512 results about "Decision networks" patented technology

The invention provides an AI-based big data distributed computing task automatic optimization method and system, and relates to the technical field of AI. The method comprises the following steps: constructing a multi-modal spatial-temporal feature sensing network to extract spatial-temporal features of tasks and resources, and training a hierarchical hybrid decision network to perform global task scheduling and local resource allocation. The global strategy network adopts a layered deep reinforcement learning architecture, and generates a decision by combining Monte Carlo tree search and priority experience playback; and the local execution network optimizes local deployment based on the graph neural network and multi-agent collaborative learning. In addition, a distributed anomaly detection network is deployed to monitor the performance in real time, self-adaptive adjustment and optimization are achieved through enhanced transfer learning, and finally the perception network is updated through knowledge distillation to achieve model evolution. According to the method, the execution efficiency and the resource utilization rate of the big data distributed computing task can be effectively improved, and the system operation cost is reduced.

The present invention provides a method and system for extracting and analyzing long text information and association based on a large model, which relates to the technical field of natural language processing, including determining sliding window parameters based on semantic density values ​​to divide long text into blocks, extracting feature vectors of text blocks and calculating semantic relevance, and constructing a hierarchical clustering feature matrix to merge text blocks to obtain semantic fragments. Afterwards, the semantic fragments are syntactically analyzed to construct a semantic dependency tree, and the semantic network is extracted and the entity relationship strength value is calculated to construct a semantic association graph. Finally, the node features are extracted using a pre-trained large model, the semantic representation is fused through a hierarchical attention mechanism, the features are enhanced by propagating an adaptive graph network, and candidate paths are generated through a hierarchical decision network, the optimal path is selected, and the associated path and probability distribution are output. The present invention can effectively extract key information from long texts and analyze the association relationship between entities, thereby improving the accuracy and efficiency of information extraction.

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention provides a network security protection method and system applied to regional digital and intelligent asset businesses, and the method comprises the steps: collecting asset data flows of a plurality of asset business nodes in a target region, carrying out the threat feature extraction of the asset data flows based on a preset threat knowledge graph, and obtaining a threat feature extraction result; generating a dynamic threat feature vector corresponding to the asset service node; inputting the dynamic threat feature vector into a pre-trained dynamic protection model, and outputting a real-time protection strategy adaptive to the asset service node through a multi-layer decision network in the dynamic protection model; performing strategy execution on the network flow of the asset service node based on the real-time protection strategy, generating a strategy execution result and feeding back the strategy execution result to the dynamic protection model; and performing adaptive optimization on decision parameters of the dynamic protection model according to a strategy execution result, and generating an updated dynamic protection model for a protection decision of a next round of asset business nodes.

The invention provides a construction method of a double-path collaborative decision network for multi-agent collaborative path optimization, and the method comprises the steps: obtaining multi-source heterogeneous real-time business data and historical business data, and constructing a standardized data set; converting the standardized data set into a dynamic environment input sequence; at least two domain agents perform full-process business logical reasoning based on the dynamic environment input sequence to generate a decision chain structure covering the full life cycle of the business; implementing full-flow path planning simulation based on the decision chain structure, and generating an initial path scheme including multi-resource collaborative scheduling; the environment perception intelligent agent puts the initial distribution path scheme into an actual logistics distribution scene for execution, and loads real-time environment change information to determine a logistics decision chain weight; and the logistics strategy agent optimizes the initial distribution path scheme based on the logistics decision chain weight to obtain a logistics business knowledge graph so as to construct a logistics double-path collaborative decision network.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision-making method, device, equipment and medium based on multi-modal semantic alignment. Executing cross-modal alignment by taking the voice semantic map as a reference to generate associated information, fusing the voice features, the visual features, the action features and the associated information to generate a fusion feature vector, inputting a decision network to generate a decision feature vector and generate a task execution instruction, obtaining execution feedback information of the task execution instruction, and updating the decision network. According to the method, input is dominated by voice instructions, visual features, action features and semantic map depth alignment and fusion are combined, input naturalness and multi-modal data analysis and decision-making efficiency are improved, and interaction adaptability and decision-making accuracy of the model in a complex scene are improved.

