841 results about "Topology information" patented technology

The invention discloses an intelligent cable fault diagnosis and positioning method and system, and relates to the technical field of intelligent operation and maintenance of a power system. The method is used for accurately identifying and positioning high-resistance faults and external damage. According to the method, electric field, current, temperature and vibration signals are synchronously collected, a multi-source fusion enhanced signal flow is constructed, and multi-physical field features are extracted; based on a coupling mechanism of an electromagnetic-thermal field and a mechanical-electric field, generating a fault type label and a space coordinate; executing targeted impedance correction for different fault types, establishing a dynamic topology network, and inputting a time-space diagram neural network to output a preliminary positioning result; and multi-source verification is carried out by further fusing salinity dielectric, a harmonic thermal field, a vibration electric field and stress topological information, a high-confidence-coefficient fault positioning result is finally output, a closed-loop diagnosis mechanism is formed, and the fault recognition accuracy and the system adaptability under complex working conditions are improved.

The invention discloses a multi-modal large model interpretable diagnosis method and system for a wind turbine generator, and relates to the technical field of wind turbine generator fault diagnosis, comprising the step of combining multi-modal data (vibration, time sequence, image and text) and topological information to realize fault diagnosis through cross-modal contrast learning and topological modeling. The method comprises the steps of multi-modal feature extraction, standardization and alignment, and feature fusion through topology embedding optimization and a cross-modal attention mechanism. In the fault diagnosis process, dynamic correction and path reliability evaluation are introduced by using a regular Agent and a topology consistent Agent, weighted fusion is performed on each modal feature and a topology structure, and finally an accurate fault type and a component positioning result are output. Through combination of knowledge retrieval and a multi-Agent decision model, the adaptability and precision of fault diagnosis are improved, especially in a complex environment, the fault mode of the wind turbine generator can be effectively identified, and the system reliability is improved.

The invention discloses a game reinforcement learning-based reactive power dispatching method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: collecting node voltage, line current, electric vehicle charging and discharging states and topological information, and constructing graph attention embedding; generating an initial reactive power regulation action in the multi-agent game reinforcement learning framework, and shaping and updating a strategy gradient through double rewards; the improved particle swarm is initialized according to the updating strategy gradient, and a self-adaptive inertia coefficient is set; iteratively searching according to the node voltage sensitivity, the line reactive margin and the expected convergence step number to obtain an optimized particle swarm; the particle speed is mapped to a main strategy network through online cooperative training, the inertia coefficient is synchronously adjusted, a cooperative optimization strategy is output, a reactive power dispatching instruction is generated, and the algorithm is updated in a closed-loop mode according to real-time feedback. According to the invention, rapid and cooperative reactive dynamic scheduling of an electric vehicle group is realized, and the voltage stability and the electric energy quality are remarkably improved.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention provides a transient recording fault intelligent judgment method combining waveform matching and cloud edge cooperation, and relates to the technical field of fault diagnosis. Comprising the following steps: collecting global topological information and fault judgment reference parameters of a master station synchronous power system, and outputting initial transient recording data; carrying out preprocessing and feature extraction on the initial transient recording data, realizing edge side layered fault detection by adopting an improved waveform matching algorithm, and outputting multi-layer fault information; constructing a global fault judgment model, updating the global fault judgment model through the hierarchical model, and optimizing the fault judgment model; multi-level fault information is input into the optimized fault judgment model for accurate fault judgment, and graded response measures are taken for real-time judgment results with different confidence degrees; according to the invention, through deep fusion and system-level optimization of multiple technical streams, a transient recording fault intelligent judgment system integrating real-time performance and accuracy is created.

