135 results about "Load optimization" patented technology

The invention discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention discloses a power grid dispatching scheme generation method and system based on load optimization, and the method comprises the steps: predicting a reference load change curve and a confidence interval of each node in a future set time period according to the load data and meteorological data of each node in a power grid topological graph in a corresponding time period; continuously determining a risk area based on the risk assessment model and determining a predicted load offset and a standard deviation so as to correspondingly optimize and broaden a reference load change curve and a confidence interval of each node in the risk area; and constructing an uncertain scene set based on the reference load change curve and the confidence interval, then constructing an objective function, and solving the objective function based on the uncertain scene set to generate an elastic scheduling scheme with the minimum power generation cost and the minimum load vacancy in the worst load scene. According to the invention, through supplementing the prediction load offset and the standard deviation, the prediction precision of the short-term load change under the condition of sudden power consumption peak or extreme weather is obviously enhanced, and the power grid dispatching level is improved.

The invention discloses a load balancing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of load balancing optimization, and the method comprises the steps: carrying out the differentiation processing of a data migration task according to a file storage threshold value, and combining a migration performance index and server resource occupation information collected in real time, the task allocation strategy is adjusted through the dynamic task allocation algorithm, so that the reasonable allocation of the files with different sizes and the dynamic load optimization in the migration process are realized. The technical problems that in the prior art, server overload and resource idling coexist due to a single migration server or a static task allocation strategy, high delay and data inconsistency risks are generated due to network congestion and uneven task allocation in the migration process, and the overall performance and reliability of a system are affected can be solved. The technical effects of optimizing the load balance degree, reducing overload and idle conditions of the server, reducing migration delay, ensuring data consistency and improving the overall performance and reliability of the system are achieved.

The invention provides a particle swarm calculation improvement method for water chilling unit load optimization control. The particle swarm calculation improvement method comprises the following steps: establishing an energy consumption simulation model, and importing the energy consumption simulation model as a target function into a simulation environment; inputting the initialized population and the parameters into an energy consumption simulation model, calculating a particle fitness value under the scheme, and recording an initial global optimal solution; introducing a self-adaptive inertia weight factor on the basis of the particle swarm algorithm, and dynamically adjusting the inertia weight according to the relationship between a particle fitness value and an average fitness value; an adaptive learning factor strategy is introduced on the basis of the particle swarm optimization; based on a variation mechanism of a genetic algorithm, a variation probability which is reduced along with the number of iterations is designed on each particle dimension; judging whether the current number of iterations is the maximum number of iterations, and if yes, outputting an optimal solution; and if not, continuing iterative calculation. The improved algorithm has higher optimization efficiency in load distribution of the water chilling unit, and 3% of energy consumption can be saved compared with a traditional expert control strategy.

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

The invention belongs to the technical field of power system resource scheduling, and provides a building flexible resource load optimal scheduling method and system based on a game mechanism, and the method comprises the steps: obtaining a distributed architecture of a building flexible resource load; performing department-level and building-level layered modeling on the acquired distributed architecture to obtain a department-level model and a building-level model; carrying out distributed optimization solution on the constructed department-level model and building-level model by adopting an alternating direction multiplier method to obtain a distributed collaborative model; considering an aggregator, and constructing a single-leader-multi-follower game model which takes the transaction center as a dominant and takes the aggregator and a power generator as followers; and solving the constructed game model, and completing optimal scheduling of the building flexible resource load based on the game mechanism.

The invention discloses a thermal power generating unit load distribution optimization method and system, and relates to the technical field of load optimization distribution, and the method comprises the steps: quantifying the contribution degree of the position of each operation state monitoring device to a load prediction error based on operation influence data, and screening out a representative monitoring point set covering a unit core energy consumption node; constructing a multi-scale influence analysis model, identifying the change influence degree of the operation state data of each representative monitoring point on the load data of the thermal power generating unit, and generating a load distribution initial scheme; running state data of representative monitoring points are monitored in real time, a multi-target optimization mechanism is introduced, the weight value of each target is dynamically adjusted, and the load distribution of the thermal power generating unit is optimized in real time in combination with a load distribution initial scheme. Accurate dynamic load distribution based on multi-scale analysis and multi-objective optimization is realized, and the comprehensive benefit of a unit is improved.