The invention relates to the technical field of artificial intelligence, in particular to an agent dynamic decision network generation method based on reinforcement learning. The method comprises the following steps: receiving information demand data input by a user; performing feature analysis on the information demand data, and extracting a business target, a constraint condition and a key parameter to obtain an information demand analysis result; identifying a task flow corresponding to the information demand analysis result, and matching an API call chain according to the task flow to obtain a task demand technology blueprint; generating an agent workflow according to the task demand technology blueprint by using a preset dynamic workflow engine; performing context analysis on the language demand analysis result to obtain context information; the agent workflow is divided into ultra-long thinking chains based on context information. In conclusion, through the reinforcement learning technology, the intelligent agent can be automatically generated and continuously optimized according to user requirements, and efficient decision making and dynamic adaptation of complex service scenes are supported.

The invention relates to the technical field of electric power engineering, and discloses an electric power engineering multi-modal RAG system based on a knowledge graph and multi-Agent collaboration, and the system comprises a multi-modal dynamic knowledge base construction module which is configured to carry out the structural processing, multi-dimensional knowledge organization and dynamic optimization of electric power engineering multi-modal data; the self-adaptive retrieval strategy engine module is configured to construct a weight decision network based on deep reinforcement learning and execute multi-channel parallel retrieval and result fusion; the iterative self-reflection reasoning module is configured to generate a reasoning path in combination with the retrieval result and verify evidence validity from multiple dimensions; the MCP tool intelligent calling module is configured to integrate multiple types of standardized MCP tools; and the multi-Agent collaborative framework is configured to provide multiple types of Agents which are specific in function and have a cross-module interaction capability. According to the method, the question and answer accuracy, the reasoning depth and the result interpretability in the complex multi-modal scene of the electric power engineering can be remarkably improved.

The invention relates to the technical field of intelligent power energy monitoring, in particular to a power grid monitoring method. The method comprises the following steps: obtaining power grid monitoring nodes, and carrying out monitoring node graph structure processing to construct a physical feature fusion network graph; performing graph structure knowledge enhancement on nodes in the physical feature fusion network graph to construct a semantic enhancement physical association graph; performing composite diagram operation processing of fault propagation risk assessment and root cause analysis based on the semantic enhanced physical association diagram, and constructing four types of intelligent agents of monitoring, coordination, analysis and decision to obtain a hierarchical collaborative decision network; and executing a local strategy learning process based on Q learning based on the hierarchical collaborative decision network so as to realize agent reinforcement learning optimization and end-to-end deployment implementation. According to the method, automatic and end-to-end construction of the intelligent agent is realized through graph structure semantic fusion and multi-stage reinforcement learning, and the intelligent level of power grid monitoring is remarkably improved.

The invention discloses a multi-perception interaction method and system based on virtual reality. The method comprises the following steps: creating a virtual scene and an intelligent agent based on an interaction demand, and constructing a perception model of the virtual scene and the intelligent agent; the intelligent agent perceives visual, auditory and tactile environments, extracts perceived feature time sequence data, divides instructions according to the perceived feature time sequence data, and calculates instruction priorities. And generating a decision network according to a preset knowledge graph, and forming an instruction chain in combination with the instruction priority, so that the intelligent agent obtains a feedback strategy. A dynamic weight value is distributed to a feedback strategy, a virtual scene is updated through fusion of an attention mechanism and a perception model, an interaction mode is obtained, meanwhile, physical and psychological parameters of a user are recorded in real time, abnormal fluctuation segments are recognized, and emotional tendency of the user is learned from corresponding interaction, so that the interaction mode is continuously optimized. According to the method, the interaction experience can be improved, the intelligent agent can better meet the user requirement, the interaction effectiveness and adaptability are enhanced, and meanwhile good interpretability is achieved.