The invention relates to a multi-branch power distribution network double-end traveling wave fault positioning method and system, and the method comprises the steps: deploying a plurality of measurement points based on the head and tail ends of a radial power distribution network and branch terminals, collecting topological information, and calculating the linear mode wave velocity; using the three-phase voltage data after the fault to extract traveling wave line mode and zero mode components through modulus decoupling; a line mode component with the strongest global mutation is selected as a reference, and a wave head time sequence table is generated through wavelet decomposition and mutation detection; and correcting the zero-mode wave velocity by combining the topology, the time sequence table and the line-mode wave velocity, and determining a fault interval and outputting a fault position after compensation and correction by using the line-mode and zero-mode wave head time difference. According to the invention, through a wave head time sequence analysis and time difference compensation mechanism based on cooperation of multiple measuring points, positioning errors caused by clock asynchronization and parameter disturbance are effectively overcome, accurate distance measurement which is not influenced by clock errors is realized, and the distance measurement precision is significantly improved; and meanwhile, the point distribution requirement is simplified, accurate judgment and positioning of a fault interval can be realized only by a branch single measuring point, and high economy and deployment flexibility are both achieved.

The embodiment of the invention discloses a chip verification environment generation method and system, electronic equipment and a storage medium, relates to the technical field of integrated circuit verification, can adapt to verification environments with different configurations, and improves verification efficiency. The method comprises the following steps: constructing or calling a verification environment template; analyzing the initial configuration information and the routing topology information, and generating a target configuration file according to an interface protocol and an interconnection rule of a register transfer level and a model; the routing topology information indicates an interconnection link topology structure between the devices, and the target configuration file indicates a component type corresponding to each device required by simulation forming of each interconnection link and a matched verification component type; loading and instantiating the register transfer level component and the model component based on the verification environment template according to the component type; and according to the verification component type, interconnection is carried out between the register transfer level component and the model component through the verification component, and a chip executable verification environment is generated. The method and the device are suitable for a scene for constructing a chip verification environment.

The invention relates to the fields of computer graphic processing, industrial software technology and the like, discloses a Cocos engine-oriented pipeline loop perception linear decomposition method, and aims to solve the problem that a complex topological graph which cannot be effectively processed and comprises a high-density nested loop in the prior art. The structure is completely and accurately decomposed into a linear drawing instruction sequence which can be accepted by a Cocos engine, and lossless conversion of original topological information is ensured; comprising the steps that after undirected topological graph data are input, a ring detection algorithm is called to recognize and record all independent ring structures in an undirected topological graph and nodes and edges contained in the independent ring structures; judging whether at least one ring is detected in the previous step, and if the at least one ring exists, assigning a determined traversal direction for each detected ring according to a preset ring direction rule; and if the ring does not exist, skipping, and directly performing depth-first search traversal on the undirected topological graph data.

The invention discloses a traffic scheduling method and an electronic device, and relates to the technical field of traffic scheduling, and the method comprises the steps: determining the priority weight of a micro-service, and predicting a target traffic according to the historical traffic information of a network device; constructing a graph model according to the topological information of the network equipment and the dependency relationship of the micro-service, and performing embedded learning on nodes in the graph model to generate a state vector representing a network state; the priority weight, the state vector and the target traffic of the micro-service serve as input of a reinforcement learning model, and a traffic scheduling strategy of the network equipment is obtained; performing iterative search according to iterative particles formed by encoding the strategy network parameters of the reinforcement learning model and the feature learning network parameters of the graph model to determine reinforcement learning model parameters; and issuing the traffic scheduling strategy to the network equipment and executing the traffic scheduling strategy so as to solve the technical problem that a traffic scheduling method in related technologies is difficult to adapt to a dynamic and complex network environment and service requirements under a micro-service architecture, and the reliability of traffic scheduling is improved.

The invention relates to a distributed photovoltaic transformer area metering data abnormity tracing method, belongs to the field of intelligent power distribution networks, and solves the problem of low metering abnormity user positioning efficiency caused by topological information loss of a low-voltage photovoltaic transformer area. Comprising the following steps: acquiring electrical data of a distribution transformer and each user when a distributed photovoltaic transformer area has no photovoltaic output at night, and constructing a transformer area trunk line approximate topology; constructing an inter-node final adjacency matrix between nodes on the basis of the acquired voltage data of each user in the afternoon photovoltaic high output time period of the transformer area; clustering based on the final adjacency matrix among the nodes to divide the users into a plurality of user clusters; constructing a cluster power curve for each user cluster, and calculating the fluctuation consistency between the voltage of each user in the cluster and the cluster power curve of the cluster to which the user belongs based on the approximate topology of the main line of the transformer area so as to identify a suspected abnormal cluster; and performing node-level identification on the suspected abnormal cluster to obtain an abnormal metering user. Automatic traceability and accurate positioning of abnormal measurement data are realized.

