902 results about "Ant colony" patented technology

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 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 non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention discloses a multispectral pollution analysis method and system for a photovoltaic cleaning unmanned aerial vehicle, and belongs to the technical field of spectrum detection.The multispectral pollution analysis method comprises the steps that a multispectral image is obtained, dark current-radiation-atmosphere-shadow full-link preprocessing is carried out, multiband weighted threshold segmentation and morphological post-processing are constructed, and a pollution area is extracted; establishing a self-learning pollution spectral feature library, and identifying pollution types by using cosine similarity; generating a pollution degree distribution diagram in combination with the pollution type-efficiency attenuation mapping table; and when the overall efficiency loss exceeds a preset value, planning a three-dimensional track based on a cluster priority score and an ant colony-genetic fusion algorithm, and returning an image to perform closed-loop verification and complementary cleaning after the unmanned aerial vehicle performs cleaning. According to the invention, the pollution identification precision, the efficiency evaluation accuracy and the cleaning intelligence level are significantly improved, and the method can be widely applied to unattended operation and maintenance of the photovoltaic power station.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention relates to the technical field of intelligent warehousing monitoring, and discloses an intelligent warehousing monitoring system based on digital twinning, which comprises a data acquisition layer, a digital twinning modeling layer, an intelligent analysis layer and an execution control layer, and is characterized in that three-dimensional coordinates and temperature distribution data of goods are acquired through a laser radar array and an infrared thermal imager; constructing a dynamic point cloud data set in combination with RFID positioning; an improved YOLOv7 model is adopted to be embedded into a CBAM attention mechanism to recognize the cargo form risk, and an LSTM time sequence model is fused to predict a temperature anomaly trajectory; an AGV obstacle avoidance path is optimized based on a genetic-ant colony hybrid algorithm, and a temperature control system and a security device are driven to perform linkage execution through an OPC UA protocol. And finally, millisecond-level synchronous mapping of the physical warehouse and the digital twinborn body, real-time deviation correction regulation and control of an equipment running track and cross-system collaborative response of an emergency strategy are realized, and the safety early warning accuracy and dynamic protection robustness of the storage environment are improved.

The invention provides a massage robot multi-mode fusion treatment system and method based on 3D vision, and relates to the technical field of massage robots. The region recognition module is used for establishing a human body recognition model and recognizing human body features in the processed image; the massage terminal is used for carrying out massage operation according to parts needing to be massaged and human body characteristics and monitoring skin pressure of the massaged parts in real time; the pain sense recognition module is used for human body pain sense recognition; the force adjusting module is used for adjusting skin pressure. According to the method, pertinence and safety of massage operation are remarkably improved through multi-modal image preprocessing and an initialized human body recognition model of a fusion segmentation model and a key point detection model; a global path strategy is generated and optimized through a hybrid ant colony algorithm, efficient and accurate motion control of the mechanical arm is achieved, through multi-modal data fusion analysis, the pain feeling of a user is sensed in real time, the massage strength is dynamically adjusted, and the comfort and the safety coefficient are improved.

The invention relates to the technical field of process scheme generation, and discloses an intelligent scheme reasoning generation method based on a process knowledge graph, and the method comprises the steps: firstly collecting multi-source process data, constructing a knowledge element extraction system, and outputting standardized knowledge entries through semantic analysis; establishing a process knowledge graph, and when a scheme reasoning demand is detected, positioning a target knowledge node through a logic association algorithm and generating an association relationship label; meanwhile, constructing a historical scheme case information base, and dynamically recording reasoning to generate a record; establishing a multi-dimensional weight configuration model, calculating a scheme matching degree score, and generating a recommendation priority sequence; solving an optimal scheme reasoning result by adopting an improved ant colony algorithm; and finally, outputting a result through the interactive verification platform, tracking and feeding back. The method improves the efficiency and quality of process scheme generation, and is suitable for process scheme formulation in industrial production.

