208 results about "Routing algorithm" patented technology

The invention relates to the technical field of temperature control, in particular to a mass concrete water cooling water temperature control method and system. The method comprises the following steps: collecting concrete key parameters through a multi-source sensor, constructing a microenvironment model based on information geometry, and generating a thermosensitive characteristic spectrum; forming a distributed thermodynamic model by utilizing a mutual information routing algorithm and heat-fluid-solid coupling analysis, and calculating an optimal cooling strategy; water flow path and flow optimization is realized in combination with fractional order particle swarm optimization; a pump set and a valve are adjusted through fuzzy self-adaptive control; and a potential instability region is identified through the spectrum energy gradient, and risk early warning is triggered. The precision and reliability of mass concrete cooling temperature control are remarkably improved, and intelligent, efficient and safe temperature control adjustment in the whole process is achieved.

The invention provides a power grid resource operation management method, device and equipment based on a service platform, and relates to the technical field of power grid management. The method comprises the following steps: when a flow exception occurs, determining an exception type, determining a self-healing strategy according to the exception type, and reconstructing a flow path according to an intelligent routing algorithm; obtaining resource data of each professional system of the power grid, and generating a resource scheduling scheme according to the resource data, the reconstructed flow path and the dynamic scheduling strategy; and performing resource scheduling according to the resource scheduling scheme, and obtaining resource scheduling real-time data to generate resource situation display information and / or resource early warning information. The stability and response efficiency of power grid resource operation can be improved.

The invention discloses an unmanned aerial vehicle cluster cross-layer cooperative routing method based on TDMA, is applied to the field of wireless communication network resource scheduling and unmanned aerial vehicle autonomous networking cooperative control, and aims to solve the problem that the overall resource utilization rate of a system is low due to hierarchical design of a routing strategy and an MAC layer channel access control strategy in an existing TDMA network. The method comprises a dynamic collaborative framework based on routing and time slot allocation and a time slot state sensing routing algorithm based on deep reinforcement learning, the framework provides real-time time slot resource allocation state input for the algorithm, the algorithm outputs an optimal path meeting the bandwidth requirement for the framework, the problem of resource mismatching of a traditional layered framework is effectively avoided, and the routing efficiency is improved. And finally, collaborative optimization of the service bandwidth achievement rate and the network resource utilization efficiency in a high-load scene is achieved.

The invention discloses an intelligent medical image diagnosis system and method based on hierarchical cross-modal conversion and dynamic feature tracking. The system adopts three-step cross-modal conversion: a first-layer small model for converting user questions to realize medical ontology matching; the second-layer multi-modal model extracts image features, and outputs text states such as JSON data with focus coordinates, density and other features; and the third-layer large model fuses the medical history and the image features to generate diagnosis suggestions, and credibility verification is carried out. A dynamic focus tracking engine is introduced, a focus evolution rule of multiple scanning is analyzed through a convolutional network, and an optical flow field is adopted to compensate artifacts. The system also integrates a multi-expert voting mechanism to simulate a clinical consultation process, and outputs consensus diagnosis and objection viewpoints. A hierarchical routing algorithm is designed for emergency treatment scenes, so that the recognition response time of emergencies such as pneumothorax is shortened. Further, the system automatically generates a full chain of evidence report that conforms to medical regulations, including a model version, a guide reference, and a data hash value.

The invention provides an artificial intelligence semantic processing system and method oriented to digital media creation, and relates to the technical field of artificial intelligence semantic process.The artificial intelligence semantic processing method comprises the steps that predicate argument relation pairs of language texts are extracted, object space relation pairs of sketch images are extracted at the same time, and a basic semantic unit set is constructed; the integrity and accuracy of cross-modal semantic understanding are ensured, further, semantic units are clustered by using a dynamic routing algorithm, a semantic concept cluster with a clear importance weight is generated, deep mining and structured representation of creation intentions are realized, and the creation intentions are quickly and accurately understood. An initial semantic relation graph is constructed, a graph attention network is used for dynamic reweighting, finally, an enhanced dynamic semantic graph is generated, complex association and a hierarchical structure between semantic concepts are effectively captured, finally, hierarchical analysis is carried out on the semantic graph, and a structured semantic blueprint is output, so that the dynamic semantic graph is obtained. And a reliable semantic processing technology is provided for creation of high-quality digital media contents.

