729 results about "Adaptive routing" patented technology

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention relates to the field of visual processing, and provides a visual classification processing method and device based on large model and multi-modal data fusion. The method comprises the following steps: inputting a to-be-classified input image and a corresponding category text description into a text encoder for multi-level feature extraction to obtain global text features and local text features; performing fine-grained cross-modal alignment on the local visual features and the local text features, calculating association weights between the image regions and the text phrases through a bidirectional cross attention mechanism, and generating aligned intermediate features; splicing and fusing the aligned middle features and the global visual features, and inhibiting background noise in a fusion result and reinforcing discriminative features in the fusion result through a feature mask algorithm in combination with the global text features to obtain multi-modal fusion features; and synchronously inputting the multi-modal fusion features into a multi-space classifier to generate respective classification results, and adaptively outputting an image classification result according to a confidence threshold in combination with a dynamic routing mechanism.

The invention relates to the technical field of material management, and discloses a dynamic rule generation and self-adaptive auditing system and method for material management, and the system comprises a rule intelligent extraction module, a rule management knowledge base, an enhanced auditing engine, a man-machine cooperation calibration module and a self-adaptive execution module. The method comprises the steps of automatic rule extraction, rule storage and management, enhanced auditing and reasoning, man-machine collaborative calibration, knowledge base real-time optimization and adaptive routing execution. According to the method, the rule is automatically extracted from the unstructured document, the problem that a traditional system rule depends on manpower and is lagged in updating is solved, dynamic optimization of the rule and confidence is achieved by introducing a man-machine collaborative feedback closed loop, the accuracy and transparency of an audit decision are improved by enhancing reasoning and explainable decision technologies, and the audit efficiency is improved. And the optimal balance between auditing efficiency and risk control is realized through self-adaptive routing execution based on credibility.

The invention provides a dense overlapping target detection method based on a wavelet enhancement sparse hybrid expert model. The method comprises the following steps: firstly, extracting multi-layer features through a backbone network to capture multi-scale spatial representation; secondly, discrete wavelet transform is introduced to each level of features, spatial features are decomposed into a frequency domain, collaborative modeling of frequency domain and spatial domain features is realized, the reservation capability of detail and texture information is improved, a lightweight dynamic hypergraph aggregation module is introduced into the deepest layer of features, a hyperedge structure is adaptively learned, and the feature fusion is realized; modeling a high-order incidence relation in a local area in an explicit manner; and thirdly, in the decoding process, candidate queries are screened and reweighted through an IoU perception query selection mechanism, and a dynamic routing mechanism of sparse hybrid experts is introduced, so that query self-adaptive specialized representation learning is realized, and the target detection precision and reliability in a complex scene are effectively improved.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

The invention relates to an APP intelligent marketing service method based on intelligent routing and multi-agent cooperation. The method comprises the following steps: receiving input information, wherein the input information comprises user active inquiry information or trigger event information generated based on user behavior monitoring; semantic analysis and intention recognition are carried out on the input information, a task planning directed acyclic graph is generated based on a recognition result, and the task planning directed acyclic graph comprises a plurality of subtask nodes and dependency relationships among the nodes; based on the task planning directed acyclic graph, the state of each functional agent and the historical performance index, executing dynamic routing so as to dispatch the plurality of sub-tasks to the corresponding functional agents; and in the execution process of the plurality of subtasks, performing dependency scheduling and state consistency management on an external tool call chain which is initiated by the functional agent and comprises a plurality of steps, and updating the shared memory associated with the user based on an execution result. By adopting the method, self-adaptive planning, robust execution and continuous optimization of marketing tasks can be realized.

The invention provides a water area monitoring system for cooperative operation of an unmanned aerial vehicle and an unmanned ship based on a Mesh ad hoc network. The water area monitoring system comprises the unmanned aerial vehicle, the unmanned ship, a Mesh ad hoc network module, a cooperative control system and a shore end control system, according to the unmanned plane, wide-area patrol and image acquisition, airspace and wide-area water area information is acquired through a sensor, fixed-point acquisition and water area information acquisition are realized through an unmanned ship, and data interaction is realized by relying on nodes of a Mesh ad hoc network. Through the multi-hop relay and dynamic routing technology of the Mesh ad hoc network, autonomous and reliable communication in a scene without a fixed base station is realized, and the communication coverage range and survivability are improved; on the basis of distributed task allocation and real-time data interaction, the equipment cooperation efficiency is remarkably improved, the response delay is shortened, and efficient completion of tasks in a complex environment is guaranteed.

