703 results about "Dynamical network" patented technology

The invention discloses a wide-narrow integrated ad hoc network method, and belongs to the technical field of network communication. The method comprises the following steps: a network node generates dynamic topology information based on a narrow-band communication module periodic broadcast state beacon; according to the link quality and the node load in the topological information, coordinating the resource allocation of broadband and narrowband channels by adopting a load-aware time slot allocation algorithm; data are classified according to service types, broadband data are modulated and transmitted by adopting orthogonal frequency division multiplexing, and narrowband control instructions are modulated and transmitted by adopting differential phase shift keying; when the link quality is deteriorated, the link is switched, and the transmitting power and the frequency band parameters are dynamically adjusted; and adaptive switching and data distribution of wide and narrow band communication modes are realized. According to the method, the problems of difficulty in topology maintenance and low bandwidth and narrowband resource allocation efficiency in a high-dynamic network scene are solved, the network adaptability, the anti-interference capability and the resource utilization rate are remarkably improved through a dynamic cooperative bandwidth and narrowband communication mode, and meanwhile, the protocol switching overhead and energy consumption are reduced.

The invention relates to a cerebrovascular three-dimensional evaluation method based on multi-scale feature fusion, and the method constructs a multi-scale feature fusion model which comprises a pilot segmentation network, a fusion segmentation network and a topology-style loss module. The pilot segmentation network is based on a dynamic Unet network, the response of the model to the complex boundary of the cerebrovascular image is enhanced, and the pathological detection performance is improved. The fusion segmentation network introduces a feature selection module, and effectively fuses the long-range dependence feature of the Transform layer and the local context information of the CNN layer, so that the model can accurately extract the cerebrovascular features under different scales. The topology-style loss module optimizes model parameters by combining a topology loss function and style self-consistency loss, restrains the topology continuity of a segmentation result, and effectively solves the problem that blood vessels are easy to break in a traditional segmentation method. The method effectively improves the cerebrovascular image segmentation precision.

The invention provides an energy storage cabin terminal remote upgrading method and system based on communication protocol optimization, and relates to the technical field of digital information transmission, and the method comprises the steps: deploying a bandwidth detection mechanism, adjusting a self-adaptive block transmission strategy, and dividing upgrading firmware into M dynamic data packets; constructing a dual-path transmission layer, and configuring a dual-path redundancy check mechanism; a missing block query rule is adopted, and a local flash memory is used for caching Hash check values of the received N dynamic data packets; analyzing a mapping relation between the block index and a multi-path redundancy check mechanism, and establishing a check mapping table; and performing real-time verification and state marking, and configuring a remote upgrading process to execute the upgrading operation of the energy storage cabin terminal equipment after incremental recombination is performed by comparing and verifying abnormal data packets. According to the invention, the technical problem of insufficient reliability of data transmission caused by lack of adaptability to a dynamic network environment due to the fact that most of energy storage cabin remote upgrading technologies in the prior art depend on a single communication path to carry out data transmission is solved.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are improved.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention discloses a hydrogen fuel engine electronic thermostat heating assembly control method based on the Internet of Things, particularly relates to the technical field of hydrogen fuel engine control, and is used for solving the problems of cooling response lag and action conflict caused by disconnection of control logic and a real-time network state of an existing temperature control system in a dynamic network environment. Electronic thermostat temperature data and network state data are collected in real time to generate a dynamic synchronization coefficient, network topology toughness evaluation and causal influence analysis are combined to predict instruction conflict areas and divide priorities, and gradient correction is performed on heating power and valve opening based on a target deviation compensation value. And a correction instruction is issued through the distributed control unit and a communication reliability weight is synchronously updated, so that dynamic coordination of a network state and a temperature control parameter is realized, and efficient and stable operation of the hydrogen fuel engine under a complex working condition is ensured.

The invention relates to the technical field of power distribution network dispatching. By providing a power grid load dynamic prediction and optimal scheduling method, device, equipment and medium, the method comprises the following steps: performing multi-source heterogeneous fusion processing on meteorological parameters, historical load curves and new energy output data to generate a dynamic load prediction map; constructing a dynamic network model, and performing power flow distribution simulation processing based on the dynamic network model to obtain a stability margin calculation result and a preset safety threshold boundary; performing stage decomposition processing on the global scheduling target to generate a progressive scheduling stage sequence; performing matching processing on the response characteristics of the power generation equipment to generate a self-adaptive progressive scheduling instruction sequence; and executing an adaptive progressive scheduling instruction sequence, performing feedback processing on the real-time state of the power grid, and generating a dynamic adjustment instruction so as to realize multi-dimensional data association modeling, stability margin quantitative analysis and dynamic instruction optimization, thereby improving the load prediction precision and reducing fault diffusion and transient oscillation.