The invention discloses a multi-modal data processing method and system, computer equipment and a readable storage medium, which can realize deep association and complementarity mining of multi-modal information and improve the accuracy and robustness of multi-modal understanding. The method comprises the following steps: an environment sensing module adjusts an environment sensing strategy according to feedback information transmitted by a self-adaptive decision module, and acquires multi-modal data according to the environment sensing strategy; the multi-modal encoding module encodes the multi-modal data into multi-modal feature vectors of the same dimension; a cross-modal fusion module fuses the multi-modal feature vectors to obtain fusion features; the self-adaptive decision-making module selects a decision-making network matched with the task type from a predefined network library according to the task type of the current decision-making task, inputs the fusion features into the decision-making network, and generates feedback information according to the decision-making process of the decision-making network; and the meta-learning controller evaluates the system performance of the current multi-modal data processing system and adjusts system parameters according to an evaluation result.

The present application provides a method and apparatus for multi-drone round-up of hierarchical collaborative learning, an electronic device and a medium. The method includes: determining, according to the current agent joint state and the current escape target state, a present target stage task based on a top-layer decision-making network of a hierarchical decision-making network, inputting a task parameter corresponding to the target stage task into a bottom-layer decision-making network of the hierarchical decision-making network, and obtaining an action decision-making result according to an agent state and received communication data by a strategy network of each agent after obtaining the task parameter; controlling, according to the action decision-making result obtained by the strategy network of each agent, each agent to perform a corresponding maneuver action to execute a multi-drone collaborative pursuit task under the target stage task.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal sequence data processing method, device and equipment and a medium. Extracting multi-scale feature hierarchies, combining the multi-scale feature hierarchies into a multi-scale feature pyramid set, performing cross-modal feature alignment to generate a multi-scale alignment feature sequence, executing local and global attention processing to generate long-distance dependency features, performing cross-layer information interaction to generate comprehensive multi-scale features, and performing multi-scale feature extraction; and dynamically fusing the multi-modal information and inputting the multi-modal information into a task decision network to obtain a target task result. According to the method, through the multi-scale feature pyramid, cross-modal alignment, attention processing and cross-layer information interaction, the problem of insufficient relevance between different modals and different scales in multi-modal long sequence data is solved, and fine modeling and dynamic fusion of multi-modal and multi-scale features are realized.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention provides an automatic generation method and system for an intelligent pavement maintenance scheme, and relates to the technical field of road maintenance, and the method comprises the steps: obtaining a pavement image, and recognizing a disease through a convolutional neural network; a multi-scale grid is utilized to adaptively extract geometric features, and dynamic evaluation vector classification is constructed; matching a basic scheme based on the evaluation result; a maintenance scheme is optimized through a cross attention enhanced hierarchical decision network; and space-time influence analysis and dynamic adjustment are carried out. The pavement maintenance efficiency and quality are improved, the resource waste is reduced, and the influence on traffic is reduced.

The invention provides a method and a system for improving intelligent decision-making ability based on multi-modal learning fusion. The method comprises the following steps: acquiring chessboard image data, strategy text data and historical match chess manual data; processing the chessboard image data by using a convolutional neural network, and extracting spatial features; processing the Go related strategy text by using a natural language processing model, and extracting semantic features of the text; extracting historical chess manual characteristics, namely common strategies and decision modes, from the history game chess manual data; extracting features of different modes by using a multi-mode neural network model, and introducing an attention mechanism to fuse and weight the features of the different modes to generate comprehensive feature representation; based on the fused comprehensive feature representation, a comprehensive decision network model is used for strategy selection and optimization; the method can generate a more accurate and comprehensive Go strategy, not only improves the ability of Go AI to understand and cope with complex situations, but also has the application potential of future multi-modal information fusion in other strategic game fields.

The invention provides a multi-satellite cooperative distributed routing method for multi-agent reinforcement learning, and the method comprises the steps: building a satellite network static topology model through a time slicing technology, and building a target function for minimizing the end-to-end time delay of a data packet; constructing a satellite agent network based on the satellite network static topology model, and obtaining satellite interaction experience data; wherein the satellite agent network comprises a multi-satellite cooperative hybrid network and a satellite decision network corresponding to each agent; training a satellite agent network according to the satellite interaction experience data to obtain a trained satellite agent network; and respectively deploying the satellite decision networks in the trained satellite agent network to corresponding agents, so that the agents perform routing decision based on the deployed satellite decision networks. Through cooperative work of the satellite decision network and the multi-satellite cooperative hybrid network, efficient distributed routing decision of a low-orbit satellite constellation can be realized, time delay is reduced, and load is balanced.