The invention discloses a liquid drop routing method under fluid constraint in a digital micro-fluidic biochip based on reinforcement learning and imitation learning, and aims to solve the technical problems of low planning efficiency, low success rate and weak generalization ability of a traditional liquid drop routing algorithm under fluid constraint. The method adopts a strategy combining imitation learning and reinforcement learning and comprises the following steps: firstly, generating expert data by utilizing an expert algorithm meeting fluid constraint after transformation, and pre-training a network model through imitation learning to enable the network model to quickly grasp a basic routing strategy; the model and environment interaction are autonomously optimized through reinforcement learning, and the adaptability to the dynamic environment is improved. And finally fusing the weights to form a final model. Through the hybrid training framework, the success rate and planning efficiency of droplet routing are remarkably improved, and the generalization ability of the model to chip environments with different sizes is enhanced.

The invention provides a multi-source data fusion analysis system for electronic commerce digitization insight, and relates to the technical field of data processing, and the system comprises a reference analysis unit used for selecting a reference analysis unit in a spatial relation structure, calculating fractal dimension features of feature vectors, carrying out multi-level recursive division on the analysis unit based on fractal dimension values, and carrying out multi-level data fusion analysis on the fractal dimension values; each level of division keeps a self-similarity characteristic, and feature partitions are obtained; the partition space distribution module is used for mapping the feature vector distribution to corresponding feature partitions and calculating partition space distribution weights based on vector density distribution and space position relations of the feature partitions; the value evaluation module is used for performing parameter adjustment on the deep learning network by using the partition space distribution weight to obtain a value evaluation result; and the value evaluation module is used for processing a value evaluation result through a multi-modal fusion algorithm, generating a personalized recommendation strategy based on a reinforcement learning framework, and calculating resources and an intelligent routing algorithm so as to complete a digital insight analysis process. The e-commerce data utilization efficiency and the decision-making effect can be improved.

The invention relates to a retrieval routing optimization method and device based on a distributed retrieval system, and relates to the field of computer technology distributed retrieval, the method comprises the following steps: obtaining inverted metadata of a document, including routing information, fragmentation information and a document storage identifier, judging whether the document is a new document, and combining the resource state and availability of each node to optimize the retrieval routing optimization of the document. And determining an optimal routing path by adopting an optimal execution fragmentation formula. A retrieval request is split into a plurality of sub-requests and is directionally issued to a target fragment, and the coordination node integrates return results of the fragments and then returns the return results to the client. The device comprises an information integration module, an inverted metadata recorder and a retrieval routing control module, and supports a dynamic configuration routing strategy, a document state refreshing mechanism and performance monitoring parameters. By introducing the document state identifier, the dynamic routing algorithm and the node resource monitoring mechanism, accurate routing and efficient processing of the retrieval request are realized, so that the retrieval performance and the resource utilization rate of the system are improved.

The present invention discloses a dual-mode communication method and system based on link redundancy check and dynamic hybrid routing, which relates to the technical field of electricity metering, including building a dual-link parallel channel through HPLC and HRF to perform data synchronization and consistency comparison; when the link is abnormal, dynamically electing an agent coordination node and building a cross-protocol hybrid link; and optimizing communication path selection and interference suppression using an intelligent routing algorithm and a noise identification mechanism. The method of the present invention significantly improves the reliability and data transmission efficiency of the communication system through a dual-link redundancy check and a hybrid link dynamic reconstruction mechanism. The parallel collection and path optimization method is adopted to shorten the meter reading time, reduce the link switching delay, and enhance the real-time performance and resource utilization. The system has good environmental adaptability and can cope with the topology changes brought about by complex power grid structures and distributed energy access.