The invention relates to a computer data processing technology, in particular to a real-time pipeline safety early warning method and system. The method comprises the steps of performing computer data processing according to pipeline topology information, dividing risk units and generating a risk unit session configuration structure; performing automatic feature extraction to generate a real-time risk feature vector; through machine learning fusion modeling, real-time features and historical baseline features are fused, and a dual-time-scale fusion feature vector is constructed; a preset intelligent model is used for reasoning and event judgment, a structured event flow account and resource adjustment parameters are generated, periodic self-optimization of sampling configuration, a model threshold value and an early warning strategy is driven, and therefore closed-loop intelligent monitoring and early warning of the state of the pipeline auxiliary equipment are achieved. According to the method, the early warning accuracy can be effectively improved, false alarm and missing alarm can be remarkably reduced, the adaptive capacity of the system is enhanced through a closed-loop learning mechanism, and an efficient solution is provided for application of the next generation of information network industry in related fields.

The invention relates to the technical field of energy storage equipment monitoring, and discloses an energy storage power station equipment state monitoring and maintenance decision system. The system comprises an equipment topology overview construction module, a multi-source monitoring module and a health state compensation module. Wherein the equipment topology overview construction module generates an equipment topology overview containing electrical connection parameters and working condition parameters based on a physical connection relationship of an energy storage power station; the multi-source monitoring module collects equipment operation state data streams from the distributed monitoring nodes and obtains environment disturbance data in real time; and the health state compensation module performs missing node compensation on the equipment operation state data stream in the equipment state fusion monitoring mode to obtain a health state index of the energy storage equipment. According to the system, equipment topology information and multi-source data are integrated, accurate assessment of the equipment health state is realized, support is provided for maintenance decision of energy storage power station equipment, and equipment operation and maintenance efficiency can be improved.

The invention relates to an energy digital metering monitoring and tracking method and system, and relates to the technical field of energy digital management, and the method comprises the steps: deploying a plurality of energy metering terminals in a physical energy network to collect energy consumption data in real time, and executing local energy carbon emission metering through an embedded carbon emission calculation assembly; uploading a metering result to a cloud monitoring center, and constructing a virtual mapping model based on a digital twinning technology in combination with topological information of a physical energy network; synchronizing the measurement data to the virtual mapping model, and performing visual tracking on the measurement data in combination with topology analysis and a particle tracking technology to generate an energy flow dynamic visual map; carbon emission footprint traceability tracking is carried out according to the atlas, and energy digitization metering is realized based on a traceability result. According to the invention, the problems of insufficient energy metering precision and incomplete carbon footprint traceability caused by the lack of dynamic visual tracking means due to the fact that differential adaptation is not carried out in combination with metering characteristics in traditional energy metering monitoring are solved.

The invention relates to the technical field of power systems, and discloses a power distribution network fault positioning method, device and equipment and a medium, and the method comprises the steps: synchronously collecting fault traveling wave data through a transformer area intelligent terminal and a feeder terminal disposed in a power distribution network; dynamically calculating traveling wave transmission parameters based on the topological information and the real-time operation parameters of the power distribution network; performing feature extraction and fusion processing on fault traveling wave data acquired by the transformer area intelligent terminal and the feeder terminal to generate a joint feature matrix; based on the joint feature matrix and the traveling wave transmission parameters, cooperative verification is carried out through the single-end positioning information and the double-end positioning information, and a fault section is determined; and performing fault type identification on the fault section by using the intelligent classification model, and performing consistency verification according to an obtained fault type identification result and the fault section to generate a fault diagnosis result. According to the invention, accurate positioning of the power distribution network fault can be realized, and the problems of positioning blind areas, high cost and easy misjudgment in the prior art are solved.

