553 results about "Pheromone" patented technology

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention relates to the technical field of autonomous navigation, in particular to an AUV (Autonomous Underwater Vehicle) multi-sensor fusion autonomous navigation system based on ant colony-particle swarm collaboration.The AUV multi-sensor fusion autonomous navigation system is characterized in that a sensor weight decision module constructs a pheromone matrix based on an ant colony algorithm, retrieves a mapping relation between historical errors and pheromone concentration in real time and dynamically generates a sensor priority sequence; adaptive weight distribution in a data conflict scene is realized; the dynamic path correction module is combined with a particle swarm algorithm and pheromone concentration gradient constraint to balance global path optimality and real-time obstacle avoidance requirements in optical camera obstacle deviation correction; and the closed-loop coupling module is used for converting sonar and geomagnetic residual errors into pheromone concentration correction values and confidence coefficient regulation factors by fusing residual error compensation and path tracking error feedback, so that an error traceability-weight calibration-path optimization closed loop is formed, sensor drift is inhibited, and the long-endurance navigation precision is improved.

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.

The invention discloses an unmanned aerial vehicle cluster dynamic path planning method and system based on a Gaussian plume model. The method comprises the steps that chemical plant leakage gas concentration, wind speed data and thermal imaging data are collected; dynamically correcting the diffusion coefficient of the Gaussian plume model according to the wind speed data, and generating a leakage source probability distribution diagram in combination with the leakage gas concentration and the thermal imaging data so as to determine the concentration gradient value and pheromone field intensity of each position of the chemical plant, thereby planning the search path of the unmanned aerial vehicle cluster; and performing grid division on the leakage source probability distribution diagram to determine a search task, performing search task distribution according to an auction algorithm and the real-time state of the unmanned aerial vehicle, performing leakage source search positioning in combination with a search path, and finally marking a leakage point and returning the leakage point to a command center of the unmanned aerial vehicle cluster in real time. Dynamic path planning of the unmanned aerial vehicle cluster is realized in combination with multi-source data fusion, the unmanned aerial vehicles are guided to preferentially search high-probability regions through task allocation, and the positioning precision of leakage sources and the resource utilization rate of the unmanned aerial vehicles are improved.

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 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 path planning method based on a pheromone enhanced ant colony algorithm, and particularly relates to the technical field of path planning. By constructing an initial pheromone matrix, the ratio of the linear Euclidean distance between a path starting point and a path end point to the sum of the Euclidean distance from the path starting point to a candidate grid position and the Euclidean distance from the path end point to the candidate grid position is calculated, and the initial pheromone concentration is distributed near a theoretical optimal path; an elite solution pheromone enhancement strategy is adopted, all generated paths are sorted according to path quality in each iteration, and a high-quality path is selected, copied and then combined with a global optimal path to be used for updating a pheromone matrix; a prospective steering optimization strategy is utilized, punishment is applied to redundant turning in the searching process, and the pheromone updating amount is determined through the path length and the turning frequency; through multi-round iteration, stable pheromone distribution is realized, so that a final optimized path is obtained, the search efficiency is improved, and the method is suitable for a path planning task in a complex environment.

The invention relates to the field of path planning, in particular to an ant colony algorithm improvement method and system for unmanned aerial vehicle path planning, and the method comprises the steps: initializing a path diagram and an ant colony, carrying out the iteration through employing the ant colony algorithm, recording a complete path and a sub-path of an ant, and calculating the optimal degree of the path according to the distance and wind direction factors, comprehensively evaluating the sub-path quality; the pheromone volatilization amount and release strategy are adjusted according to the optimization degree, the high-optimization-degree sub-path participates in pheromone release, and the rest of the sub-paths only participate in volatilization; and updating the pheromone concentration in each iteration, after the maximum iteration number is reached, selecting the path with the highest optimal degree as the optimal path, converting node coordinates of the path into GPS coordinates, uploading the GPS coordinates to the unmanned aerial vehicle, and completing path planning. According to the method, the pheromone updating strategy is improved, and only the sub-paths with high optimization degree participate in pheromone release, so that the algorithm can be prevented from falling into a local optimal solution, the global search capability is enhanced, and the unmanned aerial vehicle path planning efficiency and accuracy are improved.

