44 results about "Path cost" patented technology

The invention discloses a heterogeneous unmanned aerial vehicle task allocation and reallocation method based on an alliance formation game. The method comprises the following steps: respectively clustering unmanned aerial vehicles and tasks to obtain an unmanned aerial vehicle class cluster and a task class cluster; for each unmanned aerial vehicle class cluster and the corresponding task class cluster, the method comprises the following steps: forming a game model based on an alliance, and constructing a revenue function, including a resource contribution function, a path cost function and a threat cost function, of an unmanned aerial vehicle under each task alliance; based on a Nash equilibrium theory in the game theory, modeling a task allocation problem as a task alliance division problem of a maximized potential function according to a revenue function; solving a task alliance division problem of a maximized potential function based on processing of two stages of a maximum weighted matching stage and an unmanned aerial vehicle transfer alliance judgment stage, and realizing task allocation of the unmanned aerial vehicle; and when a certain unmanned aerial vehicle fails, tasks of the failed unmanned aerial vehicle are redistributed. According to the method, the task allocation efficiency, the system robustness and the fault-tolerant capability are improved.

The invention relates to the technical field of intelligent construction scheduling and path planning, and discloses a cold regeneration rolling path intelligent planning system for multi-machine collaborative operation, and the system comprises an operation area modeling module which is used for obtaining the boundary and terrain elevation data of a construction area and the compaction task density information of each position in the area; the measurement tensor construction module is used for generating a measurement tensor of a construction area based on the terrain elevation data and the compaction task density information, and constructing and forming a two-dimensional Riemannian manifold model with weighted measurement; and the path cost construction module is used for constructing a path cost functional based on the two-dimensional Riemannian manifold model and the compaction task density information. According to the method, on the basis of geodesic path optimization and multi-machine dynamic allocation and scheduling, the path is reconstructed in combination with equipment kinematics, and the construction effects that the operation path is reasonable, equipment scheduling is efficient, the path is smooth and executable, and real-time adaptability is achieved are achieved.

The invention relates to the field of three-dimensional warehouse automatic sowing, in particular to a multi-layer three-dimensional warehouse automatic sowing path optimization scheduling method and system, and the method comprises the following steps: obtaining sowing association data and three-dimensional warehouse association data through a data interaction module, and constructing a multi-dimensional mapping table; calling a preset material characteristic and path constraint mapping library, screening paths conforming to constraints through an improved A algorithm, calculating path cost, and outputting an initial path; the real-time load state of the AGV is input, a regional task allocation plan is output through the global dispatching center, and an optimized task allocation table is output through local obstacle avoidance optimization in combination with multi-source sensing data; and calculating task priorities, allocating resources according to the priorities, processing path conflicts, and outputting a final task scheduling instruction. According to the method, the task priority is calculated through the multi-dimensional parameters, and the AGV load is balanced in combination with distributed global computing power scheduling, so that the problem of coexistence of AGV task overload and idle is effectively solved, and operation interruption caused by hardware faults is avoided.

The invention discloses a multi-unmanned aerial vehicle cooperative task allocation and path planning method based on a genetic algorithm, and relates to an unmanned aerial vehicle path planning method. And operating a Dijkstra algorithm to accurately solve the shortest feasible path between all airports and task points, and finally generating a global path cost matrix for quick query. A complete and safe flight path that each unmanned aerial vehicle starts from an airport, sequentially accesses task points and returns is graphically displayed, a convergence curve of an algorithm, population diversity changes and performance comparison data under different task scales, unmanned aerial vehicle numbers and obstacle densities can be output, and a parameter sensitivity analysis function is supplemented. And the effectiveness, the stability and the practical value of the proposed method in a static urban environment are comprehensively verified.

The invention discloses a multi-target path optimization method, equipment and a medium, and relates to the technical field of path planning. The method comprises the following steps: constructing a weighted graph model of a to-be-planned area and verifying an adjacent matrix to obtain verified weighted graph data; shortest path distances among all node pairs are calculated to form an all-source shortest path distance matrix and a precursor record set, and connectivity detection and feasible region pruning processing are carried out on unreachable node pairs; generating an optimized access sequence covering all target nodes based on the processed full-source shortest path metric data and a nearest neighbor greedy strategy, accumulating composite path cost, and performing feasibility check of time windows, capacity and risk constraints to generate an initial path sequence and accumulated cost data; and expanding adjacent node pairs in the initial path sequence into a specific executable path on the original graph according to the precursor record set, performing neighborhood optimization on the executable path by adopting a local search operator, and outputting an optimized final path scheme and an accumulated cost report.