The invention belongs to the technical field of natural language processing and geographic information systems, and particularly relates to a region address standardization method based on knowledge graph enhanced retrieval. Cleaning and preprocessing the original address text input by the user; utilizing a fine-tuned large language model to identify a geographic entity and performing standardized expansion on variant expression to generate a query candidate set; entity linking and context retrieval are carried out based on the knowledge graph, and attributes, hierarchy and spatial topology information of associated entities are obtained; in combination with the original input and the map context, an enhanced retrieval query text is generated through large language model reconstruction; vectorized semantic retrieval is carried out through the fine-tuned embedding model, and a preliminary candidate address set is obtained; carrying out multi-dimensional refined sorting by adopting a resorting model; and generating a structured standard address by using a large language model, and outputting the structured standard address after multi-level verification. According to the method, the problems of ambiguity resolution, alias recognition and context understanding in address processing are solved, and the accuracy and robustness of address standardization are improved.

The invention relates to the technical field of power systems, and discloses a management and control system for effectively managing and controlling distributed power supply loads based on power grid automation equipment. The system comprises an acquisition module for acquiring dynamic load parameters of a distributed power supply and topological information of a target power grid; the division module extracts load sudden change characteristics and divides the power grid into coordinated regulation and control partitions in combination with topological information; the injection module injects a preset disturbance signal to a time delay sensitive area node in the partition to obtain voltage offset distribution; the positioning module is used for positioning electrical weak nodes according to the oscillation attenuation rate of the electrical nodes and topological connection distribution; a verification module performs gridding ratio verification on the weak nodes and the coordination regulation and control partition to obtain a dynamic boundary constraint condition; an output module reconstructs a distributed power supply power output priority instruction based on the condition. According to the invention, the problems of lagging response and insufficient precision of power grid management and control under large-scale access of the distributed power supply are solved.

The invention discloses a power distribution cable fault processing method and device, electronic equipment and a storage medium, and relates to the technical field of power distribution cable fault processing, and the method comprises the steps: firstly collecting multi-mode fault signals, such as a high-frequency traveling wave signal, an ultrasonic partial discharge signal and an environment parameter signal, of a power distribution cable; and distribution network topological structure information such as line length, branch number, node position, wave impedance parameters and the like is synchronously obtained. Then high-frequency traveling wave features are extracted, a positioning strategy is determined according to the number of line branches, and fault position coordinates are obtained in cooperation with the traveling wave features and topological information. Then partial discharge features and environment features are extracted respectively, the three types of features are fused to form a fusion feature set, the feature set is analyzed through a fault classification model optimized by the improved whale algorithm, and fault type information is obtained. And finally, associating fault positions and types, generating a fault processing scheme containing a positioning result, fault causes and operation and maintenance priorities, and realizing accurate positioning, classification and operation and maintenance guidance of the distribution cable faults.

The invention discloses an electricity consumption anomaly detection method based on a graph structure, and relates to the technical field of electricity consumption anomaly detection, and the method comprises the steps: obtaining historical electricity consumption data, environment variable data and power grid physical topology information of a power grid region, and carrying out the standardization processing of the historical electricity consumption data, the environment variable data and the power grid physical topology information, a standardized time sequence-environment data set is obtained; based on the standardized time sequence-environment data set, identifying a causal association relationship between electricity consumption and environment variables through a causal discovery algorithm, and constructing a causal perception heterogeneous graph containing region nodes and environment factor nodes; based on a causal perception heterogeneous graph, introducing a plurality of region nodes of which hyperedge connection is influenced by the same environmental event to form a dynamic hypergraph structure for representing a many-to-many environment-region coupling relationship; based on the dynamic hypergraph structure, a graph comparison learning task is constructed, positive and negative sample pairs are generated by applying disturbance to node features and the graph structure, and unsupervised pre-training is completed;

The invention discloses a transformer area topology identification method and system capable of being deployed in a concentrator, equipment and a medium, and the method comprises the steps: solving an initial virtual impedance matrix based on historical electrical quantity data of a transformer area, and taking the minimum deviation between an optimization result and the initial matrix as an optimization target; and taking symmetry constraint, boundary constraint and collinear constraint as constraint conditions, and performing iterative solution to obtain an optimized virtual impedance matrix, so that the optimized virtual impedance matrix is closest to an original matrix on the basis of conforming to the actual topology, noise interference can be eliminated, the topology identification precision is improved, the method can dynamically adapt to a transformer area change scene, and the method is suitable for being applied to a transformer area. The method has high robustness, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to physical topology, fuzziness of a traditional method is avoided, the whole recognition process only depends on historical electrical quantity data of the transformer area, no initial topology information is needed, and the high-precision transformer area topology can be constructed from zero.

