1018results about How to "Load balancing" patented technology

The invention provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention provides a private domain traffic peak identification and route scheduling method and system based on time sequence prediction, and relates to the technical field of private domain traffic management, and the method comprises the steps: predicting traffic data and confidence through a multi-dimensional heterogeneous graph network and a causal reasoning mechanism, and generating a dynamic threshold based on empirical mode decomposition and a generalized extreme value distribution model, and predicting a peak value by combining a fuzzy inference device. And then, constructing a distributed collaborative early warning mechanism, and generating graded early warning signals and early warning grades according to the multi-dimensional early warning indexes and node consensus. And finally, constructing a multi-target routing optimization model, performing routing resource grouping based on the link reliability score, searching candidate paths by using multi-agent reinforcement learning, determining a distribution proportion according to early warning information and a peak prediction result, optimizing a path selection strategy in a peak time period, and scheduling private domain traffic to a selected path. According to the method, the private domain flow peak value can be effectively identified, the service quality is guaranteed through dynamic routing scheduling, and the user experience is improved.

The invention discloses a big data dynamic allocation and optimal scheduling method based on reinforcement learning. The method comprises the following steps: S1, constructing a real-time operation data set; s2, preprocessing the real-time operation data set, and constructing a reinforcement learning environment model; s3, initializing a hierarchical reinforcement learning model; s4, generating an optimal data distribution and scheduling strategy based on the hierarchical reinforcement learning model; s5, adjusting data distribution and task scheduling of each computing node in real time to balance the system load and maximize the resource utilization rate; s6, the hierarchical reinforcement learning model continuously monitors the system state and dynamically updates the strategy, and self-adaptive adjustment is carried out on resource allocation and scheduling in time when the system load or environment changes; and S7, through multi-round iterative optimization, gradually converging the hierarchical reinforcement learning model to an optimal strategy. Through application of reinforcement learning, automatic optimization of resource allocation and scheduling strategies is realized, and the resource utilization rate, the scheduling efficiency and the adaptability of the system are remarkably improved.

The invention relates to the technical field of computer storage, in particular to a multi-level cache management method, system and equipment for solid-state storage equipment and a medium. The method comprises the following steps that multiple layers of caches are configured in the solid-state storage device and comprise a high-speed cache layer, a medium-speed cache layer and a low-speed cache layer, and each layer of cache has different access speeds and capacities and is used for storing data blocks with different priorities and access frequencies; dynamically scheduling the data blocks to a proper cache layer through a cache allocation algorithm according to the real-time access frequency, size, life cycle and importance of the data blocks; establishing a comprehensive data block priority evaluation model, and calculating a priority score of each data block; according to the multi-level cache management method, by configuring the multi-layer cache, the priority evaluation model and the multi-path scheduling mechanism, dynamic scheduling and load balancing of the data blocks among different cache layers are achieved, and the overall efficiency of data access is improved.

The invention provides a distributed service intelligent master selection method and system based on double-engine cooperation, and relates to the technical field of intelligent master selection, and the method comprises the steps: collecting the processing capability index, the network throughput and the resource utilization rate of a service node to construct a performance data matrix, and generating an intelligent identification code based on a node scoring function and a physical clock value; nacos and Redis signals are monitored through a double-layer event monitor, the optimal incremental step length is determined according to the score sequence distribution trend, and a target score is generated to construct a dynamic priority sequence; and periodically selecting a main node and a preheating standby node, and when the main node fails, starting increment state synchronization based on Redis affairs after voting arbitration confirmation, so as to realize main and standby non-inductive switching. According to the method, the election accuracy of the main node and the switching reliability are improved, and the system interruption risk is reduced.

The invention belongs to the technical field of computing power scheduling, and particularly relates to a computing power scheduling method in a distributed computing environment. According to the task scheduling method and system, the task needing to be processed at present is obtained through the task management system, the task is divided into the real-time task set, the priority task set and the common task set, and the related tasks are scheduled by adopting the corresponding scheduling strategy based on the set corresponding to the current task and the priority in the set. Resource scheduling and allocation in a distributed computing environment are realized; according to the method, through the steps of task classification, real-time resource monitoring, resource demand estimation, priority ranking, reasonable scheduling strategy, load calculation, resource migration and the like, the technical problem of computing resource scheduling and allocation in a distributed environment is effectively solved, timely processing of high priority is ensured, the system load is balanced, and the system performance is improved. And efficient utilization of resources and stable operation of the system are realized, so that the performance and reliability of the whole distributed computing environment are improved.