The invention discloses a metal formwork production full-process management and control system based on a cloud platform, and relates to the technical field of industrial manufacturing, and the system comprises an industrial cloud platform which is in communication connection with the following modules: a global element perception processing module, an industrial Internet-of-Things terminal used for combined deployment, and a cloud platform module. And multi-source heterogeneous data including equipment state, cutter service life information, material circulation information, personnel operation information and workpiece quality detection data are collected in real time. According to the method, the industrial Internet of Things terminal is deployed, multi-source heterogeneous data such as equipment state, cutter life and material circulation are collected in real time, a virtual production environment synchronized with a physical workshop is constructed in combination with a digital twinning technology, a production scheduling problem is converted into a path planning problem based on an ant colony algorithm, an optimal scheduling scheme is generated in real time, and the scheduling efficiency is improved. The problems that a traditional system is rigid in plan and slow in response are solved, and the flexibility and efficiency of production scheduling are remarkably improved.

The invention discloses a material supply and resource recovery collaborative scheduling method and system, and belongs to the technical field of logistics scheduling, and the method comprises the steps: obtaining a material supply instruction and a garbage recovery instruction, carrying out the image recognition of a building garbage picture, obtaining the garbage attribute, and generating a demand instruction library; the method comprises the following steps: establishing a feature knowledge base, setting a spatial clustering method, carrying out spatial clustering and time window screening, carrying out geographic coordinate analysis and time sequence sorting on material demand points and recovery points, generating an initial task pool, setting a loading matching method, and generating a material supply and recovery loading schematic diagram according to building material and garbage attributes; a circulation path is planned, a path planning method is set, an ant colony algorithm is used for initial path planning, a monitoring feedback method is set, data are returned in real time, the running state of the vehicle is monitored, and an alarm is given out immediately once abnormity is found; and the sorted and regenerated aggregate data is synchronized to a building material database, a resource feedback method is set, a reverse transportation task is identified, and a material supply and resource recovery closed loop is formed.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

Aiming at the problem of time-dependent multi-target green vehicle path optimization, the invention designs a space-time attenuation factor-driven urban logistics low-carbon robust optimization method, takes the total vehicle distribution time and carbon emission as optimization targets, and adopts double-layer ant colony pheromones to guide ant colony search, so that the optimal path optimization is realized. The adaptive capacity of ants in a high-disturbance uncertain environment is improved, the convergence of solutions is enhanced, more solution sets balancing robustness and optimality are excavated through solution robustness evaluation and a feedback mechanism thereof, the diversity of optimal solutions is improved, an optimal vehicle path robust optimization scheme is obtained, the carbon emission of urban road network distribution is reduced, and the urban road network distribution efficiency is improved. And the distribution efficiency of logistics is improved.

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

The invention provides a medical consumable management method and device based on an ant colony algorithm, and relates to the technical field of ant colony algorithms, and the method comprises the steps: constructing a medical consumable turnover frequency weight model, and generating a consumable turnover priority function; generating a goods allocation weight mapping matrix through an ant colony algorithm in combination with a priority function, wherein the mapping matrix is established according to the medical consumables and the access areas; simulating ant colony individuals to perform multi-path search in a hospital channel topological structure, and dynamically updating path pheromones to form a shortest path set; a storage space is constructed into a heterogeneous graph structure with storage locations as graph nodes and channels as graph edges, and an embedding result is fed back to an ant colony optimization algorithm as a goods allocation initial constraint through embedding attribute vectors of each node by a graph neural network; goods allocation initial constraint and a trafficability score and a blocking factor generated by camera identification data are introduced, and a consumable transportation path under an emergent task is dynamically adjusted and realized. According to the invention, full-life-cycle management of the medical consumables can be realized.

The invention relates to the technical field of intelligent airport logistics, in particular to an airport luggage flow group intelligent AI-driven digital twinning low-carbon scheduling system, which comprises a physical sensing layer, a digital twinning engine layer, a group intelligent decision-making layer, a block chain evidence storage layer and a dynamic scheduling execution layer, and forms a'sensing-modeling-decision-evidence storage-execution 'closed loop; the physical sensing layer collects equipment and luggage state data; the digital twinborn engine layer constructs a total-factor twinborn body to realize synchronous mapping and carbon accounting of a physical system; the group intelligent decision-making layer adopts an improved carbon sensitive ant colony algorithm to generate a multi-objective optimization strategy of total energy consumption, residence time and load balance; the block chain evidence storage layer ensures credibility and traceability of the carbon data; and the dynamic scheduling execution layer converts the decision into a control signal and corrects simulation and actual deviation. According to the method, low-carbon, high-efficiency and reliable luggage scheduling is realized, and the method is suitable for green operation of a smart airport.

