173 results about "Allocation algorithm" patented technology

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

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 multi-satellite collaborative regional target observation scheduling method based on stripe coverage and ant colony improvement, and relates to the field of multi-satellite collaborative observation. According to the method, firstly, a to-be-observed task list and satellite resources are obtained, and a task conflict matrix is constructed; secondly, performing discretization processing on the regional target based on the task conflict matrix, and generating a stripe coverage scheme and a stripe priority; thirdly, generating a multi-satellite cooperative task allocation scheme by adopting a self-adaptive ant colony task allocation algorithm; thirdly, generating an optimization target based on the stripe priority and a multi-satellite cooperative task allocation scheme, and constructing a scheduling decision model in combination with an energy constraint, a time window constraint and a task unique execution constraint; and finally, solving the scheduling decision model by using an improved branch and bound algorithm, and obtaining a global optimal solution for deciding whether each satellite observes a task or not at different moments. According to the invention, by improving the ant colony algorithm and the dynamic stripe coverage strategy, the task execution efficiency and the resource utilization rate of multi-satellite collaborative observation are significantly improved.

The invention discloses an assembly interface shape error grouping method for three-dimensional matching, and relates to the field of precision assembly, and the method comprises the steps: obtaining the point cloud data of an assembly interface, and calculating five-dimensional morphology parameters; a greedy distribution K-Means algorithm is adopted to carry out clustering grouping on the five-dimensional morphology parameters; performing three-dimensional visualization on a grouping result in combination with principal component analysis; and based on a three-dimensional visualization result, selecting and matching reference parts and matching parts by adopting a grouping and selecting and matching method, and realizing assembling interface shape error grouping oriented to three-dimensional selecting and matching. The problem that an existing size grouping method faces a two-dimensional size chain model and is difficult to adapt to three-dimensional matching requirements is solved.

The invention provides a VR game data synchronous transmission method based on a 5G network, and the method comprises the steps: obtaining a multi-type data stream generated during the operation of a VR game, extracting scene rendering data and player interaction data from the multi-type data stream, separating a feature value of each data type, and obtaining a data priority sequence; analyzing the real-time requirements of the scene rendering data and the interaction data according to the data priority sequence, and judging a key data packet which needs to be transmitted immediately; the method comprises the following steps: monitoring a current bandwidth fluctuation condition through a network transmission layer, calculating available bandwidth and delay parameters from network state data acquired in real time, and obtaining a resource allocation reference value in a dynamic scene; and for high-priority data packets, a dynamic resource allocation algorithm is adopted to adjust a transmission queue sequence, a resource allocation reference value is corrected through a bandwidth fluctuation parameter, and a transmission time slot allocation scheme of the key data packets is determined.

The invention discloses an AI digital human real-time rendering method based on GPU acceleration, and relates to the technical field of digital human rendering, and the method comprises the steps: S1, constructing a multi-GPU hardware feature and load monitoring module, and collecting hardware feature parameters and current load states of each GPU participating in collaborative rendering in real time; according to the method, by constructing the multi-GPU hardware feature and load monitoring module and combining a dynamic task allocation algorithm, dynamic allocation of the rendering sub-tasks to the optimal GPU is achieved, the problems of GPU performance bottleneck and resource idleness caused by traditional fixed task allocation are solved, the multi-GPU collaborative rendering efficiency is improved, and the multi-GPU collaborative rendering efficiency is improved. A scene complexity analysis module and a self-adaptive resource allocation algorithm are built, the proportion of GPU resources between an AI driving module and a rendering module is dynamically adjusted according to scene complexity, the AI digital human interaction naturalness is improved in a simple scene, the rendering frame rate and the picture quality are guaranteed in a complex scene, and the method is suitable for being used in a large-scale scene. And finally, dual optimization of rendering efficiency and picture quality in AI digital human real-time rendering is realized.

