1130 results about "Path search" patented technology

The invention provides an intelligent question and answer method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, the method comprises the following steps: S1, integrating multi-source heterogeneous data to construct an initial knowledge graph; s2, analyzing the questions of the user, executing single-hop query and judging whether a result meets requirements or not; if not, entering S3, performing multi-hop dynamic search based on an adaptive path extension algorithm, and generating a reasoning path and evidence; s4, calling a large language model to combine with an attention mechanism to perform semantic matching on a result or a path, and screening an optimal answer; and S5, correcting the knowledge graph and performing closed-loop feedback to the query process to realize continuous optimization. Through cooperation of the knowledge graph and the large language model and combination of dynamic path search and semantic matching, the problems that a traditional method is insufficient in knowledge coverage, low in reasoning efficiency and poor in answer interpretability are solved, the method has the advantages of being efficient, accurate and self-optimized, and the performance and user experience in a complex scene are remarkably improved.

The invention provides an autonomous navigation path planning method, device and equipment for a humanoid robot in a complex environment. A navigation task is determined by obtaining the current position and path requirement of the robot; constructing a constraint preposition map based on body size constraint, joint motion constraint and dynamic balance constraint, and calculating a feasible motion space of the robot in advance; acquiring environment sensing data for variation trend analysis, and predicting a future movement track of the dynamic obstacle to generate a dynamic environment prediction sequence; establishing a bidirectional planning mechanism, performing forward and reverse path search at the same time, and generating a plurality of candidate path hypotheses through meeting point detection and path fusion; analyzing a human behavior pattern to identify a potential passage conflict area, establishing a social negotiation strategy, and taking an intermediate meeting point as a social key node to adaptively adjust a candidate path; the optimal safety path is determined through multi-dimensional safety evaluation of the falling risk, the collision risk and the energy consumption risk, and intelligent autonomous navigation of the humanoid robot in a complex environment is achieved.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention relates to the field of airline planning, and discloses a digital twin system for low-altitude airline planning and a construction method, and the method comprises the steps: obtaining a spatial perception data set of a preset sensor in a target airspace, carrying out the fusion of the spatial perception data set, obtaining the fusion perception data, obtaining the historical flight hidden danger data of the target airspace, and obtaining the historical flight hidden danger data of the target airspace; performing multi-layer convolution on the fusion perception data by using the historical flight hidden danger data to obtain a risk analysis result, performing fuzzy fusion on preset risk factors, generating a dynamic obstacle risk field according to the risk factors after fuzzy fusion and the risk analysis result, performing flight simulation on the dynamic obstacle risk field, and obtaining a flight simulation result; and collecting flight simulation real-time data in the flight simulation process, generating an air route space cost map according to the flight simulation real-time data, and performing optimal path search on the air route space cost map to obtain a low-altitude air route scheme. In the face of airspace actual risks, the searching efficiency of the optimal path in low-altitude route planning is improved.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention discloses a branch topological structure automatic switching control method and system based on load balancing, and relates to the technical field of automatic switching control, and the method comprises the following steps: obtaining working condition data, and constructing a joint map; extracting a power conduction path from the trunk node to each load node based on the joint map to form a high-load segment set; aiming at the high-load transmission segment, constructing a multi-target switching feasibility set, and generating a coupling relation mapping graph; based on the coupling relation mapping map, constructing a path search model, and solving an optimal topology switching path by adopting an enhanced iterative algorithm; and generating a topology switching sequence according to the optimal topology switching path, and implementing power transfer regulation and control on a high-load transmission section. According to the method, the joint atlas is constructed, the optimal topology switching path is generated, local overload is effectively relieved, power fluctuation is weakened, and the overall load balancing level and the active regulation and control capability of the system are improved.

The invention relates to the technical field of UV printing, and discloses an intelligent scheduling method and system for UV printing nozzles based on multi-objective optimization, and the method comprises the steps: collecting the real-time state data of a physical UV printing system, and forming a state data set; establishing a digital twinborn model at the edge computing node; scheduling simulation is carried out by adopting an improved ant colony algorithm, and a scheduling scheme is generated through path search of virtual ants; the scheduling scheme is applied to a physical system, feedback data are collected, a physical mathematical bidirectional feedback system is constructed, and hierarchical pheromones of edge nodes and a cloud end are cooperatively updated; each UV printing nozzle autonomously calculates a multi-objective optimization function value, and an optimal working strategy is selected; according to the method, a completely distributed self-organizing scheduling architecture is adopted, so that the single-point fault problem and the expansibility limitation of traditional centralized scheduling are effectively solved.

