468 results about "Planning algorithms" patented technology

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention discloses a double-station robot sorting optimization method and system based on digital twinning and a terminal, double improvement of sorting efficiency and safety is achieved by constructing a digital twinning driven collaborative operation system, and the method comprises the steps that firstly, a laser radar and a polarization camera are used for forming a composite sensing unit; geometric morphology, material reflection characteristics and spatial pose data of a target object are synchronously obtained, and are input into a digital twin engine after time synchronization processing; an engine constructs a virtual sorting scene containing a material attribute database based on a physical rendering technology, sub-millimeter-level space registration is achieved through feature fusion of a binocular stereoscopic vision depth map and geometric parameters, high-precision digital twin stations are generated, a system plans a space-time constraint trajectory of a double-station robot in a virtual environment, and a target object is obtained. And an integrated discrete event simulation engine performs operation time sequence conflict prediction, and when a space overlapping risk is detected, an alternative path containing a dynamic obstacle avoidance strategy is generated through a trajectory re-planning algorithm.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention provides an AI agent emergency order insertion dynamic decision production scheduling method, a medium and a system, and belongs to the technical field of industrial agents. A dynamic weight adaptive optimization model is adopted to calculate a target weight coefficient and construct a multi-target function set, an improved non-dominated sorting genetic algorithm is adopted to solve and output a Pareto optimal solution set, and a delay risk assessment correlation matrix is combined to start an incremental re-planning algorithm to generate a local adjustment scheme. The Pareto optimal solution set and the local adjustment scheme are combined to generate a final production scheduling scheme, a real-time monitoring module is started to track the execution deviation condition, and when it is detected that the deviation degree exceeds a threshold value, a rapid rescheduling mechanism is triggered to conduct scheme correction; the technical problem of poor production scheduling scheme quality caused by low multi-agent cooperation efficiency in the emergency order insertion dynamic decision process is solved.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention provides a construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning, relates to the technical field of building construction carbon emission management and control, and solves the problem of limitation of an existing management and control process in the aspects of accounting precision, response speed, emission reduction effect and economical efficiency. The method comprises the following steps: firstly, establishing a multi-dimensional BIM model, constructing a digital twinborn body linked with a physical construction site in real time based on the model, and mapping real-time energy consumption and material data; and then monitoring and calculating the real-time carbon emission intensity of the whole construction process in combination with a dynamic carbon emission factor library, further performing multi-target optimization solution by adopting a mixed integer programming algorithm based on the intensity value and an optimization decision, outputting a low-carbon construction optimization scheme, and guiding field execution. Through BIM, digital twinning and dynamic optimization full-chain technology integration, real-time monitoring, accurate accounting, intelligent optimization and closed-loop management and control of building construction carbon emission are smoothly realized.

The invention relates to a multi-unmanned aerial vehicle disaster site collaborative search method fused with a big language model consultation mechanism. Compared with the prior art, the multi-unmanned aerial vehicle disaster site collaborative search method solves the defect that multi-unmanned aerial vehicle control cannot adaptively combine historical task experience and multiple big language model decision suggestions. The method comprises the following steps: starting a collaborative search task and initial path configuration; starting real-time monitoring on the disaster search task cooperatively executed by the plurality of unmanned aerial vehicles; a plurality of unmanned aerial vehicles cooperatively execute monitoring of a disaster search task; generating an unmanned aerial vehicle task abstract and a response specification; performing big language model consultation processing; and updating the credibility of each model. According to the method, the knowledge of the large language model and historical task data are utilized to supplement the defects of a traditional planning algorithm, continuous adaptive optimization of the search task is realized, and the safety and efficiency of an unmanned aerial vehicle system in a disaster scene are improved.

The invention discloses a storage robot intelligent obstacle avoidance system under the Internet of Things, and belongs to the technical field of the Internet of Things, and the system comprises a multi-source data collection and fusion module, a path planning module, a multi-robot cooperation module, an execution control module, a task scheduling module, a safety monitoring module and a self-optimization module. According to the method, laser radar and visual data are integrated into a dynamic occupation grid map, a dynamic obstacle trajectory prediction model is introduced, active avoidance of moving obstacles is realized, and path smoothness and energy consumption efficiency can be optimized while a safe distance is ensured by quantifying distance and time dual cost and path planning. Besides, the path is decomposed into a time interval attitude sequence, and a curvature optimization objective function is combined, so that the robot can still keep efficient and stable obstacle avoidance capability in a storage environment with dense people streams or frequent movement, and the task execution success rate is remarkably improved.

