2435 results about "Decision process" patented technology

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention provides a decision generation and execution method and system based on an AI agent, and the method comprises the steps: analyzing a user demand document through a natural language processing technology, and extracting key information to construct a structured cue word; then inputting the cue word into a private domain AI agent based on a large model, and generating a preliminary decision scheme in combination with a professional domain database; automatically generating adversarial introspection probe cues, and guiding an AI agent to carry out consistency, risk and constraint conformity evaluation on the preliminary scheme; the system collects feedback response of the AI intelligent agent, analyzes the feedback through a pre-trained graph neural network, and calculates a confidence score of a decision scheme; when the confidence reaches a preset threshold value, automatically generating an execution script according to the decision scheme; and the execution script automatically operates the target system through the preset API and generates an execution document. The whole process realizes a closed-loop intelligent decision-making process from demand understanding, scheme generation, self-verification and automatic execution, and the decision-making efficiency and reliability are remarkably improved.

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

The invention relates to the technical field of information retrieval, and discloses a multi-modal fusion and reinforcement learning collaborative retrieval enhancement generation method and system. The method comprises the following steps: receiving an original query input by a user, and generating a sub-query based on a large language model in combination with a multi-modal context of a current iteration step; forming a current state in combination with the sub-query and the multi-modal context, modeling a retrieval enhancement generation task as a Markov decision process, and adaptively selecting an optimal action from a predefined action set in the current state by utilizing a large language model according to a decision strategy; executing a corresponding multi-modal retrieval operation according to the optimal action, fusing the obtained multi-modal information, generating an intermediate answer or a final answer of the sub-query, and updating a multi-modal context by using the intermediate answer; off-line training optimization is carried out on the large language model through imitation learning and a calibration chain, and decision strategies and sub-queries are inferred online through the model after fine adjustment. According to the invention, more efficient and accurate complex query processing is realized.

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention discloses an agricultural pest question-answering method based on knowledge graph adaptive hybrid retrieval enhancement. The method comprises the following steps: 1) data acquisition and arrangement; 2) construction of a knowledge graph and a vector library; 3) constructing a question and answer pre-classification system; 4) building and training a question type automatic classification model: automatically identifying and classifying the questions input by the user by using a small classification model to form a self-adaptive retrieval classifier; 5) self-adaptive knowledge retrieval based on question types: according to the queried question types, self-adaptively and dynamically selecting different retrieval methods of the knowledge graph and the vector library; 6) selecting a corresponding thinking chain reasoning method according to retrieval results of the knowledge graph and the vector library, and constructing a Prompt cue word with high pertinence; according to the method, the knowledge graph and vector library retrieval are fused, so that the answering precision and accuracy are improved, and by integrating a thinking chain (CoT) reasoning framework, the decision-making process of disease and insect pest problem analysis and answering has a clear logic chain.

The invention discloses an intelligent low-code development method and system based on deep learning model optimization, and relates to the technical field of deep learning. A multi-dimensional domain knowledge graph is constructed, a three-dimensional space-time fusion training sample set is constructed based on the domain knowledge graph, and cross-modal feature alignment is performed on the training sample set, so that the multi-dimensional domain knowledge graph is constructed; the method comprises the following steps: generating an executable logic flow template, encoding the executable logic flow template into a Markov decision process, and performing joint strategy optimization on an optimization target of logic flow by integrating a feature importance index generated by a gradient back propagation path and a multi-target reinforcement learning framework of a Pareto leading edge analysis module. Extracting a strategy parameterization sequence after joint strategy optimization, injecting the strategy parameterization sequence into a dynamic verification sandbox environment, and performing abnormal mode detection and feedback type parameter distillation iteration on an execution track through an online variational auto-encoder to complete dynamic adjustment of the strategy; and the elasticity, the stability and the expandability of the low-code platform are improved.

The invention relates to the technical field of fault analysis, and particularly provides a monitoring fault analysis method fused with a multi-modal knowledge base, which comprises the following steps: collecting original data of a monitoring fault log, and preprocessing and storing the original data; performing data cleaning and feature extraction on the obtained original data of the monitoring fault log; constructing a searchable knowledge base based on the cleaned data; when the system triggers an alarm, mixed retrieval is executed through a dynamic routing mechanism; aggregating the plurality of retrieval results to generate an executable repair scheme; iteratively optimizing the decision process through manual feedback; and continuously optimizing the knowledge base and the diagnosis model to form a closed loop iteration mechanism. According to the scheme, the accuracy and response efficiency of fault diagnosis are improved.