The invention relates to the technical field of mobile application continuous integration automation, and discloses a multi-channel APP automatic packaging system which comprises the steps that byte code files in a released mobile terminal APP installation package are acquired, and an occupied symbol space set is generated; generating an available confusion namespace by using set complementary operation; extracting method-level code change by using an abstract syntax tree difference algorithm, and generating a change method set; generating a patch special confusion rule for the change method set by using a namespace allocation algorithm; compiling and obfuscating the change method set by applying a patch special obfuscating rule to generate a safe obfuscating patch byte code; performing packaging and signature operation on the safe obfuscation patch byte code, and outputting a minimized obfuscation safe hot repair patch pack; according to the method, the technical problems that class definition conflicts or method calling errors occur after the hot repair patch is loaded, and the downloading success rate of a user is affected due to the overlarge size of the hot repair patch are solved.

The invention discloses an edge reasoning optimization method and system based on segmented knowledge distillation. Firstly, a segmented knowledge distillation framework is constructed, a teacher-student model is divided into corresponding sub-modules with balanced parameters, parallel distillation training is adopted, middle feature dimension reduction and space alignment are achieved in combination with a principal component analysis method, the knowledge transmission efficiency is improved, and convergence is accelerated. Secondly, proposing an equipment perception self-adaptive pruning strategy, dynamically distributing a differential pruning proportion according to the real-time calculation capability and resource state of heterogeneous edge equipment, and balancing the load of low-performance equipment and the precision of high-performance equipment; and finally, establishing a deep reinforcement learning dynamic scheduling mechanism, generating a module delay-energy consumption file through offline analysis, adaptively selecting a device combination by an intelligent agent in an online stage, determining an optimal partition and deployment strategy through a threshold value distribution algorithm, and realizing joint optimization of energy consumption and reasoning time while meeting delay constraint.

The invention provides a Tiantong satellite transmission rate improving method and device based on a point-to-point protocol, relates to the field of satellite communication, and solves the technical problems of low bandwidth utilization rate and low multi-service concurrent processing efficiency. The method comprises the following steps: configuring an APN through an AT instruction set, starting PPP dialing, and establishing a physical connection with a Tiantong satellite; a plurality of virtual serial port channels are created on a single physical serial port channel by using a CMUX protocol, and each channel bears an independent PPP link. Compression type HDLC packaging is carried out on a PPP protocol stack, a frame header is reduced to 2 bytes, and a dynamic FCS verification mechanism is started. And sending the data fragments to the virtual channels through an adaptive time slot allocation algorithm according to the service priorities. And performing forward timestamp mark pre-compensation on the sending time sequence based on the 550ms satellite time delay prediction model. And sorting and recombining the channel data packets at a receiving end, and restoring the original data stream. The method is used in the Tiantong satellite transmission process.

The invention discloses a home appliance dual-network hub site selection planning method based on a hybrid heuristic algorithm. The method comprises the following steps: firstly, modeling a home appliance dual-network hub site selection planning problem into a mathematical model taking the minimum comprehensive cost as an optimization target; and then, solving by using a hybrid heuristic algorithm. The algorithm is a priority competition allocation algorithm based on a simulated annealing framework. Two attribute indexes of a comprehensive load coefficient and a distance sensitivity coefficient of a site are defined, the competition degree of the site for hub resources and hub distance sensitivity of the site are quantified, a reasonable distribution sequence is determined, resource conflicts are reduced, efficient configuration of hub resources of a sales service network is achieved, and the service efficiency of the sales service network is improved. The problem that a traditional clustering method is low in solving quality is effectively solved.

The invention relates to the crossing field of engineering cost management and building informatization (BIM) technology, and discloses a BIM-based multi-specialty collaborative engineering cost allocation and settlement auditing system, which comprises an engineering cost allocation and settlement auditing system, and the engineering cost allocation and settlement auditing system comprises a sensing layer, a platform layer, a core layer, an application layer and a guarantee layer. According to the BIM-based multi-specialty collaborative project cost allocation and settlement auditing system, a five-layer system architecture (a sensing layer, a platform layer, a core layer, an application layer and a guarantee layer) is constructed, so that the whole process from data acquisition, standardized processing and intelligent algorithm analysis to multi-role collaborative application is realized; according to the method, cost allocation and settlement auditing services under multiple specialties and multiple scenes are supported, and multi-dimensional cost intelligent allocation and automatic anomaly detection based on BIM components are realized by integrating a BIM semantization engine, a dynamic allocation algorithm engine and an AI auditing engine.

