115results about How to "Improve decision making" patented technology

The invention relates to the technical field of data management, in particular to an intelligent supervision method and system based on big data, and the method comprises the following steps: S1, multi-source data collection: collecting multi-source data in real time; s2, data preprocessing and spatio-temporal feature extraction: preprocessing the collected multi-source data, and extracting spatio-temporal features from the preprocessed multi-source data; s3, performing multi-dimensional risk assessment based on spatial-temporal characteristics: performing risk assessment on behaviors and states of the supervision objects by applying a multi-dimensional risk assessment algorithm; s4, dynamic early warning: in combination with a preset early warning rule, automatically triggering dynamic early warning, and providing a corresponding risk coping suggestion; and S5, supervision strategy optimization: optimizing and updating a supervision strategy through a self-learning mechanism based on a risk assessment result and feedback of dynamic early warning. According to the invention, the accuracy and response speed of risk identification are improved, so that the supervision work is more flexible and efficient.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

The invention discloses a planning method for a pumped storage power station unmanned aerial vehicle inspection complex task. The method comprises the following steps: based on historical inspection data of the pumped storage power station, identifying key indexes and modes in inspection through big data analysis so as to construct a multi-dimensional historical inspection data set; based on historical maintenance records and real-time monitoring data of pumped storage power station equipment, using a time sequence analysis and neural network model to construct an equipment fault prediction model; obtaining potential faults and maintenance requirements of the equipment by using the equipment fault prediction model; and constructing an intelligent path inspection model by adopting a path planning algorithm so as to formulate an intelligent path inspection plan for the unmanned aerial vehicle inspection complex task. The unmanned aerial vehicle is used for collecting real-time data of inspection, and the inspection task path is dynamically adjusted. Through the method, the efficiency, the accuracy and the adaptability of the unmanned aerial vehicle to the completion of the complicated inspection task are ensured, and a powerful technical support is provided for the safe operation of the pumped storage power station.

The invention provides a calculation unloading optimization strategy based on multi-agent deep reinforcement learning, and relates to the technical field of intelligent algorithms. The core content of the calculation unloading optimization strategy based on multi-agent deep reinforcement learning is a multi-agent deep reinforcement learning algorithm designed for an MEC multi-user calculation unloading scene. According to the method, a task Q network structure for each task is constructed by utilizing a value decomposition technology, and a corresponding task Q value is generated on the basis of considering all task relationships of the intelligent agent. The task Q values can be further aggregated to obtain the Q value of the agent, so that the problem of high time and space complexity faced by the multi-task agent in a complex parameterized action space is effectively relieved. In addition, a self-attention mechanism is utilized in the hybrid network, and corresponding weights are dynamically distributed according to the characteristics of different users. Therefore, more accurate interaction representation among the intelligent agents is realized, and the flexibility and effectiveness of overall decision making are improved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent networked PHEV multi-driving-mode autonomous decision control method based on deep learning. The method comprises the steps of designing a state space and an action space based on a deterministic strategy gradient algorithm by constructing three driving modes of a cruise scene, a car-following scene and a passing scene; a multi-target award function is constructed for driving safety, riding comfort and energy consumption economy, a Markov decision process is utilized to construct a multi-target driving mode autonomous decision control model, and it is ensured that when the intelligent network connection PHEV is driven in a complex environment, the optimal target vehicle speed can be planned by autonomously deciding different driving modes. The comprehensive optimization of safety, trafficability and energy consumption economy of the intelligent networked PHEV in the driving process is realized, and the decision-making ability of the intelligent vehicle in a complex traffic environment is further improved.

The invention provides a task planning method for an intelligent world model with a body based on visual reasoning, and the method combines the language processing capability of a large language model and an advanced visual perception technology, so that an intelligent agent can pre-reasone an environment state and simulate possible actions through the world model under the condition of obtaining a small amount of environment visual information; therefore, effective decision making and rapid adaptation to constantly changing environment states are realized in a complex environment, and the task execution efficiency and the task completion success rate of the intelligent agent in a complex scene are remarkably improved.