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

The invention discloses a power distribution system and method for a modular power supply, and aims to optimize power distribution through an intelligent means, improve the operation efficiency and stability of the system, and control a unit to check the states of all power supply modules, and evaluate the required total power output according to the load demand. Then, based on the capability of each module and a redundancy planning principle, an appropriate module combination is selected to meet the load requirement, and necessary voltage or current adjustment is carried out to ensure stable power supply. In the operation process, the control system continuously monitors the state and the overall performance of each module, and once a fault or an abnormal condition is detected, measures such as automatic switching to a standby module are taken immediately, so that the continuity and the safety of the system are ensured. In addition, through analysis and learning of historical data, the system can continuously optimize a power distribution strategy, and efficiency and reliability are further improved. And the ability of the system to deal with emergency situations is also enhanced.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention provides a WEB browsing real-time rendering method and system based on distributed rendering, and relates to the technical field of distributed rendering, and the method comprises the following steps: receiving a rendering request from a user terminal browser, obtaining page data of a target webpage according to the rendering request, preprocessing the page data, analyzing the preprocessed page data, and displaying the analyzed page data. Obtaining a DOM tree structure and a CSSOM tree structure of the page, and constructing a rendering tree adaptive to the user terminal according to the DOM tree structure, the CSSOM tree structure and the equipment parameters; dividing the rendering tree into a plurality of sub-rendering trees, transmitting sub-rendering results generated by the plurality of distributed rendering nodes to a synthesis service node, performing image compression, transmitting the compressed rendering result data to a user terminal browser, receiving and decompressing the rendering result data in the user terminal browser to obtain a rendering result bitmap, and displaying the rendering result bitmap in the synthesis service node. And displaying the information to the user for real-time interactive browsing of the user.

The invention discloses a micro-service scheduling method and system based on edge computing, and relates to the technical field of distributed edge computing, and the method comprises the steps: collecting historical service index time sequence data and service demand description information of a micro-service; respectively inputting the historical service index time sequence data of each micro-service and the corresponding service demand description information into a time sequence analysis model to determine a predicted service index of each micro-service in a future time step; generating a resource cooperation notification according to the intermediate state data of the risk micro-service and the service demand description information of the corresponding first queue request, and broadcasting the resource cooperation notification in the edge network; and when the response reply message is detected, migrating the risk micro-service and the queue request to the second target edge node. Therefore, a decision-making mechanism based on data driving is realized, a service distribution and resource scheduling strategy in an edge network is dynamically adjusted, and the fault-tolerant capability of micro-services is enhanced through service migration between edge nodes.

The invention relates to an Internet of Things equipment intelligent management platform based on edge computing, and aims to realize efficient and intelligent management of Internet of Things equipment through an edge computing technology. According to the platform, the edge nodes are deployed near the data source, so that local processing and storage of the data are realized, the data transmission delay is remarkably reduced, and the response speed of the system is improved. And the platform is provided with an intelligent management module, so that the equipment state can be monitored in real time, faults can be predicted, repair operation can be automatically executed, and the stability and reliability of the system are enhanced. And a self-adaptive data stream processing algorithm is adopted, and the data stream is dynamically adjusted according to a real-time network state and equipment requirements, so that the high efficiency of data processing is ensured. The secure transmission module protects the data transmission process through an end-to-end encryption technology, and prevents data leakage and tampering. The cloud-side collaborative resource scheduling system realizes optimal allocation of resources, and a user-defined rule engine provides high flexibility and personalized management strategies to meet different user requirements.