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

The invention provides an end-to-end automatic driving interpretable decision-making system and method based on a multi-modal causal atlas, and belongs to the technical field of intelligent driving. The method comprises the steps of data input, decision generation, causal analysis, model optimization and interactive interpretation. The objective of the invention is to construct an interpretable end-to-end automatic driving system integrating causal atlas construction, expert network mixing and interactive visualization by combining multi-modal vision-language models such as DynRsl-VLM and V3LMA with an end-to-end network and a DiffVLA diffusion planning algorithm. And the gap of decision transparency and real-time interaction requirements in the prior art is further filled.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to a community unmanned delivery control method and device, equipment and a medium. The method comprises the following steps: predicting a delivery demand thermodynamic diagram of a first preset duration in the future based on a delivery demand prediction model; based on the distribution demand thermodynamic diagram, obtaining the space utilization rate of each express cabinet in the to-be-distributed community and to-be-distributed parcel data every second preset duration, and determining a reward function value according to the space utilization rate of each express cabinet and the to-be-distributed parcel data by adopting a preset dynamic resource scheduling model, according to the reward function value, determining the number of a target express cabinet allocated to the to-be-allocated parcel and the size specification of a corresponding grid opening of the to-be-allocated parcel; and performing global path planning according to the number of the target express cabinet allocated to the to-be-allocated parcel by adopting a distribution path planning algorithm, and generating a bidding value to match the optimal execution unmanned aerial vehicle during task allocation according to a preset bidding value calculation model based on an improved contract network protocol. The resource utilization rate can be increased, the energy consumption can be reduced, and the operation cost can be reduced.

The invention discloses an autonomous planning algorithm for an automobile gluing path, and relates to the technical field of automobile manufacturing, and the algorithm comprises the specific steps: collecting multi-source data through a line laser radar and an industrial camera, carrying out the preprocessing and synchronous fusion, carrying out the point cloud processing and curved surface modeling, generating and optimizing a gluing path, and adjusting the deviation in real time. And finally, converting into a mechanical arm instruction, controlling an executing mechanism to move, and synchronously adjusting gluing parameters and the posture of a gun head to finish high-precision gluing operation. Through dynamic formula linkage design, full-process self-adaption of track planning and execution control is achieved, manual intervention is reduced, and the gluing quality and the production efficiency are guaranteed; meanwhile, a high-precision positioning and complex curved surface modeling system is constructed by adopting a multi-sensor fusion and curvature self-adaptive NURBS weight factor formula, so that the positioning reliability and the curved surface model precision are ensured, and the adaptation requirement of a complex gluing scene of an automobile body is met.

The invention belongs to the field of unmanned aerial vehicle (UAS) traffic management (UTM) and computer science, and particularly relates to a method and system for airspace modeling and path planning of a logistics unmanned aerial vehicle, and the method comprises the steps: S1, receiving multi-source data, and constructing a hierarchical mixed data model; s2, coding the control constraint data and the dynamic environment data into semantic attributes of nodes in a two-dimensional quadtree layer and / or voxels in a three-dimensional octree layer so as to generate a semantic airspace model containing computable rules; s3, based on the semantic airspace model, executing a hierarchical path planning algorithm to determine a flight path from the starting point to the ending point; and S4, combining the refined paths, and outputting a four-dimensional flight path containing a timestamp. The airspace rule is internalized into the model attribute, the real-time performance, safety and compliance of path planning are remarkably improved, the airspace resource utilization efficiency is improved, and key technical support is provided for large-scale urban unmanned aerial vehicle logistics operation.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention belongs to the field of offshore wind power, and discloses a single-pile offshore wind turbine power regulation and load relief method, which comprises the following steps of: constructing a thrust system model of wind to a rotor and a tower; constructing a dual-mass transmission system model of the wind turbine generator; constructing a tower top front-back displacement model and a pile soil front-back displacement model; constructing a rotor speed tracking error model based on an adjustment error method; constructing a tracking error model of the change rate of the front-back displacement of the tower top; constructing an optimal control strategy based on an infinite vision field optimal cost function; and designing an optimal pitch control scheme based on a robust adaptive dynamic programming algorithm. According to the design of the RADP-based propeller pitch controller provided by the invention, the MOWT model does not need to be completely known, so that the calculation cost of the controller is reduced, and the calculation efficiency is improved.

