94 results about "Collaborative strategy" patented technology

The invention relates to the technical field of intelligent agricultural robots, and provides a multi-agent collaborative anti-collision picking method based on digital twinning and deep reinforcement learning, which comprises the following steps: constructing a digital twinning model of a picking scene, and generating environmental geometric parameters, agent kinetic parameters and fruit position parameters through three-dimensional point cloud reconstruction; acquiring environment state data in real time and inputting the environment state data into the digital twin model for space-time alignment processing to generate synchronous state data; based on the synchronous data, a collaborative strategy containing a collision avoidance priority matrix, a path planning sequence and a task allocation weight is generated through a deep reinforcement learning network; an action instruction set is generated according to the strategy, and multiple agents are controlled to execute a picking task after virtual-physical space bidirectional verification of the digital twin model. According to the invention, efficient collision avoidance and accurate picking of multiple agents in a dynamic environment can be realized, and the picking efficiency, safety and system robustness are improved.

The invention provides a consensus decision question answering system based on multi-AI agent game, and relates to the technical field of artificial intelligence. The system comprises a multi-domain information aggregation module, an interaction effect deduction module, a strategy fusion calibration unit, a distributed behavior adaptive mechanism and an aggregation strategy discrimination module. The multi-domain information gathering module is used for unifying multi-source strategy information and environment situation data, the interaction effect deduction module is used for analyzing and quantifying the mutual influence relation of strategies between intelligent agents, and the strategy fusion calibration unit generates correction suggestions based on a game deduction and optimization method. The distributed behavior self-adaptive mechanism is used for locally and progressively executing a correction path in an intelligent agent; and the aggregation strategy judgment module dynamically evaluates the overall strategy state. The multi-agent consensus decision-making question-answering method realizes consensus decision-making question-answering of multiple agents in a complex environment, can effectively identify and correct non-collaborative strategy deviation, and improves the coordination, stability and immunity of a system.

The invention discloses a multi-agent cooperation system construction method, a medium and equipment, and the method comprises the steps: carrying out the task modeling of a target business scene, and constructing a task dependence graph; initial cooperation strength weights are set for the atomic tasks with the cooperation relationship, and multi-stage cooperation training courses from easy to difficult are generated based on the cooperation complexity of the atomic tasks; the intelligent agent is controlled to execute a task in a training course, interaction behavior data is collected, the overall task completion efficiency is calculated, the cooperation weight is dynamically updated, and interaction data and the updated weight are input into a reinforcement learning model to iteratively optimize a cooperation strategy; and finally, solidifying the converged cooperation strategy into the constructed multi-agent cooperation system. According to the invention, through the combination of course learning and reinforcement learning, the cooperation efficiency and robustness of the system in a complex business scene can be significantly improved.

The invention belongs to the technical field of power grids, and discloses a collaborative optimization method, system, equipment and medium based on a virtual power plant operator and a producer, and the method comprises the following steps: S1, building a power-carbon collaborative management framework of the virtual power plant operator and the producer; s2, constructing a double-layer game model based on the power-carbon collaborative management framework; the upper layer is an operator, the lower layer is a consumer, the operator calculates the node marginal electricity price and the node carbon intensity through the optimal power flow and issues the node marginal electricity price and the node carbon intensity to the consumer, and the consumer optimizes scheduling based on the node marginal electricity price and the node carbon intensity and submits an electricity purchasing and selling curve; and S3, solving the double-layer game model by using a distributed iterative algorithm of a penalty function, and realizing adaptive optimization of model solving by introducing a penalty term to obtain an optimal power-carbon collaborative strategy.

The invention relates to a thermal power plant multi-agent collaborative operation platform construction method, which comprises the following steps: collecting time sequence and static data of a whole plant, and carrying out data preprocessing; sending the data into a digital twinborn body, performing data feature fusion by using a neural network model, generating a high-dimensional feature vector, and sending the high-dimensional feature vector into a digital twinborn model for prediction; a specialized agent is constructed, and training is carried out in the digital twinborn body; a plurality of agents perform multi-step time sequence prediction by using a built-in prediction model to obtain a local optimization decision; constructing a collaborative brain based on a large model, receiving a local optimization decision, and generating a global state code; constructing a decision planner, receiving the global state code and outputting a collaborative strategy instruction; and integrating the digital twinborn body, the specialized agent, the collaborative brain and the domain knowledge base, constructing a unified collaborative operation platform, and carrying out iterative optimization on the digital twinborn prediction model and the collaborative brain. Compared with the prior art, the method has the advantage of high cooperative processing efficiency.

