1116 results about "Adaptive routing" patented technology

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) using a gateway router that dynamically coordinates the agents based on prompt characteristics, user context, and / or real-time operational factors. Received inputs (e.g., prompts) are segmented into subcomponents (e.g., sub-queries), which are routed / mapped to candidate agents based on the output parameters of the subcomponent (e.g., performance thresholds, cost thresholds) and operational parameters (e.g., cost, performance metric values, user access restrictions, timing restrictions) of each agent. The gateway router maintains dynamic routing data structures for each agent that are continuously updated based on environmental stimuli (e.g., geo-political stimuli, sensor stimuli, agent stimuli). For example, the gateway router causes agents to dynamically switch between rule engines identified by the routing tables in response to detecting environmental stimuli. Responses from the candidate agents are aggregated into an output that is responsive to the input.

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 relates to the technical field of artificial intelligence and Internet of Things, and discloses an AI interaction intelligent module based on a hybrid architecture, comprising a user interaction unit which supports voice, text and image multi-modal input and integrates intention recognition and context understanding algorithms; the data processing unit is used for carrying out structured processing on the electric appliance specification and the historical fault data and constructing a dynamically updated knowledge graph; the hybrid architecture core unit comprises a deep learning subunit for realizing natural language understanding and generation based on a Transform model, and a knowledge reasoning subunit; a fault diagnosis unit; and a feedback optimization unit. According to the method, seamless cooperation of deep learning and symbol logic is realized through a dynamic routing strategy, a high-confidence-coefficient scene generates a response through a Transform model, a medium-confidence-coefficient scene calls a knowledge graph rule for verification, and a low-confidence-coefficient scene supplements information through multiple rounds of interaction, so that the effect of improving balance efficiency and safety is achieved.

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 discloses an artificial intelligence machine vision image acquisition system, and the system comprises a multi-mode perception layer which integrates a self-adaptive optical module, inhibits metal reflection, captures a visible light to short wave infrared image, and captures a motion edge; the dynamic adaptive layer adopts an illumination compensation and motion compensation module to dynamically adjust camera parameters and micro displacement compensation, feeds back an illumination trend, outputs a motion vector to the cognitive layer, generates a confrontation sample through a GAN, simulates virtual defects in combination with a physical engine, and expands training data; the cognitive reasoning layer is used for deploying a dynamic routing network, distributing computing resources according to image complexity and optimizing feature extraction efficiency; reducing data deviation through anti-fact analysis, and generating a thermodynamic diagram to explain a detection basis; and the collaborative decision-making layer is used for rapidly screening samples by edge nodes, training a global model by cloud aggregated data, automatically triggering manual rechecking when the confidence coefficient of the model is insufficient, synchronously optimizing a training set and a causal reasoning module by a rechecking result, and improving the labeling efficiency through AR assistance.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention relates to the artificial intelligence technology, can be applied to service system platforms such as medical health and financial science and technology, and discloses a data processing method, device, equipment and medium based on a hybrid expert model.The method comprises the steps that to-be-processed data is input into the hybrid expert model, routing probability distribution is obtained, the Tsallis entropy of the routing probability distribution is calculated, and the Tsallis entropy of the routing probability distribution is calculated; generating a prediction result according to the Tsallis entropy; evaluating loss based on an auxiliary entropy loss function, constructing a subspace, and adjusting pre-training parameters in the subspace to obtain an optimized hybrid expert model; and obtaining a target prediction result according to the optimized hybrid expert model. According to the dynamic routing mechanism, experts are flexibly selected, an entropy loss function is assisted to optimize a routing decision, uncertainty is reduced, and model convergence is accelerated; and interference of new tasks on old tasks is eliminated through re-parameterization and subspace design, so that the model achieves good balance between stability and plasticity, and generalization and adaptability of model data processing are improved.

