77 results about "Weighted network" patented technology

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention relates to a construction site environment dynamic regulation and control method and system fused with AIoT. According to the method, a multi-source environment sensor network is deployed to collect original data of a construction site environment in real time, a data set with aligned timestamps is generated, an abnormal event is detected by using an isolated forest algorithm after standardized denoising processing, and a specific parameter type and a time window are marked; a causal intensity matrix between parameters is constructed through Granger causal test on the basis of environment data intercepted in an abnormal time period, a directed weighted network adjacency matrix representing a pollution propagation path is generated accordingly, and an abnormal source is accurately positioned through node influence propagation calculation and timestamp verification; and finally, a regulation and control instruction sequence is dynamically generated according to the shortest influence path of the source node in the propagation network, so that autonomous analysis of implicit association among construction site environment parameters, accurate positioning of a pollution source and dynamic distribution and instant deviation correction of a multi-stage regulation and control strategy are realized.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention discloses a low-altitude airspace planning method and system based on a micro-terminal area, and relates to the field of low-altitude airspace planning, and the method comprises the steps: generating a corresponding take-off and landing area for a low-altitude take-off and landing point or a take-off and landing field, and generating a take-off and landing hub area for an adjacent take-off and landing point or a take-off and landing field, so as to construct the micro-terminal area of the low-altitude airspace; constructing a risk area, and forming a restricted area of the low-altitude airline network together with the control area and the no-fly area; on the basis of the restricted area, generating alternative intermediate points connected with the take-off and landing points or the take-off and landing fields; based on the data of the alternative intermediate points and the data of the micro-terminal area, constructing an initial low-altitude airline network connected with all the points; calculating a weight corresponding to each side line in the initial low-altitude airline network, and optimizing the initial low-altitude airline network based on a weighted network; and removing redundant intermediate points based on the optimized network, constructing access points of the airline network and each micro-terminal area, and generating a final low-altitude airline network. According to the invention, a more reliable technical basis is provided for low-altitude route network planning.

The invention discloses a multipath channel parameter reconstruction and beam coverage prediction method, and relates to the technical field of wireless communication. Aiming at the problems of low data utilization efficiency, limited environment modeling precision, strong coupling of measured data and antenna beam configuration and the like in the prior art, the method comprises the steps of configuring antenna beams and orientation parameters, collecting reference signal receiving strength, performing deterministic channel modeling simulation, establishing a mapping model, performing sparse recovery, constructing a channel propagation model, evaluating a channel map and the like. In combination with a coherence perception weighting network (CARE-Net) algorithm and ray tracing (RT) physical prior guidance, accurate reconstruction from a low-dimensional reference signal receiving strength (RSRP) measurement value to a high-dimensional multipath parameter is realized. The method has the advantages that the reconstructed multi-path parameters are decoupled from the antenna configuration, the method has the cross-beam / cross-configuration generalization prediction capability, the channel coverage performance under different antenna configurations can be accurately predicted, a reliable basis is provided for wireless network optimization, and the method is low in cost, high in precision and strong in generalization.

The invention discloses a lightweight optical fiber vibration intrusion event identification method and system for perimeter security and protection, and belongs to the technical field of optical fiber sensing technology and mode identification, and the method comprises the steps: collecting an original vibration signal of a perimeter monitoring region through a distributed optical fiber vibration sensing system; performing wavelet threshold de-noising preprocessing on the original vibration signal to obtain a de-noised signal; performing feature extraction on the denoised signal, and constructing a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; and inputting the low-dimensional classification feature vector into a pre-trained lightweight convolutional neural network model for classification and identification, and outputting a corresponding intrusion event category. According to the method, through cooperation of front-end LDA dimension reduction and a rear-end lightweight network, the model parameter quantity and calculation overhead are greatly reduced while high recognition precision is guaranteed, efficient real-time deployment on edge equipment is achieved, and the method is suitable for intrusion detection in perimeter security and protection.

