7 results about "Task demand" patented technology

The application provides a hybrid large language model modular fusion system, an implementation method and related devices, which comprises the following steps: firstly, receiving a task request and analyzing to generate a task characteristic vector describing the task demand; then, obtaining the ability characteristic vectors of each expert model and calculating the similarity between the ability characteristic vectors and the task characteristic vector to screen a target expert model group to realize accurate matching of the models; secondly, querying the real-time load state of the computing node where the target expert model group is located and dynamically distributing the task to improve the utilization rate of computing resources; then, controlling the target expert model group to perform multi-round iterative collaborative reasoning through a preset cooperation process, taking the output of a previous expert model as the input of a subsequent expert model to strengthen the collaborative linkage capability among the models; finally, performing fusion processing on the intermediate output result to generate a standardized task output result, ensuring the standardization and accuracy of the output result and improving the stability and reliability of the system operation.

The application claims a service-aware resource collaborative allocation method, belonging to the field of mobile edge computing resource allocation, which comprises: analyzing task demand characteristics, obtaining task abstract categories combined with task demand characteristics, and mapping them into corresponding demand types. Based on the task demand type, candidate collaborative node clusters are selected through destruction operators and repair operators, then the task and resource matching problem is modeled as a combinatorial optimization problem, the collaborative benefits of candidate node combinations are calculated, and the edge node combination with the highest collaborative benefit is selected. The application mainly considers the problems of task demand fuzziness and task and resource matching imbalance in the computing power network, designs a task demand type identification algorithm and a resource collaborative allocation strategy, selects a reasonable collaborative node cluster, and reduces the time delay while improving the resource utilization.

The application provides a multi-agent dynamic workflow arrangement method and system based on an MCP protocol, and relates to the technical field of multi-agent collaborative work. The method comprises the following steps: constructing a standardized agent description and configuring an MCP protocol adapter; generating a task demand text through planning an agent, matching an optimal agent by using a cosine similarity algorithm, and obtaining an initial workflow arrangement graph including a task execution node and a task dependency relationship in combination with a logic analysis technology and a topological sorting algorithm; screening historical related content corresponding to the task execution node based on a context propagation strategy, determining a context dependency result according to the task dependency relationship, and generating context inheritance information by summarizing; accessing the optimal agent by using the MCP protocol adapter, and performing a task execution operation according to the initial workflow arrangement graph in combination with the context inheritance information to generate a task execution result, so that multi-agent efficient collaboration can be realized, and arrangement flexibility and interactive stability can be improved.

The application provides a construction method of an intelligent agent and a storage medium. The construction method determines a paradigm and a model to be carried by the intelligent agent based on task data input by a user, matches natural language description data containing the task data and documents with different constraint degrees according to the paradigm, and finally constructs the intelligent agent in combination with the natural language description data and the model. The system arrangement can be performed by replacing relevant coding with the natural language description data, the construction and operation logic of the intelligent agent is simplified, and thus the technical problem that the intelligent agent system is complex and difficult to be widely applied due to coding arrangement in the related art is improved. Meanwhile, the paradigm and the model are selected according to the task demand, unnecessary computing power consumption is avoided, and the resource input for the operation of the intelligent agent is optimized, and thus the technical problem that the intelligent agent system has high operation cost and consumes much computing power in the related art is improved.

This application relates to the technical field of smart blackboards and discloses an interactive resource scheduling and utilization optimization system for smart blackboards. The system includes a task module that instantiates multimodal interactions into task nodes through intent parsing and associates them with resource nodes to form a dynamic interactive topology; an edge weight module that dynamically calculates the weights of the topology edges based on task demand intensity, resource load, and energy consumption; a decision module that transforms task nodes into agents and, through weight-based multi-round local policy interactions and conflict negotiation, enables the system to converge to a Nash equilibrium state in resource allocation; and a resource control module that issues precise control commands based on the equilibrium matching relationship. This system solves the problems of rigid resource allocation, insufficient real-time performance, and low energy efficiency in smart blackboards under concurrent multi-task interaction, achieving adaptive, high real-time, and low-energy resource scheduling based on interactive intents.

The application discloses a multi-agent collaborative scheduling method and system for scientific and technological information analysis, relates to the technical field of scientific and technological information analysis, and comprises the following steps: a task publishing layer outputs a standardized task description by performing task demand element analysis; an intelligent coordination and scheduling layer performs closed-loop collaborative decision after receiving the standardized task description and outputs a task allocation instruction; and a professional agent execution layer drives a data acquisition agent group to acquire scientific and technological information data through a data access adapter of a data resource layer and execute a scientific and technological information analysis task, and outputs a structured processing result, which is visually presented to a user through the task publishing layer. The application solves the technical problem that a scientific and technological information processing system in the prior art lacks intelligent task allocation, leading to low information processing efficiency, achieves four-layer architecture closed-loop collaboration and efficient scheduling of a professional agent group for scientific and technological information analysis tasks, and improves the technical effects of task allocation accuracy and information processing efficiency.

The application discloses a kind of multi-micro manipulator device parallel collaborative work scheduling and path planning method and system, comprising: according to system state space model, the task information of the task to be processed is obtained;According to the spatial distribution characteristics of target task in task information and task demand, the target task is grouped, and a plurality of task subgroups are obtained;Several task subgroups are assigned to corresponding micro manipulator device, and initial task allocation scheme is generated;In the process that micro manipulator device executes task, its execution state is monitored in real time, and according to the deviation of actual execution progress and expected planning, the remaining tasks are dynamically rescheduled, and updated task scheduling sequence is generated;According to the target position of each micro manipulator device, kinematic constraint and collision constraint, the optimal path without collision is planned for each micro manipulator device, and position calibration is carried out in the process of path execution.The method proposed in the application significantly improves the efficiency of multi-device parallel collaborative work.