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 biosynthesis inhibitor of migratory locust aggregation pheromone 4-vinyl anisole and application of the biosynthesis inhibitor. The biosynthesis inhibitor of migratory locust aggregation pheromone 4-vinylanisole reduces the activity of 4-vinylphenol methyltransferase, and the 4-vinylphenol methyltransferase contains an amino acid sequence selected from the following sequences: (a) a sequence as shown in any one of SEQ ID No.1 and SEQ ID No.3; and (b) a sequence which is obtained by deleting, inserting and / or substituting one or more amino acid residues in the sequence shown in any one of SEQ ID No.1 and SEQ ID No.3 and has the activity of catalyzing generation of migratory locust aggregation pheromone 4-vinyl anisole. The inhibitor can be applied to biological prevention and control of migratory locusts, can keep locusts in a harmless scattered state instead of killing the locusts, provides a new method for biological prevention and control of migratory locusts, and has a wide application prospect.

The invention provides a path planning method and system based on HER-ACO, and belongs to the technical field of robot path planning. The method comprises the following steps: S1, acquiring an environment grid map, wherein the grid map comprises a starting point, a target point and obstacle distribution information; s2, introducing an HER algorithm to improve a traditional ant colony algorithm ACO, and searching a feasible path from a starting point to a target point in the grid map by using the improved ant colony algorithm as an initial planning path; the improvement comprises the following steps: introducing an HER thought and a dynamic pheromone volatilization mechanism; increasing a direction weight factor, a neighborhood obstacle factor and a future obstacle factor; and S3, smoothing the initial planning path based on the feasibility check of the linear path, removing redundant intermediate nodes, and outputting a final planning path. A simulation result shows that the scheme has remarkable advantages in a complex environment, particularly has excellent performance in the aspects of path quality and convergence speed, and is suitable for scenes such as robot navigation and concrete robot path planning.

The invention relates to the technical field of machine path planning, and discloses an island ant colony path planning method for an unmanned trolley, which comprises the following steps: after generating a grid map, decomposing global path search into path exploration between adjacent islands; non-uniform pheromone initialization is adopted between adjacent islands, local paths between the adjacent islands are searched respectively, and the position of a next grid is selected in the searching process by combining an improved node transition probability with a heuristic function and pheromone concentration; and when global pheromone distribution is updated, an optimal path is rewarded, and a worst path is punished. And combining the local paths of the island pairs to obtain global paths, and selecting the global path with the shortest length as the optimal driving path. According to the method, a segmented island strategy is adopted, the complexity of single search is reduced, the early random probing process is shortened through non-uniform initialization pheromones, the convergence speed is increased, the number of deadlock ants is effectively reduced, and the precision, convergence and stability of path planning are improved.

The invention belongs to the technical field of robot path planning, and provides a mobile robot three-dimensional path planning method based on an adaptive ant colony algorithm, and the method comprises the steps: determining a starting node and a target node in a rasterized three-dimensional search space; iteratively searching the optimal path from the starting node to the target node, and outputting the optimal path from the starting node to the target node obtained by the last iterative search; each iterative search comprises the following steps of: A, putting all ants to an initial node; b, generating an attenuation factor which is in negative correlation with the number of iteration search times, and adjusting heuristic information among the nodes by using the attenuation factor; c, each ant selects the next node according to the heuristic information between the nodes and the pheromone concentration between the nodes; d, updating the pheromone concentration of the road section passed by the ants; the invention further discloses a three-dimensional path planning device for the mobile robot, a computer program product and the mobile robot. According to the method, the convergence and efficiency of path search are improved, and the three-dimensional path planning time is shortened.

The invention provides a mobile robot path planning method based on an improved ant colony-guided optimization algorithm. According to the method, after a grid map environment is constructed, a dynamic selection factor is introduced to optimize a state transition rule of an ant colony algorithm, so that the efficiency at the initial stage of search is improved; a multi-step search strategy is adopted to enhance the flexibility of path planning, and an end point distance index is utilized to replace the traditional pheromone concentration, so that the risk of falling into local optimum is reduced; and carrying out smoothing processing on the path in combination with a guide optimization algorithm to realize iterative correction and global path optimization of a feasible region. The method has good path quality and convergence performance, and is suitable for a mobile robot path planning task. Simulation results show that compared with a traditional ant colony algorithm and an improved method thereof, the algorithm has obvious advantages in the aspects of path length, the number of corners, convergence efficiency and the like.