The invention provides a product full production cycle carbon footprint tracing evaluation method and system, and relates to the technical field of carbon footprint tracing, and the method comprises the steps: obtaining production activity data of each link of a product full life cycle, constructing a directed weighted network model, and executing path traversal, extracting a traversal path of which the path carbon intensity meets a preset path carbon intensity condition as a first carbon flow path, obtaining the node net carbon emission of each node, and determining the apportionment amount of each node to the third-range indirect emission; and matching the production activity data of the link corresponding to each node with the emission parameters in the carbon emission database to obtain the link carbon emission intensity of each link, and determining the link carbon emission of each link based on the link carbon emission intensity and the node net carbon emission. According to the method and the device, a small-flow high-carbon emission intensity path can be identified, and the range three-emission accounting accuracy is improved to the level of tracing to a specific supply chain node through a double-layer mapping relation of link level microscopic accounting and node level macroscopic tracing.

The invention discloses a change segmentation method and system based on learnable DCT and frequency band division and a medium, and relates to the technical field of image processing, an improved AFBDCTFEM model is taken as a core, a learnable two-dimensional DCT transformation network, an adaptive frequency band division AFB network and a Band Router dynamic weighting network are connected in series to form an integral framework capable of end-to-end back propagation, and the integral framework is used for learning the two-dimensional DCT transformation network, the adaptive frequency band division AFB network and the Band Router dynamic weighting network. While the DCT energy concentration characteristic is maintained, the network is endowed with triple capabilities of tuning a frequency base as required, dynamically selecting a frequency band and automatically estimating a weight; and a substructure of an existing image change region segmentation network is replaced in an end-to-end mode, so that efficient extraction of dual-time-phase image difference features is realized, and the fineness and robustness of image change region segmentation are remarkably improved.

The invention relates to the field of perovskite photovoltaic technology, and discloses a perovskite solar cell interface layer optimization and stability enhancement method. The method comprises the following steps: firstly, acquiring real-time operation data of a battery, and screening a to-be-optimized interface layer set; based on an interface material attribute and a target stability threshold value, taking a battery overall structure as a center node, taking a to-be-optimized interface layer as a starting node and taking the target stability threshold value as a termination node, establishing an interface performance weighting network, and exporting a weight of an edge by a matching degree of the material attribute and the threshold value; in the network, aiming at the path of each starting node from a central node to a termination node, applying a self-adaptive optimization strategy to search an optimal solution, and identifying a plurality of optimization tracks; and generating an interface layer optimization scheme according to the optimal solution, and after a selection instruction is received, presenting an interface layer detailed material parameter record corresponding to the selected scheme. According to the method, interface layer optimization and stability enhancement are realized through network construction and optimization trajectory analysis.

The embodiment of the invention provides an external risk factor cause quantitative analysis method and system, and belongs to the technical field of risk assessment. The method comprises the steps that historical accident information is collected, accident cause analysis is performed on the historical accident information, and an external risk factor set is constructed and obtained; obtaining a corresponding relation matrix; constructing a directed weighted network based on the external risk factor set and the relation matrix, and constructing an evaluation system corresponding to the directed weighted network; constructing a corresponding external factor cause quantitative evaluation model; and evaluating the current operation state information of the target chemical enterprise based on the external factor cause quantitative evaluation model to obtain a safety risk evaluation result. According to the scheme, the problem that the external risk factor causes cannot be quantified in the safety risk assessment process of the chemical production process at present is solved, and the accuracy of the safety risk assessment result of the chemical production process is improved.

This application discloses a low-altitude airspace planning method and system based on micro-terminal areas, relating to the field of low-altitude airspace planning. The method includes: generating corresponding take-off and landing areas for low-altitude take-off and landing points or fields, and generating take-off and landing hub areas for adjacent take-off and landing points or fields to construct micro-terminal areas of the low-altitude airspace; constructing risk areas, which, together with control areas and no-fly zones, constitute the restricted areas of the low-altitude flight path network; generating alternative intermediate points connecting each take-off and landing point or field based on the restricted areas; constructing an initial low-altitude flight path network connecting all points based on the data of the alternative intermediate points and the data of the micro-terminal areas; calculating the corresponding weights of each edge line in the initial low-altitude flight path network, and optimizing the initial low-altitude flight path network based on the weighted network; and removing redundant intermediate points based on the optimized network, constructing the flight path network and the entry and exit points of each micro-terminal area to generate the final low-altitude flight path network. This application provides a more reliable technical basis for low-altitude flight path network planning.