A method is provided for enhancing feature integration in capsule networks using GAN-augmented latent space. The method comprises training an autoencoder to encode input data into a latent space representation that captures essential features; training a generative adversarial network (GAN) to generate synthetic features, wherein the GAN includes (a) a generator configured to produce synthetic features from random noise, and (b) a discriminator configured to evaluate the quality of the synthetic features by comparing them with real features from the latent space representation; combining the latent space representation with the synthetic features to form an augmented latent space; generating routing coefficients for the capsule network based on the augmented latent space; and applying the routing coefficients to modulate dynamic routing between capsule layers in the capsule network.

The invention discloses a multi-task dexterous hand operation method based on self-adaptive two-way distillation, which comprises the following steps of: constructing a plurality of single-task expert strategies based on vision-touch pre-training, and collecting state-action tracks of a high-degree-of-freedom dexterous manipulator on various operation tasks; on-line distillation is utilized to distill multi-task expert data into a unified diffusion strategy sharing perception characterization and task coding, and modeling action multi-modal distribution is generated through diffusion type actions; a self-adaptive routing network is introduced, supervision weights of all experts are dynamically distributed according to the current observation state, and weighted distillation of the mixed experts is achieved; and after the performance of the unified strategy is superior to that of experts, performing reverse distillation on the unified diffusion strategy into expert strategies of all tasks by adopting action-reward combined constraint, and performing two-way iterative distillation circulation. Through forward and reverse knowledge migration between a unified strategy and a task expert, the problems of observation distribution offset and task imbalance in multi-task dexterous operation can be effectively relieved.

The invention discloses an attention-based context-aware sparse hybrid expert model routing method, which comprises the following steps of: encoding prompt information input by a user to obtain context vector representation of the prompt information, and introducing a multi-head attention mechanism to obtain multi-scale semantic interaction information; further, constructing a gating network based on attention output, and dynamically selecting Top-K expert networks for reasoning; by introducing a self-adaptive neighbor attention weight and a fusion gating mechanism, expert dispatching and weight fusion of a token level are realized; combining with the sub-output of each expert network, and aggregating according to the weight to obtain the final model output; according to the method, the understanding ability of the model for different semantic contexts is enhanced through a multi-expert structure and a dynamic routing mechanism, and the method is adaptive to multiple rounds of token generation processes, so that the accuracy and diversity of generated texts can be improved.

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 dynamic routing parameter efficient fine tuning method and system based on LoRA-MoE, and relates to the technical field of large model fine tuning. The method comprises the following steps: firstly, constructing a heterogeneous expert architecture-based LoRA module pool based on a multi-field data set; and secondly, coding the hidden layer features of the task through a dynamic gating network, realizing continuous differentiable expert activation, and improving the balance of expert allocation by adopting temperature attenuation and entropy regularization constraint. And then, dynamically selecting and carrying out weighted fusion on a plurality of LoRA parameter increments according to task semantics in a reasoning stage, so as to realize low-cost model adaptive updating. Finally, the module pool is continuously optimized through the confusion degree and manual evaluation feedback, low-efficiency modules are automatically eliminated, and a new LoRA module is generated to maintain task coverage. According to the method, the accuracy and generalization ability of the model in a complex scene can be remarkably improved on the premise of ensuring light weight, and rapid adaptation and dynamic optimization of a large model under a low-resource condition are realized.

The invention relates to the technical field of artificial intelligence, and discloses a Transformer training method for paint surface AI defect identification, which comprises the following steps: synchronously acquiring RGB images and three-dimensional point cloud data through a six-axis mechanical arm to form a multi-modal data set; generating synthetic defect data by using fluid dynamics and a ray tracing model to expand a sample set; realizing feature alignment by adopting cross-modal contrast learning; aggregating defect structure capsules through a dynamic routing attention network; performing knowledge retrieval and fusion in combination with the prototype memory matrix; and finally, the sorting mechanical arm is driven to execute sorting or rechecking operation based on the multi-task output and the uncertainty score. According to the method, the problems of insufficient structural representation of complex defects, weak generalization ability of rare defects and low reliability of sorting decision are solved, and high-precision and high-reliability automatic defect detection and sorting are realized.

The invention provides a meteorological service application system based on a general large model, and the system is characterized in that a data base module is used for obtaining multi-source heterogeneous meteorological data related to a meteorological service, and carrying out the anomaly detection, format standardization and interpolation correction processing of the multi-source heterogeneous meteorological data, and obtaining structured data; collecting each computing power index of the meteorological service application system in real time, and respectively outputting the structured data and each computing power index to an intelligent brain module; the intelligent brain module is used for performing association matching on the structured data, a meteorological service standard process stored in a knowledge base and a preset algorithm in an algorithm library based on a dynamic routing mechanism, and determining an optimal model combination from a model library by combining the computing power index; and processing the structured data based on the optimal model combination to obtain a service processing result of the meteorological service. By adopting the scheme, the accuracy of a meteorological service processing result can be effectively improved, and the overall service capability of the system is optimized.