The invention provides a hydropower station full-scene intelligent inspection and safety supervision method and device. A two-legged inspection robot with anti-explosion and anti-vibration capabilities is deployed to realize all-terrain autonomous inspection of equipment nodes in the hydropower station; based on a digital twinborn model and an edge computing platform, fusing equipment state data and personnel behavior data acquired by a robot in real time, identifying equipment defects and personnel violation behaviors through an AI algorithm, and generating graded early warning information; the robot is controlled to carry out real-time intervention through a voice module, early warning information is pushed to a safety management system, and an equipment operation authority locking mechanism is linked; through optimizing a path planning algorithm and a multi-robot cooperative scheduling strategy, rapid response and autonomous obstacle avoidance of the robot under an emergent task are realized. According to the method, full-scene automatic inspection of the hydropower station and real-time supervision of personnel behaviors can be realized, the equipment defect identification accuracy and the emergency response efficiency are remarkably improved, and the manual inspection cost and the non-planned shutdown risk are reduced.

The invention discloses a traffic planning method and device, a storage medium and a program product, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring vehicle driving data and a real-time traffic condition; according to the vehicle driving data and the real-time traffic condition, a plurality of particles are initialized, and each particle represents an acceleration sequence of the vehicle in a future preset time period; iteratively updating the particles until convergence, and obtaining a preliminarily optimized acceleration sequence; and a quadratic programming algorithm is applied to smooth the preliminarily optimized acceleration sequence, a target acceleration sequence is obtained, and the target acceleration sequence is used for controlling the vehicle to run. The technical problem that a rule-based speed planning method in the prior art lacks flexibility and is difficult to deal with a complex traffic environment is solved.

The invention discloses an intelligent luggage case obstacle avoidance method based on an ultra wide band, and relates to the technical field of intelligent mobile equipment control, and the method comprises the steps: collecting and preprocessing multi-source environment perception data, and generating an environment perception fusion data set; correcting projection in the ranging direction and constructing a local environment grid map based on grid division and grid occupation probability; comparing the bottom direction TOF height value with a ground reference height by adopting a threshold value judgment method, identifying a height difference risk area, and generating a path guide point through Cartesian coordinate conversion; based on the path guide points and the local environment grid map, generating a trajectory candidate set by adopting a dynamic window method, and removing impassable trajectories through a collision detection algorithm in combination with obstacle grid information; through a multi-target scoring function and a double-DIP controller, predicting a collision risk score of the feasible trajectory and generating an obstacle avoidance control instruction; the obstacle avoidance control instruction is executed, environment sudden change data are collected in real time, and the obstacle avoidance control instruction is adjusted in real time through a path replanning algorithm.

The invention discloses a tunnel ventilation system and a control method thereof, and relates to the technical field of tunnel ventilation energy-saving control. According to the method, traffic data, environment data and equipment starting data in a tunnel are preprocessed and divided into data sets, a traffic prediction model based on LSTM and an environment prediction model based on full connection are constructed, and training is carried out through a root-mean-square error and an average absolute percentage error; and sequentially predicting traffic and environment data by using the model, and solving an optimal ventilation equipment combination through a sequential quadratic programming algorithm by taking energy consumption minimization as a target and environmental standard reaching as a constraint, and adjusting operation. The method has the advantages that advanced regulation and control are achieved through the two-stage prediction model, pollutants are prevented from exceeding the standard, and the air quality is guaranteed; dynamic training, multi-parameter optimization and a fault tolerance mechanism are combined, energy conservation and environment regulation and control are accurately balanced, meanwhile, the operation reliability and the intelligent level of the system are improved, and manual intervention is reduced.

The invention relates to a group control optimization method and system for a cold station system, and belongs to the technical field of automatic control, and the method comprises the steps: carrying out the minimum optimization of a first-stage group control optimization objective function through a mixed integer programming algorithm with the start-stop state of each device as an optimization variable during the operation of the cold station system, and obtaining a second-stage group control optimization objective function; the start-stop state of each device of the cold station system is controlled during first-stage group control; and on the basis of the first-stage group control, performing minimum optimization on the second-stage group control optimization objective function by taking chilled water outlet water temperature, chilled pump frequency, cooling pump frequency and cooling tower frequency as optimization variables through a model prediction control algorithm, and controlling the operation state of operation equipment when the cold station system is in the second-stage group control. Therefore, energy consumption is reduced as much as possible and energy efficiency is improved.

