99 results about "Load optimization" patented technology

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 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 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 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 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 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.

The invention provides an economic dispatching method and system for a steel smelting production process under demand response, and belongs to the technical field of steel industry load optimization dispatching. The method comprises the following steps: establishing an objective function of price and incentive joint demand response according to power demand response; establishing a state task network model of the iron and steel smelting production process according to the iron and steel smelting process flow; wherein the state task network model is used for representing a coupling relationship between a material flow and a process; establishing production constraint conditions of steel smelting according to the state task network model; and jointly solving the objective function and the production constraint conditions to obtain a scheduling scheme of the steel smelting production process. The method can fully excavate the potential of the industrial load participating in the demand response, effectively optimizes the production scheduling scheme of steel smelting under the demand response, and achieves the balance of economic benefit, production flow and production safety.

The invention discloses a source network load intelligent enhanced storage optimization operation method and a related device. Comprising the following steps: inputting topological structure information of a graph model of the source network load storage system, feature information of graph nodes in the graph model of the source network load storage system and feature information of graph edges in the graph model of the source network load storage system into a trained physical information graph neural network model, the physical information graph neural network model is trained based on a physical constraint equation set of a graph model of the source network load storage system to obtain an optimized operation strategy of the source network load storage system, the source network load storage system is controlled according to the optimized operation strategy of the source network load storage system, and the physical information graph neural network model is trained based on a physical constraint equation set of the graph model of the source network load storage system. According to the method and the related device, the physical consistency and reliability of source network load storage optimization can be improved.

The invention discloses a flexible load optimal scheduling method and device based on chaotic mapping, terminal equipment and a storage medium, and belongs to the field of flexible load scheduling. The method comprises the following steps: clustering a plurality of flexible loads according to user behavior data and a power curve to generate a plurality of load clusters; the method comprises the steps that a constraint function set is established, a load curve and a load adjusting range of the cluster center of each load cluster are determined, a plurality of initial scheduling schemes are generated through chaotic mapping according to the load curve and the load adjusting range of each cluster center, and iterative optimization is carried out on the initial scheduling schemes according to the target function under the constraint of the constraint function set to obtain the optimal scheduling scheme. Generating a target scheduling scheme; and scheduling the flexible load according to the target scheduling scheme. Therefore, the method solves the problem that the existing method does not consider the space-time association rule and the dynamic adjustment capability of the load and is difficult to realize the efficient calling of the flexible resources.

The invention relates to the technical field of energy internet control, and discloses a virtual power plant load optimization control method based on demand response. The method comprises the steps that real-time operation data and a power grid instruction are automatically obtained, dynamic behavior characteristics such as the load change rate and the fluctuation period are mined after data cleaning, and a load control strategy draft is generated according to the dynamic behavior characteristics. And then configuring control node parameters and collecting performance data, obtaining a current dynamic operation constraint of the system through simulation calculation, carrying out load distribution optimization solution on the condition, and finally generating a control instruction sequence and carrying out deployment execution. According to the method, the matching degree of the control strategy and the actual load response capability is improved through load dynamic behavior characteristic analysis, the feasibility of the optimization result is ensured by utilizing the dynamic constraint defined by simulation feedback, and more accurate and safer virtual power plant load regulation is realized.

The invention discloses an eBPF-based key value storage system performance optimization system and method, and the system comprises a traffic interception and analysis module which intercepts a key value request in real time, extracts request metadata, and recombines a cross-fragment request; the dynamic threshold fragmentation module is used for updating classification thresholds in real time so as to divide large requests and small requests; the resource awareness scheduling module is used for routing a large request to an isolation queue for processing, and preferentially accelerating the response of a small request through a kernel cache; the kernel level cache management module adopts an LRU elimination strategy and a write operation consistency lock mechanism to guarantee data consistency; and the hybrid load optimization module is used for aggregating a plurality of write operation requests through a batch processing submission method and merging adjacent interrupt signals through an interrupt aggregation method. According to the method, low delay, high throughput and low resource overhead of the key value storage system can be realized, the tail delay is stably lower than 150 microseconds under the mixed load, meanwhile, the CPU utilization rate is kept to be smaller than or equal to 85%, and the method is suitable for large-scale key value storage services.