The invention discloses an optimization control method for a power system, and relates to the technical field of power control, and the method comprises the steps: constructing a multi-energy state space, and building a layered optimization architecture based on the multi-energy state space; the decision-making layer generates a multi-energy balance scheduling instruction through a multi-target collaborative optimization algorithm to realize multi-target collaborative optimization of the electric heating gas system; the execution layer comprises a plurality of energy subsystem control modules, and collaborative strategy optimization is carried out through a distributed reinforcement learning algorithm; positioning a disturbance source of topology mutation through a causal reasoning algorithm, and generating a reconstruction instruction; training a control strategy of each energy subsystem by adopting a distributed learning framework, and executing a distributed cooperative control action according to the reconstruction instruction; the network connectivity is judged through the communication state of the energy subsystem, and if the communication is normal, collaborative decision making is carried out through a distributed learning framework; and if the communication is interrupted, switching to an edge node offline autonomous mode.

The invention discloses a multi-hospital first-aid collaborative strategy recommendation method and system, and relates to the technical field of pre-hospital first-aid collaboration.The method comprises the steps that spare medical resource information is acquired in real time, and an initial collaborative strategy set is acquired based on the spare medical resource information according to department and hospital positions and basic first-aid configuration; acquiring historical medical resource use information, and constructing a medical resource candidate influence relationship based on the medical resource use frequency and the importance degree according to the historical medical resource use information; and responding to the first-aid information, matching an initial collaborative strategy according to the first-aid information, generating an auxiliary collaborative strategy according to the medical resource candidate influence relationship, and executing collaborative strategy recommendation according to the initial collaborative strategy and the auxiliary collaborative strategy. The method has the beneficial effects that the medical resource scheduling efficiency and the anti-risk capability in the emergency treatment process are improved.

The invention discloses a distributed power supply cooperative control method based on transient dynamic characteristic adaptive driving, which adopts a multi-dimensional transient performance coupling evaluation function, unifies multiple targets including frequency, voltage, power angle and operation economy into a global optimization target, and fundamentally solves the defect of single target in the prior art. A collaborative strategy network based on dynamic context awareness is adopted, and a time sequence processing technology based on an attention mechanism is utilized, so that an intelligent agent can extract key transient dynamic characteristics from a local observation sequence, and the limitation of no memory and non-self-adaption in the prior art is broken through; according to the method, a cooperative control flow of a cooperative control strategy is provided, a high-performance learning type strategy is combined with a high-reliability safety monitor, the problem that safety and performance are difficult to be compatible is solved, fundamental transformation of distributed power supply control from passive, local and static rule-based modes is achieved, and the transient stability margin of a novel power system is remarkably improved.

The invention discloses an artificial intelligence platform computing power resource scheduling management method and system, and relates to the technical field of artificial intelligence computing power scheduling, and the method comprises the specific steps: hardware topology collection traverses heterogeneous resources, and generates and updates a graph; the collaborative strategy decision is based on the atlas, and optimal precision and parallel combination are screened; load balancing deploying split tasks, matching hardware capability and planning a transmission path; executing and monitoring multi-dimensional real-time tracking; the quantization deviation is dynamically adapted and adjusted, and the strategy is timely optimized; according to the invention, through hardware topology acquisition and collaborative strategy decision, a computing power resource optimal configuration scheme is formed, and the training task starting efficiency and stability are improved; load balancing deployment and dynamic adaptation adjustment are combined, resource balancing allocation and whole-process dynamic optimization are achieved, the problems of resource imbalance and response lag are effectively solved, the computing power utilization efficiency is improved to the maximum extent, and efficient and stable training is guaranteed.

The invention discloses a large model dynamic optimization method and system for edge-cloud collaborative reasoning, relates to the technical field of large model dynamic optimization, and solves the technical problems that the prior art lacks flexibility and adaptivity and is difficult to effectively deal with complex and changeable edge application scenes. The method comprises the following steps: S1, collecting and analyzing various key information of edge equipment, a cloud end, a large model to be reasoned and a reasoning task in real time; s2, hierarchically dividing the large model to be reasoned, identifying a plurality of layers or sub-modules which can be used as model unloading points, and dynamically determining an optimal unloading point and an edge-cloud collaborative execution strategy of the large model by utilizing an intelligent optimization algorithm based on the information collected in the step S1; s3, executing a reasoning task according to the dynamic unloading and cooperation strategy generated in the step S2, and converging a final result; through the decision module based on deep multi-agent reinforcement learning (DMARL), the optimal unloading point and the cooperation strategy of the large model can be intelligently selected in real time.