The invention discloses a multi-dimensional dynamic sensing and intelligent computing power scheduling method and device oriented to a computing power network. The method comprises the following steps: deploying a plurality of target components forming multi-dimensional dynamic perception at a terminal, an edge node and a regional computing power center, and collecting computing power indexes and network indexes of each node; analyzing indexes in a preset period through a time sequence prediction model, and obtaining computing power bottleneck risk and time delay attenuation information; according to the type of a to-be-executed task, dynamically obtaining a computing power dimension weight and a time delay dimension weight, and combining a computing power bottleneck risk and time delay attenuation information to construct a cost function; determining a node corresponding to the minimum cost function and taking the node as a target node; and obtaining path costs corresponding to all feasible migration paths according to the target node, determining the migration path corresponding to the minimum path cost and taking the migration path as a target migration path, and triggering intelligent scheduling of computing power according to the migration path. According to the method, collaborative perception, intelligent decision and closed-loop control of computing power and network resources can be realized.

The invention discloses a decentralized distributed task auction and safety cooperative control method, which comprises the following steps: distributed multi-price auction: a robot calculates an income value based on residual electric quantity, task priority and path cost to realize safety conflict-free operation; the problem that traditional DMPC path planning lacks obstacle avoidance collaboration is solved, the path conflict rate is reduced from 12% to 0%, 90-degree emergency obstacle avoidance steering can be completed within 0.18 s under the working condition that the load is 3000kg, operation safety is guaranteed, and the cargo overturning risk is reduced; the cargo overturning risk is reduced by 90%, the stability of load transportation and the efficient task decision-making ability are improved, a distributed multi-price auction mechanism is combined with Pareto optimization, the task distribution average decision-making time reaches 120 ms and is far better than the task distribution average decision-making time larger than 1 s of a centralized system, and the overall operation efficiency is improved.

The invention relates to the field of logistics, in particular to a pick-up optimization method and system for intelligent logistics, and the method comprises the steps: obtaining a to-be-processed pick-up task set, courier information and real-time road network data; in path construction of each iteration of the ant colony system, according to path pheromone concentration, comprehensive heuristic information, courier area proficiency and task urgency, calculating the transfer probability of ants from a current node # imgabs0 # to a next non-accessed node # imgabs1 #; after each iteration is completed, calculating a space-time volatilization factor according to the historical traffic efficiency and the real-time congestion index, and updating global pheromones by using the space-time volatilization factor; performing pheromone enhancement on the optimal path based on the path quality of the current iteration optimal solution; the path quality is a scalar value of the total duration of the comprehensive evaluation path, the time window satisfaction degree and the comprehensive path cost; and repeating the path construction and pheromone updating steps until a preset termination condition is met, and outputting an optimal pickup path sequence.

The invention relates to the technical field of intelligent path planning, and discloses a path planning method and system based on polling management, and the method comprises the following steps: obtaining the information of a to-be-polled store; constructing a store topological graph with edge weights according to the geographical location information and the priority scores of the stores; constructing an objective function of routing inspection path optimization based on the store topological graph; inputting the objective function into a genetic algorithm for solving, and outputting an inspection path under a given path cost constraint condition; monitoring store state information in real time, interrupting current path execution when a store with an abnormal state is detected, and inserting the abnormal store into path planning input; and based on the current polled path state and the inserted abnormal store information, the genetic algorithm is executed again to locally reconstruct the remaining paths, and an updated polled path is generated. According to the invention, the inspection efficiency and the key store response capability are improved.