The invention relates to the technical field of micro-grid distributed control, in particular to a micro-grid cluster distributed control method and system based on information interconnection. The method comprises the following steps: identifying a micro-grid cluster in a region, and extracting a plurality of micro-grid nodes; obtaining a plurality of microgrid operation state parameters; performing filtering standardization processing on the plurality of micro-grid operation state parameters, and constructing a micro-grid time sequence load change curve; discrete wavelet transform is carried out on the micro-grid time sequence load change curve, load fluctuation frequency trend mining is carried out, and micro-grid load fluctuation frequency trend characteristics are generated; carrying out data stream encapsulation on the load fluctuation frequency trend characteristics of the micro-grid, and constructing a cloud environment distributed computing framework; carrying out dynamic load demand calculation on the plurality of micro-grid nodes based on a cloud environment distributed calculation framework so as to obtain multi-period load demand prediction data of each micro-grid; according to the invention, high-efficiency microgrid cluster control with high energy utilization rate is realized.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention belongs to the field of video monitoring systems, and particularly relates to a cascade-based multi-platform data synchronization method and system, and the method comprises the steps: designing an interface security mechanism of cross-platform data exchange; constructing a data synchronization service architecture, namely deploying corresponding data synchronization services at different levels of platforms to realize resource data synchronization, and supporting data synchronization of multi-level cascade or platform interconnection; performing data initialization synchronization; real-time synchronization of data change is realized; establishing a service heartbeat keep-alive mechanism; service multi-instance deployment is carried out, and efficient data processing and flexible load balancing are achieved. According to the invention, many defects of a data synchronization mechanism of an existing video monitoring system are effectively solved, and the stability, expansibility and data processing efficiency of the system are improved.

The invention discloses an intelligent scheduling method and system for an aircraft jumping hangar. The method comprises the steps that the hangar obtains local aircraft flight operation data in real time; the hangar acquires stop request information of the jump flight aircraft in real time, performs data communication with the aircraft control center in real time according to the stop request information, acquires flight operation data of the aircraft, and performs data synchronization on the state of the aircraft and the hangar; acquiring real-time scene operation data of the hangar, and performing aircraft cluster scheduling according to the real-time scene operation data of the hangar; the state data of the hangar and the aircraft are obtained and synchronized in real time, deep reinforcement learning and mixed integer programming are combined, the scheduling strategy is dynamically adjusted, load balancing and optimal resource utilization are achieved, the problems of scheduling chaos and errors caused by communication faults in an existing hangar scheduling system are solved, and the scheduling efficiency is improved. The DRL model optimizes task allocation, ensures load balance and reduces waiting time by learning real-time states of an aircraft and a hangar.

The invention relates to the technical field of computer-aided engineering, and provides a task resource scheduling method based on a containerized environment, and the method comprises the steps: receiving a target task and attribute information thereof, the attribute information of the target task comprising a type, a resource demand condition and a priority of the target task; according to the attribute information of the target task, calculating cost required for solving the target task is evaluated; according to the attribute information of the target task and / or the calculation cost required for solving the target task, distributing the target task to a container instance corresponding to the calculation cost required for solving the target task for processing; monitoring operation conditions of the nodes and the container instances in real time; and according to the attribute information of the to-be-solved task and the running conditions of the node and the container instance, dynamically adjusting the established allocation of the to-be-solved task in the container instance. According to the technical scheme, stable operation of the system, quick response to the to-be-solved task and reasonable and efficient utilization of resources can be guaranteed.

The invention provides a task allocation method, device and equipment based on a distributed cluster and a computer readable storage medium. The method comprises the following steps: acquiring to-be-allocated tasks and determining priorities among the to-be-allocated tasks; selecting a to-be-allocated task node from the low-load nodes according to the task processing capability and the task processing type of the low-load nodes; according to the task number and the priority of the to-be-allocated tasks and the task processing capability and the task processing type of the to-be-allocated task nodes, estimating whether the load of the to-be-allocated task nodes after task allocation exceeds a preset load threshold value or not; if the load does not exceed the preset load threshold, establishing a distribution relationship between the to-be-distributed task and the to-be-distributed task node; and distributing the to-be-distributed task according to the distribution relation. According to the embodiment of the invention, tasks can be distributed more accurately, node overload and software crash are avoided, and load balancing is realized.

