1575 results about "Ant colony" patented technology

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The invention provides a desilting robot intelligent control method and system based on deep learning, and relates to the technical field of intelligent control, and the method comprises the steps: collecting environment information through a multi-source sensor, extracting environment features through a convolutional neural network and a recurrent neural network, and building a three-dimensional dynamic model of a desilting robot operation environment; based on the model and a hierarchical reward function, training a dual neural network by using a depth deterministic strategy gradient algorithm to obtain an intelligent control strategy model; a motion track output by the model is optimized by an improved ant colony algorithm of a double-target pheromone updating strategy based on path length and energy consumption and then is input into a layered model prediction controller, and cooperative control over a dredging robot propeller and a dredging device is achieved. The working efficiency and the energy utilization rate of the dredging robot are effectively improved, and the dredging cost is reduced.

The invention provides an automobile part spraying control method and system, and relates to the technical field of automobile manufacturing, and the method comprises the steps: collecting spraying environment parameters in real time, generating a comprehensive digital model containing the environment parameters and part characteristics, and carrying out the intelligent partitioning of the surface of an automobile part through a clustering partitioning algorithm, and dividing the surface into a plurality of sub-regions; on the basis of each sub-region, combining with the current environment parameters, generating an optimal spraying parameter for each sub-region by utilizing an intelligent agent trained by a reinforcement learning algorithm, and on the basis of the spatial relationship between the optimal spraying parameters and the sub-regions, optimizing a global spraying sequence by adopting an ant colony path planning algorithm, and generating an optimal spraying path; the spraying robot sprays, a real-time quality evaluation result of spraying is generated, a pre-constructed fuzzy logic controller is adopted, spraying parameters are adjusted, sprayed parts are obtained, a full-period comprehensive data set is integrated, and spraying control parameters are iteratively updated.

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

The invention relates to the technical field of product quality inspection, in particular to a product quality detection system based on deep learning and machine vision, which comprises a multi-modal image acquisition module, a deep learning defect detection module and a dynamic resource scheduling module. The multi-modal image acquisition module is used for acquiring product images in all directions by using a high-resolution camera array and a self-adaptive light source; the deep learning defect detection module accurately identifies defects through dual-path residual denoising, multi-scale context sensing and graph topology defect classification; the dynamic resource scheduling module realizes reasonable scheduling of personnel and materials and balance of a production line according to a detection result, a skill map, an ant colony algorithm and the like; the augmented reality interaction module visually displays defects and provides maintenance navigation through holographic projection and multi-mode interaction; the self-evolution detection model can update the weight based on a new sample, and the multi-physics field simulation module simulates the influence of defects on the mechanical property of a product. The detection precision is high, the efficiency is high, and the product quality detection and production management level is remarkably improved.

The invention provides an unstructured database federated learning collaboration method and system based on swarm intelligence, and relates to the technical field of structured data, and the method comprises the steps: training a query pattern recognition sub-model, a path planning sub-model and a resource allocation sub-model through federated learning, and constructing a swarm intelligence collaboration network. After a user query request is analyzed by the query mode recognition sub-model, candidate query paths are generated and optimized by fusing an ant colony algorithm and a particle swarm algorithm, and a query execution scheme is generated. And the resource allocation sub-model performs resource scheduling accordingly. And generating a performance optimization target for updating local model parameters of each node, and fusing the updated parameters through a security aggregation algorithm to guide the next query optimization. According to the method, the query efficiency of the unstructured database is improved, and the resource consumption is reduced.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

The invention provides a robot path planning method and system based on fusion of an improved ant colony algorithm and DWA, and the method comprises the steps: building a grid map of the movement of a robot, and generating a global path of the robot in the grid map through employing the ant colony algorithm; transmitting the global path to a DWA dynamic window model to generate a dynamic window; adding unknown static and dynamic obstacles into the grid map, and setting a local target point by using a local target point setting method based on the global path; based on the local target point and the robot movement information, robot speed sampling is carried out in the dynamic window, and a simulation track of robot movement is obtained; evaluating the simulation track by using an obstacle avoidance evaluation function, and if the robot reaches a target point, obtaining an optimal path; and if the robot does not arrive at the target point, regenerating a simulation track for iterative calculation until the robot arrives at the target point. The improved algorithm can effectively improve the performance and adaptability of robot path planning in different environments.