The invention relates to the technical field of distributed file management, and discloses a college cross-campus distributed file collaborative management system. The system comprises a file operation sensing module, a collaborative strategy decision module and a distributed execution engine module. The file operation sensing module is deployed at each campus file server node, captures user editing operation and converts the user editing operation into an operation feature vector set with a timestamp; the collaborative strategy decision-making module generates a conflict probability matrix by analyzing the operation feature vector, and generates a transmission path scheme and a real-time synchronization instruction in combination with a network state and load data; and the distributed execution engine module is responsible for analyzing the instruction, adjusting transmission parameters, processing conflicts and generating a global audit tracking report. According to the system, refined perception, conflict pre-judgment, intelligent transmission, dynamic synchronization and comprehensive auditing of cross-campus file operation are realized, the efficiency and reliability of college cross-campus file collaborative management are improved, and the system is suitable for a multi-campus college file collaborative management scene.

The invention relates to the technical field of adaptive control, and discloses a heterogeneous energy resource multi-agent aggregation and adaptive control method, which comprises the following steps of: firstly, acquiring state information of energy equipment in real time through a sensor; based on the equipment state information, a multi-objective optimization problem is constructed, and the optimization objective is to maximize the comprehensive power generation efficiency of the system, minimize the operation cost and maintain load balance; according to the method, through a Nash equilibrium model based on the game theory, a collaboration strategy between agents is constructed, the agents are adjusted according to local information, and finally global optimal power output is achieved; and then performing adaptive control on each device, processing external disturbance by using a robust control algorithm, and enabling the system to adjust device operation parameters in real time when the environment changes. By introducing a self-adaptive dynamic optimization strategy based on an augmented Lagrangian multiplier method, real-time optimal scheduling of a multi-source system under a dynamic constraint condition is realized, and the system can still keep stable operation and global optimal control effects in a complex disturbance environment.

The invention belongs to the field of information security, and relates to a multi-agent network security operation method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a layered architecture from physical hardware to an upper application; receiving a network security global task, and decomposing the network security global task into task subsets in a coarse-grained layer; the atomic tasks are allocated to appropriate agents, a task alliance is formed, and a collaborative strategy is formulated; by improving the credible interference-free information flow control method of the operating system process, interference-free information interaction between intelligent agents is adapted; performing real-time intention synchronization and cooperation adjustment between the intelligent agents; feedback information in the task execution process is collected, the task completion condition is evaluated, and the multi-granularity collaborative decision process is optimized. The requirements of an intelligent network space security protection system are met; the method can be applied to security operation system platform scenes of multi-agent cooperation, multi-robot cooperation, multi-agent software systems, multi-digital person cooperation and the like.

The invention discloses a semantic segmentation-based space planning conflict intelligent detection method and system, and belongs to the field of computer vision and space planning decision. The method comprises the following steps: acquiring planning graph data to generate an initial semantic segmentation graph; constructing a multi-subject game environment including subjects such as planners, developers and ecological protectors; dynamically optimizing the semantic segmentation model through game experience data; generating a planning guide map by using the optimization model, and embedding the planning guide map into an environment state space; guiding each main body to make a collaborative decision, and generating a dynamic collaborative strategy; generating an intervention sequence based on the strategy, carrying out ethical base line real-time interception, and outputting a security strategy result; and feeding back a security policy result to realize continuous optimization of the model. According to the method, a deep reinforcement learning and semantic segmentation cooperation mechanism is adopted, adaptive generation of planning intention elastic inversion and conflict intervention strategies can be realized, fairness and robustness of decision making are guaranteed through ethical constraint, and the intelligence level of planning conflict detection and decision making is improved.

The invention discloses a shared energy storage and new energy station cooperative operation method and system, and relates to the field of power market and energy storage optimization management in a power system, and the method comprises the following steps: extracting historical wind and light scene data of a new energy station; according to historical wind and light scene data, a wind and light prediction error probability model is established, and after sampling and scene reduction, a limited typical scene is extracted and an uncertainty set is constructed; and on the basis of the limited typical scene and the uncertainty set, by taking the benefit maximization of the new energy station as a target and combining a virtual energy storage operation mode, constructing a shared energy storage and new energy station collaborative operation framework, and generating a double-track system optimal collaborative strategy. The method is used for solving the problems that stochastic optimization calculation is complex, traditional robust optimization benefits are limited, and energy storage resource configuration efficiency is low.