The invention discloses an intelligent dynamic security monitoring system and method based on multi-modal data fusion, and the method comprises the steps: capturing a behavior feature sequence of encrypted network traffic through a protocol analysis engine, and obtaining a hardware state vector of an equipment operation log through a security agent interface, extracting metadata fingerprints of the data session through a cross-network ferry monitor to form three types of standardized data streams; the three types of data streams are input into a space-time alignment engine for time window synchronization, feature level fusion is carried out based on an adaptive weight distribution algorithm, and a multi-modal feature matrix containing a network threat index, an equipment health degree score and a ferry risk value is generated; according to the method, an integrated safety monitoring system of cross-domain collaborative awareness and dynamic closed-loop evolution is constructed, and the risk identification precision, the response timeliness and the self-adaptive capability of the system are remarkably improved.

The invention relates to a time-sensitive network gating scheduling optimization method and system based on reinforcement learning, and belongs to the field of time-sensitive network scheduling. According to the method, data flow and network topology information are collected, a graph neural network is adopted for feature coding, a reinforcement learning model of a two-stage action space is designed for routing selection and gating decision making, and an early scheduling time slot allocation algorithm and a hierarchical training strategy are combined, so that the routing accuracy is improved. The problems that a traditional scheduling method is high in calculation complexity and gating list entries are increased sharply are effectively solved, the gating entries are minimized on the premise that time delay and jitter constraints are met, and the schedulable rate and deployment feasibility under a complex topology and multi-cycle flow set are improved.

The invention provides a resource allocation method in task unloading, relates to the technical field of network and communication, and solves the technical problem of relatively high time consumption of joint optimization of channel resources, computing resources and power consumption control in a task unloading process. According to the technical scheme, the method comprises the following steps of S1, task unloading problem modeling; s2, realizing an unloading decision; and S3, designing and realizing a resource allocation algorithm. According to the allocation method, the search behavior can be dynamically adjusted according to the real-time performance of the particles, and exploration and development of resource allocation problem solving are dynamically balanced; according to the scheme, the time overhead performance of program calculation is reduced, and a high target utility function value can be maintained.

The invention relates to a practical consensus algorithm for file resource sharing, which comprises reputation-driven task allocation: according to reputation values calculated in real time, nodes with high reputation values are preferentially selected by the algorithm to form a core consensus group to execute the most critical consensus task, and the number of nodes participating in core communication is remarkably reduced. And protocol flow optimization: fundamentally reducing the communication complexity to O (n) level by limiting the scale of the core consensus group and reconstructing a message transmission path. And node state dynamic autonomy: the algorithm has real-time evaluation and dynamic adjustment capabilities on node types. The reputation model continuously monitors node behaviors, can automatically perform degradation processing on low-reputation or fault nodes, and can timely discover and safely isolate or delete bad nodes through a specific mechanism without restarting the system. Through test verification, the reliability, safety, higher throughput and lower delay of the consensus algorithm are verified.

The invention provides an intelligent scheduling system and method for engineering mechanical equipment leasing, a medium, a program product and a terminal, and the system achieves the mutual cooperation of a complete hosting algorithm, a warehouse allocation algorithm and a stowage algorithm through the construction of the intelligent scheduling system of an order screening module, a warehouse allocation module, a stowage module and a transportation execution module. The processes of order screening, warehouse selection, vehicle selection, task issuing and the like in leasing equipment scheduling are automatically screened and executed by the system, so that the execution efficiency of the scheduling system is improved, the warehouse and the vehicle which most conform to conditions are matched for the current order through the warehouse allocation algorithm and the stowage algorithm, the time cost and the expense cost are saved, and the efficiency is improved. And the requirement of an actual scheduling scene is met.