The invention provides a flexible production plan prediction system based on a knowledge graph and a language model, and the system comprises a feature network graph building module which is used for aggregating the historical production data of a flexible factory to obtain feature network graph data; the flexible production large language model establishment module is used for training the natural language model by using the feature network graph data to obtain a flexible production large language model; the flexible production plan model establishing module is used for obtaining a flexible production plan model by adopting a stacking algorithm and training; the knowledge graph establishing module is used for obtaining a knowledge graph according to the feature network graph; and the flexible production plan prediction module is used for calling the flexible production large language model, the flexible production plan model and the knowledge graph by utilizing the language model development framework, and inputting the current customer demand to obtain a comprehensive flexible production plan. And predicting the flexible production plan through the synergistic effect of the two models and the knowledge graph.

The invention discloses a ship distributed hybrid energy system and an evaluation method thereof, the system comprises a solar photovoltaic module, a wind driven generator module, a diesel generator module, an energy storage system, a converter module, a combined heat and power generation module and a thermal load controller, and the method comprises the steps of obtaining ship data information; performing analysis according to the ship data information; according to the power load analysis, heat energy converted by the residual electric quantity in the energy storage system through the combined heat and power generation module is obtained; obtaining an optimal combination according to an analysis result of the thermal load analysis; performing power demand-based dynamic pricing analysis, environmental influence evaluation analysis and social factor evaluation analysis on the optimal combination; according to the evaluation analysis result, determining that the corresponding optimal combination is a solution for comprehensive and sustainable use of the ship energy; the problems that in the prior art, many defects exist in the aspects of an energy structure, intelligent management and system integration, and efficient utilization, intelligent management and sustainable development of an energy system cannot be achieved are solved.

The invention discloses a policy information intelligent pushing system and method based on big data analysis, and relates to the technical field of policy information intelligent pushing. Comprising a data acquisition and preprocessing module, a dynamic keyword and semantic analysis module, a cross-domain information analysis and classification module, a user portrait and push strategy adjustment module and an intelligent push and feedback optimization module. According to the method, through multi-source data collection and NLP preprocessing, the data quality and availability are ensured; a keyword library and a semantic model are dynamically generated through deep learning, and accurate matching is achieved; policy information is intelligently analyzed and classified through a cross-domain information fusion technology, so that the correlation of pushed contents is ensured; the pushing strategy is dynamically adjusted in combination with the user portrait and real-time feedback, so that the system adapts to the interest change of the user, the pushing model is continuously optimized, the personalization and the accuracy of policy information pushing are remarkably improved, and the information obtaining efficiency and the decision-making ability of the user are improved.

The invention discloses an end-to-end planning method fusing mixed trajectory representation and curriculum reinforcement learning. The method comprises the following steps: constructing a discrete-continuous mixed representation end-to-end pre-training network; constructing a course strengthening fine tuning framework based on interactive deduction; and designing a hard and soft constraint coupled hierarchical course award mechanism. According to the method, a discrete intention and continuous residual error coupling mixed trajectory characterization mechanism is introduced, and on the basis that a driving intention is quickly locked by using discrete primitives, subgrid-level geometric correction is performed on a coarse-grained trajectory through parallel regression branches. According to the invention, on the premise of not increasing the complexity of the search space, accurate trajectory planning with both long-time-sequence intention consistency and dynamics smoothness is realized. According to the method, the reinforcement learning training efficiency is effectively improved, catastrophic forgetting of a long-tail risk scene is prevented, a safety boundary is established in a strategy planning decoder, and the robustness and decision-making ability of an automatic driving system under extreme working conditions are improved.