The invention discloses a multi-unmanned aerial vehicle task collaborative planning method and system based on comprehensive cost constraint, and belongs to the technical field of unmanned aerial vehicle system design and application, and the method comprises the steps: carrying out the data initialization in an image of a target region, and determining task point information and unmanned aerial vehicle information in the image; determining a distance cost and a path cost based on task point information and unmanned aerial vehicle information; then combining the path cost and the distance cost to obtain a fitness function of the comprehensive cost; task allocation is carried out on the unmanned aerial vehicle through a fitness function based on a genetic algorithm, and an optimal task allocation result is obtained; and finally, performing sequence optimization on the optimal task allocation result based on a grey wolf algorithm to complete collaborative planning of the tasks of the multiple unmanned aerial vehicles. According to the method, for the multi-unmanned aerial vehicle collaborative planning problem, the path cost value is predicted, the task is allocated to each unmanned aerial vehicle through the genetic algorithm, the optimal task allocation is obtained by adopting the fitness function of the comprehensive cost, the total operation cost reaches the lowest, and the working efficiency is higher.

The invention relates to a path planning method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a first path from a starting point to an ending point; according to a first probability rule, mutation nodes are determined from the intermediate nodes, and in the first probability rule, a first probability is in positive correlation with the number of adjacent nodes of the intermediate nodes; determining a target node from idle nodes of the road network; performing mutation on the mutation node based on the target node to obtain a second path from the starting point to the terminal point; determining a target path from the first path and the second path according to the path cost; taking the target path as a new first path for iteration; by adopting the method provided by the invention, the problem of poor global optimization capability in the prior art can be improved.

The invention relates to the technical field of optical communication, in particular to a unified cost calculation method and device based on Astar path finding. On the basis of Astar heuristic search, relay cost attenuation is adopted, and the minimum relay cost h is obtained; describing wavelength distribution and a near wave variable wave scene by adopting discrete normal distribution to obtain a passing path cost g; and obtaining a unified cost according to the minimum relay cost h and the path passing cost g. According to the invention, path calculation speed, smooth variable waves and more accurate relay estimation cost can be ensured under the condition of large business volume.

The invention discloses a robot path planning method based on a dynamic heuristic function, and the method comprises the steps: S1, initializing a binary grid map, and generating a random obstacle; s2, algorithm parameters are set, a list is opened, list initialization is closed, and a path planning starting point and a path planning ending point are set; s3, expanding the current node, and obtaining 8-direction adjacent nodes; s4, continuously calculating the obstacle density and calculating a dynamic weighted heuristic function based on the Chebyshev distance; s5, updating the node cost, judging whether the path is a better path, if the path is the better path, adding the path to the open list, and updating the father node relationship; and S6, calculating the path cost until a global optimal path is obtained, and carrying out path smoothing processing on the global optimal path. The problems of heuristic function fixed weight and a large number of invalid search areas can be solved, sharp turning points of the path can be reduced, the path feasibility is realized, and the mechanical loss of the robot is reduced.

The application discloses a multi-target path optimization method and device and a medium, and relates to the technical field of path planning. The method comprises the following steps: constructing a weighted graph model of a region to be planned and verifying an adjacency matrix to obtain verified weighted graph data; calculating the shortest path distance between all node pairs to form a full-source shortest path distance matrix and a predecessor record set, performing connectivity detection and feasible region pruning processing on unreachable node pairs; generating an optimized access sequence covering all target nodes based on the processed full-source shortest path metric data and a nearest neighbor greedy strategy, accumulating a composite path cost, performing a feasibility test on a time window, a capacity and a risk constraint to generate an initial path sequence and accumulated cost data; expanding adjacent node pairs in the initial path sequence into specific executable paths on the original graph according to the predecessor record set, and performing neighborhood optimization on the executable paths by using a local search operator, and outputting an optimized final path scheme and an accumulated cost report.

The present application relates to a kind of multi-constraint multilevel path planning method, to solve the problem that the flexibility of path planning is low, and optimal path cannot be accurately found in the prior art path planning method only considering the obstruction of passage type obstacle, method includes the following steps: obtaining multi-layer map information, path planning starting point and target point position;Each layer map information includes the passage type and passage cost multiplier of each passage node in the layer map;The passage type includes normal passage type, slow passage type and obstruction passage type;According to the passage type and passage cost multiplier of each passage node in each layer map, the optimal path from starting point to target point is calculated using improved path planning algorithm.Make the optimal path result more accurate, and the path cost of planning place is shorter.