The invention discloses a fine adjustment method for real-time load of a hydropower station, which relates to the technical field of hydropower station dispatching operation and comprises the following steps of: adopting an adjustment mode of firstly determining a starting-up combination and then optimizing load distribution; the method comprises the following steps: firstly, constructing a unit operation state comprehensive evaluation model considering multiple factors such as a maintenance plan, start-stop time constraint, accumulated power generation hours and a water consumption coefficient, and scientifically determining a start-up sequence; and then, comprehensively considering targets of reducing power generation water consumption of a power station and preventing a unit from frequently crossing a vibration area, and establishing a load optimal distribution model, thereby realizing fine adjustment of real-time load. By the adoption of the method, the hydropower station can quickly respond to the power grid load fluctuation requirement, meanwhile, the number of times of unit startup and shutdown and vibration area crossing is effectively reduced, an effective solution is provided for intelligent operation of the hydropower station under the condition that new energy is connected to the power system in a high-proportion mode, and the method is suitable for the scene of frequent load fluctuation under the new energy high-permeability background.

The invention discloses an energy storage control system load optimization and emergency support control method, system and device. The method comprises the following steps: firstly, constructing a multi-level water head sensor network to monitor water head data in real time, and dynamically determining power generation duration based on a hydraulic model; thirdly, combining the power generation duration capacity and the unit efficiency characteristic curve, and using an optimization algorithm to obtain an optimal load distribution scheme; then, a start control strategy is constructed, unit start-stop and load regulation unified management is achieved through an automatic control algorithm of a computer monitoring platform, and the system operation state is obtained; according to the state, a support scheme is determined through a capacity calculation model, and different requirements of a power grid can be flexibly met; when the power grid is abnormal, a corresponding scheme is called and executed in time, normal operation of the power grid can be recovered quickly, safety and stability are guaranteed, and influences on users are reduced. The method establishes a perfect emergency response mechanism, improves the emergency processing capability of the power system, and effectively improves the water energy utilization efficiency and the power grid stability.

The invention discloses a double-master-control dynamic cooperation hybrid power system and a control method. The system comprises a double-master-control dynamic cooperation framework, a master control unit A and a master control unit B synchronize data through a communication bus and dynamically distribute tasks according to a load rate; the hybrid energy storage expansion unit is formed by connecting a lithium iron phosphate battery pack and a capacitor bank in parallel through a bidirectional DC / DC converter; a dual-channel redundancy communication mechanism is adopted, a CAN bus is adopted for parallel communication with the Ethernet, automatic switching is achieved in the case of faults, and no data is lost; the predictive fault switching module is used for predicting a fault risk based on the LSTM neural network and starting standby main control unit preloading; and a machine learns a load optimization engine, deploys a reinforcement learning algorithm to dynamically adjust an output ratio and performs online optimization. The method comprises the steps of system initialization, master control task allocation and the like. The task processing efficiency and flexibility of the system are improved, reliable and stable communication is guaranteed, the fault risk is reduced, and the adaptability to load change and the energy utilization efficiency are improved.

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 load optimization scheduling method and system based on a thermodynamic and electrical coupling model, relates to the technical field of load scheduling, and solves the technical problem that the existing load adjustment method neglects the coupling relationship between thermodynamic characteristics and electrical parameters of devices, resulting in poor adjustment effect. The method comprises the steps of obtaining operation data of load equipment; processing the operation data to obtain initial data; extracting and fusing multi-dimensional data features of the initial data to obtain a model input vector; constructing a thermodynamic and electrical parameter coupling model; the optimal scheduling strategy is generated based on the coupling model and the optimization algorithm, and the technical problem is solved.

The invention relates to the technical field of server testing, in particular to a server multi-core computing processor load optimization testing method, device and equipment and a medium, and the method comprises the following steps: automatically identifying hardware configuration information of a server; generating a multi-dimensional pressure test plan based on the hardware configuration information; executing the test plan, and monitoring the load rate, the memory bandwidth, the cache hit rate and the I / O performance index of each CPU core in real time; dynamically adjusting a task allocation strategy based on a set load difference threshold to realize task dynamic optimization, and if the difference between the maximum core load rate and the minimum core load rate is monitored to exceed the threshold, triggering a task migration operation; system performance data are collected again, performance indexes before and after optimization are compared and analyzed, and system performance bottlenecks are recognized; and automatically generating a performance test report. The utilization rate and the overall task throughput of the multi-core processor are improved, and the problem that a static task allocation strategy is difficult to adapt to dynamic load changes is solved.