The invention provides a high-power charging pile self-adjusting method, medium and system for a high-temperature environment, and belongs to the technical field of electrical variable measurement, and the method comprises the steps: extracting time domain features and frequency domain features of an environment parameter, a charging temperature, an input parameter and an output parameter; a correlation matrix of each parameter is calculated through a Pearson's correlation coefficient and principal component analysis, and a stability matrix and a variability matrix are obtained through singular value decomposition. Based on the two matrixes, an autoregressive integrated moving average model and a conditional autoregressive model are respectively constructed, and the long-term trend and short-term fluctuation of each parameter are predicted. And then establishing a game model, and solving to obtain an optimal output parameter by taking the long-term stability and the short-term stability of the output parameter as a target, and taking the optimal output parameter as a charging pile output parameter in a next time period. The charging process is continuously optimized with the preset duration as the cycle period, and the technical problem that real-time optimization of charging adjustment is difficult to achieve due to the fact that an existing charging control technology is mainly based on a single temperature threshold value is solved.

The embodiment of the invention provides a salvage unmanned ship for water surface tiny foreign matter image recognition and an image capturing and processing method thereof, and belongs to the technical field of water surface cleaning and water quality monitoring. The salvage unmanned ship comprises a ship body; the image acquisition and processing module is used for capturing an image of the water surface and identifying tiny foreign matters through a target detection algorithm; the garbage collecting device is mounted on the ship body and can collect tiny foreign matters on the water surface according to an identification result so as to clean garbage floating on the water surface; wherein the target detection algorithm is a Cascade R-CNN target detection algorithm, and the Cascade R-CNN target detection algorithm is combined with a feature pyramid network to process a multi-scale target through feature extraction from bottom to top and feature fusion from top to bottom. The Cascade R-CNN target detection algorithm is combined with the feature pyramid network to effectively solve the problems of low water surface garbage cleaning efficiency, difficulty in small foreign matter detection and the like in the prior art.

The present invention proposes a communication-sensitive multi-agent collaboration method, which is applied to the field of vehicle network communication technology. The method of the present invention includes: the agent uses an information filtering module to encode the locally observed message into a hidden state vector, that is, a compressed filtered message, and before sending the message, the shared medium access control module is used to score the message, and the edge computing node sorts the message value according to the score, and sends a confirmation message to the agent with the topK message value. After the agent receives the confirmation, it sends the message; the edge computing node receives all messages, uses the message distillation module to extract valid information, and summarizes and distributes it to each agent; in the process of summarizing the agent messages, the graph convolution aggregation method based on the graph information bottleneck is used to retain the agent topology structure information, and the summarized message is distilled. The present invention reduces the transmission of redundant and invalid information between agents, saves communication resources, and improves the decision-making ability and collaboration efficiency of multiple agents.

The invention discloses a hydraulic support group cloud measurement method based on AR calibration matching. A physical system, a remote cloud data processing system and a VR system are included. In the physical system, AR glasses provide an interactive interface for virtual-real interaction and data display, and an inspector performs manual matching and dragging adjustment through an MRTK toolkit to complete detection. In the remote cloud data processing system, the cloud service computing system carries out data storage and preliminary analysis, the centralized control worker and the GPT decision making system send a mixed decision making result to the AR glasses through secondary data processing to make a detection data supplement request, and finally the field inspection worker completes measurement. In the VR system, a twin hydraulic support group model is constructed and corrected, an overall working space is constructed, and pose reconstruction precision verification is carried out, so that virtual pose real-time measurement of the hydraulic support group is realized.

The invention relates to the technical field of flue gas denitration, in particular to an ammonia water injection quantity accurate regulation and control method for a flue gas denitration system. Comprising the following steps: S1, constructing a function relational expression between the ammonia water injection amount and the denitration efficiency; s2, constructing a comprehensive objective function based on the ammonia water injection amount and the denitration efficiency; s3, collecting historical operation data of the flue gas denitration system, constructing a reinforcement learning model, and iteratively training the reinforcement learning model by using the historical operation data to minimize the comprehensive objective function; s4, acquiring the real-time operation state data of the current flue gas denitration system, and predicting the ammonia water injection amount by using the reinforcement learning model output model; and S5, constructing a correction relational expression, and obtaining the optimal ammonia water injection amount based on the correction relational expression. By constructing the reinforcement learning model and the correction relational expression and combining the real-time working condition data and the historical operation data, the system can dynamically adjust the ammonia water injection amount, the denitration efficiency is ensured to be maximized, and meanwhile ammonia escape is effectively controlled.