The invention discloses an unmanned aerial vehicle airport charging scheduling method and system, and the method comprises the steps: S1, obtaining the state data of each charging pile and the state data of each returning unmanned aerial vehicle, and constructing a compatibility matrix and a time cost matrix; s2, performing preliminary screening and sorting on the returning unmanned aerial vehicles based on the task emergency degree to obtain a to-be-scheduled queue; s3, based on the compatibility matrix and the time cost matrix, adopting an optimal allocation algorithm to allocate charging piles to the to-be-scheduled queue to obtain pile position allocation data; and S4, based on the pile position allocation data, allocating departure time to each unmanned aerial vehicle and planning a conflict-free path to obtain time-space scheduling information. According to the invention, the optimal distribution algorithm is adopted to distribute the charging pile for each unmanned aerial vehicle, and reasonable scheduling is carried out when multiple unmanned aerial vehicles return to the field for charging at the same time through departure time distribution and a time slot reservation mechanism, so that path conflicts are avoided, collision or blockage among the unmanned aerial vehicles is prevented, and the utilization rate and the overall scheduling efficiency of airport channels of the unmanned aerial vehicles are improved.

The invention relates to a multi-gun charging dynamic power distribution method and system and a medium, and the method comprises the steps: constructing a multi-dimensional priority scoring matrix, and distributing a dynamic priority for each charging gun; a three-stage power distribution algorithm is adopted, basic power is guaranteed firstly, then residual power is distributed according to priorities, and finally the power which is not fully utilized is optimized and adjusted; intelligent adaptive feedback is carried out, the actual charging state of each charging gun is monitored in real time, and a power distribution strategy is adjusted every 30 seconds according to a monitoring result; context awareness and emergency processing are carried out, and special power distribution strategies are designed for different scenes; quick response starting is carried out, and the maximum power requirement is preferentially met within the preset time period when vehicle charging starts; the peak shifting power distribution based on data prediction prevents the storage and charging all-in-one machine from being overloaded in a certain time period, the intelligent power distribution under limited power grid capacity and energy storage electric quantity is realized, the resource utilization efficiency and the user satisfaction are remarkably improved, and the method has wide application prospects.

The invention discloses an enterprise service demand prediction and resource optimization configuration system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, the multi-source heterogeneous data is integrated through the multi-modal data fusion algorithm, the demand change rule is accurately captured through the time sequence prediction algorithm, the demand prediction precision is remarkably improved, and the problem that the demand perception ability is limited due to insufficient data utilization in a traditional method is solved; the resource optimization allocation algorithm is combined with the comprehensive utility function of the resource utilization rate and the customer satisfaction, the resource combination is dynamically adjusted, the resource allocation efficiency is greatly improved, the defect that a traditional resource allocation mode is static and lack of flexibility is overcome, and the user experience is improved by continuously monitoring customer interaction and service feedback data. Model parameters are dynamically updated, a resource allocation algorithm is optimized, continuous optimization of resource allocation is achieved, the problem that an existing method lacks an effective feedback mechanism is solved, long-term efficient operation of a system is ensured, and more competitive operation support is provided for enterprises.

The invention relates to an elastic distributed computing power resource control method and system driven by low-carbon energy, and belongs to the technical field of computer distributed systems and green energy management. The method comprises the following steps: acquiring load data of distributed computing power nodes, identifying a server resource use signal, analyzing a carbon efficiency mapping relation between the nodes through an energy consumption weight detection method, and constructing an energy-computing power characteristic incidence matrix; extracting a unit energy consumption computing power output coefficient of each computing power node, performing low-carbon computing power resource priority scheduling through an elastic task allocation algorithm, and generating a node resource allocation instruction; establishing a distributed autonomous control model, and correcting a node execution state through an energy-computing power coupling feedback mechanism to obtain a distributed computing power resource balance interval; and establishing an elastic distributed computing power resource closed-loop control system, performing self-adaptive correction on task allocation weights, and controlling energy-task cooperative compensation between computing power nodes according to energy consumption feedback of the computing power nodes.