The embodiment of the invention discloses an unmanned aerial vehicle path planning method and system. The method comprises the following steps: S1, constructing an environment model of unmanned aerial vehicle operation; s2, setting an objective function and constraint conditions of unmanned aerial vehicle path planning; s3, based on the environment model, the objective function and the constraint condition, determining information of an approximate optimal path by adopting an ant colony algorithm; and S4, applying the information of the approximate optimal path to a sequential quadratic programming algorithm to determine the optimal path. According to the unmanned aerial vehicle path planning method provided by the invention, on the basis of the ant colony algorithm, the SQP algorithm is fused for secondary optimization, so that the problems that the traditional ant colony algorithm is easy to fall into local optimum and slow in convergence speed are solved, the path length of unmanned aerial vehicle path planning is successfully shortened, the number of iterations is reduced, and the path planning efficiency is improved. According to the method, more efficient and more accurate path optimization is realized, and the optimized algorithm has relatively high adaptability and robustness.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.

The invention discloses a multi-algorithm fusion transmission path planning method and device, terminal equipment and a storage medium, and belongs to the field of transmission path planning, and the method comprises the steps: obtaining state data of each node of a power grid, repeatedly executing a position updating operation until a convergence condition is satisfied, and obtaining a target particle position and a target ant position; the position updating operation comprises the step of updating the particle speed and the particle position according to the current particle speed, the particle position and the pheromone; according to the current ant position, the pheromone and the global optimal solution, the ant position and the pheromone are updated; when the convergence condition is not met, taking the updated parameter as the current parameter of the next iteration; otherwise, outputting the updated particle position and ant position; and determining a final transmission path according to the target position. By implementing the method, the advantages of the particle swarm algorithm and the ant colony algorithm can be combined, so that the problem that a single algorithm is easy to fall into local optimum or slow in convergence in transmission path planning in the prior art is solved.

The invention provides a rounded-corner container transportation path dynamic optimization scheduling method and system, and the method comprises the steps: quantifying three constraints of the size, the gravity center and the loading and unloading priority of a rounded-corner container, converting the three constraints into a matching formula, a path constraint threshold value and a weight rule, and constructing a weighted multi-objective optimization function in combination with the transportation cost, the time and the cargo damage risk; a genetic algorithm and ant colony algorithm mixed framework is built, a constraint adaptation layer is embedded to filter invalid solutions, and the iteration efficiency is improved; two types of algorithm operators are improved, and a constraint satisfaction degree, a loading and unloading priority and a dynamic parameter adjustment mechanism are fused; dividing multiple regions into sub-region optimization by adopting a divide-and-conquer strategy, and adapting to a large-scale dynamic scene through cross-region collaboration and local re-optimization; a full-dimension verification scheduling scheme in iteration is carried out, algorithm parameters are automatically adjusted based on constraint violation information, and iteration is terminated or constraint relaxation is started according to preset conditions; an improved algorithm is integrated to a dynamic scheduling system, real-time data are connected, parameters are optimized through a self-learning module, and a manual intervention interface is reserved.

The invention relates to the technical field of intelligent environmental protection, in particular to an unmanned aerial vehicle urban atmospheric pollution monitoring and preventing system based on radar and AI cooperation, which comprises a laser radar scanning module, an edge computing gateway, a cloud server, a small unmanned aerial vehicle inspection module, an unmanned aerial vehicle spraying module and a fog gun vehicle module, the laser radar scanning module scans and generates point cloud data in real time, and pollution information is obtained by combining positioning and aerosol inversion; the edge computing gateway uses a DBSCAN clustering algorithm and an improved YOLOv8 model to determine pollution hot spot coordinates, grades and pollution source types; the cloud server dynamically dispatches all the modules in sequence, an improved ant colony algorithm is adopted to plan a route, and real-time obstacle avoidance and accurate spraying are achieved; the small unmanned aerial vehicle inspection module obtains an image of a polluted area, the unmanned aerial vehicle spraying module carries out precise spraying treatment, and the fog gun vehicle module carries out dust suppressant spraying on a dust raising source. The defects of a traditional atmospheric pollution monitoring and treatment means are overcome, and accurate monitoring and efficient treatment are achieved.

