7 results about "Task group" patented technology

The invention discloses an unmanned cluster collaborative management control loop construction method and system, and relates to unmanned cluster and task planning, and the method comprises the steps that a central unmanned ship searches and detects target information, so as to sense a distributed target ship group; obtaining related information of the target boat group obtained through perception, and generating a sea condition initial situation; an event self-adaptive decision response engine is established, a two-layer composite decision is implemented, the first layer of the two-layer composite decision updates a coping strategy for situation migration, and the second layer of the two-layer composite decision carries out resource configuration calculation according to a current strategy and organizes and applies strategies in a strategy set to complete construction of a control loop; and sending the target set of strategy calculation and the action scheme generated by the strategy to each unmanned ship. The method can be used for task organization and decision making of water safety supervision autonomous patrol executed by the unmanned surface vessel cluster, and the task organization and decision making efficiency is improved.

This application discloses a task execution method, apparatus, and device, comprising: acquiring tasks to be executed and assigning the tasks to be executed to items in a task group corresponding to a first priority; wherein each task group has a unique first priority; determining the task margin of the tasks to be executed corresponding to each item in the task group, and reordering the tasks to be executed corresponding to the items according to the task margin to obtain a reordered task queue corresponding to the task group; wherein the task margin represents the execution urgency of the tasks to be executed; polling the task group by priority round-robin to determine the tasks to be executed from the corresponding reordered task queue, and executing the tasks to be executed using a work engine. This application solves the problem of neglecting low-latency execution due to first-come-first-served tasks, and improves the efficiency of task execution and work engine scheduling.

The embodiment of the invention provides a task allocation method and device, scheduling equipment, a storage medium and a program product, and the task allocation method comprises the steps: sorting all tasks in a to-be-allocated task wave according to a roadway position corresponding to each task in the to-be-allocated task wave, and carrying out the task grouping based on a sorting result, and obtaining an initial solution; based on at least one of a destruction operator, an insertion operator and an exchange operator, adjusting the task group in the initial solution to obtain at least one adjustment solution; calculating the time cost of the initial solution and each adjustment solution, and determining a target solution from the initial solution and the at least one adjustment solution based on the time cost; and performing grouping and task allocation on the to-be-allocated task waves based on the target solution. In combination with one or more of destruction, insertion and exchange operators, dynamic grouping optimization of order tasks is realized, and the resource utilization rate and the overall efficiency of order processing are improved.

The invention discloses a large ship reconstruction time estimation method based on a spatial constraint critical path, relates to the technical field of shipbuilding, and proposes the following scheme: obtaining large ship reconstruction demand data including a subtask set composed of a plurality of subtasks, the subtasks including time consumption, three-dimensional space coordinates and resource demand vectors, and the time consumption, the three-dimensional space coordinates and the resource demand vectors being equal to the time consumption, the three-dimensional space coordinates and the resource demand vectors; a dependency relationship set, a spatial conflict rule base and a global resource pool among all the sub-tasks in the sub-task set are obtained, the dependency relationship is a time sequence constraint edge among the sub-tasks, and three-dimensional space coordinate conflict detection is carried out based on the sub-task set and the dependency relationship set in combination with the spatial conflict rule base. According to the method, the directed acyclic graph is constructed, and the key path is judged based on the double-time equality criterion, so that the problem of key path distortion caused by neglecting space and resource constraints in a traditional key path method is solved, time, space and resource triple constraint coupled key path accurate identification is realized, and the theoretical value and actual deviation of a construction period are reduced.

The application discloses a task model driven unmanned cluster command control simulation method, and belongs to the technical field of unmanned cluster command control, and comprises the following steps: establishing a universal task model, respectively establishing a formation level task model, a group level task model and a platform level task model based on the universal task model, respectively realizing the business process and function simulation of unmanned cluster multi-layer command control from formation cooperative decision, task group cooperative decision to unmanned platform autonomous control, and driving multi-level command business twist automation and command decision intelligence based on the formation level task model, the group level task model and the platform level task model, realizing manned or unmanned cooperative decision under a multi-domain task scene, inter-group and intra-group cooperative control of a UAV group, an unmanned ship group and an unmanned underwater vehicle group. The application meets the unmanned cluster task capability, command control capability verification and effect demonstration application requirement under a typical confrontation task scene.

This invention relates to an improved CBBA (Continuous Computational Bayes Analysis) task allocation method for unmanned surface vessels (USVs) based on MOAWC-Kmeans clustering, belonging to the field of unmanned autonomous collaborative control technology. The method first uses the MOAWC-Kmeans algorithm to intelligently pre-cluster tasks, employing adaptive weight calculation, intelligent initialization, constraint-optimized allocation, and a boundary task secondary optimization mechanism to achieve task grouping. Second, distributed task allocation is performed based on the improved CBBA algorithm, with each USV prioritizing bidding for tasks within its pre-clustered cluster. This method introduces a hard time window constraint mechanism, dynamically pruning to remove non-compliant tasks. During path construction, a two-layer strategy combining greedy construction and local search is used to optimize the path structure in real time. After negotiation convergence, unassigned tasks are greedily inserted according to the time window urgency index, and the Or-opt path reconstruction mechanism is used for deep path reconstruction. Finally, load balancing adjustments further optimize cluster efficiency. Compared with existing technologies, this invention significantly improves the task completion rate, path efficiency, and load balancing of heterogeneous USV clusters, making it suitable for complex application scenarios such as maritime search and rescue and patrol monitoring.

This invention relates to the field of vehicle collaborative operation technology, providing a multi-vehicle collaborative operation method and system based on a wireless network, comprising the following steps: loading vehicle parameters, broadcasting the vehicle parameters in real time, receiving vehicle parameters sent by other vehicles, and generating an online vehicle list; determining a global leader based on the online vehicle list, the online vehicle list containing several vehicle parameters; sending task information to the global leader; analyzing the online vehicle list and task information through the global leader to determine the task group receiving the task information, the task group containing one or more vehicle IDs; determining a coordinator for each task group, the coordinator decomposing the task, determining the task path for each vehicle in the task group, and sending the task path to the corresponding vehicle. This invention implements a decentralized task allocation and path planning mechanism, reducing the risk of single point of failure and achieving higher processing efficiency.