A reinforcement learning-based energy efficiency optimization method for UAV-assisted MEC networks, belonging to the field of mobile communication, is proposed. First, the system is modeled and state information is collected. The state information is input into the policy model to generate flight and energy transfer actions, enabling UAV movement and service device selection. Next, an energy transfer and task offloading model is defined, and a gradient-based device offloading time allocation algorithm is initiated. Then, a binary search for the equilibrium marginal utility gradient is initialized, followed by a single iterative search, updating the search interval, and completing the equilibrium marginal utility gradient search. The final time allocation scheme is then determined, energy transfer and task offloading are executed, performance data is collected, the reward function is calculated, and experience is stored. Finally, the Critic network, Actor network, and target network are updated, and the optimal policy is obtained through iterative optimization. This invention significantly improves the overall energy efficiency and task processing throughput of the system.

The application discloses a three-dimensional matching-oriented assembly interface shape error grouping method, and relates to the field of precision assembly, and comprises the following steps: acquiring assembly interface point cloud data and calculating five-dimensional topographic parameters; adopting a greedy allocation K-Means algorithm to cluster and group the five-dimensional topographic parameters; combining principal component analysis to perform three-dimensional visualization on the grouping result; and based on the three-dimensional visualization result, adopting a grouping matching method to match a reference part and a matching part, so as to realize three-dimensional matching-oriented assembly interface shape error grouping. The application solves the problem that the existing size grouping method is two-dimensional size chain model-oriented and is difficult to adapt to three-dimensional matching requirements.

The invention belongs to the field of track association, and particularly relates to a layered track association method for complex formation targets. The method comprises an upper-layer algorithm and a lower-layer algorithm. The upper-layer algorithm receives the spatial deviation parameter fed back by the lower-layer algorithm, performs deviation compensation on the sensor according to the spatial deviation parameter, calculates a correlation degree matrix by adopting gray theory track correlation, and calculates and outputs a track correlation result by using a distribution algorithm; and the lower-layer algorithm receives the track association result output by the upper-layer algorithm, constructs a corresponding relation of a spatial topological structure on the basis of the track association result, performs secondary association of the tracks by adopting similarity of the spatial topological structure, estimates data based on the screened tracks and outputs spatial deviation parameters of the sensor. A multi-scene simulation result shows that the track association method provided by the invention can effectively complete the association problem in a complex dense target scene, and shows relatively high robustness in a Monte Carlo simulation test.

This invention discloses a large-scale visual-language model collaborative inference method for cloud-edge-device systems. Its features include: deploying a high-performance cloud-based LVLM model and a cloud-based archive in the cloud; deploying an edge-based LVLM model and an edge-based archive at the edge, wherein the edge LVLM model incorporates a retrieval-enhanced generation algorithm, and the edge archive and the cloud archive can dynamically interact in terms of knowledge; a task scheduler evaluates the complexity of received query tasks based on a task allocation algorithm, and allocates the query tasks to the edge for local processing or to the cloud for processing based on the complexity evaluation results. This invention can improve the inference efficiency within cloud-edge-device systems while maintaining high accuracy, effectively solving the technical problem of low inference efficiency caused by limited computing resources, intermittent communication windows, and bandwidth limitations of edge devices.

The invention discloses a file management method and device, electronic equipment and a computer readable medium, and relates to the technical field of file management.The file management method comprises the steps that under the condition that a target file uploaded by a user side is received, a temporary hash value is determined for the target file according to a preset allocation algorithm; taking the temporary hash value as a unique identifier to store an original file copy of the target file; determining a real hash value of the original file copy through an asynchronous task; and according to the real hash value, carrying out duplicate removal processing on the file copy. When a large file or a high-concurrency import scene is processed, the file uploading speed can be increased.