The invention relates to the technical field, in particular to an AI classroom teaching quality evaluation method and system based on image recognition, and the method comprises the steps: obtaining a test paper RGB image and structured metadata, and generating multi-modal input data through graying, denoising and affine transformation; dynamically adjusting the weight through a double-branch feature extraction network (CNN + ViT), and extracting the global structure of the printed form and the local detail features of the handwritten form; generating a mixed font segmentation mask by using space attention, channel attention and dynamic routing regulation; optimizing handwritten answer recognition in combination with an educational knowledge graph; and fusing the answer expression and the attention data to generate a teaching quality evaluation report. The system comprises a data acquisition unit, a preprocessing unit, a feature extraction unit, a segmentation unit, an identification verification unit and an evaluation unit. According to the scheme, the accuracy of character segmentation and recognition in a mixed font scene is improved, and data support is provided for precise teaching in an education scene.

The invention relates to the field of low-altitude economy and geographic information, in particular to a low-altitude scene-based twinborn data rapid updating method, which comprises the following steps of: acquiring multi-source sensing data of a target low-altitude area in real time; comparing with a historical twinborn model, and identifying a change area and evaluating a priority by using an artificial intelligence algorithm; dynamically generating an optimal unmanned aerial vehicle data acquisition route by combining the airspace information and priority of the change area; then the unmanned aerial vehicle is controlled to collect updated data along the route; performing lightweight real-time three-dimensional reconstruction on the updated data to form a local updated model; and finally, fusing the model with a historical twinborn model to generate a final target twinborn model. Through intelligent change identification, dynamic route planning, lightweight reconstruction and incremental updating, the updating efficiency is significantly improved, the resource consumption is reduced, the real-time accuracy of the model is ensured, and the method is suitable for multiple fields such as low-altitude economy and urban governance.

The invention relates to the technical field of artificial intelligence, and discloses a business process configuration method based on Activiti and AI decision, and the method comprises the steps: collecting the historical execution data of a business process through a process instance monitoring module, generating a process behavior feature data set, carrying out the training processing of an AI decision model based on the process behavior feature data set, and carrying out the analysis of the AI decision model. A business process decision tree model is constructed, AI decision routing labels are configured for decision nodes in an Activiti process engine, non-intrusive intelligent decision integration is achieved, and when a process instance is executed to a key decision node, a system automatically calls the pre-trained business process decision tree model to conduct real-time path analysis and generate an optimal dynamic routing instruction. The problem of decision stiffness caused by the fact that a traditional workflow depends on artificial experience configuration is solved, the accuracy and adaptability of flow branch selection in a complex service scene are improved, and meanwhile the compatibility and stability of an original flow engine are guaranteed.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention discloses a dynamic routing optimization transmission method based on a neural network. The method comprises the following steps: S1, collecting original state data in a network environment; s2, preprocessing the original state data; s3, constructing a causal graph based on the historical abnormal event log, and performing vectorization representation on the causal graph; s4, modeling is carried out on the state features and the topological features, state and structure fusion processing is carried out, and a performance prediction tensor is output through a multi-layer perceptron; s5, constructing a joint optimization target based on the embedded vector and the performance prediction tensor, and screening an optimal path set; and S6, generating a routing control instruction according to the optimal path set, and issuing the routing control instruction to the network forwarding equipment. According to the method, causal graph embedding and neural network modeling are fused, dynamic path optimization is realized, and the method has the advantages of high intelligence, high stability and high adaptability.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

According to the multi-source threat detection method based on the hybrid expert model, real-time collection and structured processing of network flow, system logs and user behavior data are achieved through a multi-mode intelligent collection engine, and high-quality multi-source input is provided for upper-layer analysis; the double-branch feature extractor carries out deep analysis on the network flow time sequence mode and the log semantic context to generate fine-grained feature vectors; the hybrid expert reasoning framework is based on expert models in three fields of a dynamic routing gating network, intelligent scheduling network behaviors, log semantics and user portraits, combines space-time alignment features through a cross-modal attention mechanism, and constructs an interpretable attack evidence chain in combination with a causal reasoning engine. Finally, a full-link closed loop from multi-modal data acquisition, feature collaborative extraction and intelligent threat reasoning is realized, and while millisecond-level real-time response is ensured, the complex internal threat detection accuracy is obviously improved.

The invention discloses an enterprise appeal intelligent perception and closed-loop processing system based on multi-modal AI, and relates to the technical field of intelligent government affairs, the system realizes accurate semantic understanding and deep intention recognition of multi-modal fusion, and the intelligent perception and closed-loop processing of enterprise appeals are realized through a cross-modal attention mechanism and a unified semantic representation model. According to the method, deep fusion and complementary analysis of multi-source heterogeneous data such as voices, texts, images and the like are realized, the limitation of a traditional single-mode or simple splicing mode is broken through, the accuracy and robustness of intention recognition are remarkably improved, and misjudgment is fundamentally reduced; the method comprises the following steps: constructing a dynamic self-adaptive routing mechanism based on Actor-Critic reinforcement learning, constructing a work order assignment problem into a sequence decision problem, driving a model to learn a dynamic fusion and weight distribution strategy of multiple decision factors through a reward mechanism, and adopting an online strategy iteration optimization mechanism to realize an optimal assignment decision, so as to improve the work order assignment efficiency. And the shunting accuracy and efficiency are obviously improved.