The invention provides a carbon emission calculation method and device based on power grid prediction, and the method comprises the steps: constructing a graph structure containing a plurality of nodes and edges based on the topological information and operation data of a power grid; the nodes and the edges respectively represent the connection relationship between the devices in the power grid; on the basis of the graph neural network model, feature extraction and information spreading are carried out on the graph structure, a prediction result is obtained, and the prediction result comprises state information of each node; a physical constraint mechanism is arranged in the graph neural network model, and the physical constraint mechanism is used for constraining a prediction result to conform to a physical rule of power grid operation; and outputting a prediction result conforming to the physical rule of power grid operation, thereby solving the problem that the prediction result in the traditional scheme is unavailable and uncredible in the key industrial field, enabling the prediction result to be seamlessly connected to the power grid dispatching and decision-making process with extremely high requirements on safety and stability, and improving the reliability of the power grid. Therefore, the practicability of the intelligent prediction technology of the power grid is greatly improved.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention provides a fast recovery method for hadoop cluster data, which relates to the technical field of erasure codes, and comprises the following steps of: calculating an importance score of a data block to be recovered, determining a target copy number based on a nonlinear copy number adjustment function, calculating a load weight in combination with network topology information, and selecting candidate nodes; and predicting a node fault probability by using a long short-term memory network, calculating a migration priority based on the importance score and the fault probability, and selecting an optimal target node to execute data recovery. According to the invention, the intelligence and differentiation of the data recovery process are realized, and the data recovery efficiency and reliability are improved.

The invention relates to an activation value re-segmentation communication method for a heterogeneous AI acceleration chip server. The activation value re-segmentation communication method comprises the following steps: scanning through chips, network interface cards and PCIe levels in the AI acceleration chip server, extracting network topology information of the server, calculating delay and bandwidth from each chip to each network interface card, and generating an affinity matrix; based on the affinity matrix, each chip is bound to a network interface card in an exclusive or shared mode through maximum matching; based on the server network topology information, the chip-network interface card affinity and the network interface card and PCIe single line bandwidth, activation value communication mode matching is carried out, and communication modes comprise a transmitting / receiving mode, a transmitting / receiving + collecting mode and a transmitting / receiving + broadcasting mode; and double-level multi-channel communication between the chip and the network interface card is realized. The method has the advantages of remarkably improving the bandwidth utilization rate, being highly adaptive to topology difference, being high in heterogeneous compatibility and the like.

The invention provides an electric connector signal transmission optimization method and system, and relates to the technical field of communication, and the method comprises the steps: obtaining an electrical characteristic parameter and transmission path topological information, recognizing a frequency turning point, calculating a loss coefficient, constructing a frequency selectivity compensation mapping rule, and obtaining a frequency division compensation strategy; and calculating a double-component correction factor, carrying out nonlinear adaptive adjustment to obtain a target compensation parameter, and finally generating a multi-path compensation control signal. According to the invention, loss characteristics in signal transmission can be accurately identified, frequency selective compensation is realized, and the signal transmission quality and stability of the electric connector are improved.

Provided herein are techniques to facilitate providing consumer-defined workloads for a network slice of a slice provider network. In at least one example, a method may include transmitting a query to a slice provider function for topology information of an instantiated network slice in which the instantiated network slice includes slice resources provided at a plurality of network locations. The method may further include obtaining the topology information that identifies, at least in part, the plurality of network locations at which the slice resources and identifying, based at least in part on the topology information, at least one network location of the instantiated network slice at which to instantiate a consumer-defined workload, and transmitting a request to instantiate the consumer-defined workload that includes at least one location identifier of the at least one network location and a workload identifier of the consumer-defined workload to be instantiated.