The invention discloses a vehicle spatial position uncertainty modeling method based on a factor graph, and relates to the technical field of automatic driving. Establishing a factor graph model, and constructing state variable nodes and factor nodes; an objective function of the factor graph optimization problem is converted into a nonlinear least square problem, and optimal state estimation is obtained through iterative solution by adopting a Gauss-Newton method; on the basis of an objective function under a Gaussian Newton method framework, a posterior covariance matrix is obtained through calculation of a Hesse matrix and inversion; performing incremental optimization on the factor graph, and updating a global Jacobian matrix through matrix decomposition and Givens transformation; and carrying out confidence ellipse geometric quantization, and representing the positioning uncertainty in a geometric form. Based on a factor graph and a confidence ellipse theory, through multi-sensor space-time constraint fusion and geometric interpretable representation, a vehicle positioning uncertainty modeling framework is constructed, and the safety and robustness of an automatic driving system downstream decision planning algorithm are improved.

The invention discloses an unmanned vehicle mixed path planning method based on a grid map. The method comprises the following steps: constructing a 3D point cloud map in a campus; performing mixed path planning by using the grid map, realizing global path planning based on the optimized A * algorithm, and obtaining a global optimal path from the unmanned vehicle starting point to the distribution terminal point; when the unmanned vehicle tracks and moves according to the global optimal path, local path planning is carried out based on the optimized dynamic window strategy, so that the unmanned vehicle completes obstacle avoidance processing in the moving process; aiming at an unstructured campus environment, navigation efficiency and safety are guaranteed, an improved mixed path planning algorithm integrating global path planning and local path planning is provided, the unmanned vehicle can effectively realize obstacle avoidance for dynamic and static obstacles on the basis of an optimal global path, and autonomous navigation is completed.

This invention discloses a method for planning U-turns of automated vehicles in narrow passages in mines, comprising: S1, determining the starting area for U-turn planning; S2, performing tunnel area planning to generate interval navigation paths and local paths; S3, performing chamber area planning: the automated vehicle travels to the starting area for U-turn planning based on the tunnel area planning results, establishes a discrete map of the chamber planning area and sets a step size Sn, expands nodes until the target point is reached, and generates a collision-free path; S4, post-processing the collision-free path generated in step S3: a nonlinear optimization planning problem is modeled using a constructed objective function for the discrete points of the collision-free path, and a corresponding solver is used to obtain the final smooth path solution. This method can quickly and effectively complete U-turn trajectory planning in narrow areas using the chamber space, achieving relatively coherent U-turn actions, ensuring the real-time performance of the planning algorithm, and improving vehicle traffic efficiency.

The invention discloses an intersection signal control method based on minimum blocking probability and highest traffic capacity, and relates to the technical field of intelligent traffic systems. A bilevel planning optimization model is constructed, an upper layer model takes minimization of intersection blocking probability as a core target, a blocking probability function is established through dynamic quantification of traffic flow random volatility, and a sequential quadratic programming algorithm is fused to optimize a signal period and effective green light time parameters of each phase; the lower layer model is based on a Webster delay model, and feeds back the traffic state in real time by taking the minimization of the total delay of the vehicle as a target. The upper layer model and the lower layer model realize collaborative optimization through a closed loop mechanism of parameter adjustment-delay feedback-iterative optimization, and meanwhile, a dynamic weight coefficient is introduced to flexibly coordinate multi-target conflicts of the blocking probability, delay and traffic capacity. According to the method, the dynamic response capability of signal timing to the real-time traffic demand is remarkably improved, the peak delay of the intersection is effectively reduced by 20%, the average peak delay is effectively reduced by 15%, and the road network toughness is enhanced.

The invention discloses an unmanned aerial vehicle real-time path optimization system based on edge calculation, and relates to the field of unmanned aerial vehicle path planning, and the system comprises sensing data collection modules which are deployed at all unmanned aerial vehicle platforms and are used for synchronously collecting multi-source sensor data and generating multi-source sensing data packets; the global map fusion module is used for receiving the data packet and generating a global consistency probability map with time-varying characteristics; the collaborative task planning module is used for carrying out task allocation by adopting a market auction mechanism based on the probability map and generating a global path sequence integrating path length, safety and energy consumption; the local obstacle avoidance execution module is used for receiving the planning scheme, generating a control instruction by combining real-time sensor data and adopting a model predictive control and re-planning algorithm, and realizing trajectory tracking and dynamic obstacle avoidance; and the closed-loop feedback updating module analyzes the execution log, generates an optimization instruction through deviation evaluation and incremental learning, and feeds back the optimization instruction to the fusion and planning module.