The invention provides an NoC low-delay data transmission method based on a dynamic routing algorithm, relates to the technical field of data transmission, and aims to solve the technical problems that an existing NoC routing algorithm cannot adapt to a link dynamic load, is lack of congestion trend prejudgment, is slow in path search convergence and is insufficient in QoS differential scheduling. The method comprises the following steps: constructing a dynamic sensing module containing a double-branch LSTM time sequence prediction model, collecting states such as link bandwidth and queue length in real time, and predicting a congestion trend in 50-100ms in the future; constructing a multi-objective evaluation function taking delay as a core, and dynamically adjusting the weight according to the QoS level; solving an optimal path by adopting an improved ant colony algorithm which introduces a congestion penalty and pruning strategy; during transmission, dynamic reselection is triggered through a pre-calculation + fast matching mechanism; and iteratively optimizing the prediction model based on incremental learning. According to the method, congestion is avoided in advance, the path search efficiency and the transmission reliability are improved, the delay is remarkably reduced compared with a traditional algorithm, and the method is adaptive to delay sensitive scenes such as a high-performance processor and an AI chip.

The invention provides a blind identification and intelligent classification prediction method and system for a signal modulation mode, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a multi-scale time domain feature through wavelet packet decomposition, obtaining an instantaneous frequency feature through Hilbert transform, and constructing a feature correlation matrix; blind identification of a signal modulation mode is realized by using a dynamic routing algorithm and a self-correction mechanism; distinguishing discrete and continuous modulation based on the feature matrix and the signal-to-noise ratio; and signal modulation mode prediction is realized by adopting hybrid filtering and layered Markov decision. According to the invention, the recognition accuracy and prediction stability of the signal modulation mode are improved.

The invention relates to the field of network communication, and discloses a fast message pushing method and device based on converged communication and a medium, and the method comprises the steps: collecting message content data through a multi-source heterogeneous interface, carrying out the normalization processing of the data, and generating standard message metadata; key feature vectors are extracted from the standard message metadata by adopting a deep semantic analysis model, and a message priority evaluation matrix is generated in combination with a user portrait database; based on a multi-channel intelligent routing algorithm, generating an optimal transmission path decision tree according to the priority evaluation matrix and the real-time network state data; and distributing the message to the target communication channel according to the decision tree path through a dynamic load balancing engine, and executing multi-protocol adaptive transmission. According to the method, a whole-process message pushing method from matrix evaluation to intelligent routing and then to multi-protocol adaptive transmission is constructed, a high-reliability and low-delay communication solution is provided for a scene needing rapid message pushing, and the method has remarkable technical breakthrough and commercial application value.

The invention provides a workflow processing method, system and device based on a routing algorithm and a medium, and belongs to the technical field of workflow approval, and the method specifically comprises the steps: obtaining an approval request transmitted by an API gateway, constructing a semantic map, extracting an entity relationship, calculating a dynamic weight in combination with the real-time state and historical information of an approval terminal, and generating a feature vector; analyzing the request to obtain basic data, re-constructing a semantic map based on the basic data, and fusing the terminal state and the historical record to generate a feature vector; processing the basic data and the feature vectors through a rule engine priority layer to obtain preliminary routing judgment; adjusting a routing value through a DQN optimization layer and then determining an optimal path; and finally, executing an approval process and pushing a task, receiving feedback and generating a visual report, and forming closed-loop optimization. Through multi-layer processing and optimization, the accuracy and efficiency of approval routing are improved, blocking is reduced, the process stability is enhanced, transparency is improved through a visual report, and workflow processing is integrally optimized.

Embodiments of this application disclose an in-network computing packet forwarding method, a forwarding node, and a computer storage medium, and belong to the field of communication technologies. In the method, a forwarding node receives a plurality of in-network computing packets; determines an in-network computing identifier of each in-network computing packet; performs, based on the in-network computing identifier of each in-network computing packet, aggregation computing on in-network computing packets that belong to a same in-network computing message in the plurality of in-network computing packets, to obtain at least one aggregated packet; and forwards each aggregated packet by using a hash routing algorithm based on an in-network computing identifier corresponding to each aggregated packet.