The invention discloses a power grid supply and demand cooperative control method and system based on a soft bus and atomization service. The method comprises the following steps: abstracting equipment data of user side equipment into atomization service, and registering the atomization service to a distributed soft bus; obtaining a supply and demand cooperation strategy, and arranging and combining the atomization service to generate a response sequence; and executing the response sequence, and calling the corresponding atomization service through the distributed soft bus to control the user side equipment to operate. According to the method disclosed by the invention, a networking cooperation system facing the user side equipment of the power grid-user flexible interaction scene is constructed through the distributed soft bus and the atomization service, so that the networking communication efficiency and reliability are improved; a supply and demand cooperation strategy is generated in response to the real-time operation state of the equipment, operation of the user side equipment is controlled, the problems that heterogeneous equipment is difficult to access, cooperation control response is slow, and the cooperation strategy is solidified are solved, and minute-level even second-level rapid autonomous supply and demand cooperation of the power grid on the side end side is achieved.

The invention discloses an AI-based multi-device cooperative control method in an Internet of Things environment. By constructing a'perception-decision-control-feedback-self-adaption 'full-closed-loop architecture, terminal components such as an intelligent equipment group and a sensor group of an equipment layer, a multi-source data acquisition and enhancement preprocessing module of a perception layer, a dynamic equipment capability graph of an AI decision layer, an incremental AI decision model and other core units are integrated; and a cross-protocol instruction distribution and closed-loop feedback module of the control layer realizes deep processing of multi-source heterogeneous data, real-time perception of equipment capability, dynamic adjustment of task priority and autonomous optimization of a coordination strategy, thoroughly solves the technical problems of rigid coordination logic, response lag and unreasonable resource allocation of a traditional method, and improves the reliability of the system. And the intelligent level and the operation efficiency of the Internet of Things system are greatly improved.

The invention provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps of: registering a dependency relationship between a task context and a concerned event triggering condition to the collaboration center, updating a dynamic dependency view through the collaboration center, analyzing knowledge updating time through the collaboration center, and generating a task context identifier set of the intelligent agent; constructing a fact set based on the task context identifier set, and inputting the fact set into a rule inference engine to execute rules in a collaboration rule base to generate a strategy so as to obtain a collaboration strategy; according to the method, the problem of efficiency bottleneck of event routing in the distributed intelligent system is solved from the system architecture level, and the real-time response capability and expansibility of the system in a high-concurrency scene are greatly improved on the premise of ensuring decision consistency.

The invention discloses a school history culture intelligent guide system and method based on a multilingual knowledge graph, and relates to the technical field of knowledge graphs and intelligent guide, and the system comprises a multilingual school history knowledge graph construction module which constructs a multilingual ontology model and generates the multilingual knowledge graph through cross-lingual semantic alignment; the edge collaborative perception interaction module is used for collecting interaction data and realizing low-delay response based on an edge-cloud collaborative strategy; the cross-language semantic retrieval and recommendation module is used for calculating semantic similarity and generating personalized recommendation; the knowledge-enhanced multilingual intelligent question and answer module constructs an enhancement prompt and generates a question and answer result; according to the school history culture international communication system and method, the problems of language barriers and single forms in school history culture international communication are solved, and the tour guide service quality is improved.

The invention relates to the technical field of industrial equipment internet of things systems, and discloses an edge computing laminating machine remote parameter debugging and fault diagnosis system which can trace process root causes from equipment abnormities or look forward equipment risks during process optimization by synchronously collecting equipment and process data and utilizing a causal network to perform bidirectional reasoning so as to improve the efficiency of process optimization. Therefore, a collaborative strategy of deep integration of the equipment and the process is generated, transformation from passive maintenance to active collaborative intervention is realized, and the comprehensive efficiency and reliability of a production system are greatly improved. The system can automatically adapt to changes of a production environment through a closed-loop self-learning mechanism based on feedback data, and long-term accuracy is kept. And in combination with cloud edge collaboration, group knowledge can be quickly fused, and deployment and popularization of intelligent applications are accelerated. The remarkable comprehensive benefits of reducing non-planned shutdown, improving product quality and equipment reliability, reducing operation and maintenance cost and expert dependence and supporting intelligent scale deployment are achieved.