The invention discloses a material creep behavior prediction method and device based on a physical mechanism weighted network, and aims to solve the problems of low prediction precision and poor physical interpretability of an existing data-driven model under a small sample condition. The method comprises the following steps: constructing a physical mechanism weighting network which comprises a plurality of weighting mechanism units; each weighting mechanism unit is formed by coupling a weighting layer and a physical mechanism layer, the physical mechanism layer is packaged with a preset creep physical mechanism model, and the weighting layer is configured to represent a competition and conversion relationship of different physical mechanisms under an external load condition through a learnable weighting function; training the physical mechanism weighting network by using creep experiment data of a material so as to determine parameters of the weighting function; and predicting the creep behavior of the target material under a given stress-temperature condition by using the trained physical mechanism weighting network. According to the method, high-precision and physically interpretable creep prediction under small samples and a wide load domain is realized.

The application discloses a face emotion recognition method based on a high-order recursive attention weighting network, which is a HRAM-CNN network model, and learning and training of face emotion recognition are performed, and then an image to be recognized is input into the trained HRAM-CNN network model to perform emotion judgment on each image; the HRAM-CNN network model is a high-order recursive attention mechanism HRAM which is put into a convolution layer of a convolution network model CNN. The application effectively combines the advantages of VisionTransformer and CNN, provides a high-order recursive attention mechanism combining the self-attention mechanism and the channel attention mechanism, and proposes that a large 7*7 kernel is used to construct the attention mechanism, so that adjacent information is effectively captured, and the feature extraction capability of the convolution process is further enhanced; the trained network can perform emotion judgment on each verification image.

The application discloses a physical information guided SAR airplane target detection method, comprising the following steps: constructing an airplane target slice data set and extracting scattering key points; based on a Gaussian mixture model, generating a probability density image corresponding to the airplane target slice as a true value heat map by using the scattering key points; constructing a target scattering key point prediction network and training the same, wherein the true value heat map corresponding to the airplane target slice and a predicted heat map predicted by the prediction network are used to calculate a network loss during the training process; constructing a feature reweighting network; combining the feature reweighting network and the trained target scattering key point prediction network to form a joint model, and connecting the joint model to a deep learning backbone network and a target detection network, so as to construct a physical information guided SAR airplane target detection network; and using a SAR airplane detection image data set to train the SAR airplane target detection network, and saving the trained network model for use in detecting and identifying unknown target categories of SAR airplane detection images.

The application discloses a traditional Chinese medicine compound analysis method based on a multi-dimensional data and information weighted network, and belongs to the field of traditional Chinese medicine compound analysis. The method comprises the following steps: constructing a multi-dimensional disease gene set; acquiring a traditional Chinese medicine compound set and a target set; determining disease gene nodes, compound nodes and target nodes respectively, and constructing a heterogeneous correlation network; performing propagation node screening on the heterogeneous correlation network, and weighting the edge set of the heterogeneous correlation network; running three algorithms on the updated heterogeneous correlation network respectively, and performing equal-weight average fusion on the propagation results of the three algorithms to obtain a final weighted network correlation degree score; sorting the final weighted network correlation degree score from high to low to obtain a key target candidate list; and selecting the first N candidate targets in the key target candidate list for enrichment analysis. The application solves the problems of single data source, lack of functional semantic support in network construction, static node evaluation index, and disconnection between enrichment analysis and core algorithm in the prior art.