This invention relates to the fields of audio and video playback technology and network communication technology, specifically a dynamic control method and system for video playback caching. By introducing intelligent prediction models, dynamic weight allocation algorithms, and scene association mechanisms, it achieves precise adjustment of video player caching duration, download speed, and external parameters, thereby maximizing the utilization of the player's local cached content, improving bandwidth utilization, reducing CDN bandwidth costs, and minimizing playback stuttering. Furthermore, this invention provides dynamic parameter and intelligent algorithm configuration interfaces for external users, allowing them to adjust caching control strategies based on multiple different parameters and business scenarios to achieve the optimal caching strategy for their own business.

This invention discloses a large-scale hybrid constellation distributed downlink spectrum sharing method based on game theory, comprising the following steps: establishing a link channel model in the hybrid constellation; using a perceived satisfaction function as an evaluation index of user performance; formulating a channel allocation game to capture the interaction and conflict between links, formalizing the channel allocation problem as a latent game; to solve the Nash equilibrium solution of the game model, dividing the system cycle into several equal topology cycles, with the topology of the ground-satellite link remaining unchanged within each topology cycle; dividing the topology cycle into a link establishment superframe and a data transmission superframe; and then using a distributed channel allocation algorithm based on stochastic learning for allocation. This application transforms a dynamic social problem into a static channel allocation problem; establishes a mathematical model based on game theory to represent user interaction; balances convergence speed and overall network satisfaction, and improves spectrum efficiency.

The invention relates to the technical field of data processing, and discloses a data stream processing method and device based on dynamic bandwidth allocation, equipment and a medium, when a data request of a client is received, a new redirection request is generated by judging a data stream type and applying redirection logic when the data stream type is confirmed to be a target data type, and the data stream is redirected. Therefore, the data streams can be quickly and accurately classified and guided, congestion of the data streams on unnecessary paths is avoided, and the condition of resource competition is relieved. According to the method, the channel specially used for transmitting the target data stream is determined, and independent transmission paths are provided for different types of data streams, so that mutual interference among data is reduced, and the occurrence probability of system jamming is remarkably reduced. According to the method, the traffic resource of each channel is calculated and allocated by applying a traffic dynamic allocation algorithm, so that the processing capability of high-concurrency scenes of financial and medical data processing is greatly improved, and the processing efficiency and the response speed of data streams are remarkably improved.

The application discloses an AI cluster perception-based dynamic power distribution system of a heterogeneous computing platform, which comprises a data acquisition module, a performance quantization module, a power consumption analysis module and a power distribution module.The data acquisition module acquires the real-time power consumption and utilization rate of each computing unit in the cluster, and forms a heterogeneous unit data set after preprocessing.The performance quantization module obtains the single-resource bottleneck coefficient of each resource type based on the heterogeneous unit data set, and calculates the comprehensive bottleneck coefficient of each unit by constructing an AI-based bottleneck perception space-time network.The power consumption analysis module identifies the critical path unit and the idle unit based on the comprehensive bottleneck coefficient by using a weighted power consumption redistribution algorithm based on critical path perception, and calculates the releasable power consumption factor of the critical path unit and the idle unit to perform optimized distribution in combination with the comprehensive bottleneck coefficient, so that the best target power consumption range of the computing platform is obtained.The power distribution module performs setting operation by calling the underlying hardware power consumption management interface based on the power consumption upper limit value set of different units, so that dynamic power distribution is realized.

The invention discloses an electric power data acquisition method and device based on dynamic plug-in, equipment and a medium, various electric power equipment drives are packaged into independent plug-ins by constructing a dynamic plug-in management model, and flexible plug-and-play of the equipment is realized. And then, configuring a corresponding protocol analysis template for the access equipment, uniformly analyzing the collected original data and mapping the data into a standard data format, thereby solving the problem of multi-source protocol adaptation. On the basis, multi-dimensional priority evaluation and quantitative scoring are carried out on the standard data, then system processing resources are distributed in a differentiated mode through a dynamic resource distribution algorithm according to the scoring result, and the transmission and processing efficiency of high-importance data is guaranteed preferentially. According to the invention, the equipment compatibility, scene adaptability and resource utilization efficiency of the power data acquisition architecture can be significantly improved. The method can be widely applied to the technical field of electric power.