The application discloses a chip based on a reconfigurable neuromorphic interconnection architecture, comprising a layered dynamic reconfigurable interconnection network layer and a memristor-CMOS reconfigurable interconnection unit, and the layered dynamic reconfigurable interconnection network layer and the memristor-CMOS reconfigurable interconnection unit are dynamically adjusted in a routing path through a dynamic routing algorithm. The application has the beneficial effects that: the dynamic routing algorithm is combined with the layered dynamic reconfigurable interconnection network layer and the memristor-CMOS reconfigurable interconnection unit, thereby performing dynamic prediction of network congestion and dynamic adjustment of a routing path, and the CMOS switch and the memristor switch are connected in parallel, avoiding the limitation of traditional single devices or static interconnections, so that the network performance and reliability are improved, and the delay and power consumption of data transmission are reduced.

The invention provides a file co-processing method and system based on distributed multithreading and dynamic sub-libraries, and relates to the technical field of file data processing, and the method comprises the steps: obtaining a to-be-processed file, and generating a file list according to batch information; a production-consumer model is adopted, a producer main thread scans a file list, a task queue is generated, a consumer thread pool concurrently executes downloading tasks, and the maximum concurrency number is controlled through semaphore; file data are uniformly distributed to a plurality of physical storage nodes on the basis of a fragment routing algorithm, real-time updating of a mapping relation between fragments and instances is managed by utilizing a dynamic routing table, resources are preferentially allocated to high-priority file tasks on the basis of a service routing strategy, and the tasks are dynamically allocated to specified database fragments; multi-file parallel processing on a processing node is realized through a three-stage parallel model, dynamic Feign calling is carried out on a service instance dynamic route and a weight load, multi-state table state synchronous updating is carried out based on a two-stage state machine, and a processing log is saved.

The invention discloses a method for deploying a spiking neural network on neuromorphic hardware, and belongs to the technical field of collaborative design of neural network software and hardware. In order to solve the problem of high delay of spiking neural network deployment under different hardware topological structures and routing algorithms, modeling is mainly carried out on a mapping scheme of a neural network between hardware cores under the constraint of preset hardware parameters; and performing iterative optimization on the initial deployment scheme through the deployment scheme selection strategy to meet the response time constraint. The method can be suitable for various hardware architecture and route combinations, response time after deployment is effectively shortened, and system operation efficiency is improved.

The invention discloses a multicast routing and load balancing method applied to a low earth orbit satellite network, which belongs to the field of low earth orbit satellite network communication, and comprises the following steps: acquiring topology and link state information of the low earth orbit satellite network, modeling a multicast tree construction problem into a joint path optimization game, and solving through a cooperative game routing algorithm; by introducing an incentive link cost allocation mechanism and combining with a dynamically adjusted cost weight, multicast path selection is optimized, link cost is effectively allocated, and load balancing is realized; and designing a composite cost function fusing link load and delay to calculate the total cost of the path. According to the method provided by the invention, an innovative and efficient solution is provided for the multicast routing and load balancing problems in the low earth orbit satellite network, and the transmission efficiency, the service quality, the network stability and the overall throughput of the multicast service in the low earth orbit satellite network can be enhanced. And obvious advantages are shown in the aspects of improving the network performance, guaranteeing the service quality and enhancing the network robustness.

The invention relates to the field of data retrieval, in particular to an efficient data retrieval method based on a neural network, which comprises the following steps: obtaining a lightweight biomedical language model through a knowledge distillation method, and generating previous health record feature vectors; analyzing through a dynamic routing algorithm to obtain a query semantic complexity score value, and dynamically selecting a retrieval path for retrieval to obtain a preliminary retrieval result; when it is monitored that the accumulated number of newly-added rare disease cases exceeds a preset threshold value, the lightweight biomedical language model is updated, and feature vectors of the newly-added rare disease cases are seamlessly inserted into a second-level approximate nearest neighbor index; adjusting similarity scores according to a symptom time sequence attention mechanism, and reordering by combining treatment effectiveness feedback to obtain a final retrieval result. The challenges faced by previous health record data retrieval can be solved, the retrieval efficiency and accuracy are improved, and the method adapts to data dynamic changes.