The invention is suitable for the technical field of electric vehicle and power grid interaction, and provides an electric vehicle charging and power grid load optimization scheduling method based on reinforcement learning, and the method comprises the steps: collecting power grid operation state data, electric vehicle user behavior data, weather environment data and traffic flow data; constructing a power grid dynamic model and a user state model; constructing a reinforcement learning scheduling model, wherein the reinforcement learning scheduling model comprises a state space, an action space and a reward function; a deep Q network is adopted to train and learn an initial strategy for the discrete action space, the continuous charging and discharging power is optimized based on a deep deterministic strategy gradient, and an optimized reinforcement learning scheduling model is obtained; the state space input of the optimized scheduling model is updated according to the collected data, the weight coefficient of the reward function is dynamically adjusted, a real-time charging and discharging scheduling instruction is generated, the electric vehicle charging requirement and the power grid load can be balanced, the power grid resource utilization rate can be optimized, and the system operation cost and the electric energy loss can be reduced.

The invention discloses a load optimization control method and system for a generator set, belongs to the technical field of power systems, and solves the problem that complex dynamic changes are difficult to capture when a set load is calculated through a difference value between a theoretical load and a real load in an existing method. The method comprises the steps of selecting operation correlation parameters based on a feature priority strategy, constructing a load prediction model based on a long short-term memory network, identifying and predicting operation load data, outputting a load rate prediction threshold interval of a generator set, and calculating a load distribution value corresponding to the generator set in a prediction period based on an optimal distribution function. According to the method, the operation correlation parameters are selected based on the feature priority strategy to obtain the correlation feature set which is high in correlation degree with different types of generator sets and has large influence on the load rates of the generator sets, the adaptability of the load prediction model to the different types of generator sets is improved, and the accuracy of adjusting the operation loads of the generator sets is ensured.

The invention discloses a building flexible load regulation and control method, equipment and a medium, relates to the technical field of Internet of Things and intelligent control, and solves the problems of high communication cost and low response speed of an existing regulation and control method. The method comprises the following steps: acquiring environmental parameters and equipment operation data in a building, and transmitting the environmental parameters and the equipment operation data to an edge computing node through a low-power wide area network; the environmental parameters and the equipment operation data are preprocessed, logic judgment is conducted on the preprocessed data through a machine learning model, and a preliminary regulation and control instruction is generated; performing load prediction on the global information of the building and the preprocessed data abstract, generating a load optimization strategy, and issuing the load optimization strategy to an edge computing node; and regulating and controlling the preliminary regulation and control instruction according to a load optimization strategy to generate a final regulation and control instruction. Through a hierarchical architecture of edge computing and cloud collaboration, dual effects of fast local response and high global efficiency are obtained, and at the same time, low-cost control equipment is used to reduce deployment and communication costs.

The invention relates to an unmanned aerial vehicle load optimization control system based on multi-sensor fusion, and the system employs an electro-optical sensor, an infrared sensor and a laser radar to collect multi-source data, and generates a state vector through the fusion of an extended Kalman filtering algorithm. A multi-source energy consumption model including a power system, a cradle head execution mechanism and load operation is established, the total energy consumption is calculated in real time, and a model prediction control framework is adopted to optimize a release strategy including the position, the opportunity and the attitude. An L1 self-adaptive control algorithm is introduced, modeling errors and external disturbance are compensated in real time, the response time is smaller than 50 ms, and the task completion rate reaches 87%. The system adopts a modular design and is compatible with an ROS 2 architecture and a PX 4 architecture, the positioning error is less than 1m, the delivery error radius is less than 5m, and the energy consumption is reduced by 15-25%.

The invention discloses a multi-task allocation and scheduling method for a multi-core data stream processor. The method comprises the following steps: step 1, building a system architecture; 2, task collection and sparseness calculation; step 3, dynamic priority calculation; step 4, implementing a load prediction mechanism; step 5, task scheduling and distribution; and step 6, evaluating and optimizing the system. By introducing a dynamic priority adjustment mechanism, the system can respond to changes of task characteristics and kernel states in real time, the scheduling efficiency is remarkably improved, and the dynamic adaptability ensures that key tasks can preferentially obtain processing resources, so that the processing delay is reduced; through a load prediction mechanism, the load among the cores is effectively balanced, and the situation that some cores are overloaded and other cores are idle is avoided, so that the overall resource utilization rate of the system is improved, dynamic scheduling and load optimization are integrated, the system can keep efficient operation in a complex data stream processing scene, and the throughput and real-time response capability of data processing are improved.