The invention relates to an ocean platform pipeline wiring method and system based on an ant colony optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S11, environment modeling: discretizing a three-dimensional space of an ocean platform into a grid model, marking an obstacle region and a feasible region, and calculating a grid node energy value; s12, parameter initialization: constructing a heuristic information matrix based on pipeline inlet and outlet coordinates and equipment parameters, and determining a node transition probability by the matrix through a direction probability allocation strategy and a distance sensitive function; s13, path optimization: generating candidate paths through dynamic smooth search, local pheromone adaptive adjustment and collaborative ecological niche search, and updating pheromones; and S14, constraint verification and result output: verifying whether the path meets the engineering constraint or not, and outputting the three-dimensional coordinate, length and bend number of the optimal pipeline path after iterative optimization. According to the method, efficient, safe and economical wiring of the offshore platform pipeline is achieved.

The invention is suitable for the technical field of crowd simulation and emergency evacuation, and provides an emotional infection and social force model coupling-based crowd evacuation method, which comprises the following steps of: constructing an emotional infection model based on an SEIRS framework, dividing individual emotional states into a susceptible state, an exposed state, an infected state and a recovery state, the infection rate and the recovery rate are dynamically adjusted through a feedback function; establishing a social force model, and embedding an emotion value into calculation of a target driving force and a repulsive force between individuals to realize real-time adjustment of the motion behavior by the emotion; global path planning and an ant colony pheromone mechanism are fused, a density perception cost function and pheromone guiding force are introduced, and dynamic blockage avoidance is achieved; a bidirectional feedback mechanism is designed, and local path reconstruction is triggered when congestion is detected. According to the method, bidirectional coupling of emotion propagation and behavior decision is realized, the authenticity and adaptability of evacuation simulation are improved, and a scientific basis can be provided for emergency management and evacuation strategy optimization in public places.

The embodiment of the invention provides a mobile communication PCI (Peripheral Component Interconnect) decision-making method and device based on an improved ant colony algorithm, and the method comprises the steps: obtaining MR (Measurement Report) data of a base station and a real-time network state; the base station MR measurement report data comprises a conflict matrix, a confusion matrix and a modulo 3 interference matrix; extracting a network topology feature vector according to the MR measurement report data of the base station; according to the network topology feature vector and the real-time network state, obtaining a heuristic weight and a pheromone volatilization rate parameter; based on an ant colony algorithm, obtaining an improved ant colony algorithm according to the heuristic weight and the pheromone occurrence rate parameter; based on the quantum revolving door, determining a three-level objective function according to the conflict matrix, the confusion matrix and the modulo 3 interference matrix; and based on an improved ant colony algorithm, generating an optimal PCI allocation scheme according to the conflict matrix, the confusion matrix, the modulo 3 interference matrix, the three-level objective function and a preset constraint condition. The problem of optimization target imbalance is solved, and the reliability of the PCI decision method can be ensured.

The invention provides a photovoltaic panel self-adaptive cleaning path planning method and system based on environmental perception, and relates to the technical field of photovoltaic panel cleaning, and the method comprises the steps: calculating a cleanliness value through obtaining historical weather data and state monitoring data, and triggering cleaning when the cleanliness value is lower than a dynamic cleaning threshold value; optimizing a cleaning path by adopting a multi-pheromone ant colony algorithm and an environment self-adaptive multi-neighborhood simulated annealing algorithm; and subareas are divided based on the dirt density, and multi-robot collaborative cleaning is achieved. The cleaning efficiency is improved, energy consumption is reduced, and intelligentization and precision of photovoltaic panel cleaning are achieved.

The invention discloses an online health management method and system based on big data, and relates to the technical field of online health management, a virtual user executes a selected health service to simulate the process that a real virtual user participates in a health activity in actual life, and after the virtual user completes the health service, the virtual user can take part in the health activity. The pheromone concentration is updated according to the effect of the plan, the virtual user carries out selection and execution of the health care service and updating of the pheromone concentration according to the pheromone concentration and a heuristic rule, convergence is carried out after a preset number of iterations is achieved, natural attenuation of the pheromone concentration is simulated based on the volatilization rate of the pheromone concentration, and the health care service is obtained. After the last round of iteration is completed, according to the pheromone concentration and the health care service effect, the optimal health care service is selected as the final personalized recommendation. The management system effectively generates more personalized health and care services for the virtual users, progressive optimization helps the management system continuously adapt to health changes and demand evolution of the virtual users, and the long-term effectiveness of the health and care services is improved.