The invention belongs to off-line reinforcement learning, and particularly relates to an off-line reinforcement learning method combining generalized advantage estimation and modal decomposition interaction. In order to solve the problems of insufficient track splicing capability and intra-modal interaction and inter-modal interaction of multi-modal information in a track in an off-line reinforcement learning method based on TransFormer, the track splicing capability of a model in an off-line data set is improved by adopting generalized advantage estimation, a modal decomposition interaction module utilizes an encoder-decoder structure, and a mode decomposition interaction module utilizes an encoder-decoder structure, so that the track splicing capability of the model in the off-line data set is improved. And the encoder effectively processes time sequence information in a single mode by utilizing modal internal interaction based on ConvFormer. Relative to this, the correlation between the state and the action is captured based on the interaction between the modes of the TransFormer, so that the characterization capability of the model on the state and action sequence is enhanced. The decoder further fuses multi-modal information by using convolution and cross-attention mechanisms, thereby realizing more accurate motion prediction.

The invention discloses an intelligent trajectory planning prediction method and device, equipment and a storage medium, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining and detecting aerial view features, occupied network voxel features and scene semantic map features; generating a self-vehicle trajectory plan based on the detected aerial view features, the occupied network voxel features and the scene semantic map features, performing trajectory lifting and fusing a potential trajectory, and determining a self-vehicle optimized trajectory plan; and determining a prediction target trajectory based on the self-vehicle optimization trajectory planning, the occupied network voxel features and the scene semantic map features, and completing intelligent trajectory planning prediction based on the prediction target trajectory. According to the method and the device, the self-vehicle trajectory planning is generated by integrating different features through integrating multi-source information, richer scene information is provided, trajectory improvement is performed, potential trajectories are interactively fused, data which are not seen in a data set are better processed, a relation matrix is calculated to optimize trajectory prediction, and prediction accuracy and reliability are remarkably improved.

The invention discloses a submarine task simulation method based on multiple agents, belongs to the technical field of military simulation, and aims at solving the problem that a traditional submarine task analysis method has limitation in the aspect of simulating a complex battlefield environment and a dynamic scene. The method comprises the following steps: modeling each component unit of the submarine into an intelligent agent; constructing an interaction relationship among the intelligent agents, and simulating interaction and information transmission among internal composition units of the submarine; therefore, sea-to-land task simulation and underwater task simulation of the submarine are realized.

The invention discloses a vehicle path planning method and system for vehicle-infrastructure cooperation. The method comprises the following steps: S1, cloud global path planning; s2, edge calculation center local path planning; s3, the vehicle terminal executes and feeds back; and S4, multi-source data fusion and traffic flow prediction. According to the invention, on-end global path planning is carried out, the cloud carries out global path planning on each automatic driving vehicle according to a logistics scheduling demand or a vehicle driving destination, and an edge calculation center carries out local path planning according to a specific traffic condition of a current area where the vehicle is located after receiving the overall path planning sent by the cloud. After the instructions are executed and fed back to the vehicle terminal, the vehicle terminal can execute corresponding operations such as acceleration, deceleration and steering after receiving the instructions, so that the problem of path planning in a large-scale road network is solved, and the problems including development of a path planning algorithm suitable for a dynamic environment are solved. And parallel computing, distributed computing and other technologies are utilized to improve the operation efficiency of the algorithm.

The invention relates to an object point cloud reconstruction method based on a guided cross-modal robot, and the method comprises the steps: obtaining a depth image and a corresponding RGB image of an object under a single visual angle of the robot, carrying out the preprocessing, and obtaining the incomplete data of the point cloud of the object; a point cloud classification encoder is adopted to encode the point cloud classification task, a point cloud classification decoder is adopted to decode the point cloud classification task, and a classification result of the object is obtained; based on the object point cloud incomplete data, a point cloud data encoder is adopted to carry out position embedded coding, and point cloud features containing position codes are obtained; acquiring tactile data of an object at a single-view blind area position of the robot, and encoding the tactile data by a tactile space encoder to obtain tactile features; a visual touch cross-modal fusion module is adopted to fuse the point cloud features and the touch features, and a classification result is utilized to guide in the fusion process; and decoding the fused features by adopting a pyramid decoder to obtain a complete point cloud of the object. Compared with the prior art, the method has the advantages of high precision and the like.