The invention provides a fan load optimization control method and system based on a load estimator, and belongs to the technical field of fan loads.A fan simulation model is established, and a fan blade thrust curve is generated through the fan simulation model; establishing a load estimator equivalent model of a fan pneumatic system under the condition that the fan blade thrust curve is known; determining a fan load safety threshold through the load estimator equivalent model; establishing a fan variable pitch system control model; and based on the fan load safety threshold value, the fan load is adjusted through a fan variable pitch control system, and specifically, the pitch angle and the rotating speed are adjusted to achieve fan load adjustment. Therefore, the timeliness of the response of the control system and the accuracy of load control can be improved, the load of the fan can be accurately controlled under the complex working condition of the fan, overload of the fan is prevented, the controllability of safe operation is improved, and the service life of the fan is prolonged.

The invention relates to the field of off-grid source load optimization, in particular to an off-grid source load storage scheduling control optimal configuration system based on calcium carbide production load, which comprises an integrated regulation and control center module; an off-grid source load storage scheduling control optimization system architecture for calcium carbide production load; a source network load coordination control module; the off-grid source load storage scheduling control is used for optimizing the source network load coordination control of the calcium carbide production load; a source load planning module; an off-grid source load storage scheduling control optimization source load plan for calcium carbide production load; and an emergency stability control module. According to the off-grid source-load storage optimization configuration technology, calcium carbide production load characteristics are fully considered, and the calcium carbide production load characteristics serve as flexible adjustment resources to participate in source-load storage optimization scheduling; the scale of calcium carbide production equipment, the wind power installation scale and the energy storage scale are reasonably determined, valuable reference is provided for investment decision-making work, and the calcium carbide production industry is assisted to accelerate'green power calcium carbide 'transformation.

The invention discloses a load optimization scheduling method, system and device for a virtual power plant and a medium, and the method comprises the steps: obtaining the target output data of a power grid system and the target electricity price data of the virtual power plant; a double-layer model is constructed, the double-layer model comprises a lower-layer uncertainty prediction model and an upper-layer multi-load optimization scheduling model, and the lower-layer uncertainty prediction model is used for predicting probability distribution of electricity price and renewable energy source output of the virtual power plant in a future time period; the upper-layer multi-load optimal scheduling model is used for generating a multi-load optimal scheduling strategy of the virtual power plant; inputting the target electricity price data and the target output data into the double-layer model to obtain a multi-load optimization scheduling strategy output by the double-layer model; and performing multi-load scheduling control on the virtual power plant according to the multi-load optimal scheduling strategy. The method can effectively improve the virtual power plant load optimization scheduling effect. The invention relates to the technical field of power dispatching.

The application relates to the field of screen control, in particular to a control system based on intelligent multi-screen cooperation, which comprises a processing unit, an analysis unit, a load optimization unit and a display optimization unit. The processing unit comprises a plurality of sub-processing modules and is used for processing target videos respectively. The analysis unit is used for determining the display state of each target screen according to a picture misadjustment reference value and a display decline reference value, and determining an optimization mode as load distribution optimization or environment optimization according to a first display state proportion and a first display state aggregation degree. The load optimization unit is used for determining the screen category of the target screen according to an image complexity reference value and a dynamic reference value, and determining an optimization processing mode as task distribution optimization or task order optimization based on a balance coefficient. The display optimization unit is used for determining the environmental light influence state corresponding to each target screen according to an illumination intensity coefficient and an illumination distribution coefficient, and determining whether to adjust the brightness of the target screen according to the environmental light influence state. The application improves the display effect under the control of the multi-spliced screen.

The invention relates to the technical field of sewage treatment, discloses a sewage treatment dynamic load optimization method and system based on rhodobacter online monitoring, and aims to solve the problems of unstable process, effluent fluctuation, increase of energy consumption and material consumption and the like caused by lack of real-time accurate biological phase indication when the existing sewage treatment is used for coping with dynamic change of inflow load. According to the method and the system, biomass and physical and chemical parameters are obtained through online monitoring of rhodobacter and sewage parameters; after data preprocessing, dynamic load analysis is carried out to predict inflow water / biological load and a rhodobacter growth trend; the optimization decision module generates and controls the execution module to implement key operation parameter adjustment; and the man-machine interaction alarm module carries out visual management. According to the method, real-time quantitative evaluation of the biological phase is realized, the dynamic load adaptability, the operation efficiency and the effluent stability of the system are remarkably improved, and the energy consumption and the material consumption are reduced.

The embodiment of the application discloses a distributed cooperative load optimization scheduling method and system for power distribution cabinets. The technical scheme provided by the embodiment of the application accurately calculates the target power value and the corresponding maximum sustainable time of the second power distribution cabinet target load which can be safely reduced according to the adjustable type of the second power distribution cabinet target load and the set comfort constraint boundary, and executes scheduling control, realizes accurate quantization of adjustable capacity and dynamic matching of time dimension, improves the accuracy and execution success rate of cooperative response between distributed power distribution cabinets, and effectively realizes dynamic balance and optimization of regional load on the premise of ensuring equipment safety and user experience.