The invention provides an intelligent grading and judging method for wooden furniture plates, which comprises the following steps of: constructing a high-quality image database which is acquired by an industrial camera at multiple angles and is bound with material labels, performing multi-stage denoising, enhancement and normalization preprocessing, extracting a key texture feature channel through a lightweight convolutional neural network and a channel attention mechanism, and performing classification and judgment on the key texture feature channel; according to the method, weight distribution and online optimization are carried out on feature channels in combination with a dynamic feature reweighting network, intelligent compensation of feature expression ability after lightweight pruning and quantification of the model is realized, in addition, a weight drift monitoring and local recalibration mechanism is arranged, the discrimination robustness of the model in detection of different batches of plates is guaranteed, and the detection accuracy is improved. According to the method, the edge deployment efficiency is improved, and meanwhile, the accuracy and adaptability of automatic judgment of the plate grade are enhanced.

The disclosure provides a traditional Chinese medicine efficacy evaluation method and device based on a node-weighted network, equipment and a storage medium. The method determines target disease protein targets and corresponding target weights based on the comprehensive scoring results of candidate disease protein targets by a public database and a large language model. When determining the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, the prescription ratio, chemical component information, and the interaction probability between the chemical components and the protein targets of each medicinal material in the traditional Chinese medicine prescription to be evaluated are comprehensively considered. The target disease protein targets and the corresponding disease protein target weights, as well as the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, are added to a pre-constructed protein interaction network. The obtained node-weighted network focuses on the real pharmacological basis, effectively improves the network biological interpretation, and the multi-dimensional network index determined accordingly is used to evaluate the regulation effect of the traditional Chinese medicine prescription to be evaluated on the target disease, which is more accurate.

The application is suitable for the technical field of charging station site selection, and provides a method for electric vehicle charging station site selection and capacity optimization based on structural entropy, comprising: constructing an initial feature vector of a candidate parking lot based on business information and geographic location information of the candidate parking lot; constructing a weighted network graph and initializing the weighted network graph as a coding tree; calculating the structural entropy of the candidate parking lot based on the coding tree, generating an enhanced feature vector based on the structural entropy and the initial feature vector; clustering the candidate parking lot based on the enhanced feature vector to obtain multiple clusters, and defining each cluster as a demand area; constructing an energy utilization rate function based on the demand area, taking the energy utilization rate function as a fitness function of a genetic algorithm; and optimizing the electric vehicle charging station construction scheme by using the genetic algorithm to obtain an optimal electric vehicle charging station construction scheme which maximizes the fitness function and meets the construction constraint condition. The application can improve the energy utilization rate so that the energy can be fully utilized.

The application discloses a subway network resilience evaluation method and device, and relates to the technical field of traffic resilience evaluation. The method comprises the following steps: constructing a directed weighted network model of a subway network according to subway stations and running routes; determining subway network variable indexes according to the directed weighted network model, and performing initial load distribution of nodes of the directed weighted network model according to the subway network variable indexes; performing iterative attacks on the directed weighted network model with the initial load distributed, re-distributing node loads after each attack, and calculating subway network efficiency after each attack according to re-distribution results; and evaluating subway network resilience according to the subway network efficiency after each attack. The application can more accurately identify key nodes in the network, the evaluation result of the network resilience is more comprehensive and reliable, and the operation efficiency and management level of the subway network are improved.

This invention discloses a microblog group identification method based on community discovery, comprising the following steps: S1, data collection and cleaning; S2, feature extraction and representation; S3, establishing a classification model; S4, community tagging and influence analysis. In this invention, an optimized Dynamic Topic Model (DTM) is used to mine specific groups within the microblog community. Microblog posts from the past year are selected as the research object, and the similarity of topics in posts from different authors is used as the weight of links between authors, mapping the microblog network into a directed weighted network. Community discovery is performed using the Label Propagation Algorithm (LPA), identifying the inherent community structure within the social relationship network. This invention conducts in-depth analysis of user relationships within the microblog network, and based on identification methods for user-generated content characteristics, user association characteristics, and environmental characteristics, it mines potential topics to identify users with similar interests and active user groups in specific fields.