The present invention relates to the technical fields of reinforcement learning and power grid fault diagnosis, and specifically to a power grid fault diagnosis method, system and device based on prior knowledge reinforcement learning, which obtains alarm data texts of power grid faults under different environmental states, firstly performs text matching of key features on the alarm data texts and key feature rules in prior knowledge, and directly outputs actions if the match is successful, and converts the alarm data texts that fail to match into embedded vectors after embedding processing, and further processes the embedded vectors to obtain expected values ​​of different actions under the current environmental state, and then obtains the action with the highest estimated expected value and the corresponding power grid fault type under the current environmental state based on switch features, switch state features and estimated expected values ​​of different actions, and introduces experience playback to update the estimated expected value, thereby effectively improving the effectiveness and accuracy of power grid fault diagnosis.

The invention discloses an unmanned aerial vehicle model prediction control method, system and device based on Actor-Critic and an artificial potential field, and a medium. A network of multiple static multi-obstacle environments and dynamic multi-obstacle environments, a kinetic model of a four-rotor unmanned aerial vehicle, an award value based on the artificial potential field, and an unmanned aerial vehicle model prediction control algorithm based on Actor-Critic and the artificial potential field is constructed in sequence; training an algorithm model according to the set network parameters, and selecting an optimal weight file; inputting the optimal weight file and a network of an unmanned aerial vehicle model prediction control algorithm based on Actor-Critic and an artificial potential field into a plurality of static multi-obstacle environments and dynamic multi-obstacle environments for testing; obtaining an unmanned aerial vehicle flight path diagram, a reward value r, a path length L, a path maximum torsion degree LS, a path global torsion degree GS, and an index result of whether collision occurs or not and threat; the system, the equipment and the medium are used for implementing the method. The method can effectively plan the path and timely avoid the obstacle, and has the advantages of high real-time performance, strong robustness, timely obstacle avoidance and the like.

The invention discloses an industrial control equipment data acquisition system and method, and the system comprises a sensor module which is installed on industrial control equipment and is used for detecting various parameters of the control equipment; the data acquisition module is connected with the sensor module and is used for acquiring an output signal of the sensor in real time; the data processing module is used for preprocessing the collected data, improving the data quality and reducing the storage space; the communication module is used for transmitting the processed data to a remote monitoring system and supports wired and wireless communication modes; the control module is used for comprehensively processing and analyzing data and providing decision support for industrial control; according to the data acquisition system and the data acquisition method, the operation data of the industrial control equipment can be efficiently and accurately acquired, the real-time performance and the precision are remarkably improved, the data quality is effectively improved through the optimal design of the data processing module, noise and redundant data are reduced, and meanwhile, storage and transmission loads are reduced.

The invention provides a virtual simulation training system and method for a forest fire fighting commander. The virtual simulation training system comprises a physical layer, a service layer, an element simulation layer and an application layer. The physical layer provides a hardware infrastructure; the service layer provides a data processing function; the element simulation layer simulates key elements in the forest fire fighting process and comprises a fire investigation module, a field communication module, an information submission module, a situation research and judgment module, a tactical making module, an instruction issuing module and a tactical execution module. The application layer provides a user interaction interface and comprises a training end situation display function, a training end interaction function and a guiding end function. According to the system, real-time analysis of the fire scene situation is realized by adopting multi-source data fusion and a deep learning technology, and the training effect is improved through dynamic scene injection and an automatic evaluation function. The system can provide a highly vivid training environment for forest fire suppression commanders, effectively improve command decision and coordination ability, reduce training cost, and enhance emergency disposal effect.