The application relates to the technical field of power systems, and discloses a multi-time-scale computing power load management method and system, wherein the method comprises the following steps: obtaining historical operation data of various types of data centers; based on the historical operation data, the computing power load characteristics of each type of data center under a multi-time scale and corresponding processing adaptation rules are analyzed in sequence; in response to a load optimization signal, real-time operation data of a target data center obtained is integrated and processed based on the computing power load characteristics and the corresponding processing adaptation rules, so that multi-source real-time data under a multi-time scale are obtained; the multi-source real-time data are input into a computing power load dynamic prediction model, so that computing power load prediction results of the target data center under different time scales are obtained; and based on the computing power load prediction results, time-adjustable tasks of the target data center are migrated and scheduled, so that load management optimization of the target data center is realized. The method improves the utilization efficiency of the computing power load.

The invention provides an integrated energy system load optimization scheduling method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-dimensional data and target load data of a corresponding dimension; screening core features by using the maximum information coefficient; training an LSTM model based on federated learning; using the trained LSTM to output target load prediction data under the current multi-dimensional data corresponding to each key feature; correcting the predicted target load based on a PI feedback mode to obtain a corrected predicted target load; in combination with the corrected target load prediction data and the core feature decision variables, establishing a target function by taking the minimum comprehensive cost as a target; solving the target function by combining multiple agents, and outputting the adjustment amount of each core feature decision variable; and adjusting the integrated energy system according to the adjustment amount so as to realize load optimization scheduling of the integrated energy system. And the scheduling economy and response real-time performance of the integrated energy system are effectively improved.

The invention relates to the technical field of fuel cells, and discloses a dynamic load optimization system of a fuel cell multi-physics field coupling simulation model. The method comprises the following steps: establishing a multi-source data collaborative capture module, a multi-physics field coupling analysis module, a dynamic load optimization module, an execution module, an early warning module, an adjustment module, an evaluation module, a cross-scale connection module and a model iteration updating module; the multi-physics coupling analysis module performs analysis and calculation based on a four-dimensional parameter set, the dynamic load optimization module formulates a distribution strategy, the execution module executes a distribution instruction, the early warning module monitors the operation state of each unit of the electric pile in real time, and the adjustment module adjusts the operation parameters of the electric pile according to the feedback of the early warning module. The evaluation module is used for evaluating the overall operation performance of the electric pile, the cross-scale connection module is used for realizing data connection between a macro scale and a micro scale, and the model iteration updating module is used for iteratively updating a simulation model.

The application discloses a hydrogen refueling charging collaborative control method based on real-time load prediction and relates to the technical field of new energy vehicle hydrogen refueling charging control, which comprises the following steps: collecting multi-dimensional operation data through a hydrogen refueling station multi-scene load monitoring and analysis platform, mining load correlation and coupling relationship through a hydrogen refueling station load response adaptive model, deducing fluctuation trends and generating regulation and control parameters by using a load fluctuation compensation prediction algorithm, outputting load balance regulation and control instructions by using a dynamic load balance prediction model, and then constructing a collaborative control strategy set to dynamically regulate and control equipment operation parameters, energy distribution ratios and response time sequences. The method solves the defects of insufficient data fusion and lack of regulation and control collaboration in the prior art, realizes accurate multi-scene load adaptation and closed-loop regulation and control, and improves system operation efficiency and load optimization effect.

The invention provides an SM9-based client-multi-server rapid self-adaptive key negotiation method. The SM9-based client-multi-server rapid self-adaptive key negotiation method mainly comprises the following steps: an initialization stage, a private key extraction stage, a broadcasting stage, an offline preparation stage, an online response stage and a session key generation stage. The method has the following characteristics: 1, the method is suitable for a client-multi-server scene, a client initiates a communication request, a plurality of servers are automatically adapted to communicate with the client according to states such as self load and communication delay, and a key negotiation process only needs three times of communication; 2, aiming at a client-server communication mode, computing load optimization is carried out; different from the traditional P2P key negotiation with the same calculation amount of both parties, the method migrates the calculation overhead to the server side as much as possible; and 3, the client does not need to carry out complex bilinear pairing operation, and particularly, the calculated amount in an online response stage can be almost ignored. And meanwhile, an online / offline optimization technology is adopted, so that the client can carry out pre-calculation before communication, and the online response efficiency is improved.