The invention discloses an intelligent traffic data analysis and decision generation system based on multi-model dynamic collaboration, and the system comprises a multi-mode collection module which collects voice, text, video and structured data and loads a traffic knowledge graph; the feature processing module is used for extracting semantic, time sequence and spatial features and generating a fusion vector; the semantic modeling module is used for generating an intention vector in combination with the knowledge graph and the interaction record; the dynamic routing module selects an edge or cloud model according to the intention vector to generate a query statement; the Prompt memory module fuses the historical template and the alignment parameters to generate a query draft; the query verification module is used for executing semantic and structure verification and outputting a correction statement; the query execution module generates a response vector; the causal interpretation module generates an interpretation vector; and the response generation module outputs a multi-granularity result according to the user role. According to the method, the cooperative processing capability and semantic analysis precision of complex traffic query are improved.

The invention relates to the technical field of computer network communication, and relates to a reconfigurable router architecture based on buffer sharing and a dynamic routing method. The router architecture comprises a demultiplexer used for distributing data packets to centralized buffer areas of other ports according to port load states; the virtual channel distribution module is configured to determine an output port of the data packet according to a routing algorithm and generate a corresponding virtual channel ID; the centralized buffer area supports multi-port sharing and is divided into a plurality of logic sub-channels according to virtual channel IDs, and each sub-channel manages a data packet queue through a head pointer and a tail pointer; the credit management module is used for monitoring the idle capacity of the centralized buffer area and transmitting congestion information through a reserved bit of a data packet; and the congestion register dynamically records the congestion state of the neighbor node through the congestion information table. According to the invention, the problems of unbalanced load, low utilization rate of a buffer area and high congestion control cost of a traditional router are solved.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

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

The embodiment of the invention discloses a multi-agent task collaboration method and device and a medium, and the method is characterized in that the method comprises the steps: registering functions corresponding to all agents to a dynamic service directory through an MCP protocol; disassembling the to-be-executed task to obtain a plurality of to-be-executed sub-tasks, matching each to-be-executed sub-task with the capability range of each agent in the dynamic service directory, and establishing a communication channel between each to-be-executed sub-task and the corresponding agent; performing dynamic routing distribution on the to-be-executed sub-tasks through the cooperative bus, and selecting an optimal execution node according to the real-time requirements of the sub-tasks and the node load state of each corresponding agent; according to a subtask dependency relationship defined by a directed acyclic graph, determining an execution mode of each subtask to be executed; and executing each to-be-executed sub-task at the optimal execution node based on the communication channel and the execution mode of each to-be-executed sub-task.

The invention discloses a distributed model training-oriented routing reconstruction method in a hybrid photoelectric network, and belongs to the field of network communication optimization. The method comprises the following steps: constructing a collaborative routing model based on a current network flow state and topological information, and introducing an adaptive weight mechanism to dynamically adjust the priority of optical channels and electric packet forwarding, thereby realizing efficient collaborative utilization of multi-layer forwarding resources. According to the method, path reconstruction can be rapidly completed according to task characteristics and a network dynamic state, so that the system resource utilization rate is improved, and the maximum task completion time delay is reduced. Compared with a traditional fixed routing and semi-dynamic routing method, the method has the advantages that the average completion time delay and the maximum time delay are improved by more than 45% in the large-flow environment generated by large-scale model training, and the method has good expandability and real-time performance and is particularly suitable for burst flow intensive intelligent computing cluster scenes.

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

The invention discloses a multi-modal sequence recommendation method based on a double-gating hybrid expert model and Fourier denoising. The method comprises the following steps: S1, multi-modal feature coding: extracting text / visual features by using a BERT / ViT pre-training model; s2, frequency domain feature denoising: carrying out frequency domain noise filtering by adopting Fourier transform; s3, establishing a double-gating hybrid expert model: adopting a parallel path to realize self-adaptive multi-modal fusion and time sequence interest modeling; and S4, multi-task joint optimization: performing joint optimization on the model by integrating triple auxiliary contrast learning, extracting text and image features by utilizing a pre-training model BERT / ViT, performing frequency domain noise reduction on multi-modal features by introducing Fourier transform, remarkably improving the robustness of modal representation, realizing fine-grained modal interaction through dynamic routing by an input dependent expert, and performing multi-task joint optimization. A shared expert uses time coding with a gating mechanism to model periodic evolution of user interests, the feature fusion quality is optimized, the FT-MSR integrates triple auxiliary learning tasks, and the problem of data sparsity is relieved.