The embodiment of the invention provides a resource management method and system based on cloud edge collaboration. According to the method, firstly, an intelligent AI management platform can be constructed to better allocate computing, storage and network resources; and then resource requirements of the static resources and the dynamic resources can be accurately estimated by monitoring calling frequencies of data interfaces of the static resources and the dynamic resources, so that dynamic allocation of the computing resources is realized, enterprises can be helped to better manage and utilize the computing resources, the stability and reliability of the system are improved, the operation cost is reduced, and the economic benefit is increased. And greater value is created for enterprises. Meanwhile, intelligent scheduling of resources can be achieved based on load balancing, tasks can be automatically distributed to nodes with low loads according to the load conditions of all the nodes, and therefore load balancing is achieved, and the overall performance of the system is improved. Through the dynamic resource allocation process, the resource allocation is more reasonable.

The invention relates to the technical field of data processing, provides a strategy selection method for node backup in a multi-domain network, and solves the problem that an existing backup management method cannot effectively deal with complex resource distribution in the multi-domain network. A weighted undirected graph is used for representing a network structure, a multi-domain network model is trained, a random forest algorithm is used for evaluating storage network element node attributes, node priority ranking is determined, reasonable allocation of backup tasks is achieved, and in the task allocation process, node resource use conditions are monitored in real time, and a backup strategy is dynamically adjusted. According to the method, the backup efficiency and the data reliability are improved, the storage and calculation cost is reduced, and the method is suitable for large-scale enterprise cloud environments, financial institution cross-domain data backup and other scenes.

The invention discloses a data security system based on dynamic data splitting, belongs to the field of data security, and aims to ensure safe, reliable and efficient management of data files in a full life cycle. Comprising a data splitting module, a data distribution module, an access control module and a data recombination module. And the data splitting module reads and converts the data file, dynamically adjusts a splitting strategy, and splits the data file in combination with a sensitive information encryption technology, so that the security is improved. And the data distribution module optimizes data block distribution by using a consistent Hash algorithm, dynamically monitors storage node loads, and prevents data loss through redundant backup. And the access control module realizes user identity verification and authority distribution, and accurately controls data access. And the data recombination module is responsible for recovering and synchronously updating the split data and keeping the data consistency. On the whole, comprehensive protection is provided for data, and strict safety regulations and efficient processing requirements are met.

The invention discloses a high-density WLAN (Wireless Local Area Network) optimization management system and method, which are suitable for a high-density equipment access environment so as to improve network performance and user experience. Firstly, through real-time network state monitoring, channel allocation is dynamically adjusted based on data such as channel utilization rate, equipment access density and interference condition; in combination with a channel switching mechanism of equipment priority, the connection stability of key equipment is ensured; signal overlapping and interference are reduced through adaptive transmitting power control and channel linkage optimization; multi-level load balancing is realized among a plurality of access points, and network resource distribution is optimized; and through intelligent user access optimization, equipment behaviors and moving tracks are predicted, and seamless access is ensured. According to the invention, channel conflicts can be reduced, and network stability and user experience are improved.

The invention relates to the related technical field of integrated service management, in particular to a digital intelligent technology and software and hardware integrated service management method and platform and a storage medium, and the method comprises the steps: connecting an industrial internet, receiving integrated service requirements, deploying software and hardware based on specifications, fitting a service scene, setting a topological architecture conforming to the specifications, and collecting data to establish a performance model. According to the software and hardware matching topology architecture, a communication architecture is adopted, a monitoring mechanism is set, and abnormal response management is performed on each component and each service in the software and hardware matching topology architecture, so that the technical problems that a service scene is fixed and a static deployment mode is difficult to quickly respond to dynamically changing service requirements in a software and hardware integration process are solved; the technical effects that the industrial internet is connected, the integrated service demand information is received in real time, changes of service demands are flexibly coped with in the software and hardware configuration process, dynamic configuration and adjustment are carried out according to different service scenes and demands, service changes are rapidly responded, and the flexibility and adaptability of resource configuration are improved are achieved.

The invention discloses a client information management method based on a cloud environment and a related device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the information safety analysis based on a client information composition parameter generated through the information composition analysis of client information, and obtaining an information safety parameter; acquiring category identification information of the customer information based on the category logic chain, and acquiring corresponding local density based on the preset information field and the category identification information; classifying the customer information based on the local density and the information security parameters; the client information of each category is stored and encrypted; obtaining a mapping relationship between the storage performance information and the encrypted customer information of each category, and determining an information storage strategy based on the mapping relationship in combination with a node partitioning strategy; and storing the encrypted customer information of each category into a storage node corresponding to a cloud storage center. According to the invention, the reliability of classified storage of the customer information is improved, and the classified storage of the customer information achieves a more ideal effect.

