177 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 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 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 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 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.

The invention discloses a modeling and control method and system for industrial factory building air conditioner load optimization prediction. The method comprises the steps that indoor and outdoor parameter data of an air conditioner system in each area of an industrial factory building are collected; according to a thermal comfort index-predicted average value pwv model and the parameter data, determining the thermal comfort of each area; according to the thermal comfort degree state of each area of the industrial factory building, a preset event triggering control strategy and a controller, the control strategy of the current air conditioner and the state of the controller are determined; according to the current control strategy of the air conditioner and the state of the controller, an intermittent control strategy of the controller is determined, and the intermittent control strategy is divided into a control interval and a non-control interval; and inputting the data of the control interval and the non-control interval into a pre-constructed physical information neural network model to obtain the predicted energy consumption of each area of the industrial factory building. And the air-conditioning system load prediction level is effectively improved.

The invention discloses a route dynamic generation method and system for virtual private line distribution, and particularly relates to the technical field of network optimization, and the method comprises the following steps: collecting a flow index in a set time window, calculating a flow load index, and dividing a low-flow environment, a medium-flow environment and a high-flow environment; determining whether to execute path adjustment analysis or not according to the environment type, calculating a time overhead value to evaluate path re-calculation cost, and calculating a path calculation load value to evaluate the calculation capability of the controller; preset strategy adjustment is performed according to the traffic environment, and whether a standby special line is started or not is intelligently decided by combining CNN model analysis, so that the network resource utilization rate is improved, the calculation load is reduced, and the path distribution is optimized; the method can solve the problems that a standby special line is easy to idle for a long time or unnecessary switching is easy to cause and resources are wasted in a traditional fixed path distribution mode, ensures that path adjustment is only carried out under the conditions of sufficient computing resources and acceptable overhead, and improves the network 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 method, device and equipment, a medium and a vehicle, and belongs to the technical field of computers. The method comprises the steps of obtaining a target load parameter of the automatic driving system; the target load parameter is matched based on a preset corresponding relation between the load parameter and a load score, a target load score matched with the target load parameter is obtained, and the target load score is in positive correlation with the load size of the automatic driving system; judging whether the target load score is greater than a load score threshold; and under the condition that the target load score is greater than the load score threshold, migrating a target task meeting a first preset condition in the automatic driving system to a target system for operation. Therefore, under the condition that the load of the automatic driving system is too large, at least part of tasks in the automatic driving system can be migrated to other systems to run, so that the load of the automatic driving system can be reduced as much as possible, and the tasks in the automatic driving system can be ensured to run normally.

The invention relates to the technical field of network node management, in particular to a distributed network node management system based on the Internet of Things, which comprises a network performance evaluation module, a node load optimization module, a node state monitoring module and a fault prevention operation module. According to the invention, by collecting the delay and data packet loss rate data of each network node in real time and evaluating the real-time performance of the network node, performance monitoring and stability evaluation are realized, the node processing capability is evaluated in combination with bandwidth information, and load distribution is optimized, so that network state changes can be responded more flexibly, resource utilization is optimized, and the network quality is improved. The deviation degree and the abnormal state of the node can be found in time through the operation key indexes of the Internet of Things technology monitoring equipment, the continuity and the stability of the network service are guaranteed, and the network service interruption is prevented by carrying out detailed health assessment on the network node and adjusting the network configuration parameters and activating the standby node according to the assessment result. And the reliability and efficiency of the network are improved.