The invention provides a false data injection attack identification method based on an adaptive residual weighted PINN. The method comprises the following steps: constructing a physical equation describing a system operation state; a self-adaptive residual weighting network is constructed, and the self-adaptive residual weighting network takes sensor measurement data with noise or attack as input and takes the reconstructed system state as output; training the adaptive residual weighting network by using a gradient descent algorithm; mapping sensor data into a system state meeting physical consistency in real time by using an adaptive residual weighting network; and according to a weight coefficient and physical residual information which are calculated in real time by the self-adaptive residual weighting network, identifying a false data injection attack through a dual-criterion mechanism.

This invention discloses a method and system for measuring the sound insulation of in-ear hearing protectors, relating to the field of sound insulation measurement technology. The method includes: calibrating a test microphone and an external microphone to obtain sensitivity at different frequencies; pre-selecting sound source points for noise simulation; dividing the pre-processed sound signal into several short time periods, obtaining frequency domain signals through Fourier transform, dividing into harmonics according to the fundamental frequency and obtaining voltage characteristics; calculating the sound pressure amplitude and sound pressure level at each harmonic based on sensitivity, and calculating the sound insulation and sound insulation rate for each harmonic; combining the sound insulation of each harmonic within all short time periods to obtain the sound insulation of each harmonic within the complete time period, and then calculating the comprehensive sound insulation based on an A-weighted network; turning on the test sound source at each sound source point, calculating the sound insulation rate of each harmonic at each sound source point, extracting the high, medium, and low frequency sound insulation rates, and fitting a function of the distance variation based on the straight-line distance between the sound source point and the test microphone; thus achieving accurate measurement and analysis of the sound insulation of in-ear hearing protectors.

The invention discloses an atmospheric pollution transmission modeling and pollution driving factor identification method based on multiple networks, and the method comprises the steps: taking an atmospheric monitoring station as a common node set, and constructing a corresponding directed weighted network layer according to pollutant transmission, meteorological driving, geographical constraint, pollution source contribution and urban activity influence, the directed weighted network layers share the same node set, are not fused, and are superposed to form a directed weighted multiple network; performing label propagation community detection on the directed weighted multiple network to obtain a detection result; determining a site set corresponding to each community in the detection result as a pollution associated area; and calculating layer correlation according to the internal directed weighted edge connection cumulative strength of the pollution correlation area on each network layer, and determining the network layer with the maximum layer correlation as a dominant pollution driving factor of the corresponding pollution correlation area.

The invention discloses a zero sample prediction and numerical phase diagram generation method and system for a creep behavior of a metal material, and belongs to the technical field of digital design and performance prediction of materials. The method comprises the following steps: firstly, constructing a cross-material generality rule mapping relation library, and establishing quantitative correlation among material macroscopic attributes, crystal structures, phase change temperatures, physical mechanism weight distribution and model parameters; for a to-be-predicted target metal material, a preset model and parameters can be called from the relation library only by inputting the crystal structure and the melting point of the to-be-predicted target metal material, a high-precision physical mechanism weighted network prediction model is automatically assembled, and accurate prediction of the creep behavior of the to-be-predicted target metal material in the full stress-temperature domain is achieved. Through continuous calculation and visualization, a quantitative and continuous creep numerical phase diagram with physical interpretability is generated, and competition and evolution of different creep mechanisms are visually revealed. According to the method, normal form transformation from experience fitting to physical prediction is realized, and a core tool is provided for rapid evaluation and digital design of new materials.

The present disclosure discloses a method for SFC deployment based on VNF-dependent software migration in a multi-domain network, and relates to the field of communications technologies. The method jointly optimizes the SFC deployment and the migration of VNF-dependent software to resolve issues concerning service provision in the multi-domain network. According to the method, information related to the multi-domain network is first initialized, and then a multi-layer network architecture is constructed to support SFC deployment. Then, a multi-layer weighted network is established by using an analytic hierarchy process (AHP), and an SFC deployment policy and a VNF-dependent software migration policy are executed on this basis. In the present disclosure, the impact between SFC deployment and VNF-dependent software migration decisions is comprehensively considered, achieving efficient SFC deployment and VNF-dependent software migration in the multi-domain network.