The invention relates to a probability distribution determination method and device for marginal carbon emission factors and computer equipment. The method comprises the following steps: sampling according to first probability distribution of renewable energy output and second probability distribution of a power system load to obtain a parameter sample of the sampling; matching the parameter sample with each critical domain in the critical domain list; in response to a result that the parameter sample is not matched with the critical domain, solving by utilizing a multi-period economic dispatching model to obtain a marginal carbon emission factor corresponding to the parameter sample, the constraint conditions of the multi-period economic dispatching model comprise the power balance constraint of each period and the inequality constraint formed by the line power flow constraint, the unit output constraint and the unit climbing constraint, and the constraint conditions are generated by utilizing the network topology information and the unit configuration information; and generating probability distribution of the marginal carbon emission factors by using the marginal carbon emission factors obtained by each sampling. By adopting the method, the calculation efficiency and accuracy can be improved.

The invention discloses a topology identification method based on intelligent measurement data of a low-voltage transformer area. According to the technical scheme, the topology identification method comprises the steps that 1, a master station issues a timing command to an intelligent measurement terminal and an electric meter; step 2, the master station issues a transformer area electric meter file to the intelligent measurement terminal, configures an acquisition task and issues the acquisition task to the intelligent measurement terminal; step 3, establishing an electric quantity sum linear regression model based on a Lasso algorithm by using daily frozen electric quantity data, and converting the identification problem of the transformer area user-transformer relation into regression coefficient solving; step 4, on the basis of user change relation identification, performing dimension reduction on historical voltage data by using a t-SNE algorithm to improve the identification efficiency of the model; and step 5, clustering the voltage data after dimension reduction by using agglomerated hierarchical clustering, identifying a user phase and a meter box to which the user belongs, and completing identification of a topological structure. According to the method, the power grid topology information of the transformer area can be automatically identified, the efficiency and the accuracy of topology identification are improved, and the method has far-reaching significance for construction of an intelligent power grid.

The invention relates to the technical field of medical instruments, and discloses a breast cancer sentinel lymph node positioning system and method, and the system comprises a data collection subsystem, a space positioning subsystem and a central processing subsystem. The method comprises the following steps: acquiring an anatomical structure image, a radioactivity counting rate and fluorescence intensity after performing primary fluorescence, radioactivity and secondary pulse type fluorescence tracer injection in sequence; establishing a global coordinate system and generating an anatomical region of interest; constructing a weighted directed topological graph based on the initial fluorescence data, and obtaining a baseline flow velocity; extracting radionuclide dynamic characteristics based on the radioactive data; a functional flow rate attenuation coefficient is calculated based on the second pulsed fluorescence data. And a multi-factor scoring model is applied, topological information, dynamic characteristics and functional flow velocity attenuation coefficients are fused, and a comprehensive score is calculated to identify the sentinel lymph node. According to the method, the lymphatic flow topological structure, nuclide dynamics and functional flow velocity information are combined, and the positioning accuracy and reliability are improved.

A routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is provided. The routing and scheduling method includes the following steps: S1, initializing a cycle index detection mechanism, a queue mapping and a queue mapping constraint of a multi-cycle CSQF; S2, constructing a DFRLLS model; S3, optimizing the DFRLLS model based on GDRL; S4, off-line training a learning strategy of a GDRL model; S5, making a decision on-line based on a trained GDRL model. The routing and scheduling method based on multi-cycle CSQF mechanism and GDRL is adopted, and GCN network is used to extract topology information between networks. Compared with the method of only using reinforcement learning, the routing and scheduling method can achieve more flow scheduling, and its performance is also stable under complex network topology, multi-cycle CSQF can reduce the start-to-end delay of the flow compared to the CSQF.

A method and system for updating a topology on router nodes in a distributed storage system are described. The method can include obtaining, by a first service of a control plane service, topology information from an inventory data store by querying a second service of the control plane service that manages the inventory data store for the topology information. The method can further include generating, by the first service of the control plane service, a topology payload based on the obtained topology information. The method can also include sending, by the first service of the control plane service, a request comprising the topology payload to a router node.