusing decomposed component sequences to obtain a multivariate fusion sequence; performing feature screening on the processed related influence factor data to obtain an influence factor feature set; and inputting the multivariate fusion sequence and the influence factor feature set into an improved multi-task learning model for joint training and prediction, and outputting a joint prediction result of the multivariate load. According to the multi-element load joint prediction method based on multi-source data decomposition and multi-task coupling learning, the model precision, generalization ability and operation efficiency can be effectively improved, and the multi-element load joint prediction method is suitable for cooling, heating and power load scheduling and energy optimization management of a comprehensive energy system.

The invention provides a flexible distributed energy network multi-agent collaborative optimization method based on MARL, and the method comprises the steps: building a distributed energy network multi-agent interaction framework which comprises a local energy transaction model for distinguishing an intelligent soft switching path, a network cost model based on a power transmission distribution factor, and a power distribution network operator and user income model; a physical system operation constraint model is established and covers intelligent soft switch operation characteristics, network topology radial constraint, energy storage charging and discharging dynamic states, controllable distributed power supply and photovoltaic output limitation, translational load scheduling and DistFlow power flow constraint. And finally, constructing a collaborative optimization mechanism based on a multi-agent near-end strategy optimization algorithm, modeling the system into a partially observable Markov decision process, adopting a centralized training distributed execution framework, and guaranteeing transaction security and data privacy through block chain evidence storage, RSA encryption and an intelligent contract. The system economy and the operation flexibility are improved.

The invention relates to the field of power management, in particular to a load power consumption management control method and system based on time-of-use electricity price, and the method comprises the steps: obtaining the historical power consumption data of each user, and constructing a fitness function of a load scheduling scheme; setting an initial population; calculating a variable coefficient of each user in each time period; for each individual, weighting a preset initial variation rate according to the variation coefficient of each user in each time period, and obtaining a weighted variation rate of each user in each time period; and iteratively updating the initial population by using the weighted variation rate, outputting the individual with the highest fitness in the current population as the optimal load scheduling scheme, and performing optimal scheduling of the user load according to the optimal load scheduling scheme. According to the method, the variation rate is dynamically adjusted, and excellent individuals can be quickly identified in the iteration process, so that redundancy calculation is reduced, the efficiency of the algorithm is improved, and the algorithm can give an optimal scheduling scheme in time.