The invention discloses a traceless splicing method for knitted garment production, and relates to the technical field of traceless splicing of knitted garments, a fabric physical parameter library is imported, model grid attributes are correlated through CAD software, a principal curvature algorithm is operated to calculate and output a Gaussian curvature distribution thermodynamic diagram, and stress distribution in a tensile state is analyzed and simulated in combination with finite elements; marking a stress high-risk area; and calling a geodesic path planning algorithm to generate an initial seam path, importing the fabric texture map, optimizing the initial seam path through a texture flow field alignment algorithm, and outputting coordinate data of the optimized three-dimensional seam path. According to the method, through innovation of digital driving precise planning, material gradient adaptation and process dynamic regulation and control, the experience-dependent and scientific controllable crossing of traceless splicing of the knitted garments is realized, and a core technical support is provided for large-scale production of the high-quality knitted garments.

The invention discloses an AGV navigation technology based on a normal-temperature quantum sensing chip, and belongs to the crossing field of AGV navigation and quantum sensing technologies. According to the technology, environment magnetic field characteristics or passive magnetic field marking signals are captured through a normal-temperature quantum sensing chip, and high-precision autonomous navigation of the AGV is realized by combining magnetic field map construction, multi-modal data fusion and a dynamic path planning algorithm. The system mainly comprises a normal-temperature quantum sensing module, a magnetic field characteristic processing unit, a path planning system and a positioning correction module, does not need to depend on entity guide media such as a magnetic stripe and a two-dimensional code, and solves the problems of poor flexibility, high maintenance cost and weak interference resistance of traditional fixed path navigation. Experiments show that the positioning precision of the technology can reach the millimeter level, the technology adapts to complex environments such as industrial workshops and warehousing, route adjustment does not need ground modification, the flexibility and stability of AGV navigation are remarkably improved, and the technology is suitable for multi-scene application such as flexible production and intelligent warehousing.

The invention discloses a complex environment-oriented body agent navigation and three-dimensional reconstruction method, and relates to the technical field of body agent navigation, and the method comprises the following steps: collecting environment data around a navigation body, using a fine-grained octree for close-range key areas (narrow slits and dead corners), adopting coarse voxel acceleration for a far area, and adopting coarse voxel acceleration for a near-range key area (narrow slits and dead corners); extracting large planes such as wall surfaces, beam columns and the like by using RANSAC (Random Sample Consensus), and constructing an environment'abstract skeleton 'for navigation reference; establishing a temporary three-dimensional space based on the point cloud of the whole body environment, dividing a plurality of detection planes in the space, performing position correction on an obstacle edge dot matrix in the detection planes, determining a movable range, and generating a safe motion path in a feasible space by applying a local planning algorithm; octree + differential coding is carried out on a local map, and only a current activity area and crisis point cloud are reserved; local full-stack deployment enables navigation to be free of dependence on remote communication, and sensing, mapping and navigation closed loop can still be completed when a link is broken.

The invention provides a fire-fighting unmanned aerial vehicle dispatching method and system based on multi-vehicle multi-nest intelligent matching, and belongs to the technical field of unmanned aerial vehicle dispatching. According to the method, a multi-dimensional state sensing system is constructed to obtain and preprocess fire-fighting unmanned aerial vehicle cluster and multi-nest operation information, and a global state data set is obtained; based on the data set, a nest matching priority evaluation model is established through an analytic hierarchy process, and accurate matching of the unmanned aerial vehicle and the nest is realized through a dynamic matching mechanism; multi-task pre-allocation and dynamic reallocation are completed by adopting a cluster collaborative scheduling optimization algorithm fusing multiple constraints, and a cross-nest switching track is generated in combination with an improved RRT track planning algorithm; a temporary landing preparation strategy is generated through the remaining voyage, the required electric quantity and risk judgment, and unmanned aerial vehicle parking supply, data uploading and position adjustment are achieved by means of a high-precision landing guide technology and a data synchronization protocol. And the requirements of high real-time performance and accuracy of multi-machine and multi-nest collaborative scheduling in a fire-fighting scene are met.