The invention provides a computer room energy consumption and computing power balance optimization method and system based on swarm intelligence, and relates to the technical field of data center management, and the method comprises the steps: collecting server node real-time operation data, constructing a multi-objective optimization function, predicting an energy consumption and computing power change curve through a recurrent neural network, and generating an initial task distribution scheme; and a global optimal allocation scheme is searched by adopting a parallel ant colony algorithm, the running state of the server is monitored, and task migration and energy consumption adjustment are executed, so that collaborative optimization of energy consumption reduction of the machine room and balanced allocation of computing power is realized, and the service quality and the resource utilization efficiency are improved.

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention belongs to the technical field of agricultural automation, and provides a closed park-oriented tractor multi-machine collaborative operation scheduling system and a control method, and the method comprises the steps: employing a multi-source heterogeneous sensor network to construct an environment perception layer, generating an environment state tensor and a dynamic environment model through space-time registration and data fusion, and obtaining a dynamic environment model; a complex task is decoupled into an atomic operation unit, a single tractor optimal task sequence is generated by utilizing dynamic planning, a genetic algorithm is improved to realize global operation distribution, and an optimal path is planned for tractors in a closed park through an ant colony algorithm in combination with a dynamic environment model to control the tractors to perform collaborative operation. Deploying an edge computing node to receive the tractor pose data in real time, judging an actual conflict risk by calculating space-time conflict density, fitting a prediction trajectory function to detect a prediction conflict risk, and if the actual conflict risk or the prediction conflict risk exists, triggering conflict coordination; and selecting a hierarchical coordination strategy for the tractor according to the conflict risk type to realize obstacle avoidance control.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses an intelligent short-pulse laser processing cutting path optimization method and system. The intelligent short-pulse laser processing cutting path optimization method comprises the steps that S1, coordinate data of all cutting points in a laser processing area are obtained and processed; s2, simulating the heat distribution condition in the laser processing process according to the coordinate data, and outputting a heat distribution diagram; s3, based on the heat distribution diagram, calculating a moving path of the laser head through a path planning algorithm, and optimizing the path to minimize the total moving distance and reduce the influence of a heat accumulation area; s4, the optimized path is adjusted according to specific machining requirements, and parameters are dynamically adjusted through a real-time feedback mechanism; and S5, outputting the optimal cutting path, and applying the optimal cutting path to actual machining equipment to improve the machining efficiency and quality. According to the method, the cutting point coordinate data of the laser processing area are obtained and processed, the heat distribution condition is simulated, path planning is conducted by adopting the ant colony algorithm in combination with the heat distribution diagram, the idle stroke time is minimized, and the influence of a heat gathering area is reduced.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention discloses an intelligent robot control method based on multi-sensor cooperation, and relates to the field of intelligent robots, and the method comprises the following operation steps: S1, carrying out the architecture deployment of sensors; s2, establishing communication; s3, edge intelligent calculation enhancement; s4, a dynamic adaptive fusion strategy; s5, carrying out group intelligent decision making; and S6, carrying out sensor role dynamic conversion. According to the intelligent robot control method based on multi-sensor cooperation, the sensor data credibility can be adjusted in real time according to the environment condition, the fusion weight can be dynamically allocated, the data accuracy is ensured through the cross check of the adjacent sensor data, and meanwhile, the intelligent robot control method based on multi-sensor cooperation can be realized through the ant colony cooperation algorithm in combination with the self-adaptive pheromone volatilization regulation and control. The path exploration strategy can be flexibly adjusted according to task complexity and environment dynamic change, the search efficiency can be effectively improved, various environments can be more quickly adapted, and the optimal action path can be quickly found.

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention discloses an AR intelligent photovoltaic inspection method and system, and the method comprises the following steps: S1, obtaining a panoramic image and heat distribution data of a photovoltaic module, and generating a data set; s2, multi-modal data preprocessing is carried out, key fault features are extracted, and fault areas are marked; s3, focusing and analyzing the marked area, and identifying a thermal abnormal point; s4, constructing an electrical topological graph, and generating a fault point priority; s5, performing ant colony optimization to adjust the inspection path, and marking the position of a fault point; s6, displaying the path, the fault point and the risk heat map in the AR equipment; and S7, generating an inspection report and performing feedback optimization. According to the invention, through multi-modal data fusion, dynamic path optimization and augmented reality technologies, efficient detection, accurate positioning and visual display of photovoltaic module faults are realized, and the inspection efficiency and the intelligent level are improved.