The invention relates to the field of power system operation and maintenance, and particularly provides a power grid state early warning and alarming method and system based on dynamic characteristic atlas and risk conduction path analysis, a terminal and a medium, and the method comprises the steps: synchronously collecting and preprocessing multi-dimensional data of power grid monitoring nodes, calculating the real-time abnormality of the nodes and the dynamic association strength between the nodes, and calculating the real-time abnormality of the nodes; constructing a weighted network map evolved along with time; an initial risk source is identified based on a map, risk conduction is simulated to calculate a whole network node influence score, a key fragile path is identified, a dynamic risk level is obtained, and finally graded early warning information is generated. According to the method, false alarms caused by normal fluctuation of working conditions are reduced, the early recognition capability of a hidden abnormal mode is improved, a risk conduction process is simulated, current node abnormality evaluation, risk diffusion range and degree pre-judgment and key fragile path tracing recognition are realized, and weak links of a system are locked in advance.

The invention is suitable for the technical field of charging station site selection, and provides an electric vehicle charging station site selection and capacity optimization method based on structure entropy, and the method comprises the steps: constructing an initial feature vector of a candidate parking lot based on the business information and geographic position information of the candidate parking lot; constructing a weighted network graph, and initializing the weighted network graph into a coding tree; calculating the structure entropy of the candidate parking lot based on the coding tree, and generating an enhanced feature vector based on the structure entropy and the initial feature vector; clustering the candidate parking lots based on the enhanced feature vectors to obtain a plurality of clusters, and defining each cluster as a demand area; constructing an energy utilization rate function based on the demand area, and taking the energy utilization rate function as a fitness function of a genetic algorithm; and optimizing the electric vehicle charging station building scheme by using a genetic algorithm to obtain an optimal electric vehicle charging station building scheme which has the maximum fitness function and meets the station building constraint condition. According to the invention, the energy utilization rate can be improved, so that energy can be fully utilized.

The invention provides a traditional Chinese medicine curative effect evaluation method and device based on a node weighted network, equipment and a storage medium. According to the method, a target disease protein target and a corresponding target weight are determined on the basis of a comprehensive scoring result of candidate disease protein targets by a public database and a large language model. When the medicinal material protein target and the corresponding medicinal material protein target weight are determined, the prescription proportion and the chemical component information of each medicinal material in the to-be-evaluated traditional Chinese medicine prescription and the interaction probability between the chemical component and the medicinal material target are comprehensively considered. A target disease protein target, a corresponding disease protein target weight, a medicinal material protein target and a corresponding medicinal material protein target weight are added to a pre-constructed protein-protein interaction network, and an obtained node weighted network focuses on a real pharmacological basis, so that the biological interpretation force of the network is effectively improved; the accuracy of evaluating the regulation effect of the to-be-evaluated traditional Chinese medicine prescription on the target disease by using the determined multi-dimensional network index is higher.

This invention discloses a ranking information recommendation method based on distribution entropy, belonging to the field of recommendation strategy technology, including the following steps: S1, obtaining ranking information: each ranking entity ranks all or a portion of the evaluated objects separately; S2, networking the ranking data: constructing an undirected weighted network; S3, the ranking information recommendation method based on distribution entropy: introducing global edge weight distribution entropy, quantifying the uniformity of edge weight distribution, and designing a recommendation scoring mechanism based on the quantification results. This invention, employing the above-mentioned ranking information recommendation method based on distribution entropy, not only provides an intuitive and scientific means to objectively evaluate the quality of ranking data, but also, through a low-computational-cost, high-generalization-ability mechanism, suppresses the Matthew effect in the evaluation system from the source, ensuring that the final obtained ranking set has extremely high coverage and fairness, providing solid data support for scientific decision-making in the field of computer and auxiliary equipment repair.

A method and apparatus for optimizing network delivery of fifth generation (5G) edge application services by weighting network latency and application server load, capacity, and preference to determine where to transmit new packet flows. By using a weighting approach, network traffic can be balanced among multiple instances (e.g., applications) with the same address.