The invention discloses an enterprise digital management method based on data mining. The method comprises the following steps: acquiring information flow data and resource allocation data; analyzing according to the information flow data and the resource allocation data to obtain an initial network diagram; performing clustering analysis according to the initial network diagram, and performing path optimization to obtain a dynamic network diagram; performing feature extraction on the dynamic network diagram to obtain prediction analysis data; according to the dynamic network diagram and the prediction analysis data, network nodes are identified, and key nodes and high-risk nodes are obtained; in combination with the key nodes and the high-risk nodes, identifying a key propagation path to obtain the key propagation path; and based on the key nodes, the high-risk nodes and the key propagation paths, predicting key positions where resource bottlenecks and conflicts occur, and generating resource optimization suggestions. According to the method, the dynamic change of the implicit dependency relationship of the enterprise can be identified, high-risk nodes and key propagation paths are positioned, and resource configuration is optimized.

The invention discloses a network protection method and system based on an attack and defense game model, and relates to the field of network protection, and the method comprises the steps: firstly constructing a dynamic network graph through integrating an original log, asset information and threat intelligence; and then, identifying and predicting potential Top-K attack hypotheses from the dynamic atlas by using the trained graph neural network model. Then, the attack hypotheses are used as new evidences to update the Bayesian belief network, and the cognition of the intention and ability of the attacker is further improved. Most importantly, aiming at each attack hypothesis, a sub-game model is generated, and on the basis of the updated Bayesian belief network, real-time defense strategy solving is carried out by utilizing MCTS. The introduction of the MCTS enables the optimal defense strategy to be efficiently explored and recommended in incomplete information and complex decision space, avoids the infeasibility of exhaustion calculation, and achieves the real-time efficient defense decision of a large-scale network.

The invention provides a traffic flow spatio-temporal feature adaptive extraction method based on a dynamic Kolmogorov-Arnold network, and aims to solve the defects of a traditional model in the aspects of dynamic spatio-temporal modeling, structural adaptability and feature expression efficiency. According to the method, a network structure is dynamically adjusted through differential topology search and a parameterized primary function library, a double-flow coupling architecture is designed to extract space and time dependent features respectively, and adaptive fusion of spatial and temporal features is realized by using a gating mechanism. The method comprises the following specific steps: performing space-time normalization and graph structure coding to generate a node feature matrix; extracting multi-scale spatial-temporal features in parallel by the dynamic graph convolution KANs and the time sequence convolution KANs; feature alignment and cooperative enhancement are realized based on a local correlation matrix and a bidirectional interactive attention mechanism; and the lightweight KAN decoder is combined with the dynamic basis function library to output a prediction result. Experiments show that the MAE is reduced to 1.78 vehicles per minute and the reasoning speed is improved by 1.8 times in a traffic accident emergency scene on a PeMS08 data set, and the method is suitable for an intelligent traffic management and vehicle-road cooperation system.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The invention discloses an intelligent driving vehicle dynamic network redundancy configuration and switching method in a complex information source scene, and relates to the technical field of intelligent traffic and communication. According to the method, communication quality data of a plurality of communication links at different positions of the unmanned taxi in the driving process are detected, the data are broadcasted to other unmanned taxies around through a vehicle-mounted communication system, and communication quality data broadcasted by other unmanned taxies are received; the method does not depend on a centralized server or a cloud platform, and a signal map is fused and generated in a decentralized manner only based on self-detected and received data; dynamically configuring a network redundancy strategy according to the signal map and a preset task priority; after the strategy is executed, scores are recorded and broadcasted, scores of other vehicles are received, and the strategy is optimized and adjusted in advance. According to the method, the problem of unstable communication in a complex information source scene is solved, and the communication reliability and efficiency of the intelligent driving vehicle are improved.

The invention relates to the field of intelligent building safety, and discloses an intelligent fire evacuation guiding method which comprises the following steps: collecting fire information and crowd information, and establishing a dynamic evacuation network model; dynamically calculating the weight of an evacuation path based on the fire information and the crowd information; according to the dynamic weight, an optimal evacuation path in the evacuation area is planned, and an intelligent fire evacuation guidance system is further provided and comprises a data acquisition module used for acquiring fire information and crowd information; the dynamic modeling module is used for constructing a dynamic network model of the evacuation area; the path planning module is used for planning an optimal evacuation path based on the dynamic network model; and the light control module is used for controlling the display state of the path guiding light. According to the invention, through dynamic weight calculation, real-time path optimization, light dynamic adjustment and crowd flow prediction, an efficient, safe and flexible intelligent evacuation guidance effect in a fire scene is realized.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides a time service precision evaluation method and system for a network time server, and relates to the technical field of computer networks, and the method comprises the steps: 1, collecting the operation data of the network time server in a dynamic network environment in real time, including core index data and auxiliary index data, carrying out the normalization of the core index data, and carrying out the calculation of the auxiliary index data; generating a standardized core index data set; step 2, dynamically integrating the standardized core index data set according to the fluctuation characteristics and relevance of each core index to generate a comprehensive core index characterization value; and step 3, in combination with auxiliary index data, carrying out dynamic compensation correction on the comprehensive core index representation value, and generating a comprehensive time service precision evaluation value. According to the invention, comprehensive, accurate and dynamic evaluation and abnormity diagnosis of the time service precision of the network time server are realized, and the network time service quality and the operation and maintenance efficiency are effectively improved.