A transportation system for generating transportation recommendations may (1) receive a transportation request associated with a user; (2) identify, using the transportation request, a first location and a second location associated with the transportation request; (3) determine, using the first location and the second location, routes between the first location and the second location; (4) receive travel data associated with each of the plurality of the routes, the travel data including information indicating a current or predicted future travel condition along each of the routes; (5) generate, using historic transportation characteristics associated with the user and the travel data, a safety or other score for each route, the score indicating an estimated level of safety of traveling along the route; and / or (6) generate a user interface providing indicators associated with the scores of the routes.

The invention discloses a multi-dimensional information-based adaptive gateway traffic scheduling method, which comprises the following steps of: acquiring server performance indexes, network delay, bandwidth and geographic position data in real time, and establishing a historical database; performance load weights are generated through weighted calculation of CPU, memory and disk indexes, and routing priorities are dynamically adjusted by adopting exponential function mapping; performing normalization processing on network bandwidth, delay and geographic distance, and calculating a service access weight to optimize a transmission path in combination with a step function; analyzing historical traffic data based on an LSTM model to predict a future trend, and generating a traffic prediction weight through a sigmoid function to realize pre-equalization; and combining the three types of weights, weighting to generate dynamic routing configuration, eliminating abnormal nodes in real time in combination with a health check mechanism, triggering high-load early warning and linking a predictive balancing strategy. According to the method, multi-dimensional index collaborative decision-making is realized, routing distribution is dynamically optimized, network delay is effectively reduced, and system throughput and stability in a high-concurrency scene are improved.

The invention relates to a method and a system for identifying and tracing counterfeited content based on multi-agent collaboration. The method comprises the following steps of: acquiring to-be-identified content input by a user in a natural language dialogue or file uploading mode; the type of a task input by a user is judged through an intention recognition agent, and a corresponding detection and traceability agent is called through a modal self-adaptive routing mechanism; calling an identification agent of a corresponding mode to judge whether the to-be-identified content is generated in a counterfeit manner or not; when the content is judged to be counterfeited, analyzing a generation source of the counterfeited content through a traceability agent, and identifying a generation model category; and generating a natural language interpretation text for the detection and traceability result by an interpretation agent in combination with an expert cue word template and a counterfeit feature knowledge base. Compared with the prior art, integration and intellectualization of detection and traceability are realized. The problems of task splitting, unexplainable result, lack of traceability, complex interaction and the like of an existing detection system are effectively solved.

The invention relates to the technical field of power systems, and discloses a photovoltaic equipment fault diagnosis method, system and equipment and a medium. The method comprises the steps of obtaining a structured multi-mode fault data set based on a disclosed photovoltaic data set and collected multi-source heterogeneous data generated in the operation process of photovoltaic equipment; selecting an initial multi-modal large language model, and constructing a feature-oriented dynamic routing architecture according to a pre-training weight matrix of the initial multi-modal large language model; based on the structured multi-modal fault data set, utilizing a pre-constructed joint training loss function to carry out fine tuning training on the initial multi-modal large language model to obtain a target multi-modal large language model; and inputting the real-time operation data of the photovoltaic equipment into the target multi-mode large language model for reasoning to obtain a fault diagnosis result of the photovoltaic equipment. According to the invention, efficient and accurate diagnosis of the photovoltaic equipment fault is realized.

The invention discloses an offshore regional communication system, and relates to the technical field of offshore aquaculture platform communication, the offshore regional communication system comprises a multi-level communication platform, a distributed adaptive networking engine and a cross-domain cross-medium communication gateway, the multi-level communication platform is integrated with a satellite communication module, a water surface communication module and an underwater communication module, a local edge calculation function is realized; the distributed self-adaptive networking engine is in communication connection with the multi-level communication platform and is used for carrying out neighbor discovery, global topology convergence, dynamic routing calculation and hub automatic switching on the multi-level communication platform; and the cross-domain cross-medium communication gateway is in communication connection with the multi-level communication platform and the distributed self-adaptive networking engine, and is used for performing signal format conversion and intelligent routing among the satellite communication domain, the water surface communication domain and the underwater communication domain for the multi-level communication platform. The communication system provided by the invention can realize cross-domain cooperation of satellite, water surface and underwater communication, and has self-sensing, self-repairing and self-optimizing capabilities.