The invention relates to the technical field of power systems, and discloses a multi-time-scale computing power load management method and system, and the method comprises the steps: obtaining the historical operation data of all types of data centers; sequentially analyzing computing power load characteristics and corresponding processing adaptation rules of various types of data centers under multiple time scales on the basis of the historical operation data; in response to the load optimization signal, performing integration processing on the obtained real-time operation data of the target data center based on computing power load characteristics and corresponding processing adaptation rules to obtain multi-source real-time data under multiple time scales; inputting the multi-source real-time data into the computing power load dynamic prediction model to obtain computing power load prediction results of the target data center under different time scales; and carrying out migration scheduling on the time-adjustable task of the target data center based on the calculation power load prediction result so as to realize load management optimization of the target data center. According to the method, the utilization efficiency of the computing power load is improved.

The invention discloses a load optimization method of an automobile controller and related equipment, which can configure codes of application software in an initial software integration stage so as to control a rack on which the codes are established, and can optimize the load of the application software after each time of code submission. Resource reports such as a static resource report and a stack use report and a real vehicle load test result are automatically generated and are finally used as a basis for enabling the load rate to meet a standard, and analysis reports and alarm mechanisms in the two aspects are integrated into a development and integration process by positively monitoring static resources of a controller and the use condition of a stack area; the load problem can be actively found and solved in the early stage of a project, and the maturity and reliability of controller software are greatly improved, so that the problems of operation clamping stagnation caused by over-high load of the controller, vehicle function failure caused by resetting and the like are solved, and traffic safety is guaranteed. The invention is widely applied to the technical field of automobiles.

The invention relates to the technical field of hydropower dispatching operation, in particular to a real-time load optimal distribution method and device between cascade hydropower stations and a medium, a hydropower station group system is generalized according to a hydraulic relationship and reservoir inflow, and a cascade reservoir group is simulated; constructing a load distribution model according to the constraint relationship, building a solving framework, designing an environment state, a reward function and an intelligent agent of the solving framework, and training the intelligent agent; and solving the load distribution model based on the solving framework, screening selectable load distribution schemes according to constraint conditions and distribution requirements, and outputting a final load distribution scheme in combination with a water level control target and water energy utilization efficiency, thereby realizing dynamic simulation of a real-time operation process of the cascade power station and optimization of an inter-station load distribution scheme. In a novel electric power system, regulation and control of multi-target tasks such as flood control and power generation can be taken into consideration, the decision-making efficiency in the model solving process is effectively improved, and quick response to load distribution under the high-intensity peak regulation and frequency modulation background is achieved.

The embodiment of the invention provides a load optimization method and device for program switching, and relates to the technical field of video processing, and the method comprises the steps: allocating memory resources and thread pools required by a first video decoding task on different NUMAs; monitoring the load of the video playing server, the frame buffer time length and the completion progress of the first video decoding task in real time; adjusting the load of the video playing server according to the frame caching duration; when the completion progress of the first video decoding task meets a first preset condition, starting to decode a second video decoding task, and allocating memory resources and thread pools required by the second video decoding task on different NUMAs; carrying out decoding processing on the second video decoding task by adopting a decoding rule matched with the type of the two-video decoding task; and adjusting the thread count occupied by the second video decoding task according to the real-time monitored load condition occupied by the first video decoding task. Through the program switching load optimization method, the program playing fluency can be improved.

The invention discloses an Internet of Things equipment state updating method and system based on Redis cache, belongs to the technical field of Internet of Things, and aims to solve the technical problem of how to report the state of Internet of Things equipment on the premise of considering real-time performance and database load optimization. Comprising the following steps: after an Internet of Things platform receives state information, reading cache state information from a Redis cache; comparing the core field information in the current report state information with the core state field in the cache state information; the currently reported state information is written into the MySQL database, and the Redis cache is synchronously updated; when the Internet of Things platform does not receive the state information reported by the Internet of Things equipment within the preset time, the Internet of Things platform is judged to be in an offline state; and when the Redis service is unavailable, switching to a direct writing MySQL mode and recording an abnormal log, and when the data written into the Redis service is unavailable, temporarily storing the current state to a local temporary cache, and retrying to write through an exponential backoff mechanism.