The invention discloses an intelligent flow arrangement method based on fusion expert network and deep reinforcement learning, which comprises the following steps: collecting network node and link state data in real time, and constructing a time sequence input vector and a topological graph structure; a time sequence neural network and a graph neural network are used for extracting traffic spatial-temporal features and node topological features respectively, future traffic is predicted through a classification network after fusion, and coarse-grained arrangement of network slices of different service levels is completed; modeling resource scheduling into a multi-agent Markov decision process, and designing a state space, an action space and a reward function; a deep reinforcement learning agent is initialized, and training is carried out through interaction experience; fusing a pre-trained expert strategy network, and constructing a total loss function to optimize network parameters; and finally generating an intelligent strategy capable of dynamically optimizing the flow path and resource allocation according to the real-time state. According to the invention, efficient resource scheduling under multi-service differentiation service quality requirements can be realized.

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 invention discloses a multi-aircraft cooperative formation flight path planning method based on a leader and follower model, and relates to the technical field of environment perception and unmanned aerial vehicle cluster cooperation. The method comprises the following steps of: firstly, designing a multi-agent double delay depth deterministic strategy gradient (LFMATD3) based on a leader-follower model, and converting an optimization problem model into a Markov decision process model by introducing an artificial potential field model and a reward function; and secondly, constructing an independent agent for each unmanned aerial vehicle, optimizing a behavior strategy of the unmanned aerial vehicle by combining a reward function based on an algorithm framework of deep reinforcement learning, and performing flight path planning and realizing dynamic formation control. The method provided by the invention can effectively improve the formation stability and collaboration of the unmanned aerial vehicles in a complex environment.

The invention relates to the technical field of artificial intelligence and insurance, and discloses an intelligent insurance decision core system based on a dynamic dialogue strategy. The system comprises a user interaction module which collects user risk attribute data through voice and text multi-mode input; the insurance product database is used for storing multi-source insurance product structured data; and the intelligent decision-making module is used for generating personalized insurance suggestions by applying a random forest, a gradient boosting tree and other multi-model fusion technologies. A dynamic dialogue strategy engine is further arranged, and a dynamic interaction sequence is generated based on context-aware reinforcement learning. The data synchronization method of the system can obtain latest insurance product data, the multi-model fusion technology improves the decision accuracy, the dynamic dialogue strategy realizes intelligent interaction, the problems of incomplete information collection, inaccurate decision, inflexible interaction and the like in the traditional insurance decision process can be effectively solved, and more efficient and personalized insurance decision service is provided for users.

The decision process of a first machine learning (ML) model may be explained based on a second ML model implemented on an apparatus. The apparatus may obtain a prediction about an image made based on the first ML model. The apparatus may further determine visual concepts associated with the image that may have been used by the first ML model to make the prediction, and determine respective contributions of the visual concepts to the prediction made by the first ML model. The apparatus may then generate, based on the second ML model, a textual description that explains the respective contributions of the visual concepts to the prediction made by the first ML model. The second ML model may determine respective image features associated with the visual concepts, map the determined image features to corresponding text features, and generate the textual description based at least on the text features.

The invention belongs to the field of reservoir optimization intelligent scheduling, and particularly provides a reservoir multi-objective optimization intelligent scheduling method based on deep reinforcement learning and a deterministic strategy gradient algorithm, which comprises the following steps: collecting original hydrological data and reservoir characteristic curves of a target reservoir; establishing a multi-objective optimization scheduling model including an objective function and constraint conditions; mapping the multi-objective optimization scheduling model into a Markov decision process, and establishing a corresponding reinforcement learning environment; a deep reinforcement learning deterministic strategy gradient algorithm is selected to perform interactive training with the environment, and a reservoir multi-objective optimization intelligent scheduling model is established; and performing multi-target optimization intelligent scheduling on the reservoir through the reservoir multi-target optimization intelligent scheduling model. The method solves the problems that in the prior art, a deep reinforcement learning method generally adopts a fixed weight to design an excitation function, so that the flexibility of a learned scheduling strategy is insufficient, and real-time dynamic coordination optimization of each reservoir scheduling target is difficult to realize according to an environment state.

The invention belongs to the technical field of mobile communication, and particularly relates to a task unloading and resource allocation method for edge computing. According to the method, a three-layer network structure is established, a distributed decision framework is constructed through reinforcement learning, the task emergency degree is dynamically evaluated, and computing resources are distributed in a differentiated mode; and task unloading and resource allocation are optimized in combination with an edge-cloud collaborative architecture, so that calculation load balancing is realized. According to the method, aiming at a cloud edge-end collaborative edge calculation model, the total cost of a system is defined as a joint optimization problem of task unloading time delay and energy consumption, the problem model is converted into a Markov decision process, and multi-agent and multi-user oriented deep reinforcement learning algorithm agent near-end strategy optimization (MAPPO) is designed; and obtaining an optimal unloading decision through mutual learning among multiple agents. According to the method, the total cost of the system can be effectively reduced, the rationality of edge computing task unloading and resource allocation decision is realized, and meanwhile, the use experience of a user can be improved.