The invention discloses a cable trench inspection robot path planning method and device and a medium, a cable trench three-dimensional semantic map is constructed through multi-sensor fusion, and a laser radar and a depth camera are combined to accurately identify the spatial distribution of a cable support, a suspension cable and an obstacle. An improved directional path search algorithm is adopted, firewall passing sequential logic and lifting platform kinematics constraints are integrated, and a multi-mode inspection path is generated. The environment change is sensed in real time in the inspection process, the path is adjusted online through a dynamic path optimization engine, a planning strategy is iteratively optimized based on historical data, a digital twin system is introduced to realize path pre-verification, and the firewall interaction efficiency and the exception handling capacity are optimized by adopting reinforcement learning. According to the invention, the technical problems of poor real-time performance of path planning and low reliability of facility interaction in a complex cable trench environment are solved, and the inspection efficiency and safety are significantly improved.

The invention is suitable for the technical field of robots, and provides a path planning method and a robot, and the method comprises the steps: obtaining the environment information of a target area, building a grid map according to the environment information, and enabling the grid map to comprise a starting point area, an end point area and an obstacle area; determining a global path through a path search algorithm according to the grid map; in the process of controlling the robot to move along the global path, performing local path optimization through a dynamic window algorithm; wherein the dynamic window algorithm calculates the score of each candidate local path through a trajectory cost function and determines the optimal local path, and a strategy network output score obtained by predicting the candidate paths through a pre-trained deep learning strategy model is introduced into the trajectory cost function. The adaptive capacity of the robot in a dynamic environment can be improved.

The invention provides a data quality detection and improvement system for a large language model, and relates to the technical field of code quality management. The data analysis and characteristic extraction module is used for extracting structured and semantic characteristics of codes and constructing a code distribution diagram; the multi-dimensional quality detection module comprehensively evaluates the data set quality from three aspects of semantic diversity, structural consistency and error mode; the code optimization and verification module generates an optimization suggestion based on a detection result, and verifies the effectiveness of an optimization scheme through dynamic evaluation and unit testing; the feedback and self-adaptive optimization module is combined with developer feedback and runtime performance test to dynamically adjust a repair strategy, and the complex code repair efficiency is improved through quantum computing auxiliary path search; according to the method, full-process automation from quality detection to optimization verification is achieved, the method is suitable for multi-dependency and multi-module complex code repair tasks, and the quality and efficiency of the code life cycle are improved.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses a transformer substation positioning and navigation method and system integrating positioning and visual auxiliary positioning. The method comprises the following steps: establishing a background electromagnetic field distribution baseline graph associated with a three-dimensional reference map; electromagnetic field data of the transformer substation in an emergency response mode is collected through the unmanned aerial vehicle, and the abnormal field intensity of a space electromagnetic field and a fault source pointing vector pointing to a fault source are calculated; constructing a risk adaptive elliptical forbidden zone; calculating a passage cost index Cto of the unmanned aerial vehicle passing through each three-dimensional grid in the three-dimensional reference map by using the fault source pointing vector and the risk adaptive elliptical forbidden zone; and searching a path which is from the current position of the unmanned aerial vehicle to a preset target point, bypasses the risk adaptive elliptical forbidden zone, faces the fault source and has the lowest total passing cost index Cto by adopting a preset path search algorithm. According to the method, the risk self-adaptive elliptical forbidden zone is constructed and is used as the three-dimensional grid forbidden assignment processing, so that the dynamic global path avoidance of the sudden dangerous zone is realized.

The embodiment of the invention provides a multi-AGV path planning method and a related device. The method comprises the steps that in the single AGV path searching stage, a dynamic weight coefficient, a steering punishment mechanism and a Bezier curve smoothing strategy are introduced on the basis of a traditional A * algorithm, the operation efficiency of the algorithm can be improved, the unnecessary turning frequency in a path can be reduced, and the smoothness of the path can be improved. In a multi-AGV path conflict coordination stage, most traditional avoidance strategies use a fixed priority strategy, and the relative importance of tasks among the AGVs is not considered, however, according to the method, the avoidance among the AGVs can be implemented according to the relative importance of the tasks by setting a dynamic priority strategy. In the multi-AGV online navigation stage, a traditional DWA algorithm is prone to falling into local optimum, and the situation can be avoided by adopting the improved DWA algorithm, so that local obstacle avoidance can be achieved, and falling into local optimum can also be avoided.