The present invention discloses a pig farming intelligent decision-making management system based on multimodal information monitoring, which relates to the technical field of pig farming management. The intelligent decision-making management system includes: an information monitoring module, an abnormal warning module, an analysis and decision module, a management and control module, and an intelligent collaboration module, wherein the information monitoring module is used to divide the pig house into several enclosure areas through enclosure fences, and use the monitoring equipment configured in each enclosure area to monitor the multimodal information of the pig herd growth process in the enclosure area in real time. The present invention realizes the intelligent management of the whole process of pig farming by comprehensively applying information monitoring, abnormal warning, analysis and decision, management and control, and intelligent collaboration modules; comprehensively grasps the growth status of the pig herd by real-time collection of multimodal information in the enclosure area; analyzes information data in real time through edge computing equipment, quickly identifies and warns of abnormal situations, ensures timely handling of potential problems, and avoids the development of abnormal problems.

The invention discloses a multi-agent implicit collaborative decision-making method based on dark tactical guidance, which is used for solving the problems of unreliable communication and even complete lack of collaboration in a multi-agent system. A'dark tactical 'concept is introduced to guide behaviors of the intelligent agent. The method comprises three core components: a tactical distribution network, an intelligent agent strategy network and a group of dark tactics, and the three components are synchronously optimized in a training stage; during execution, each agent selects a dark tactics which is most adaptive to the current overall and individual states through the tactics distribution network according to information of other agents which are observed by the agent and can be acquired by the agent; the selected dark tactics are then entered into the policy network along with individual observations of the agents to generate action decisions. According to the method, cooperation and competition among the intelligent agents can be considered, the benefit of the overall strategy of the intelligent agent cluster is maximized, and the cooperation efficiency of the multiple intelligent agents in an uncertain environment is improved.

The application belongs to the technical field of fault identification, and discloses an equipment processing device fault identification method and system based on artificial intelligence. The method comprises the following steps: in a cloud data center, using an artificial intelligence algorithm, an equipment processing device fault identification engine is constructed, and a multi-modal data acquisition device is connected through an edge computing gateway; using the multi-modal data acquisition device, real-time monitoring multi-modal data of the equipment processing device is acquired, and is uploaded to the cloud data center through the edge computing gateway; in the cloud data center, according to the real-time monitoring multi-modal data, using the equipment processing device fault identification engine, fault identification is carried out, and real-time fault identification results and real-time fault maintenance strategies are obtained. The application solves the problems of single data dimension, limited model depth and precision, and lack of response mechanism in the prior art.

This invention discloses a method, apparatus, electronic device, and storage medium for training an intelligent agent policy network. The method includes: determining the linear score of a first training sample; inputting the current first training sample into a state-action value network, a state value network, and a policy network respectively, and determining a first actual output, a second actual output, and a third actual output; determining a first loss value based on the second actual output, the cumulative reward at a first historical time, the linear score, and a first loss function, and adjusting the parameters of the state-action value network; determining a second loss value corresponding to the state value network, and adjusting the parameters of the state value network accordingly; determining a third loss value corresponding to the policy network, and adjusting the parameters of the policy network based on the third loss value, to obtain the target policy network. This technical solution improves the training effect and speed of the policy network, enabling more accurate and convenient training of the target policy network.

This invention discloses a heat station control system based on virtual-real fusion, comprising: a data acquisition module, a data processing module, a metaverse module, a virtual-real fusion module, a control module, and a response module. These modules are respectively used to acquire data, process data, and construct a heat station metaverse, linking the metaverse with the real heat station and combining the metaverse with the real heat station. This integrates virtual humans, natural humans, and robots, and merges physical dispatchers with digital avatars, forming a human-machine integrated decision-making scheme. It boasts advantages such as good real-time performance, strong immersion, high decision-making ability, and strong execution, improving the efficiency of heat station control. Furthermore, the metaverse environment enables rapid response to control commands and observation of heat station operation animations, enhancing the transparency of the heat station's operation process.