The invention belongs to the related technical field of underwater gravity matching navigation, and discloses a submersible vehicle route planning method, application and equipment based on an improved ant colony algorithm, and the method comprises the steps: (1) constructing an adaptability index matrix based on gravity adaptability characteristics, and carrying out the normalization and information entropy weighting to obtain comprehensive adaptability characteristics; (2) constructing an environment grid model according to the comprehensive adaptability characteristics; (3) taking a free space as a search area, carrying out path search based on an environment grating model and an improved ant colony algorithm, and further obtaining a track node sequence meeting the adaptability constraint, the path continuity and the shortest path target to complete track planning; wherein the improved ant colony algorithm is obtained by introducing an elite ant strategy to enhance the globally optimal path pheromone of the ant colony algorithm and adaptively adjusting a pheromone concentration factor, a heuristic function factor and a pheromone volatilization coefficient in the ant colony algorithm by using a particle swarm optimization algorithm. The search efficiency and the automation degree are improved.

The invention discloses an education content recommendation method and system based on group intelligence, and the method comprises the steps: constructing a user dynamic cognition knowledge graph, and carrying out the dynamic attenuation of the memory intensity attribute of a knowledge point node based on a cognition forgetting model in combination with the memory stability; defining utilization pheromones on edges of the knowledge graph, and strategically injecting exploration pheromones according to forgetting states of users or knowledge breadth indexes; guiding a swarm intelligence algorithm to construct a learning path according to a rule jointly influenced by bimodal pheromones; and finally determining an optimal path and generating recommendation. According to the method, accurate intervention on a learning process and intelligent guidance on knowledge exploration are realized through deep fusion of a recognition scientific model and a swarm intelligence algorithm of bimodal pheromones, the learning stability is remarkably improved, information cocoon houses are effectively broken, and the interpretability of recommendation is enhanced through path intention analysis.

The invention provides a die-casting robot micro-spraying path planning and control method and system, and relates to the technical field of path planning, and the method comprises the steps: obtaining three-dimensional data of a workpiece to extract feature point information, calculating spraying accessibility data, carrying out path planning through employing an ant colony algorithm, and dynamically adjusting a search weight through employing a self-adaptive pheromone updating strategy. And an optimal spraying path sequence is generated, and track smooth transition is realized through track interpolation and optimization. The spraying quality and uniformity can be improved, waste of spraying materials is reduced, and the stability and accuracy of robot movement are improved.

The invention relates to the technical field of path planning, in particular to a navigation device path planning method and system, and the method comprises the steps: building a dynamic ocean grid map, dividing an ocean region into dynamic grids, and constructing a multi-factor cost matrix; selecting a path, and constructing a path selection probability formula of the ship; pheromones are updated, for each feasible path, the path length and the corresponding pheromone increment are calculated, the pheromones of the corresponding points on the paths are updated, and the navigation marks are updated after each round of iteration; and updating the dynamic parameters once every preset time step, stopping updating until the maximum number of iterations is reached, and outputting a global optimal path and total cost.

The invention belongs to the technical field of engineering design, and discloses a new meta-heuristic method inspired by a piranha fierce hunting strategy, and the algorithm comprises the steps: initializing a numerical value, initializing the positions Xi of individuals in a piranha school, the number N of the individuals, and the maximum number tmax of iterations; s3, searching: performing multi-dimensional perception on all piranha according to a formula (1), a formula (3), a formula (4) and a formula (6), then performing greedy decision search movement according to a formula (8), and turning to S3; s4, interaction: each piranha randomly selects other piranha according to a formula (9) for interaction, the piranha preferentially moves, and the step S4 is executed; hunting and killing: hunting and killing each piranha to the position of the global optimal prey according to a formula (10), and updating the position of the individual and the current global optimal position Xbest; s2, judging whether to terminate or not, judging whether an optimization precision requirement or the maximum number of iterations tmax is met or not, if yes, outputting the optimal value of the target function, and if not, turning to S2. According to the invention, accurate and efficient target locking is realized. A closely cooperative optimization system is constructed by simulating a plurality of natural communication mechanisms such as sound signals, light guide, pheromone transmission, vibration response and the like.

The invention discloses an unmanned cluster biological heuristic collaborative crowd intelligence method for disaster search, which comprises the following steps of: firstly, designing an improved YOLOv5 target detection model, guiding a local relation modeling module in combination with smoke characteristics, enhancing the smoke target identification capability, and constructing a dynamic fire event detection and positioning mechanism through multi-source sensor information fusion; secondly, aiming at a multi-point fire scene, respectively constructing a distributed ant colony collaborative search model based on a pheromone triple, and introducing an exploration and convergence desire function to form a real-time dynamic intelligent decision-making mechanism; according to the method, the fire behavior recognition precision and response efficiency are improved, and the search area coverage rate and the resource scheduling efficiency are improved.