The invention relates to the technical field of nuclear power equipment quality management and control, in particular to a nuclear power unit quality defect source analysis and total value chain tracing method, which comprises the following steps: S01, collecting total value chain multi-modal data including structured data, logs, texts and sensor time sequence data, and carrying out BIO labeling, BERT coding, BiGRU-Attention feature extraction and CRF sequence labeling; constructing a mixed storage data resource pool; s02, mining association rules by adopting a dynamic pruning improved Apriori algorithm, processing sample imbalance in combination with chi-square test and an NSGAII algorithm, and analyzing defect causes; s03, a dynamic knowledge graph containing multiple types of nodes, relations and threshold attributes is constructed, and rapid defect tracing is achieved through entity linking and probability path searching; the objective of the invention is to solve the problems of difficulty in defect source calibration, low tracing efficiency and the like in nuclear power equipment quality control.

The invention provides a path planning method and system based on four-AGV cooperative carrying, and relates to the technical field of automatic logistics transportation. The method comprises the steps that a high-precision grid map is constructed, a global path is generated in combination with a simulation improved A * algorithm, dynamic weight factors are adaptively adjusted according to the obstacle density, and the path search efficiency is optimized; a time window constraint method is adopted to divide path segments and dynamically allocate time windows; real-time sensing of dynamic obstacles is fused, and a local path is adjusted based on Kalman filtering and a dynamic priority scheduling algorithm; a one-master and three-slave cooperative control architecture is adopted, a master AGV tracks a simulation path, and slave AGVs dynamically adjust control parameters through a fuzzy adaptive PID algorithm; the synchronous jacking operation of the four AGVs feeds back the state to the central server in real time through the Ethernet, so that the synchronism and safety of collaborative carrying are ensured. According to the invention, the technical problems of high path conflict rate, low trajectory tracking precision and slow obstacle avoidance response of a multi-AGV system in a complex environment are solved.

The invention provides a tunnel drilling and blasting slag removal vehicle autonomous path planning method and device based on a Naligation 2 behavior tree scheduling framework, and the method comprises the following steps: S1, carrying out the data collection and map construction of a tunnel scene after drilling and blasting; s2, identifying a blasting residue area and generating a target point; s3, constructing a behavior tree scheduling structure and planning a path; and S4, performing path search, execution and feedback evaluation based on a path planning algorithm. According to the method, multi-source sensor mapping and deep learning semantic segmentation technologies are combined, high-precision recognition and traffic area extraction of residual obstacles in an unstructured tunnel environment after drilling and blasting are performed are performed, complex scenes faced by slag removal path planning are fully considered, a behavior tree scheduling mechanism is adopted to fuse path planning, target scheduling and an anomaly recovery strategy, and the path planning efficiency is improved. And a path execution system of real-time response and fault-tolerant capability is further constructed, and the operation continuity and robustness of the robot in a complex operation environment are improved.

The invention provides a design method and device of a metallurgical pipeline and a program product, path planning design of the metallurgical pipeline is carried out in a pre-constructed three-dimensional metallurgical factory scene, the three-dimensional metallurgical factory scene is constructed through an imported building structure model, and the three-dimensional metallurgical factory scene comprises arrangement information of equipment and structural members of a building. The design method comprises the following steps: S1, acquiring a starting point position, an end point position, a prohibited area, process parameters and constraint conditions of a metallurgical pipeline input by a user; and S2, according to the starting point position, the terminal point position, the forbidden area, the process parameters and the constraint conditions, performing path search in the three-dimensional metallurgical factory scene through a predetermined path planning algorithm and a pre-trained pipeline layout prediction model to obtain a metallurgical pipeline planning path. By means of the technical scheme, the design efficiency can be improved, the construction cost is reduced, and the building safety is ensured.

The invention discloses a dynamic multi-step adversarial attack method based on a graph neural network minimum disturbance budget, and belongs to the technical field of multi-step attack and information security. The dynamic multi-step adversarial attack algorithm is mainly composed of four parts of multi-step disturbance budget determination, dynamic disturbance propagation of a graph convolutional network, an optimal attack path search strategy and algorithm evaluation. Firstly, a multi-step attack loss function based on a maximized target node classification error is constructed, and accurate control over a disturbance path is achieved. Secondly, a dynamic search strategy is utilized, nodes which are most easily attacked are preferentially selected in each step, disturbance budget is reasonably distributed, and the persistence and concealment of attacks are improved through gradual diffusion; and then, combining multiple disturbance constraints of an adjacent matrix and node characteristics, and optimizing an attack process from two dimensions of disturbance intensity and balance. Finally, the effectiveness and superiority of the method provided by the invention are verified by comparing an ablation experiment with the overall performance of other similar algorithms.