The invention relates to the field of construction engineering, in particular to a roadbed construction slope displacement monitoring system based on cloud computing. A sensor network is used for collecting roadbed construction data in a roadbed, wavelet transform is used for denoising the data, a dynamic time warping algorithm in a cloud computing unit is used for aligning time sequence data in the initial roadbed construction data, and the weight of each sensor data is dynamically distributed based on an entropy weight method. A hybrid prediction model is established based on an LSTM long short-term memory network and FEA finite element analysis, a slope stress field result simulated by FEA is used as a physical constraint layer of the LSTM, an attention layer is embedded in the physical constraint layer, hyper-parameters of the initial hybrid prediction model are optimized by using a Cs-Ant improved cuckoo-ant colony combinatorial algorithm, and a slope stress field is obtained. And inputting the feature roadbed construction data into the target Cs-FEA-LSTM hybrid prediction model for prediction. The manual analysis cost is effectively reduced, and the level of roadbed engineering full-period monitoring is improved.

The invention provides a multifunctional integrated cleaning robot for a photovoltaic module of a power station, which belongs to the technical field of photovoltaic module cleaning and comprises a control chip, a robot body device, an automatic cleaning device, a dust collection device, a water spraying system device, a solar charging device, an intelligent navigation device, an environment monitoring device, a communication interface device and a power management device. An intelligent cleaning control module is arranged in the control chip. The method comprises the following steps: constructing a three-dimensional map through environment perception; evaluating the pollution state of the photovoltaic module by using a sensor to generate a thermodynamic diagram; a double-layer optimization model is adopted for path planning and resource allocation, an upper layer adopts an improved ant colony algorithm to take charge of a global path, and a lower layer optimizes cleaning parameters based on a particle swarm algorithm; cleaning operation is executed, and real-time monitoring is carried out; evaluating the cleaning effect; water resource and energy intelligent management is carried out; multi-machine collaboration and task allocation are realized; processing an abnormal condition; the cleaning strategy is continuously optimized through machine learning, and efficient and accurate photovoltaic module cleaning is achieved.

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

The invention discloses a distributed multi-mobile robot path planning and obstacle avoidance method based on ACO-DWA, and belongs to the technical field of path planning. The invention discloses a distributed multi-mobile robot path planning and obstacle avoidance method based on ACO-DWA. The method comprises the following steps: constructing an environment model; performing global path planning by using an ant colony algorithm ACO; establishing a motion model of the robot, and selecting an optimal speed group; and designing a conflict judgment rule, introducing a priority strategy, and solving conflicts according to the importance and urgency of robot tasks. According to the distributed multi-mobile-robot path planning and obstacle avoidance method based on ACO-DWA, an improved dynamic window method (DWA) and an ant colony optimization (ACO) algorithm are utilized, effective local path planning and global path optimization are achieved in the environment with high-density obstacles and unknown static obstacles, and safe obstacle avoidance of the robots is ensured. Meanwhile, a motion conflict judgment rule and a priority strategy in the multi-robot system are provided to coordinate the motion between the robots and between the robots and obstacles, and safe and efficient navigation is ensured.

The invention discloses an energy management method of an electric bicycle system and the electric bicycle system.The method comprises the steps that battery parameters, motor parameters and whole vehicle kinetic parameters of an electric bicycle are obtained, and digital mapping processing is carried out to obtain a digital twin system; acquiring operation parameters of the battery, the motor and the whole vehicle, and performing distributed feature extraction processing to obtain feature data; training the bionic optimization model fusing the genetic algorithm and the ant colony algorithm to obtain a scenarized energy optimization strategy; performing hierarchical regulation and control processing on the control system to obtain a hierarchical control instruction sequence; and performing dynamic regulation and control processing on energy distribution to obtain a real-time energy optimization control instruction. According to the technical scheme of the invention, intelligent prediction and dynamic optimization distribution of energy demands are realized.

The invention discloses a spinning equipment real-time monitoring system based on an itinerant robot and stroboscopic control, particularly relates to the field of spinning equipment monitoring, is used for solving the problems of low monitoring precision and insufficient inspection efficiency, and is used for accurately evaluating the state of spinning equipment and generating a score based on historical operation data and real-time feedback. And then planning and dynamically adjusting an inspection path by adopting an ant colony optimization and reinforcement learning algorithm to ensure that high-risk equipment is preferentially monitored. Resource allocation is optimized and inspection efficiency is improved through accurate simulation of path time consumption and a coverage range; meanwhile, handshake connection is encrypted through multiple protocols, equipment operation parameters are extracted in real time, stroboscopic control is dynamically adjusted, and the definition of image collection and data safety are guaranteed; high-frame-rate high-definition camera shooting and self-adaptive stroboscopic control are combined, high-quality image data are collected and preprocessed, deep analysis is carried out by using a convolutional neural network and a multivariable correlation model, abnormal early warning is generated in real time, and an inspection path is optimized.