The invention discloses a multi-unmanned aerial vehicle cooperative task dynamic allocation method, which belongs to the technical field of unmanned aerial vehicles, and comprises the following steps: constructing an initial allocation model of a multi-unmanned aerial vehicle cooperative task according to static factors; performing optimal grouping on the task points by adopting an improved Kmeans clustering algorithm; determining an optimal sequence of the task points in each group based on an ant colony algorithm; based on the initial allocation model and the grouping, adopting an improved genetic algorithm to carry out initial allocation solution; after initial allocation is carried out according to an initial allocation solving result, a dynamic reallocation model of the multi-unmanned aerial vehicle cooperative task is constructed according to the dynamic factors; based on the dynamic redistribution model, carrying out dynamic redistribution solving by adopting an operation planning thought, and carrying out dynamic redistribution according to a dynamic redistribution solving result; according to the method, the genetic algorithm and the operation planning are combined, the advantages of the genetic algorithm and the operation planning are fused, more efficient multi-unmanned-aerial-vehicle cooperative task allocation is achieved, and the method is suitable for cooperative multi-task scenes such as routing inspection monitoring, disaster rescue, logistics distribution and fixed-point surveying and mapping.

The invention, which relates to the technical field of the logistics supply chain, discloses a digital intelligence park supply chain warehouse distribution integrated service system comprising a dynamic warehouse resource collaborative scheduling module, a multi-modal intelligent path planning module and a block chain federal learning security center module. Data are collected in real time through 5G-IoT, storage locations and equipment are dynamically allocated in combination with a quantum genetic algorithm, and the storage proportion of cold and hot regions is optimized; planning a path in real time by using an improved ant colony algorithm and a dynamic cost function, and supporting multi-carrier collaborative distribution of unmanned vehicles and unmanned aerial vehicles; a hierarchical block chain and federated learning technology is adopted to ensure data encryption storage and cross-enterprise security sharing; the system improves the storage efficiency and the inventory turnover rate, shortens the distribution time, reduces the energy consumption, guarantees the data privacy and compliance, and is suitable for the supply chain management of a modern park.

The invention discloses a multi-warehouse multi-logistics vehicle path planning method based on a self-adaptive neighborhood ant colony system, provides a self-adaptive warehouse selection strategy based on historical evolution information, autonomously selects a proper warehouse for each logistics vehicle, achieves the purpose of optimizing the position of the warehouse, and improves the path planning efficiency. A customer selection mechanism based on a self-adaptive neighborhood is designed for the ant colony system, so that the complexity of customer selection is effectively reduced; through continuous iterative optimization, a high-quality and high-precision multi-logistics-vehicle service path is finally output, and the service cost of each logistics vehicle is balanced as much as possible while the total service cost of all the logistics vehicles is reduced. According to the invention, solution is carried out through an ant colony optimization algorithm, and a self-adaptive warehouse selection strategy, a customer selection mechanism based on a self-adaptive neighborhood, an ant selection technology based on path estimation service cost and a path combination and segmentation strategy fused with 2-opt are designed, so that the quality of a path planning scheme is remarkably improved; and meanwhile, the time consumed for constructing the planning scheme is greatly shortened.

The invention relates to a multi-mobile-robot path planning method based on swarm intelligence, and belongs to the technical field of robot path planning. The method comprises the following steps: setting the size of a map, starting and target positions and colors of a robot, creating a preset grid map, and initializing an ant colony algorithm, a genetic algorithm and a pheromone system; iteratively searching paths for the mobile robots with different starting points at the same time through an ant colony algorithm; a genetic algorithm is used for optimizing the path, conflict detection and processing are carried out after the path is optimized, and it is ensured that the final path is free of conflicts; and when the maximum number of iterations is reached, outputting the shortest path that each robot arrives at the target node and no collision exists between the robots. According to the path planning method, unnecessary turning can be reduced, the convergence speed of path searching is obviously improved, robot conflicts can be avoided, and the actual requirements of multi-robot path planning are met.