The invention discloses a false information detection model and method based on multi-modal semantic difference and bidirectional prompt, and belongs to the technical field of information processing and artificial intelligence. A model including prompt learning, single-mode feature extraction, semantic difference deepening, multi-mode fusion and a prediction module is constructed, a mixed template of background prompt and virtual prompt is utilized to strengthen semantic understanding, and a clustering-guided triple loss function is combined to enlarge semantic difference. And realizing cross-modal'partial-overall 'relation modeling by means of a two-way attention mechanism and a dynamic routing algorithm of a capsule network, and finally outputting authenticity prediction. According to the method, the task understanding capability and cross-scene adaptability of the model are effectively improved, the feature discrimination capability is enhanced, multi-modal deep fusion is realized, the accuracy and stability of false information detection are improved, and F1 scores of experiments on microblog and Tutt data sets respectively reach 88.6% and 83.9%, which shows that the detection performance of the method is superior to that of a mainstream baseline model.

The invention relates to the technical field of computer graphic processing, and discloses a method for automatically drawing a DCS (Distributed Control System) picture into a real-time database picture, which comprises the following steps of: firstly, identifying a DCS source file format, directly analyzing a public text format or intercepting a bottom layer rendering instruction for a private binary format, and extracting standardized discrete data; constructing an intermediate vector model, and performing coordinate normalization and curve smoothing processing on the static primitives by using an affine transformation matrix; meanwhile, syntactic analysis is conducted on the control script to construct an abstract syntax tree, and dynamic logic is converted; then recombining a composite object based on spatial semantics, and repairing pipeline topology connection by utilizing automatic adsorption and an orthogonal routing algorithm; and finally, calling a target system interface to complete object instantiation and persistent storage. According to the invention, the automatic high-fidelity migration of the DCS picture to the real-time database is realized, the labor cost is reduced, and the consistency and accuracy of the monitoring picture are ensured.

The invention discloses a link congestion control load balancing routing method based on routing domain division. The method belongs to the technical field of low-orbit satellite routing switching application. The problem that a traditional routing algorithm is high in complexity and slow in response time under the condition of a large network scale is solved. A giant constellation is divided into non-overlapping routing domains, global routing is divided into intra-domain local routing calculation and cross-domain routing interaction, on-satellite computing power consumption is reduced, and the method is adaptive to a large-scale satellite network. Meanwhile, a traditional distributed routing algorithm relates to signaling flooding when the state of a satellite link is changed, the signaling overhead is huge in a large-scale satellite network, local response is carried out in the congestion process by means of a sub-domain architecture and inter-satellite link availability prediction, and the signaling overhead of the whole network is reduced, so that the availability of the routing algorithm is improved.

The invention discloses a giant satellite base layered multi-domain routing method and device based on long and short chain collaboration, and relates to the field of satellite communication, and the method comprises the steps: constructing a satellite network layered multi-domain architecture which comprises a GEO satellite layer, an MEO satellite layer and an LEO satellite layer; dynamically clustering the LEO satellite network by adopting an LEO satellite clustering algorithm to form a plurality of distributed clouds; constructing a satellite network dynamic topological graph; based on the satellite network dynamic topological graph, adopting a discrete time virtual topological routing algorithm to obtain a long-chain global optimal path; for a plurality of distributed clouds, a deep reinforcement learning model is adopted to determine a short-chain local optimal path; if the short-chain local optimal path is consistent with the corresponding part in the long-chain global optimal path, the long-chain global optimal path is adopted, and if the short-chain local optimal path is not consistent with the corresponding part in the long-chain global optimal path, the short-chain local optimal path is adopted to replace the corresponding part in the long-chain global optimal path to form an actual routing path. According to the method, the problem of conflict between high dynamics and multiple targets of the satellite-ground network can be solved.

The present application generally relates to electronic trading systems, and more specifically to systems and methods for electronic trade order routing. A routing algorithm may generate an execution style recommendation for incoming IG corporate orders. For example, the style recommendation may be shown as a new column in the trading application dashboard that suggests a course of action to traders for each order. In one implementation, automatic decision making may occur based on the style recommendation.