The invention discloses a catering equipment task allocation method based on dynamic path planning. The catering equipment task allocation method comprises the following steps: S1, collecting data of mobile catering equipment; s2, performing task matching in combination with the equipment capability and task characteristics based on an eagle algorithm, and generating a preliminary allocation result; s3, constructing a path cost function, and evaluating path length and obstacle density; s4, calling a water wave optimization algorithm to generate an optimal path corresponding to each device; s5, the path cost and the equipment load are jointly considered, and task and path distribution is optimized; s6, issuing the final scheme to an equipment control system to execute a task; s7, dynamically monitoring a path and an equipment state, and identifying execution abnormity; and S8, completing the task when no abnormity exists, and updating and restarting the task to complete the scheduling closed loop when the abnormity occurs. According to the invention, intelligent task scheduling of catering equipment is realized, and path adaptability and system stability are improved.

The invention discloses a path planning method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a starting point and an ending point of a to-be-planned path; obtaining a target path and target path cost from the starting point to the ending point based on a path planning model constructed by a hybrid improved algorithm and a cost function; wherein the hybrid improved algorithm is obtained by combining an improved ant colony algorithm and an improved random tree algorithm, the improved ant colony algorithm is used for generating a first path from a starting point to an end point of the round for each round, and the improved random tree algorithm is used for obtaining a current path of the round according to the first path; and determining a target path according to the current path. According to the embodiment, the improved ant colony algorithm and the improved random tree algorithm are fused, and the cost function is combined for path evaluation, so that the convergence speed of path planning is improved, the problem of easy falling into a local optimal path is solved to a great extent, and the quality of path planning is improved.

The invention discloses a path cost calculation method and device based on discrete graph key point recognition and a medium, and relates to the technical field of path planning, and the method comprises the steps: obtaining a working environment image corresponding to an intelligent agent working environment, and a current node and a target node corresponding to an intelligent agent; dividing the nodes into key nodes and non-key nodes based on the communication relationship, and drawing a skeleton graph based on the key nodes and the non-key nodes; calculating the distance between any two key nodes in the skeleton diagram based on a multi-source Dijkstra algorithm, and creating a distance table based on the distance; and judging whether the current node is a key node or not and whether the target node is a key node or not, and determining the optimal path cost corresponding to the intelligent agent based on the judgment result and the distance table. The method and the device are used for solving the problems of low efficiency and poor accuracy when path cost calculation is carried out by directly utilizing a discrete graph structure in the prior art, and the efficiency and the accuracy of path cost calculation are improved.

The application is suitable for the technical field of path planning, and provides a path re-planning method and device, a self-moving device and a readable storage medium. The method comprises the following steps: fusing a global cost map of a target area and an expected path cost map to obtain a fusion map, wherein the expected path cost map is a cost map generated according to an expected path; and determining a target path between a starting point and an ending point of the expected path according to the cost values of each point in the fusion map. According to the application, the expected path cost map is also used for planning in the process of planning the path, that is, the expected path is considered when the path is planned, a new path that is feasible and close to the expected path is obtained, and the situation that there is no solution for a local path can be avoided.

The invention belongs to the technical field of multi-AGV scheduling optimization, and relates to a task allocation management method applied to storage and transportation and related equipment, and the method comprises the steps: calculating a shortest distance matrix and a shortest path matrix of storage map position data according to a Floyd algorithm; reading a task database, and obtaining a task order from the task database; determining passing working nodes and terminal nodes according to the task order; according to the shortest distance matrix and the shortest path matrix, calculating shortest path node vectors and required path costs of all the AGVs for executing the task order; according to an AGV density algorithm and the shortest path node vector, calculating AGV total density of each AGV path area; calculating the total cost of each AGV according to a bidding algorithm, the path cost and the AGV total density of each AGV path area; screening out a target AGV with the minimum total cost according to the total cost of each AGV; and distributing the task order to the target AGV. The bidding scheduling strategy is introduced, and it is ensured that tasks are distributed with the optimal efficiency.