The invention relates to the technical field of power system fault recovery, in particular to a power distribution network fault transfer optimization method fusing a knowledge base under the participation of a virtual power plant, and the method comprises the steps: firstly modeling a power distribution network fault transfer process into a Markov decision process to construct a power grid environment model, and then extracting power grid topological features through a graph neural network; the method comprises the following steps: extracting and fusing time sequence features in combination with a Transform structure, then introducing expert knowledge to carry out imitation learning, providing an initial strategy for an intelligent agent, then adopting PPO and DQN cooperative training to optimize an intelligent agent strategy, finally aggregating distributed energy with the help of a virtual power plant, realizing resource coordination and fault load transfer, and dynamically correcting the strategy through closed-loop feedback. Therefore, dynamic adaptability, resource cooperation efficiency and strategy reliability of power distribution network fault recovery are improved, power supply recovery time is shortened, and safe and stable operation of a power grid is guaranteed.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention provides an intelligent decision-making method and device for cross-modal data, equipment and a storage medium, and the method comprises the steps: carrying out the blockchain storage of collected multi-modal process data, and generating an on-chain hash certificate; performing cross-modal space-time alignment and feature fusion on the multi-modal process data according to the space-time metadata analyzed by the Hash certificate on the chain to generate a process knowledge graph; constructing an interactive teaching model according to the process knowledge graph; determining a multi-dimensional contribution measurement value according to the use data of the interactive teaching model; and based on the multi-dimensional contribution magnitude and the hierarchical smart contract architecture, determining the data access permission through a community voting mechanism. Through implementation of the scheme, the process knowledge graph and the interactive teaching model, the inheritor operation data and the material response data form a causal association model, a traceable decision basis is provided for accurate reproduction of an endangered process, and meanwhile, the intelligent contract can automatically execute a decision to ensure high efficiency and transparency of the decision process.

The present application belongs to the technical field of parallel task scheduling. Specifically, disclosed are a heterogeneous computing resource scheduling method and apparatus based on multi-objective optimization. The method comprises: selecting at least two performance indicators from both a task dimension and a resource dimension as optimization objectives, and establishing a multi-objective optimization model for heterogeneous computing resource scheduling; converting a computation task request process into a computation task waiting model on the basis of a queuing theory; on the basis of an observed resource state, constructing a multi-task adaptive scheduling model based on reinforcement learning; on the basis of the multi-objective optimization model and the computation task waiting model, constructing a Markov decision process model from a multi-task-oriented heterogeneous computing resource scheduling problem and a resource mapping process; and on the basis of the Markov decision process model and the multi-task adaptive scheduling model, realizing adaptive multi-task heterogeneous computing resource scheduling. The embodiments of the present application solve the problem of it being difficult for a homogeneous computing resource scheduling method to adapt to heterogeneous computing resource scheduling.

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention relates to the field of intelligent automatic driving, in particular to a knowledge-driven end-to-end automatic driving method based on a sparse expert mechanism and a diffusion model. Comprising the following steps: S1, sensing information processing and state coding; s2, sparse expert module construction and multi-task training; constructing a sparse expert module composed of a plurality of experts, and obtaining a reusable driving skill through multi-task behavior cloning training; s3, generating a diffusion strategy network and an action sequence; and on the basis of a diffusion model, a future multi-step control action sequence is generated from the current state condition, and a continuous and stable driving decision is formed. And S4, a continuous learning and task migration mechanism. According to the method, a combinable and explainable modular driving knowledge structure is constructed, so that the strategy modeling capability is remarkably improved; a diffusion generation mechanism effectively improves the smoothness and stability of the decision process; and a continuous learning and task migration mechanism of structural decoupling improves the long-term adaptability and deployment efficiency of the system.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention discloses a water conservancy multi-modal intelligent decision-making method based on a model association protocol, and the method comprises the following steps: obtaining hydrological, meteorological and remote sensing data, and carrying out the preprocessing and feature extraction respectively; performing structured coding on the extracted multi-modal features by using a data serialization protocol to generate corresponding serialization codes, and performing packaging and transmission by using an MCP protocol; at a receiving end, de-encapsulation is carried out by using a corresponding MCP protocol, the data is restored to original multi-modal features, and each modal feature is fused through a dynamic weight distribution mechanism and a cross-modal attention mechanism; inputting the fused features into a big decision model driven by a knowledge graph, and generating a flood risk level and a corresponding decision through mixed decision reasoning by means of the knowledge graph and a rule engine; according to the method, the problem of data islands is solved, efficient fusion of multi-source data is realized, the decision process is more intelligent and interpretable, and the flood risk level and the prediction accuracy of the corresponding decision are improved.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention relates to a calculation task scheduling method and system considering the uncertainty risk of a data center. The task scheduling process of the data center is modeled based on the Markov decision process to obtain the task scheduling model, the task information and the electricity price serve as uncertain parameters, the decision process of task scheduling is represented by adopting the strategy network, and the risk of too high operation cost is measured based on the conditional value-at-risk. And embedding the conditional value-at-risk as a constraint into a reinforcement learning framework, optimizing the operation cost of the data center under the condition of ensuring the controllable risk, solving a task scheduling strategy through an improved strategy gradient algorithm fused with Monte Carlo sampling, training the task scheduling model, and obtaining a task scheduling result. Outputting a task scheduling strategy of the data center through the trained task scheduling model; task scheduling can be carried out according to the time-varying electricity price, the operation cost of the data center is reduced, risks caused by uncertainty can be sensed, and potential losses are avoided.

