228 results about "Load optimization" patented technology

The invention provides a digital power grid power cloud resource real-time hierarchical distribution method, which comprises the steps of dividing power cloud resources into a plurality of sub-resource pools according to a physical architecture and a topological structure of a digital power grid, setting a special resource pool for key equipment and sensitive loads of the power grid, and dynamically adjusting network topology and a routing strategy. A low-delay and high-reliability network transmission channel is provided through a priority queue and a bandwidth guarantee mechanism; for each sub-resource pool, extracting periodicity, tendency and randomness characteristics of a load by analyzing historical load data and a current system state of power grid equipment, and updating a load prediction result in real time; after incremental resources are migrated, a power grid control request is routed to a corresponding computing node by adopting a consistent Hash algorithm in a sub-resource pool, load balancing is realized by dynamically adjusting the number and distribution of virtual nodes of a Hash ring, and weights of the virtual nodes are dynamically adjusted according to load optimization indexes of the nodes so as to adapt to power grid load changes.

The invention relates to the technical field of network routing, and discloses a dynamic load balancing method and system for network routing, and the method comprises the steps: carrying out the periodic sampling and feature extraction of a processing load of a network node and a transmission load of a network link, and obtaining a load state vector and a network load degree; constructing a load balancing decision matrix, and performing comparison operation on the load balancing decision matrix and the multi-stage load threshold to obtain a load optimization instruction sequence; identifying a high-load source node set and a low-load target node set based on the load optimization instruction sequence, and performing load prediction modeling to obtain a task migration scheme; performing path optimization of reinforcement learning based on the task migration scheme to obtain a multi-routing path set; and carrying out load partition clustering and routing strategy distribution on the multi-routing path set to obtain a regional routing mapping table, and the method reduces the network delay, improves the resource utilization rate and realizes dynamic load balance.

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 the technical field of low-power-consumption AI processors, and discloses a load balancing method of a low-power-consumption AI processor, a chip and a storage medium, the method comprises the following steps: performing cache resource division on a plurality of neural mimicry cores in the low-power-consumption AI processor to obtain a double-layer cache structure; performing characteristic analysis on the target calculation task, and constructing a calculation task directed acyclic graph and a dependency intensity matrix; performing task segmentation to obtain a subtask division scheme; creating a task-core mapping table; organizing the neural mimicry core into a plurality of concentric ring layers based on the task-core mapping table to obtain a multi-layer ring cooperative computing network; and calculating a plurality of performance indexes of each neural mimicry core based on the multilayer annular cooperative computing network, and performing dynamic load optimization to obtain a dynamic load optimization strategy. According to the invention, the load balancing of the low-power AI processor is realized, and the calculation efficiency and the energy utilization rate are improved.

The invention discloses a BIM (Building Information Modeling)-based building energy efficiency optimization system and method, particularly relates to the field of building energy efficiency management, and is used for solving the problems of dynamic optimization and collaborative maintenance of building equipment aging and energy efficiency reduction. Initial attenuation data, historical operation data and environmental influence parameters of the equipment are embedded into the data, and dynamic adjustment and real-time tracking of the aging state of the equipment are realized in combination with a sensor network and edge calculation. The maintenance period of the equipment is predicted based on a random forest regression algorithm, a collaborative maintenance plan is generated in combination with physical position information in the BIM, and load optimization and time sequence coordination during multi-equipment maintenance are ensured. By calculating the dynamic adaptability nonlinear response coefficient and the performance regeneration residual nonlinear deviation coefficient, the load adjustment opportunity is accurately judged, the load distribution between the equipment is optimized, the overall energy efficiency of the building is effectively improved, and the full-life-cycle management and energy efficiency optimization of the building equipment are realized.