The invention discloses a subway service robot real-time path planning method based on a graph neural network, and the method comprises the following steps: scanning the environment in a subway station, completing the map construction, and obtaining basic environment data; performing target detection and personnel statistics to form an environment and passenger flow fusion map; taking the fused map as input, setting a starting point and a target position, and outputting a global path search initial state; calling an improved A * algorithm to carry out node expansion, calculating a cost estimation function, and generating candidate paths; performing redundant node deletion and arc smoothing processing on the candidate path, performing step length dynamic adjustment, and outputting an optimized global path; performing local path planning by adopting an improved dynamic window method, and outputting an update path in real time; a passenger evacuation scheme is generated and is output through a display screen and voice broadcast, evacuation is guided, and passing is guaranteed. According to the invention, real-time path planning of the subway service robot is realized.

The invention particularly relates to a robot outdoor large-scale scene autonomous navigation method based on an open digital map, which comprises the following steps: extracting a road network structure, constructing a road network topological graph, and modeling a road network space by using an R tree; associating a starting point with a target point according to the road network topological graph of the R-tree structure, and performing global path planning based on a Dijkstra algorithm to obtain path points from the starting point to the target point; acquiring laser radar sensor point clouds, and generating a local voxel map by using the point clouds after the ground points are filtered out; performing local target point mapping by combining the global path line segment and a local voxel map to generate a local target point; performing local front-end path search based on a JPS algorithm to obtain a local front-end path from the current position of the robot to a local target point; a safety corridor is constructed according to the local front-end path, and trajectory optimization is carried out; and tracking the optimized trajectory, and planning the speed by considering the nonlinear model of the robot and the actual motion state. The method provided by the invention can realize outdoor large-range long-distance autonomous navigation.

The invention provides a low-altitude space path planning method and system, and relates to the technical field of low-altitude space management and path planning, and the method comprises the steps: S1, dividing a target airspace into a plurality of levels of three-dimensional voxels, and generating a unified-level three-dimensional code for each voxel; s2, voxelizing a task starting point, a task ending point and constraint elements based on unified hierarchical three-dimensional coding to generate a confinement three-dimensional corridor so as to limit a subsequent search space; s3, in the confinement three-dimensional corridor, hierarchical path search from the coarse hierarchy to the target hierarchy is executed, and a discrete path is output; s4, when the dynamic change of the environment is detected, performing increment re-planning on the basis of the discrete path, and updating the affected local path; and S5, smooth processing and safety verification are carried out on the discrete path or the updated path, and a final continuous flight path is generated. According to the invention, the real-time performance, the safety and the verifiability of the low-altitude flight task path can be realized.

The invention discloses a safety-enhanced trajectory planning method based on an Astar algorithm, and belongs to the technical field of intelligent robot navigation and path planning, and the method comprises the following steps: 1, constructing a grid map, and marking an obstacle region; 2, establishing a security evaluation mechanism, evaluating the surrounding environment of the path node, and quantifying a security index; 3, in a node expansion process, combining a heuristic item and a security index in a cost function to realize priority search for a security region during path selection; 4, key nodes of the obtained initial safety path are extracted based on a Douglas-Peucker algorithm to generate a rectangular safety corridor, and smoothing processing of the initial path is achieved based on a segmented Bezier curve. On the basis of guaranteeing path optimality, safety is used as an auxiliary guiding basis in the path searching process, and the path searching efficiency is improved. Therefore, consideration of the path length and the environmental risk is realized, and the navigation path with higher execution safety is generated.

The invention provides a product defect tracing method based on knowledge graph reasoning. The method comprises the following steps: constructing a knowledge graph based on a product hierarchical structure of a bill of materials and a manufacturing process hierarchical structure of a process route; when it is detected that data of a certain node in the knowledge graph is changed, calculating an update priority weight of a change node based on the occurrence frequency of the change node in a historical defect path and the hierarchy depth in the double-hierarchy structure; determining an updating range according to the updating priority weight, and updating the knowledge graph; and positioning defect nodes in the updated knowledge graph according to system response, executing path search from the defect nodes, identifying potential root cause nodes, and realizing product defect tracing. According to the method, accurate and real-time defect traceability of the product is realized based on multi-level feature correlation analysis of causal reasoning and dynamic knowledge graph construction and enhanced reasoning.