This invention discloses a multi-agent collaborative script generation method, apparatus, electronic device, and medium to address the technical problems of current script generation methods, such as a single memory system, a lack of industrial-grade features in the orchestration engine, and poor quality of generated text. The method includes: acquiring a script creation theme input by the user; starting a DAG orchestration engine to decompose the script creation task corresponding to the theme, obtaining multiple ordered sub-tasks; executing each sub-task sequentially using an interactive collaborative strategy; for each sub-task, calling the corresponding agent to generate task text based on a three-layer memory-based collaborative working mechanism according to the script creation theme; and after executing all sub-tasks, using the task text output by the last sub-task as the script text for the script creation theme.

The application relates to the technical field of tunnel drilling and blasting construction, in particular to a drilling and blasting method tunnel construction scheme simulation method and system based on digital twinning, which comprises the following steps: collecting geological and rock mass data, historical blasting data, site equipment data, site personnel data, real-time environment and working condition data; constructing a digital twinning model of a tunnel to be blasted, and updating the digital twinning model; designing an original drilling and blasting plan based on historical experience and mechanism analysis, and obtaining an optimal drilling and blasting construction scheme by using the digital twinning model and a hierarchical collaborative strategy; performing a field drilling and blasting experiment by using the optimal drilling and blasting construction scheme, and obtaining real blasting data to optimize the digital twinning model. The application realizes auditing and screening of a blasting construction scheme by constructing a high-fidelity tunnel digital twinning model and using a hierarchical collaborative strategy, and unifies efficiency and precision while obtaining the optimal drilling and blasting construction scheme.

The invention discloses a building flexible resource collaborative optimization configuration method, which comprises the steps of obtaining equipment resource data, material resource data and human resource data of a to-be-regulated building, obtaining time-shifting resources and non-time-shifting resources on the basis of the human resource data, dividing start-stop resources in the non-time-shifting resources into flexible resources, and storing the flexible resources into a server; the method comprises the steps of obtaining a resource configuration time sequence, calculating an association target and task resource loss among different resources according to the resource configuration time sequence, performing differential pre-configuration by using the association target to obtain differential pre-loss, and obtaining a pre-regulation curve based on the task resource loss and the differential pre-loss. And carrying out cooperative configuration on the flexible resources and the rigid resources through the pre-regulation curve according to the construction task. According to the method, a pre-regulation curve is generated through hierarchical classification and incremental case-based reasoning, and a rigid-flexible resource cooperation strategy is dynamically adjusted in combination with historical construction data and current project difference, so that a configuration scheme meets actual construction requirements, and scheduling deviation and rework cost are reduced.

The application provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps: registering the dependency relationship between the task context and the event trigger condition concerned by the agent to the coordination center, updating the dynamic dependency view through the coordination center, analyzing the knowledge update time through the coordination center, and generating a task context identifier set of the agent; constructing a fact set based on the task context identifier set, inputting the fact set into a rule reasoning engine, executing the rules in the collaborative rule base to generate a strategy, and obtaining a collaborative strategy; and distributing the collaborative strategy to the corresponding agent and executing a cleaning process at the end of the task generation period. The application solves the efficiency bottleneck of event routing in a distributed intelligent system from the system architecture level, greatly improves the real-time response capability and scalability of the system in a high-concurrency scenario while ensuring decision consistency.

The invention relates to the technical field of power distribution systems, in particular to an intelligent collaborative decision-making method for a power distribution system, which comprises the following steps of: S1, acquiring sampling frequency, uploading delay and data integrity identifiers of sensing nodes of each sub-region, analyzing the structural consistency of operating data, identifying a time alignment defect or a logic coupling fault, and acquiring a data integrity identifier of a sensing node of each sub-region; generating a heterogeneous drift characteristic spectrum; s2, combining historical data, a topology mechanism and a boundary rule, reconstructing control input, and generating a data consistency control input set; and S3, on the basis of the input set, the scheduling target and the system constraint, constructing and distributing a collaborative strategy to realize intelligent collaborative regulation and control. According to the method, the heterogeneous drift characteristic spectrum is constructed, the time-space consistency operation data is reconstructed, and the cooperative strategy set with the adaptive execution capability is generated, so that accurate identification, effective repair and intelligent cooperative regulation of the power distribution system under the condition of data inconsistency are realized.