The invention relates to the field of artificial intelligence cross-modal retrieval, and provides a video text cross-modal retrieval method and system based on spatio-temporal feature fusion. The method comprises the following steps: carrying out key frame sampling and time sequence partitioning on an input video, extracting static visual features through a spatial feature network, and extracting motion features through a time dynamic network; a self-adaptive gating fusion module is adopted to dynamically calculate spatial-temporal feature weights and perform weighted fusion; extracting text semantic features by using a pre-training language model; constructing a double-flow projection network to map video fusion features and text features to a unified measurement space, and optimizing a feature distance by adopting a contrast loss function containing difficult negative sample mining and intra-modal constraint; and outputting a retrieval result according to the cosine similarity sequence. The system comprises four units, wherein the gating fusion module is integrated with an FPGA acceleration circuit. According to the method, mAP (at) 10 is equal to 0.78 in a UCF-101 data set, the time sequence action retrieval accuracy rate is 92.8%, and the single video retrieval delay is 23 milliseconds.

The invention provides a dynamic network security defense method and system oriented to real-time network state adaptation, and the method comprises the steps: building a standardized state vector sequence through collecting key indexes, such as network flow rate, abnormal connection ratio, topology change frequency, protocol distribution deviation degree and the like; based on the sequence, utilizing a time sequence prediction model embedded with a structured attention mechanism to predict attack risk trends of a plurality of time windows in the future in a prospective manner; and in combination with the current state and the risk trend, dynamically selecting an optimal strategy path in a predefined defense action map, mapping the optimal strategy path into a standardized control command which can be executed by equipment, and dispatching, issuing and executing the standardized control command according to the priority. According to the method, closed-loop self-adaptive defense from state perception and risk prediction to strategy generation and execution is realized, and the real-time performance, intelligence and engineering deployability of a network security system are remarkably improved.

The invention belongs to the technical field of dynamic network risk prediction based on knowledge graph driving, and discloses a dynamic network risk prediction method and system based on knowledge graph driving, and the method comprises the steps: firstly obtaining a network security event and a context to construct a knowledge graph, and then detecting entity semantic drift through a preset rule and a deep semantic model, and carrying out node splitting, fusion, renaming or label updating and other structural remodeling on the knowledge graph based on a detection result, and finally carrying out risk prediction by utilizing a graph neural network model. The problem that in a traditional method, a static model cannot adapt to dynamic semantic changes is solved, the recognition capacity of a novel attack mode is improved, the risk detection false report and missing report rate is reduced, and the risk prediction efficiency is improved.

The invention provides an underwater dynamic network resource scheduling and time slot conflict optimization method, which comprises the following steps of: firstly, acquiring network state characteristics by using underwater sensor nodes to perform cluster head election and cluster member allocation; secondly, dynamically optimizing the sending priority of the nodes in the cluster by utilizing a Q-learning theory, and carrying out time slot allocation; constructing a reward function based on intra-cluster node distribution information, and iteratively and dynamically adjusting the sending priority of the nodes; and finally, a time slot allocation conflict detection mechanism is formulated to avoid resource competition and transmission conflicts among the nodes in the cluster, so that the optimal node allocation scheme is gradually converged. According to the method, the problems of communication conflicts among multiple cluster nodes of the underwater network, low resource allocation efficiency, inaccurate dynamic sending queues and the like are comprehensively considered, the node sending priority and time slot allocation are optimized through Q-learning, and the channel utilization rate is effectively improved. In a complex dynamic underwater network environment, the technology has applicability and effectiveness, and an underwater node sending strategy can be optimized more reasonably.

The invention discloses a facial emotion analysis method based on spatial-temporal feature network construction. The method comprises the following steps: S1, video frame segmentation and feature extraction; s2, dividing the video into a plurality of time periods according to the set time window length and time step length; and for each time period, constructing a facial feature network representing association between facial features based on the feature sequences of all frames in the time period. The networks obtained through calculation in all the time periods are synthesized, and a dynamic network sequence is obtained; s3, performing dynamic network feature extraction and dynamic key point analysis based on the facial feature dynamic network; and S4, performing emotion calculation or psychological state evaluation based on the dynamic network features or the dynamic key points. The method solves the problems that an existing method is difficult to comprehensively capture complex dynamic characteristics of facial expressions and lacks dynamic correlation modeling and dynamic key point recognition capabilities.