The embodiment of the invention provides a system-on-chip design method and device, a deadlock detection method and device, electronic equipment and a storage medium. The system-on-chip design method comprises the steps that a network-on-chip included in a system-on-chip in design is mapped into a first directed connected graph; determining a first directed connectivity dependency graph according to the first directed connectivity graph and a routing algorithm of the network-on-chip; mapping subsystems included in the system-on-chip in the design into a second directed connected graph; determining a second directed connectivity dependency graph according to the second directed connectivity graph and a logic routing algorithm of the subsystem; determining a third directed connected graph according to the first directed connected graph and the second directed connected graph; determining a third directed connectivity dependency graph according to the first directed connectivity dependency graph and the second directed connectivity dependency graph; and performing deadlock detection on the system-on-chip in design according to the third directed connectivity graph and the third directed connectivity dependency graph, and optimizing the system-on-chip in design according to a deadlock detection result. According to the scheme, the design cost of the system-on-chip can be reduced.

The invention relates to the technical field of network configuration for interconnection and intercommunication, in particular to a network configuration method, system and device for interconnection and intercommunication and a medium. Determining a network topology structure and dividing a logic area; distributing a unique multicast address field for each area; deploying a self-adaptive routing protocol; distributing preset channels in the core exchange layer through quantum communication; creating an intelligent traffic sculpture engine, predicting burst traffic by using a neural network and presetting a buffer space; implementing a heterogeneous equipment compatibility strategy; constructing an annular redundant architecture; starting a block chain verification mechanism; and deploying a holographic topology monitoring system. Through intelligent linkage of a dynamic routing algorithm and an optical layer self-healing loop, business migration of regional faults can be completed within a human perception threshold, and finance transaction sensitive operation can achieve zero-interruption extreme availability.

This invention discloses a method for detecting salient objects with a partial-object relationship based on disentangled capsule routing, comprising: extracting basic deep features from an input image to obtain basic deep features at five different scales; further extracting multi-receptive field deep features from the basic deep features; utilizing a pre-trained capsule network based on a disentangled routing algorithm to analyze the deep features at three deep scales to obtain corresponding partial-object relationship features; fusing the corresponding partial-object relationship features with the deep features to obtain a fused feature at three deep scales; and further fusing the deep features at the first and second scales and the fused feature at three deep scales to obtain a fused feature, and generating a saliency map based on the fused feature. This invention solves the problems of the existing techniques, such as the large number of network parameters and slow inference speed, by achieving better foreground and background segmentation and faster network inference speed. It can be used in image preprocessing in computer vision.

This invention discloses a routing method and system for a multi-layered, multi-path switching network. The method includes the following steps: constructing a directed graph based on the multi-layered network, wherein nodes in the directed graph represent switches, edges represent communication links, each node has the ability to switch between different switching technologies, and the communication link has the ability to transmit data according to the switching technologies of its two endpoints; for each node in the directed graph, initializing the routing table of that node and iteratively calculating the optimal routing table; given a starting node and a target node, obtaining the next hop of the minimum-cost continuous path from the starting node to the target node according to the optimal routing table of the current node, and sending data packets hop-by-hop to the next node. The routing algorithm proposed in this invention can solve the minimum-cost path problem between all node pairs.

Systems and methods of configuring and using a machine-learned safety risk model to predict a corresponding risk of vehicular collision for different candidate routes are disclosed herein. In some example embodiments, a computer system obtains accident data and feature data for historical routes that have been communicated electronically as navigation guidance, trains a safety risk model using the accident data and the feature data of the historical routes as training data in a machine learning process, and then evaluates one or more routing algorithms by generating a corresponding set of routes for each routing algorithm and generating a corresponding performance measurement for each set of routes using the trained safety risk model.

This invention discloses an efficient routing method for ultra-high bandwidth (UBF) service flows in Time-Sensitive Networks (TSNs), aiming to solve problems such as load balancing of UBF and improve network performance. The main steps include distinguishing UBF from other data flows to meet their respective needs; then, employing a multi-objective optimization efficient routing algorithm to select the optimal path for transmission for each service flow. This invention can improve the operational efficiency and performance of TSNs, especially in scenarios involving mixed transmission of ultra-high bandwidth and regular service flows, providing more reliable services for real-time applications.