The invention discloses an electric power communication multi-route intelligent planning method and device based on scene classification. The multi-route intelligent planning method in the electric power field comprises the steps that S1, multi-source heterogeneous data fusion processing is carried out; s2, classifying three-dimensional scene features; s3, dynamic routing modeling is carried out; s4, a hybrid intelligent optimization algorithm; and S5, real-time verification and feedback optimization are carried out. According to the multi-route intelligent planning method in the electric power field, the problems of static route planning, poor adaptability and insufficient multi-source data fusion capability in a traditional electric power communication network are solved, intelligent perception and dynamic adaptation to a complex communication environment are realized, and the multi-source data fusion capability is improved. And the network resource scheduling efficiency, the flexibility of the routing strategy and the overall service quality are improved.

The invention relates to a construction method and system of an intelligent question-answering system based on a planning knowledge graph library. The method comprises the following steps: acquiring multi-source space planning data in real time; carrying out fusion on the planning data; vectorizing the fused planning data; a plurality of preset retrieval enhancement models are modularized, and interaction of vectorized planning data among the modules is realized through a message queue; through a natural language processing method and a message queue method, constructing a plurality of knowledge maps which can be accessed by the preset retrieval enhancement model; training a pre-training model through the knowledge graph; in response to a user question, retrieving an enhancement model through dynamic routing selection, and retrieving entities and relationships in the knowledge graph; and based on the retrieval result, generating a questioning result through a pre-training model. According to the method, intelligent question answering, scheme generation and evaluation of space planning are realized through the knowledge graph and the pre-training model constructed by the multi-source data, the working efficiency of the space planning is improved, and the manual workload and the error rate are reduced.

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.

Systems, switches, network endpoints, and methods are provided. In one example, a system is described that includes a latency measurement circuit to measure traffic on a network from an endpoint sender to an endpoint receiver across multiple paths. The system also includes a packet marking circuit to provide a routing mark for a packet destined for the endpoint receiver according to a network traffic measurement provided by the latency measurement circuit, where the routing mark provides an indication that supports routing for the packet to reach the endpoint receiver via a chosen path or subset of paths among the multiple paths.

The invention provides a power distribution network adaptive inspection method and system based on image recognition, and a medium, and belongs to the technical field of power distribution network line inspection. The self-adaptive routing inspection method comprises the following steps: S1, driving a routing inspection unmanned aerial vehicle to perform self-adaptive routing inspection along a power distribution network line, and obtaining a routing inspection image in real time; s2, preprocessing the inspection image, and obtaining an identification result of the inspection image based on an inspection image defect identification model; s3, judging whether the identification result is within a defect warning range, if so, sending warning information and marking a corresponding inspection image; s4, obtaining a prediction result of the inspection image based on the inspection image defect prediction model; and S5, judging whether the prediction result is within the potential defect range, and if so, sending early warning information and marking a corresponding inspection image. According to the method, the convenience and efficiency of power distribution network line inspection and maintenance can be effectively improved in a mode of combining self-adaptive inspection, defect identification and prediction.

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 relates to the technical field of energy prediction, and discloses a hierarchical routing-based full-modal hybrid expert model energy prediction method, which comprises the following steps: S1, constructing a hierarchical expert network structure which comprises a plurality of expert layers, and each expert layer is configured to process feature data of a specific modal, wherein the expert layer is divided into a meteorological processing layer, an electricity price processing layer, a load processing layer and an illumination processing layer based on modal types; and S2, receiving multi-modal characteristic data, wherein the multi-modal characteristic data comprises meteorological data, electricity price data, load data and illumination data. A layered expert network structure and a dynamic routing distribution mechanism are constructed, a special expert layer is adopted to perform feature extraction and local prediction, and a dynamic distribution strategy of a routing module is combined, so that the compatibility of a prediction model to high-dimensional heterogeneous data is improved, the calculation complexity of model training and deployment is reduced, and the prediction efficiency is improved. And the real-time performance and resource constraint requirements in a virtual power plant scene are met.