The invention relates to the technical field of ammeter monitoring, in particular to a detection method and system for ammeter load optimization, and the method comprises the following steps: collecting voltage and current data based on a load access terminal, judging synchronous offset, recognizing a dominant channel change mode, adjusting sampling rhythm configuration, and constructing amplitude mapping between voltage and power; and analyzing a multi-cycle response trend and classifying load states to obtain a load feature distribution label. According to the invention, by constructing a time sequence comparison structure of voltage and current signals in the same period, the identification capability of channel synchronization characteristics is enhanced, the interference of periodic rhythm drift on data sampling is avoided, the channel dominant trend is judged according to the spacing change proportion, the difference source has clear guidance, and the fluctuation identification precision is improved. In combination with a dynamic regulation and control strategy of a sampling rhythm, adaptive distribution of sampling frequency bands of key channels is realized, the detection granularity is improved, linkage expression among multi-channel signals is completed, and the depiction depth of a load response structure is enhanced.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a database load optimization method based on dynamic fragmentation, which comprises the following steps: establishing an association relationship between fragments and storage nodes by configuring basic logic fragments and dynamic logic fragments; monitoring a data access record, identifying hotspot data and marking the hotspot data as a to-be-migrated data block; when the fragment load exceeds a threshold value, migrating the high-load data block to the dynamic logic fragment; and when the access frequency is reduced, moving the low-load data block back to the basic logic fragment. According to the method, through configuration and management of the dynamic logic fragments, in combination with real-time monitored access records and load state analysis, the high-load data blocks are recognized and migrated to the dynamic logic fragments for storage, and the overload problem of the basic logic fragments is relieved. Meanwhile, the low-load data blocks are moved back to the basic logic fragments, dynamic adjustment and balance of storage resources are further achieved, the load balancing capacity of the system is improved, and the requirements for high concurrency and real-time load changes in a financial scene are met.

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 cabinet-level computing power server dynamic temperature regulation and load optimization method, and relates to the technical field of data centers, and the method comprises the following steps: collecting data, respectively building a thermodynamic model and a price prediction model, predicting a thermodynamic state, an electric power spot market price, and a renewable energy source supply condition, outputting and executing an optimal refrigeration system control plan by adopting an MPC algorithm; in combination with electric power spot market price data, cabinet-level computing power server energy consumption data and renewable energy supply data, the environment temperature of the cabinet-level computing power server and refrigeration system output are adjusted in real time through an MPC algorithm, and the future state of the cabinet-level computing power server can be accurately predicted. The MPC algorithm predicts the thermodynamic state of the cabinet-level computing power server and dynamically optimizes the temperature regulation strategy, the real-time performance is good, the temperature and the load can be adjusted in time, the stability and the energy efficiency of the cabinet-level computing power server are ensured, and safe and stable operation of the data center and the minimization of the energy consumption cost are ensured.

The invention provides a distribution network line dynamic optimization method based on multi-fault mode analysis, and relates to the technical field of distribution network management. Comprising the following steps: acquiring electrical parameters and real-time monitoring data of each fault section in a distribution network line, and acquiring a topological structure, a load demand and historical fault data of the distribution network line; carrying out load distribution optimization on the distribution network lines by adopting a dynamic load optimization algorithm according to the topological structure and the load demand of the distribution network lines in combination with the electrical parameters and the historical fault data, and adjusting the load distribution of each distribution network line based on optimization; and when a distribution network fault occurs, positioning a fault point and implementing a repair measure by combining the topological information of the distribution network and the electrical parameters of the fault section. According to the invention, by constructing the multi-fault mode analysis model, the fault occurrence position of the distribution network is detected in real time, and the fault point is quickly positioned; and in combination with an optimized repair strategy, the fault recovery time is effectively shortened, and the distribution network recovery efficiency is improved.

The invention relates to the technical field of task scheduling, in particular to a container computing-oriented dynamic scheduling system and method, and the system comprises a resource state modeling module which obtains CPU, memory and network bandwidth parameters and analyzes multi-node resource characteristics based on resource use data of nodes in a container cluster. According to the invention, through dynamic resource modeling, analysis of probability change of multi-node resources and accurate prediction of resource fluctuation, a scheduling system can rapidly deal with dynamic resource adjustment, real-time resource comparison is combined, refined load optimization and reallocation are executed, global resource balance is enhanced, resource waste and node overload conditions are avoided, and the scheduling efficiency is improved. In the task scheduling process, matching is carried out based on the priority of tasks and resource multi-dimensional data, scheduling intelligence and high efficiency are ensured, loads are monitored in real time, task distribution is flexibly adjusted, the loads are prevented from being concentrated in a few nodes, task migration among the nodes is optimized, the bottleneck is eliminated, and the system stability and the overall performance are remarkably improved.

The invention relates to the technical field of variable-speed wind generating set control, in particular to a wind generating set active power coordination control method for load optimization of a variable-pitch system. The method comprises the steps that a fuzzy controller is designed to update a variable pitch range limiting parameter # imgabs0 #, and # imgabs1 # and a rotating speed range limiting parameter are used for conducting coordinated control over a pitch angle and torque so that a unit can accurately track an active power instruction issued by a wind power plant, and the load of a variable pitch system can be reduced. According to the method, the rotating speed and the rotating speed variation serve as input variables of a fuzzy controller, a fuzzy rule is established to dynamically update a variable pitch range limiting parameter # imgabs2 #, rotating speed range limiting parameters # imgabs3 # and # imgabs4 # are further introduced, the rotating speed adjusting error at the moment is redefined, and active power control of pitch angle and torque coordination is designed according to the rotating speed adjusting error. The method is simple and easy to implement, the range that the rotating speed participates in active power adjustment at any pitch angle is effectively expanded, and the load of a variable pitch system is reduced.