The invention belongs to the technical field of unmanned aerial vehicle clusters, and particularly relates to an unmanned aerial vehicle cluster hierarchical behavior decision-making method oriented to ground hunting, which comprises the following steps of: 1, determining various constraint conditions and income targets in a ground hunting task so as to establish a random game model oriented to the ground hunting task; the second step of considering the prediction problem of the motion trail of the surrounding target and the optimization problem of the multi-agent decision algorithm, and establishing an unmanned aerial vehicle cluster behavior decision network based on the improved MASAC algorithm, and the third step of online decision making. According to the method, the random game model oriented to the ground surrounding task can be established, then the position information of the surrounding target at the next moment can be predicted, the predicted trajectory data is input into the bottom layer MASAC decision network, and finally the optimal unmanned aerial vehicle cluster behavior decision strategy oriented to the ground surrounding task is obtained through offline training. And the ground surrounding of the target is completed by adopting an offline trained unmanned aerial vehicle cluster behavior decision-making method.

The invention relates to an unmanned aerial vehicle group collaboration and task allocation optimization method and system based on edge computing, in particular to the field of communication, efficient task allocation and threat early warning are achieved through dynamic modeling of a multi-modal sequence prediction model and a heterogeneous relation graph, firstly, real-time environment and historical task data are fused, and the real-time environment and historical task data are fused; generating space threat probability distribution and an environment dynamic coefficient; then, a dynamic adjacency matrix is used for adjusting a subgraph embedding vector, a threat-driven topological structure is reconstructed in real time, a decision-making layer outputs a task instruction and value evaluation based on a hierarchical decision-making network, task acceptance, task abandoning and path selection are intelligently optimized, and task conflicts are solved through a federal consensus mechanism; according to the method, the cooperation efficiency and the task execution accuracy of the unmanned aerial vehicle group in a complex environment are effectively improved, and task allocation and resource use are optimized.

The invention relates to the technical field of low-altitude aircraft obstacle avoidance, and discloses an autonomous obstacle avoidance method and system for a low-altitude intelligent dynamic monitoring aircraft, and the method comprises the steps: carrying out the state vector construction of a position coordinate, a flight speed and a course angle of a low-altitude aircraft cluster, and obtaining a state space of the low-altitude aircraft cluster; performing Bezier curve calculation and genetic optimization on the control point sequence in the low-altitude aircraft cluster state space to obtain an optimal detection route parameter; performing geometric distance calculation and double-point removal processing according to the waypoint sequence of the optimal detection route to obtain a patrol airspace equation; performing multi-level reward function calculation through a hierarchical reinforcement learning framework to obtain a high-low layer decision network; time sequence differential learning and strategy gradient optimization are carried out based on the high-low layer decision network to obtain a distributed control strategy, so that global optimization control of the low-altitude aircraft cluster is realized, and stable operation of the low-altitude aircraft cluster in an unknown wind disturbance environment is ensured.

The invention discloses a fan health state detection method and device based on multi-modal data, equipment and a medium, and relates to the technical field of intelligent fan monitoring, and the method comprises the steps: collecting fan images, sound and sensor data, extracting spatial features, synchronous vibration features and time sequence features through employing a multi-modal processing technology, and carrying out the detection of the health state of a fan; the features are integrated through the multi-mode self-adaptive space-time coupling decision network to generate health scores, the health state of the fan is evaluated according to the scores, early warning is performed, early risk detection and active early warning are achieved, and the accuracy and timeliness of fan fault detection are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a task processing method and device based on visual attention enhancement, equipment and a medium. Visual hierarchical features are extracted, a double fovea attention module processes and fuses high-level visual features, a side suppression network obtains enhanced visual features, and a cross-modal fusion module generates fusion features by taking the enhanced visual features as query vectors and taking language components and action components as key and value vectors; and fusing the feature input decision network to generate target category and position information, generating feedback information based on actual label difference, and updating module parameters to complete a target task. According to the invention, through combination of a bionic vision mechanism and multi-modal attention fusion, the visual feature extraction and background suppression capability is improved, and the target capture efficiency and recognition precision in a complex scene can be improved.