The invention discloses a multi-robot path planning method based on multi-heterogeneous population coevolution and parallel search. The method comprises the following steps: firstly, modeling an environment by adopting a two-dimensional grid method, and generating a multi-robot initial path of a total population by utilizing a risk perception A * algorithm; then, dividing the total population into three sub-populations and corresponding external cooperative populations through a hierarchical roulette selection strategy; the three types of heterogeneous algorithms are executed in parallel, namely the collaborative genetic algorithm, the collaborative particle swarm algorithm and the collaborative ant colony algorithm are executed in parallel and synchronously, and a global solution set is generated in combination with a double-solution pool fusion mechanism. And then, a priority-based conflict resolution strategy is adopted, and time-space conflicts are solved by adopting time migration, path detour and local path planning strategies. And finally, path smoothing is realized through a cubic B-spline curve based on path reconstruction. According to the method, heterogeneous population coevolution is combined with parallel calculation, so that the efficiency, safety and cooperation capability of multi-robot path planning are remarkably improved.

The invention provides a robot motion path planning method and device based on machine learning, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining and storing the position information of an obstacle and a target point in an environment; constructing an artificial potential field according to the current position of the robot, the speed of the robot, the target point position, the target speed, the obstacle information and the motion state of the robot; the artificial potential field is optimized through a variable neighborhood search algorithm, and a reinforcement learning strategy is introduced into the variable neighborhood search algorithm; nodes on the basic path generated by the artificial potential field method serve as initial starting points of ants in the ant colony algorithm, and a final path is obtained through multi-round iterative optimization through an ant release pheromone mechanism, a transition probability selection mechanism and a pheromone volatilization updating mechanism; and evaluating the optimized path and dynamically adjusting the path. The robot motion path planning method can cope with complex environment changes, and effectively improves the safety and efficiency of path planning.

The invention discloses a chaos annealing ant colony-based adversarial blocking timeliness emergency material transportation toughness intelligent decision-making method, and relates to the technical field of intelligent emergency scheduling and optimization. The method comprises the steps of 1, extracting scene data and converting the scene data into model parameters; step 2, constructing a mathematical model for survivability optimization; 3, initializing algorithm parameters and generating an initial path; 4, dynamically adjusting the temperature and the pheromone volatilization rate; 5, generating an adversarial blocking perception path and implementing detour repair; 6, updating pheromone distribution based on an annealing criterion; and step 7, locking the critical path and outputting an optimal transportation scheme. According to the method, timely delivery of materials can be guaranteed in a complex road network blocking and emergency demand scene, the timeliness and toughness of an emergency transportation system are improved through a chaos annealing mechanism and an adversarial blocking simulation technology, the method can be widely applied to the fields of natural disaster rescue, public health event response and the like, and a scientific basis is provided for emergency decision making.

The invention provides a marine oil spill emergency resource scheduling method and system based on an improved ant colony algorithm, and relates to the technical field of data processing.The method comprises the steps that on the basis of a modified path transition probability function, multi-target ant colony collaborative optimization is executed on a dynamic resource scheduling architecture, a population is initialized to construct a scheduling path solution, and the scheduling path solution is optimized; calculating a time consumption target and an ecological loss target, iteratively updating pheromone distribution until convergence, and generating a multi-agent non-dominated scheduling strategy; according to a multi-agent non-dominated scheduling strategy, calculating a resource scheduling failure condition probability and an ecological sensitive area damage expected value; and if the scheduling failure conditional probability exceeds a risk tolerance threshold value, an ecological damage expected value is taken as a nonlinear penalty weight, and an anti-disturbance rescheduling mechanism is triggered to generate a flexible scheduling strategy. According to the method, the timeliness and accuracy of emergency resource transportation are improved, and collaborative optimization of'emergency efficiency-ecological protection 'is realized.