The invention relates to the technical field of air conditioner control, and discloses an air conditioner load data control method and system.The method comprises the steps that firstly, meteorological parameters and load parameters of an air conditioner are collected, and data cleaning, timestamp definition and standardization processing are conducted; then, defining a feature vector according to the processed data, generating a feature data set, and constructing a load prediction model by using a neural network algorithm; and pre-training the model by using the source domain data set through a transfer learning strategy, and enhancing the target domain data to generate a new sample. Next, parameters of the pre-training model are migrated to the new task model, and partial network layer parameters are frozen to form an initialized reinforcement learning model; and finally, training unfrozen layer parameters by using the target domain data set, and finely adjusting the model to realize real-time and accurate optimal setting of the air conditioner temperature, thereby achieving the purpose of load optimization control. Through combination of transfer learning and reinforcement learning, the generalization ability, robustness and adaptability of the model are effectively improved.

The invention discloses a virtual power plant load optimization method based on source load uncertainty, and the method comprises the steps: introducing discrete random variables based on the source load uncertainty, including the source load uncertainty based on normal distribution and the output and load of a photovoltaic power plant based on probability distribution; the probability distribution condition of the output and the load of the photovoltaic power station can be better described, and a foundation is laid for the construction of a photovoltaic output and load model; a virtual power plant load optimization scheduling model is established by taking maximization of energy consumption and minimization of operation cost as targets, virtual power plant load optimization scheduling is realized, high-efficiency operation of the virtual power plant can be ensured, a good peak clipping and valley filling effect on an electricity price peak-valley trend can be achieved, economic benefits are relatively considerable, and the method has high reliability and is suitable for popularization and application. Scientific guidance is provided for power demand side management, and the problem that an existing virtual power plant load optimization scheduling method is extremely prone to causing standby insufficiency under the condition that the source load is uncertain due to the fact that standby demands are not considered is solved.

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 an energy consumption optimization method and system for a computing power server, and relates to the technical field of server optimization, and the method comprises the steps: monitoring the operation data of the computing power server in real time, and recognizing the task and load conditions; constructing a load prediction model to predict the future load of the computing power server, and carrying out the energy consumption optimization of the computing power server according to a predicted load scheduling task; continuously optimizing the load prediction model based on a reinforcement learning algorithm; and displaying the load condition of the computing power server in real time, and storing the operation data of the computing power server to a database. The load prediction model is constructed by collecting the processing tasks and the load data of the computing power server to predict the future load, the accuracy of load prediction is improved, task processing scheduling is performed by predicting the future load to achieve load optimization of the computing power server, and load energy consumption adjustment of the processing tasks of the computing power server is effectively achieved.

The invention discloses a data center energy consumption optimization method based on electricity price and load prediction, and relates to the technical field of scheduling optimization of a demand side, and the method comprises the steps: collecting parameters related to the energy consumption of a data center, constructing a data center energy consumption model based on workload transfer, proposing an electricity price and workload prediction model based on an LSTM neural network, and optimizing the energy consumption of the data center. The method comprises the following steps: constructing a data center load optimization scheduling decision model in a market environment by taking minimization of power purchase cost as an objective function and considering constraint conditions, and performing simulation verification on the model by utilizing an IEEE30 node system to obtain the electricity price of a prediction day, the arrived workload, the workload scheduling condition and the power purchase cost. According to the data center energy consumption optimization method based on the electricity price and load prediction, through introduction of the LSTM model, short-term and long-term fluctuation of the electricity price can be accurately predicted.

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 provides a self-adaptive seabed load optimization method and device, and relates to the technical field of ocean engineering. The method comprises the following steps: acquiring physical parameters of a seabed soil sample, load distribution of ocean engineering equipment and mechanical response data in an interaction process of the ocean engineering equipment and a seabed; and constructing a seabed mechanical simulation model. Based on the seabed mechanical simulation model and the simulation result data set, training a reinforcement learning model, and generating an optimal load distribution scheme; predicting seabed response results under different load conditions through the trained neural network; and dynamically adjusting the load distribution of the ocean engineering equipment according to the seabed response result and the optimal load distribution scheme. According to the method, real-time self-adaptive optimization of load distribution of the ocean engineering equipment can be realized, the operation stability and efficiency of the ocean engineering equipment are improved, seabed deformation can be remarkably reduced, and the influence on the environment is reduced.