The invention relates to the technical field of scenic spot route planning, and provides a scenic spot global collaborative touring planning method and system, and the method comprises the steps: constructing a scenic spot environment depth model, and deploying an ant colony algorithm on the constructed scenic spot environment depth model through key parameter initialization and tabu table initialization; path selection is carried out by executing ant colony movement, and real-time information updating and ant colony collaborative obstacle avoidance are carried out in the movement process; constructing a pheromone updating rule to update global pheromones, and judging and outputting an optimal path through iterative convergence; and performing multi-dimensional verification on the output optimal path, and generating a personalized touring scheme according to the path passing the verification. According to the invention, collaborative planning and dynamic optimization of the scenic spot sightseeing path are realized, the sightseeing efficiency of tourists is effectively improved, the queuing time is reduced, the information interaction comprehensiveness is enhanced, and the overall operation efficiency and tourist experience of the scenic spot are remarkably improved.

The invention provides a mobile robot path planning method based on an improved ant colony algorithm, and relates to the technical field of robot path planning, and the method is technically characterized in that the method comprises the following steps: carrying out environment modeling by adopting a grid method, and constructing a multi-target performance evaluation function; an improved ant colony algorithm is adopted to carry out optimal path search, path planning is carried out on a grid map from a starting node to a target node, and the improvement of the ant colony algorithm comprises the steps of distributing pheromone concentration by adopting a non-uniform initialization method; improving a state transition rule; differentiating a pheromone updating strategy; and smoothing the path. An initial pheromone distribution optimization strategy is introduced, heuristic information based on a triangular inequality, a steering angle suppression function and a state transition rule of artificial potential field force are combined, a differential pheromone updating strategy based on a multi-target performance evaluation index is adopted, and finally a planned path is smoothed. Therefore, the convergence speed, the path quality and the environment adaptability of the algorithm are remarkably improved.

The invention relates to the field of data processing, in particular to an energy consumption monitoring and predicting method and system, which introduces an energy consumption efficiency factor related to real-time energy consumption of equipment and integrates the energy consumption efficiency factor into a global pheromone updating mechanism of an ant colony algorithm, so that a scheduling scheme dynamically deviates to equipment with better energy consumption. Meanwhile, in order to deal with emergencies such as emergency order insertion, a disturbance degree model is constructed and is used for evaluating the priority and urgency of the events. When the disturbance degree exceeds a threshold value, the system triggers rescheduling and adjusts heuristic information, emergencies are processed preferentially, finally a perceptible and adaptive closed-loop optimization system is formed, and the energy efficiency and robustness of scheduling are improved. According to the method, energy consumption efficiency factors are introduced to update ant colony algorithm pheromones, heuristic information is dynamically adjusted according to event disturbance degrees, and closed-loop adaptive scheduling of energy consumption and emergencies is achieved.

The invention provides a distributed heterogeneous task scheduling method and system based on an ant colony algorithm, and the method is constructed on a scheduling framework of a distributed heterogeneous task scene, and when a new task instance exists, a scheduler firstly checks whether the resource of each node meets the demand of the task instance; if the resources of the nodes meet the requirements, resource utilization rate heuristic values of the nodes are calculated; and in combination with the pheromone concentration and the heuristic value of the node, calculating to obtain a mixing score of the node and carrying out normalization processing. By comparing the mixed scores of the nodes, selecting the node with the highest score as a target node of the to-be-scheduled task instance; and updating the pheromone concentration of the node, and persistently storing the updated pheromone concentration table. Through a dynamic pheromone updating mechanism and a heuristic search strategy of the ant colony algorithm, the task scheduling decision is optimized, the cluster resource utilization rate and the task execution efficiency are improved, the global optimization capability of the ant colony algorithm is fully utilized, and more intelligent and more efficient resource scheduling is realized.

The invention discloses a cooperative control method for a multi-domain unmanned system. The method comprises the following steps: acquiring an original sensor sequence and a late message set; according to the original sensor sequence, calculating a dominant function and anti-fact regret to obtain a value signal; generating a time delay weight according to the late message set; constructing a space-time source item based on the value signal and the time delay weight; constructing a neural pheromone field by solving partial differential equation dynamics by using space-time source items and pheromone field parameters; under the guidance of the neural pheromone field, sampling to generate a candidate path set; and selecting a cooperative path from the candidate path set for the unmanned system to execute. According to the method, the problems of value characterization and timeliness under asynchronous communication are solved, deep coupling of a decision strategy and a physical model is realized, and the robustness and the adaptive ability of collaborative decision are improved.