The invention belongs to the technical field of communication, and particularly relates to a management method and system based on a new-generation satellite internet combined base station, and the method comprises the steps: calculating comprehensive satellite-satellite and satellite-ground link state measurement through nonlinear weighted combined link bandwidth, time delay, stability and prediction bit error rate; evaluating the resource occupation condition of the ground station, and calculating the node processing load; for a star-star link, evaluating a quantum key distribution channel condition based on quantum load configuration and a key pool state, and generating a discrete quantum security level; in the routing calculation level, the deterministic change of the network topology is predicted by using the satellite ephemeris, and a timeliness link state notification is generated; the node processing load is included in the total cost of the path; calculating differentiated link costs for different service types by adjusting the weight of link state measurement; for services with specific security requirements, all links with substandard quantum security levels can be directly excluded from routing computation, or the cost is increased by increasing penalty factors.

The application provides a multi-target waypoint planning method and system fusing an improved A* algorithm and an SPFA algorithm, and the method mainly comprises the following steps: a mobile navigator rasterizes the surrounding environment, and calibrates a target point set and a starting point of the navigator to simulate a task scene; a distance value calculation of the starting point to the target point is performed through construction of the improved A* algorithm; a negative edge weight map with point priority, direction and without negative loop is established through fusion of the improved A* algorithm and the SPFA algorithm, and the best waypoint order can be obtained according to the point priority in the negative edge weight map; the navigator moves according to the waypoint order to traverse the task target points, and global path planning and local path planning algorithms are realized. Through the application, a large number of path points and inflection points are reduced, the total path length is shortened, the total path cost is reduced, the overall efficiency is improved, and a reasonable and fast feasible path is planned in a multi-target waypoint task in a complex environment.

This invention provides a delivery route scheduling optimization method, system, and storage medium based on spatial indexing. The method divides the delivery geographic space into spatial partitions using a spatial index structure and records order ownership relationships. It utilizes spatial filling curves to perform one-dimensional dimensionality reduction mapping on pick-up and delivery locations to generate one-dimensional sorting codes, which are then arranged in ascending order to obtain an order sequence with spatial proximity clustering characteristics. Constrained by rider load capacity, consecutive orders are extracted along the order sequence to form candidate order combination combinations. The travel cost of these candidate combination combinations is estimated segment by segment using the spatial index structure, and the cyclic path cost is accumulated according to the access order. The cyclic path costs of all candidate combination combinations are compared, and the one with the lowest cost is selected as the scheduling execution result. Rider assignment information and pick-up / delivery order suggestions are output. This method effectively reduces the computational complexity of delivery route planning and improves the spatial rationality and route economy of order combination.

A distributed routing method for a long-distance cross-data center RDMA (Remote Direct Memory Access) network belongs to the field of communication, and comprises the following steps: pre-calculating path quality fraction coding propagation delay and link capacity; designing a switch real-time congestion estimator to calculate a congestion score; calculating the path cost by adopting a weighted fusion formula; the optimal path is selected through cost filtering and diversity preserving hash. By fusing the path quality and the real-time congestion signal of the switch, intelligent path selection of the cross-data center RDMA network flow is realized, and the problems of path isomerism asymmetry, congestion signal delay outdated, concurrent flow routing conflict and the like in the existing routing method are solved. According to the method, prospective congestion perception is provided on a data plane to suppress tail delay, the RDMA is kept friendly, low-risk increment deployment can be realized on programmable exchange hardware at a line speed and supported, medians and tail FCTs are reduced by 76% and 64%, and the flow transmission performance of the cross-data center RDMA network is remarkably improved.

The invention provides a virtual navigator path planning method based on SAC-RRT *, which innovatively introduces formation geometric envelope parameters to map multi-agent entity constraints into virtual navigator geometric constraints, replaces traditional RRT uniform sampling with non-deterministic Gaussian sampling of an SAC algorithm, and realizes adaptive step length extension and formation-level collision detection in combination with a Critic network. Meanwhile, the path cost optimization quantity of RRT rewiring is creatively converted into rewards to be fed back to the SAC network to complete backward fusion, network training is enhanced in cooperation with a real and virtual dual-experience playback strategy, formation trafficability factors are fused in a state space, a multi-dimensional reward function optimization decision is designed, and finally, the formation trafficability is improved through three times of B spline curve smoothing processing. The multi-agent formation path planning considering safety, optimality and convergence efficiency is realized, and the problems that a traditional method ignores formation geometric constraints, sampling blindness is large, and reinforcement learning and sampling algorithm fusion is not deep are effectively solved.