The invention provides a field medical sharp instrument recovery management method and system based on a module anti-permeation structure, and relates to the technical field of image analysis, and the method comprises the steps: recognizing a field medical sharp instrument through multi-modal image fusion and a deep learning model, extracting key features, and carrying out the precise positioning, segmentation and semantic analysis, classifying by combining a causal reasoning network to obtain category and danger level information, and generating and optimizing a recovery scheduling strategy by utilizing an improved space-time diagram neural network and a hierarchical reinforcement learning algorithm and combining an improved auction mechanism and a security constraint model based on the classification information and the danger level information. An optimal recovery route is planned through an improved ant colony algorithm and a heuristic search strategy in combination with a Pareto optimization algorithm.

The invention relates to the technical field of PCB processing, and discloses a multi-layer circuit board drilling method and device based on machine vision, and the method comprises the steps: obtaining a multi-layer circuit board image; preprocessing the multi-layer circuit board image to obtain a to-be-identified image; inputting the to-be-recognized image into a pre-trained defect detection model to obtain defect position data; carrying out point location identification and matching on the to-be-identified image to obtain an optimal hole location coordinate, and carrying out correction according to the optimal hole location coordinate to obtain hole location information data; according to the hole site information data and the defect position data, nodes are constructed respectively, distance calculation is carried out, and a hole site node graph is generated; according to the hole site node graph, performing path planning by applying an ant colony algorithm, and performing iterative search to obtain an optimal drilling path; and drilling the multilayer circuit board according to the optimal drilling path. The method has the following effect that the drilling efficiency of the multilayer circuit board can be improved.

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention discloses an RFSOC-based FAST core array distributed collaborative observation and data fusion method and system, and the method comprises the steps: S1, system initialization: a master node RFSOC generates a global clock, achieves the phase synchronization of multiple board cards through an SYSREF differential signal, and completes the calibration of a three-stage clock tree; s2, signal acquisition and preprocessing: directly sampling a 3-8GHz radio frequency signal through an ADC (Analog to Digital Converter), and performing digital down-conversion to obtain a baseband signal; s3, intelligent resource scheduling: identifying a signal type based on an ESN (Echo State Neural Network), and dynamically allocating FPGA logic resources through an improved ant colony algorithm; s4, heterogeneous calculation acceleration: executing a 128-channel digital beam forming pipeline on the FPGA; s5, cross-domain data fusion; s6, collaborative observation planning; and S7, outputting data. The method is suitable for multi-beam synthesis, cross-region joint observation and mass data real-time processing scenes, and provides key technical support for solving the frontier scientific problems such as rapid radio storm origin and black hole activity monitoring.

The invention discloses a multi-unmanned aerial vehicle target exploration and tracking method and system based on multi-agent ant colony reinforcement learning. The method comprises the following steps: dividing an actual environment for target tracking of unmanned aerial vehicles into grid units; performing target state initialization based on the grid units to obtain an initial observation position of the unmanned aerial vehicle; based on an ant colony algorithm and the initial observation position of the unmanned aerial vehicle, the transition probability of the unmanned aerial vehicle among the grid units is calculated; establishing a reward function based on the transition probability and the grid unit; based on the reward function and the existing exploration information and tracking information, performing centralized training on the multiple unmanned aerial vehicles by using a multi-agent near-end strategy optimization algorithm to obtain an optimal joint decision; and based on the optimal joint decision, enabling the unmanned aerial vehicle to complete exploration and tracking of multiple targets to be explored. According to the technical scheme, the possibility that the unmanned aerial vehicle finds the target in the unknown area is increased, and the target can be effectively followed.

The invention provides an unmanned aerial vehicle autonomous inspection method and system for a transformer substation. The core content of the unmanned aerial vehicle autonomous inspection method comprises seven steps of task pre-planning, flight path generation, BIM model generation, edge point extraction, inspection path generation, inspection point planning and inspection task execution. The steps and links are combined with various advanced technologies such as LiDAR scanning, SLAM technology, reinforcement learning, genetic algorithm, ant colony algorithm and edge detection, efficient, intelligent and safe inspection of the transformer substation is realized, and powerful support is provided for operation and maintenance management of the transformer substation. Wherein the obstacle avoidance path is autonomously planned through the reinforcement learning dynamic training obstacle avoidance model, dynamic obstacle avoidance and path optimization are realized, and the flight risk is reduced. And autonomous flight and task execution in a complex environment are supported. And real-time mapping and positioning of the unmanned aerial vehicle are realized by using the SLAM technology, and the preprocessing dependence is reduced.