The invention discloses a public security event emergency resource allocation method based on multi-agent collaborative optimization. The method comprises the following steps: S1, forming a three-dimensional state tensor field; s2, initializing an event agent, a resource agent and a scheduling agent based on the three-dimensional state tensor field, constructing a resource scheduling solution space, configuring an evolution hybrid perception model for each agent, and outputting a scheduling potential map; s3, path search based on target driving is carried out in the resource scheduling solution space, honey source position selection is carried out based on the scheduling potential map through a bee-bird search algorithm, and a resource matching path is generated; s4, establishing a bidirectional coupling mechanism between the hummingbird search algorithm and the evolution hybrid perception model, and realizing dynamic collaborative optimization; and S5, outputting a final resource scheduling scheme and executing the final resource scheduling scheme in real time by each agent to form a scheduling decision closed loop of the emergency response of the public security event. According to the method, the multi-agent evolution hybrid perception model and the hummingbird search algorithm are fused, and public security event resource scheduling optimization is realized.

The invention discloses an end-side safe path planning method based on a small language model, which comprises the following steps: generating a task prompt by fusing the small language model SLM with an A * algorithm and a structured map token, storing the task prompt in a vector database, retrieving historical map information similar to the current task from the database when the path planning task is executed, and performing path planning according to the historical map information. The method comprises the following steps: constructing a COT logic chain prompt, guiding a small language model to gradually generate path key nodes and weights thereof in combination with the constructed COT logic chain prompt, then executing greedy path search by an A * algorithm by taking the key nodes as local targets, and finally generating a safe and efficient path. The method provided by the invention has end-side deployment capability, can be operated in scenes without network, resource limitation and the like, greatly reduces node traversal and improves planning efficiency compared with the existing A * algorithm, is particularly suitable for path planning tasks in a large-scale or high-density obstacle environment, gives consideration to intelligent reasoning and planning safety, and has wide application prospects. The method has good interpretability and expansibility, and has a wide application prospect.

The invention discloses an intelligent parking dynamic scheduling optimization method based on multi-source data fusion, and the method comprises the following steps: S1, collecting multi-source data, and carrying out the preprocessing of the multi-source data; s2, constructing a heterogeneous graph structure, and generating a node feature matrix and a heterogeneous adjacency tensor; s3, extracting a state fusion vector, constructing a joint scoring matrix, and generating a candidate path set; s4, setting target parameters, executing path search and pheromone iteration, and outputting a regional path set; s5, adjusting a node preference weight, executing regional path reordering, and outputting an optimal vehicle parking path; and S6, recording the whole scheduling process, updating the network parameter weight through the sliding window memory pool, and generating an incremental learning sample set. According to the invention, high-precision, low-delay and personalized intelligent parking dynamic scheduling under the driving of multi-source data can be realized, and the parking efficiency and the user experience are effectively improved.

The invention is suitable for the technical field of unmanned aerial vehicle path planning, and particularly relates to an unmanned aerial vehicle real-time path optimization system based on edge calculation, and the system comprises a data collection unit which is used for collecting the original environment data of a multi-mode sensor, and transmitting the original environment data to an edge processing unit; the edge processing unit is used for performing data fusion on the original environment data input by the data acquisition unit and outputting fusion features; and the path optimization unit is used for performing path search and path optimization according to the fusion features output by the edge processing unit, generating a flight path and controlling the unmanned aerial vehicle through the flight path. According to the method, the deeply-fused environmental spatial-temporal characteristics are deeply coupled with a path optimization algorithm, the splitting of perception and planning is broken, an end-to-end closed-loop optimization process is realized, the adaptability and overall robustness of the system are greatly improved, and through a local smoothing and multi-target evaluation mechanism, the real-time performance of the system is improved. And a continuous and highly feasible flight path of the unmanned aerial vehicle can be output in real time.

The invention provides a hierarchical exploration thinking-driven large-model complex graph question and answer processing method and device, relates to the technical field of artificial intelligence, and aims to solve the technical problem that an existing complex graph question and answer method is insufficient in the aspects of reasoning depth, path search strategies and cross-layer information fusion ability. The method comprises the steps of obtaining a to-be-processed complex graph question and answer task; inputting the complex graph question and answer task into a pre-constructed large model, adding an induction mark for a node set by using a first-layer structure, and generating an induction graph; extracting a core entity feature and a trunk logic structure feature in the problem description by using a second-layer structure, and generating a context prompt; according to the induction graph and context prompts, reasoning and exploring are conducted through the third-layer structure, a preliminary question and answer result is generated, and the semantic relation and the logic relation of the preliminary question and answer result are verified; and in response to verification failure, iteratively updating the induction graph and regenerating a question and answer result until verification is passed, and outputting a target question and answer result.