The invention discloses a flexible job shop scheduling method based on deep reinforcement learning, and belongs to the field of intelligent manufacturing and intelligent scheduling, and the method comprises the steps: firstly, taking the minimization of the maximum job completion time and the maximization of the average machine utilization rate as optimization targets, and obtaining a target model; then representing a flexible job shop scheduling problem by using a disjunction graph model to obtain a scheduling state; according to the scheduling state and the target model, an end-to-end learning framework is constructed, and the framework is combined with a multi-attention network for deep feature extraction and a decision network for extensible decision, and a Markov decision process is utilized to obtain a job scheduling model; and finally, the multiple attention network constructs a production adaptability operation-AGV-machine scheme by using the complex relationship among the operation, the AGV and the machine to support the decision of the decision network, and obtains a scheduling schedule by using the job scheduling model to complete flexible job shop scheduling. According to the method, complex modes and relations in data can be better captured, and the performance and generalization ability of the model are improved.

The invention discloses a multi-modal diagnosis and treatment scheme recommendation system based on reinforcement learning, and relates to the technical field of artificial intelligence, and the system comprises a data processing module which is used for obtaining multi-modal medical data of a patient and carrying out the preprocessing of the multi-modal medical data to obtain a multi-modal data set; the feature extraction and fusion module is used for performing feature extraction and fusion based on the multi-modal data set to obtain multi-modal features as the current state of the patient; the training set acquisition module is used for constructing a decision network and inputting the current state of the patient to obtain a current diagnosis and treatment scheme, taking the current state of the patient and the current diagnosis and treatment scheme as data groups, and circulating the process to obtain a plurality of data groups to form a training set; the network optimization module is used for optimizing the decision network based on the training set and the core objective function to obtain an optimized decision network; and the optimal scheme output module is used for obtaining the current state of the patient to be recommended and inputting the current state into the optimized decision network to obtain an optimal diagnosis and treatment scheme. And the disease diagnosis accuracy and comprehensiveness are improved.

The invention belongs to the cross technical field of artificial intelligence and robot autonomous navigation, and discloses an unmanned aerial vehicle obstacle avoidance navigation method based on hierarchical pulse reinforcement learning. A layered reinforcement learning framework is adopted for obstacle avoidance navigation, a pulse neural network is adopted as a reinforcement learning model, and a layered pulse reinforcement learning model is obtained; the layered pulse reinforcement learning model comprises a low-level obstacle avoidance decision-making module and a high-level sub-target point decision-making network; the obstacle avoidance decision module is used for local obstacle avoidance; the sub-target point decision network is used for planning a global path; and setting a state machine to coordinate the operation of the obstacle avoidance decision-making module and the sub-target point decision-making network. According to the invention, task requirements of local obstacle avoidance and global navigation can be adaptively balanced, so that the unmanned aerial vehicle can make adaptive adjustment more autonomously when executing a task. And while the decision-making efficiency is ensured, the computing resource consumption and power consumption are reduced.

The invention discloses a ground source heat pump system optimization scheduling method based on a probability graph digital twinborn model, and belongs to the field of ground source heat pump system operation optimization, and the method comprises the following steps: S1, constructing a digital twinborn model based on a dynamic probability graph decision network in a modeling stage; s2, calibrating the digital twin model in a calibration stage; and S3, the digital twinborn model is updated in the operation stage, and the optimal operation method of the ground source heat pump system is output in real time through the updated digital twinborn model. A physical entity-twin system is abstracted into a group of coupled dynamic systems, and the systems evolve along with time in a physical state space and a digital state space and interact with each other through observation data and control input; the problems that multi-physical-field coupling modeling and dynamic optimization control of the ground source heat pump system are complex in calculation and high in calculation resource requirement are solved, and real-time and automatic optimization control of the ground source heat pump system under the complex and changeable underground environment and load requirement is achieved.