The invention provides an AI agent emergency order insertion dynamic decision production scheduling method, a medium and a system, and belongs to the technical field of industrial agents. A dynamic weight adaptive optimization model is adopted to calculate a target weight coefficient and construct a multi-target function set, an improved non-dominated sorting genetic algorithm is adopted to solve and output a Pareto optimal solution set, and a delay risk assessment correlation matrix is combined to start an incremental re-planning algorithm to generate a local adjustment scheme. The Pareto optimal solution set and the local adjustment scheme are combined to generate a final production scheduling scheme, a real-time monitoring module is started to track the execution deviation condition, and when it is detected that the deviation degree exceeds a threshold value, a rapid rescheduling mechanism is triggered to conduct scheme correction; the technical problem of poor production scheduling scheme quality caused by low multi-agent cooperation efficiency in the emergency order insertion dynamic decision process is solved.

The invention discloses an HCPS workshop system-oriented scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: taking the minimization of completion time as a target, and constructing a target optimization model; independently establishing a worker efficiency fluctuation model for each worker; the method comprises the following steps: modeling an HCPS workshop system as a Markov decision process, and designing decision points; designing a state space; and designing an action space: designing a reward function based on an ARSI training mechanism. According to the method, the multi-agent deep reinforcement learning is introduced, so that the accuracy and efficiency of workshop scheduling are remarkably improved, and particularly, the production process is effectively optimized by independently modeling each worker and considering factors such as fatigue and skills of the worker.

The invention discloses a heterogeneous capacity constraint unmanned aerial vehicle path planning method and device based on deep reinforcement learning and a storage medium. According to the method, a path planning problem is modeled as a Markov decision process, and an encoder-double decoder strategy network based on an attention mechanism is adopted for solving. Wherein the encoder network is used for carrying out feature extraction on all task node information so as to generate task node embedding containing a global dependency relationship; in each decision-making step, the dual-stage decoder network firstly determines an optimal execution unmanned aerial vehicle through an unmanned aerial vehicle selection decoder, then selects a next target task node for the unmanned aerial vehicle through a node selection decoder, and generates a complete path scheme in an iteration mode. According to the scheme, a high-quality and high-robustness path planning scheme can be efficiently and intelligently generated, heterogeneous constraint and three-dimensional space problems are effectively processed, and good generalization ability is achieved.

The invention discloses a shipping container freight rate prediction method based on artificial intelligence, and relates to the technical field of freight rate prediction.The method comprises the steps that a port state prediction model is constructed through a graph neural network GNN, a port congestion index is calculated based on the port throughput, the ship queuing condition, the berth utilization rate and the transportation scheduling condition, and a port network graph is constructed to predict the freight rate of a shipping container; describing a connection relationship between ports by using an adjacent matrix, performing information propagation by using GNN, and calculating updated port features; a port congestion index is adopted to train an LSTM regression model, the future port state is predicted, and therefore the mutual influence between ports is dynamically captured; and based on the predicted port state, a reinforcement learning RL training agent is adopted to construct a cargo circulation prediction model, a Markov decision process MDP is adopted to define a cargo circulation state, and a deep Q network DQN is adopted to optimize a cargo circulation path, so that the agent can adjust a cargo circulation strategy in different port states, and the cargo circulation prediction accuracy is improved. Therefore, market change is adapted and prediction reliability is improved.