The invention provides a large-model cross-modal collaborative understanding method and device. The fusion efficiency and the understanding capability of multi-modal information can be improved. The large-model cross-modal collaborative understanding method comprises the following steps: preprocessing visual data, language data and sound data to obtain preprocessed visual data, language data and sound data; wherein the preprocessing comprises the steps of performing adaptive size adjustment and normalization on visual data, performing word segmentation and dynamic truncation on language data, and performing band-pass filtering and spectral noise reduction on sound data; extracting visual features, language features and sound features based on the preprocessed visual data, language data and sound data; on the basis of an adaptive mapping network and a mixed granularity cross-modal attention mechanism, performing feature alignment on the visual features, the language features and the sound features to obtain aligned feature vectors; and based on a dynamic routing architecture, fusing the aligned feature vectors to generate a unified multi-modal representation.

The invention relates to the technical field of adaptive routing optimization, and particularly discloses a topology self-recognition and routing optimization method and system for a concentrator and a collector, and the method comprises the steps: carrying out the topology modeling of a multi-layer heterogeneous network; executing high-speed link state monitoring based on the initial topological graph; global loop detection and link optimization are completed based on the link state matrix; a weighted robust multi-dimensional path selection method is adopted to configure a main path and a standby path for each node; and monitoring the path state in real time and dynamically switching. In the prior art, a static route, a single protocol path or a fixed main and standby path is mainly adopted, and especially in a cross-protocol, cross-level and dynamic multi-node environment in a smart city, rapid path switching and high-reliability route redundancy guarantee cannot be realized. Due to the fact that the multi-dimensional path selection method and the cross-level and cross-protocol self-adaptive switching strategy are adopted, the problems of routing interruption and performance reduction are avoided, and the reliability of the whole network is improved.

The invention particularly relates to an emergency broadcast intelligent early warning system applied to disaster prevention and reduction, and relates to the technical field of communication. The redundant communication and self-adaptive switching module is configured to establish a multi-mode communication network, monitor link states in real time and realize intelligent switching and load balancing of communication links based on a dynamic algorithm; an intelligent decision-making and precise broadcasting module; and the emergency broadcast terminal is connected with the offline execution module. According to the invention, a multi-mode communication architecture is established, each link monitors parameters such as signal strength, delay, packet loss rate and the like in real time, and intelligent switching and load balancing are carried out through a dynamic routing algorithm; and meanwhile, on the basis of memory thermal characteristic analysis, link faults are pre-judged in advance, switching is triggered, the success rate of early warning information transmission under extreme disasters is ensured, and the problem of transmission interruption of traditional single link communication under the scenes of power failure, base station damage and the like is solved.

The invention discloses a substation unmanned aerial vehicle inspection dynamic route planning method and device. The method comprises the steps of collecting and constructing a substation 3DGS model, optimizing details, dividing areas, recognizing equipment parts, inversely calculating waypoint parameters, planning waypoints of a safety channel and a flight channel, generating a basic model data set, and dynamically generating an inspection route in combination with tasks. The device comprises a model construction module, a region division module and the like. On the basis of a 3DGS model and equipment part identification, through a multi-level waypoint system and a dynamic planning algorithm, full-process automation from model construction to route generation is achieved, it is ensured that unmanned aerial vehicle routing inspection is safe and efficient, equipment updating and task changing are adapted, the problems that traditional two-dimensional planning is insufficient in precision, and static route flexibility is poor are solved, and the unmanned aerial vehicle routing inspection efficiency is improved. And the automation and intelligence level of substation inspection is improved.

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.