The invention relates to an information guidance strategy generation method, device and equipment in a multi-subject interaction scene, and relates to the technical field of reinforcement learning. The method comprises the following steps: defining a plurality of execution subjects participating in information guidance, and constructing a social network basic model; building a multi-agent strategy iteration framework, and initializing a strategy pool of each execution main body; generating a strategy interaction matrix through multiple rounds of simulation; solving an equilibrium strategy of each execution main body under the strategy interaction matrix, training an optimal response strategy and supplementing the optimal response strategy to a strategy pool to obtain an optimization strategy set; according to the optimization strategy set, the budget constraint and the node selection cost rule, the execution subjects select seed nodes from the social network basic model in sequence to deploy an information spreading starting point; and after the budget of all the execution subjects is used up, completing viewpoint evolution of all nodes in the social network basic model according to a viewpoint updating mechanism, and outputting an information guide strategy. According to the invention, a multi-agent cooperation strategy, dynamic viewpoint updating and resource optimization distribution can be integrated.

The application relates to the technical field of power distribution systems, in particular to a power distribution system intelligent collaborative decision-making method, which comprises the following steps: S1, collecting the sampling frequency, uploading delay and data integrity identification of each sub-region sensing node, analyzing the consistency of operation data structure, identifying time alignment defects or logical coupling faults, and generating a heterogeneous drift feature map; S2, combining historical data, topological mechanisms and boundary rules, reconstructing control input, and generating a data consistency control input set; S3, based on the input set, scheduling targets and system constraints, constructing and distributing a collaborative strategy, and realizing intelligent collaborative regulation and control. According to the application, the heterogeneous drift feature map is constructed, the spatio-temporal consistency operation data is reconstructed, and the collaborative strategy set with adaptive execution capability is generated, so that the accurate identification, effective repair and intelligent collaborative regulation and control of the power distribution system under the condition of inconsistent data are realized.

This invention relates to the field of signal interference identification technology, and discloses a distributed communication cabinet group collaborative control method and system based on the Internet of Things, including: automatically collecting and semantically mapping full-dimensional data; calculating node collaborative adaptability to complete heterogeneous node admission judgment; dividing a decentralized dynamic collaborative domain; electing domain coordination nodes and building a distributed collaborative infrastructure; calculating decentralized distributed clock synchronization and deviation within the domain; planning a unified collaborative timing window and completing full-link timing binding; combining node twins to complete cross-validation and anomaly identification of collaborative data; building a three-layer hierarchical decision architecture; screening feasible collaborative strategies through multi-objective rigid constraints; completing distributed command interlocking and priority control; calculating and pre-diagnosing node failure probability through node twins; planning fault pre-collaborative backup strategies and executing three-level gradient self-healing; and completing incremental iteration of the collaborative decision model and full lifecycle collaborative operation and maintenance through federated learning.

The invention provides a central air conditioner pipeline construction collaborative management method and system, and belongs to the technical field of computers.The method comprises the steps that on the basis of a redisk period set by a user, redisk data of the previous redisk period are called as target redisk data, the target redisk data are analyzed, and a construction stage is determined; calculating a progress deviation between the construction stage and a preset overall construction plan, and analyzing a quantitative index associated with the progress deviation, an uncompleted process reason and a cross operation record to obtain a leading reason; performing matching degree and efficiency score calculation on a preset differential collaborative response strategy library based on the leading reason to obtain a strategy sorting list; determining an optimal collaboration strategy based on the strategy sorting list, and inputting the optimal collaboration strategy into a preset BIM model to obtain a strategy verification report; verifying the strategy verification report based on constraint conditions of the overall construction plan; and if the verification is passed, determining a target executor and a work responsibility involved in the optimal cooperation strategy. The collaborative management level is improved.

This invention relates to the field of large model-driven inference technology, specifically disclosing a method for understanding and coordinating the interaction intentions of multi-agent systems based on large models. The method includes the following steps: S1, perception of environmental and multi-source heterogeneous interaction information; S2, hierarchical intention understanding driven by the large model; S3, generation of intention-guided collaborative strategies; and S4, action execution and closed-loop online optimization. Through end-to-end technological innovation, this invention significantly improves the accuracy of intention understanding, efficiency of collaborative control, and scene adaptability of multi-agent systems, providing core technical support for multi-agent collaborative applications in complex scenarios. It possesses extremely high engineering application value and industrial promotion potential.