The invention discloses a task processing method and device based on a dynamic LoRA network, computer equipment and a storage medium, and the method comprises the steps: receiving a task processing text, recognizing a feature vector of the task processing text, and enabling the feature vector to at least indicate the semantic and context features of the task processing text; calculating correlation with a plurality of to-be-selected LoRA networks according to the feature vector of the task processing text; determining an activation probability of each to-be-selected LoRA network according to the correlation, and determining a to-be-activated target LoRA network according to the activation probability based on a pre-configured gating network; fusing the target LoRA network into a basic model to obtain a fusion model; and performing task processing based on the semantic and context features of the task processing text by using a fusion model so as to load the target LoRA network and then output a processing result of the task processing text. By dynamically accessing the LoRA network, the accuracy and efficiency of task processing are improved.

The invention discloses a content caching and replacing method and system based on context awareness, and relates to the field of content caching, and the method comprises the steps: obtaining context information, constructing a context feature vector, constructing a training sample, training an XGBoost regression model, and carrying out the prediction, thereby obtaining the future content access popularity; a multi-objective optimization function and constraint conditions are designed, an NSGA-II multi-objective evolutionary algorithm is used for solving, a comprehensive scoring function is defined, and a solution with the maximum comprehensive scoring function value is selected from an obtained solution set to serve as a final caching strategy; and designing a cache scoring function, calculating to obtain a cache score, and replacing the cache content by adopting a greedy strategy. According to the content caching and replacing method, the caching hit rate and the resource utilization efficiency can be improved, the service capability and the response efficiency of the edge node in a complex environment can be further enhanced, and therefore a more intelligent and more efficient content caching and replacing strategy is achieved in a dynamic network scene.

The invention discloses a road network traffic diversion method based on dynamic topology reconstruction. According to the method, the position of the abnormal event and the influenced node set are accurately positioned by identifying the abnormal event and determining the influence range. By dynamically updating node representation, event characteristics are propagated to nodes in an affected range, and dynamic attenuation is performed according to the distance between the nodes and a core node and the severity of the event. On the basis, the connection weight of the affected area is reduced, the connection weight of the bypassing path is enhanced, and the dynamic adjacent matrix is reconstructed. And finally, based on the reconstructed adjacent matrix, combining with a path planning algorithm, comprehensively evaluating the passing efficiency and congestion degree of the path, generating an optimal shunting path, and distributing the flow in proportion. According to the invention, through dynamic network reconstruction and intelligent path planning, traffic congestion caused by abnormal events is effectively relieved, and the emergency response capability and the overall traffic efficiency of the traffic network are improved.

The invention provides a heterogeneous integrated circuit thermomechanical stress collaborative analysis method and system, and relates to the technical field of integrated circuit thermomechanical analysis, and the method comprises the steps: building a finite element analysis grid through obtaining geometric, material and temperature stress data, building a causal reasoning network guided by physical knowledge to recognize a thermal stress evolution path, and obtaining a thermal stress evolution path; and forming a thermal stress evolution analysis map, and constructing a dynamic network to carry out system characteristic analysis so as to realize thermal stress instability early warning.

A system and method for adaptive neural network architecture implementing sophisticated supervision and signal transmission capabilities. The system comprises a layered neural network monitored by a hierarchical supervisory system that collects operational data and implements architectural modifications. A meta-supervisory system oversees the supervisory process, tracking adaptation patterns and extracting generalizable principles from successful modifications. The system implements novel signal transmission pathways that enable direct communication between non-adjacent network regions through adaptive transformation components and coordinated timing mechanisms. This multi-level approach enables dynamic network adaptation while maintaining operational stability through careful monitoring and controlled modification procedures. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled signal propagation.

The invention discloses a supporting power supply technical index evaluation method and system for a novel electric power system, and the method comprises the steps: constructing the technical index of each supporting power supply, comprehensively covering the performances of each dimension of the supporting power supply, determining the subjective weight of each index through employing a dynamic network analytic hierarchy process, and carrying out the evaluation of the technical indexes. The objective weight of each index is calculated by using a weight fusion method based on an entropy method, a machine learning algorithm and grey correlation analysis, the actual contributions of the indexes in different scenes are comprehensively reflected, the subjective weight and the objective weight of each index are dynamically fused to obtain the comprehensive weight of each index, and the comprehensive weight of each index is calculated. According to the method, the influence of subjective weight and objective weight is effectively balanced, the improved TOPSIS method is used for sorting the plurality of supporting power supplies based on the comprehensive weight of each index of the supporting power supplies, and the technical index evaluation results of the plurality of supporting power supplies are obtained, so that the comprehensiveness, flexibility and simplicity of the technical index evaluation of the supporting power supplies are improved.