The invention discloses a railway signal scheduling optimization method and system based on an AlphaEvove algorithm, and relates to the technical field of railway signal scheduling, and the method comprises the following steps: S1, constructing a track state diagram; s2, constructing a track state characterization model, a strategy network and a value network; s3, constructing a Monte Carlo tree, performing search by using selection, expansion, simulation and return stages in Monte Carlo tree search, and generating a preliminary scheduling strategy after search; s4, introducing a plurality of candidate scheduling sequences from the preliminary scheduling strategy as a population, generating new scheduling samples by using an evolution strategy, and screening the new scheduling samples and updating a strategy network based on path cost; and S5, selecting a scheduling scheme with least conflicts and minimum delay in the candidate samples as an optimal scheduling scheme, and outputting the optimal scheduling scheme. Compared with a traditional railway dispatching method based on rules or a single heuristic algorithm, the railway dispatching method has higher learning ability and generalization ability and can continuously adapt to different operation scenes.

The invention provides a path retrieval system and method for vehicle recovery, and relates to the technical field of intelligent logistics, and the method comprises the steps: constructing a recovery network topology, building a path cost evaluation function, carrying out the heuristic path retrieval of the recovery network topology, obtaining a candidate path set, and carrying out the dynamic optimization of the non-cooperative game weight of the candidate path set. An equilibrium weight ratio is obtained; cost weight feedback updating is carried out on the path cost evaluation function based on an equilibrium weight ratio, path evolution is carried out on the candidate path set, and an evolution path set is obtained; performing node state exploration and cost updating decision on the evolution candidate path set to obtain the disposal cost of each path in the evolution candidate path set; and determining the recovery path of the target vehicle from the evolution path set according to the disposal cost, thereby realizing game equilibrium optimization based on multi-target weight and exploration decision of uncertain node state, and further performing global static optimization and local dynamic adaptation on the vehicle recovery path.

The invention discloses a multi-robot combined task allocation method based on energy consumption perception and a standby mechanism, and belongs to the technical field of robot task allocation, and the method comprises the steps: S1, constructing a system topological structure according to a scheduling environment of a robot, and constructing a shortest path cost matrix through a Floyd algorithm; s2, acquiring a task set, and dividing the task set into a plurality of combined tasks according to a predefined rule to obtain a combined task set; s3, generating an optimal access sequence and minimum path cost of the combined task set by adopting a double-strategy optimal access sequence calculation method according to the combined task set; and S4, calling the robot to complete the combined task according to the optimal access sequence and the minimum path cost of the combined task set. By adopting the combined task allocation, the task allocation efficiency can be remarkably improved, and path conflicts and resource competition generated in the execution process after high-correlation tasks are allocated to a plurality of robots can be avoided, so that the system coordination cost is reduced.

This application relates to a service path evaluation method based on an SRv6 deterministic network, comprising the following steps: defining key QoS attributes of the required service according to ITU standards; calculating the evaluation weights of each QoS and path cost of the service; calculating the service request time for each time period; calculating the final service cost of each path; and using an improved Topsis method to evaluate and rank all path methods and select the optimal path. By evaluating and ranking all path methods using the improved Topsis method, the optimal path selection for a specific service can be achieved in an SRv6 deterministic network.

The invention provides a traffic project path comparison and selection method and system, and belongs to the technical field of traffic planning. The method comprises the following steps: constructing a basic traffic network, adding each scheme line position of a project, and setting traffic restriction information; creating an OD cell centroid node, and communicating the centroid node with the traffic network; constructing a project-free network and each scheme network, and setting impedance and limiting conditions; calculating the shortest path line and cost of each OD interval in the project-free network and each scheme network by taking mileage, time, cost and carbon emission as cost factors of impedance; on the basis of the acquired shortest path line and cost, generating an expected path comparison diagram of each OD interval in the project-free network and the optimal scheme network; and interval evaluation is carried out by adopting an entropy weight TOPSIS method, and a global optimal scheme is determined in combination with a weighted comprehensive scoring method. By calculating the expected path cost and the GIS circuit diagram under different impedances in the multi-scheme network, the analysis content of scheme comparison and selection is enriched, and the necessity of scheme construction is enhanced.