The invention provides a power distribution network line fault diagnosis system and method, and belongs to the technical field of power system data processing, and the system comprises a data collection module which divides a power distribution network into different physical regions, and deploys a preset sensor group in each physical region to collect the related data of the power distribution network; the map construction module is used for determining the health state of each physical region and further constructing a regional health map; the data analysis module is used for deploying a monitoring tool, acquiring monitoring data of each physical area and analyzing the monitoring data so as to acquire a fault influence coefficient of each line of the power distribution network; the strategy making module is used for determining a corresponding prevention and control strategy and a self-healing strategy based on the fault influence coefficient of each line; and the load optimization module is used for determining a load distribution coefficient of each line based on the fault influence coefficient of each line so as to optimize power grid distribution. Effective power distribution network line fault diagnosis and analysis are achieved, load distribution of a power grid is optimized, and the self-healing capacity 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 provides a method for improving unit load splicing capacity, and relates to the technical field of power system load optimization, and the method comprises the steps: determining a calculation boundary condition of a power grid system in a preset region and an operation mode of a power plant unit based on the information of the power grid system; dividing the power plant units into fast adjusting units and slow adjusting units based on the adjusting time of the power plant units; dividing output gears of the power plant units based on the unit capacities of the rapid adjustment unit and the slow adjustment unit; based on the predetermined health state level of the power plant unit, optimizing the output gear of the power plant unit; in each operation mode, stability checking is carried out based on the output gear of the power plant unit; constructing an optimization model taking active power maximization of the load station as a target function; introducing stability check as a constraint condition of the optimization model; the optimization model is solved through an optimization algorithm, the maximum value of the power plant unit load connection is obtained, and the unit load connection capacity can be fully exerted after the large-capacity thermal power unit is put into operation.

The invention provides a regional power distribution network scheduling method and system based on flexible load optimization, and relates to the technical field of power distribution network scheduling, and the method comprises the steps: extracting a first type in preset flexible load types, and carrying out the traversing in a scheduling database, and obtaining a first database; if the first power distribution network feature accords with a predetermined power distribution network constraint, sending a scheduling evaluation instruction, the predetermined power distribution network constraint being a conditional constraint obtained by analyzing a historical power distribution network feature of the power distribution network of the target region; evaluating and analyzing the first scheduling feature to obtain a first scheduling fitness; adjusting by taking the first scheduling feedback coefficient as a weight to obtain a first effective scheduling fitness; and if the preset fitness threshold is reached, taking the first scheduling scheme as a target day-ahead scheduling scheme. The technical problem that a power distribution network scheduling strategy in the prior art is mainly based on historical data or a fixed mode and lacks a flexible adjustment mechanism for real-time load change, so that the stability and reliability of a power grid are affected is solved.

The invention relates to a load balancing algorithm based on deep learning. The load balancing algorithm comprises the following steps: step 1, system initialization and model preparation; 2, receiving and preprocessing a user request; step 3, collecting and analyzing server load data; 4, intelligent load distribution decision making; 5, processing and forwarding the request; step 6, an error processing and retry mechanism; 7, evaluating and optimizing the model and the strategy; 8, the user requests are completed, and the system is in a standby state. According to the invention, through the technical means of deep learning modeling, reinforcement learning mechanism, adaptive queue management and global load optimization, intelligent prediction, dynamic adjustment and global optimization of the server load are realized; and the positive effects of improving the load balancing efficiency, shortening the response time, improving the resource utilization rate, improving the user experience and the like are achieved.

The invention discloses a power grid load optimization regulation and control method and system, and the method comprises the steps: constructing a self-adaptive prediction module, an intelligent regulation and control system and a power failure risk assessment module, and enabling the self-adaptive prediction module to carry out the dynamic prediction of the operation state of a power grid based on the current state data of a power grid system, and generating a target load regulation and control strategy of the power grid; the intelligent regulation and control system generates an updated state reward value according to the target load regulation and control strategy and a state reward function used for evaluating load balance of the power grid, and regulates and controls the power grid according to the target load regulation and control strategy when it is judged that the updated state reward value is smaller than a reward value threshold value; and the power failure risk assessment module is used for identifying different operation modes of the power grid, assessing power failure risks in the modes and feeding back the power failure risks to the adaptive prediction model so as to continuously optimize the regulation and control strategy. According to the invention, the problems of untimely monitoring, slow regulation and control response, rigid scheduling strategy and the like of stable section management during the power failure of the power grid in the prior art are solved, the reliable operation of the stable section during the power failure of the power grid is ensured, and the efficiency and flexibility of power grid scheduling are improved.

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.