The invention provides a parallel water pump intelligent algorithm optimization combination and control method, which comprises the following steps of: configuring a sensor and a calculation module on an edge node of each water pump, acquiring and preprocessing operation data of each water pump in real time, and generating a unified real-time feature vector and a time sequence feature; dynamically calculating the service life state of the water pump according to the real-time feature vector, and quantifying the residual service life; designing a dynamic start-stop scheme based on a fuzzy logic algorithm according to the service life state, and balancing load requirements and water pump service life distribution; and based on federal learning, the residual life of the water pump is optimized in a global perspective, and an operation strategy based on a start-stop scheme is dynamically optimized and adjusted. A complete solution is formed, and a brand new technical path is provided for efficient and reliable operation of the parallel water pumps.

The invention relates to a satellite internet task planning method based on intention understanding and a large model, which belongs to the field of task planning and comprises the following steps: constructing a satellite internet intention library and a satellite internet knowledge tensor library based on RAG enhanced retrieval; receiving a communication intention demand report, and performing intention acceptance according to a user communication intention; performing RAG enhanced retrieval through the accepted communication intention and the knowledge tensor database to obtain a tensor retrieval result, and performing reasoning to obtain an intention analysis result; communication intention verification is carried out according to the intention analysis result and a satellite internet intention library, if legality is met, execution is continued, and otherwise, the process is ended and abnormal warning is carried out; task planning and scheduling are carried out through a satellite internet intention library and an equipment and resource library, and a network planning parameter result is obtained; and then network parameter configuration is carried out on the application layer to realize satellite planning, station planning and terminal planning. According to the method, the total resource occupancy rate and the service capacity can be effectively improved, and meanwhile load balancing is achieved.

The invention discloses an online monitoring system and method based on the use state of monitoring equipment, and particularly relates to the technical field of equipment monitoring. According to the method, whether the recovery performance of the RAID level is abnormal or not is judged by writing and reading data in an RAID array, the performance of a storage server is evaluated and divided according to the cache hit rate fluctuation condition of a read-write request in a cache in a storage system and the capacity of a storage device for processing an uncompleted I / O request, and a data writing strategy is optimized; when the system runs under a high load, video data are distributed to a plurality of storage nodes by configuring the storage system to realize load balancing and dynamically store the number of the nodes, so that the performance and the reliability of the storage system are remarkably improved, the load of single storage equipment is reduced, the service life of the equipment is prolonged, and the write-in delay and the data loss are reduced; the stable operation of the system under high load is ensured, and the maintenance cost and the downtime are reduced.

The invention provides a 5G intelligent networking method and system based on edge computing. The method comprises the following steps: deploying edge computing nodes, and designing a hierarchical network architecture and a dynamic routing mechanism; deploying an edge model on the edge computing node, and carrying out real-time processing and anomaly detection; dynamically distributing network resources, and carrying out network switching and load balancing by utilizing an edge model; decomposing the complex task into a plurality of sub-tasks, and performing distribution and parallel processing of the sub-tasks; updating and optimizing the edge model, and dynamically adjusting a networking strategy; and data security protection is realized, and network data and user feedback are monitored. The system comprises an edge deployment module, a data analysis module, a networking strategy module, a collaborative computing module, a collaborative optimization module and a security protection module. According to the invention, network resources can be efficiently utilized, intelligent networking and network switching are carried out, load balancing is realized, the response speed, the processing capability and the adaptive capability of the network are greatly improved, and the experience feeling of a user is improved.

The invention provides an HIVE grouping operation performance optimization and scheduling method based on key column partitioning, which relates to the technical field of data processing, and comprises the following steps: preprocessing data by constructing a key column distribution histogram and determining a key column partitioning strategy; optimizing data partitions based on resource evaluation and a dynamic programming algorithm; and calculating an optimal data transmission path by adopting a minimum spanning tree algorithm, and dynamically adjusting an allocation scheme. According to the method, HIVE grouping operation performance can be improved, dynamic balance of calculation loads is achieved, the problem of data skew is effectively solved, and large-scale data processing efficiency is improved.