The invention relates to the field of electronic component workshop AGV logistics scheduling, and discloses an improved PPO scheduling method and system in an electronic component workshop AGV composite operation mode, a medium and equipment, and the method comprises the steps: carrying out the rasterization of a workshop environment map through grid modeling, enabling the AGV to carry out an A * search algorithm in a grid map through a plurality of task orders, obtaining batch sample data, and carrying out the calculation of the A * search algorithm in the grid map; pre-training is completed according to model parameters of the sample pre-training strategy network; according to the new task order, the AGV interacts with the environment, an observation state, an action space and a reward value are obtained, after matrix processing is carried out on the observation state, the observation state serves as input data of a PPO algorithm, a strategy network loss value and a value network loss value of the PPO algorithm are calculated, network model parameters are updated through a gradient descent method, and PPO training is completed; and generating a real-time task order on line, and guiding the AGV to complete path planning and scheduling tasks through the decision network models trained in the first two stages. According to the method, the electronic component workshop AGV scheduling strategy can be obtained through rapid learning, the learning efficiency is remarkably improved, and stability is achieved.

The invention discloses a dynamic scheduling optimization method based on deep reinforcement learning and a heterogeneous graph neural network. The method comprises the following steps: firstly, converting a flexible job shop scheduling problem into a Markov decision process, and setting a state, an action, a state transition and a reward function; then constructing a basic heterogeneous graph and an enhanced heterogeneous graph, and defining the types and the number of nodes and edges and a directed relationship between the edges; performing three-stage feature embedding by adopting a heterogeneous graph attention network to obtain machine node embedding, operation node embedding, distribution instance node embedding and global state features; and inputting an action state vector formed after each feature is processed, optimizing a decision network according to a reward function, updating parameters to obtain a flexible job shop scheduling model, and completing FJSP solving. The method can effectively consider the flexible job shop scheduling problem under transport time and machine reachability constraints, can also process scheduling problems of different scales, has good generalization ability, and shows excellent performance in large-scale application.

The invention discloses a multi-energy-storage thermal power generating unit load prediction and optimal scheduling method and system, and relates to the technical field of multi-energy-storage thermal power generating units, and the method comprises the steps: collecting the operation parameters and external environment parameters of a thermal power generating unit in real time, and constructing a real-time state parameter matrix; constructing a load prediction model based on the historical state parameter matrix, importing the real-time state parameter matrix into the load prediction model, outputting a load trend prediction curve, and triggering an early warning signal through a secondary discrimination mechanism; identifying a load disturbance value based on the load trend prediction curve, obtaining a load disturbance sequence, and decoupling the load disturbance sequence into a plurality of components; inputting the plurality of vectors into a preset decision network, dynamically correcting a constraint condition built in the decision network in combination with the early warning signal, introducing an improved dragonfly algorithm for optimization iteration, and generating an optimization scheduling instruction; according to the method, the adaptability of optimal scheduling and high-precision prediction of the load trend are improved.

The invention relates to the technical field of machining automation control, and discloses an intelligent tool changing decision-making method based on tool wear perception, which comprises the following steps: acquiring real-time state data of a tool through vibration, acoustic emission, force and temperature sensors, and fusing features of a multi-modal graph neural network to obtain tool wear feature data. And inputting the parameters into a Bayesian decision network, optimizing a tool changing strategy by using a dynamic probabilistic reasoning structure, and generating decision optimization parameters. And a multi-target tool changing optimization model with the highest machining efficiency and the longest service life of the tool as targets is constructed, and an optimal tool changing strategy is determined by adopting an improved particle swarm algorithm. Based on this, a hierarchical decision control model is established and comprises a global evaluation layer, a dynamic adjustment layer and an execution control layer, and intelligent control of tool changing action is realized. In addition, a self-healing control module is embedded in the system to deal with abnormal wear of the cutter. The machining efficiency is improved, the service life of the cutter is prolonged, the machining quality is guaranteed, and intelligent development of machining is promoted.